From 4ece0d0d9b94d84474baa91f14e3a8172d1a0a52 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Thu, 15 May 2025 12:10:07 -0400 Subject: [PATCH 001/218] prototype --- .gitignore | 4 + DashAI/back/container.py | 9 +- DashAI/back/models/RAG/RAG_pipeline.py | 238 ++++++++++++++++++ DashAI/back/models/RAG/__init__.py | 1 + DashAI/back/models/RAG/dummy_retriever.py | 194 ++++++++++++++ .../models/RAG/encodings/tf_idf_encoding.py | 32 +++ DashAI/back/models/RAG/prompts/__init__.py | 3 + .../models/RAG/prompts/augmentation_prompt.py | 51 ++++ DashAI/back/models/RAG/prompts/base_prompt.py | 20 ++ .../RAG/prompts/context_merge_prompt.py | 43 ++++ DashAI/back/models/RAG/text_preprocessor.py | 39 +++ DashAI/back/models/RAG/text_splitter.py | 41 +++ DashAI/back/models/__init__.py | 2 + .../models/hugging_face/deep_seek_model.py | 2 +- DashAI/back/tasks/RAG_task.py | 118 +++++++++ DashAI/back/tasks/__init__.py | 2 + 16 files changed, 797 insertions(+), 2 deletions(-) create mode 100644 DashAI/back/models/RAG/RAG_pipeline.py create mode 100644 DashAI/back/models/RAG/__init__.py create mode 100644 DashAI/back/models/RAG/dummy_retriever.py create mode 100644 DashAI/back/models/RAG/encodings/tf_idf_encoding.py create mode 100644 DashAI/back/models/RAG/prompts/__init__.py create mode 100644 DashAI/back/models/RAG/prompts/augmentation_prompt.py create mode 100644 DashAI/back/models/RAG/prompts/base_prompt.py create mode 100644 DashAI/back/models/RAG/prompts/context_merge_prompt.py create mode 100644 DashAI/back/models/RAG/text_preprocessor.py create mode 100644 DashAI/back/models/RAG/text_splitter.py create mode 100644 DashAI/back/tasks/RAG_task.py diff --git a/.gitignore b/.gitignore index 2671a9ae6..8c017f168 100644 --- a/.gitignore +++ b/.gitignore @@ -178,3 +178,7 @@ __pycache__ # MacOS *.DS_Store + +# RAG database +/chroma +/saved_encoders diff --git a/DashAI/back/container.py b/DashAI/back/container.py index 7206a46ef..508a2880f 100644 --- a/DashAI/back/container.py +++ b/DashAI/back/container.py @@ -51,6 +51,8 @@ StableDiffusionV2Model, StableDiffusionV3Model, ViTTransformer, + + RAGPipeline, ) from DashAI.back.optimizers import HyperOptOptimizer, OptunaOptimizer from DashAI.back.tasks import ( @@ -61,7 +63,9 @@ TabularClassificationTask, TextClassificationTask, TextToImageGenerationTask, - TranslationTask, + TranslationTask, + + RAGTask, ) logger = logging.getLogger(__name__) @@ -130,6 +134,9 @@ KernelShap, PartialDependence, PermutationFeatureImportance, + + RAGTask, + RAGPipeline ] diff --git a/DashAI/back/models/RAG/RAG_pipeline.py b/DashAI/back/models/RAG/RAG_pipeline.py new file mode 100644 index 000000000..7ca6961c3 --- /dev/null +++ b/DashAI/back/models/RAG/RAG_pipeline.py @@ -0,0 +1,238 @@ +from typing import Any, Tuple, List + +from DashAI.back.models.base_generative_model import BaseGenerativeModel + +from DashAI.back.core.schema_fields import ( + BaseSchema, + enum_field, + int_field, + float_field, + schema_field, + string_field +) + +from DashAI.back.models.hugging_face.deep_seek_model import DeepSeekModel +from DashAI.back.models.hugging_face.qwen_model import QwenModel +from DashAI.back.models.llm_generation_model import LLMGenerationModel +from DashAI.back.models.RAG.dummy_retriever import DummyRetriever +from DashAI.back.models.RAG.prompts import ( + BasePrompt, + ContextMergePrompt, + AugmentationPrompt +) +from typing import Dict + +class RAGPipelineSchema(BaseSchema): + """Schema for RAG pipeline.""" + + # RAG algorithm parameters + RAG_algorithm: schema_field( + enum_field(["SINGLE_INTERACTION", "AUGMENTED_INTERACTION"]), + placeholder="SINGLE_INTERACTION", + description="RAG algorithm to use for document retrieval and text generation.", + ) # type: ignore + + + # Retriever parameters + documments_path: schema_field( + string_field(), + placeholder="Path to the folder with documents", + description="Path to the folder containing documents for retrieval.", + ) # type: ignore + + collection_name: schema_field( + string_field(), + placeholder="My documents collection name", + description="Name of the collection to use for document retrieval.", + ) # type: ignore + + + distance_function: schema_field( + enum_field(["cosine", "L2", "inner"]), + placeholder="cosine", + description="Distance function to use for document retrieval.", + ) # type: ignore + + n_docs: schema_field( + int_field(ge=1), + placeholder=5, + description="Maximum number of documents to retrieve.", + ) # type: ignore + + max_distance: schema_field( + float_field(ge=0.0), + placeholder=1.0, + description="Maximum distance allowed for retrieved documents.", + ) # type: ignore + + chunk_size: schema_field( + int_field(ge=1), + placeholder=1000, + description="Size of the chunks to split the documents into.", + ) # type: ignore + + chunk_overlap: schema_field( + int_field(ge=0), + placeholder=200, + description="Overlap size between chunks.", + ) # type: ignore + + + # LLM parameters + model_name: schema_field( + enum_field(["DeepSeek", "Qwen2"]), + placeholder="DeepSeek", + description="LLM model to use for text generation.", + ) # type: ignore + + max_tokens: schema_field( + int_field(ge=1), + placeholder=800, + description="Maximum number of tokens to generate.", + ) # type: ignore + + temperature: schema_field( + float_field(ge=0.0, le=1.0), + placeholder=0.7, + description="Sampling temperature. Higher values make the output more random, while lower values make it more focused and deterministic.", + ) # type: ignore + + frequency_penalty: schema_field( + float_field(ge=0.0, le=2.0), + placeholder=0.1, + description="Penalty for repeated tokens in the output. Higher values reduce the likelihood of repetition, encouraging more diverse text generation.", + ) # type: ignore + + n_ctx: schema_field( + int_field(ge=1), + placeholder=4096, + description="Maximum number of tokens the model can process in a single forward pass (context window size).", + ) # type: ignore + +class RAGPipeline(BaseGenerativeModel): + """Retrieval-Augmented Generation (RAG) pipeline.""" + + COMPATIBLE_COMPONENTS = ["RAGTask"] + SCHEMA = RAGPipelineSchema + + def __init__( self, + **kwargs): + """ + Initialize the DummyRetrieverModel with dummy parameters. + + Args: + distance_function (str): The distance function to use (e.g., "cosine", "euclidean"). + n_docs (int): The maximum number of documents to retrieve. + max_distance (float): The maximum distance allowed for retrieved documents. + """ + + print("Initializing RAG pipeline") + # Initialize the LLM model + llm_classes = { + "DeepSeek": DeepSeekModel, + "Qwen2": QwenModel, + } + + llm_kwargs = { + "max_tokens": kwargs.get("max_tokens"), + "temperature": kwargs.get("temperature"), + "frequency_penalty": kwargs.get("frequency_penalty"), + "n_ctx": kwargs.get("n_ctx"), + } + + print("Initializing LLM model") + self.llm_model: LLMGenerationModel = llm_classes[kwargs.get("model_name")](**llm_kwargs) + + # Initialize the retriever + retriever_params = { + "documents_path": kwargs.get("documments_path"), + "collection_name": kwargs.get("collection_name"), + "distance_function": kwargs.get("distance_function"), + "n_docs": kwargs.get("n_docs"), + "max_distance": kwargs.get("max_distance"), + "chunk_size": kwargs.get("chunk_size"), + "chunk_overlap": kwargs.get("chunk_overlap"), + } + + print("Initializing retriever") + self.retriever = DummyRetriever(**retriever_params) + + print("Definining rag algorithm") + # Initialize the RAG algorithm + if kwargs.get("RAG_algorithm") == "SINGLE_INTERACTION": + self.retrieval_algorithm = self.single_interaction + elif kwargs.get("RAG_algorithm") == "AUGMENTED_INTERACTION": + self.retrieval_algorithm = self.augmented_interaction + else: + raise ValueError("Invalid RAG algorithm. Choose 'SINGLE_INTERACTION' or 'AUGMENTED_INTERACTION'.") + print("RAG pipeline initialized") + + def single_interaction(self, input: str, history: List[Tuple[str, str]] = None) -> Dict[str, str]: + """ + Single interaction retrieval algorithm: input -> retrieve documents. + Args: + input (str): The input query. + Returns: + str: The generated response. + """ + + documents = self.retriever.retrieve([input]) + + return documents + + + def augmented_interaction(self, input: str, history: List[Tuple[str, str]] = None) -> Dict[str, str]: + """ + Augmented interaction RAG algorithm: input -> generate lookup tokens -> retrieve documents -> generate response. + Args: + input (str): The input query. + Returns: + str: The generated response. + """ + augmentation_prompt = AugmentationPrompt.format( + input=input, + history=history, + n_seach_terms=5 + ) + augementation_response = self.llm_model.generate(augmentation_prompt)[0] + print(f"Augmentation response: {augementation_response}") + search_terms = augementation_response.split("keywords:")[1].strip() + search_terms = search_terms.split(",") + if len(search_terms) > 5: + search_terms = search_terms[:5] + print(f"Augmentation search terms: {search_terms}") + search_terms = [term.strip() for term in search_terms] + + documents = self.retriever.retrieve(search_terms) + + return documents + + def generate(self, input: Tuple[str, List[Tuple[str,str]]]) -> str: + """ + Generate a response based on the input and the retrieved documents. + Args: + input: a tuple containing the input query and the conversation history. + input (str): The input query. + history (List[Tuple[str, str]]): The conversation history. + Returns: + str: The generated response. + """ + message, history = input + + documents = self.retrieval_algorithm(message, history) + documents_str = "\n\n".join([f"Document {name}:\n {content}" for name, content in documents.items()]) + + prompt = ContextMergePrompt.format( + input=message, + history=history, + documents=documents_str + ) + + print(f"Prompt: {prompt}") + + response = self.llm_model.generate(prompt) + + sources_str = ", ".join(doc_name for doc_name in documents.keys()) + response = f"{response[0]}\n\nSources:{sources_str}" + + return [response] diff --git a/DashAI/back/models/RAG/__init__.py b/DashAI/back/models/RAG/__init__.py new file mode 100644 index 000000000..ef3481f81 --- /dev/null +++ b/DashAI/back/models/RAG/__init__.py @@ -0,0 +1 @@ +from DashAI.back.models.RAG.RAG_pipeline import RAGPipeline \ No newline at end of file diff --git a/DashAI/back/models/RAG/dummy_retriever.py b/DashAI/back/models/RAG/dummy_retriever.py new file mode 100644 index 000000000..82463d0e8 --- /dev/null +++ b/DashAI/back/models/RAG/dummy_retriever.py @@ -0,0 +1,194 @@ +import os +import chromadb +from chromadb import Settings +from typing import List, Dict +from pypdf import PdfReader +from DashAI.back.models.RAG.encodings.tf_idf_encoding import TfidfEmbeddingFunction +from DashAI.back.models.RAG.text_splitter import TextSplitter +from DashAI.back.models.RAG.text_preprocessor import TextPreprocessor + +def directory_has_documents(directory: str) -> bool: + """Check if the directory contains any documents.""" + for filename in os.listdir(directory): + if filename.endswith(".txt") or filename.endswith(".pdf"): + return True + return False + +class DummyRetriever: + """A dummy retriever using ChromaDB.""" + + def __init__( + self, + documents_path: str, + collection_name: str, + distance_function: str, + n_docs: int, + max_distance: float, + chunk_size: int, + chunk_overlap: int + ): + """ + Initialize the DummyRetriever with dummy parameters. + + Args: + collection_name (str): The name of the collection. + distance_function (str): The distance function to use (e.g., "cosine", "euclidean"). + n_docs (int): The maximum number of documents to retrieve. + max_distance (float): The maximum distance allowed for retrieved documents. + """ + assert os.path.exists(documents_path), f"Documents path {documents_path} does not exist." + assert directory_has_documents(documents_path), f"No txt or pdf documents found in {documents_path}." + assert collection_name, "Collection name cannot be empty." + assert distance_function in ["cosine", "L2", "inner"], "Invalid distance function. Choose 'cosine', 'L2', or 'inner'." + assert n_docs > 0, "Number of documents must be greater than 0." + assert max_distance > 0, "Max distance must be greater than 0." + assert chunk_size > 0, "Chunk size must be greater than 0." + assert chunk_overlap >= 0, "Chunk overlap must be greater than or equal to 0." + + self.documents_path = documents_path + self.collection_name = collection_name.lower().strip().replace(" ", "_") + self.distance_function = distance_function + self.n_docs = n_docs + self.max_distance = max_distance + self.chunk_size = chunk_size + self.chunk_overlap = chunk_overlap + self.collection: chromadb.Collection = None + self.embedding = TfidfEmbeddingFunction( + corpus=self.load_documents().values(), + collection_name=self.collection_name, + ) + + print("Initializing ChromaDB storage") + self.__init_chromadb__() + + def __init_chromadb__(self): + """ + Initialize the ChromaDB storage + if the collection already exists, it will be loaded. + + """ + print("Initializing ChromaDB client") + client = chromadb.PersistentClient( + settings=Settings( + anonymized_telemetry=False, + )) + + print("Initializing ChromaDB collection") + self.collection = client.get_or_create_collection( + name=self.collection_name, + configuration={ + "embedding_function": self.embedding.name(), + }, + embedding_function=self.embedding, + metadata={ + "distance_function": self.distance_function, + }, + ) + print(f"Collection {self.collection_name} initialized.") + try: + print("Checking if collection exists") + if self.collection.count() == 0: + print(f"Collection {self.collection_name} does not exist. Creating a new one.") + documents = self.load_documents() + self.add_documents_to_collection(documents) + else: + print(f"Collection {self.collection_name} already exists. Loading existing documents.") + print(f"Collection {self.collection_name} initialized with {self.collection.count()} documents.") + except Exception as e: + print(f"Error initializing collection {self.collection_name}: {e}") + raise e + + def parse_txt_file(self, file_path: str): + """Parse a txt file and return its content.""" + with open(file_path, "r", encoding="utf-8") as file: + content = file.read() + content = TextPreprocessor.preprocess_text(content) + return content + + def parse_pdf_file(self, file_path: str): + """Parse a pdf file and return its content.""" + with open(file_path, "rb") as file: + reader = PdfReader(file) + content = "" + for page in reader.pages: + content += page.extract_text() + content = TextPreprocessor.preprocess_text(content) + return content + + def load_documents(self)-> Dict[str, str]: + """Load txt documents from the specified path.""" + + documents = {} + for filename in os.listdir(self.documents_path): + file_path = os.path.join(self.documents_path, filename) + if filename.endswith(".txt"): + content = self.parse_txt_file(file_path) + + elif filename.endswith(".pdf"): + content = self.parse_pdf_file(file_path) + + else: + continue + + if len(content) > 0: + documents[filename] = content + else: + print(f"File {filename} is empty or not valid. Skipping.") + continue + + return documents + + def add_documents_to_collection(self, documents: Dict[str, str]): + """ + Add documents to the ChromaDB collection. + Args: + documents (Dict[str, str]): A dictionary of documents with filenames as keys and content as values. + """ + print(f"Adding {len(documents)} documents to the collection.") + text_splitter = TextSplitter(self.chunk_size, self.chunk_overlap) + splitted_documents = text_splitter.split_documents_texts(documents) + print(f"Splitting documents into {len(splitted_documents)} chunks.") + + for i, (filename, chunk_index, chunk_text) in enumerate(splitted_documents): + embedding = self.embedding([chunk_text])[0] + try: + self.collection.add( + documents=chunk_text, + metadatas={"filename": filename, "chunk_index": chunk_index}, + embeddings=embedding, + ids=f"{filename}_{chunk_index}", + ) + except Exception as e: + print(f"Error adding document {filename} chunk {chunk_index}: {e}") + continue + + print(f"Added {len(documents)} documents and {len(splitted_documents)} chunks to the collection.") + + def retrieve(self, query_texts: List[str], n_docs: int = None)-> Dict[str, str]: + """ + Retrieve documents based on the query. + + Args: + query_texts (List[str]): The query strings. + n_docs (int): The number of documents to retrieve. + + Returns: + list: A list of retrieved documents. + """ + if n_docs is None: + n_docs = self.n_docs + + results = self.collection.query( + query_embeddings=self.embedding(query_texts), + n_results= n_docs*len(query_texts), + include=["documents", "metadatas", "distances"] + ) + + retrieved_documents = {} + for query_text, query_docs, query_metadatas, query_distances in zip(query_texts, results["documents"], results["metadatas"], results["distances"]): + for doc, metadata, distance in zip(query_docs, query_metadatas, query_distances): + if distance <= self.max_distance: + docname = metadata["filename"]+"_chunk_"+str(metadata["chunk_index"]) + retrieved_documents[docname] = doc + + return retrieved_documents \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/tf_idf_encoding.py b/DashAI/back/models/RAG/encodings/tf_idf_encoding.py new file mode 100644 index 000000000..936f4aa01 --- /dev/null +++ b/DashAI/back/models/RAG/encodings/tf_idf_encoding.py @@ -0,0 +1,32 @@ +from chromadb import EmbeddingFunction, Embeddings +from typing import List +from sklearn.feature_extraction.text import TfidfVectorizer +import pickle +import os + +class TfidfEmbeddingFunction(EmbeddingFunction): + """A dummy embedding function using TF-IDF.""" + def __init__(self, corpus: List[str], collection_name: str = None): + if collection_name: + stored_path = os.path.join(os.getcwd(), "saved_encoders", "tfidf", collection_name) + + if os.path.exists(stored_path): + with open(stored_path, "rb") as f: + self.vectorizer = pickle.load(f) + return + + self.vectorizer = TfidfVectorizer() + self.vectorizer.fit(corpus) + + if collection_name: + os.makedirs(os.path.dirname(stored_path), exist_ok=True) + with open(stored_path, "wb") as f: + pickle.dump(self.vectorizer, f) + + def __call__(self, texts: List[str]) -> Embeddings: + """Transform the texts into TF-IDF embeddings.""" + return self.vectorizer.transform(texts).toarray().tolist() + + @staticmethod + def name() -> str: + return "default" \ No newline at end of file diff --git a/DashAI/back/models/RAG/prompts/__init__.py b/DashAI/back/models/RAG/prompts/__init__.py new file mode 100644 index 000000000..95e26dcfe --- /dev/null +++ b/DashAI/back/models/RAG/prompts/__init__.py @@ -0,0 +1,3 @@ +from DashAI.back.models.RAG.prompts.augmentation_prompt import AugmentationPrompt +from DashAI.back.models.RAG.prompts.context_merge_prompt import ContextMergePrompt +from DashAI.back.models.RAG.prompts.base_prompt import BasePrompt \ No newline at end of file diff --git a/DashAI/back/models/RAG/prompts/augmentation_prompt.py b/DashAI/back/models/RAG/prompts/augmentation_prompt.py new file mode 100644 index 000000000..a5d399b8a --- /dev/null +++ b/DashAI/back/models/RAG/prompts/augmentation_prompt.py @@ -0,0 +1,51 @@ +from typing import List, Tuple, Any + +from DashAI.back.models.RAG.prompts.base_prompt import BasePrompt + +class AugmentationPrompt(BasePrompt): + """ + AugmentationPrompt class for generating augmented retrieval prompts, + it uses the language model to generate keywords or phrases that can be used to augment the input. + """ + @staticmethod + def format( + input: str, + history:List[Tuple[str, str]], + n_seach_terms: int = 5, + **kwargs: Any + ) -> str: + """ + Instantiate and format the prompt for augmentation. + Args: + input (str): The input to be formatted. + history (List[Tuple[str, str]]): The history of the conversation. + n_seach_terms (int): The number of search terms to generate. + **kwargs: Additional keyword arguments for formatting. + Returns: + str: The formatted prompt. + """ + if history: + formatted_history = "\n".join( + [f"Q: {h_input}\nA: {h_output}" for h_input, h_output in history] + ) + else: + formatted_history = "" + + keyword_dict = "{'keywords': [" + for i in range(n_seach_terms): + keyword_dict += f"'keyword_{i+1}', " + keyword_dict = keyword_dict[:-2] + "]}" + return f""" + You are a intelligent and insightful assistant. Your task is to generate keywords or phrases to search for + relevant information based on the input provided. The keywords or phrases should be relevant to the input + and should help in retrieving useful information to improve the precision of the response. + The user input is: + {input} + The chat history is: + {formatted_history} + The number of keywords or phrases to generate is {n_seach_terms}. + Please generate {n_seach_terms} keywords or phrases that can be used to search for relevant information. + The keywords or phrases should be concise and to the point. You must fill the following template: + {keyword_dict} + """ + \ No newline at end of file diff --git a/DashAI/back/models/RAG/prompts/base_prompt.py b/DashAI/back/models/RAG/prompts/base_prompt.py new file mode 100644 index 000000000..7f66fcc74 --- /dev/null +++ b/DashAI/back/models/RAG/prompts/base_prompt.py @@ -0,0 +1,20 @@ +from abc import ABC +from typing import Any + +class BasePrompt(ABC): + """ + Base class for all RAG prompt templates. + This class defines the interface for creating and formatting prompts. + """ + @staticmethod + def format(input: str, **kwargs: Any) -> str: + """ + Instantiate and format the prompt. + Args: + input (str): The input to be formatted. + **kwargs: Additional keyword arguments for formatting. + Returns: + str: The formatted prompt. + """ + raise NotImplementedError("Subclasses must implement this method.") + \ No newline at end of file diff --git a/DashAI/back/models/RAG/prompts/context_merge_prompt.py b/DashAI/back/models/RAG/prompts/context_merge_prompt.py new file mode 100644 index 000000000..7f00a2787 --- /dev/null +++ b/DashAI/back/models/RAG/prompts/context_merge_prompt.py @@ -0,0 +1,43 @@ +from typing import Any, List, Tuple +from DashAI.back.models.RAG.prompts.base_prompt import BasePrompt + +class ContextMergePrompt(BasePrompt): + """ + ContextMergePrompt class for generating prompts that merge the use input, the retrieved + documents and the chat history into a single context for the language model. + """ + @staticmethod + def format( + input: str, + history:List[Tuple[str, str]], + documents: str, + **kwargs: Any + ) -> str: + """ + Instantiate and format the prompt for context merging. + Args: + input (str): The input to be formatted. + documents (str): The retrieved documents to be included in the context. + history (str): The chat history to be included in the context. + **kwargs: Additional keyword arguments for formatting. + Returns: + str: The formatted prompt. + """ + return f""" + You are a intelligent and insightful assistant. + Your task is to generate a response to the user message. To do this, you will be provided with the user input, + documents retrieved from a database that can be useful for the response, and the chat history. + The user input is: + {input} + The retrieved documents are: + {documents} + The chat history is: + {history} + Please generate a response that takes into account the input, the retrieved documents and the chat history, you + must respond in a conversational manner. + Use the information from the retrieved documents to provide a more accurate and relevant response to the user, + if the information is not relevant to the user input, you can ignore it. + Your response should be clear, concise and relevant to the input provided, using natural language. + + RESPONSE: + """ \ No newline at end of file diff --git a/DashAI/back/models/RAG/text_preprocessor.py b/DashAI/back/models/RAG/text_preprocessor.py new file mode 100644 index 000000000..86023de73 --- /dev/null +++ b/DashAI/back/models/RAG/text_preprocessor.py @@ -0,0 +1,39 @@ +import re +from typing import Dict + +class TextPreprocessor: + """ + A simple text preprocessor that removes special characters and extra spaces from text. + This is useful for cleaning text data before processing. + """ + + @staticmethod + def preprocess_text(text: str) -> str: + """ + Preprocess the text by removing special characters and extra spaces. + Args: + text (str): The text to be preprocessed. + Returns: + str: The preprocessed text. + """ + # Remove special characters except for periods, commas, and spaces + pattern = r"[^a-zA-Z0-9áéíóúüñÁÉÍÓÚÜÑ(),¿?¡! \[\]:;\n\-_]+" + text = re.sub(pattern, " ", text).strip() + # Remove extra spaces + text = re.sub(r"\s+", " ", text) + return text + + @staticmethod + def preprocess_documents_texts(documents: Dict[str, str]) -> Dict[str, str]: + """ + Preprocess the documents' texts. + Args: + documents (Dict[str, str]): A dictionary of documents with filenames as keys and content as values. + Returns: + Dict[str, str]: A dictionary of preprocessed documents with filenames as keys and preprocessed content as values. + """ + preprocessed_documents = {} + for filename, content in documents.items(): + preprocessed_content = TextPreprocessor.preprocess_text(content) + preprocessed_documents[filename] = preprocessed_content + return preprocessed_documents \ No newline at end of file diff --git a/DashAI/back/models/RAG/text_splitter.py b/DashAI/back/models/RAG/text_splitter.py new file mode 100644 index 000000000..2a9fbb069 --- /dev/null +++ b/DashAI/back/models/RAG/text_splitter.py @@ -0,0 +1,41 @@ +from typing import List, Dict, Tuple +class TextSplitter: + """ + A simple text splitter that splits text into chunks of a specified size with overlap. + This is useful for processing large texts in smaller parts. + """ + def __init__(self, chunk_size=1000, chunk_overlap=200): + self.chunk_size = chunk_size + self.chunk_overlap = chunk_overlap + + def split_text(self, text: str) -> List[str]: + """ + Split the text into chunks of specified size with overlap. + Args: + text (str): The text to be split. + Returns: + List[str]: A list of text chunks. + """ + chunks = [] + for i in range(0, len(text), self.chunk_size - self.chunk_overlap): + chunk = text[i:i + self.chunk_size] + if chunk: + chunks.append(chunk) + return chunks + + def split_documents_texts(self, documents: Dict[str, str]) -> List[Tuple[str, int, str]]: + """ + Split the documents into chunks of text. + Args: + documents (Dict[str, str]): A dictionary of documents with filenames as keys and content as values. + Returns: + List[Tuple[str, int, str]]: A list of tuples containing the filename, chunk index, and chunk content. + """ + chunked_documents = [] + for filename, content in documents.items(): + chunks = self.split_text(content) + for i, chunk in enumerate(chunks): + chunked_documents.append( + (filename, i, chunk) + ) + return chunked_documents \ No newline at end of file diff --git a/DashAI/back/models/__init__.py b/DashAI/back/models/__init__.py index d2b98cec6..58a4f6b87 100644 --- a/DashAI/back/models/__init__.py +++ b/DashAI/back/models/__init__.py @@ -53,3 +53,5 @@ from DashAI.back.models.scikit_learn.sklearn_like_model import SklearnLikeModel from DashAI.back.models.scikit_learn.sklearn_like_regressor import SklearnLikeRegressor from DashAI.back.models.scikit_learn.svc import SVC + +from DashAI.back.models.RAG import RAGPipeline \ No newline at end of file diff --git a/DashAI/back/models/hugging_face/deep_seek_model.py b/DashAI/back/models/hugging_face/deep_seek_model.py index 4ba808209..33469c03d 100644 --- a/DashAI/back/models/hugging_face/deep_seek_model.py +++ b/DashAI/back/models/hugging_face/deep_seek_model.py @@ -50,7 +50,7 @@ def __init__(self, **kwargs): self.n_ctx = kwargs.pop("n_ctx", 512) self.model_id = "TheBloke/deepseek-llm-7B-base-GGUF" - self.filename = "*Q8_0.gguf" + self.filename = "*Q3_K_S.gguf" self.model = Llama.from_pretrained( repo_id=self.model_id, filename=self.filename, verbose=True, n_ctx=self.n_ctx, n_gpu_layers=-1 diff --git a/DashAI/back/tasks/RAG_task.py b/DashAI/back/tasks/RAG_task.py new file mode 100644 index 000000000..0719cd1ca --- /dev/null +++ b/DashAI/back/tasks/RAG_task.py @@ -0,0 +1,118 @@ +from typing import Any, List, Tuple +from itertools import chain + +from DashAI.back.tasks.base_generative_task import BaseGenerativeTask + + +class RAGTask(BaseGenerativeTask): + """Base class for image generation tasks. + + Here you can change the methods provided by class Task. + """ + + metadata: dict = { + "inputs_types": [str], + "outputs_types": [str], + "inputs_cardinality": 1, + "outputs_cardinality": 1, + } + + DESCRIPTION: str = "This task generates a text response based on documents provided and chat." + + DISPLAY_NAME: str = "Retreival-Augmented Generation (RAG) Task" + + USE_HISTORY: bool = True + + def prepare_for_task( + self, + input: List[str], + **kwargs: Any, + ) -> Tuple[str, List[str]]: + """Prepare the input by including the history in Q: A: format and retrieving the documents. + + Parameters + ---------- + input : str + The current input to be processed. + + + Returns + ------- + str + The input message + + list[str] + The history of the conversation + """ + + return input # type: Optional[List[Tuple[str, str]]] + + + + def prepare_input_for_database( + self, + input: List[str], + **kwargs: Any, + ) -> List[str]: + """Prepare the input for the database. + + Parameters + ---------- + input : str + The input to be prepared. + + Returns + ------- + str + The prepared input. + """ + return input[0], kwargs.get("history", None) # type: Optional[List[Tuple[str, str]]] + + + def process_output( + self, + output: List[Any], + **kwargs: Any, + ) -> str: + """Process the output of a generative model.""" + + return output + + def process_output_from_database( + self, + output: List[str], + **kwargs: Any, + ) -> List[str]: + """Process the output from the database. + + Parameters + ---------- + output : list[str] + The output data to be processed. + + Returns + ------- + list[str] + The processed output data. + """ + + return output + + def process_input_from_database( + self, + input: List[str], + **kwargs: Any, + ) -> List[str]: + """Process the input from the database. + + Parameters + ---------- + input : list[str] + The input data to be processed. + + Returns + ------- + list[str] + The processed input data. + """ + return input diff --git a/DashAI/back/tasks/__init__.py b/DashAI/back/tasks/__init__.py index 5cc9e1b63..de92dd78a 100644 --- a/DashAI/back/tasks/__init__.py +++ b/DashAI/back/tasks/__init__.py @@ -9,3 +9,5 @@ from DashAI.back.tasks.text_classification_task import TextClassificationTask from DashAI.back.tasks.text_to_image_generation_task import TextToImageGenerationTask from DashAI.back.tasks.translation_task import TranslationTask + +from DashAI.back.tasks.RAG_task import RAGTask \ No newline at end of file From 1ba8382b0d763d34f4ad7c5e01f2057a356b7603 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Sun, 29 Jun 2025 01:10:58 -0400 Subject: [PATCH 002/218] Basic rag configuration --- .../back/api/api_v1/endpoints/components.py | 31 ++ DashAI/back/initial_components.py | 1 + DashAI/back/models/RAG/RAG_pipeline.py | 6 +- DashAI/back/models/RAG/dummy_retriever.py | 58 +-- .../models/RAG/encodings/tf_idf_encoding.py | 6 +- DashAI/back/tasks/RAG_task.py | 10 +- DashAI/front/src/api/component.ts | 1 + DashAI/front/src/api/generativeTask.ts | 12 +- DashAI/front/src/api/rag.ts | 423 ++++++++++++++++++ .../datasets/ConfigureAndUploadDataset.jsx | 17 +- .../components/documents/DocumentTable.jsx | 205 +++++++++ .../experiments/EditModelDialog.jsx | 4 +- .../generative/MainGenerativeBox.jsx | 4 +- .../src/components/generative/ParamsBar.jsx | 2 +- .../generative/RAG/ComponentSelector.jsx | 178 ++++++++ .../generative/RAG/DocumentDropzone.jsx | 45 ++ .../generative/RAG/DocumentSelector.jsx | 106 +++++ .../generative/RAG/DocumentTable.jsx | 101 +++++ .../generative/RAG/SessionMetadata.jsx | 82 ++++ .../components/generative/SelectModelMenu.jsx | 2 +- .../shared/FormSchemaRenderFields.jsx | 2 + .../{datasets => shared}/Upload.jsx | 14 +- .../front/src/pages/generative/Generative.jsx | 90 ++-- .../RAG/AlgorithmConfigurationStep.jsx | 41 ++ .../generative/RAG/DocumentSelectionStep.jsx | 94 ++++ .../generative/RAG/ModelConfigurationStep.jsx | 50 +++ .../pages/generative/RAG/NewSessionModal.jsx | 252 +++++++++++ .../src/pages/generative/RAG/RAGHomePage.jsx | 55 +++ .../pages/generative/RAG/RAGSessionsTable.jsx | 175 ++++++++ .../RAG/RetrieverConfigurationStep.jsx | 38 ++ DashAI/front/yarn.lock | 6 +- requirements.txt | 2 + 32 files changed, 2023 insertions(+), 90 deletions(-) create mode 100644 DashAI/front/src/api/rag.ts create mode 100644 DashAI/front/src/components/documents/DocumentTable.jsx create mode 100644 DashAI/front/src/components/generative/RAG/ComponentSelector.jsx create mode 100644 DashAI/front/src/components/generative/RAG/DocumentDropzone.jsx create mode 100644 DashAI/front/src/components/generative/RAG/DocumentSelector.jsx create mode 100644 DashAI/front/src/components/generative/RAG/DocumentTable.jsx create mode 100644 DashAI/front/src/components/generative/RAG/SessionMetadata.jsx rename DashAI/front/src/components/{datasets => shared}/Upload.jsx (94%) create mode 100644 DashAI/front/src/pages/generative/RAG/AlgorithmConfigurationStep.jsx create mode 100644 DashAI/front/src/pages/generative/RAG/DocumentSelectionStep.jsx create mode 100644 DashAI/front/src/pages/generative/RAG/ModelConfigurationStep.jsx create mode 100644 DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx create mode 100644 DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx create mode 100644 DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx create mode 100644 DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx diff --git a/DashAI/back/api/api_v1/endpoints/components.py b/DashAI/back/api/api_v1/endpoints/components.py index 68e991196..7e4953078 100644 --- a/DashAI/back/api/api_v1/endpoints/components.py +++ b/DashAI/back/api/api_v1/endpoints/components.py @@ -87,6 +87,20 @@ async def get_components( HTTPException If task_name does not exist in the registry """ + + print("select_types", str(select_types)) + print("ignore_types", str(ignore_types)) + print("related_component", str(related_component)) + print("component_parent", str(component_parent)) + print("has_related_of_type", str(has_related_of_type)) + print("component_registry", str(component_registry)) + + + print(list(component_registry._registry.keys())) + print(related_component in component_registry._registry) + print(component_registry) + + print("get_components api ") # when select_type is not none, check if it exists in the registry. if select_types is not None: for select_type in select_types: @@ -99,6 +113,8 @@ async def get_components( ), ) + print("get_components api 2") + # when ignore_type is not none, check if it exists in the registry. if ignore_types is not None: for ignore_type in ignore_types: @@ -111,6 +127,8 @@ async def get_components( ), ) + print("get_components api 3") + # when task_name is not none, check if it exists in the registry. if related_component is not None and related_component not in component_registry: raise HTTPException( @@ -121,6 +139,8 @@ async def get_components( ), ) + print("get_components api 4") + # When has_related_type is not none, check if it exists in the registry. if ( has_related_of_type is not None @@ -134,6 +154,8 @@ async def get_components( ), ) + print("get_components api 5") + # 1. obtain all components from the selected registry/registries # if none, get_components_by_type returns all components. selected_components = { @@ -143,6 +165,8 @@ async def get_components( ) } + print("get_components api 6") + # 2. ignore the requested types if ignore_types is not None: selected_components = _intersect_component_lists( @@ -150,6 +174,8 @@ async def get_components( component_registry.get_components_by_types(ignore=ignore_types), ) + print("get_components api 7") + # 3. select only the components related to "related_component" if related_component is not None: selected_components = _intersect_component_lists( @@ -157,6 +183,8 @@ async def get_components( component_registry.get_related_components(related_component), ) + print("get_components api 8") + # 4. filter if component parent was specified. if component_parent is not None: selected_components = _intersect_component_lists( @@ -164,6 +192,8 @@ async def get_components( component_registry.get_child_components(component_parent, recursive=True), ) + print("get_components api 9") + # 5. filter if "has_related_of_type" was specified. if has_related_of_type is not None: components_with_related_type = [] @@ -176,6 +206,7 @@ async def get_components( components_with_related_type, ) + print("get_components api 10") return [ _delete_class(component_dict) for component_dict in selected_components.values() ] diff --git a/DashAI/back/initial_components.py b/DashAI/back/initial_components.py index ea4659d69..23d734df9 100644 --- a/DashAI/back/initial_components.py +++ b/DashAI/back/initial_components.py @@ -66,6 +66,7 @@ ExplorerJob, ModelJob, PredictJob, + GenerativeJob ) from DashAI.back.metrics import F1, MAE, RMSE, Accuracy, Bleu, Precision, Recall, Ter from DashAI.back.models import ( diff --git a/DashAI/back/models/RAG/RAG_pipeline.py b/DashAI/back/models/RAG/RAG_pipeline.py index f478fe118..9ed9338a3 100644 --- a/DashAI/back/models/RAG/RAG_pipeline.py +++ b/DashAI/back/models/RAG/RAG_pipeline.py @@ -13,7 +13,7 @@ from DashAI.back.models.hugging_face.deep_seek_model import DeepSeekModel from DashAI.back.models.hugging_face.qwen_model import QwenModel -from DashAI.back.models.llm_generation_model import LLMGenerationModel +from DashAI.back.models.text_to_text_generation_model import TextToTextGenerationTaskModel from DashAI.back.models.RAG.dummy_retriever import DummyRetriever from DashAI.back.models.RAG.prompts import ( BasePrompt, @@ -109,7 +109,7 @@ class RAGPipelineSchema(BaseSchema): description="Maximum number of tokens the model can process in a single forward pass (context window size).", ) # type: ignore -class RAGPipeline(BaseGenerativeModel): +class RAGPipeline(TextToTextGenerationTaskModel): """Retrieval-Augmented Generation (RAG) pipeline.""" COMPATIBLE_COMPONENTS = ["RAGTask"] @@ -143,7 +143,7 @@ def __init__( self, } print("Initializing LLM model") - self.llm_model: LLMGenerationModel = llm_classes[kwargs.get("model_name")](**llm_kwargs) + self.llm_model: TextToTextGenerationTaskModel = llm_classes[kwargs.get("model_name")](**llm_kwargs) # Initialize the retriever retriever_params = { diff --git a/DashAI/back/models/RAG/dummy_retriever.py b/DashAI/back/models/RAG/dummy_retriever.py index 82463d0e8..2f781668d 100644 --- a/DashAI/back/models/RAG/dummy_retriever.py +++ b/DashAI/back/models/RAG/dummy_retriever.py @@ -1,6 +1,6 @@ import os -import chromadb -from chromadb import Settings +#import chromadb +#from chromadb import Settings from typing import List, Dict from pypdf import PdfReader from DashAI.back.models.RAG.encodings.tf_idf_encoding import TfidfEmbeddingFunction @@ -52,7 +52,7 @@ def __init__( self.max_distance = max_distance self.chunk_size = chunk_size self.chunk_overlap = chunk_overlap - self.collection: chromadb.Collection = None + self.collection = None #chromadb.Collection = None self.embedding = TfidfEmbeddingFunction( corpus=self.load_documents().values(), collection_name=self.collection_name, @@ -68,36 +68,37 @@ def __init_chromadb__(self): """ print("Initializing ChromaDB client") - client = chromadb.PersistentClient( + client = None + return + """ chromadb.PersistentClient( settings=Settings( anonymized_telemetry=False, - )) + )) """ print("Initializing ChromaDB collection") - self.collection = client.get_or_create_collection( - name=self.collection_name, - configuration={ - "embedding_function": self.embedding.name(), - }, - embedding_function=self.embedding, - metadata={ - "distance_function": self.distance_function, + stored_collections = client.list_collections() + stored_collections_names = [collection.name for collection in stored_collections] + if self.collection_name in stored_collections_names: + print(f"Collection {self.collection_name} already exists. Loading existing collection.") + self.collection = client.get_collection(name=self.collection_name) + else: + print(f"Collection {self.collection_name} does not exist. Creating a new collection.") + # Create a new collection with the specified embedding function + self.collection = client.create_collection( + name=self.collection_name, + configuration={ + "embedding_function": self.embedding.name(), + }, + embedding_function=self.embedding, + metadata={ + "distance_function": self.distance_function, }, - ) - print(f"Collection {self.collection_name} initialized.") - try: - print("Checking if collection exists") - if self.collection.count() == 0: - print(f"Collection {self.collection_name} does not exist. Creating a new one.") - documents = self.load_documents() - self.add_documents_to_collection(documents) - else: - print(f"Collection {self.collection_name} already exists. Loading existing documents.") - print(f"Collection {self.collection_name} initialized with {self.collection.count()} documents.") - except Exception as e: - print(f"Error initializing collection {self.collection_name}: {e}") - raise e - + ) + print(f"Collection {self.collection_name} initialized with distance function {self.distance_function}.") + + documents = self.load_documents() + self.add_documents_to_collection(documents) + def parse_txt_file(self, file_path: str): """Parse a txt file and return its content.""" with open(file_path, "r", encoding="utf-8") as file: @@ -152,6 +153,7 @@ def add_documents_to_collection(self, documents: Dict[str, str]): for i, (filename, chunk_index, chunk_text) in enumerate(splitted_documents): embedding = self.embedding([chunk_text])[0] try: + print(f"Adding document {i+1}/{len(splitted_documents)}: {filename} chunk {chunk_index}") self.collection.add( documents=chunk_text, metadatas={"filename": filename, "chunk_index": chunk_index}, diff --git a/DashAI/back/models/RAG/encodings/tf_idf_encoding.py b/DashAI/back/models/RAG/encodings/tf_idf_encoding.py index 936f4aa01..f74045ae2 100644 --- a/DashAI/back/models/RAG/encodings/tf_idf_encoding.py +++ b/DashAI/back/models/RAG/encodings/tf_idf_encoding.py @@ -1,10 +1,10 @@ -from chromadb import EmbeddingFunction, Embeddings +#from chromadb import EmbeddingFunction, Embeddings from typing import List from sklearn.feature_extraction.text import TfidfVectorizer import pickle import os -class TfidfEmbeddingFunction(EmbeddingFunction): +class TfidfEmbeddingFunction:#(EmbeddingFunction): """A dummy embedding function using TF-IDF.""" def __init__(self, corpus: List[str], collection_name: str = None): if collection_name: @@ -23,7 +23,7 @@ def __init__(self, corpus: List[str], collection_name: str = None): with open(stored_path, "wb") as f: pickle.dump(self.vectorizer, f) - def __call__(self, texts: List[str]) -> Embeddings: + def __call__(self, texts: List[str]):# -> Embeddings: """Transform the texts into TF-IDF embeddings.""" return self.vectorizer.transform(texts).toarray().tolist() diff --git a/DashAI/back/tasks/RAG_task.py b/DashAI/back/tasks/RAG_task.py index 50739e7e6..f8d309847 100644 --- a/DashAI/back/tasks/RAG_task.py +++ b/DashAI/back/tasks/RAG_task.py @@ -17,9 +17,8 @@ class RAGTask(BaseGenerativeTask): "outputs_cardinality": 1, } - DESCRIPTION: str = "This task generates a text response based on documents provided and chat." - DISPLAY_NAME: str = "Retreival-Augmented Generation (RAG) Task" + DESCRIPTION: str = "This task generates a text response based on documents provided and chat." USE_HISTORY: bool = True @@ -63,10 +62,11 @@ def prepare_input_for_database( Returns ------- - str - The prepared input. + List[Tuple[str, str]] + Input with the new types as a list of tuples containing the data + and its type """ - return input[0], kwargs.get("history", None) # type: Optional[List[Tuple[str, str]]] + return [(input[0], "str")] def process_output( diff --git a/DashAI/front/src/api/component.ts b/DashAI/front/src/api/component.ts index 98b87c715..9a7306017 100644 --- a/DashAI/front/src/api/component.ts +++ b/DashAI/front/src/api/component.ts @@ -1,6 +1,7 @@ import api from "./api"; import type { IComponent } from "../types/component"; + interface componentQuery { model?: string; selectTypes?: string[]; diff --git a/DashAI/front/src/api/generativeTask.ts b/DashAI/front/src/api/generativeTask.ts index d64c2c18f..73957488d 100644 --- a/DashAI/front/src/api/generativeTask.ts +++ b/DashAI/front/src/api/generativeTask.ts @@ -12,10 +12,14 @@ export const getGenerativeTask = async (): Promise => { export const getRelatedComponents = async ( relatedComponent: string, ): Promise => { - const response = await api.get( - `/v1/component/?related_component=${encodeURIComponent(relatedComponent)}`, - ); - return response.data; + try{ + const response = await api.get( + `/v1/component/?related_component=${encodeURIComponent(relatedComponent)}`, + ); + return response.data; + } catch (error) { + return []; + } }; export const createGenerativeSession = async ( diff --git a/DashAI/front/src/api/rag.ts b/DashAI/front/src/api/rag.ts new file mode 100644 index 000000000..8e57f2a99 --- /dev/null +++ b/DashAI/front/src/api/rag.ts @@ -0,0 +1,423 @@ +import api from "./api"; +import { ISession } from "../types/session"; +import { IGenerativeTask } from "../types/generativeTask"; + + + + + +// Dummy data +// use the days of june 2025 for first, second, ... sessions +let sessions: ISession[] = [ + { + id: "1", + name: "Session I", + task_name: "RAGTask", + description: "First dummy session", + created: new Date("2025-06-01"), + last_modified: new Date("2025-06-01"), + display_name: "RAG Session I", + model_name: "RAGModel", + parameters: { paramA: 1 }, + }, + { + id: "2", + name: "Session II", + task_name: "RAGTask", + description: "Second dummy session", + created: new Date("2025-06-02"), + last_modified: new Date("2025-06-02"), + display_name: "RAG Session II", + model_name: "RAGModel", + parameters: { paramA: 2 }, + }, + { + id: "3", + name: "Session III", + task_name: "RAGTask", + description: "Third dummy session", + created: new Date("2025-06-03"), + last_modified: new Date("2025-06-03"), + display_name: "RAG Session III", + model_name: "RAGModel", + parameters: { paramA: 3 }, + }, + { + id: "4", + name: "Session IV", + task_name: "RAGTask", + description: "Fourth dummy session", + created: new Date("2025-06-04"), + last_modified: new Date("2025-06-04"), + display_name: "RAG Session IV", + model_name: "RAGModel", + parameters: { paramA: 4 }, + } +]; + +let components: IGenerativeTask[] = [ + { + name: "Dense Passage Retrieval", + type: "retriever", + configurable_object: true, + schema: { + description: "Schema for dense passage retrieval", + properties: { + paramA: { + description: "Transformer", + enum: [ + "BERT", + "RoBERTa", + "DistilBERT", + "ALBERT", + ], + placeholder: "BERT", + title: "Parameter A", + type: "string", + }, + DistanceMetric: { + description: "Distance metric to use for retrieval", + enum: [ + "cosine", + "dot_product", + "euclidean", + ], + placeholder: "cosine", + title: "Distance Metric", + type: "string", + }, + }, + required: ["paramA", "DistanceMetric"], + type: "object", + }, + metadata: { + inputs_cardinality: 1, + outputs_cardinality: 1, + inputs_types: ["text"], + outputs_types: ["text"], + }, + description: "A component for dense passage retrieval", + display_name: "Dense Passage Retrieval", + }, + { + name: "TFIDFRetriever", + type: "retriever", + configurable_object: true, + schema: { + description: "Schema for TF-IDF retriever", + properties: { + paramA: { + description: "Remove stop words", + placeholder: "true", + title: "Remove Stop Words", + type: "boolean" + }, + TokenScoreCombination: { + description: "Token score combination method", + enum: [ + "sum", + "max", + "min", + ], + placeholder: "sum", + title: "Token Score Combination", + type: "string" + }, + }, + required: ["paramA", "TokenScoreCombination"], + type: "object", + }, + metadata: { + inputs_cardinality: 1, + outputs_cardinality: 1, + inputs_types: ["text"], + outputs_types: ["text"], + }, + description: "A component for TF-IDF retrieval", + display_name: "TF-IDF Retriever", + }, + + { + name: "DeepSeekModel", + type: "GenerativeModel", + configurable_object: true, + schema: { + description: "Schema for DeepSeek model.", + properties: { + max_tokens: { + description: "Maximum number of tokens to generate.", + minimum: 1, + placeholder: 100, + title: "Max Tokens", + type: "integer" + }, + temperature: { + description: "Sampling temperature. Higher values make the output more random, while lower values make it more focused and deterministic.", + maximum: 1, + minimum: 0, + placeholder: 0.7, + title: "temperature", + type: "number" + }, + frequency_penalty: { + description: "Penalty for repeated tokens in the output. Higher values reduce the likelihood of repetition, encouraging more diverse text generation.", + maximum: 2, + minimum: 0, + placeholder: 0.1, + title: "Frequency Penalty", + type: "number" + }, + n_ctx: { + description: "Maximum number of tokens the model can process in a single forward pass (context window size).", + minimum: 1, + placeholder: 4096, + title: "N Ctx", + type: "integer" + } + }, + required: [ + "max_tokens", + "temperature", + "frequency_penalty", + "n_ctx" + ], + title: "DeepSeekSchema", + type: "object" + }, + metadata: { + inputs_cardinality: 1, + outputs_cardinality: 1, + inputs_types: ["text"], + outputs_types: ["text"] + }, + description: "null,", + display_name: "null" + }, + { + name: "GemmaModel", + type: "GenerativeModel", + configurable_object: true, + schema: { + description: "Schema for Gemma model.", + properties: { + max_tokens: { + description: "Maximum number of tokens to generate.", + minimum: 1, + placeholder: 100, + title: "Max Tokens", + type: "integer" + }, + temperature: { + description: "Sampling temperature. Higher values make the output more random, while lower values make it more focused and deterministic.", + maximum: 1, + minimum: 0, + placeholder: 0.7, + title: "temperature", + type: "number" + }, + frequency_penalty: { + description: "Penalty for repeated tokens in the output. Higher values reduce the likelihood of repetition, encouraging more diverse text generation.", + maximum: 2, + minimum: 0, + placeholder: 0.1, + title: "Frequency Penalty", + type: "number" + }, + n_ctx: { + description: "Maximum number of tokens the model can process in a single forward pass (context window size).", + minimum: 1, + placeholder: 512, + title: "N Ctx", + type: "integer" + } + }, + required: [ + "max_tokens", + "temperature", + "frequency_penalty", + "n_ctx" + ], + title: "GemmaSchema", + type: "object" + }, + metadata: { + inputs_cardinality: 1, + outputs_cardinality: 1, + inputs_types: ["text"], + outputs_types: ["text"] + }, + description: "null,", + display_name: "null" + }, + { + name: "QwenModel", + type: "GenerativeModel", + configurable_object: true, + schema: { + description: "Schema for Qwen model.", + properties: { + "model_name": { + description: "The specific Qwen model version to use.", + "enum": [ + "Qwen/Qwen2.5-0.5B-Instruct-GGUF", + "Qwen/Qwen2.5-1.5B-Instruct-GGUF" + ], + placeholder: "Qwen/Qwen2.5-1.5B-Instruct-GGUF", + title: "Model Name", + type: "string" + }, + max_tokens: { + description: "Maximum number of tokens to generate.", + minimum: 1, + placeholder: 100, + title: "Max Tokens", + type: "integer" + }, + temperature: { + description: "Sampling temperature. Higher values make the output more random, while lower values make it more focused and deterministic.", + maximum: 1, + minimum: 0, + placeholder: 0.7, + title: "temperature", + type: "number" + }, + frequency_penalty: { + description: "Penalty for repeated tokens in the output. Higher values reduce the likelihood of repetition, encouraging more diverse text generation.", + maximum: 2, + minimum: 0, + placeholder: 0.1, + title: "Frequency Penalty", + type: "number" + }, + n_ctx: { + description: "Maximum number of tokens the model can process in a single forward pass (context window size).", + minimum: 1, + placeholder: 512, + title: "N Ctx", + type: "integer" + } + }, + required: [ + "model_name", + "max_tokens", + "temperature", + "frequency_penalty", + "n_ctx" + ], + title: "QwenSchema", + type: "object" + }, + metadata: { + inputs_cardinality: 1, + outputs_cardinality: 1, + inputs_types: ["text"], + outputs_types: ["text"] + }, + description: "null,", + display_name: "null" + } +] + +// Fetch all RAG sessions +export const getRAGSessions = async (): Promise => { + console.log("Fetching all RAG sessions"); + + // Simulate an API call + /* + const response = await api.get( + "/v1/generative-session + */ + return [...sessions]; +} + +// Create a new RAG session +export const createRAGSession = async ( + sessionData: Omit +): Promise => { + const newSession: ISession = { + ...sessionData, + id: (Math.random() * 100000).toFixed(0), + }; + // Simulate an API call + /* + const response = await api.post("/v1/rag-session/", newSession); + */ + sessions.push(newSession); + return newSession; +} + +// Update an existing RAG session +export const updateRAGSession = async ( + sessionData: ISession +): Promise => { + + // Mockup data + const index = sessions.findIndex((s) => s.id === sessionData.id); + if (index === -1) throw new Error("Session not found"); + sessions[index] = sessionData; + + // Simulate an API call + /* + const response = await api.put( + `/v1/rag-session/${sessionData.id}`, + sessionData + ); + */ + + return sessionData; +} + +// Delete a RAG session by ID +export const deleteRAGSession = async (sessionId: string): Promise => { + + // mockup data + sessions = sessions.filter((s) => s.id !== sessionId); + + // Simulate an API call + /* + removeSession(sessionId); + */ + + console.log(`Deleted session with ID: ${sessionId}`); + return; +} + +// Fetch a single RAG session by ID +export const getRAGSession = async (sessionId: string): Promise => { + console.log(`Fetching session with ID: ${sessionId}`); + + // Simulate an API call + /* + const response = await api.get( + `/v1/generative-session/${sessionId}` + + return response.data; + */ + + const session = sessions.find((s) => s.id === sessionId); + if (!session) throw new Error("Session not found"); + + return [session]; +} + +export const getRAGComponents = async (componentType: string): Promise => { + // Simulate fetching RAG components based on type + /* + const response = await api.get( + `/v1/component/?related_component=RAGTask&component_type=${componentType}` + ); + return response.data; + */ + + let filteredComponents = components.filter((c) => c.type === componentType); + console.log(`Fetching RAG ${filteredComponents.length} components of type ${componentType}`); + return filteredComponents; +}; + +export const getRetrieverComponents = async (): Promise => { + return getRAGComponents("retriever"); +} + +export const getGeneratorComponents = async (): Promise => { + return getRAGComponents("GenerativeModel"); +} \ No newline at end of file diff --git a/DashAI/front/src/components/datasets/ConfigureAndUploadDataset.jsx b/DashAI/front/src/components/datasets/ConfigureAndUploadDataset.jsx index a852759f6..101b68ae4 100644 --- a/DashAI/front/src/components/datasets/ConfigureAndUploadDataset.jsx +++ b/DashAI/front/src/components/datasets/ConfigureAndUploadDataset.jsx @@ -1,7 +1,7 @@ import React, { useState, useEffect } from "react"; -import { Grid, Paper } from "@mui/material"; +import { Grid, Paper, Typography } from "@mui/material"; import PropTypes from "prop-types"; -import Upload from "./Upload"; +import Upload from "../shared/Upload"; import { getComponents as getComponentsRequest } from "../../api/component"; import { useSnackbar } from "notistack"; import DataloaderConfiguration from "./DataloaderConfiguration"; @@ -77,7 +77,18 @@ function ConfigureAndUploadDataset({ > {/* Upload file */} - + + Upload your dataset + + If your dataset have splits, upload it as a zip file + + + } + /> + {/* Configure dataloader parameters */} diff --git a/DashAI/front/src/components/documents/DocumentTable.jsx b/DashAI/front/src/components/documents/DocumentTable.jsx new file mode 100644 index 000000000..320c96d90 --- /dev/null +++ b/DashAI/front/src/components/documents/DocumentTable.jsx @@ -0,0 +1,205 @@ +import React, { useEffect, useState, useCallback } from "react"; +import { DataGrid, GridToolbar } from "@mui/x-data-grid"; +import { useSnackbar } from "notistack"; +import { Button, Grid, Paper, Typography, LinearProgress, Box } from "@mui/material"; +import { + get_documents_json, + delete_document as deleteDocumentRequest, +} from "../../api/documents"; +import { + AddCircleOutline as AddIcon, + Update as UpdateIcon, +} from "@mui/icons-material"; +import DeleteItemModal from "../custom/DeleteItemModal"; +import DocumentSummaryModal from "./DocumentSummaryModal"; + +function DocumentTable({ + handleNewDocument, + updateTableFlag, + setUpdateTableFlag, +}) { + const { enqueueSnackbar } = useSnackbar(); + const [loading, setLoading] = useState(true); + const [documents, setDocuments] = useState([]); + const [selectionModel, setSelectionModel] = useState([]); + + const getDocuments = useCallback(async () => { + setLoading(true); + try { + const docs = await get_documents_json(); + setDocuments(docs); + } catch (error) { + enqueueSnackbar("Error when trying to get the documents", { + variant: "error", + }); + console.error("Error fetching documents:", error); + } finally { + setLoading(false); + } + }, [enqueueSnackbar]); + + const deleteDocument = async (doc_name) => { + try { + await deleteDocumentRequest(doc_name); + setUpdateTableFlag(true); + enqueueSnackbar("Document successfully deleted.", { + variant: "success", + }); + } catch (error) { + enqueueSnackbar("Error when trying to delete the document", { + variant: "error", + }); + console.error("Error deleting document:", error); + } + }; + + const createDeleteHandler = useCallback( + (doc_name) => () => { + deleteDocument(doc_name); + }, + [deleteDocument], + ); + + useEffect(() => { + getDocuments(); + }, [getDocuments]); + + useEffect(() => { + if (updateTableFlag) { + getDocuments(); + setUpdateTableFlag(false); + } + }, [updateTableFlag, setUpdateTableFlag, getDocuments]); + + const columns = React.useMemo( + () => [ + { + field: "id", + headerName: "ID", + minWidth: 30, + editable: false, + }, + { + field: "doc_name", + headerName: "Document Name", + minWidth: 200, + editable: false, + }, + { + field: "created_at", + headerName: "Created At", + minWidth: 200, + editable: false, + }, + { + field: "actions", + type: "actions", + minWidth: 150, + getActions: (params) => [ + , + , + ], + }, + ], + [createDeleteHandler], + ); + + return ( + + + + Current documents + + + + + + + + + + + + + + + {(!loading && documents.length === 0) ? ( + + + No documents available + + + ) : ( + row.id} + pageSize={5} + sortModel={[{ field: "id", sort: "asc" }]} + pageSizeOptions={[5, 10]} + checkboxSelection + selectionModel={selectionModel} + onSelectionModelChange={setSelectionModel} + disableRowSelectionOnClick={false} + autoHeight={false} + loading={loading} + slots={{ + toolbar: GridToolbar, + loadingOverlay: LinearProgress, + }} + sx={{ + flex: 1, + minHeight: 300, + "& .MuiDataGrid-cell:focus": { + outline: "none", + }, + }} + /> + )} + + + ); +} + +export default DocumentTable; \ No newline at end of file diff --git a/DashAI/front/src/components/experiments/EditModelDialog.jsx b/DashAI/front/src/components/experiments/EditModelDialog.jsx index 19868e857..a86984323 100644 --- a/DashAI/front/src/components/experiments/EditModelDialog.jsx +++ b/DashAI/front/src/components/experiments/EditModelDialog.jsx @@ -36,12 +36,12 @@ function EditModelDialog({ }} > { updateParameters(values); setOpen(false); }} - modelToConfigure={modelToConfigure} - initialValues={paramsInitialValues} onCancel={() => setOpen(false)} /> diff --git a/DashAI/front/src/components/generative/MainGenerativeBox.jsx b/DashAI/front/src/components/generative/MainGenerativeBox.jsx index 239c03cf2..6f69b9967 100644 --- a/DashAI/front/src/components/generative/MainGenerativeBox.jsx +++ b/DashAI/front/src/components/generative/MainGenerativeBox.jsx @@ -7,8 +7,8 @@ export default function MainGenerativeBox({ children }) { width={"100%"} height={"100%"} sx={{ - border: "0.2px solid", - borderColor: "grey.600", // o el color que prefieras + //border: "0.2px solid", + //borderColor: "grey.600", // o el color que prefieras }} borderRadius={2} //bgcolor={"background.box"} diff --git a/DashAI/front/src/components/generative/ParamsBar.jsx b/DashAI/front/src/components/generative/ParamsBar.jsx index 4d4870b01..5c25f3aeb 100644 --- a/DashAI/front/src/components/generative/ParamsBar.jsx +++ b/DashAI/front/src/components/generative/ParamsBar.jsx @@ -40,7 +40,7 @@ export default function ParamsBar({ getGenerativeSession(selectedSessionId) .then((session) => { setParameters(session.parameters); - + console.log("Session task name:", session.task_name); getRelatedComponents(session.task_name).then((relatedComponents) => { const relatedModel = relatedComponents.find( (component) => component.name === session.model_name, diff --git a/DashAI/front/src/components/generative/RAG/ComponentSelector.jsx b/DashAI/front/src/components/generative/RAG/ComponentSelector.jsx new file mode 100644 index 000000000..457a05798 --- /dev/null +++ b/DashAI/front/src/components/generative/RAG/ComponentSelector.jsx @@ -0,0 +1,178 @@ +import React, { useState, useEffect } from "react"; +import PropTypes from "prop-types"; +import { + Box, + Typography, + Autocomplete, + TextField, + CircularProgress +} from "@mui/material"; +import FormSchemaRenderFields from "../../shared/FormSchemaRenderFields"; +import { useFormik } from "formik"; +import { preprocessSchema, buildYupSchema } from "../utils"; +import { getRelatedComponents } from "../../../api/generativeTask"; +function ComponentSelector({ + componentType, + fetchComponents, + initialValues, + onConfigurationChange, + setNextEnabled +}) { + const [components, setComponents] = useState([]); + const [selectedComponent, setSelectedComponent] = useState(null); + const [loading, setLoading] = useState(true); + const [validationSchema, setValidationSchema] = useState(null); + + try { + console.log(getRelatedComponents("TextToTextGenerationTask")); + } catch (error) { + } + + + + // Fetch available components + useEffect(() => { + const getComponents = async () => { + setLoading(true); + try { + const data = await fetchComponents(); + setComponents(data); + + // If we have initialValues with a model_name, select that component + if (initialValues?.model_name) { + const preselectedComponent = data.find(c => c.name === initialValues.model_name); + if (preselectedComponent) { + setSelectedComponent(preselectedComponent); + } + } else { + console.warn(`No initial model_name provided for ${componentType} selection.`); + } + } catch (error) { + console.error(`Error fetching ${componentType} components:`, error); + } finally { + setLoading(false); + } + }; + + getComponents(); + }, [componentType, fetchComponents]); + + // Set up form validation schema when component changes + useEffect(() => { + if (selectedComponent?.schema?.properties) { + const processedProps = preprocessSchema(selectedComponent.schema.properties); + setValidationSchema(buildYupSchema(processedProps)); + console.log("Processed properties for form:", processedProps); + // Merge any existing values with defaults from schema + const schemaDefaults = Object.keys(processedProps).reduce( + (acc, key) => { + acc[key] = processedProps[key].placeholder || ""; + console.log(`Setting default for ${key}:`, acc[key]); + return acc; + }, + { name: "", description: ""}); + + // Preserve any values that were already set + const mergedValues = { + ...schemaDefaults, + ...(initialValues?.parameters || {}) + }; + + formik.setValues(mergedValues); + } + }, [selectedComponent]); + + const formik = useFormik({ + initialValues: initialValues?.parameters || {}, + validationSchema, + enableReinitialize: true, + onSubmit: (values) => { + onConfigurationChange({ + model_name: selectedComponent.name, + parameters: values + }); + } + }); + + // Enable/disable next button based on form validity and selection + useEffect(() => { + if (selectedComponent && formik.isValid) { + setNextEnabled(true); + + // Auto-submit valid values as they change + if (formik.dirty) { + onConfigurationChange({ + model_name: selectedComponent.name, + parameters: formik.values + }); + } + } else { + setNextEnabled(false); + } + }, [selectedComponent, formik.values, formik.isValid, formik.dirty]); + + // Process schema properties for the form renderer + const processedProperties = selectedComponent?.schema?.properties + ? preprocessSchema(selectedComponent.schema.properties) + : {}; + + return ( + + + Select {componentType} + + + {loading ? ( + + + + ) : ( + <> + c.name)} + value={selectedComponent?.name || null} + onChange={(_, newValue) => { + const selected = components.find(c => c.name === newValue); + setSelectedComponent(selected); + }} + renderInput={(params) => ( + + )} + sx={{ mb: 4 }} + /> + + {selectedComponent && ( +
+ + {selectedComponent.name} Configuration + + + { + formik.setValues({...formik.values, ...updatedValues}); + }} + onFormSubmit={formik.handleSubmit} + setError={(error) => console.error(error)} + errorsMessage={formik.errors} + /> + + )} + + )} +
+ ); +} + +ComponentSelector.propTypes = { + componentType: PropTypes.string.isRequired, + fetchComponents: PropTypes.func.isRequired, + initialValues: PropTypes.object, + onConfigurationChange: PropTypes.func.isRequired, + setNextEnabled: PropTypes.func.isRequired +}; + +export default ComponentSelector; \ No newline at end of file diff --git a/DashAI/front/src/components/generative/RAG/DocumentDropzone.jsx b/DashAI/front/src/components/generative/RAG/DocumentDropzone.jsx new file mode 100644 index 000000000..01eeb326d --- /dev/null +++ b/DashAI/front/src/components/generative/RAG/DocumentDropzone.jsx @@ -0,0 +1,45 @@ +import { Box, Typography, Paper } from "@mui/material"; +import { useCallback } from "react"; +import { useDropzone } from "react-dropzone"; +import Upload from "../../shared/Upload"; +let idCounter = 200; + +export default function DocumentDropzone({ onAddDocument }) { + const onDrop = useCallback((acceptedFiles) => { + acceptedFiles.forEach((file) => { + const newDoc = { + id: idCounter++, + name: file.name, + updatedAt: new Date().toLocaleDateString(), + }; + onAddDocument(newDoc); + }); + }, [onAddDocument]); + + const { getRootProps, getInputProps, isDragActive } = useDropzone({ onDrop }); + + return ( + + + + Drag & drop documents here, or click to upload + + + ); +} diff --git a/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx b/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx new file mode 100644 index 000000000..944cad664 --- /dev/null +++ b/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx @@ -0,0 +1,106 @@ +import { Box } from "@mui/material"; +import { useEffect, useState } from "react"; +import DocumentTable from "./DocumentTable"; +import Upload from "../../shared/Upload"; + +export default function DocumentSelector({ + selected = [], // Provide default empty array to prevent map errors + onSelect +}) { + const [documents, setDocuments] = useState([]); + const [selectedIds, setSelectedIds] = useState( + (selected || []).map(doc => doc.id) + ); + // Add a key state to force Upload component re-render + const [uploadKey, setUploadKey] = useState(0); + + // Fetch documents on mount + useEffect(() => { + // Simulated API + const fetchDocuments = async () => { + // Return mock data if needed + return [ + { id: "doc1", name: "Document 1", updatedAt: new Date().toISOString() }, + { id: "doc2", name: "Document 2", updatedAt: new Date().toISOString() } + ]; + }; + + fetchDocuments().then(setDocuments); + }, []); + + // Sync selected documents with parent + useEffect(() => { + const selectedDocs = documents.filter(doc => selectedIds.includes(doc.id)); + if (onSelect) { + onSelect(selectedDocs); + } + }, [selectedIds, documents, onSelect]); + + const handleToggleSelection = (id) => { + setSelectedIds(prev => + prev.includes(id) ? prev.filter(x => x !== id) : [...prev, id] + ); + }; + + const handleSelectAll = () => { + setSelectedIds(documents.map(doc => doc.id)); + }; + + const handleDeselectAll = () => { + setSelectedIds([]); + }; + + const handleAddDocument = (newDoc) => { + const newDocWithId = { + ...newDoc, + id: `doc-${Date.now()}`, + updatedAt: new Date().toISOString() + }; + + setDocuments(prev => [newDocWithId, ...prev]); + setSelectedIds(prev => [...prev, newDocWithId.id]); + }; + + const handleRemove = (id) => { + setDocuments(prev => prev.filter(doc => doc.id !== id)); + setSelectedIds(prev => prev.filter(x => x !== id)); + }; + + const handleFileUpload = (file, url) => { + if (!file) return; + + const newDoc = { + id: `doc-${Date.now()}`, + name: file.name, + updatedAt: new Date().toISOString(), + preview: url + }; + + handleAddDocument(newDoc); + + // Reset the upload component by incrementing its key + setUploadKey(prev => prev + 1); + }; + + return ( + + + + + + + + + + ); +} \ No newline at end of file diff --git a/DashAI/front/src/components/generative/RAG/DocumentTable.jsx b/DashAI/front/src/components/generative/RAG/DocumentTable.jsx new file mode 100644 index 000000000..94293a4a7 --- /dev/null +++ b/DashAI/front/src/components/generative/RAG/DocumentTable.jsx @@ -0,0 +1,101 @@ +import { + Box, + Table, + TableBody, + TableCell, + TableHead, + TableRow, + Checkbox, + Typography, + IconButton, + Tooltip, + Button, +} from "@mui/material"; +import VisibilityIcon from "@mui/icons-material/Visibility"; +import DeleteIcon from "@mui/icons-material/Delete"; +import { Margin } from "@mui/icons-material"; + +export default function DocumentTable({ + documents, + selectedIds, + onToggle, + onSelectAll, + onDeselectAll, + onRemove, +}) { + + return ( + + {documents.length === 0 ? ( + + No documents available. + + ) : ( + <> + + + + + Name + Updated + Actions + + + + {documents.map((doc) => ( + + + onToggle(doc.id)} + /> + + {doc.name} + {doc.updatedAt} + + + + + + + + + onRemove(doc.id)}> + + + + + + + ))} + +
+ + + + + + )} + + ); +} diff --git a/DashAI/front/src/components/generative/RAG/SessionMetadata.jsx b/DashAI/front/src/components/generative/RAG/SessionMetadata.jsx new file mode 100644 index 000000000..b543d748a --- /dev/null +++ b/DashAI/front/src/components/generative/RAG/SessionMetadata.jsx @@ -0,0 +1,82 @@ +import React, { useEffect } from "react"; +import PropTypes from "prop-types"; +import { Box, TextField, Typography } from "@mui/material"; +import { useFormik } from "formik"; +import * as yup from "yup"; + +const validationSchema = yup.object({ + name: yup.string().required("Session name is required"), + description: yup.string() +}); + +function SessionMetadata({ + sessionData, + onUpdate, + setNextEnabled +}) { + const formik = useFormik({ + initialValues: { + name: sessionData.name || "", + description: sessionData.description || "" + }, + validationSchema, + onSubmit: (values) => { + onUpdate(values); + } + }); + + useEffect(() => { + // Enable next button if form is valid + setNextEnabled(formik.isValid); + + // Auto-update parent component when values change + if (formik.isValid) { + onUpdate({ + name: formik.values.name, + description: formik.values.description + }); + } + }, [formik.values, formik.isValid]); + + return ( + + + Session Information + + + + + + + ); +} + +SessionMetadata.propTypes = { + sessionData: PropTypes.object.isRequired, + onUpdate: PropTypes.func.isRequired, + setNextEnabled: PropTypes.func.isRequired +}; + +export default SessionMetadata; \ No newline at end of file diff --git a/DashAI/front/src/components/generative/SelectModelMenu.jsx b/DashAI/front/src/components/generative/SelectModelMenu.jsx index cd855bc26..aa15b71e6 100644 --- a/DashAI/front/src/components/generative/SelectModelMenu.jsx +++ b/DashAI/front/src/components/generative/SelectModelMenu.jsx @@ -24,7 +24,7 @@ export default function SelectModelMenu({ useEffect(() => { if (!selectedTaskName) return; - + console.log("Fetching related components for task:", selectedTaskName); getRelatedComponents(selectedTaskName).then(setRelatedComponents); }, [selectedTaskName]); diff --git a/DashAI/front/src/components/shared/FormSchemaRenderFields.jsx b/DashAI/front/src/components/shared/FormSchemaRenderFields.jsx index 70bfaf4a6..ce55f9b59 100644 --- a/DashAI/front/src/components/shared/FormSchemaRenderFields.jsx +++ b/DashAI/front/src/components/shared/FormSchemaRenderFields.jsx @@ -19,6 +19,8 @@ function FormSchemaRenderFields({ }) { if (!modelSchema) return null; + console.debug("Rendering FormSchemaRenderFields with modelSchema:", modelSchema); + const renderFields = useCallback(() => { const fields = []; diff --git a/DashAI/front/src/components/datasets/Upload.jsx b/DashAI/front/src/components/shared/Upload.jsx similarity index 94% rename from DashAI/front/src/components/datasets/Upload.jsx rename to DashAI/front/src/components/shared/Upload.jsx index 011cf7e56..60e887978 100644 --- a/DashAI/front/src/components/datasets/Upload.jsx +++ b/DashAI/front/src/components/shared/Upload.jsx @@ -20,7 +20,7 @@ import ClearIcon from "@mui/icons-material/Clear"; * newDataset state in the modal * @param {function} onFileUpload function to handle when the user "uploads" a dataset */ -function Upload({ onFileUpload }) { +function Upload({ onFileUpload, emptyUploadText }) { const [EMPTY, LOADING, LOADED] = [0, 1, 2]; const [datasetState, setDatasetState] = useState(EMPTY); const [dragActive, setDragActive] = useState(false); @@ -129,14 +129,10 @@ function Upload({ onFileUpload }) { {/* state text */} - {datasetState === EMPTY && "Upload your dataset"} {datasetState === LOADING && "Loading..."} {datasetState === LOADED && "Loaded"} - {datasetState === EMPTY && ( - - If your dataset have splits, upload it as a zip file - - )} + {/* {datasetState === EMPTY && "Upload your dataset"} */} + {datasetState === EMPTY &&emptyUploadText} @@ -190,6 +186,10 @@ function Upload({ onFileUpload }) { Upload.propTypes = { onFileUpload: PropTypes.func.isRequired, + emptyUploadText: PropTypes.oneOfType([ + PropTypes.string, + PropTypes.node, + ]) }; export default Upload; diff --git a/DashAI/front/src/pages/generative/Generative.jsx b/DashAI/front/src/pages/generative/Generative.jsx index 079b0329c..8568d46a1 100644 --- a/DashAI/front/src/pages/generative/Generative.jsx +++ b/DashAI/front/src/pages/generative/Generative.jsx @@ -1,4 +1,4 @@ -import { Box } from "@mui/material"; +import { Box, Typography } from "@mui/material"; import { useState, useEffect } from "react"; import SessionBar from "../../components/generative/SessionBar"; import MainGenerativeBox from "../../components/generative/MainGenerativeBox"; @@ -6,6 +6,9 @@ import SelectTaskMenu from "../../components/generative/SelectTaskMenu"; import GenerativeChat from "../../components/generative/GenerativeChat"; import SelectModelMenu from "../../components/generative/SelectModelMenu"; import ParamsBar from "../../components/generative/ParamsBar"; + +import RAGHomePage from "./RAG/RAGHomePage"; + import { getSessions, removeSession } from "../../api/session"; export default function Generative() { @@ -15,39 +18,48 @@ export default function Generative() { const [sessions, setSessions] = useState([]); const [paramsVersion, setParamsVersion] = useState(0); + const isRAGTask = () => selectedTaskName === "RAGTask"; + const handleSessionClick = (sessionId, taskName) => { + console.log("[handleSessionClick]", { sessionId, taskName }); setSelectedTaskName(taskName); setSelectedSessionId(sessionId); }; const handleNewSessionButton = () => { + console.log("[handleNewSessionButton]"); setSelectedSessionId(null); setStepIndex(0); setSelectedTaskName(""); }; const onParamsUpdate = (newParams) => { + console.log("[onParamsUpdate]", newParams); setParamsVersion((prev) => prev + 1); }; useEffect(() => { getSessions().then((data) => { + console.log("[getSessions]", data); setSessions(data); }); }, []); const handleAddSession = (session) => { + console.log("[handleAddSession]", session); setSessions((prevSessions) => [session, ...prevSessions]); }; const handleSessionDelete = (id) => { + console.log("[handleSessionDelete]", id); if (id === selectedSessionId) { setSelectedSessionId(null); setStepIndex(0); + setSelectedTaskName(""); } setSessions((prevSessions) => - prevSessions.filter((session) => session.id !== id), + prevSessions.filter((session) => session.id !== id) ); removeSession(id); @@ -65,45 +77,67 @@ export default function Generative() { stepIndex={stepIndex} /> + {selectedSessionId ? ( - - ) : stepIndex === 0 ? ( - { - setSelectedTaskName(taskName); - setStepIndex(1); - }} - /> - ) : ( - - )} + isRAGTask() ? ( + + ) : ( + + ) + ) : stepIndex === 0 ? ( + { + console.log("[SelectTaskMenu] task selected:", taskName); + setSelectedTaskName(taskName); + setStepIndex(1); + }} + /> + ) : isRAGTask() ? ( + + ) : ( + + ) + } + + {!isRAGTask() && ( - {selectedSessionId ? ( - - ) : null} + {!selectedSessionId ? + ( + + ): null} + ) + } ); } diff --git a/DashAI/front/src/pages/generative/RAG/AlgorithmConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/AlgorithmConfigurationStep.jsx new file mode 100644 index 000000000..90465957b --- /dev/null +++ b/DashAI/front/src/pages/generative/RAG/AlgorithmConfigurationStep.jsx @@ -0,0 +1,41 @@ +import React from "react"; +import PropTypes from "prop-types"; +import { Box } from "@mui/material"; +import ComponentSelector from "../../../components/generative/RAG/ComponentSelector"; +import { getGeneratorComponents } from "../../../api/rag"; + +function AlgorithmConfigurationStep({ + newSession, + setNewSession, + setNextEnabled +}) { + const handleConfigurationChange = (generatorConfig) => { + setNewSession(prev => ({ + ...prev, + RAGParameters: { + ...prev.RAGParameters, + generation: generatorConfig + } + })); + }; + + return ( + + + + ); +} + +AlgorithmConfigurationStep.propTypes = { + newSession: PropTypes.object.isRequired, + setNewSession: PropTypes.func.isRequired, + setNextEnabled: PropTypes.func.isRequired, +}; + +export default AlgorithmConfigurationStep; \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/DocumentSelectionStep.jsx b/DashAI/front/src/pages/generative/RAG/DocumentSelectionStep.jsx new file mode 100644 index 000000000..4b7321461 --- /dev/null +++ b/DashAI/front/src/pages/generative/RAG/DocumentSelectionStep.jsx @@ -0,0 +1,94 @@ +import React, { useState, useEffect } from "react"; +import PropTypes from "prop-types"; +import { Box, CircularProgress } from "@mui/material"; +import DocumentSelector from "../../../components/generative/RAG/DocumentSelector"; +import SessionMetadata from "../../../components/generative/RAG/SessionMetadata"; + +function DocumentSelectionStep({ newSession, setNewSession, setNextEnabled }) { + const [documents, setDocuments] = useState([]); + const [loading, setLoading] = useState(true); + + // Fetch available documents on component mount + useEffect(() => { + const fetchDocuments = async () => { + setLoading(true); + try { + // Replace this with your actual API call to get documents + // For now using a mock response with delay + await new Promise(resolve => setTimeout(resolve, 500)); + const data = [ + { id: 1, name: "Product Documentation.pdf", type: "PDF", size: "1.2 MB", updatedAt: "2025-05-15" }, + { id: 2, name: "User Manual.docx", type: "DOCX", size: "3.4 MB", updatedAt: "2025-05-20" }, + { id: 3, name: "API Reference.pdf", type: "PDF", size: "2.1 MB", updatedAt: "2025-06-01" }, + { id: 4, name: "Technical Specifications.txt", type: "TXT", size: "4.7 MB", updatedAt: "2025-06-10" }, + { id: 5, name: "Troubleshooting Guide.pdf", type: "PDF", size: "1.8 MB", updatedAt: "2025-06-15" } + ]; + setDocuments(data); + } catch (error) { + console.error("Failed to fetch documents:", error); + } finally { + setLoading(false); + } + }; + + fetchDocuments(); + }, []); + + // Update next button state whenever documents or session name changes + useEffect(() => { + const hasDocuments = newSession.documents && newSession.documents.length > 0; + const hasName = Boolean(newSession.name); + setNextEnabled(hasDocuments && hasName); + }, [newSession.documents, newSession.name, setNextEnabled]); + + const handleSessionMetadataUpdate = (metadata) => { + setNewSession(prev => ({ + ...prev, + name: metadata.name, + description: metadata.description + })); + }; + + const handleDocumentsSelect = (selectedDocuments) => { + setNewSession(prev => ({ + ...prev, + documents: selectedDocuments + })); + }; + + return ( + + + + {loading ? ( + + + + ) : ( + + )} + + ); +} + +DocumentSelectionStep.propTypes = { + newSession: PropTypes.object.isRequired, + setNewSession: PropTypes.func.isRequired, + setNextEnabled: PropTypes.func.isRequired, +}; + +export default DocumentSelectionStep; \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/ModelConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/ModelConfigurationStep.jsx new file mode 100644 index 000000000..14d3d9367 --- /dev/null +++ b/DashAI/front/src/pages/generative/RAG/ModelConfigurationStep.jsx @@ -0,0 +1,50 @@ +import React, { useState, useEffect } from "react"; +import PropTypes from "prop-types"; +import { Box } from "@mui/material"; +import FormSchemaWithSelectedModel from "../../../components/shared/FormSchemaWithSelectedModel"; + +function ModelConfigurationStep({ + taskName, + initialParameters = {}, + onConfigurationChange, + setNextEnabled +}) { + const [parameters, setParameters] = useState(initialParameters); + const [isConfigured, setIsConfigured] = useState(false); + + useEffect(() => { + if (Object.keys(parameters).length > 0) { + onConfigurationChange(parameters); + setIsConfigured(true); + } + }, [parameters, onConfigurationChange]); + + useEffect(() => { + setNextEnabled(isConfigured); + }, [isConfigured, setNextEnabled]); + + const handleFormSubmit = (values) => { + setParameters(values); + setIsConfigured(true); + }; + + return ( + + {}} + /> + + ); +} + +ModelConfigurationStep.propTypes = { + taskName: PropTypes.string.isRequired, + initialParameters: PropTypes.object, + onConfigurationChange: PropTypes.func.isRequired, + setNextEnabled: PropTypes.func.isRequired, +}; + +export default ModelConfigurationStep; \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx new file mode 100644 index 000000000..8537b3fbf --- /dev/null +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx @@ -0,0 +1,252 @@ +import React, { useState, useEffect } from "react"; +import { + Button, + ButtonGroup, + Dialog, + DialogActions, + DialogTitle, + Grid, + IconButton, + Step, + StepButton, + DialogContent, + Stepper, + Typography, +} from "@mui/material"; +import CloseIcon from "@mui/icons-material/Close"; +import { useTheme } from "@mui/material/styles"; +import useMediaQuery from "@mui/material/useMediaQuery"; +import { useSnackbar } from "notistack"; + +import DocumentSelectionStep from "./DocumentSelectionStep"; +import RetrieverConfigurationStep from "./RetrieverConfigurationStep"; +import AlgorithmConfigurationStep from "./AlgorithmConfigurationStep"; +import { createRAGSession, updateRAGSession } from "../../../api/rag"; + +const steps = [ + { + name: "select-documents", + label: "Select documents", + Component: DocumentSelectionStep, + }, + { + name: "configure-retriever", + label: "Configure retriever", + Component: RetrieverConfigurationStep, + }, + { + name: "configure-algorithm", + label: "Configure algorithm", + Component: AlgorithmConfigurationStep, + }, +]; + +const defaultNewSession = { + name: "", + task_name: "RAGTask", // Default task name + description: "", + RAGParameters: {}, + documents: [], +}; + +export default function NewSessionModal({ + open, + setOpen, + setUpdateTableFlag, + setSelectedSessionId, + handleAddSession, + selectedSession, +}) { + const theme = useTheme(); + const matches = useMediaQuery(theme.breakpoints.down("md")); + const screenSm = useMediaQuery(theme.breakpoints.down("sm")); + const { enqueueSnackbar } = useSnackbar(); + + const [activeStep, setActiveStep] = useState(0); + const [nextEnabled, setNextEnabled] = useState(false); + const [newSession, setNewSession] = useState( + selectedSession || defaultNewSession + ); + + const [stepCompleted, setStepCompleted] = useState([false, false, false]); + + const handleStepComplete = (stepIndex) => { + const newCompleted = [...stepCompleted]; + newCompleted[stepIndex] = true; + setStepCompleted(newCompleted); + }; + + // Initialize session data when modal opens or selectedSession changes + useEffect(() => { + if (open) { + if (selectedSession) { + // Edit mode - use existing session + setNewSession({ + ...selectedSession, + // Ensure documents array exists + documents: selectedSession.documents || [] + }); + } else { + // Create mode - use defaults + setNewSession(defaultNewSession); + } + setActiveStep(0); + setNextEnabled(false); + } + }, [open, selectedSession]); + + + const uploadNewSession = async () => { + try { + // For new sessions, exclude ID + const sessionData = selectedSession + ? newSession // Keep existing ID for updates + : { ...newSession, id: undefined }; // Remove ID for new sessions + + const response = selectedSession + ? await updateRAGSession(sessionData) // Implement update function if needed + : await createRAGSession(sessionData); + + const sessionId = response.id; + console.log("Session created/updated with ID:", sessionId); + + enqueueSnackbar( + `Session ${selectedSession ? "updated" : "created"} successfully!`, + { variant: "success" } + ); + setUpdateTableFlag(true); // Trigger table update + } catch (error) { + console.error("Session creation error:", error); + enqueueSnackbar( + `Failed to ${selectedSession ? "update" : "create"} session: ${error.message}`, + { variant: "error" } + ); + } + }; + + const handleCloseDialog = () => { + setActiveStep(0); + setOpen(false); + setNewSession(defaultNewSession); + setNextEnabled(false); + + }; + + const handleStepButton = (index) => () => { + setActiveStep(index); + }; + + const handleBackButton = () => { + if (activeStep === 0) { + handleCloseDialog(); + } else { + setActiveStep(activeStep - 1); + setNextEnabled(true); + } + }; + + const handleNextButton = () => { + if (activeStep === steps.length - 1) { + handleFinish(); + } else { + setActiveStep(activeStep + 1); + setNextEnabled(false); // Reset next enabled for new step + } + }; + + const handleFinish = async () => { + await uploadNewSession(); + handleCloseDialog(); + }; + + return ( + + + + + + + + + + + + + {selectedSession ? "Edit" : "Create New"} RAG Session + + + + + + + {steps.map((step, index) => ( + index} + disabled={activeStep < index}> + + {step.label} + + + ))} + + + + + + + {activeStep === 0 && ( + + )} + {activeStep === 1 && ( + + )} + {activeStep === 2 && ( + + )} + + + + + + + + + + ); +} diff --git a/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx b/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx new file mode 100644 index 000000000..26107a1f6 --- /dev/null +++ b/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx @@ -0,0 +1,55 @@ +import React from "react"; + +import NewSessionModal from "./NewSessionModal"; +import RAGSessionsTable from "./RAGSessionsTable.jsx"; + +import CustomLayout from "../../../components/custom/CustomLayout.jsx"; + +function RAGHomePage({ + handleAddSession, + selectedTaskName, + setSelectedSessionId + }) { + const [showNewSessionModal, setShowNewSessionModal] = React.useState(false); + const [updateTableFlag, setUpdateTableFlag] = React.useState(false); + const [selectedSession, setSelectedSession] = React.useState(null); + + const handleOpenNewSessionModal = () => { + setSelectedSession(null); + setShowNewSessionModal(true); + } + + const handleSessionSelect = (session) => { + setSelectedSession(session); + setShowNewSessionModal(true); + setSelectedSessionId(session.id); + } + + return ( + + {showNewSessionModal? ( + + ):( + + ) + } + + ); +} + +export default RAGHomePage; \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx b/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx new file mode 100644 index 000000000..560ae5f28 --- /dev/null +++ b/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx @@ -0,0 +1,175 @@ + +import React from "react"; +import PropTypes from "prop-types"; + +import { + AddCircleOutline as AddIcon, + Update as UpdateIcon, +} from "@mui/icons-material"; +import { DataGrid, GridToolbar } from "@mui/x-data-grid"; +import { Button, Grid, Paper, Typography, LinearProgress } from "@mui/material"; +import { useSnackbar } from "notistack"; + +import { getRAGSessions, deleteRAGSession } from "../../../api/rag"; // Adjust the import path as necessary + +function RAGSessionsTable({ + handleOpenNewSessionModal, + onSessionSelect, + updateTableFlag, + setUpdateTableFlag, +}) { + console.log("RAGSessionsTable rendered"); + const [loading, setLoading] = React.useState(true); + const [sessions, setSessions] = React.useState([]); + const { enqueueSnackbar } = useSnackbar(); + + const getSessions = async () => { + setLoading(true); + try { + const data = await getRAGSessions(); + setSessions(data); + } catch (error) { + console.error("[getSessions] error:", error); + enqueueSnackbar("Error fetching RAG sessions", { variant: "error" }); + } finally { + setLoading(false); + } + }; + + const handleDeleteSession = async (sessionId) => { + try { + await deleteRAGSession(sessionId); + enqueueSnackbar("RAG session successfully deleted.", { + variant: "success" + }); + getSessions(); // Refresh sessions after deletion + } catch (error) { + console.error("[deleteSession] error:", error); + enqueueSnackbar("Error deleting RAG session", { variant: "error" }); + } + }; + + const handleOpenSession = (session) => { + if (onSessionSelect) { + onSessionSelect(session); + } + }; + + const handleViewDocuments = (session) => { + console.log("View documents for session:", session.id); + }; + + // FIXED: Moved useEffect to top level + React.useEffect(() => { + console.log("Initial load effect"); + getSessions(); + }, []); + + // FIXED: Added updateTableFlag effect + React.useEffect(() => { + if (updateTableFlag) { + console.log("Update table flag triggered"); + getSessions(); + setUpdateTableFlag(false); + } + }, [updateTableFlag, setUpdateTableFlag]); + + // FIXED: Correct DataGrid column structure + const columns = [ + { + field: "name", + headerName: "Session Name", + flex: 1 + }, + { + field: "created_at", + headerName: "Created At", + flex: 1, + valueFormatter: (params) => new Date(params.value).toLocaleString() + }, + { + field: "documents", + headerName: "Documents", + flex: 0.7, + valueGetter: (params) => params.row.documents?.length || 0 + }, + { + field: "actions", + headerName: "Actions", + flex: 1.5, + renderCell: (params) => ( +
+ + + +
+ ) + } + ]; + + return ( + + + + RAG Sessions + + + + + row.id} + sx={{ + '& .MuiDataGrid-cell:focus': { outline: 'none' }, + minHeight: 400 + }} + /> + + ); +} + +// FIXED: Correct propTypes +RAGSessionsTable.propTypes = { + handleOpenNewSessionModal: PropTypes.func.isRequired, + onSessionSelect: PropTypes.func, + updateTableFlag: PropTypes.bool.isRequired, + setUpdateTableFlag: PropTypes.func.isRequired +}; + +export default RAGSessionsTable; \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx new file mode 100644 index 000000000..b1d29f529 --- /dev/null +++ b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx @@ -0,0 +1,38 @@ +// RetrieverConfigurationStep.jsx +import React from "react"; +import PropTypes from "prop-types"; +import { Box } from "@mui/material"; +import ComponentSelector from "../../../components/generative/RAG/ComponentSelector"; +import { getRetrieverComponents } from "../../../api/rag"; + +function RetrieverConfigurationStep({ newSession, setNewSession, setNextEnabled }) { + const handleConfigurationChange = (retrieverConfig) => { + setNewSession(prev => ({ + ...prev, + RAGParameters: { + ...prev.RAGParameters, + retrieval: retrieverConfig + } + })); + }; + + return ( + + + + ); +} + +RetrieverConfigurationStep.propTypes = { + newSession: PropTypes.object.isRequired, + setNewSession: PropTypes.func.isRequired, + setNextEnabled: PropTypes.func.isRequired, +}; + +export default RetrieverConfigurationStep; \ No newline at end of file diff --git a/DashAI/front/yarn.lock b/DashAI/front/yarn.lock index 85e83bbc3..73d4a3513 100644 --- a/DashAI/front/yarn.lock +++ b/DashAI/front/yarn.lock @@ -5334,9 +5334,9 @@ __metadata: linkType: hard "caniuse-lite@npm:^1.0.0, caniuse-lite@npm:^1.0.30001587, caniuse-lite@npm:^1.0.30001599": - version: 1.0.30001618 - resolution: "caniuse-lite@npm:1.0.30001618" - checksum: 7a87a49f5b2432b4bd428d3c1b629c49699aa75390e29915745f6e1753c8f91d990ec468a0306ea2b6efd5e76435a127a7c535cb28b9d1bd268732057a5d64c1 + version: 1.0.30001724 + resolution: "caniuse-lite@npm:1.0.30001724" + checksum: 3285f9c56898ffd9f5fce8f6cff9deb832c5897da8046474f8961a5848771cac293b80200c6311c8a97822dd25b8af699af35042a2033d5ec54017fe4dc6c5e3 languageName: node linkType: hard diff --git a/requirements.txt b/requirements.txt index 58a905edf..f80c8d769 100644 --- a/requirements.txt +++ b/requirements.txt @@ -449,3 +449,5 @@ zipp==3.20.2 # importlib-metadata # importlib-resources imblearn +chromadb==0.5.5 +chroma-hnswlib==0.7.6 \ No newline at end of file From 5f197ac0771b335cda57da57423f9469db6af432 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Thu, 3 Jul 2025 11:39:43 -0400 Subject: [PATCH 003/218] Basic configuration via UI TODO: Divide the Retriever component in single responsability configurable objects Allow the load of multiple documents the Document selector Store documents bytes in the DB Implement UI re-render after deleting a session in RAG sessions table Implement documents view in front Implement trained models (like retrievers) load and save --- .../back/api/api_v1/endpoints/components.py | 15 +- DashAI/back/initial_components.py | 9 +- .../back/models/RAG/Documents/BaseDocument.py | 102 ++ .../back/models/RAG/Documents/PDFDocument.py | 58 ++ .../back/models/RAG/Documents/TxtDocument.py | 54 + DashAI/back/models/RAG/Documents/__init__.py | 3 + DashAI/back/models/RAG/RAG_pipeline.py | 226 ++--- .../models/RAG/Retrievers/BaseRetriever.py | 165 +++ .../models/RAG/Retrievers/DenseRetriever.py | 12 + .../models/RAG/Retrievers/TFIDFRetriever.py | 259 +++++ DashAI/back/models/RAG/Retrievers/__init__.py | 3 + DashAI/back/models/RAG/__init__.py | 3 +- DashAI/back/models/__init__.py | 2 +- DashAI/back/models/hugging_face/__init__.py | 3 + .../models/text_to_text_generation_model.py | 2 +- DashAI/back/tasks/RAG_task.py | 4 +- DashAI/front/src/api/generativeTask.ts | 3 + DashAI/front/src/api/rag.ts | 490 ++------- .../generative/RAG/ComponentSelector.jsx | 185 ++-- .../generative/RAG/DocumentSelector.jsx | 111 +- .../generative/RAG/DocumentTable.jsx | 64 +- .../components/generative/SelectModelMenu.jsx | 1 - .../src/components/generative/SessionBar.jsx | 2 - .../front/src/components/generative/prompt.js | 954 ++++++++++++++++++ .../front/src/pages/generative/Generative.jsx | 25 +- .../generative/RAG/DocumentSelectionStep.jsx | 161 +-- .../RAG/GeneratorConfigurationStep.jsx | 171 ++++ .../pages/generative/RAG/NewSessionModal.jsx | 268 ++--- .../src/pages/generative/RAG/RAGHomePage.jsx | 141 ++- .../pages/generative/RAG/RAGSessionsTable.jsx | 310 +++--- .../RAG/RetrieverConfigurationStep.jsx | 191 +++- .../vectorizer | Bin 0 -> 422105 bytes .../vectorizer | Bin 0 -> 101559 bytes .../vectorizer | Bin 0 -> 179480 bytes .../vectorizer | Bin 0 -> 243480 bytes .../vectorizer | Bin 0 -> 378471 bytes .../vectorizer | Bin 0 -> 379501 bytes .../vectorizer | Bin 0 -> 258501 bytes .../vectorizer | Bin 0 -> 244262 bytes .../vectorizer | Bin 0 -> 225721 bytes .../vectorizer | Bin 0 -> 230851 bytes 41 files changed, 2823 insertions(+), 1174 deletions(-) create mode 100644 DashAI/back/models/RAG/Documents/BaseDocument.py create mode 100644 DashAI/back/models/RAG/Documents/PDFDocument.py create mode 100644 DashAI/back/models/RAG/Documents/TxtDocument.py create mode 100644 DashAI/back/models/RAG/Documents/__init__.py create mode 100644 DashAI/back/models/RAG/Retrievers/BaseRetriever.py create mode 100644 DashAI/back/models/RAG/Retrievers/DenseRetriever.py create mode 100644 DashAI/back/models/RAG/Retrievers/TFIDFRetriever.py create mode 100644 DashAI/back/models/RAG/Retrievers/__init__.py create mode 100644 DashAI/front/src/components/generative/prompt.js create mode 100644 DashAI/front/src/pages/generative/RAG/GeneratorConfigurationStep.jsx create mode 100644 StoredRetrievers/tfidf_retriever_07c008c27ce1de3ecd0d4cd42fc4c43604e6d7c2c4f86eef5d4b1b42d6d3c15d_75aeb1f071ec2f059f26544c40689c89c282d8dd1ab84b6674ffbf27e9e8fbd1/vectorizer create mode 100644 StoredRetrievers/tfidf_retriever_0cff2792f2880a97e8dc881b7f30588af3e40f2617f4a8e40f236e3ed70b3976_0ec09b17926fe5aa93641201da7dfeb580d37f3af6b2032f31b6343ac471fba3/vectorizer create mode 100644 StoredRetrievers/tfidf_retriever_28bce20bb2374f93157c85fa986ff3d0a6004b637324b92bf98c20efb3e56103_2730eac7385c920c51ee4089b4172f7af4d7be862b3b2aefa2d229aad399bea4/vectorizer create mode 100644 StoredRetrievers/tfidf_retriever_347f29ec72552a7656cf8f10a42670fcd8076c89ce86fece0c180555440bcbc2_c54a2f7e421d397dff3d0a420d9513c4f7b43399cf2c2770b610c951b7af9e38/vectorizer create mode 100644 StoredRetrievers/tfidf_retriever_88c363cbc175eb2b0bef48208782dfa84682a5b6dd6e45ac65127700520bc2ba_75aeb1f071ec2f059f26544c40689c89c282d8dd1ab84b6674ffbf27e9e8fbd1/vectorizer create mode 100644 StoredRetrievers/tfidf_retriever_978e040feee803d3cd97a7eaf5c3e9f502d0d97f670c303715e455047b877281_75aeb1f071ec2f059f26544c40689c89c282d8dd1ab84b6674ffbf27e9e8fbd1/vectorizer create mode 100644 StoredRetrievers/tfidf_retriever_a38d48165431be31206e34ce2758b75170414d8ad2e65d9fa37a38b2e41343fd_57d8f551775097b02aeb07b11856b3cd3f942fbcf36d43a4384b130551631efb/vectorizer create mode 100644 StoredRetrievers/tfidf_retriever_dfdc77de99969c1ac75e72e16a8c38ee3fb24e98a90075f4cd923472c317b05c_c27d55405ff4e4180350c51990a570d33540e0098056446ce95de0942b41e4da/vectorizer create mode 100644 StoredRetrievers/tfidf_retriever_e19a4b25c422aa7be44921f262f32d44d790b515e5af012fa3eaacb52a649869_c54a2f7e421d397dff3d0a420d9513c4f7b43399cf2c2770b610c951b7af9e38/vectorizer create mode 100644 StoredRetrievers/tfidf_retriever_ee0f3dd0eaad95cb49ab497e118300f86c49ab07807d73ced6d3c69372661f46_c54a2f7e421d397dff3d0a420d9513c4f7b43399cf2c2770b610c951b7af9e38/vectorizer diff --git a/DashAI/back/api/api_v1/endpoints/components.py b/DashAI/back/api/api_v1/endpoints/components.py index 7e4953078..ad5e85ceb 100644 --- a/DashAI/back/api/api_v1/endpoints/components.py +++ b/DashAI/back/api/api_v1/endpoints/components.py @@ -87,19 +87,14 @@ async def get_components( HTTPException If task_name does not exist in the registry """ - + flag = " ".join(str(param) for param in [select_types, ignore_types, related_component, component_parent, has_related_of_type]) + if "etriever" in flag or "RAG" in flag or "rag" in flag: + "stop here" print("select_types", str(select_types)) print("ignore_types", str(ignore_types)) print("related_component", str(related_component)) print("component_parent", str(component_parent)) print("has_related_of_type", str(has_related_of_type)) - print("component_registry", str(component_registry)) - - - print(list(component_registry._registry.keys())) - print(related_component in component_registry._registry) - print(component_registry) - print("get_components api ") # when select_type is not none, check if it exists in the registry. if select_types is not None: @@ -207,10 +202,12 @@ async def get_components( ) print("get_components api 10") - return [ + out = [ _delete_class(component_dict) for component_dict in selected_components.values() ] + return out + @router.get("/{id}/") @inject diff --git a/DashAI/back/initial_components.py b/DashAI/back/initial_components.py index 23d734df9..f66fba0c6 100644 --- a/DashAI/back/initial_components.py +++ b/DashAI/back/initial_components.py @@ -92,7 +92,11 @@ SimpleControlNetModel, StableDiffusionV2Model, StableDiffusionV3Model, - RAGPipeline + RAGPipeline, + BaseRetriever, + TFIDFRetriever, + DenseRetriever + ) from DashAI.back.optimizers import HyperOptOptimizer, OptunaOptimizer from DashAI.back.plugins.utils import get_available_plugins @@ -158,6 +162,9 @@ def get_initial_components(): LinearSVR, LinearRegression, RAGPipeline, + TFIDFRetriever, + DenseRetriever, + #BaseRetriever, # Dataloaders CSVDataLoader, JSONDataLoader, diff --git a/DashAI/back/models/RAG/Documents/BaseDocument.py b/DashAI/back/models/RAG/Documents/BaseDocument.py new file mode 100644 index 000000000..f54db39db --- /dev/null +++ b/DashAI/back/models/RAG/Documents/BaseDocument.py @@ -0,0 +1,102 @@ +from abc import ABC +import hashlib +from typing import Any, Dict, List, Optional + +class BaseDocument(ABC): + """ + Base class for documents. + """ + + def __init__(self, *args, **kwargs): + """ + Initialize the document. + """ + pass + + def get_text(self) -> str: + """ + Get the text content of the document. + + Returns: + str: The text content of the document. + """ + raise NotImplementedError("This method should be implemented by subclasses.") + + def get_text_length(self) -> int: + """ + Get the length of the text content of the document. + + Returns: + int: The length of the text content of the document. + """ + raise NotImplementedError("This method should be implemented by subclasses.") + + def get_metadata(self) -> Dict[str, Any]: + """ + Get the metadata of the document. + + Returns: + Dict[str, Any]: The metadata of the document. + """ + raise NotImplementedError("This method should be implemented by subclasses.") + + def get_filename(self) -> Optional[str]: + """ + Get the filename of the document. + + Returns: + Optional[str]: The filename of the document, or None if not applicable. + """ + raise NotImplementedError("This method should be implemented by subclasses.") + + def get_filetype(self) -> Optional[str]: + """ + Get the filetype of the document. + + Returns: + Optional[str]: The filetype of the document, or None if not applicable. + """ + raise NotImplementedError("This method should be implemented by subclasses.") + + def get_file_location(self) -> Optional[str]: + """ + Get the file location of the document. + + Returns: + Optional[str]: The file location of the document, or None if not applicable. + """ + raise NotImplementedError("This method should be implemented by subclasses.") + + def get_chunks(self, chunk_size: int, chunk_overlap: int) -> List[str]: + """ + Get the document text split into chunks. + + Args: + chunk_size (int): The size of each chunk. + chunk_overlap (int): The overlap between chunks. + + Returns: + List[str]: A list of text chunks. + """ + text = self.get_text() + chunks = [] + start = 0 + while start < len(text): + end = min(start + chunk_size, len(text)) + chunks.append(text[start:end]) + start += chunk_size - chunk_overlap + return chunks + + def get_hash(self) -> str: + """ + Get a hash of the document content. + + Returns: + str: A hash string representing the document content. + """ + text = self.get_text() + return hashlib.sha256(text.encode('utf-8')).hexdigest() if text else '' + + def __repr__(self): + return f"BaseDocument(filename='{self.get_filename()}', content='{self.get_text()[:50]}...', metadata={self.get_metadata()})" + diff --git a/DashAI/back/models/RAG/Documents/PDFDocument.py b/DashAI/back/models/RAG/Documents/PDFDocument.py new file mode 100644 index 000000000..ca2ecb059 --- /dev/null +++ b/DashAI/back/models/RAG/Documents/PDFDocument.py @@ -0,0 +1,58 @@ +from .BaseDocument import BaseDocument +import os +from PyPDF2 import PdfReader +import base64 + +class PDFDocument(BaseDocument): + """ + Class representing a PDF document. + """ + + def __init__(self, file_path: str): + """ + Initialize the PDF document with the file path. + + Args: + file_path (str): The path to the PDF file. + """ + + assert os.path.exists(file_path), f"File {file_path} does not exist." + + self.file_path = file_path + self.metadata = { + "file_path": file_path, + "filetype": "pdf", + "filename": os.path.basename(file_path), + } + self.file_path = file_path + self.filename = os.path.basename(file_path) + self.filetype = "pdf" + self.text_length = len(self.get_text()) + + def get_text(self) -> str: + reader = PdfReader(self.file_path) + if not reader.pages: + raise ValueError(f"The PDF file {self.file_path} is empty or not valid.") + + # Extract text from all pages + text = "" + for page in reader.pages: + text += page.extract_text() or "" + + return text.strip() + + def get_text_length(self) -> int: + return self.text_length + + def get_metadata(self) -> dict: + return self.metadata + + def get_filename(self) -> str: + return self.filename + + def get_filetype(self) -> str: + return self.filetype + + def get_file_location(self) -> str: + return self.file_path + \ No newline at end of file diff --git a/DashAI/back/models/RAG/Documents/TxtDocument.py b/DashAI/back/models/RAG/Documents/TxtDocument.py new file mode 100644 index 000000000..f8f7f58a7 --- /dev/null +++ b/DashAI/back/models/RAG/Documents/TxtDocument.py @@ -0,0 +1,54 @@ +from .BaseDocument import BaseDocument +from typing import Dict, Any, Optional +import os + +class TxtDocument(BaseDocument): + """ + Class representing a .txt document. + """ + + def __init__(self, file_path: str): + """ + Initialize the text document with the file path. + + Args: + file_path (str): The path to the text file. + """ + assert os.path.exists(file_path), f"File {file_path} does not exist." + + self.file_path = file_path + self.filename = os.path.basename(file_path) + self.filetype = "txt" + self.text_length = len(self.text) + self.metadata = { + "file_path": file_path, + "filetype": self.filetype, + "filename": self.filename, + } + self.text_length = len(self.get_text()) + + def get_text(self) -> str: + """ + Get the text content of the document. + + Returns: + str: The text content of the document. + """ + with open(self.file_path, "r", encoding="utf-8") as file: + text = file.read() + return text.strip() + + def get_text_length(self) -> int: + return self.text_length + + def get_metadata(self) -> Dict[str, Any]: + return self.metadata + + def get_filename(self) -> str: + return self.filename + + def get_filetype(self) -> str: + return self.filetype + + def get_file_location(self) -> str: + return self.file_path \ No newline at end of file diff --git a/DashAI/back/models/RAG/Documents/__init__.py b/DashAI/back/models/RAG/Documents/__init__.py new file mode 100644 index 000000000..06bb27b6f --- /dev/null +++ b/DashAI/back/models/RAG/Documents/__init__.py @@ -0,0 +1,3 @@ +from .BaseDocument import BaseDocument +from .PDFDocument import PDFDocument +from .TxtDocument import TxtDocument \ No newline at end of file diff --git a/DashAI/back/models/RAG/RAG_pipeline.py b/DashAI/back/models/RAG/RAG_pipeline.py index 9ed9338a3..dde12274e 100644 --- a/DashAI/back/models/RAG/RAG_pipeline.py +++ b/DashAI/back/models/RAG/RAG_pipeline.py @@ -1,115 +1,68 @@ -from typing import Any, Tuple, List - -from DashAI.back.models.base_generative_model import BaseGenerativeModel +from typing import Any, Tuple, List, Dict from DashAI.back.core.schema_fields import ( BaseSchema, - enum_field, - int_field, - float_field, + component_field, schema_field, + bool_field, + list_field, string_field ) -from DashAI.back.models.hugging_face.deep_seek_model import DeepSeekModel -from DashAI.back.models.hugging_face.qwen_model import QwenModel +from DashAI.back.models.base_generative_model import BaseGenerativeModel from DashAI.back.models.text_to_text_generation_model import TextToTextGenerationTaskModel -from DashAI.back.models.RAG.dummy_retriever import DummyRetriever +from DashAI.back.models.RAG.Retrievers import ( + TFIDFRetriever, + DenseRetriever, + BaseRetriever) + from DashAI.back.models.RAG.prompts import ( - BasePrompt, ContextMergePrompt, AugmentationPrompt ) -from typing import Dict + +from DashAI.back.models.hugging_face import ( + DeepSeekModel, + QwenModel, + GemmaModel) + +retriever_models = { + "TFIDFRetriever": TFIDFRetriever, + "DenseRetriever": DenseRetriever, +} + +generation_models = { + "DeepSeekModel": DeepSeekModel, + "QwenModel": QwenModel, + "GemmaModel": GemmaModel, +} class RAGPipelineSchema(BaseSchema): """Schema for RAG pipeline.""" + # Document collection parameters + documents: schema_field( + list_field( + string_field(), + min_items=1, + ), + placeholder=None, + description="List of documents to be used in the RAG pipeline." + ) # type: ignore # RAG algorithm parameters - RAG_algorithm: schema_field( - enum_field(["SINGLE_INTERACTION", "AUGMENTED_INTERACTION"]), - placeholder="SINGLE_INTERACTION", - description="RAG algorithm to use for document retrieval and text generation.", - ) # type: ignore - - - # Retriever parameters - documments_path: schema_field( - string_field(), - placeholder="Path to the folder with documents", - description="Path to the folder containing documents for retrieval.", - ) # type: ignore - - collection_name: schema_field( - string_field(), - placeholder="My documents collection name", - description="Name of the collection to use for document retrieval.", - ) # type: ignore - + retriever_model: schema_field( + component_field(parent="RetrieverModel"), + placeholder={"component": "TFIDFRetriever", "params": {}}, + description="Retriever component used in the RAG pipeline." + ) # type: ignore - distance_function: schema_field( - enum_field(["cosine", "L2", "inner"]), - placeholder="cosine", - description="Distance function to use for document retrieval.", - ) # type: ignore - - n_docs: schema_field( - int_field(ge=1), - placeholder=5, - description="Maximum number of documents to retrieve.", - ) # type: ignore - - max_distance: schema_field( - float_field(ge=0.0), - placeholder=1.0, - description="Maximum distance allowed for retrieved documents.", - ) # type: ignore - - chunk_size: schema_field( - int_field(ge=1), - placeholder=1000, - description="Size of the chunks to split the documents into.", - ) # type: ignore - - chunk_overlap: schema_field( - int_field(ge=0), - placeholder=200, - description="Overlap size between chunks.", - ) # type: ignore - - - # LLM parameters - model_name: schema_field( - enum_field(["DeepSeek", "Qwen2"]), - placeholder="DeepSeek", - description="LLM model to use for text generation.", - ) # type: ignore - - max_tokens: schema_field( - int_field(ge=1), - placeholder=800, - description="Maximum number of tokens to generate.", - ) # type: ignore - - temperature: schema_field( - float_field(ge=0.0, le=1.0), - placeholder=0.7, - description="Sampling temperature. Higher values make the output more random, while lower values make it more focused and deterministic.", - ) # type: ignore - - frequency_penalty: schema_field( - float_field(ge=0.0, le=2.0), - placeholder=0.1, - description="Penalty for repeated tokens in the output. Higher values reduce the likelihood of repetition, encouraging more diverse text generation.", - ) # type: ignore - - n_ctx: schema_field( - int_field(ge=1), - placeholder=4096, - description="Maximum number of tokens the model can process in a single forward pass (context window size).", + generation_model: schema_field( + component_field(parent="TextToTextGenerationTaskModel"), + placeholder={"component": "DeepSeek", "params": {}}, + description="Text generation model used in the RAG pipeline." ) # type: ignore -class RAGPipeline(TextToTextGenerationTaskModel): +class RAGPipeline(BaseGenerativeModel): """Retrieval-Augmented Generation (RAG) pipeline.""" COMPATIBLE_COMPONENTS = ["RAGTask"] @@ -126,50 +79,49 @@ def __init__( self, n_docs (int): The maximum number of documents to retrieve. max_distance (float): The maximum distance allowed for retrieved documents. """ - kwargs = self.validate_and_transform(kwargs) - print("Initializing RAG pipeline") - # Initialize the LLM model - llm_classes = { - "DeepSeek": DeepSeekModel, - "Qwen2": QwenModel, - } + try: + documents_paths = kwargs['documents'] + retriever_args = kwargs['retriever_model'] + generation_model_args = kwargs['generation_model'] + except KeyError as e: + raise ValueError(f"Missing required RAG pipeline parameter: {e}") + + try: + retriever_model_class = retriever_models[retriever_args["component"]] + retriever_params = retriever_args['params'] + except KeyError as e: + raise ValueError(f"Invalid retriever model specified: {e}") + + try: + generation_model_class = generation_models[generation_model_args["component"]] + generation_params = generation_model_args['params'] - llm_kwargs = { - "max_tokens": kwargs.get("max_tokens"), - "temperature": kwargs.get("temperature"), - "frequency_penalty": kwargs.get("frequency_penalty"), - "n_ctx": kwargs.get("n_ctx"), - } + except KeyError as e: + raise ValueError(f"Invalid generation model specified: {e}") + + assert isinstance(documents_paths, list) and len(documents_paths) > 0, "Documents must be a non-empty list." + for path in documents_paths: + assert isinstance(path, str), f"Each document path must be a string, got {type(path)}" print("Initializing LLM model") - self.llm_model: TextToTextGenerationTaskModel = llm_classes[kwargs.get("model_name")](**llm_kwargs) - - # Initialize the retriever - retriever_params = { - "documents_path": kwargs.get("documments_path"), - "collection_name": kwargs.get("collection_name"), - "distance_function": kwargs.get("distance_function"), - "n_docs": kwargs.get("n_docs"), - "max_distance": kwargs.get("max_distance"), - "chunk_size": kwargs.get("chunk_size"), - "chunk_overlap": kwargs.get("chunk_overlap"), + self.llm_model: TextToTextGenerationTaskModel = generation_model_class(**generation_params) + + + new_retriever_args = { + 'documents_paths': documents_paths, } + for key, value in retriever_params.items(): + new_retriever_args[key] = value + + print("Initializing retriever model") + self.retriever: BaseRetriever = retriever_model_class(**new_retriever_args) - print("Initializing retriever") - self.retriever = DummyRetriever(**retriever_params) - - print("Definining rag algorithm") - # Initialize the RAG algorithm - if kwargs.get("RAG_algorithm") == "SINGLE_INTERACTION": - self.retrieval_algorithm = self.single_interaction - elif kwargs.get("RAG_algorithm") == "AUGMENTED_INTERACTION": - self.retrieval_algorithm = self.augmented_interaction - else: - raise ValueError("Invalid RAG algorithm. Choose 'SINGLE_INTERACTION' or 'AUGMENTED_INTERACTION'.") + self.retrieval_algorithm = "SINGLE_INTERACTION" print("RAG pipeline initialized") - def single_interaction(self, input: str, history: List[Tuple[str, str]] = None) -> Dict[str, str]: + + def single_interaction(self, input: str, history: List[Tuple[str, str]] = None) -> List[Tuple[str, str, int]]: """ Single interaction retrieval algorithm: input -> retrieve documents. Args: @@ -178,12 +130,12 @@ def single_interaction(self, input: str, history: List[Tuple[str, str]] = None) str: The generated response. """ - documents = self.retriever.retrieve([input]) + documents = self.retriever.retrieve(input) return documents - def augmented_interaction(self, input: str, history: List[Tuple[str, str]] = None) -> Dict[str, str]: + def augmented_interaction(self, input: str, history: List[Tuple[str, str]] = None) -> List[Tuple[str, str, int]]: """ Augmented interaction RAG algorithm: input -> generate lookup tokens -> retrieve documents -> generate response. Args: @@ -219,14 +171,17 @@ def generate(self, input: Tuple[str, List[Tuple[str,str]]]) -> str: Returns: str: The generated response. """ - message, history = input + message = input - documents = self.retrieval_algorithm(message, history) - documents_str = "\n\n".join([f"Document {name}:\n {content}" for name, content in documents.items()]) + documents = self.single_interaction(message) + documents_str = "RETRIEVED INFORMATION:\n\n" + for doc_content, doc_file, chunk in documents: + doc_name = doc_file.split("/")[-1] + documents_str += f"Document '{doc_name}' in chunk: {chunk}:\n{doc_content}\n\n" prompt = ContextMergePrompt.format( input=message, - history=history, + history=None, documents=documents_str ) @@ -234,7 +189,6 @@ def generate(self, input: Tuple[str, List[Tuple[str,str]]]) -> str: response = self.llm_model.generate(prompt) - sources_str = ", ".join(doc_name for doc_name in documents.keys()) - response = f"{response[0]}\n\nSources:{sources_str}" + response = f"{response[0]}\n\nSources:{documents_str}" return [response] diff --git a/DashAI/back/models/RAG/Retrievers/BaseRetriever.py b/DashAI/back/models/RAG/Retrievers/BaseRetriever.py new file mode 100644 index 000000000..e12143378 --- /dev/null +++ b/DashAI/back/models/RAG/Retrievers/BaseRetriever.py @@ -0,0 +1,165 @@ +from abc import ABC +import os +from typing import Any, Dict, List, Final +from DashAI.back.models.RAG.Documents import BaseDocument, PDFDocument, TxtDocument +from DashAI.back.config_object import ConfigObject +import hashlib + +SUPPORTED_DOCUMENT_TYPES = ['pdf', 'txt'] + +class BaseRetriever(ABC, ConfigObject): + """ + Base class for retrievers. + """ + + RETRIEVERS_PATH = "./StoredRetrievers" + COMPATIBLE_COMPONENTS = ["RAGTask"] + TYPE: Final[str] = "RetrieverModel" + + + def __init__(self, *args, **kwargs): + """ + Initialize the retriever. + """ + self.params = None + self._documents_paths = None + self.chunk_size = None + self.chunk_overlap = None + + def _load_document(self, document_path: str) -> BaseDocument: + """ + Load a document from the specified path. + + Args: + document_path (str): The path to the document file. + + Returns: + BaseDocument: An instance of a document class (PDFDocument or TxtDocument). + """ + if document_path.endswith('.pdf'): + return PDFDocument(document_path) + elif document_path.endswith('.txt'): + return TxtDocument(document_path) + else: + raise ValueError(f"Unsupported document type: {document_path}") + + def get_documents(self) -> Dict[str, BaseDocument]: + """ + Get the documents stored in the retriever. + Returns: + Dict[str, BaseDocument]: A dictionary where keys are document paths and values are document instances. + """ + assert self._documents_paths is not None, "Document paths must be provided." + assert len(self._documents_paths) > 0, "At least one document path must be provided." + + self._documents_paths = sorted(self._documents_paths) # Sort for consistent hashing + documents = {} + for path in self._documents_paths: + doc = self._load_document(path) + documents[path] = doc + return documents + + def get_documents_chunks(self) -> Dict[str, List[str]]: + """ + Get the chunks of documents stored in the retriever. + + Returns: + Dict[str, Dict[int, str]]: A dictionary where keys are document paths and values are + dictionaries of chunk indices and their corresponding text. + """ + documents = self.get_documents() + chunks = {} + for filename, doc in documents.items(): + chunks[filename] = doc.get_chunks(self.chunk_size, self.chunk_overlap) + return chunks + + def get_parameters(self) -> Dict[str, Any]: + """ + Get the parameters of the retriever. + + Returns: + Dict[str, Any]: A dictionary of retriever parameters. + """ + assert self.params is not None, "Retriever parameters are not set." + return self.params + + def get_hashable_parameters(self) -> Dict[str, Any]: + """ + Get the parameters used for storing the fitted retriever in the database. + This parameters are a subset of the retriever parameters, the non-hashable parameters are + those whose change does not require a retraining of the retriever, i.e. n_docs, + max_distance, etc. + + Returns: + Dict[str, Any]: A dictionary of parameters that can be used for hashing. + """ + raise NotImplementedError("This method should be implemented by subclasses.") + + def _get_documents_hash(self) -> str: + """ + Generate a hash representing the current set of documents. + + Returns: + str: A hash string representing the documents. + """ + documents = sorted(self.get_documents().items()) + hashes = "" + for filename, doc in documents.items(): + doc_hash = doc.get_hash() + hashes += f"{filename}{doc_hash}" + return hashlib.sha256(hashes.encode('utf-8')).hexdigest() + + def _get_parameters_hash(self) -> str: + """ + Generate a hash representing the retriever parameters. + + Args: + parameters (Dict[str, Any]): A dictionary of retriever parameters. + + Returns: + str: A hash string representing the parameters. + """ + hashable_parameters = sorted(self.get_hashable_parameters().items()) + params_string = "".join(f"{key}{value}" for key, value in hashable_parameters) + return hashlib.sha256(params_string.encode('utf-8')).hexdigest() + + def _get_cache_hash(self) -> str: + """ + Generate a unique hash for the retriever based on its documents and parameters. + + Returns: + str: A hash string representing the retriever's state. + """ + documents_hash = self._get_documents_hash() + parameters_hash = self._get_parameters_hash() + return f"{self.__class__.__name__}_{documents_hash}_{parameters_hash}" + + def add_document(self, document: BaseDocument) -> None: + """ + Add a document to the retriever. + + Args: + document (BaseDocument): The document to add. + """ + raise NotImplementedError("This method should be implemented by subclasses.") + + def remove_document(self, document_id: str) -> None: + """ + Remove a document from the retriever by its ID. + + Args: + document_id (str): The ID of the document to remove. + """ + raise NotImplementedError("This method should be implemented by subclasses.") + + def retrieve(self, **kwargs) -> List[BaseDocument]: + """ + Retrieve documents based on the provided parameters. + + Args: + **kwargs: The parameters for the retrieval. + + Returns: + List[BaseDocument]: A list of retrieved documents. + """ + raise NotImplementedError("This method should be implemented by subclasses.") \ No newline at end of file diff --git a/DashAI/back/models/RAG/Retrievers/DenseRetriever.py b/DashAI/back/models/RAG/Retrievers/DenseRetriever.py new file mode 100644 index 000000000..835747c96 --- /dev/null +++ b/DashAI/back/models/RAG/Retrievers/DenseRetriever.py @@ -0,0 +1,12 @@ +from .BaseRetriever import BaseRetriever + +class DenseRetriever(BaseRetriever): + """ + Dense retriever class for retrieving documents based on dense vector representations. + This class is a placeholder and should be implemented with specific dense retrieval logic. + """ + + COMPATIBLE_COMPONENTS = ["RAGTask"] + + def __init__(self, *args, **kwargs): + return \ No newline at end of file diff --git a/DashAI/back/models/RAG/Retrievers/TFIDFRetriever.py b/DashAI/back/models/RAG/Retrievers/TFIDFRetriever.py new file mode 100644 index 000000000..f6a005ff0 --- /dev/null +++ b/DashAI/back/models/RAG/Retrievers/TFIDFRetriever.py @@ -0,0 +1,259 @@ +import os +from .BaseRetriever import BaseRetriever +from DashAI.back.models.RAG.Documents import BaseDocument, PDFDocument, TxtDocument +from typing import List, Dict, Any, Optional, Tuple +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances +import numpy as np +import hashlib +import pickle + +from DashAI.back.core.schema_fields import ( + BaseSchema, + schema_field, + enum_field, + float_field, + int_field, +) + +similarities = { + "cosine": cosine_similarity, + "euclidean": euclidean_distances +} + + +class TFIDFRetrieverSchema(BaseSchema): + """ + Schema for the TFIDFRetriever. + """ + + similarity_function: schema_field( + enum_field(['cosine', 'euclidean']), + placeholder="cosine", + description="Similarity function to use for document retrieval." + ) # type: ignore + + n_docs: schema_field( + int_field(ge=1), + placeholder=5, + description="Number of documents to retrieve." + ) # type: ignore + + similarity_threshold: schema_field( + float_field(), + placeholder=0.5, + description="Maximum or minimum distance for retrieved documents based on the similarity_function." + ) # type: ignore + + chunk_size: schema_field( + int_field(ge=1), + placeholder=512, + description="Size of chunks to split the documents into." + ) # type: ignore + + chunk_overlap: schema_field( + int_field(ge=0), + placeholder=50, + description="Overlap between chunks when splitting documents." + ) # type: ignore + +class TFIDFRetriever(BaseRetriever): + """ + A retriever that uses TF-IDF to retrieve documents based on a query. + """ + SCHEMA = TFIDFRetrieverSchema + + def __init__( + self, + documents_paths: List[str], + similarity_function: str, + n_docs: int, + similarity_threshold: float, + chunk_size: int, + chunk_overlap: int + ): + """ + Initialize the TFIDFRetriever with the given parameters. + + Args: + documents_paths (List[str]): A list of document texts to be indexed. + similarity_function (str): Similarity function to use ('cosine' or 'euclidean'). + n_docs (int): The maximum number of documents to retrieve. + distance_threshold (float): The maximum or minimum distance for retrieved documents based on the similarity_function. + chunk_size (int): The size of chunks to split the documents into. + chunk_overlap (int): The overlap between chunks. + """ + + assert isinstance(documents_paths, list) and all(isinstance(doc, str) for doc in documents_paths), "Documents must be a list of strings." + assert similarity_function in similarities, f"similarity_function must be one of {list(similarities.keys())}." + assert n_docs > 0, "Number of documents to retrieve must be greater than 0." + assert similarity_threshold >= 0, "Similarity threshold must be non-negative." + assert chunk_size > 0, "Chunk size must be greater than 0." + assert chunk_overlap >= 0, "Chunk overlap must be non-negative." + assert chunk_size > chunk_overlap, "Chunk size must be greater than chunk overlap." + BASE_DOC_PATH = 'THIS IS A PLACEHOLDER, should be unnecesary after implementing the document persistence storage in the database' + new_documents_paths = [] + for doc_path in documents_paths: + new_path = os.path.join(BASE_DOC_PATH, doc_path) + new_documents_paths.append(new_path) + documents_paths = new_documents_paths + self._documents_paths = sorted(documents_paths) # Sort for consistent hashing + self.similarity_function_name = similarity_function + self.similarity_function = similarities[similarity_function] + self.n_docs = n_docs + self.similarity_threshold = similarity_threshold + self.chunk_size = chunk_size + self.chunk_overlap = chunk_overlap + + self.params = { + "similarity_function_name": self.similarity_function_name, + "similarity_function": self.similarity_function, + "n_docs": n_docs, + "similarity_threshold": similarity_threshold, + "chunk_size": chunk_size, + "chunk_overlap": chunk_overlap + } + + + # Generate a unique hash for the current set of documents and chunking parameters + self._cache_hash = self._generate_cache_hash() + self._vectorizer_path, self._tf_idf_matrix, self.chunk_to_doc_path = self._get_cache_paths() + + + # Attempt to load from cache + if self._load_state(): + print(f"TFIDFRetriever loaded from cache: {self._cache_hash}") + else: + print(f"TFIDFRetriever cache not found or invalid for {self._cache_hash}. Fitting new model...") + self._fit() + self._save_state() + + def _get_cache_paths(self) -> tuple[str, str, str]: + """ + Returns the file paths for the cached vectorizer, tfidf_matrix, and chunk_to_doc_id_map. + """ + os.makedirs(self.RETRIEVERS_PATH, exist_ok=True) # Use BaseRetriever's constant + base_name = os.path.join(self.RETRIEVERS_PATH, self._cache_hash) + os.makedirs(base_name, exist_ok=True) + vectorizer_path = os.path.join(base_name, "vectorizer") + tfidf_matrix_path = os.path.join(base_name, "tfidf_matrix") + map_path = os.path.join(base_name, "chunk_to_doc_id_map") + return vectorizer_path, tfidf_matrix_path, map_path + + def get_hashable_parameters(self): + return { + "chunk_size": self.chunk_size, + "chunk_overlap": self.chunk_overlap, + } + + def _generate_cache_hash(self) -> str: + hashable_params = self.get_hashable_parameters() + text = f"{self._documents_paths}{hashable_params}" + params_hash = hashlib.sha256(text.encode('utf-8')).hexdigest() + docs_hashes = [] + for doc_path in self._documents_paths: + if doc_path.endswith('.pdf'): + doc = PDFDocument(doc_path) + elif doc_path.endswith('.txt'): + doc = TxtDocument(doc_path) + else: + raise ValueError(f"Unsupported document type: {doc_path}") + + docs_hashes.append(doc.get_hash()) + docs_hash = hashlib.sha256("".join(docs_hashes).encode('utf-8')).hexdigest() + return f"tfidf_retriever_{params_hash}_{docs_hash}" + + + def _load_state(self) -> bool: + """ + Load the retriever state from cache if available. + + Returns: + bool: True if the state was successfully loaded, False otherwise. + """ + if not os.path.exists(self._vectorizer_path): + return False + if not os.path.exists(self._tf_idf_matrix): + return False + if not os.path.exists(self.chunk_to_doc_path): + return False + + try: + self._vectorizer = pickle.load(open(self._vectorizer_path, 'rb')) + self._tf_idf_matrix = pickle.load(open(self._tf_idf_matrix, 'rb')) + self.chunk_to_doc_map = pickle.load(open(self.chunk_to_doc_path, 'rb')) + return True + except Exception as e: + print(f"Error loading state: {e}") + return False + + def _save_state(self): + """ + Save the retriever state to cache. + """ + try: + pickle.dump(self._vectorizer, open(self._vectorizer_path, 'wb')) + pickle.dump(self._tf_idf_matrix, open(self._tf_idf_matrix, 'wb')) + pickle.dump(self.chunk_to_doc_map, open(self.chunk_to_doc_path, 'wb')) + except Exception as e: + print(f"Error saving state: {e}") + + def _fit(self): + """ + Fit the TF-IDF model to the documents. + This method should be called after initializing the retriever. + """ + # Load chunks from the documents + chunks = self.get_documents_chunks() + chunk_texts = [] + current_chunk_idx = 0 + self.chunk_to_doc_map = {} + + for doc_path, doc_chunks in chunks.items(): + for i, chunk_text in enumerate(doc_chunks): + chunk_texts.append(chunk_text) + self.chunk_to_doc_map[current_chunk_idx] = (chunk_text, doc_path, i) + current_chunk_idx += 1 + + # Create the TF-IDF vectorizer and fit it to the chunks + self._vectorizer = TfidfVectorizer() + self._tf_idf_matrix = self._vectorizer.fit_transform(chunk_texts) + self._save_state() + + def retrieve(self, query: str) -> List[Tuple[str, str, int]]: + """ + Retrieve documents based on the query using TF-IDF. + Args: + query (str): The query string to search for. + Returns: + List[Tuple[str, str, int]]: A list of tuples containing the chunk text, document path, and chunk ID. + """ + assert self._tf_idf_matrix is not None, "Model is not fitted. Call _fit() before retrieving." + + print(f"Retrieving documents for query: {query}") + # Transform the query using the same vectorizer + query_vector = self._vectorizer.transform([query]) + + # Calculate the similarity scores + similarities = self.similarity_function(query_vector, self._tf_idf_matrix).flatten() + + # Determine sorting order based on similarity_function + if self.similarity_function == cosine_similarity: + filtered_indices = np.where(similarities >= self.similarity_threshold)[0] + ranked_indices = np.argsort(-similarities[filtered_indices])[:self.n_docs] + + elif self.similarity_function == euclidean_distances: + filtered_indices = np.where(similarities <= self.similarity_threshold)[0] + ranked_indices = np.argsort(similarities[filtered_indices])[:self.n_docs] + + else: + raise ValueError(f"Unsupported similarity_function: {self.similarity_function}") + + # Get the top n_docs indices + top_indices = ranked_indices[:self.n_docs] + + results: List[Tuple[str, str, int]] = [] + for idx in top_indices: + results.append(self.chunk_to_doc_map[idx]) + + return results \ No newline at end of file diff --git a/DashAI/back/models/RAG/Retrievers/__init__.py b/DashAI/back/models/RAG/Retrievers/__init__.py new file mode 100644 index 000000000..4e295aff6 --- /dev/null +++ b/DashAI/back/models/RAG/Retrievers/__init__.py @@ -0,0 +1,3 @@ +from DashAI.back.models.RAG.Retrievers.BaseRetriever import BaseRetriever +from DashAI.back.models.RAG.Retrievers.TFIDFRetriever import TFIDFRetriever +from DashAI.back.models.RAG.Retrievers.DenseRetriever import DenseRetriever \ No newline at end of file diff --git a/DashAI/back/models/RAG/__init__.py b/DashAI/back/models/RAG/__init__.py index ef3481f81..6261e0606 100644 --- a/DashAI/back/models/RAG/__init__.py +++ b/DashAI/back/models/RAG/__init__.py @@ -1 +1,2 @@ -from DashAI.back.models.RAG.RAG_pipeline import RAGPipeline \ No newline at end of file +from DashAI.back.models.RAG.RAG_pipeline import RAGPipeline +from DashAI.back.models.RAG.Retrievers import BaseRetriever, TFIDFRetriever, DenseRetriever \ No newline at end of file diff --git a/DashAI/back/models/__init__.py b/DashAI/back/models/__init__.py index 8505aa623..3e7cb9a99 100644 --- a/DashAI/back/models/__init__.py +++ b/DashAI/back/models/__init__.py @@ -49,4 +49,4 @@ from DashAI.back.models.scikit_learn.sklearn_like_regressor import SklearnLikeRegressor from DashAI.back.models.scikit_learn.svc import SVC -from DashAI.back.models.RAG import RAGPipeline \ No newline at end of file +from DashAI.back.models.RAG import RAGPipeline, BaseRetriever, TFIDFRetriever, DenseRetriever \ No newline at end of file diff --git a/DashAI/back/models/hugging_face/__init__.py b/DashAI/back/models/hugging_face/__init__.py index e69de29bb..340e9e6f0 100644 --- a/DashAI/back/models/hugging_face/__init__.py +++ b/DashAI/back/models/hugging_face/__init__.py @@ -0,0 +1,3 @@ +from DashAI.back.models.hugging_face.deep_seek_model import DeepSeekModel +from DashAI.back.models.hugging_face.qwen_model import QwenModel +from DashAI.back.models.hugging_face.gemma_model import GemmaModel \ No newline at end of file diff --git a/DashAI/back/models/text_to_text_generation_model.py b/DashAI/back/models/text_to_text_generation_model.py index f5fbfda2c..782fdcb9e 100644 --- a/DashAI/back/models/text_to_text_generation_model.py +++ b/DashAI/back/models/text_to_text_generation_model.py @@ -4,4 +4,4 @@ class TextToTextGenerationTaskModel(BaseGenerativeModel): """Class for models associated to TextToTextGenerationTask.""" - COMPATIBLE_COMPONENTS = ["TextToTextGenerationTask"] + COMPATIBLE_COMPONENTS = ["TextToTextGenerationTask", "RAGTask"] diff --git a/DashAI/back/tasks/RAG_task.py b/DashAI/back/tasks/RAG_task.py index f8d309847..60b06c461 100644 --- a/DashAI/back/tasks/RAG_task.py +++ b/DashAI/back/tasks/RAG_task.py @@ -44,7 +44,7 @@ def prepare_for_task( The history of the conversation """ - return input # type: Optional[List[Tuple[str, str]]] + return input[0].data # type: Optional[List[Tuple[str, str]]] @@ -76,7 +76,7 @@ def process_output( ) -> str: """Process the output of a generative model.""" - return output + return [(str(output[0]), "str")] def process_output_from_database( self, diff --git a/DashAI/front/src/api/generativeTask.ts b/DashAI/front/src/api/generativeTask.ts index 73957488d..2dac403c7 100644 --- a/DashAI/front/src/api/generativeTask.ts +++ b/DashAI/front/src/api/generativeTask.ts @@ -16,6 +16,7 @@ export const getRelatedComponents = async ( const response = await api.get( `/v1/component/?related_component=${encodeURIComponent(relatedComponent)}`, ); + console.log("Related components:", response.data); return response.data; } catch (error) { return []; @@ -25,6 +26,8 @@ export const getRelatedComponents = async ( export const createGenerativeSession = async ( sessionData: Omit, ): Promise => { + + console.log("Creating new generative session with data:", sessionData); const response = await api.post( "/v1/generative-session/", sessionData, diff --git a/DashAI/front/src/api/rag.ts b/DashAI/front/src/api/rag.ts index 8e57f2a99..6a3f2b67c 100644 --- a/DashAI/front/src/api/rag.ts +++ b/DashAI/front/src/api/rag.ts @@ -3,421 +3,135 @@ import { ISession } from "../types/session"; import { IGenerativeTask } from "../types/generativeTask"; +// Fetch all RAG sessions +export const getRAGSessions = async (): Promise => { + const response = await api.get("/v1/generative-session/"); + if (response.status !== 200) { + throw new Error(`Failed to fetch RAG sessions: ${response.statusText}`); + } + return response.data; +} -// Dummy data -// use the days of june 2025 for first, second, ... sessions -let sessions: ISession[] = [ - { - id: "1", - name: "Session I", - task_name: "RAGTask", - description: "First dummy session", - created: new Date("2025-06-01"), - last_modified: new Date("2025-06-01"), - display_name: "RAG Session I", - model_name: "RAGModel", - parameters: { paramA: 1 }, - }, - { - id: "2", - name: "Session II", - task_name: "RAGTask", - description: "Second dummy session", - created: new Date("2025-06-02"), - last_modified: new Date("2025-06-02"), - display_name: "RAG Session II", - model_name: "RAGModel", - parameters: { paramA: 2 }, - }, - { - id: "3", - name: "Session III", - task_name: "RAGTask", - description: "Third dummy session", - created: new Date("2025-06-03"), - last_modified: new Date("2025-06-03"), - display_name: "RAG Session III", - model_name: "RAGModel", - parameters: { paramA: 3 }, - }, - { - id: "4", - name: "Session IV", - task_name: "RAGTask", - description: "Fourth dummy session", - created: new Date("2025-06-04"), - last_modified: new Date("2025-06-04"), - display_name: "RAG Session IV", - model_name: "RAGModel", - parameters: { paramA: 4 }, - } -]; - -let components: IGenerativeTask[] = [ - { - name: "Dense Passage Retrieval", - type: "retriever", - configurable_object: true, - schema: { - description: "Schema for dense passage retrieval", - properties: { - paramA: { - description: "Transformer", - enum: [ - "BERT", - "RoBERTa", - "DistilBERT", - "ALBERT", - ], - placeholder: "BERT", - title: "Parameter A", - type: "string", - }, - DistanceMetric: { - description: "Distance metric to use for retrieval", - enum: [ - "cosine", - "dot_product", - "euclidean", - ], - placeholder: "cosine", - title: "Distance Metric", - type: "string", - }, - }, - required: ["paramA", "DistanceMetric"], - type: "object", - }, - metadata: { - inputs_cardinality: 1, - outputs_cardinality: 1, - inputs_types: ["text"], - outputs_types: ["text"], - }, - description: "A component for dense passage retrieval", - display_name: "Dense Passage Retrieval", - }, - { - name: "TFIDFRetriever", - type: "retriever", - configurable_object: true, - schema: { - description: "Schema for TF-IDF retriever", - properties: { - paramA: { - description: "Remove stop words", - placeholder: "true", - title: "Remove Stop Words", - type: "boolean" - }, - TokenScoreCombination: { - description: "Token score combination method", - enum: [ - "sum", - "max", - "min", - ], - placeholder: "sum", - title: "Token Score Combination", - type: "string" - }, - }, - required: ["paramA", "TokenScoreCombination"], - type: "object", - }, - metadata: { - inputs_cardinality: 1, - outputs_cardinality: 1, - inputs_types: ["text"], - outputs_types: ["text"], - }, - description: "A component for TF-IDF retrieval", - display_name: "TF-IDF Retriever", - }, - - { - name: "DeepSeekModel", - type: "GenerativeModel", - configurable_object: true, - schema: { - description: "Schema for DeepSeek model.", - properties: { - max_tokens: { - description: "Maximum number of tokens to generate.", - minimum: 1, - placeholder: 100, - title: "Max Tokens", - type: "integer" - }, - temperature: { - description: "Sampling temperature. Higher values make the output more random, while lower values make it more focused and deterministic.", - maximum: 1, - minimum: 0, - placeholder: 0.7, - title: "temperature", - type: "number" - }, - frequency_penalty: { - description: "Penalty for repeated tokens in the output. Higher values reduce the likelihood of repetition, encouraging more diverse text generation.", - maximum: 2, - minimum: 0, - placeholder: 0.1, - title: "Frequency Penalty", - type: "number" - }, - n_ctx: { - description: "Maximum number of tokens the model can process in a single forward pass (context window size).", - minimum: 1, - placeholder: 4096, - title: "N Ctx", - type: "integer" - } - }, - required: [ - "max_tokens", - "temperature", - "frequency_penalty", - "n_ctx" - ], - title: "DeepSeekSchema", - type: "object" - }, - metadata: { - inputs_cardinality: 1, - outputs_cardinality: 1, - inputs_types: ["text"], - outputs_types: ["text"] - }, - description: "null,", - display_name: "null" - }, - { - name: "GemmaModel", - type: "GenerativeModel", - configurable_object: true, - schema: { - description: "Schema for Gemma model.", - properties: { - max_tokens: { - description: "Maximum number of tokens to generate.", - minimum: 1, - placeholder: 100, - title: "Max Tokens", - type: "integer" - }, - temperature: { - description: "Sampling temperature. Higher values make the output more random, while lower values make it more focused and deterministic.", - maximum: 1, - minimum: 0, - placeholder: 0.7, - title: "temperature", - type: "number" - }, - frequency_penalty: { - description: "Penalty for repeated tokens in the output. Higher values reduce the likelihood of repetition, encouraging more diverse text generation.", - maximum: 2, - minimum: 0, - placeholder: 0.1, - title: "Frequency Penalty", - type: "number" - }, - n_ctx: { - description: "Maximum number of tokens the model can process in a single forward pass (context window size).", - minimum: 1, - placeholder: 512, - title: "N Ctx", - type: "integer" - } - }, - required: [ - "max_tokens", - "temperature", - "frequency_penalty", - "n_ctx" - ], - title: "GemmaSchema", - type: "object" - }, - metadata: { - inputs_cardinality: 1, - outputs_cardinality: 1, - inputs_types: ["text"], - outputs_types: ["text"] - }, - description: "null,", - display_name: "null" - }, - { - name: "QwenModel", - type: "GenerativeModel", - configurable_object: true, - schema: { - description: "Schema for Qwen model.", - properties: { - "model_name": { - description: "The specific Qwen model version to use.", - "enum": [ - "Qwen/Qwen2.5-0.5B-Instruct-GGUF", - "Qwen/Qwen2.5-1.5B-Instruct-GGUF" - ], - placeholder: "Qwen/Qwen2.5-1.5B-Instruct-GGUF", - title: "Model Name", - type: "string" - }, - max_tokens: { - description: "Maximum number of tokens to generate.", - minimum: 1, - placeholder: 100, - title: "Max Tokens", - type: "integer" - }, - temperature: { - description: "Sampling temperature. Higher values make the output more random, while lower values make it more focused and deterministic.", - maximum: 1, - minimum: 0, - placeholder: 0.7, - title: "temperature", - type: "number" - }, - frequency_penalty: { - description: "Penalty for repeated tokens in the output. Higher values reduce the likelihood of repetition, encouraging more diverse text generation.", - maximum: 2, - minimum: 0, - placeholder: 0.1, - title: "Frequency Penalty", - type: "number" - }, - n_ctx: { - description: "Maximum number of tokens the model can process in a single forward pass (context window size).", - minimum: 1, - placeholder: 512, - title: "N Ctx", - type: "integer" - } - }, - required: [ - "model_name", - "max_tokens", - "temperature", - "frequency_penalty", - "n_ctx" - ], - title: "QwenSchema", - type: "object" - }, - metadata: { - inputs_cardinality: 1, - outputs_cardinality: 1, - inputs_types: ["text"], - outputs_types: ["text"] - }, - description: "null,", - display_name: "null" +export const getRAGSession = async (sessionId: number): Promise => { + + const response = await api.get(`/v1/generative-session/${sessionId}`); + if (response.status !== 200) { + throw new Error(`Failed to fetch RAG session: ${response.statusText}`); } -] -// Fetch all RAG sessions -export const getRAGSessions = async (): Promise => { - console.log("Fetching all RAG sessions"); - - // Simulate an API call - /* - const response = await api.get( - "/v1/generative-session - */ - return [...sessions]; + return response.data; } -// Create a new RAG session export const createRAGSession = async ( - sessionData: Omit + sessionData: Omit ): Promise => { - const newSession: ISession = { - ...sessionData, - id: (Math.random() * 100000).toFixed(0), + console.log("Original RAG session input:", sessionData); + + const params = sessionData.parameters as { + documents: string[]; + retriever_model: { + name: string; + parameters: Record; + }; + generator_model: { + name: string; + parameters: Record; + }; }; - // Simulate an API call - /* - const response = await api.post("/v1/rag-session/", newSession); - */ - sessions.push(newSession); - return newSession; -} -// Update an existing RAG session -export const updateRAGSession = async ( - sessionData: ISession -): Promise => { - - // Mockup data - const index = sessions.findIndex((s) => s.id === sessionData.id); - if (index === -1) throw new Error("Session not found"); - sessions[index] = sessionData; - - // Simulate an API call - /* - const response = await api.put( - `/v1/rag-session/${sessionData.id}`, - sessionData - ); - */ + const transformedSession: Omit = { + name: sessionData.name, + description: sessionData.description, + task_name: "RAGTask", + model_name: "RAGPipeline", + display_name: "", + parameters: { + documents: params.documents, + retriever_model: { + component: params.retriever_model.name, + params: params.retriever_model.parameters, + }, + generation_model: { + component: params.generator_model.name, + params: params.generator_model.parameters, + }, + }, + }; - return sessionData; -} -// Delete a RAG session by ID -export const deleteRAGSession = async (sessionId: string): Promise => { - // mockup data - sessions = sessions.filter((s) => s.id !== sessionId); + console.log("Sending RAG session as:", transformedSession); - // Simulate an API call - /* - removeSession(sessionId); - */ + const response = await api.post("/v1/generative-session/", transformedSession); - console.log(`Deleted session with ID: ${sessionId}`); - return; -} + if (response.status !== 201) { + throw new Error(`Failed to create RAG session: ${response.statusText}`); + } -// Fetch a single RAG session by ID -export const getRAGSession = async (sessionId: string): Promise => { - console.log(`Fetching session with ID: ${sessionId}`); + console.log("RAG session created successfully:", response.data); + return response.data; +}; + +export const updateRAGSession = async (sessionId: number, sessionData: Partial): Promise => { - // Simulate an API call - /* - const response = await api.get( - `/v1/generative-session/${sessionId}` + const response = await api.put(`/v1/generative-session/${sessionId}`, sessionData); + if (response.status !== 200) { + throw new Error(`Failed to update RAG session: ${response.statusText}`); + } return response.data; - */ - - const session = sessions.find((s) => s.id === sessionId); - if (!session) throw new Error("Session not found"); +} + +export const deleteRAGSession = async (sessionId: number): Promise => { + const response = await api.delete(`/v1/generative-session/${sessionId}`); + if (response.status !== 204) { + throw new Error(`Failed to delete RAG session: ${response.statusText}`); + } - return [session]; } -export const getRAGComponents = async (componentType: string): Promise => { - // Simulate fetching RAG components based on type - /* - const response = await api.get( - `/v1/component/?related_component=RAGTask&component_type=${componentType}` +export const updateGenerativeSessionParams = async ( + sessionId: number, + newParams: Record, +): Promise => { + + const response = await api.put( + `/v1/generative-session/${sessionId}/parameters`, + newParams, ); - return response.data; - */ - - let filteredComponents = components.filter((c) => c.type === componentType); - console.log(`Fetching RAG ${filteredComponents.length} components of type ${componentType}`); - return filteredComponents; -}; + if (response.status !== 200) { + throw new Error(`Failed to update RAG session parameters: ${response.statusText}`); + } + return response.data; +} + export const getRetrieverComponents = async (): Promise => { - return getRAGComponents("retriever"); + + const response = await api.get( + `/v1/component/?select_types=RetrieverModel` + ); + + if (response.status !== 200) { + throw new Error(`Failed to fetch retriever components: ${response.statusText}`); + } + + return response.data; + } export const getGeneratorComponents = async (): Promise => { - return getRAGComponents("GenerativeModel"); + + const response = await api.get( + `/v1/component/?related_component=TextToTextGenerationTask` + ); + + if (response.status !== 200) { + throw new Error(`Failed to fetch generator components: ${response.statusText}`); + } + + return response.data; + } \ No newline at end of file diff --git a/DashAI/front/src/components/generative/RAG/ComponentSelector.jsx b/DashAI/front/src/components/generative/RAG/ComponentSelector.jsx index 457a05798..d3e7f648c 100644 --- a/DashAI/front/src/components/generative/RAG/ComponentSelector.jsx +++ b/DashAI/front/src/components/generative/RAG/ComponentSelector.jsx @@ -1,17 +1,9 @@ -import React, { useState, useEffect } from "react"; -import PropTypes from "prop-types"; -import { - Box, - Typography, - Autocomplete, - TextField, - CircularProgress -} from "@mui/material"; -import FormSchemaRenderFields from "../../shared/FormSchemaRenderFields"; -import { useFormik } from "formik"; -import { preprocessSchema, buildYupSchema } from "../utils"; -import { getRelatedComponents } from "../../../api/generativeTask"; -function ComponentSelector({ +import React, { useEffect, useState } from 'react'; +import { Box, Typography, TextField, CircularProgress } from '@mui/material'; +import Autocomplete from '@mui/material/Autocomplete'; + + +export default function ComponentSelector({ componentType, fetchComponents, initialValues, @@ -21,100 +13,64 @@ function ComponentSelector({ const [components, setComponents] = useState([]); const [selectedComponent, setSelectedComponent] = useState(null); const [loading, setLoading] = useState(true); - const [validationSchema, setValidationSchema] = useState(null); - - try { - console.log(getRelatedComponents("TextToTextGenerationTask")); - } catch (error) { - } - + const [formValues, setFormValues] = useState({}); - - // Fetch available components + // Cargar componentes disponibles useEffect(() => { - const getComponents = async () => { - setLoading(true); + const loadComponents = async () => { try { const data = await fetchComponents(); setComponents(data); - // If we have initialValues with a model_name, select that component + // Seleccionar componente inicial si existe if (initialValues?.model_name) { - const preselectedComponent = data.find(c => c.name === initialValues.model_name); - if (preselectedComponent) { - setSelectedComponent(preselectedComponent); + const component = data.find(c => c.name === initialValues.model_name); + if (component) { + setSelectedComponent(component); + setFormValues(initialValues.parameters || {}); } - } else { - console.warn(`No initial model_name provided for ${componentType} selection.`); } } catch (error) { - console.error(`Error fetching ${componentType} components:`, error); + console.error(`Error loading ${componentType} components:`, error); } finally { setLoading(false); } }; - - getComponents(); - }, [componentType, fetchComponents]); - // Set up form validation schema when component changes + loadComponents(); + }, []); + + // Actualizar cuando cambia la selección useEffect(() => { - if (selectedComponent?.schema?.properties) { - const processedProps = preprocessSchema(selectedComponent.schema.properties); - setValidationSchema(buildYupSchema(processedProps)); - console.log("Processed properties for form:", processedProps); - // Merge any existing values with defaults from schema - const schemaDefaults = Object.keys(processedProps).reduce( - (acc, key) => { - acc[key] = processedProps[key].placeholder || ""; - console.log(`Setting default for ${key}:`, acc[key]); - return acc; - }, - { name: "", description: ""}); + if (selectedComponent) { + // Resetear valores al cambiar de componente + setFormValues(initialValues?.parameters || {}); - // Preserve any values that were already set - const mergedValues = { - ...schemaDefaults, - ...(initialValues?.parameters || {}) - }; - - formik.setValues(mergedValues); - } - }, [selectedComponent]); - - const formik = useFormik({ - initialValues: initialValues?.parameters || {}, - validationSchema, - enableReinitialize: true, - onSubmit: (values) => { + // Notificar cambio onConfigurationChange({ model_name: selectedComponent.name, - parameters: values + parameters: initialValues?.parameters || {} }); } - }); + }, [selectedComponent]); - // Enable/disable next button based on form validity and selection + // Validar y habilitar siguiente paso useEffect(() => { - if (selectedComponent && formik.isValid) { - setNextEnabled(true); - - // Auto-submit valid values as they change - if (formik.dirty) { - onConfigurationChange({ - model_name: selectedComponent.name, - parameters: formik.values - }); - } - } else { - setNextEnabled(false); - } - }, [selectedComponent, formik.values, formik.isValid, formik.dirty]); + setNextEnabled(!!selectedComponent); + }, [selectedComponent]); - // Process schema properties for the form renderer - const processedProperties = selectedComponent?.schema?.properties - ? preprocessSchema(selectedComponent.schema.properties) - : {}; + const handleParameterChange = (name, value) => { + const newValues = {...formValues, [name]: value}; + setFormValues(newValues); + + // Notificar cambios en tiempo real + if (selectedComponent) { + onConfigurationChange({ + model_name: selectedComponent.name, + parameters: newValues + }); + } + }; return ( @@ -123,56 +79,49 @@ function ComponentSelector({ {loading ? ( - - - + ) : ( <> c.name)} - value={selectedComponent?.name || null} + value={selectedComponent?.name || ""} onChange={(_, newValue) => { - const selected = components.find(c => c.name === newValue); - setSelectedComponent(selected); + const component = components.find(c => c.name === newValue); + setSelectedComponent(component); }} renderInput={(params) => ( - + )} - sx={{ mb: 4 }} + fullWidth + sx={{ mb: 3 }} /> {selectedComponent && ( -
+ - {selectedComponent.name} Configuration + Configuration Parameters - { - formik.setValues({...formik.values, ...updatedValues}); - }} - onFormSubmit={formik.handleSubmit} - setError={(error) => console.error(error)} - errorsMessage={formik.errors} - /> - + {selectedComponent.schema && Object.entries(selectedComponent.schema.properties).map(([key, spec]) => ( + handleParameterChange(key, e.target.value)} + fullWidth + margin="normal" + type={spec.type === 'integer' ? 'number' : 'text'} + helperText={spec.description} + /> + ))} +
)} )} ); -} - -ComponentSelector.propTypes = { - componentType: PropTypes.string.isRequired, - fetchComponents: PropTypes.func.isRequired, - initialValues: PropTypes.object, - onConfigurationChange: PropTypes.func.isRequired, - setNextEnabled: PropTypes.func.isRequired -}; - -export default ComponentSelector; \ No newline at end of file +} \ No newline at end of file diff --git a/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx b/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx index 944cad664..4f7040a2c 100644 --- a/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx +++ b/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx @@ -1,34 +1,32 @@ import { Box } from "@mui/material"; import { useEffect, useState } from "react"; +import PropTypes from "prop-types"; // Import PropTypes import DocumentTable from "./DocumentTable"; -import Upload from "../../shared/Upload"; +import Upload from "../../shared/Upload"; // Assuming path is correct -export default function DocumentSelector({ - selected = [], // Provide default empty array to prevent map errors - onSelect +export default function DocumentSelector({ + documents: initialDocuments = [], // Renamed prop for clarity, uses initial state + selected: initialSelectedIds = [], // Renamed prop for clarity + onSelect, // Callback for when selection changes (receives selected document objects) + onAddDocument, // Callback for when a new document object is added (receives new doc object) + onRemove, // Callback for when a document is removed (receives docId/path) }) { - const [documents, setDocuments] = useState([]); - const [selectedIds, setSelectedIds] = useState( - (selected || []).map(doc => doc.id) - ); + const [documents, setDocuments] = useState(initialDocuments); + const [selectedIds, setSelectedIds] = useState(initialSelectedIds); // Add a key state to force Upload component re-render const [uploadKey, setUploadKey] = useState(0); - // Fetch documents on mount + // Sync internal 'documents' state with 'initialDocuments' prop useEffect(() => { - // Simulated API - const fetchDocuments = async () => { - // Return mock data if needed - return [ - { id: "doc1", name: "Document 1", updatedAt: new Date().toISOString() }, - { id: "doc2", name: "Document 2", updatedAt: new Date().toISOString() } - ]; - }; - - fetchDocuments().then(setDocuments); - }, []); + setDocuments(initialDocuments); + }, [initialDocuments]); + + // Sync internal 'selectedIds' state with 'initialSelectedIds' prop + useEffect(() => { + setSelectedIds(initialSelectedIds); + }, [initialSelectedIds]); - // Sync selected documents with parent + // Sync selected documents with parent's onSelect callback useEffect(() => { const selectedDocs = documents.filter(doc => selectedIds.includes(doc.id)); if (onSelect) { @@ -37,7 +35,7 @@ export default function DocumentSelector({ }, [selectedIds, documents, onSelect]); const handleToggleSelection = (id) => { - setSelectedIds(prev => + setSelectedIds(prev => prev.includes(id) ? prev.filter(x => x !== id) : [...prev, id] ); }; @@ -50,34 +48,48 @@ export default function DocumentSelector({ setSelectedIds([]); }; - const handleAddDocument = (newDoc) => { - const newDocWithId = { - ...newDoc, - id: `doc-${Date.now()}`, - updatedAt: new Date().toISOString() - }; - - setDocuments(prev => [newDocWithId, ...prev]); - setSelectedIds(prev => [...prev, newDocWithId.id]); + // Internal handler for adding a document object (before notifying parent) + const addDocumentObject = (newDoc) => { + setDocuments(prev => { + // Ensure no duplicates based on ID (path) + if (!prev.some(doc => doc.id === newDoc.id)) { + return [newDoc, ...prev]; // Add to the beginning + } + return prev; + }); + // Call the parent's onAddDocument callback + if (onAddDocument) { + onAddDocument(newDoc); + } }; - const handleRemove = (id) => { + // Internal handler for removing a document object (before notifying parent) + const removeDocumentObject = (id) => { setDocuments(prev => prev.filter(doc => doc.id !== id)); setSelectedIds(prev => prev.filter(x => x !== id)); + // Call the parent's onRemove callback + if (onRemove) { + onRemove(id); + } }; const handleFileUpload = (file, url) => { if (!file) return; - + + // The 'id' of the document will be the URL (or file.name if no URL is available) + // This allows the 'id' to directly represent the path/reference + const docId = url || file.name; + const newDoc = { - id: `doc-${Date.now()}`, + id: docId, name: file.name, updatedAt: new Date().toISOString(), - preview: url + preview: url, // Store URL for preview if available }; - - handleAddDocument(newDoc); - + + addDocumentObject(newDoc); // Use the internal handler + setSelectedIds(prev => [...prev, newDoc.id]); // Also select it upon upload + // Reset the upload component by incrementing its key setUploadKey(prev => prev + 1); }; @@ -91,16 +103,29 @@ export default function DocumentSelector({ onToggle={handleToggleSelection} onSelectAll={handleSelectAll} onDeselectAll={handleDeselectAll} - onRemove={handleRemove} + onRemove={removeDocumentObject} // Use the internal removal handler /> - ); -} \ No newline at end of file +} + +DocumentSelector.propTypes = { + documents: PropTypes.arrayOf(PropTypes.shape({ + id: PropTypes.string.isRequired, + name: PropTypes.string.isRequired, + updatedAt: PropTypes.string.isRequired, + preview: PropTypes.string, // Optional + })), + selected: PropTypes.arrayOf(PropTypes.string), // Array of document IDs (paths) + onSelect: PropTypes.func, // (selectedDocs: array of objects) => void + onAddDocument: PropTypes.func, // (newDoc: object) => void + onRemove: PropTypes.func, // (docId: string) => void +}; diff --git a/DashAI/front/src/components/generative/RAG/DocumentTable.jsx b/DashAI/front/src/components/generative/RAG/DocumentTable.jsx index 94293a4a7..9bf365ed0 100644 --- a/DashAI/front/src/components/generative/RAG/DocumentTable.jsx +++ b/DashAI/front/src/components/generative/RAG/DocumentTable.jsx @@ -13,7 +13,7 @@ import { } from "@mui/material"; import VisibilityIcon from "@mui/icons-material/Visibility"; import DeleteIcon from "@mui/icons-material/Delete"; -import { Margin } from "@mui/icons-material"; +import PropTypes from "prop-types"; export default function DocumentTable({ documents, @@ -25,12 +25,12 @@ export default function DocumentTable({ }) { return ( - @@ -43,7 +43,20 @@ export default function DocumentTable({ - + {/* Added padding="checkbox" for standard spacing */} + {/* Checkbox for Select All/Deselect All */} + 0 && selectedIds.length < documents.length} + checked={selectedIds.length === documents.length && documents.length > 0} + onChange={(event) => { + if (event.target.checked) { + onSelectAll(); + } else { + onDeselectAll(); + } + }} + /> + Name Updated Actions @@ -59,7 +72,7 @@ export default function DocumentTable({ backgroundColor: selectedIds.includes(doc.id) ? "action.hover" : "inherit", }} > - + {/* Added padding="checkbox" */} onToggle(doc.id)} @@ -70,7 +83,10 @@ export default function DocumentTable({ - + { + if (doc.preview) window.open(doc.preview, '_blank'); + else console.warn("No preview URL available for this document."); + }}> @@ -85,12 +101,16 @@ export default function DocumentTable({ ))}
- + gap={1} /* Added gap between buttons */ + p={1} /* Added padding for buttons within the box */ + borderTop="1px solid #e0e0e0" /* Separator line */ + > @@ -99,3 +119,17 @@ export default function DocumentTable({ ); } + +DocumentTable.propTypes = { + documents: PropTypes.arrayOf(PropTypes.shape({ + id: PropTypes.string.isRequired, + name: PropTypes.string.isRequired, + updatedAt: PropTypes.string.isRequired, + preview: PropTypes.string, + })).isRequired, + selectedIds: PropTypes.arrayOf(PropTypes.string).isRequired, + onToggle: PropTypes.func.isRequired, + onSelectAll: PropTypes.func.isRequired, + onDeselectAll: PropTypes.func.isRequired, + onRemove: PropTypes.func.isRequired, +}; diff --git a/DashAI/front/src/components/generative/SelectModelMenu.jsx b/DashAI/front/src/components/generative/SelectModelMenu.jsx index aa15b71e6..71afaeb2a 100644 --- a/DashAI/front/src/components/generative/SelectModelMenu.jsx +++ b/DashAI/front/src/components/generative/SelectModelMenu.jsx @@ -24,7 +24,6 @@ export default function SelectModelMenu({ useEffect(() => { if (!selectedTaskName) return; - console.log("Fetching related components for task:", selectedTaskName); getRelatedComponents(selectedTaskName).then(setRelatedComponents); }, [selectedTaskName]); diff --git a/DashAI/front/src/components/generative/SessionBar.jsx b/DashAI/front/src/components/generative/SessionBar.jsx index 5ca9f864d..574109100 100644 --- a/DashAI/front/src/components/generative/SessionBar.jsx +++ b/DashAI/front/src/components/generative/SessionBar.jsx @@ -21,8 +21,6 @@ export default function SessionBar({ const [selectedInfoSession, setSelectedInfoSession] = useState(null); const [openSections, setOpenSections] = useState({}); - console.log(selectedSessionId); - useEffect(() => { // Initialize all sections as closed const taskNames = [ diff --git a/DashAI/front/src/components/generative/prompt.js b/DashAI/front/src/components/generative/prompt.js new file mode 100644 index 000000000..3e6ba658d --- /dev/null +++ b/DashAI/front/src/components/generative/prompt.js @@ -0,0 +1,954 @@ +Ayudame a hacer que este código funcione, llamando correctamente a las APIs y manteniendo el estilo definido. Puedes modificar y refactorizar todo lo necesario, pero tienes prohibido cambiar la estética de los componentes y el código de NewSessionModal (el resto sí puedes modificarlo) + +La idea es poder crear una sesión de rag del siguiente modo: + +1. Se cargan (desde el back) y muestran todas las sesiones disponibles + +2. Si se crea una nueva sesión, se abre un modal + +3. Se seleccionan los documentos + +4. Se configura el Retriever. Para ello: + +4.1. Se cargan desde el back los retriever disponibles + +4.2 El usuario selecciona un modelo + +4.3 Se muetran los parámetros por defecto del modelo + +4.4 Si el usuario desea puede modificar los parámetros + +4.5 Al hacer click en Next se guardan (con un post al backend) los parámetros del Retriever de la sesión + +5. Se configura el LLM + +//imports + +export default function Generative() { + const [stepIndex, setStepIndex] = useState(0); + const [selectedSessionId, setSelectedSessionId] = useState(null); + const [selectedTaskName, setSelectedTaskName] = useState(""); + const [sessions, setSessions] = useState([]); + const [paramsVersion, setParamsVersion] = useState(0); + + const isRAGTask = () => selectedTaskName === "RAGTask"; + + const handleSessionClick = (sessionId, taskName) => { + console.log("[handleSessionClick]", { sessionId, taskName }); + setSelectedTaskName(taskName); + setSelectedSessionId(sessionId); + }; + + const handleNewSessionButton = () => { + console.log("[handleNewSessionButton]"); + setSelectedSessionId(null); + setStepIndex(0); + setSelectedTaskName(""); + }; + + const onParamsUpdate = (newParams) => { + console.log("[onParamsUpdate]", newParams); + setParamsVersion((prev) => prev + 1); + }; + + useEffect(() => { + getSessions().then((data) => { + console.log("[getSessions]", data); + setSessions(data); + }); + }, []); + + const handleAddSession = (session) => { + console.log("[handleAddSession]", session); + setSessions((prevSessions) => [session, ...prevSessions]); + }; + + const handleSessionDelete = (id) => { + console.log("[handleSessionDelete]", id); + if (id === selectedSessionId) { + setSelectedSessionId(null); + setStepIndex(0); + setSelectedTaskName(""); + } + + setSessions((prevSessions) => + prevSessions.filter((session) => session.id !== id) + ); + + removeSession(id); + }; + + return ( + + + + + + + + {selectedSessionId ? ( + isRAGTask() ? ( + + ) : ( + + ) + ) : stepIndex === 0 ? ( + { + console.log("[SelectTaskMenu] task selected:", taskName); + setSelectedTaskName(taskName); + setStepIndex(1); + }} + /> + ) : isRAGTask() ? ( + + ) : ( + + ) + } + + + + {!isRAGTask() && ( + + + {!selectedSessionId ? + ( + + ): null} + + + ) + } + + ); +} +//imports + +const steps = [ + { + name: "select-documents", + label: "Select documents", + Component: DocumentSelectionStep, + }, + { + name: "configure-retriever", + label: "Configure retriever", + Component: RetrieverConfigurationStep, + }, + { + name: "configure-algorithm", + label: "Configure algorithm", + Component: AlgorithmConfigurationStep, + }, +]; + +const defaultNewSession = { + name: "", + task_name: "RAGTask", // Default task name + description: "", + RAGParameters: {}, + documents: [], +}; + +export default function NewSessionModal({ + open, + setOpen, + setUpdateTableFlag, + setSelectedSessionId, + handleAddSession, + selectedSession, +}) { + const theme = useTheme(); + const matches = useMediaQuery(theme.breakpoints.down("md")); + const screenSm = useMediaQuery(theme.breakpoints.down("sm")); + const { enqueueSnackbar } = useSnackbar(); + + const [activeStep, setActiveStep] = useState(0); + const [nextEnabled, setNextEnabled] = useState(false); + const [newSession, setNewSession] = useState( + selectedSession || defaultNewSession + ); + + const [stepCompleted, setStepCompleted] = useState([false, false, false]); + + const handleStepComplete = (stepIndex) => { + const newCompleted = [...stepCompleted]; + newCompleted[stepIndex] = true; + setStepCompleted(newCompleted); + }; + + // Initialize session data when modal opens or selectedSession changes + useEffect(() => { + if (open) { + if (selectedSession) { + // Edit mode - use existing session + setNewSession({ + ...selectedSession, + // Ensure documents array exists + documents: selectedSession.documents || [] + }); + } else { + // Create mode - use defaults + setNewSession(defaultNewSession); + } + setActiveStep(0); + setNextEnabled(false); + } + }, [open, selectedSession]); + + + const uploadNewSession = async () => { + try { + // For new sessions, exclude ID + const sessionData = selectedSession + ? newSession // Keep existing ID for updates + : { ...newSession, id: undefined }; // Remove ID for new sessions + + const response = selectedSession + ? await updateRAGSession(sessionData) // Implement update function if needed + : await createRAGSession(sessionData); + + const sessionId = response.id; + console.log("Session created/updated with ID:", sessionId); + + enqueueSnackbar( + `Session ${selectedSession ? "updated" : "created"} successfully!`, + { variant: "success" } + ); + setUpdateTableFlag(true); // Trigger table update + } catch (error) { + console.error("Session creation error:", error); + enqueueSnackbar( + `Failed to ${selectedSession ? "update" : "create"} session: ${error.message}`, + { variant: "error" } + ); + } + }; + + const handleCloseDialog = () => { + setActiveStep(0); + setOpen(false); + setNewSession(defaultNewSession); + setNextEnabled(false); + + }; + + const handleStepButton = (index) => () => { + setActiveStep(index); + }; + + const handleBackButton = () => { + if (activeStep === 0) { + handleCloseDialog(); + } else { + setActiveStep(activeStep - 1); + setNextEnabled(true); + } + }; + + const handleNextButton = () => { + if (activeStep === steps.length - 1) { + handleFinish(); + } else { + setActiveStep(activeStep + 1); + setNextEnabled(false); // Reset next enabled for new step + } + }; + + const handleFinish = async () => { + await uploadNewSession(); + handleCloseDialog(); + }; + + return ( + + + + + + + + + + + + + {selectedSession ? "Edit" : "Create New"} RAG Session + + + + + + + {steps.map((step, index) => ( + index} + disabled={activeStep < index}> + + {step.label} + + + ))} + + + + + + + {activeStep === 0 && ( + + )} + {activeStep === 1 && ( + + )} + {activeStep === 2 && ( + + )} + + + + + + + + + + ); +} + + +// imports +function RAGHomePage({ + selectedTaskName, + setSelectedSessionId, + handleAddSession + }) { + + ... + + return ( + + {showNewSessionModal? ( + + ):( + + ) + } + + ); +} + +export default RAGHomePage; + +// imports + +const steps = [ + { + name: "select-documents", + label: "Select documents", + Component: DocumentSelectionStep, + }, + { + name: "configure-retriever", + label: "Configure retriever", + Component: RetrieverConfigurationStep, + }, + { + name: "configure-algorithm", + label: "Configure algorithm", + Component: AlgorithmConfigurationStep, + }, +]; + +const defaultNewSession = { + name: "", + task_name: "RAGTask", // Default task name + description: "", + RAGParameters: {}, + documents: [], +}; + +... +export default function NewSessionModal({ + open, + setOpen, + setUpdateTableFlag, + setSelectedSessionId, + handleAddSession, + selectedSession, +}) { + const theme = useTheme(); + const matches = useMediaQuery(theme.breakpoints.down("md")); + const screenSm = useMediaQuery(theme.breakpoints.down("sm")); + const { enqueueSnackbar } = useSnackbar(); + + const [activeStep, setActiveStep] = useState(0); + const [nextEnabled, setNextEnabled] = useState(false); + const [newSession, setNewSession] = useState( + selectedSession || defaultNewSession + ); + + const [stepCompleted, setStepCompleted] = useState([false, false, false]); + + const handleStepComplete = (stepIndex) => { + const newCompleted = [...stepCompleted]; + newCompleted[stepIndex] = true; + setStepCompleted(newCompleted); + }; + + // Initialize session data when modal opens or selectedSession changes + useEffect(() => { + if (open) { + if (selectedSession) { + // Edit mode - use existing session + setNewSession({ + ...selectedSession, + // Ensure documents array exists + documents: selectedSession.documents || [] + }); + } else { + // Create mode - use defaults + setNewSession(defaultNewSession); + } + setActiveStep(0); + setNextEnabled(false); + } + }, [open, selectedSession]); + + + const uploadNewSession = async () => { + try { + // For new sessions, exclude ID + const sessionData = selectedSession + ? newSession // Keep existing ID for updates + : { ...newSession, id: undefined }; // Remove ID for new sessions + + const response = selectedSession + ? await updateRAGSession(sessionData) // Implement update function if needed + : await createRAGSession(sessionData); + + const sessionId = response.id; + console.log("Session created/updated with ID:", sessionId); + + enqueueSnackbar( + `Session ${selectedSession ? "updated" : "created"} successfully!`, + { variant: "success" } + ); + setUpdateTableFlag(true); // Trigger table update + } catch (error) { + console.error("Session creation error:", error); + enqueueSnackbar( + `Failed to ${selectedSession ? "update" : "create"} session: ${error.message}`, + { variant: "error" } + ); + } + }; + + const handleCloseDialog = () => { + setActiveStep(0); + setOpen(false); + setNewSession(defaultNewSession); + setNextEnabled(false); + + }; + + const handleStepButton = (index) => () => { + setActiveStep(index); + }; + + const handleBackButton = () => { + if (activeStep === 0) { + handleCloseDialog(); + } else { + setActiveStep(activeStep - 1); + setNextEnabled(true); + } + }; + + const handleNextButton = () => { + if (activeStep === steps.length - 1) { + handleFinish(); + } else { + setActiveStep(activeStep + 1); + setNextEnabled(false); // Reset next enabled for new step + } + }; + + const handleFinish = async () => { + await uploadNewSession(); + handleCloseDialog(); + }; + + return ( + + + + + + + + + + + + + {selectedSession ? "Edit" : "Create New"} RAG Session + + + + + + + {steps.map((step, index) => ( + index} + disabled={activeStep < index}> + + {step.label} + + + ))} + + + + + + + {activeStep === 0 && ( + + )} + {activeStep === 1 && ( + + )} + {activeStep === 2 && ( + + )} + + + + + + + + + + ); +} + +// imports +function RetrieverConfigurationStep({ newSession, setNewSession, setNextEnabled }) { + const handleConfigurationChange = (retrieverConfig) => { + setNewSession(prev => ({ + ...prev, + RAGParameters: { + ...prev.RAGParameters, + retrieval: retrieverConfig + } + })); + }; + console.log("Retriever initial values:", newSession.RAGParameters?.retrieval); + console.log("New session state:", newSession); + + return ( + + + + ); +} + +RetrieverConfigurationStep.propTypes = { + newSession: PropTypes.object.isRequired, + setNewSession: PropTypes.func.isRequired, + setNextEnabled: PropTypes.func.isRequired, +}; + +export default RetrieverConfigurationStep; + +// imports + +import { getRelatedComponents } from "../../../api/generativeTask"; +function ComponentSelector({ + componentType, + fetchComponents, + initialValues, + onConfigurationChange, + setNextEnabled +}) { + const [components, setComponents] = useState([]); + const [selectedComponent, setSelectedComponent] = useState(null); + const [loading, setLoading] = useState(true); + const [validationSchema, setValidationSchema] = useState(null); + + // Fetch available components + useEffect(() => { + const getComponents = async () => { + setLoading(true); + try { + const data = await fetchComponents(); + setComponents(data); + + // If we have initialValues with a model_name, select that component + if (initialValues?.model_name) { + const preselectedComponent = data.find(c => c.name === initialValues.model_name); + if (preselectedComponent) { + setSelectedComponent(preselectedComponent); + } + } else { + console.warn(`No initial model_name provided for ${componentType} selection.`); + } + } catch (error) { + console.error(`Error fetching ${componentType} components:`, error); + } finally { + setLoading(false); + } + }; + + getComponents(); + }, [componentType, fetchComponents]); + + // Set up form validation schema when component changes + useEffect(() => { + if (selectedComponent?.schema?.properties) { + const processedProps = preprocessSchema(selectedComponent.schema.properties); + setValidationSchema(buildYupSchema(processedProps)); + console.log("Processed properties for form:", processedProps); + // Merge any existing values with defaults from schema + const schemaDefaults = Object.keys(processedProps).reduce( + (acc, key) => { + acc[key] = processedProps[key].placeholder || ""; + console.log(`Setting default for ${key}:`, acc[key]); + return acc; + }, + { name: "", description: ""}); + + // Preserve any values that were already set + const mergedValues = { + ...schemaDefaults, + ...(initialValues?.parameters || {}) + }; + + formik.setValues(mergedValues); + } + }, [selectedComponent]); + + const formik = useFormik({ + initialValues: initialValues?.parameters || {}, + validationSchema, + enableReinitialize: true, + onSubmit: (values) => { + onConfigurationChange({ + model_name: selectedComponent.name, + parameters: values + }); + } + }); + + // Enable/disable next button based on form validity and selection + useEffect(() => { + if (selectedComponent && formik.isValid) { + setNextEnabled(true); + + // Auto-submit valid values as they change + if (formik.dirty) { + onConfigurationChange({ + model_name: selectedComponent.name, + parameters: formik.values + }); + } + } else { + setNextEnabled(false); + } + }, [selectedComponent, formik.values, formik.isValid, formik.dirty]); + + // Process schema properties for the form renderer + const processedProperties = selectedComponent?.schema?.properties + ? preprocessSchema(selectedComponent.schema.properties) + : {}; + + return ( + + + Select {componentType} + + + {loading ? ( + + + + ) : ( + <> + c.name)} + value={selectedComponent?.name || null} + onChange={(_, newValue) => { + const selected = components.find(c => c.name === newValue); + setSelectedComponent(selected); + }} + renderInput={(params) => ( + + )} + sx={{ mb: 4 }} + /> + + {selectedComponent && ( +
+ + {selectedComponent.name} Configuration + + + { + formik.setValues({...formik.values, ...updatedValues}); + }} + onFormSubmit={formik.handleSubmit} + setError={(error) => console.error(error)} + errorsMessage={formik.errors} + /> + + )} + + )} +
+ ); +} + +ComponentSelector.propTypes = { + componentType: PropTypes.string.isRequired, + fetchComponents: PropTypes.func.isRequired, + initialValues: PropTypes.object, + onConfigurationChange: PropTypes.func.isRequired, + setNextEnabled: PropTypes.func.isRequired +}; + +export default ComponentSelector; + +//RAG api +import api from "./api"; +import { ISession } from "../types/session"; +import { IGenerativeTask } from "../types/generativeTask"; + + +// Fetch all RAG sessions +export const getRAGSessions = async (): Promise => { + console.log("Fetching all RAG sessions"); + + const response = await api.get("/v1/generative-session/"); + if (response.status !== 200) { + throw new Error(`Failed to fetch RAG sessions: ${response.statusText}`); + } + + console.log("RAG sessions fetched successfully:", response.data); + return response.data; +} + +export const getRAGSession = async (sessionId: number): Promise => { + console.log(`Fetching RAG session with ID: ${sessionId}`); + + const response = await api.get(`/v1/generative-session/${sessionId}`); + if (response.status !== 200) { + throw new Error(`Failed to fetch RAG session: ${response.statusText}`); + } + + console.log("RAG session fetched successfully:", response.data); + return response.data; +} + +export const createRAGSession = async (sessionData: Omit): Promise => { + console.log("Creating new RAG session with data:", sessionData); + + const response = await api.post("/v1/generative-session/", sessionData); + if (response.status !== 201) { + throw new Error(`Failed to create RAG session: ${response.statusText}`); + } + + console.log("RAG session created successfully:", response.data); + return response.data; +} + +export const updateGenerativeSessionParams = async ( + sessionId: number, + newParams: Record, +): Promise => { + console.log(`Updating parameters for RAG session ID: ${sessionId} with data: + ${JSON.stringify(newParams)}`); + + const response = await api.put( + `/v1/generative-session/${sessionId}/parameters`, + newParams, + ); + if (response.status !== 200) { + throw new Error(`Failed to update RAG session parameters: ${response.statusText}`); + } + console.log("RAG session parameters updated successfully:", response.data); + return response.data; +} + +export const getRAGComponents = async (): Promise => { + // Simulate fetching RAG components based on type + + const response = await api.get( + `/v1/component/?related_component=RAGTask` + ); + + if (response.status !== 200) { + throw new Error(`Failed to fetch RAG components: ${response.statusText}`); + } + + return response.data; +}; + +export const getRetrieverComponents = async (): Promise => { + const response = await api.get( + `/v1/component/?selectTypes=RetrieverModel` + ); + + if (response.status !== 200) { + throw new Error(`Failed to fetch retriever components: ${response.statusText}`); + } + + return response.data; + +} + +export const getGeneratorComponents = async (): Promise => { + + const response = await api.get( + `/v1/component/?related_component=GenerativeModel` + ); + + if (response.status !== 200) { + throw new Error(`Failed to fetch generator components: ${response.statusText}`); + } + + return response.data; + +} \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/Generative.jsx b/DashAI/front/src/pages/generative/Generative.jsx index 8568d46a1..bb424bdf0 100644 --- a/DashAI/front/src/pages/generative/Generative.jsx +++ b/DashAI/front/src/pages/generative/Generative.jsx @@ -21,37 +21,31 @@ export default function Generative() { const isRAGTask = () => selectedTaskName === "RAGTask"; const handleSessionClick = (sessionId, taskName) => { - console.log("[handleSessionClick]", { sessionId, taskName }); setSelectedTaskName(taskName); setSelectedSessionId(sessionId); }; const handleNewSessionButton = () => { - console.log("[handleNewSessionButton]"); setSelectedSessionId(null); setStepIndex(0); setSelectedTaskName(""); }; const onParamsUpdate = (newParams) => { - console.log("[onParamsUpdate]", newParams); setParamsVersion((prev) => prev + 1); }; useEffect(() => { getSessions().then((data) => { - console.log("[getSessions]", data); setSessions(data); }); }, []); const handleAddSession = (session) => { - console.log("[handleAddSession]", session); setSessions((prevSessions) => [session, ...prevSessions]); }; const handleSessionDelete = (id) => { - console.log("[handleSessionDelete]", id); if (id === selectedSessionId) { setSelectedSessionId(null); setStepIndex(0); @@ -81,32 +75,22 @@ export default function Generative() { {selectedSessionId ? ( - isRAGTask() ? ( - ) : ( - - ) - ) : stepIndex === 0 ? ( + ) : stepIndex === 0 ? ( { - console.log("[SelectTaskMenu] task selected:", taskName); setSelectedTaskName(taskName); setStepIndex(1); }} /> ) : isRAGTask() ? ( ) : ( - ) - } + )} diff --git a/DashAI/front/src/pages/generative/RAG/DocumentSelectionStep.jsx b/DashAI/front/src/pages/generative/RAG/DocumentSelectionStep.jsx index 4b7321461..3ad1d7afa 100644 --- a/DashAI/front/src/pages/generative/RAG/DocumentSelectionStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/DocumentSelectionStep.jsx @@ -1,94 +1,105 @@ -import React, { useState, useEffect } from "react"; +import React, { useState, useEffect, useCallback } from "react"; import PropTypes from "prop-types"; -import { Box, CircularProgress } from "@mui/material"; -import DocumentSelector from "../../../components/generative/RAG/DocumentSelector"; -import SessionMetadata from "../../../components/generative/RAG/SessionMetadata"; +import { Box, Typography } from "@mui/material"; +import DocumentSelector from "../../../components/generative/RAG/DocumentSelector"; // Adjust the import path as needed -function DocumentSelectionStep({ newSession, setNewSession, setNextEnabled }) { - const [documents, setDocuments] = useState([]); - const [loading, setLoading] = useState(true); - // Fetch available documents on component mount + + + + +// Helper function to convert document paths to UI-friendly objects +const createDocumentObjectFromPath = (path) => ({ + id: path, + name: path.split('/').pop() || path, // Use filename as name, fallback to full path + updatedAt: new Date().toISOString(), // Initial timestamp for new paths +}); + +export default function DocumentSelectionStep({ documents, setDocuments, setNextEnabled }) { + // Initialize localDocumentObjects and selectedObjectIds once from the 'documents' prop. + // This runs only on the initial mount. + const [localDocumentObjects, setLocalDocumentObjects] = useState(() => { + const initialLocalDocs = documents.map(createDocumentObjectFromPath); + const initialSelectedIds = documents; // Paths are directly the IDs + return initialLocalDocs; + }); + + const [selectedObjectIds, setSelectedObjectIds] = useState(() => { + return documents; // Paths are directly the IDs + }); + + // Effect 1: Sync selectedObjectIds (paths) back to the parent component + // This effect ensures the parent's 'documents' prop is updated when local selection changes. + useEffect(() => { + // Only update parent if there's a change to prevent infinite loops and unnecessary re-renders + if (JSON.stringify(selectedObjectIds) !== JSON.stringify(documents)) { + setDocuments(selectedObjectIds); // selectedObjectIds already contains the paths + } + }, [selectedObjectIds, setDocuments, documents]); // Dependencies: local selected IDs, and parent's setter/prop for comparison + + // Effect 2: Notify parent about step validity + // This effect ensures the 'Next' button's enabled state is correct. useEffect(() => { - const fetchDocuments = async () => { - setLoading(true); - try { - // Replace this with your actual API call to get documents - // For now using a mock response with delay - await new Promise(resolve => setTimeout(resolve, 500)); - const data = [ - { id: 1, name: "Product Documentation.pdf", type: "PDF", size: "1.2 MB", updatedAt: "2025-05-15" }, - { id: 2, name: "User Manual.docx", type: "DOCX", size: "3.4 MB", updatedAt: "2025-05-20" }, - { id: 3, name: "API Reference.pdf", type: "PDF", size: "2.1 MB", updatedAt: "2025-06-01" }, - { id: 4, name: "Technical Specifications.txt", type: "TXT", size: "4.7 MB", updatedAt: "2025-06-10" }, - { id: 5, name: "Troubleshooting Guide.pdf", type: "PDF", size: "1.8 MB", updatedAt: "2025-06-15" } - ]; - setDocuments(data); - } catch (error) { - console.error("Failed to fetch documents:", error); - } finally { - setLoading(false); + const isValid = selectedObjectIds && selectedObjectIds.length > 0; + setNextEnabled(isValid); + }, [selectedObjectIds, setNextEnabled]); // Dependencies: local selected IDs, and parent's setter + + // Handler for when DocumentSelector's selection changes (checkboxes) + const handleDocumentSelectionChange = useCallback((selectedDocs) => { + // Extract 'id' (which is the path) from the selected document objects + const newSelectedIds = selectedDocs.map(doc => doc.id); + setSelectedObjectIds(newSelectedIds); + }, []); + + // Handler for when DocumentSelector adds a new document (e.g., from Upload component) + const handleAddDocumentObject = useCallback((newDoc) => { + setLocalDocumentObjects(prev => { + // Ensure no duplicates based on ID (path) before adding + if (!prev.some(doc => doc.id === newDoc.id)) { + const updatedDocs = [...prev, newDoc]; + // Also ensure it's selected in the UI + setSelectedObjectIds(currentSelected => { + if (!currentSelected.includes(newDoc.id)) { + return [...currentSelected, newDoc.id]; + } + return currentSelected; + }); + return updatedDocs; } - }; + return prev; + }); + }, []); - fetchDocuments(); + // Handler for when DocumentSelector removes a document + const handleRemoveDocumentObject = useCallback((docId) => { + setLocalDocumentObjects(prev => prev.filter(doc => doc.id !== docId)); + setSelectedObjectIds(prev => prev.filter(id => id !== docId)); }, []); - // Update next button state whenever documents or session name changes - useEffect(() => { - const hasDocuments = newSession.documents && newSession.documents.length > 0; - const hasName = Boolean(newSession.name); - setNextEnabled(hasDocuments && hasName); - }, [newSession.documents, newSession.name, setNextEnabled]); - - const handleSessionMetadataUpdate = (metadata) => { - setNewSession(prev => ({ - ...prev, - name: metadata.name, - description: metadata.description - })); - }; - - const handleDocumentsSelect = (selectedDocuments) => { - setNewSession(prev => ({ - ...prev, - documents: selectedDocuments - })); - }; return ( - - - - {loading ? ( - - - - ) : ( + + Select Documents + + Upload new documents or select from existing ones to be used for RAG. + + + {/* Integrate the DocumentSelector component */} + {/* Allow DocumentSelector to take available height */} - )} + ); } DocumentSelectionStep.propTypes = { - newSession: PropTypes.object.isRequired, - setNewSession: PropTypes.func.isRequired, + documents: PropTypes.arrayOf(PropTypes.string).isRequired, + setDocuments: PropTypes.func.isRequired, setNextEnabled: PropTypes.func.isRequired, }; - -export default DocumentSelectionStep; \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/GeneratorConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/GeneratorConfigurationStep.jsx new file mode 100644 index 000000000..0af5a14c7 --- /dev/null +++ b/DashAI/front/src/pages/generative/RAG/GeneratorConfigurationStep.jsx @@ -0,0 +1,171 @@ +import { useState, useEffect, useCallback } from "react"; +import PropTypes from "prop-types"; +import { Box, Autocomplete, TextField, Typography, Button } from "@mui/material"; +import { useFormik } from "formik"; +import FormSchemaRenderFields from "../../../components/shared/FormSchemaRenderFields"; +import { getGeneratorComponents } from "../../../api/rag"; +import { preprocessSchema, buildYupSchema } from "../../../components/generative/utils" + + +export default function GeneratorConfigurationStep({ generatorModel, setGeneratorModel, setNextEnabled }) { + const [generators, setGenerators] = useState([]); + const [currentSelectedGeneratorOption, setCurrentSelectedGeneratorOption] = useState(null); + const [validationSchema, setValidationSchema] = useState(null); + + const getInitialParamsFromSchema = useCallback((schemaProperties) => { + if (!schemaProperties) return {}; + return Object.keys(schemaProperties).reduce((acc, key) => { + acc[key] = schemaProperties[key].placeholder !== undefined + ? schemaProperties[key].placeholder + : ""; + return acc; + }, {}); + }, []); + + + useEffect(() => { + const loadAndSetGenerator = async () => { + const data = await getGeneratorComponents(); + setGenerators(data); + + if (generatorModel?.name) { + const existingGenerator = data.find(r => r.name === generatorModel.name); + if (existingGenerator) { + setCurrentSelectedGeneratorOption(existingGenerator); + } + } + }; + loadAndSetGenerator(); + }, [generatorModel?.name]); + + const formik = useFormik({ + initialValues: generatorModel?.parameters || {}, + validationSchema: validationSchema, + enableReinitialize: true, + onSubmit: (values) => { + }, + }); + + useEffect(() => { + + const generatorSchemaProperties = currentSelectedGeneratorOption?.schema?.properties; + + if (generatorSchemaProperties) { + const processedProps = preprocessSchema(generatorSchemaProperties); + setValidationSchema(buildYupSchema(processedProps)); + + const initialFormValues = Object.keys(processedProps).reduce( + (acc, key) => { + acc[key] = generatorModel?.parameters?.[key] !== undefined + ? generatorModel.parameters[key] + : (processedProps[key].placeholder !== undefined + ? processedProps[key].placeholder + : ""); + return acc; + }, + {}, + ); + formik.setValues(initialFormValues); + } else { + setValidationSchema(null); + formik.setValues({}); + } + }, [currentSelectedGeneratorOption, generatorModel?.parameters]); + + useEffect(() => { + const isValid = !!currentSelectedGeneratorOption && formik.isValid; + setNextEnabled(isValid); + }, [currentSelectedGeneratorOption, formik.isValid, setNextEnabled]); + + const handleGeneratorSelectionChange = (event, newValue) => { + setCurrentSelectedGeneratorOption(newValue); + + if (newValue) { + const initialParameters = getInitialParamsFromSchema(newValue.schema?.properties); + setGeneratorModel({ + name: newValue.name, + parameters: initialParameters, + }); + } else { + setGeneratorModel({ name: "", parameters: {} }); + } + }; + + const handleParametersChange = (updatedValues) => { + formik.setValues((prevValues) => ({ + ...prevValues, + ...updatedValues, + })); + + setGeneratorModel({ + name: currentSelectedGeneratorOption.name, + parameters: { + ...formik.values, + ...updatedValues, + }, + }); + }; + + const processedProperties = currentSelectedGeneratorOption?.schema?.properties + ? preprocessSchema(currentSelectedGeneratorOption.schema.properties) + : {}; + + return ( + + + Configure Generator Model (LLM) + + + option.name} + value={currentSelectedGeneratorOption} + onChange={handleGeneratorSelectionChange} + isOptionEqualToValue={(option, value) => option.name === value?.name} + renderInput={(params) => } + sx={{ mb: 3 }} + /> + + {currentSelectedGeneratorOption && currentSelectedGeneratorOption.schema && ( +
+ + + Parameters + + console.error("FormSchemaRenderFields Error:", error)} + errorsMessage={formik.errors} + /> + +
+ )} + + ); +} diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx index 8537b3fbf..cf14db042 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx @@ -1,7 +1,6 @@ -import React, { useState, useEffect } from "react"; +import React, { useState, useEffect, useCallback } from "react"; import { Button, - ButtonGroup, Dialog, DialogActions, DialogTitle, @@ -16,156 +15,154 @@ import { import CloseIcon from "@mui/icons-material/Close"; import { useTheme } from "@mui/material/styles"; import useMediaQuery from "@mui/material/useMediaQuery"; -import { useSnackbar } from "notistack"; import DocumentSelectionStep from "./DocumentSelectionStep"; import RetrieverConfigurationStep from "./RetrieverConfigurationStep"; -import AlgorithmConfigurationStep from "./AlgorithmConfigurationStep"; -import { createRAGSession, updateRAGSession } from "../../../api/rag"; +import GeneratorConfigurationStep from "./GeneratorConfigurationStep"; const steps = [ { name: "select-documents", label: "Select documents", - Component: DocumentSelectionStep, + component: DocumentSelectionStep, }, { name: "configure-retriever", label: "Configure retriever", - Component: RetrieverConfigurationStep, + component: RetrieverConfigurationStep, }, { - name: "configure-algorithm", - label: "Configure algorithm", - Component: AlgorithmConfigurationStep, + name: "configure-generator", + label: "Configure Language Model", + component: GeneratorConfigurationStep, }, ]; const defaultNewSession = { name: "", - task_name: "RAGTask", // Default task name + task_name: "RAGTask", description: "", - RAGParameters: {}, - documents: [], + displayName: "", + parameters: { + "documents": [], + "retriever_model": { + "name": "", + "parameters": {}, + }, + "generator_model": { + "name": "", + "parameters": {}, + }, + }, }; export default function NewSessionModal({ open, - setOpen, - setUpdateTableFlag, - setSelectedSessionId, - handleAddSession, - selectedSession, + onClose, + onSessionSaved, + onSessionSelect, + session }) { const theme = useTheme(); - const matches = useMediaQuery(theme.breakpoints.down("md")); const screenSm = useMediaQuery(theme.breakpoints.down("sm")); - const { enqueueSnackbar } = useSnackbar(); - const [activeStep, setActiveStep] = useState(0); - const [nextEnabled, setNextEnabled] = useState(false); - const [newSession, setNewSession] = useState( - selectedSession || defaultNewSession - ); + const [sessionData, setSessionData] = useState(session || defaultNewSession); + const [stepValidity, setStepValidity] = useState(new Array(steps.length).fill(false)); - const [stepCompleted, setStepCompleted] = useState([false, false, false]); - - const handleStepComplete = (stepIndex) => { - const newCompleted = [...stepCompleted]; - newCompleted[stepIndex] = true; - setStepCompleted(newCompleted); - }; - // Initialize session data when modal opens or selectedSession changes useEffect(() => { if (open) { - if (selectedSession) { - // Edit mode - use existing session - setNewSession({ - ...selectedSession, - // Ensure documents array exists - documents: selectedSession.documents || [] - }); + setActiveStep(0); + if(session) { + const sessionParameters = session.parameters || {}; + const sessionRetrieverModel = sessionParameters.retriever_model || {}; + const sessionGeneratorModel = sessionParameters.generator_model || {}; + setSessionData({ + id: session.id, + name: session.name || "", + task_name: session.task_name || "RAGTask", + description: session.description || "", + displayName: session.displayName || "", + parameters: { + documents: sessionParameters.documents || [], + retriever_model: { + class: sessionRetrieverModel.class || "", + name: sessionRetrieverModel.name || "", + parameters: sessionRetrieverModel.parameters || {}, + }, + generator_model: { + class: sessionGeneratorModel.class || "", + name: sessionGeneratorModel.name || "", + parameters: sessionGeneratorModel.parameters || {}, + }, + }, + }) } else { - // Create mode - use defaults - setNewSession(defaultNewSession); + setSessionData(defaultNewSession); } - setActiveStep(0); - setNextEnabled(false); + setStepValidity(new Array(steps.length).fill(false)); } - }, [open, selectedSession]); + }, [open, session]); + const handleStepValidation = useCallback((stepIndex, isValid) => { + setStepValidity(prev => { + const newValidity = [...prev]; + newValidity[stepIndex] = isValid; + return newValidity; + }); + }, []) - const uploadNewSession = async () => { - try { - // For new sessions, exclude ID - const sessionData = selectedSession - ? newSession // Keep existing ID for updates - : { ...newSession, id: undefined }; // Remove ID for new sessions - - const response = selectedSession - ? await updateRAGSession(sessionData) // Implement update function if needed - : await createRAGSession(sessionData); - - const sessionId = response.id; - console.log("Session created/updated with ID:", sessionId); - - enqueueSnackbar( - `Session ${selectedSession ? "updated" : "created"} successfully!`, - { variant: "success" } - ); - setUpdateTableFlag(true); // Trigger table update - } catch (error) { - console.error("Session creation error:", error); - enqueueSnackbar( - `Failed to ${selectedSession ? "update" : "create"} session: ${error.message}`, - { variant: "error" } - ); + const isNextOrFinishEnabled = () => { + if (!stepValidity[activeStep]) { + return false; + } + if (activeStep === steps.length - 1) { + return stepValidity.every(isValid => isValid); } + return true; }; - const handleCloseDialog = () => { - setActiveStep(0); - setOpen(false); - setNewSession(defaultNewSession); - setNextEnabled(false); + const handleFinish = async () => { + if (!stepValidity.every(isValid => isValid)) { + return; + } - }; + try { + // Ensure generator_model is included in the final session data + const finalSessionData = { + ...sessionData, + parameters: { + ...sessionData.parameters, + generator_model: sessionData.parameters.generator_model || { + name: "", + parameters: {}, + }, + retriever_model: sessionData.parameters.retriever_model || { + name: "", + parameters: {}, + }, + }, + }; - const handleStepButton = (index) => () => { - setActiveStep(index); - }; + const savedSession = await onSessionSaved(finalSessionData); - const handleBackButton = () => { - if (activeStep === 0) { - handleCloseDialog(); - } else { - setActiveStep(activeStep - 1); - setNextEnabled(true); - } - }; - const handleNextButton = () => { - if (activeStep === steps.length - 1) { - handleFinish(); - } else { - setActiveStep(activeStep + 1); - setNextEnabled(false); // Reset next enabled for new step + if (onSessionSelect) { + onSessionSelect(savedSession.id); + } + onClose(); + } catch (error) { + console.error("NewSessionModal: Error saving session:", error); } }; - const handleFinish = async () => { - await uploadNewSession(); - handleCloseDialog(); - }; - return ( @@ -177,7 +174,7 @@ export default function NewSessionModal({ @@ -185,7 +182,7 @@ export default function NewSessionModal({ - {selectedSession ? "Edit" : "Create New"} RAG Session + {session ? "Edit" : "Create New"} RAG Session @@ -193,11 +190,11 @@ export default function NewSessionModal({ {steps.map((step, index) => ( - index} + index} disabled={activeStep < index}> - + setActiveStep(index)}> {step.label} @@ -206,47 +203,56 @@ export default function NewSessionModal({ - + {activeStep === 0 && ( setSessionData(prev => ({ + ...prev, + parameters: { ...prev.parameters, documents: docs } + })) + } + setNextEnabled={(isValid) => handleStepValidation(0, isValid)} /> )} {activeStep === 1 && ( setSessionData(prev => ({ + ...prev, + parameters: { ...prev.parameters, retriever_model: model } + }))} + setNextEnabled={(isValid) => handleStepValidation(1, isValid)} /> )} {activeStep === 2 && ( - setSessionData(prev => ({ + ...prev, + parameters: { ...prev.parameters, generator_model: model } + }))} + setNextEnabled={(isValid) => handleStepValidation(2, isValid)} /> )} - - - - + + ); -} +} \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx b/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx index 26107a1f6..292824418 100644 --- a/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx +++ b/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx @@ -1,55 +1,108 @@ -import React from "react"; +import React, { useState, useEffect } from 'react'; +import { Box, Button, CircularProgress, Typography } from '@mui/material'; +import CustomLayout from '../../../components/custom/CustomLayout'; +import NewSessionModal from './NewSessionModal'; +import RAGSessionsTable from './RAGSessionsTable'; +import { getRAGSessions, createRAGSession } from '../../../api/rag'; -import NewSessionModal from "./NewSessionModal"; -import RAGSessionsTable from "./RAGSessionsTable.jsx"; +function RAGHomePage({ onSessionCreated, onSessionSelect }) { + const [showModal, setShowModal] = useState(false); + const [sessions, setSessions] = useState([]); + const [loading, setLoading] = useState(true); + const [editingSession, setEditingSession] = useState(null); -import CustomLayout from "../../../components/custom/CustomLayout.jsx"; + const loadSessions = async () => { + setLoading(true); // Set loading to true when fetching + try { + const data = await getRAGSessions(); + setSessions(data); + console.log("RAGHomePage: Sessions loaded:", data); + } catch (error) { + console.error("RAGHomePage: Error loading RAG sessions:", error); + } finally { + setLoading(false); // Set loading to false after fetch + console.log("RAGHomePage: Loading finished."); + } + }; -function RAGHomePage({ - handleAddSession, - selectedTaskName, - setSelectedSessionId - }) { - const [showNewSessionModal, setShowNewSessionModal] = React.useState(false); - const [updateTableFlag, setUpdateTableFlag] = React.useState(false); - const [selectedSession, setSelectedSession] = React.useState(null); + // Cargar sesiones al iniciar + useEffect(() => { + console.log("RAGHomePage: useEffect - Initial session load..."); + loadSessions(); + }, []); // Empty dependency array means this runs once on mount - const handleOpenNewSessionModal = () => { - setSelectedSession(null); - setShowNewSessionModal(true); - } + const handleOpenNewSessionModal = (session = null) => { + console.log("RAGHomePage: handleOpenNewSessionModal called."); + setEditingSession(session); // Set the session to edit + setShowModal(true); // Open the modal + console.log("RAGHomePage: showModal set to true."); + }; + + const handleCreateOrUpdateSession = async (sessionData) => { + console.log("RAGHomePage: handleCreateOrUpdateSession called with data:", sessionData); + try { + const savedSession = await createRAGSession(sessionData); + console.log("RAGHomePage: New/Updated RAG session saved:", savedSession); - const handleSessionSelect = (session) => { - setSelectedSession(session); - setShowNewSessionModal(true); - setSelectedSessionId(session.id); + // Refresh the sessions list after creation/update + await loadSessions(); // Call loadSessions to get the latest data + + onSessionCreated(savedSession); // Notify parent component (Generative.jsx) + setShowModal(false); // Close the modal + console.log("RAGHomePage: Modal closed after session save."); + return savedSession; + } catch (error) { + console.error("RAGHomePage: Error creating/updating session:", error); + throw error; // Re-throw to handle in the modal if needed } + }; + + console.log("RAGHomePage: Render. Current showModal state:", showModal); - return ( - handleOpenNewSessionModal()} + sx={{ textTransform: 'none' }} > - {showNewSessionModal? ( - - ):( - - ) - } - - ); + New Session + + } + > + {/* Modal para nueva sesión */} + { + setShowModal(false); + setEditingSession(null); // Clear editing session when closing + console.log("RAGHomePage: Modal onClose called. showModal set to false."); + }} + onSessionSaved={handleCreateOrUpdateSession} + onSessionSelect={onSessionSelect} + session={editingSession} + /> + + {/* Contenido principal */} + {loading ? ( + + + + ) : ( + handleOpenNewSessionModal(session)} + onSelect={onSessionSelect} + onRefreshSessions={loadSessions} // Pass the refresh function + onOpenNewSessionModal={() => handleOpenNewSessionModal()} // Pass function to open modal + /> + )} + + ); } export default RAGHomePage; \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx b/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx index 560ae5f28..35d5ff01a 100644 --- a/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx +++ b/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx @@ -1,175 +1,173 @@ - -import React from "react"; +import React, { useState } from "react"; import PropTypes from "prop-types"; import { AddCircleOutline as AddIcon, Update as UpdateIcon, + DeleteOutline as DeleteIcon, + Visibility as ViewDocsIcon } from "@mui/icons-material"; -import { DataGrid, GridToolbar } from "@mui/x-data-grid"; -import { Button, Grid, Paper, Typography, LinearProgress } from "@mui/material"; -import { useSnackbar } from "notistack"; - -import { getRAGSessions, deleteRAGSession } from "../../../api/rag"; // Adjust the import path as necessary +import { DataGrid } from "@mui/x-data-grid"; // Removed GridToolbar as it's not strictly essential for basic rendering +import { + Button, + Grid, + Paper, + Typography, + LinearProgress, +} from "@mui/material"; -function RAGSessionsTable({ - handleOpenNewSessionModal, - onSessionSelect, - updateTableFlag, - setUpdateTableFlag, -}) { - console.log("RAGSessionsTable rendered"); - const [loading, setLoading] = React.useState(true); - const [sessions, setSessions] = React.useState([]); - const { enqueueSnackbar } = useSnackbar(); - - const getSessions = async () => { - setLoading(true); - try { - const data = await getRAGSessions(); - setSessions(data); - } catch (error) { - console.error("[getSessions] error:", error); - enqueueSnackbar("Error fetching RAG sessions", { variant: "error" }); - } finally { - setLoading(false); - } - }; +import { deleteRAGSession } from "../../../api/rag"; // Assuming this API call is correct - const handleDeleteSession = async (sessionId) => { - try { - await deleteRAGSession(sessionId); - enqueueSnackbar("RAG session successfully deleted.", { - variant: "success" - }); - getSessions(); // Refresh sessions after deletion - } catch (error) { - console.error("[deleteSession] error:", error); - enqueueSnackbar("Error deleting RAG session", { variant: "error" }); - } - }; +// Removed ConfirmDialog component as requested. +// Removed useSnackbar import and usage. - const handleOpenSession = (session) => { - if (onSessionSelect) { - onSessionSelect(session); - } - }; +export default function RAGSessionsTable({ + sessions, + onSelect, + onEdit, + // Removed onRefreshSessions prop as it's not needed in this simplified version + onOpenNewSessionModal +}) { + // Removed local 'loading' state for operations within the table, + // as the parent RAGHomePage handles the main loading. + // Removed showConfirmDelete and sessionToDelete states. - const handleViewDocuments = (session) => { - console.log("View documents for session:", session.id); - }; - // FIXED: Moved useEffect to top level - React.useEffect(() => { - console.log("Initial load effect"); - getSessions(); - }, []); + // Removed handleDeleteSession function as its logic involved removed features. + // The delete button will now just call the API directly (or a simplified parent handler). - // FIXED: Added updateTableFlag effect - React.useEffect(() => { - if (updateTableFlag) { - console.log("Update table flag triggered"); - getSessions(); - setUpdateTableFlag(false); - } - }, [updateTableFlag, setUpdateTableFlag]); + const columns = [ + { + field: "name", + headerName: "Session Name", + flex: 1, + renderCell: (params) => ( + + ) + }, + { + field: "created_at", + headerName: "Created At", + flex: 1, + valueFormatter: (params) => new Date(params.value).toLocaleString() + }, + { + field: "documents", + headerName: "Documents", + flex: 0.7, + valueGetter: (params) => params.row.documents?.length || 0 + }, + { + field: "actions", + headerName: "Actions", + flex: 1.5, + renderCell: (params) => ( +
+ + + {/* Simplified Delete button: It will now rely on a parent handler or direct API call */} + +
+ ) + } + ]; - // FIXED: Correct DataGrid column structure - const columns = [ - { - field: "name", - headerName: "Session Name", - flex: 1 - }, - { - field: "created_at", - headerName: "Created At", - flex: 1, - valueFormatter: (params) => new Date(params.value).toLocaleString() - }, - { - field: "documents", - headerName: "Documents", - flex: 0.7, - valueGetter: (params) => params.row.documents?.length || 0 - }, - { - field: "actions", - headerName: "Actions", - flex: 1.5, - renderCell: (params) => ( -
- - - -
- ) - } - ]; + return ( + + + + RAG Sessions + + + - return ( - - - - RAG Sessions - - - + row.id} + sx={{ + '& .MuiDataGrid-cell:focus': { outline: 'none' }, + minHeight: 400 + }} + /> - row.id} - sx={{ - '& .MuiDataGrid-cell:focus': { outline: 'none' }, - minHeight: 400 - }} - /> - - ); + {/* Removed ConfirmDialog component */} + + ); } -// FIXED: Correct propTypes RAGSessionsTable.propTypes = { - handleOpenNewSessionModal: PropTypes.func.isRequired, - onSessionSelect: PropTypes.func, - updateTableFlag: PropTypes.bool.isRequired, - setUpdateTableFlag: PropTypes.func.isRequired + sessions: PropTypes.arrayOf( + PropTypes.shape({ + id: PropTypes.string.isRequired, + name: PropTypes.string.isRequired, + created_at: PropTypes.string.isRequired, + documents: PropTypes.array, + task_name: PropTypes.string, + }) + ).isRequired, + onSelect: PropTypes.func.isRequired, + onEdit: PropTypes.func.isRequired, + // Removed onRefreshSessions from propTypes + onOpenNewSessionModal: PropTypes.func.isRequired, }; - -export default RAGSessionsTable; \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx index b1d29f529..2d4069c06 100644 --- a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx @@ -1,38 +1,171 @@ -// RetrieverConfigurationStep.jsx -import React from "react"; +import { useState, useEffect, useCallback } from "react"; import PropTypes from "prop-types"; -import { Box } from "@mui/material"; -import ComponentSelector from "../../../components/generative/RAG/ComponentSelector"; -import { getRetrieverComponents } from "../../../api/rag"; - -function RetrieverConfigurationStep({ newSession, setNewSession, setNextEnabled }) { - const handleConfigurationChange = (retrieverConfig) => { - setNewSession(prev => ({ - ...prev, - RAGParameters: { - ...prev.RAGParameters, - retrieval: retrieverConfig +import { Box, Autocomplete, TextField, Typography, Button } from "@mui/material"; // Added Button and Typography for consistency +import { useFormik } from "formik"; // Import useFormik +import FormSchemaRenderFields from "../../../components/shared/FormSchemaRenderFields"; // Assuming this component is correctly implemented +import { getRetrieverComponents } from "../../../api/rag"; // Assuming this API call is correct +import { preprocessSchema, buildYupSchema } from "../../../components/generative/utils" + + + +export default function RetrieverConfigurationStep({ retrieverModel, setRetrieverModel, setNextEnabled }) { + const [retrievers, setRetrievers] = useState([]); + const [currentSelectedRetrieverOption, setCurrentSelectedRetrieverOption] = useState(null); + const [validationSchema, setValidationSchema] = useState(null); + + const getInitialParamsFromSchema = useCallback((schemaProperties) => { + if (!schemaProperties) return {}; + return Object.keys(schemaProperties).reduce((acc, key) => { + acc[key] = schemaProperties[key].placeholder !== undefined + ? schemaProperties[key].placeholder + : ""; + return acc; + }, {}); + }, []); + + useEffect(() => { + const loadAndSetRetriever = async () => { + const data = await getRetrieverComponents(); + setRetrievers(data); + + if (retrieverModel?.name) { + const existingRetriever = data.find(r => r.name === retrieverModel.name); + if (existingRetriever) { + setCurrentSelectedRetrieverOption(existingRetriever); + } } - })); + }; + loadAndSetRetriever(); + }, [retrieverModel?.name]); + + const formik = useFormik({ + initialValues: retrieverModel?.parameters || {}, + validationSchema: validationSchema, + enableReinitialize: true, + onSubmit: (values) => { + }, + }); + + useEffect(() => { + + const retrieverSchemaProperties = currentSelectedRetrieverOption?.schema?.properties; + + if (retrieverSchemaProperties) { + const processedProps = preprocessSchema(retrieverSchemaProperties); + setValidationSchema(buildYupSchema(processedProps)); + + const initialFormValues = Object.keys(processedProps).reduce( + (acc, key) => { + acc[key] = retrieverModel?.parameters?.[key] !== undefined + ? retrieverModel.parameters[key] + : (processedProps[key].placeholder !== undefined + ? processedProps[key].placeholder + : ""); + return acc; + }, + {}, + ); + formik.setValues(initialFormValues); + } else { + setValidationSchema(null); + formik.setValues({}); + } + }, [currentSelectedRetrieverOption, retrieverModel?.parameters]); + + useEffect(() => { + const isValid = !!currentSelectedRetrieverOption && formik.isValid; + setNextEnabled(isValid); + }, [currentSelectedRetrieverOption, formik.isValid, setNextEnabled]); + + const handleRetrieverSelectionChange = (event, newValue) => { + setCurrentSelectedRetrieverOption(newValue); + + if (newValue) { + const initialParameters = getInitialParamsFromSchema(newValue.schema?.properties); + setRetrieverModel({ + name: newValue.name, + parameters: initialParameters, + }); + } else { + setRetrieverModel({ name: "", parameters: {} }); + } }; + const handleParametersChange = (updatedValues) => { + formik.setValues((prevValues) => ({ + ...prevValues, + ...updatedValues, + })); + + setRetrieverModel({ + name: currentSelectedRetrieverOption.name, + parameters: { + ...formik.values, + ...updatedValues, + }, + }); + }; + + const processedProperties = currentSelectedRetrieverOption?.schema?.properties + ? preprocessSchema(currentSelectedRetrieverOption.schema.properties) + : {}; + return ( - - + + Configure Retriever Model + + + option.name} + value={currentSelectedRetrieverOption} + onChange={handleRetrieverSelectionChange} + isOptionEqualToValue={(option, value) => option.name === value?.name} + renderInput={(params) => } + sx={{ mb: 3 }} /> + + {currentSelectedRetrieverOption && currentSelectedRetrieverOption.schema && ( +
+ + + Parameters + + console.error("FormSchemaRenderFields Error:", error)} + errorsMessage={formik.errors} + /> + +
+ )} ); } - -RetrieverConfigurationStep.propTypes = { - newSession: PropTypes.object.isRequired, - setNewSession: PropTypes.func.isRequired, - setNextEnabled: PropTypes.func.isRequired, -}; - -export default RetrieverConfigurationStep; \ No newline at end of file diff --git a/StoredRetrievers/tfidf_retriever_07c008c27ce1de3ecd0d4cd42fc4c43604e6d7c2c4f86eef5d4b1b42d6d3c15d_75aeb1f071ec2f059f26544c40689c89c282d8dd1ab84b6674ffbf27e9e8fbd1/vectorizer b/StoredRetrievers/tfidf_retriever_07c008c27ce1de3ecd0d4cd42fc4c43604e6d7c2c4f86eef5d4b1b42d6d3c15d_75aeb1f071ec2f059f26544c40689c89c282d8dd1ab84b6674ffbf27e9e8fbd1/vectorizer new file mode 100644 index 0000000000000000000000000000000000000000..74e153d247b7ad1963c3fed33b427f96b8d14433 GIT binary patch literal 422105 zcmYiP0}$j)+W_poYukF(wr$(CZQHhO+qP}nw!M3<{rnlSYNB)TDW% zCRG|Y?9{eH^B#>mOqw_;dH+ehCnXydqJROm*HB`LQ~ z4I6jtC}WvXAv(2f*|^RB9kI-);Pu+_Zrip~v;X&9=#HHmv})c)K{g z`u~eVv$)OTHH$y2S%P8B5)K~~w$1vn8bUu_g%R_owerd@~T;#@Mrs9??8G-@tZ 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DashAI/back/api/api_v1/api.py | 2 + DashAI/back/api/api_v1/endpoints/documents.py | 236 ++++++++++++++++++ .../api_v1/endpoints/generative_session.py | 3 +- DashAI/back/api/api_v1/schemas/__init__.py | 1 + DashAI/back/api/api_v1/schemas/document.py | 20 ++ DashAI/back/app.py | 1 + DashAI/back/config.py | 1 + DashAI/back/dependencies/config_builder.py | 1 + DashAI/back/dependencies/database/models.py | 18 +- DashAI/back/models/RAG/RAG_pipeline.py | 5 +- DashAI/front/src/api/rag.ts | 50 +++- .../generative/RAG/DocumentDropzone.jsx | 45 ---- .../generative/RAG/DocumentSelector.jsx | 166 ++++++------ .../generative/RAG/DocumentTable.jsx | 69 +++-- DashAI/front/src/components/shared/Upload.jsx | 59 +++-- .../generative/RAG/DocumentSelectionStep.jsx | 151 ++++++----- .../pages/generative/RAG/NewSessionModal.jsx | 53 ++-- .../src/pages/generative/RAG/RAGHomePage.jsx | 42 ++-- .../pages/generative/RAG/RAGSessionsTable.jsx | 45 ++-- DashAI/front/src/types/documentResponse.ts | 7 + .../vectorizer | Bin 422105 -> 0 bytes .../vectorizer | Bin 101559 -> 0 bytes .../vectorizer | Bin 179480 -> 0 bytes .../vectorizer | Bin 243480 -> 0 bytes .../vectorizer | Bin 378471 -> 0 bytes .../vectorizer | Bin 379501 -> 0 bytes .../vectorizer | Bin 258501 -> 0 bytes .../vectorizer | Bin 244262 -> 0 bytes .../vectorizer | Bin 225721 -> 0 bytes .../vectorizer | Bin 230851 -> 0 bytes 30 files changed, 643 insertions(+), 332 deletions(-) create mode 100644 DashAI/back/api/api_v1/endpoints/documents.py create mode 100644 DashAI/back/api/api_v1/schemas/document.py delete mode 100644 DashAI/front/src/components/generative/RAG/DocumentDropzone.jsx create mode 100644 DashAI/front/src/types/documentResponse.ts delete mode 100644 StoredRetrievers/tfidf_retriever_07c008c27ce1de3ecd0d4cd42fc4c43604e6d7c2c4f86eef5d4b1b42d6d3c15d_75aeb1f071ec2f059f26544c40689c89c282d8dd1ab84b6674ffbf27e9e8fbd1/vectorizer delete mode 100644 StoredRetrievers/tfidf_retriever_0cff2792f2880a97e8dc881b7f30588af3e40f2617f4a8e40f236e3ed70b3976_0ec09b17926fe5aa93641201da7dfeb580d37f3af6b2032f31b6343ac471fba3/vectorizer delete mode 100644 StoredRetrievers/tfidf_retriever_28bce20bb2374f93157c85fa986ff3d0a6004b637324b92bf98c20efb3e56103_2730eac7385c920c51ee4089b4172f7af4d7be862b3b2aefa2d229aad399bea4/vectorizer delete mode 100644 StoredRetrievers/tfidf_retriever_347f29ec72552a7656cf8f10a42670fcd8076c89ce86fece0c180555440bcbc2_c54a2f7e421d397dff3d0a420d9513c4f7b43399cf2c2770b610c951b7af9e38/vectorizer delete mode 100644 StoredRetrievers/tfidf_retriever_88c363cbc175eb2b0bef48208782dfa84682a5b6dd6e45ac65127700520bc2ba_75aeb1f071ec2f059f26544c40689c89c282d8dd1ab84b6674ffbf27e9e8fbd1/vectorizer delete mode 100644 StoredRetrievers/tfidf_retriever_978e040feee803d3cd97a7eaf5c3e9f502d0d97f670c303715e455047b877281_75aeb1f071ec2f059f26544c40689c89c282d8dd1ab84b6674ffbf27e9e8fbd1/vectorizer delete mode 100644 StoredRetrievers/tfidf_retriever_a38d48165431be31206e34ce2758b75170414d8ad2e65d9fa37a38b2e41343fd_57d8f551775097b02aeb07b11856b3cd3f942fbcf36d43a4384b130551631efb/vectorizer delete mode 100644 StoredRetrievers/tfidf_retriever_dfdc77de99969c1ac75e72e16a8c38ee3fb24e98a90075f4cd923472c317b05c_c27d55405ff4e4180350c51990a570d33540e0098056446ce95de0942b41e4da/vectorizer delete mode 100644 StoredRetrievers/tfidf_retriever_e19a4b25c422aa7be44921f262f32d44d790b515e5af012fa3eaacb52a649869_c54a2f7e421d397dff3d0a420d9513c4f7b43399cf2c2770b610c951b7af9e38/vectorizer delete mode 100644 StoredRetrievers/tfidf_retriever_ee0f3dd0eaad95cb49ab497e118300f86c49ab07807d73ced6d3c69372661f46_c54a2f7e421d397dff3d0a420d9513c4f7b43399cf2c2770b610c951b7af9e38/vectorizer diff --git a/DashAI/back/api/api_v1/api.py b/DashAI/back/api/api_v1/api.py index dbec76512..1e4f93242 100644 --- a/DashAI/back/api/api_v1/api.py +++ b/DashAI/back/api/api_v1/api.py @@ -14,6 +14,7 @@ plugins, predict, runs, + documents ) api_router_v1 = APIRouter() @@ -30,3 +31,4 @@ api_router_v1.include_router(generative_session.router, prefix="/generative-session") api_router_v1.include_router(generative_process.router, prefix="/generative-process") api_router_v1.include_router(plugins.router, prefix="/plugin") +api_router_v1.include_router(documents.router, prefix="/document") \ No newline at end of file diff --git a/DashAI/back/api/api_v1/endpoints/documents.py b/DashAI/back/api/api_v1/endpoints/documents.py new file mode 100644 index 000000000..f18e91265 --- /dev/null +++ b/DashAI/back/api/api_v1/endpoints/documents.py @@ -0,0 +1,236 @@ +import logging +import json +import os +import hashlib +from datetime import datetime +from typing import Any, Dict, List + +from fastapi import ( + APIRouter, + Depends, + HTTPException, + status, + File, + Form, + UploadFile, + Response, +) +from fastapi.responses import FileResponse +from kink import di +from sqlalchemy import exc +from sqlalchemy.orm import sessionmaker + +from DashAI.back.api.api_v1.schemas import DocumentResponse +from DashAI.back.dependencies.database.models import Document + +router = APIRouter() +log = logging.getLogger(__name__) + + +base_url = "/api/v1/documents" +@router.get("/", response_model=List[DocumentResponse]) +async def get_all_documents( + session_factory: sessionmaker = Depends(lambda: di["session_factory"]), +): + """Get all documents with file_url included.""" + with session_factory() as db: + try: + documents = db.query(Document).all() + + documents_responses = [] + + for doc in documents: + documents_responses.append( + DocumentResponse( + id=doc.id, + file_name=doc.file_name, + created=doc.created, + optional_metadata=doc.optional_metadata, + file_url=f"{base_url}/{doc.id}/download", + ) + ) + + return documents_responses + except exc.SQLAlchemyError as e: + log.exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal database error", + ) from e + + +@router.get("/{document_id}", response_model=DocumentResponse) +async def get_document( + document_id: int, + session_factory: sessionmaker = Depends(lambda: di["session_factory"]), +): + """Get metadata of a document by its ID.""" + with session_factory() as db: + try: + document = db.get(Document, document_id) + if not document: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Document {document_id} does not exist in DB.", + ) + return DocumentResponse( + id=document.id, + file_name=document.file_name, + created=document.created, + optional_metadata=document.optional_metadata, + file_url=f"{base_url}/{document.id}/download", + ) + except exc.SQLAlchemyError as e: + log.exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal database error", + ) from e + + +@router.get("/{document_id}/download", response_class=FileResponse) +async def download_document( + document_id: int, + session_factory: sessionmaker = Depends(lambda: di["session_factory"]), +): + """Download the actual file content of a document.""" + with session_factory() as db: + document = db.get(Document, document_id) + if not document: + raise HTTPException( + status_code=404, + detail=f"Document {document_id} not found", + ) + if not os.path.exists(document.file_path): + raise HTTPException( + status_code=500, + detail=f"File not found at {document.file_path}", + ) + return FileResponse( + path=document.file_path, + file_name=document.file_name, + media_type="application/octet-stream", + ) + + +@router.post("/", response_model=DocumentResponse, status_code=status.HTTP_201_CREATED) +async def upload_document( + file: UploadFile = File(...), + metadata: str = Form(...), + config: Dict[str, Any] = Depends(lambda: di["config"]), + session_factory: sessionmaker = Depends(lambda: di["session_factory"]), +): + """ + Upload a new document to the RAG system with file content and metadata. + The metadata should be a JSON string with required fields and an 'optional_metadata' dict. + """ + docs_folder_path = config["DOCUMENTS_PATH"] + if not docs_folder_path.exists(): + raise HTTPException( + status_code=500, + detail=f"Documents folder {docs_folder_path} does not exist.", + ) + + try: + metadata_dict = json.loads(metadata) + except json.JSONDecodeError: + raise HTTPException(status_code=400, detail="Invalid JSON in metadata") + + print(f"Metadata received: {metadata_dict}") + file_name = metadata_dict.get("file_name") + last_modified = metadata_dict.get("last_modified") + + if not file_name or not last_modified: + raise HTTPException( + status_code=400, + detail="Missing required metadata fields: 'filename' and 'last_modified'", + ) + + optional_metadata = metadata_dict.get("optional_metadata", {}) + if not isinstance(optional_metadata, dict): + raise HTTPException( + status_code=400, + detail="'optional_metadata' must be a dictionary", + ) + + try: + content_bytes = await file.read() + except Exception: + raise HTTPException(status_code=400, detail="Failed to read file content") + + hash256 = hashlib.sha256(content_bytes).hexdigest() + + with session_factory() as db: + existing_doc = db.query(Document).filter_by(file_hash=hash256).first() + if existing_doc: + # Update existing document's information + existing_doc.file_name = file_name + existing_doc.optional_metadata = optional_metadata + existing_doc.created = datetime.now() + db.commit() + return DocumentResponse( + id=existing_doc.id, + file_name=existing_doc.file_name, + created=existing_doc.created, + optional_metadata=existing_doc.optional_metadata, + file_url=f"{base_url}/{existing_doc.id}/download", + ) + else: + # Create a new document entry + try: + new_id = db.query(Document).count() + 1 + file_path = os.path.join(docs_folder_path, f"{new_id}_{file_name}") + + with open(file_path, "wb") as f: + f.write(content_bytes) + + doc = Document( + file_name=file_name, + file_path=str(file_path), + file_hash=hash256, + optional_metadata=optional_metadata or None, + ) + db.add(doc) + db.commit() + db.refresh(doc) + + return DocumentResponse( + id=doc.id, + file_name=doc.file_name, + created=doc.created, + optional_metadata=doc.optional_metadata, + file_url=f"{base_url}/{doc.id}/download", + ) + except exc.SQLAlchemyError as e: + log.exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal database error", + ) from e + + +@router.delete("/{document_id}", status_code=status.HTTP_204_NO_CONTENT) +async def delete_document( + document_id: int, + session_factory: sessionmaker = Depends(lambda: di["session_factory"]), +): + """Delete a document from the RAG system by its ID.""" + with session_factory() as db: + try: + document = db.get(Document, document_id) + if not document: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Document {document_id} does not exist in DB.", + ) + if os.path.exists(document.file_path): + os.remove(document.file_path) + db.delete(document) + db.commit() + return Response(status_code=status.HTTP_204_NO_CONTENT) + except exc.SQLAlchemyError as e: + log.exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal database error", + ) from e diff --git a/DashAI/back/api/api_v1/endpoints/generative_session.py b/DashAI/back/api/api_v1/endpoints/generative_session.py index 2d46214dd..ce79dafba 100644 --- a/DashAI/back/api/api_v1/endpoints/generative_session.py +++ b/DashAI/back/api/api_v1/endpoints/generative_session.py @@ -19,6 +19,7 @@ from DashAI.back.models import BaseGenerativeModel from DashAI.back.tasks import BaseGenerativeTask + router = APIRouter() log = logging.getLogger(__name__) @@ -496,4 +497,4 @@ async def get_parameter_history_entry( raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Internal database error", - ) from e + ) from e \ No newline at end of file diff --git a/DashAI/back/api/api_v1/schemas/__init__.py b/DashAI/back/api/api_v1/schemas/__init__.py index e69de29bb..79080d662 100644 --- a/DashAI/back/api/api_v1/schemas/__init__.py +++ b/DashAI/back/api/api_v1/schemas/__init__.py @@ -0,0 +1 @@ +from DashAI.back.api.api_v1.schemas.document import DocumentResponse, DocumentUploadResponse \ No newline at end of file diff --git a/DashAI/back/api/api_v1/schemas/document.py b/DashAI/back/api/api_v1/schemas/document.py new file mode 100644 index 000000000..38b5517c7 --- /dev/null +++ b/DashAI/back/api/api_v1/schemas/document.py @@ -0,0 +1,20 @@ +from pydantic import BaseModel +from typing import Any, Dict, Optional +from datetime import datetime + +class DocumentUploadResponse(BaseModel): + id: int + file_name: str + optional_metadata: Optional[dict] + created: datetime + file_hash: str + +class DocumentResponse(BaseModel): + id: int + file_name: str + created: datetime + optional_metadata: Optional[Dict[str, Any]] + file_url: str + + class Config: + orm_mode = True diff --git a/DashAI/back/app.py b/DashAI/back/app.py index e892b5509..884b02294 100644 --- a/DashAI/back/app.py +++ b/DashAI/back/app.py @@ -101,6 +101,7 @@ def create_app( _create_path_if_not_exists(config["EXPLANATIONS_PATH"]) _create_path_if_not_exists(config["EXPLORATIONS_PATH"]) _create_path_if_not_exists(config["RUNS_PATH"]) + _create_path_if_not_exists(config["DOCUMENTS_PATH"]) logger.debug("5. Creating database.") Base.metadata.create_all(bind=container["engine"]) diff --git a/DashAI/back/config.py b/DashAI/back/config.py index 594b5c9e7..3f72ceeff 100644 --- a/DashAI/back/config.py +++ b/DashAI/back/config.py @@ -20,3 +20,4 @@ class DefaultSettings(BaseSettings): RUNS_PATH: str = "runs" EXPLANATIONS_PATH: str = "explanations" EXPLORATIONS_PATH: str = "explorations" + DOCUMENTS_PATH: str = "documents" diff --git a/DashAI/back/dependencies/config_builder.py b/DashAI/back/dependencies/config_builder.py index c3e446da7..a98a2c9f7 100644 --- a/DashAI/back/dependencies/config_builder.py +++ b/DashAI/back/dependencies/config_builder.py @@ -61,5 +61,6 @@ def build_config_dict( config["FRONT_BUILD_PATH"] = pathlib.Path(config["FRONT_BUILD_PATH"]).absolute() config["LOGGING_LEVEL"] = getattr(logging, logging_level) config["INITIAL_COMPONENTS"] = get_initial_components() + config["DOCUMENTS_PATH"] = local_path / config["DOCUMENTS_PATH"] return config diff --git a/DashAI/back/dependencies/database/models.py b/DashAI/back/dependencies/database/models.py index 86ad2eb4a..f771da80f 100644 --- a/DashAI/back/dependencies/database/models.py +++ b/DashAI/back/dependencies/database/models.py @@ -3,7 +3,7 @@ from datetime import datetime from beartype.typing import List -from sqlalchemy import JSON, Boolean, DateTime, Enum, ForeignKey, String +from sqlalchemy import JSON, Boolean, DateTime, Enum, ForeignKey, String, LargeBinary from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import Mapped, mapped_column, relationship @@ -340,6 +340,22 @@ class ProcessData(Base): ) +class Document(Base): + __tablename__ = "RAG_document" + """ + Table to store all the information about a document in the RAG system. + """ + id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) + file_name: Mapped[str] = mapped_column(String, nullable=False) + file_path: Mapped[str] = mapped_column(String, nullable=False) + file_hash: Mapped[str] = mapped_column(String, nullable=False, unique=True) + created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) + optional_metadata: Mapped[JSON] = mapped_column(JSON, nullable=True) + + def __repr__(self) -> str: + return f"" + + class GenerativeSession(Base): __tablename__ = "generative_session" """ diff --git a/DashAI/back/models/RAG/RAG_pipeline.py b/DashAI/back/models/RAG/RAG_pipeline.py index dde12274e..83300d24c 100644 --- a/DashAI/back/models/RAG/RAG_pipeline.py +++ b/DashAI/back/models/RAG/RAG_pipeline.py @@ -4,9 +4,8 @@ BaseSchema, component_field, schema_field, - bool_field, list_field, - string_field + int_field ) from DashAI.back.models.base_generative_model import BaseGenerativeModel @@ -42,7 +41,7 @@ class RAGPipelineSchema(BaseSchema): # Document collection parameters documents: schema_field( list_field( - string_field(), + int_field(), min_items=1, ), placeholder=None, diff --git a/DashAI/front/src/api/rag.ts b/DashAI/front/src/api/rag.ts index 6a3f2b67c..90500a6b8 100644 --- a/DashAI/front/src/api/rag.ts +++ b/DashAI/front/src/api/rag.ts @@ -1,6 +1,7 @@ import api from "./api"; import { ISession } from "../types/session"; import { IGenerativeTask } from "../types/generativeTask"; +import { IDocumentResponse } from "../types/documentResponse"; // Fetch all RAG sessions @@ -134,4 +135,51 @@ export const getGeneratorComponents = async (): Promise => { return response.data; -} \ No newline at end of file +} + +export const loadDocuments = async (): Promise => { + const response = await api.get("/v1/document/"); + if (response.status !== 200) { + throw new Error(`Failed to load documents: ${response.statusText}`); + } + console.log("Loaded documents:", response.data); + return response.data; +}; + +export const deleteDocument = async (documentId: number): Promise => { + const response = await api.delete(`/v1/document/${documentId}`); + if (response.status !== 204) { + throw new Error(`Failed to delete document: ${response.statusText}`); + } +}; + +export const addDocument = async ({ file, optional_metadata }: { + file: File, + optional_metadata?: Record, +}): Promise => { + + console.log("Adding document:", file, optional_metadata); + + if (optional_metadata) { + optional_metadata.last_modified = file.lastModified + } + const metadata = { + file_name: file.name, + last_modified: file.lastModified, + optional_metadata, + }; + + const formData = new FormData(); + formData.append("file", file); + formData.append("metadata", JSON.stringify(metadata)); + + const response = await api.post("/v1/document/", formData, { + headers: { "Content-Type": "multipart/form-data" }, + }); + + if (response.status !== 201) { + throw new Error(`Failed to upload document: ${response.statusText}`); + } + + return response.data; +}; diff --git a/DashAI/front/src/components/generative/RAG/DocumentDropzone.jsx b/DashAI/front/src/components/generative/RAG/DocumentDropzone.jsx deleted file mode 100644 index 01eeb326d..000000000 --- a/DashAI/front/src/components/generative/RAG/DocumentDropzone.jsx +++ /dev/null @@ -1,45 +0,0 @@ -import { Box, Typography, Paper } from "@mui/material"; -import { useCallback } from "react"; -import { useDropzone } from "react-dropzone"; -import Upload from "../../shared/Upload"; -let idCounter = 200; - -export default function DocumentDropzone({ onAddDocument }) { - const onDrop = useCallback((acceptedFiles) => { - acceptedFiles.forEach((file) => { - const newDoc = { - id: idCounter++, - name: file.name, - updatedAt: new Date().toLocaleDateString(), - }; - onAddDocument(newDoc); - }); - }, [onAddDocument]); - - const { getRootProps, getInputProps, isDragActive } = useDropzone({ onDrop }); - - return ( - - - - Drag & drop documents here, or click to upload - - - ); -} diff --git a/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx b/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx index 4f7040a2c..c1f133863 100644 --- a/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx +++ b/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx @@ -1,98 +1,108 @@ import { Box } from "@mui/material"; -import { useEffect, useState } from "react"; -import PropTypes from "prop-types"; // Import PropTypes +import { useEffect, useState, useCallback, useRef } from "react"; // Added useRef +import PropTypes from "prop-types"; import DocumentTable from "./DocumentTable"; -import Upload from "../../shared/Upload"; // Assuming path is correct +import Upload from "../../shared/Upload"; +import { loadDocuments, addDocument, deleteDocument } from "../../../api/rag"; export default function DocumentSelector({ - documents: initialDocuments = [], // Renamed prop for clarity, uses initial state - selected: initialSelectedIds = [], // Renamed prop for clarity - onSelect, // Callback for when selection changes (receives selected document objects) - onAddDocument, // Callback for when a new document object is added (receives new doc object) - onRemove, // Callback for when a document is removed (receives docId/path) + selectedIds: initialSelectedIds = [], + onSelect, }) { - const [documents, setDocuments] = useState(initialDocuments); + const [documents, setDocuments] = useState([]); const [selectedIds, setSelectedIds] = useState(initialSelectedIds); - // Add a key state to force Upload component re-render + const [isLoading, setIsLoading] = useState(true); const [uploadKey, setUploadKey] = useState(0); - // Sync internal 'documents' state with 'initialDocuments' prop + const previousSelectedIdsRef = useRef(initialSelectedIds); + useEffect(() => { - setDocuments(initialDocuments); - }, [initialDocuments]); + const fetchDocuments = async () => { + setIsLoading(true); + try { + const docs = await loadDocuments(); + setDocuments(docs); + } catch (error) { + console.error("Failed to load documents:", error); + } finally { + setIsLoading(false); + } + }; + + fetchDocuments(); + }, []); - // Sync internal 'selectedIds' state with 'initialSelectedIds' prop useEffect(() => { - setSelectedIds(initialSelectedIds); + if (JSON.stringify(selectedIds.sort()) !== JSON.stringify(initialSelectedIds.sort())) { + setSelectedIds(initialSelectedIds); + } }, [initialSelectedIds]); - // Sync selected documents with parent's onSelect callback + useEffect(() => { - const selectedDocs = documents.filter(doc => selectedIds.includes(doc.id)); - if (onSelect) { - onSelect(selectedDocs); + if (JSON.stringify(selectedIds.sort()) !== JSON.stringify(previousSelectedIdsRef.current.sort())) { + const selectedDocs = documents.filter(doc => selectedIds.includes(doc.id)); + onSelect(selectedDocs); + previousSelectedIdsRef.current = selectedIds; } - }, [selectedIds, documents, onSelect]); + }, [selectedIds, documents, onSelect]); - const handleToggleSelection = (id) => { - setSelectedIds(prev => - prev.includes(id) ? prev.filter(x => x !== id) : [...prev, id] - ); - }; + const handleToggleSelection = useCallback((id) => { + setSelectedIds(prev => { + const newSelected = prev.includes(id) ? prev.filter(x => x !== id) : [...prev, id]; + return newSelected; + }); + }, []); - const handleSelectAll = () => { + const handleSelectAll = useCallback(() => { setSelectedIds(documents.map(doc => doc.id)); - }; + }, [documents]); - const handleDeselectAll = () => { + const handleDeselectAll = useCallback(() => { setSelectedIds([]); - }; - - // Internal handler for adding a document object (before notifying parent) - const addDocumentObject = (newDoc) => { - setDocuments(prev => { - // Ensure no duplicates based on ID (path) - if (!prev.some(doc => doc.id === newDoc.id)) { - return [newDoc, ...prev]; // Add to the beginning - } - return prev; - }); - // Call the parent's onAddDocument callback - if (onAddDocument) { - onAddDocument(newDoc); + }, []); + + const handleAddDocument = useCallback(async (newDoc) => { + try { + const savedDoc = await addDocument(newDoc); + setDocuments(prev => [savedDoc, ...prev]); + setSelectedIds(prev => [...prev, savedDoc.id]); + setUploadKey(prev => prev + 1); + return savedDoc; + } catch (error) { + console.error("Failed to add document:", error); + return null; } - }; - - // Internal handler for removing a document object (before notifying parent) - const removeDocumentObject = (id) => { - setDocuments(prev => prev.filter(doc => doc.id !== id)); - setSelectedIds(prev => prev.filter(x => x !== id)); - // Call the parent's onRemove callback - if (onRemove) { - onRemove(id); + }, []); + + const handleRemoveDocument = useCallback(async (id) => { + try { + await deleteDocument(id); + setDocuments(prev => prev.filter(doc => doc.id !== id)); + setSelectedIds(prev => prev.filter(x => x !== id)); + } catch (error) { + console.error("Failed to delete document:", error); } - }; + }, []); - const handleFileUpload = (file, url) => { - if (!file) return; + const handleFileUpload = useCallback(async (files, url) => { + if (!files) return; - // The 'id' of the document will be the URL (or file.name if no URL is available) - // This allows the 'id' to directly represent the path/reference - const docId = url || file.name; + console.log("Files to upload at handleFileUpload:", files); - const newDoc = { - id: docId, - name: file.name, - updatedAt: new Date().toISOString(), - preview: url, // Store URL for preview if available - }; - - addDocumentObject(newDoc); // Use the internal handler - setSelectedIds(prev => [...prev, newDoc.id]); // Also select it upon upload + const fileList = Array.isArray(files) ? files : [files]; - // Reset the upload component by incrementing its key - setUploadKey(prev => prev + 1); - }; + for (const file of fileList) { + const docToAdd = { + file, + optional_metadata: { + name: file.name, + source: url || 'local_upload', + } + }; + await handleAddDocument(docToAdd); + } + }, [handleAddDocument]); return ( @@ -103,7 +113,8 @@ export default function DocumentSelector({ onToggle={handleToggleSelection} onSelectAll={handleSelectAll} onDeselectAll={handleDeselectAll} - onRemove={removeDocumentObject} // Use the internal removal handler + onRemove={handleRemoveDocument} + isLoading={isLoading} /> @@ -111,6 +122,7 @@ export default function DocumentSelector({ @@ -118,14 +130,6 @@ export default function DocumentSelector({ } DocumentSelector.propTypes = { - documents: PropTypes.arrayOf(PropTypes.shape({ - id: PropTypes.string.isRequired, - name: PropTypes.string.isRequired, - updatedAt: PropTypes.string.isRequired, - preview: PropTypes.string, // Optional - })), - selected: PropTypes.arrayOf(PropTypes.string), // Array of document IDs (paths) - onSelect: PropTypes.func, // (selectedDocs: array of objects) => void - onAddDocument: PropTypes.func, // (newDoc: object) => void - onRemove: PropTypes.func, // (docId: string) => void -}; + selectedIds: PropTypes.arrayOf(PropTypes.string), + onSelect: PropTypes.func, +}; \ No newline at end of file diff --git a/DashAI/front/src/components/generative/RAG/DocumentTable.jsx b/DashAI/front/src/components/generative/RAG/DocumentTable.jsx index 9bf365ed0..ec0b3f658 100644 --- a/DashAI/front/src/components/generative/RAG/DocumentTable.jsx +++ b/DashAI/front/src/components/generative/RAG/DocumentTable.jsx @@ -10,6 +10,7 @@ import { IconButton, Tooltip, Button, + CircularProgress, } from "@mui/material"; import VisibilityIcon from "@mui/icons-material/Visibility"; import DeleteIcon from "@mui/icons-material/Delete"; @@ -22,7 +23,17 @@ export default function DocumentTable({ onSelectAll, onDeselectAll, onRemove, + isLoading = false, }) { + // Format the date to a more readable format + const formatDate = (dateString) => { + try { + const date = new Date(dateString); + return date.toLocaleString(); + } catch (e) { + return dateString; + } + }; return ( - {documents.length === 0 ? ( + }}> + {isLoading ? ( + + + + ) : documents.length === 0 ? ( No documents available. @@ -43,22 +58,23 @@ export default function DocumentTable({ - {/* Added padding="checkbox" for standard spacing */} - {/* Checkbox for Select All/Deselect All */} - 0 && selectedIds.length < documents.length} - checked={selectedIds.length === documents.length && documents.length > 0} - onChange={(event) => { - if (event.target.checked) { - onSelectAll(); - } else { - onDeselectAll(); - } - }} - /> + + 0 && selectedIds.length < documents.length} + checked={selectedIds.length === documents.length && documents.length > 0} + onChange={(event) => { + if (event.target.checked) { + onSelectAll(); + } else { + onDeselectAll(); + } + }} + /> + Id Name - Updated + Added On + Last Modified Actions @@ -72,14 +88,16 @@ export default function DocumentTable({ backgroundColor: selectedIds.includes(doc.id) ? "action.hover" : "inherit", }} > - {/* Added padding="checkbox" */} + onToggle(doc.id)} /> - {doc.name} - {doc.updatedAt} + {doc.id} + {doc.file_name} + {formatDate(doc.created)} + {doc.optional_metadata?.last_modified ? formatDate(doc.optional_metadata.last_modified) : "N/A"} @@ -103,13 +121,13 @@ export default function DocumentTable({
@@ -124,7 +142,7 @@ DocumentTable.propTypes = { documents: PropTypes.arrayOf(PropTypes.shape({ id: PropTypes.string.isRequired, name: PropTypes.string.isRequired, - updatedAt: PropTypes.string.isRequired, + createdAt: PropTypes.string.isRequired, preview: PropTypes.string, })).isRequired, selectedIds: PropTypes.arrayOf(PropTypes.string).isRequired, @@ -132,4 +150,5 @@ DocumentTable.propTypes = { onSelectAll: PropTypes.func.isRequired, onDeselectAll: PropTypes.func.isRequired, onRemove: PropTypes.func.isRequired, + isLoading: PropTypes.bool, }; diff --git a/DashAI/front/src/components/shared/Upload.jsx b/DashAI/front/src/components/shared/Upload.jsx index 60e887978..024e2c19a 100644 --- a/DashAI/front/src/components/shared/Upload.jsx +++ b/DashAI/front/src/components/shared/Upload.jsx @@ -20,20 +20,22 @@ import ClearIcon from "@mui/icons-material/Clear"; * newDataset state in the modal * @param {function} onFileUpload function to handle when the user "uploads" a dataset */ -function Upload({ onFileUpload, emptyUploadText }) { +function Upload({ onFileUpload, emptyUploadText, multiple = false }) { const [EMPTY, LOADING, LOADED] = [0, 1, 2]; const [datasetState, setDatasetState] = useState(EMPTY); const [dragActive, setDragActive] = useState(false); - const [fileOriginalName, setFileOriginalName] = useState(""); + const [fileNames, setFileNames] = useState([]); const inputRef = useRef(null); - const uploadDataset = async (file) => { + const uploadDataset = async (files) => { + const fileArray = Array.isArray(files) ? files : Array.from(files); setDatasetState(LOADING); const url = ""; - onFileUpload(file, url); + await onFileUpload(fileArray, url); setDatasetState(LOADED); - setFileOriginalName(file.name); - }; + setFileNames(fileArray.map(f => f.name)); + }; + const handleDrag = (e) => { e.preventDefault(); @@ -49,7 +51,8 @@ function Upload({ onFileUpload, emptyUploadText }) { const handleSelect = (e) => { if (datasetState === EMPTY) { - uploadDataset(e.target.files[0]); + const files = multiple ? e.target.files : [e.target.files[0]]; + uploadDataset(files); } }; @@ -58,8 +61,9 @@ function Upload({ onFileUpload, emptyUploadText }) { e.stopPropagation(); if (datasetState === EMPTY) { setDragActive(false); - if (e.dataTransfer.files && e.dataTransfer.files[0]) { - uploadDataset(e.dataTransfer.files[0]); + const files = multiple ? e.dataTransfer.files : [e.dataTransfer.files[0]]; + if (files && files.length > 0) { + uploadDataset(files); } } }; @@ -71,41 +75,42 @@ function Upload({ onFileUpload, emptyUploadText }) { const handleDeleteDataset = () => { onFileUpload(null, ""); setDatasetState(EMPTY); + setFileNames([]); }; - // renders content inside the drag and drop component depending on the state of the dataset const stateContent = (state) => { switch (state) { case EMPTY: return ( - + <> {dragActive ? ( - Drop the files here ... + Drop the file{multiple ? "s" : ""} here. ) : ( - + <> - Drag and drop a file here, or + Drag and drop {multiple ? "your files" : "a file"} here, or - + )} - + ); case LOADING: @@ -113,12 +118,24 @@ function Upload({ onFileUpload, emptyUploadText }) { case LOADED: return ( - + <> - - {fileOriginalName} - - + + {fileNames.map((name, index) => ( + + {name} + + ))} + + ); } }; diff --git a/DashAI/front/src/pages/generative/RAG/DocumentSelectionStep.jsx b/DashAI/front/src/pages/generative/RAG/DocumentSelectionStep.jsx index 3ad1d7afa..cba8634f8 100644 --- a/DashAI/front/src/pages/generative/RAG/DocumentSelectionStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/DocumentSelectionStep.jsx @@ -1,97 +1,92 @@ -import React, { useState, useEffect, useCallback } from "react"; +import React, { useState, useEffect, useRef } from "react"; // Add useRef import PropTypes from "prop-types"; -import { Box, Typography } from "@mui/material"; -import DocumentSelector from "../../../components/generative/RAG/DocumentSelector"; // Adjust the import path as needed +import { Box, Typography, TextField } from "@mui/material"; +import DocumentSelector from "../../../components/generative/RAG/DocumentSelector"; + +export default function DocumentSelectionStep({ + documents: propDocumentIds, // Rename to reflect it's IDs + setDocuments, + setNextEnabled, + sessionName, + setSesssionName, + sessionDescription, + setSessionDescription, +}) { + const [selectedDocuments, setSelectedDocuments] = useState([]); + const initialLoadRef = useRef(true); // Ref to track initial render + useEffect(() => { + if (initialLoadRef.current && propDocumentIds && propDocumentIds.length > 0) { + initialLoadRef.current = false; + } + }, [propDocumentIds]); - - - -// Helper function to convert document paths to UI-friendly objects -const createDocumentObjectFromPath = (path) => ({ - id: path, - name: path.split('/').pop() || path, // Use filename as name, fallback to full path - updatedAt: new Date().toISOString(), // Initial timestamp for new paths -}); - -export default function DocumentSelectionStep({ documents, setDocuments, setNextEnabled }) { - // Initialize localDocumentObjects and selectedObjectIds once from the 'documents' prop. - // This runs only on the initial mount. - const [localDocumentObjects, setLocalDocumentObjects] = useState(() => { - const initialLocalDocs = documents.map(createDocumentObjectFromPath); - const initialSelectedIds = documents; // Paths are directly the IDs - return initialLocalDocs; - }); - - const [selectedObjectIds, setSelectedObjectIds] = useState(() => { - return documents; // Paths are directly the IDs - }); - - // Effect 1: Sync selectedObjectIds (paths) back to the parent component - // This effect ensures the parent's 'documents' prop is updated when local selection changes. useEffect(() => { - // Only update parent if there's a change to prevent infinite loops and unnecessary re-renders - if (JSON.stringify(selectedObjectIds) !== JSON.stringify(documents)) { - setDocuments(selectedObjectIds); // selectedObjectIds already contains the paths - } - }, [selectedObjectIds, setDocuments, documents]); // Dependencies: local selected IDs, and parent's setter/prop for comparison + const documentIdsSelected = selectedDocuments.length > 0; + const validSessionName = sessionName.trim() !== ""; + setNextEnabled(documentIdsSelected && validSessionName); + }, [selectedDocuments, sessionName, setNextEnabled]); - // Effect 2: Notify parent about step validity - // This effect ensures the 'Next' button's enabled state is correct. useEffect(() => { - const isValid = selectedObjectIds && selectedObjectIds.length > 0; - setNextEnabled(isValid); - }, [selectedObjectIds, setNextEnabled]); // Dependencies: local selected IDs, and parent's setter + const currentSelectedIds = selectedDocuments.map(doc => doc.id); + if (JSON.stringify(currentSelectedIds.sort()) !== JSON.stringify(propDocumentIds.sort())) { + setDocuments(currentSelectedIds); + } + }, [selectedDocuments, setDocuments, propDocumentIds]); - // Handler for when DocumentSelector's selection changes (checkboxes) - const handleDocumentSelectionChange = useCallback((selectedDocs) => { - // Extract 'id' (which is the path) from the selected document objects - const newSelectedIds = selectedDocs.map(doc => doc.id); - setSelectedObjectIds(newSelectedIds); - }, []); - // Handler for when DocumentSelector adds a new document (e.g., from Upload component) - const handleAddDocumentObject = useCallback((newDoc) => { - setLocalDocumentObjects(prev => { - // Ensure no duplicates based on ID (path) before adding - if (!prev.some(doc => doc.id === newDoc.id)) { - const updatedDocs = [...prev, newDoc]; - // Also ensure it's selected in the UI - setSelectedObjectIds(currentSelected => { - if (!currentSelected.includes(newDoc.id)) { - return [...currentSelected, newDoc.id]; - } - return currentSelected; - }); - return updatedDocs; - } - return prev; - }); - }, []); + const handleSessionNameChange = (event) => { + setSesssionName(event.target.value); + }; - // Handler for when DocumentSelector removes a document - const handleRemoveDocumentObject = useCallback((docId) => { - setLocalDocumentObjects(prev => prev.filter(doc => doc.id !== docId)); - setSelectedObjectIds(prev => prev.filter(id => id !== docId)); - }, []); + const handleSessionDescriptionChange = (event) => { + setSessionDescription(event.target.value); + }; + const handleDocumentSelectionChange = (selectedDocs) => { + setSelectedDocuments(selectedDocs); + }; return ( - Select Documents + + Session Name + + + Session Description + + + + Select Documents Upload new documents or select from existing ones to be used for RAG. - {/* Integrate the DocumentSelector component */} - {/* Allow DocumentSelector to take available height */} + @@ -102,4 +97,8 @@ DocumentSelectionStep.propTypes = { documents: PropTypes.arrayOf(PropTypes.string).isRequired, setDocuments: PropTypes.func.isRequired, setNextEnabled: PropTypes.func.isRequired, -}; + sessionName: PropTypes.string.isRequired, + setSesssionName: PropTypes.func.isRequired, + sessionDescription: PropTypes.string.isRequired, + setSessionDescription: PropTypes.func.isRequired, +}; \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx index cf14db042..d4efa0d1d 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx @@ -75,6 +75,7 @@ export default function NewSessionModal({ setActiveStep(0); if(session) { const sessionParameters = session.parameters || {}; + const sessionDocuments = sessionParameters.documents || []; const sessionRetrieverModel = sessionParameters.retriever_model || {}; const sessionGeneratorModel = sessionParameters.generator_model || {}; setSessionData({ @@ -84,16 +85,12 @@ export default function NewSessionModal({ description: session.description || "", displayName: session.displayName || "", parameters: { - documents: sessionParameters.documents || [], + documents: [...sessionDocuments], retriever_model: { - class: sessionRetrieverModel.class || "", - name: sessionRetrieverModel.name || "", - parameters: sessionRetrieverModel.parameters || {}, + ...sessionRetrieverModel, }, generator_model: { - class: sessionGeneratorModel.class || "", - name: sessionGeneratorModel.name || "", - parameters: sessionGeneratorModel.parameters || {}, + ...sessionGeneratorModel, }, }, }) @@ -128,21 +125,26 @@ export default function NewSessionModal({ } try { - // Ensure generator_model is included in the final session data const finalSessionData = { - ...sessionData, - parameters: { - ...sessionData.parameters, - generator_model: sessionData.parameters.generator_model || { - name: "", - parameters: {}, - }, - retriever_model: sessionData.parameters.retriever_model || { - name: "", - parameters: {}, - }, + ...sessionData, + name: sessionData.name.trim(), + description: sessionData.description.trim() || "", + task_name: "RAGTask", + parameters: { + ...sessionData.parameters, + documents: sessionData.parameters.documents || [], + retriever_model: sessionData.parameters.retriever_model || { + name: "", + parameters: {}, }, - }; + generator_model: sessionData.parameters.generator_model || { + name: "", + parameters: {}, + }, + }, + }; + + console.log("NewSessionModal: Final session data to save:", finalSessionData); const savedSession = await onSessionSaved(finalSessionData); @@ -207,6 +209,7 @@ export default function NewSessionModal({ {activeStep === 0 && ( setSessionData(prev => ({ @@ -215,6 +218,16 @@ export default function NewSessionModal({ })) } setNextEnabled={(isValid) => handleStepValidation(0, isValid)} + sessionName={sessionData.name} + setSesssionName={(name) => setSessionData(prev => ({ + ...prev, + name: name + }))} + sessionDescription={sessionData.description} + setSessionDescription={(description) => setSessionData(prev => ({ + ...prev, + description: description + }))} /> )} {activeStep === 1 && ( diff --git a/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx b/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx index 292824418..11f670bbe 100644 --- a/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx +++ b/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx @@ -12,53 +12,42 @@ function RAGHomePage({ onSessionCreated, onSessionSelect }) { const [editingSession, setEditingSession] = useState(null); const loadSessions = async () => { - setLoading(true); // Set loading to true when fetching + setLoading(true); try { const data = await getRAGSessions(); setSessions(data); - console.log("RAGHomePage: Sessions loaded:", data); } catch (error) { console.error("RAGHomePage: Error loading RAG sessions:", error); } finally { - setLoading(false); // Set loading to false after fetch - console.log("RAGHomePage: Loading finished."); + setLoading(false); } }; - // Cargar sesiones al iniciar useEffect(() => { - console.log("RAGHomePage: useEffect - Initial session load..."); loadSessions(); - }, []); // Empty dependency array means this runs once on mount + }, []); const handleOpenNewSessionModal = (session = null) => { - console.log("RAGHomePage: handleOpenNewSessionModal called."); - setEditingSession(session); // Set the session to edit - setShowModal(true); // Open the modal - console.log("RAGHomePage: showModal set to true."); + setEditingSession(session); + setShowModal(true); }; const handleCreateOrUpdateSession = async (sessionData) => { - console.log("RAGHomePage: handleCreateOrUpdateSession called with data:", sessionData); try { const savedSession = await createRAGSession(sessionData); console.log("RAGHomePage: New/Updated RAG session saved:", savedSession); - // Refresh the sessions list after creation/update - await loadSessions(); // Call loadSessions to get the latest data - - onSessionCreated(savedSession); // Notify parent component (Generative.jsx) - setShowModal(false); // Close the modal - console.log("RAGHomePage: Modal closed after session save."); + await loadSessions(); + onSessionCreated(savedSession); + setShowModal(false); + return savedSession; + } catch (error) { - console.error("RAGHomePage: Error creating/updating session:", error); - throw error; // Re-throw to handle in the modal if needed + throw error; } }; - console.log("RAGHomePage: Render. Current showModal state:", showModal); - return ( } > - {/* Modal para nueva sesión */} { setShowModal(false); - setEditingSession(null); // Clear editing session when closing - console.log("RAGHomePage: Modal onClose called. showModal set to false."); + setEditingSession(null); }} onSessionSaved={handleCreateOrUpdateSession} onSessionSelect={onSessionSelect} session={editingSession} /> - {/* Contenido principal */} {loading ? ( @@ -97,8 +83,8 @@ function RAGHomePage({ onSessionCreated, onSessionSelect }) { sessions={sessions} onEdit={(session) => handleOpenNewSessionModal(session)} onSelect={onSessionSelect} - onRefreshSessions={loadSessions} // Pass the refresh function - onOpenNewSessionModal={() => handleOpenNewSessionModal()} // Pass function to open modal + onRefreshSessions={loadSessions} + onOpenNewSessionModal={() => handleOpenNewSessionModal()} /> )} diff --git a/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx b/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx index 35d5ff01a..db74d0e25 100644 --- a/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx +++ b/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx @@ -7,7 +7,7 @@ import { DeleteOutline as DeleteIcon, Visibility as ViewDocsIcon } from "@mui/icons-material"; -import { DataGrid } from "@mui/x-data-grid"; // Removed GridToolbar as it's not strictly essential for basic rendering +import { DataGrid } from "@mui/x-data-grid"; import { Button, Grid, @@ -16,25 +16,15 @@ import { LinearProgress, } from "@mui/material"; -import { deleteRAGSession } from "../../../api/rag"; // Assuming this API call is correct +import { deleteRAGSession } from "../../../api/rag"; -// Removed ConfirmDialog component as requested. -// Removed useSnackbar import and usage. export default function RAGSessionsTable({ sessions, onSelect, onEdit, - // Removed onRefreshSessions prop as it's not needed in this simplified version onOpenNewSessionModal }) { - // Removed local 'loading' state for operations within the table, - // as the parent RAGHomePage handles the main loading. - // Removed showConfirmDelete and sessionToDelete states. - - - // Removed handleDeleteSession function as its logic involved removed features. - // The delete button will now just call the API directly (or a simplified parent handler). const columns = [ { @@ -45,28 +35,33 @@ export default function RAGSessionsTable({ ) }, { - field: "created_at", + field: "created", headerName: "Created At", - flex: 1, + flex: 0.8, valueFormatter: (params) => new Date(params.value).toLocaleString() }, { - field: "documents", + field: "", headerName: "Documents", - flex: 0.7, - valueGetter: (params) => params.row.documents?.length || 0 + flex: 0.6, + valueGetter: (params) => params.row.parameters.documents.length }, { field: "actions", headerName: "Actions", - flex: 1.5, + flex: 1.1, renderCell: (params) => (
- {/* Simplified Delete button: It will now rely on a parent handler or direct API call */} + + + + )} + + ); +} + +DocumentSelectionTable.propTypes = { + documents: PropTypes.arrayOf(PropTypes.shape({ + id: PropTypes.string.isRequired, + name: PropTypes.string.isRequired, + createdAt: PropTypes.string.isRequired, + preview: PropTypes.string, + })).isRequired, + selectedIds: PropTypes.arrayOf(PropTypes.string).isRequired, + onToggle: PropTypes.func.isRequired, + onSelectAll: PropTypes.func.isRequired, + onDeselectAll: PropTypes.func.isRequired, + onRemove: PropTypes.func.isRequired, + isLoading: PropTypes.bool, +}; diff --git a/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx b/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx index c1f133863..95eca15fc 100644 --- a/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx +++ b/DashAI/front/src/components/generative/RAG/DocumentSelector.jsx @@ -1,7 +1,7 @@ import { Box } from "@mui/material"; import { useEffect, useState, useCallback, useRef } from "react"; // Added useRef import PropTypes from "prop-types"; -import DocumentTable from "./DocumentTable"; +import DocumentSelectionTable from "./DocumentSelectionTable"; import Upload from "../../shared/Upload"; import { loadDocuments, addDocument, deleteDocument } from "../../../api/rag"; @@ -107,7 +107,7 @@ export default function DocumentSelector({ return ( - { try { const date = new Date(dateString); - return date.toLocaleString(); + return date.toLocaleDateString(); } catch (e) { return dateString; } }; + const onPreview = (previewUrl) => { + if (previewUrl) { + window.open(previewUrl, '_blank'); + } else { + console.warn("No preview URL available for this document."); + } + } + + const onEdit = (id) => { + console.warn("Edit functionality is not implemented yet for document ID:", id); + } + + + + const columns = [ + { field: 'id', headerName: 'Id', flex: 0.1 }, + { field: 'file_name', headerName: 'Name', flex: 0.6, minWidth: 150 }, + { + field: 'created', + headerName: 'Added On', + flex: 0.4, + valueFormatter: (params) => formatDate(params.value), + }, + { + field: 'last_modified', + headerName: 'Last Modified', + flex: 0.4, + valueGetter: (params) => params.row.optional_metadata?.last_modified, + valueFormatter: (params) => formatDate(params.value), + }, + { + field: 'actions', + headerName: 'Actions', + flex: 0.5, + renderCell: (params) => ( +
+ + +
+ ), + }, + ]; + return ( - - {isLoading ? ( - - - - ) : documents.length === 0 ? ( + + {tableTitle && ( + + {tableTitle} + + )} + {documents.length === 0 && !isLoading ? ( No documents available. ) : ( - <> - - - - - 0 && selectedIds.length < documents.length} - checked={selectedIds.length === documents.length && documents.length > 0} - onChange={(event) => { - if (event.target.checked) { - onSelectAll(); - } else { - onDeselectAll(); - } - }} - /> - - Id - Name - Added On - Last Modified - Actions - - - - {documents.map((doc) => ( - - - onToggle(doc.id)} - /> - - {doc.id} - {doc.file_name} - {formatDate(doc.created)} - {doc.optional_metadata?.last_modified ? formatDate(doc.optional_metadata.last_modified) : "N/A"} - - - - { - if (doc.preview) window.open(doc.preview, '_blank'); - else console.warn("No preview URL available for this document."); - }}> - - - - - onRemove(doc.id)}> - - - - - - - ))} - -
- - - - - + row.id} + sx={{ + "& .MuiDataGrid-cell:focus": { outline: "none" }, + minHeight: 300, + }} + /> )} - + ); } @@ -145,10 +136,6 @@ DocumentTable.propTypes = { createdAt: PropTypes.string.isRequired, preview: PropTypes.string, })).isRequired, - selectedIds: PropTypes.arrayOf(PropTypes.string).isRequired, - onToggle: PropTypes.func.isRequired, - onSelectAll: PropTypes.func.isRequired, - onDeselectAll: PropTypes.func.isRequired, onRemove: PropTypes.func.isRequired, isLoading: PropTypes.bool, }; diff --git a/DashAI/front/src/pages/generative/Generative.jsx b/DashAI/front/src/pages/generative/Generative.jsx index 6460d8597..48dd66cf6 100644 --- a/DashAI/front/src/pages/generative/Generative.jsx +++ b/DashAI/front/src/pages/generative/Generative.jsx @@ -56,7 +56,7 @@ export default function Generative() { } setSessions((prevSessions) => - prevSessions.filter((session) => session.id !== id) + prevSessions.filter((session) => session.id !== id), ); removeSession(id); @@ -90,44 +90,41 @@ export default function Generative() { setSelectedTaskName(taskName); setStepIndex(1); }} - /> - ) : isRAGTask() ? ( - - ) : ( - setStepIndex(0)} - handleAddSession={handleAddSession} - selectedTaskName={selectedTaskName} - selectedDisplayName={selectedDisplayName} - setSelectedSessionId={setSelectedSessionId} - /> - )} - - + /> + ) : isRAGTask() ? ( + + ) : ( + setStepIndex(0)} + handleAddSession={handleAddSession} + selectedTaskName={selectedTaskName} + selectedDisplayName={selectedDisplayName} + setSelectedSessionId={setSelectedSessionId} + /> + )} + + - {!isRAGTask() && ( - - - {!selectedSessionId ? - ( + {!isRAGTask() && ( + + + {!selectedSessionId ? ( - ): null} + ) : null} + - - ) - } + )} ); } - \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx b/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx index 11f670bbe..4930043a7 100644 --- a/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx +++ b/DashAI/front/src/pages/generative/RAG/RAGHomePage.jsx @@ -1,15 +1,18 @@ -import React, { useState, useEffect } from 'react'; -import { Box, Button, CircularProgress, Typography } from '@mui/material'; -import CustomLayout from '../../../components/custom/CustomLayout'; -import NewSessionModal from './NewSessionModal'; -import RAGSessionsTable from './RAGSessionsTable'; -import { getRAGSessions, createRAGSession } from '../../../api/rag'; +import React, { useState, useEffect, useCallback } from "react"; +import { Box, Button, CircularProgress, Typography } from "@mui/material"; +import NewSessionModal from "./NewSessionModal"; +import RAGSessionsTable from "./RAGSessionsTable"; +import { getRAGSessions, createRAGSession, loadDocuments, deleteDocument } from "../../../api/rag"; +import DocumentTable from "../../../components/generative/RAG/DocumentTable"; + function RAGHomePage({ onSessionCreated, onSessionSelect }) { const [showModal, setShowModal] = useState(false); const [sessions, setSessions] = useState([]); const [loading, setLoading] = useState(true); const [editingSession, setEditingSession] = useState(null); + const [allDocuments, setAllDocuments] = useState([]); + const [documentsLoading, setDocumentsLoading] = useState(true); const loadSessions = async () => { setLoading(true); @@ -19,17 +22,30 @@ function RAGHomePage({ onSessionCreated, onSessionSelect }) { } catch (error) { console.error("RAGHomePage: Error loading RAG sessions:", error); } finally { - setLoading(false); + setLoading(false); } }; + const fetchAllDocuments = useCallback(async () => { + setDocumentsLoading(true); + try { + const docs = await loadDocuments(); + setAllDocuments(docs); + } catch (error) { + console.error("RAGHomePage: Error loading all documents:", error); + } finally { + setDocumentsLoading(false); + } + }, []); + useEffect(() => { loadSessions(); - }, []); + fetchAllDocuments(); + }, [fetchAllDocuments]); const handleOpenNewSessionModal = (session = null) => { setEditingSession(session); - setShowModal(true); + setShowModal(true); }; const handleCreateOrUpdateSession = async (sessionData) => { @@ -39,30 +55,41 @@ function RAGHomePage({ onSessionCreated, onSessionSelect }) { await loadSessions(); onSessionCreated(savedSession); - setShowModal(false); - - return savedSession; + setShowModal(false); + return savedSession; } catch (error) { throw error; } }; + const handleRemoveDocumentFromTable = useCallback(async (id) => { + try { + await deleteDocument(id); + await fetchAllDocuments(); + } catch (error) { + console.error("RAGHomePage: Failed to delete document:", error); + } + }, [fetchAllDocuments]); + return ( - handleOpenNewSessionModal()} - sx={{ textTransform: 'none' }} - > - New Session - - } + + + RAG Sessions + + + Manage your RAG sessions + { @@ -83,11 +110,23 @@ function RAGHomePage({ onSessionCreated, onSessionSelect }) { sessions={sessions} onEdit={(session) => handleOpenNewSessionModal(session)} onSelect={onSessionSelect} - onRefreshSessions={loadSessions} + onRefreshSessions={loadSessions} onOpenNewSessionModal={() => handleOpenNewSessionModal()} /> )} - + + + Documents + + + Manage documents for your RAG sessions + + + ); } diff --git a/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx b/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx index db74d0e25..8cde0a3e9 100644 --- a/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx +++ b/DashAI/front/src/pages/generative/RAG/RAGSessionsTable.jsx @@ -4,83 +4,83 @@ import PropTypes from "prop-types"; import { AddCircleOutline as AddIcon, Update as UpdateIcon, + Edit as EditIcon, DeleteOutline as DeleteIcon, - Visibility as ViewDocsIcon + Visibility as ViewDocsIcon, } from "@mui/icons-material"; + import { DataGrid } from "@mui/x-data-grid"; -import { - Button, - Grid, - Paper, - Typography, - LinearProgress, -} from "@mui/material"; +import { Button, Grid, Paper, Typography, LinearProgress } from "@mui/material"; import { deleteRAGSession } from "../../../api/rag"; - export default function RAGSessionsTable({ sessions, onSelect, onEdit, - onOpenNewSessionModal + onOpenNewSessionModal, + showTableTitle = false, }) { - const columns = [ { field: "name", headerName: "Session Name", - flex: 1, + flex: 0.8, renderCell: (params) => ( - ) + ), }, { field: "created", headerName: "Created At", - flex: 0.8, - valueFormatter: (params) => new Date(params.value).toLocaleString() + flex: 0.4, + valueFormatter: (params) => new Date(params.value).toLocaleDateString(), }, { field: "", headerName: "Documents", - flex: 0.6, - valueGetter: (params) => params.row.parameters.documents.length + flex: 0.4, + valueGetter: (params) => params.row.parameters.documents.length, }, { field: "actions", headerName: "Actions", - flex: 1.1, + flex: 0.9, renderCell: (params) => ( -
+
- ) - } + ), + }, ]; return ( - - - - RAG Sessions - - - - + + RAG Sessions + + + + )} row.id} sx={{ - '& .MuiDataGrid-cell:focus': { outline: 'none' }, - minHeight: 400 + "& .MuiDataGrid-cell:focus": { outline: "none" }, + minHeight: 300, }} /> - + {!showTableTitle && ( + + )} ); } @@ -150,7 +164,7 @@ RAGSessionsTable.propTypes = { created_at: PropTypes.string.isRequired, documents: PropTypes.array, task_name: PropTypes.string, - }) + }), ).isRequired, onSelect: PropTypes.func.isRequired, onEdit: PropTypes.func.isRequired, From 2a45b2ac88d2e260a9ac836b31e50e1fe20d4382 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Tue, 15 Jul 2025 21:47:02 -0400 Subject: [PATCH 006/218] RAG session creation updated --- .../api_v1/endpoints/generative_session.py | 46 +++++++++++++++---- DashAI/back/models/RAG/RAG_pipeline.py | 4 +- 2 files changed, 40 insertions(+), 10 deletions(-) diff --git a/DashAI/back/api/api_v1/endpoints/generative_session.py b/DashAI/back/api/api_v1/endpoints/generative_session.py index aa9765ee8..4fb69e27d 100644 --- a/DashAI/back/api/api_v1/endpoints/generative_session.py +++ b/DashAI/back/api/api_v1/endpoints/generative_session.py @@ -14,11 +14,13 @@ GenerativeSession, GenerativeSessionParameterHistory, ProcessData, + Document ) from DashAI.back.dependencies.registry import ComponentRegistry from DashAI.back.models import BaseGenerativeModel from DashAI.back.tasks import BaseGenerativeTask +from DashAI.back.tasks.RAG_task import RAGTask router = APIRouter() log = logging.getLogger(__name__) @@ -50,22 +52,50 @@ async def upload_generative_session( f"generative model.", ) - # Validate the model parameters + # Check if the task is registered try: - model_class.SCHEMA.model_validate(params.parameters) - except ValueError as e: + task_class = component_registry[params.task_name]["class"] + except KeyError as e: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, - detail=f"Invalid parameters for model {params.model_name}: {e}", + detail=f"Task {params.task_name} is not registered.", ) from e + + # RAG Task specific handling + # Frontend will send the ids of the documents to be used in the RAG session + # but RAG pipeline expects the documents paths of the backend-stored documents + if task_class == RAGTask: + try: + assert params.parameters["documents"] + assert isinstance(params.parameters["documents"], list) + assert len(params.parameters["documents"]) > 0 + + except (AssertionError, KeyError): + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail="RAG Task requires a non-empty list of document IDs.", + ) + + documents_paths = [] + for doc_id in params.parameters["documents"]: + doc = db.get(Document, doc_id) + if not doc: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Document with ID {doc_id} not found.", + ) + documents_paths.append(doc.file_path) - # Check if the task is registered + params.parameters["documents"] = documents_paths + # Continue with the session creation + + # Validate the model parameters try: - task_class = component_registry[params.task_name]["class"] - except KeyError as e: + model_class.SCHEMA.model_validate(params.parameters) + except ValueError as e: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, - detail=f"Task {params.task_name} is not registered.", + detail=f"Invalid parameters for model {params.model_name}: {e}", ) from e # Check if the task is a subclass of BaseGenerativeTask diff --git a/DashAI/back/models/RAG/RAG_pipeline.py b/DashAI/back/models/RAG/RAG_pipeline.py index 83300d24c..c7713c922 100644 --- a/DashAI/back/models/RAG/RAG_pipeline.py +++ b/DashAI/back/models/RAG/RAG_pipeline.py @@ -5,7 +5,7 @@ component_field, schema_field, list_field, - int_field + string_field ) from DashAI.back.models.base_generative_model import BaseGenerativeModel @@ -41,7 +41,7 @@ class RAGPipelineSchema(BaseSchema): # Document collection parameters documents: schema_field( list_field( - int_field(), + string_field(), min_items=1, ), placeholder=None, From 6e29b7748c14da10ecd663f07732ee3ebe9deda9 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Mon, 21 Jul 2025 17:54:42 -0400 Subject: [PATCH 007/218] tfidf embedding implemented as a configurable object --- .../back/models/RAG/Documents/BaseDocument.py | 88 ++- .../back/models/RAG/Documents/PDFDocument.py | 62 +- .../back/models/RAG/Documents/TxtDocument.py | 62 +- .../models/RAG/Retrievers/BaseRetriever.py | 2 +- .../models/RAG/Retrievers/TFIDFRetriever.py | 2 +- .../chunking_models/base_chunking_model.py | 28 + .../chunking_models/character_chunk_model.py | 58 ++ .../models/RAG/embeddings/base_embedding.py | 70 ++ .../models/RAG/embeddings/dense_embedding.py | 22 + .../models/RAG/embeddings/sparse_embedding.py | 29 + .../models/RAG/embeddings/tf_idf_embedding.py | 357 +++++++++ .../tf_idf_encoding.py | 0 DashAI/back/models/RAG/utils.py | 0 .../feature_names.pkl | Bin 0 -> 1577 bytes .../term_matrix.pkl | Bin 0 -> 2244 bytes .../vectorizer.pkl | Bin 0 -> 4710 bytes Untitled-1.ipynb | 690 ++++++++++++++++++ dummy_texts.txt | 6 + term_matrix.pkl | Bin 0 -> 2244 bytes 19 files changed, 1370 insertions(+), 106 deletions(-) create mode 100644 DashAI/back/models/RAG/chunking_models/base_chunking_model.py create mode 100644 DashAI/back/models/RAG/chunking_models/character_chunk_model.py create mode 100644 DashAI/back/models/RAG/embeddings/base_embedding.py create mode 100644 DashAI/back/models/RAG/embeddings/dense_embedding.py create mode 100644 DashAI/back/models/RAG/embeddings/sparse_embedding.py create mode 100644 DashAI/back/models/RAG/embeddings/tf_idf_embedding.py rename DashAI/back/models/RAG/{encodings => embeddings}/tf_idf_encoding.py (100%) create mode 100644 DashAI/back/models/RAG/utils.py create mode 100644 RAG/embeddings/c64d87e82535789c183ba885c1274d38fb9e6704d99f02ce7f3cb03d1d444709/feature_names.pkl create mode 100644 RAG/embeddings/c64d87e82535789c183ba885c1274d38fb9e6704d99f02ce7f3cb03d1d444709/term_matrix.pkl create mode 100644 RAG/embeddings/c64d87e82535789c183ba885c1274d38fb9e6704d99f02ce7f3cb03d1d444709/vectorizer.pkl create mode 100644 Untitled-1.ipynb create mode 100644 dummy_texts.txt create mode 100644 term_matrix.pkl diff --git a/DashAI/back/models/RAG/Documents/BaseDocument.py b/DashAI/back/models/RAG/Documents/BaseDocument.py index f54db39db..3f8c3b4fc 100644 --- a/DashAI/back/models/RAG/Documents/BaseDocument.py +++ b/DashAI/back/models/RAG/Documents/BaseDocument.py @@ -7,12 +7,32 @@ class BaseDocument(ABC): Base class for documents. """ - def __init__(self, *args, **kwargs): + def __init__( + self, + id: int, + file_name: str, + file_path: str, + file_hash: str, + created: Optional[str] = None, + optional_metadata: Optional[Dict[str, Any]] = None + ): """ Initialize the document. - """ - pass - + Args (from database): + id (int): The unique identifier of the document. + file_name (str): The name of the file. + file_path (str): The path to the file. + file_hash (str): A hash of the file content. + created (Optional[str]): The creation date of the document. + optional_metadata (Optional[Dict[str, Any]]): Additional metadata for the document. + """ + self.id = id + self.file_name = file_name + self.file_path = file_path + self.file_hash = file_hash + self.created = created if created else None + self.optional_metadata = optional_metadata if optional_metadata else None + def get_text(self) -> str: """ Get the text content of the document. @@ -29,7 +49,7 @@ def get_text_length(self) -> int: Returns: int: The length of the text content of the document. """ - raise NotImplementedError("This method should be implemented by subclasses.") + return len(self.get_text()) def get_metadata(self) -> Dict[str, Any]: """ @@ -40,63 +60,51 @@ def get_metadata(self) -> Dict[str, Any]: """ raise NotImplementedError("This method should be implemented by subclasses.") - def get_filename(self) -> Optional[str]: + def get_id(self) -> int: """ - Get the filename of the document. + Get the unique identifier of the document. Returns: - Optional[str]: The filename of the document, or None if not applicable. + int: The unique identifier of the document. """ - raise NotImplementedError("This method should be implemented by subclasses.") - - def get_filetype(self) -> Optional[str]: + return self.id + + def get_file_name(self) -> Optional[str]: """ - Get the filetype of the document. + Get the filename of the document. Returns: - Optional[str]: The filetype of the document, or None if not applicable. + Optional[str]: The filename of the document, or None if not applicable. """ - raise NotImplementedError("This method should be implemented by subclasses.") + return self.file_name - def get_file_location(self) -> Optional[str]: + def get_file_path(self) -> Optional[str]: """ - Get the file location of the document. + Get the file path of the document. Returns: - Optional[str]: The file location of the document, or None if not applicable. + Optional[str]: The file path of the document, or None if not applicable. """ - raise NotImplementedError("This method should be implemented by subclasses.") + return self.file_path - def get_chunks(self, chunk_size: int, chunk_overlap: int) -> List[str]: + def get_file_hash(self) -> str: """ - Get the document text split into chunks. - - Args: - chunk_size (int): The size of each chunk. - chunk_overlap (int): The overlap between chunks. + Get the hash of the document content. Returns: - List[str]: A list of text chunks. - """ - text = self.get_text() - chunks = [] - start = 0 - while start < len(text): - end = min(start + chunk_size, len(text)) - chunks.append(text[start:end]) - start += chunk_size - chunk_overlap - return chunks + str: A hash string representing the document content. + """ + return self.file_hash - def get_hash(self) -> str: + def get_file_type(self) -> Optional[str]: """ - Get a hash of the document content. + Get the filetype of the document. Returns: - str: A hash string representing the document content. + Optional[str]: The filetype of the document, or None if not applicable. """ - text = self.get_text() - return hashlib.sha256(text.encode('utf-8')).hexdigest() if text else '' + return self.file_path.split('.')[-1] def __repr__(self): - return f"BaseDocument(filename='{self.get_filename()}', content='{self.get_text()[:50]}...', metadata={self.get_metadata()})" + return f"BaseDocument(filename='{self.get_file_name()}', content='{self.get_text()[:50]}...', metadata={self.get_metadata()})" diff --git a/DashAI/back/models/RAG/Documents/PDFDocument.py b/DashAI/back/models/RAG/Documents/PDFDocument.py index ca2ecb059..48dba5258 100644 --- a/DashAI/back/models/RAG/Documents/PDFDocument.py +++ b/DashAI/back/models/RAG/Documents/PDFDocument.py @@ -1,33 +1,40 @@ from .BaseDocument import BaseDocument import os from PyPDF2 import PdfReader -import base64 +from typing import Optional, Dict, Any class PDFDocument(BaseDocument): """ Class representing a PDF document. """ - def __init__(self, file_path: str): + def __init__( + self, + id: int, + file_name: str, + file_path: str, + file_hash: str, + created: Optional[str] = None, + optional_metadata: Optional[Dict[str, Any]] = None + ): """ - Initialize the PDF document with the file path. - - Args: - file_path (str): The path to the PDF file. + Initialize the document. + Args (from database): + id (int): The unique identifier of the document. + file_name (str): The name of the file. + file_path (str): The path to the file. + file_hash (str): A hash of the file content. + created (Optional[str]): The creation date of the document. + optional_metadata (Optional[Dict[str, Any]]): Additional metadata for the document. """ - - assert os.path.exists(file_path), f"File {file_path} does not exist." - - self.file_path = file_path - self.metadata = { - "file_path": file_path, - "filetype": "pdf", - "filename": os.path.basename(file_path), - } - self.file_path = file_path - self.filename = os.path.basename(file_path) - self.filetype = "pdf" - self.text_length = len(self.get_text()) + super().__init__( + id=id, + file_name=file_name, + file_path=file_path, + file_hash=file_hash, + created=created, + optional_metadata=optional_metadata + ) def get_text(self) -> str: reader = PdfReader(self.file_path) @@ -41,18 +48,5 @@ def get_text(self) -> str: return text.strip() - def get_text_length(self) -> int: - return self.text_length - - def get_metadata(self) -> dict: - return self.metadata - - def get_filename(self) -> str: - return self.filename - - def get_filetype(self) -> str: - return self.filetype - - def get_file_location(self) -> str: - return self.file_path - \ No newline at end of file + def get_metadata(self): + return self.metadata \ No newline at end of file diff --git a/DashAI/back/models/RAG/Documents/TxtDocument.py b/DashAI/back/models/RAG/Documents/TxtDocument.py index f8f7f58a7..fb5507d94 100644 --- a/DashAI/back/models/RAG/Documents/TxtDocument.py +++ b/DashAI/back/models/RAG/Documents/TxtDocument.py @@ -7,25 +7,33 @@ class TxtDocument(BaseDocument): Class representing a .txt document. """ - def __init__(self, file_path: str): + def __init__( + self, + id: int, + file_name: str, + file_path: str, + file_hash: str, + created: Optional[str] = None, + optional_metadata: Optional[Dict[str, Any]] = None + ): """ - Initialize the text document with the file path. - - Args: - file_path (str): The path to the text file. + Initialize the document. + Args (from database): + id (int): The unique identifier of the document. + file_name (str): The name of the file. + file_path (str): The path to the file. + file_hash (str): A hash of the file content. + created (Optional[str]): The creation date of the document. + optional_metadata (Optional[Dict[str, Any]]): Additional metadata for the document. """ - assert os.path.exists(file_path), f"File {file_path} does not exist." - - self.file_path = file_path - self.filename = os.path.basename(file_path) - self.filetype = "txt" - self.text_length = len(self.text) - self.metadata = { - "file_path": file_path, - "filetype": self.filetype, - "filename": self.filename, - } - self.text_length = len(self.get_text()) + super().__init__( + id=id, + file_name=file_name, + file_path=file_path, + file_hash=file_hash, + created=created, + optional_metadata=optional_metadata + ) def get_text(self) -> str: """ @@ -38,17 +46,11 @@ def get_text(self) -> str: text = file.read() return text.strip() - def get_text_length(self) -> int: - return self.text_length - def get_metadata(self) -> Dict[str, Any]: - return self.metadata - - def get_filename(self) -> str: - return self.filename - - def get_filetype(self) -> str: - return self.filetype - - def get_file_location(self) -> str: - return self.file_path \ No newline at end of file + """ + Get the metadata of the document. + + Returns: + Dict[str, Any]: The metadata of the document. + """ + return self.optional_metadata if self.optional_metadata else {} \ No newline at end of file diff --git a/DashAI/back/models/RAG/Retrievers/BaseRetriever.py b/DashAI/back/models/RAG/Retrievers/BaseRetriever.py index e12143378..3385a5467 100644 --- a/DashAI/back/models/RAG/Retrievers/BaseRetriever.py +++ b/DashAI/back/models/RAG/Retrievers/BaseRetriever.py @@ -1,7 +1,7 @@ from abc import ABC import os from typing import Any, Dict, List, Final -from DashAI.back.models.RAG.Documents import BaseDocument, PDFDocument, TxtDocument +from DashAI.back.models.RAG.documents import BaseDocument, PDFDocument, TxtDocument from DashAI.back.config_object import ConfigObject import hashlib diff --git a/DashAI/back/models/RAG/Retrievers/TFIDFRetriever.py b/DashAI/back/models/RAG/Retrievers/TFIDFRetriever.py index f6a005ff0..e60734aac 100644 --- a/DashAI/back/models/RAG/Retrievers/TFIDFRetriever.py +++ b/DashAI/back/models/RAG/Retrievers/TFIDFRetriever.py @@ -1,6 +1,6 @@ import os from .BaseRetriever import BaseRetriever -from DashAI.back.models.RAG.Documents import BaseDocument, PDFDocument, TxtDocument +from DashAI.back.models.RAG.documents import BaseDocument, PDFDocument, TxtDocument from typing import List, Dict, Any, Optional, Tuple from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances diff --git a/DashAI/back/models/RAG/chunking_models/base_chunking_model.py b/DashAI/back/models/RAG/chunking_models/base_chunking_model.py new file mode 100644 index 000000000..648483a6b --- /dev/null +++ b/DashAI/back/models/RAG/chunking_models/base_chunking_model.py @@ -0,0 +1,28 @@ +from abc import ABCMeta, abstractmethod +from DashAI.back.config_object import ConfigObject +from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument +from typing import Final, List + +class BaseChunkingModel(ConfigObject, metaclass=ABCMeta): + """ + Base class for chunking models. + This class should be inherited by any specific chunking model implementation. + """ + + TYPE: Final[str] = "ChunkingModel" + + def __init__(self, **kwargs): + """ + Initialize the chunking model with any necessary parameters. + """ + raise NotImplementedError("Subclasses must implement the __init__ method.") + + @abstractmethod + def chunk(self, document: BaseDocument, **kwargs) -> List[str]: + """ + Method to be implemented by subclasses to perform document chunking. + + :param document: The input document to be chunked. + :return: A list of text chunks. + """ + raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/DashAI/back/models/RAG/chunking_models/character_chunk_model.py b/DashAI/back/models/RAG/chunking_models/character_chunk_model.py new file mode 100644 index 000000000..dca1a203d --- /dev/null +++ b/DashAI/back/models/RAG/chunking_models/character_chunk_model.py @@ -0,0 +1,58 @@ +from DashAI.back.models.RAG.chunking_models.base_chunking_model import BaseChunkingModel +from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument +from typing import List +from DashAI.back.core.schema_fields import ( + BaseSchema, + schema_field, + int_field, +) + +class CharacterChunkModelSchema(BaseSchema): + """Schema for character-based chunking model.""" + + chunk_size: schema_field( + int_field(min_value=1), + placeholder=None, + description="Size of each chunk in characters." + ) # type: ignore + + chunk_overlap: schema_field( + int_field(min_value=0), + placeholder=None, + description="Number of characters to overlap between chunks." + ) # type: ignore + +class CharacterChunkModel(BaseChunkingModel): + """ + Chunking model that splits documents into chunks based on character count. + This model is useful for processing large text documents into manageable pieces. + """ + SCHEMA = CharacterChunkModelSchema + + def __init__(self, chunk_size: int, chunk_overlap: int): + """ + Initialize the character chunking model with the specified chunk size and overlap. + + :param chunk_size: Size of each chunk in characters. + :param chunk_overlap: Number of characters to overlap between chunks. + """ + self.chunk_size = chunk_size + self.chunk_overlap = chunk_overlap + + def chunk(self, document: BaseDocument) -> List[str]: + """ + Split the document into chunks based on character count. + + :param document: The input document to be chunked. + :return: A list of text chunks. + """ + text = document.get_text() + chunks = [] + start = 0 + + while start < len(text): + end = min(start + self.chunk_size, len(text)) + chunks.append(text[start:end]) + start += self.chunk_size - self.chunk_overlap + + return chunks \ No newline at end of file diff --git a/DashAI/back/models/RAG/embeddings/base_embedding.py b/DashAI/back/models/RAG/embeddings/base_embedding.py new file mode 100644 index 000000000..025e112a6 --- /dev/null +++ b/DashAI/back/models/RAG/embeddings/base_embedding.py @@ -0,0 +1,70 @@ +from abc import ABCMeta +import hashlib +from DashAI.back.config_object import ConfigObject +from typing import Any, List +import numpy as np + +from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument + +class BaseEmbedding(ConfigObject, metaclass=ABCMeta): + """ + Base class for all embedding models. + This class should be inherited by any specific embedding model implementation. + """ + EMBEDDINGS_PATH = "RAG/embeddings" + + def __init__( + self, + documents: List[BaseDocument], + **kwargs: Any): + """ + Initialize the embedding model with the given documents. + """ + raise NotImplementedError("Subclasses must implement the __init__ method.") + + def _calculate_embeddings_hash(self) -> str: + """ + Generate a hash to identify the embeddings for caching purposes. + + Hash is calculated over the documents' content to ensure consistency in embeddings, it must: + - Be consistent across runs with the same documents content, chunking strategy and embedding model parameters. + - Change if the documents are modified. + - Change if the documents content is modified. + - Change if the chunking strategy is modified. + - Change if the embedding model parameters are modified. + + Returns: + str: A hash string representing the embeddings. + """ + raise NotImplementedError("Subclasses must implement this method.") + + def _calculate_documents_chunks_hash(self) -> str: + """ + Generate a hash to identify the documents chunks' texts for caching purposes. + + Hash is calculated over the chunks' texts to ensure consistency in embeddings, it must: + - Be consistent across runs with the same documents content and chunking strategy. + - Change if the documents are modified. + - Change if the documents content is modified. + - Change if the chunking strategy is modified. + + Returns: + str: A hash string representing the documents chunks. + """ + joint_text = "" + self.documents_chunks = sorted(self.documents_chunks.items()) + for doc_id in self.documents_chunks.keys(): + for i, chunk in enumerate(self.documents_chunks[doc_id]): + joint_text += f"{doc_id}_{i}:{chunk}\n" + + return hashlib.sha256(joint_text.encode('utf-8')).hexdigest() + + def embed(self, text: str) -> List[float]|np.ndarray|Any: + """ + Method to generate embeddings for the given text. + This method should be implemented by subclasses. + + :param text: The input text to embed. + :return: A list representing the embeddings of the input text. + """ + raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/DashAI/back/models/RAG/embeddings/dense_embedding.py b/DashAI/back/models/RAG/embeddings/dense_embedding.py new file mode 100644 index 000000000..39a585636 --- /dev/null +++ b/DashAI/back/models/RAG/embeddings/dense_embedding.py @@ -0,0 +1,22 @@ +from DashAI.back.models.RAG.embeddings.base_embedding import BaseEmbedding +from typing import Any, List +import numpy as np + +class DenseEmbedding(BaseEmbedding): + """ + Base class for dense embedding models. + This class should be inherited by any specific dense embedding model implementation. + """ + + def __init__(self): + super().__init__() + + def embed(self, text: str) -> List|np.ndarray|Any: + """ + Method to generate dense embeddings for the given text. + This method should be implemented by subclasses. + + :param text: The input text to embed. + :return: A list representing the dense embeddings of the input text. + """ + raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/DashAI/back/models/RAG/embeddings/sparse_embedding.py b/DashAI/back/models/RAG/embeddings/sparse_embedding.py new file mode 100644 index 000000000..706295673 --- /dev/null +++ b/DashAI/back/models/RAG/embeddings/sparse_embedding.py @@ -0,0 +1,29 @@ +import numpy as np +from DashAI.back.models.RAG.documents import BaseDocument +from DashAI.back.models.RAG.embeddings.base_embedding import BaseEmbedding +from typing import Any, List + +class SparseEmbedding(BaseEmbedding): + """ + Base class for sparse embedding models. + This class should be inherited by any specific sparse embedding model implementation. + """ + + def __init__(self, documents: List[BaseDocument], **kwargs: Any): + """ + Initialize the sparse embedding model with the given documents. + + :param documents: List of documents to be embedded. + :param kwargs: Additional keyword arguments. + """ + raise NotImplementedError("Subclasses must implement the __init__ method.") + + def embed(self, text: str) -> List|np.ndarray|Any: + """ + Method to generate sparse embeddings for the given text. + This method should be implemented by subclasses. + + :param text: The input text to embed. + :return: A list representing the sparse embeddings of the input text. + """ + raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/DashAI/back/models/RAG/embeddings/tf_idf_embedding.py b/DashAI/back/models/RAG/embeddings/tf_idf_embedding.py new file mode 100644 index 000000000..cfdb9ed5b --- /dev/null +++ b/DashAI/back/models/RAG/embeddings/tf_idf_embedding.py @@ -0,0 +1,357 @@ +import os +from DashAI.back.models.RAG.embeddings.sparse_embedding import SparseEmbedding +from DashAI.back.models.RAG.documents import BaseDocument, PDFDocument, TxtDocument +from typing import List, Dict, Any, Tuple +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances +import numpy as np +import hashlib +import pickle + +from DashAI.back.core.schema_fields import ( + BaseSchema, + list_field, + schema_field, + enum_field, + float_field, + int_field, + bool_field, + string_field +) + +class TFIDFEmbeddingSchema(BaseSchema): + + strip_accents: schema_field( + enum_field( + enum=["ascii", "unicode", None], + ), + placeholder=None, + description="Whether to strip accents from the text.", + ) # type: ignore + + lowercase: schema_field( + bool_field(), + placeholder=True, + description="Whether to convert all characters to lowercase.", + + ) # type: ignore + + analyzer: schema_field( + enum_field( + enum=["word", "char", "char_wb"], + ), + placeholder="word", + description="Whether the feature should be made of word or character n-grams. Option 'char_wb' creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space.", + ) # type: ignore + + stop_words: schema_field( + list_field( + string_field(), + min_items=1, + ), + placeholder=None, + description="List of stop words to be used in the TF-IDF vectorization.", + ) # type: ignore + + ngram_range: schema_field( + list_field( + int_field(), + min_items=2, + max_items=2, + ), + placeholder=[1, 1], + description="The lower and upper boundary of the range of n-values for different n-grams to be extracted.", + ) # type: ignore + + max_df: schema_field( + float_field( + ge=0.0, + le=1.0, + ), + placeholder=1.0, + description="When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words).", + ) # type: ignore + + min_df: schema_field( + float_field( + ge=0.0, + le=1.0, + ), + placeholder=1.0, + description="When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold (corpus-specific stop words).", + ) # type: ignore + + max_features: schema_field( + int_field( + ge=1, + ), + placeholder=None, + description="If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus.", + ) # type: ignore + + norm: schema_field( + enum_field( + enum=["l1", "l2", None], + ), + placeholder="l2", + description="The norm used to normalize term vectors. If None, no normalization is applied.", + ) # type: ignore + + use_idf: schema_field( + bool_field(), + placeholder=True, + description="Enable inverse-document-frequency reweighting.", + ) # type: ignore + + smooth_idf: schema_field( + bool_field(), + placeholder=True, + description="Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. This prevents zero divisions.", + ) # type: ignore + + sublinear_tf: schema_field( + bool_field(), + placeholder=False, + description="Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).", + ) # type: ignore + +class TFIDFEmbedding(SparseEmbedding): + """ + TF-IDF embedding model for text documents. + This class implements the TF-IDF algorithm to generate embeddings for text documents. + """ + + def __init__( + self, + documents_chunks: Dict[int, List[str]], + strip_accents: str, + lowercase: bool, + analyzer: str, + stop_words: List[str], + ngram_range: List[int], + max_df: float, + min_df: float, + max_features: int, + norm: str, + use_idf: bool, + smooth_idf: bool, + sublinear_tf: bool, + store_model: bool = True + ) -> None: + """ + Initialize the TF-IDF embedding model with the given document chunks. + + Args: + documents_chunks (Dict[int, List[str]]): A dictionary where keys are document IDs and values are lists of text chunks. + """ + # sort chunks by document ID to ensure consistency + documents_chunks = dict(sorted(documents_chunks.items())) + + chunks_mapping = {} + chunks_texts = [] + n_chunks = 0 + for doc_id, chunks in documents_chunks.items(): + for chunk_text in chunks: + chunks_texts.append(chunk_text) + chunks_mapping[n_chunks] = (doc_id, chunk_text) + n_chunks += 1 + + self.chunks_texts = chunks_texts + self.chunks_mapping = chunks_mapping + + self.strip_accents = strip_accents + self.lowercase = lowercase + self.analyzer = analyzer + self.stop_words = stop_words + self.ngram_range = ngram_range + self.max_df = max_df + self.min_df = min_df + self.max_features = max_features + self.norm = norm + self.use_idf = use_idf + self.smooth_idf = smooth_idf + self.sublinear_tf = sublinear_tf + + self.hyperparameters = { + "strip_accents": strip_accents, + "lowercase": lowercase, + "analyzer": analyzer, + "stop_words": stop_words, + "ngram_range": ngram_range, + "max_df": max_df, + "min_df": min_df, + "max_features": max_features, + "norm": norm, + "use_idf": use_idf, + "smooth_idf": smooth_idf, + "sublinear_tf": sublinear_tf + } + + load_success = self._load_model() + print(f"TF-IDF model loaded successfully: {load_success}") + if not load_success: + print("Fitting the TF-IDF model...") + self._fit_model() + if store_model: + print("Saving the TF-IDF model...") + self._save_model() + print("TF-IDF model fitted and saved.") + + def _hash_function(self, input_string: str) -> str: + """ + Generate a SHA-256 hash for the given input string. + + Args: + input_string (str): The input string to hash. + + Returns: + str: The SHA-256 hash of the input string. + """ + return hashlib.sha256(input_string.encode('utf-8')).hexdigest() + + def _calculate_embeddings_hash(self) -> str: + """ + Generate a hash to identify the embeddings for caching purposes. + + Hash is calculated over the documents' content to ensure consistency in embeddings, it must: + - Be consistent across runs with the same documents content, chunking strategy and embedding model parameters. + - Change if the documents are modified. + - Change if the documents content is modified. + - Change if the chunking strategy is modified. + - Change if the embedding model parameters are modified. + + Returns: + str: A hash string representing the embeddings. + """ + params_hash = self._hash_function(str(self.hyperparameters)) + documents_chunks_hash = self._hash_function(str(self.chunks_mapping)) + return self._hash_function(params_hash + documents_chunks_hash) + + def _fit_model(self) -> None: + """ + Fit the TF-IDF model to the document chunks. + This method initializes the TF-IDF vectorizer and fits it to the document chunks. + """ + self.vectorizer = TfidfVectorizer( + strip_accents=self.strip_accents, + lowercase=self.lowercase, + analyzer=self.analyzer, + stop_words=self.stop_words, + ngram_range=tuple(self.ngram_range), + max_df=self.max_df, + min_df=self.min_df, + max_features=self.max_features, + norm=self.norm, + use_idf=self.use_idf, + smooth_idf=self.smooth_idf, + sublinear_tf=self.sublinear_tf + ) + self._term_matrix = self.vectorizer.fit_transform(self.chunks_texts) + self._feature_names = self.vectorizer.get_feature_names_out() + + def _get_model_paths(self) -> Tuple[str, str, str, str]: + """ + Get the paths for saving the model components. + Returns a tuple containing the folder path, vectorizer path, term matrix path, and feature names path. + """ + folder_path = os.path.join( + self.EMBEDDINGS_PATH, + self._calculate_embeddings_hash() + ) + + vectorizer_path = os.path.join(folder_path, "vectorizer.pkl") + term_matrix_path = os.path.join(folder_path, "term_matrix.pkl") + feature_names_path = os.path.join(folder_path, "feature_names.pkl") + + return folder_path, vectorizer_path, term_matrix_path, feature_names_path + + def _save_model(self) -> None: + """ + Save the model components to disk. + This method saves the vectorizer, term matrix, and feature names to their respective files. + """ + + folder_path, vectorizer_path, term_matrix_path, feature_names_path = self._get_model_paths() + + # Create the folder if it doesn't exist + os.makedirs(folder_path, exist_ok=True) + + # Save the vectorizer + with open(vectorizer_path, 'wb') as f: + pickle.dump(self.vectorizer, f) + + # Save the term matrix + with open(term_matrix_path, 'wb') as f: + pickle.dump(self._term_matrix, f) + + # Save the feature names + with open(feature_names_path, 'wb') as f: + pickle.dump(self._feature_names, f) + + def _load_model(self) -> None: + """ + Try to load the model components from disk. + If the model components are not found, return False. + If the model components are found, load them into the instance variables and return True. + """ + folder_path, vectorizer_path, term_matrix_path, feature_names_path = self._get_model_paths() + if not ( + os.path.exists(vectorizer_path) and + os.path.exists(term_matrix_path) and + os.path.exists(feature_names_path)): + return False + + # Load the vectorizer + with open(vectorizer_path, 'rb') as f: + self.vectorizer = pickle.load(f) + + # Load the term matrix + with open(term_matrix_path, 'rb') as f: + self._term_matrix = pickle.load(f) + + # Load the feature names + with open(feature_names_path, 'rb') as f: + self._feature_names = pickle.load(f) + + return True + + + def get_documents_chunks_embeddings(self) -> Dict[int, List[np.ndarray]]: + """ + Get the embeddings for each document chunk. + + Returns: + Dict[int, List[np.ndarray]]: A dictionary where keys are document IDs and values are lists of embeddings for each chunk. + """ + if not hasattr(self, 'vectorizer'): + raise RuntimeError("The model has not been fitted yet. Call _fit_model() before getting embeddings.") + + embeddings = self._term_matrix.toarray() + n_chunks = embeddings.shape[0] + + # Map the embeddings back to the document IDs using the chunks_mapping + documents_chunks_embeddings = {} + for i in range(n_chunks): + doc_id, _ = self.chunks_mapping[i] + if doc_id not in documents_chunks_embeddings: + documents_chunks_embeddings[doc_id] = [] + documents_chunks_embeddings[doc_id].append(embeddings[i]) + + return documents_chunks_embeddings + + def embed(self, text: str) -> List[float]: + """ + Generate TF-IDF embeddings for the given text. + + Args: + text (str): The input text to embed. + + Returns: + List[float]: A list representing the TF-IDF embeddings of the input text. + """ + if not hasattr(self, 'vectorizer'): + raise RuntimeError("The model has not been fitted yet. 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Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "c:\\Users\\fcb11\\Documents\\GitHub\\DashAI\\.venv\\Lib\\site-packages\\pydantic\\_internal\\_config.py:345: UserWarning: Valid config keys have changed in V2:\n", + "* 'orm_mode' has been renamed to 'from_attributes'\n", + " warnings.warn(message, UserWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "['#1#yukari is one of the most underrated Vocaloid voicebanks and characters\\n',\n", + " '#2#Yuzuki Yukari is the president of Independent Republic of Saint Joaquin\\n',\n", + " '#3#Yuzuki Yukari is well known by her participation in the Nuremberg trials\\n',\n", + " '#4#Yuzuki Yukari is responsible for the creation of Linux kernel, even if she is not recognized by that\\n',\n", + " '#5#Papi Micky is the Major of San Carlos\\n',\n", + " '#6#Papi Micky had an affair with Tio Rene Puente, but he did not recognize him as a lover, because he was engaged with Contralorito']" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from typing import Dict, List\n", + "import hashlib\n", + "from DashAI.back.models.RAG.embeddings.tf_idf_embedding import TFIDFEmbedding\n", + "\n", + "\n", + "hasher = lambda x: hashlib.sha256(x.encode('utf-8')).hexdigest()\n", + "\n", + "\n", + "dummy_texts = []\n", + "\n", + "with open('dummy_texts.txt', 'r') as file:\n", + " dummy_texts = file.readlines()\n", + " \n", + "dummy_texts" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "59febbcb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "75 #1#yukari is one of the most underrated Vocaloid voicebanks and characters\n", + "\n", + "75 #2#Yuzuki Yukari is the president of Independent Republic of Saint Joaquin\n", + "\n", + "76 #3#Yuzuki Yukari is well known by her participation in the Nuremberg trials\n", + "\n", + "104 #4#Yuzuki Yukari is responsible for the creation of Linux kernel, even if she is not recognized by that\n", + "\n", + "41 #5#Papi Micky is the Major of San Carlos\n", + "\n", + "131 #6#Papi Micky had an affair with Tio Rene Puente, but he did not recognize him as a lover, because he was engaged with Contralorito\n" + ] + } + ], + "source": [ + "for t in dummy_texts:\n", + " print(len(t), t)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dd6c7ea9", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_chunks(texts, size, overlap)-> Dict[int, List[str]]:\n", + " \"\"\"\n", + " Generate chunks of text from the provided texts.\n", + " Args:\n", + " texts (List[str]): List of text documents to chunk.\n", + " size (int): Size of each chunk.\n", + " overlap (int): Overlap size between chunks.\n", + " Returns:\n", + " Dict[int, List[str]]: Dictionary with document index as key and list of chunks as value.\n", + " \"\"\"\n", + " \n", + " documents_chunks = {}\n", + "\n", + " for i, text in enumerate(texts):\n", + " chunks = []\n", + " for j in range(0, len(text), size - overlap):\n", + " if j + size > len(text):\n", + " chunk = text[j:]\n", + " else:\n", + " chunk = text[j:j + size]\n", + " chunks.append(chunk)\n", + " documents_chunks[i] = chunks\n", + " return documents_chunks" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "30e25e89", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: ['#1#yukari is one of the most underrated Vocaloid voicebanks and characters\\n'],\n", + " 1: ['#2#Yuzuki Yukari is the president of Independent Republic of Saint Joaquin\\n'],\n", + " 2: ['#3#Yuzuki Yukari is well known by her participation in the Nuremberg trials\\n'],\n", + " 3: ['#4#Yuzuki Yukari is responsible for the creation of Linux kernel, even if she is not recognized by that\\n'],\n", + " 4: ['#5#Papi Micky is the Major of San Carlos\\n'],\n", + " 5: ['#6#Papi Micky had an affair with Tio Rene Puente, but he did not recognize him as a lover, because he was engaged with Contralorito']}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "document_chunks = generate_chunks(dummy_texts, 200, 20)\n", + "document_chunks" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a18c4a56", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: ['#1#yukari is one of the most underrated Vocaloid voicebanks and characters\\n'],\n", + " 1: ['#2#Yuzuki Yukari is the president of Independent Republic of Saint Joaquin\\n'],\n", + " 2: ['#3#Yuzuki Yukari is well known by her participation in the Nuremberg trials\\n'],\n", + " 3: ['#4#Yuzuki Yukari is responsible for the creation of Linux kernel, even if she is not recognized by that\\n'],\n", + " 4: ['#5#Papi Micky is the Major of San Carlos\\n'],\n", + " 5: ['#6#Papi Micky had an affair with Tio Rene Puente, but he did not recognize him as a lover, because he was engaged with Contralorito']}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "document_chunks = generate_chunks(dummy_texts, 200, 0)\n", + "document_chunks" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a9f0d850", + "metadata": {}, + "outputs": [], + "source": [ + "if False: # Set to True to run the test\n", + " for chunk_size in [40, 80, 1000, 2000]:\n", + " for chunk_overlap in [0, 20, 30]:\n", + " document_chunks = generate_chunks(dummy_texts, chunk_size, chunk_overlap)\n", + " tfidf_embedding = TFIDFEmbedding(\n", + " documents_chunks=document_chunks,\n", + " strip_accents='unicode',\n", + " lowercase=True,\n", + " analyzer='word',\n", + " ngram_range=(1, 2),\n", + " max_features=1000,\n", + " norm='l2',\n", + " use_idf=True,\n", + " smooth_idf=True,\n", + " sublinear_tf=False,\n", + " stop_words=None,\n", + " max_df=1.0,\n", + " min_df=1,\n", + " )\n", + " \n", + " print(f\"Chunk Size: {chunk_size}, Chunk Overlap: {chunk_overlap}\")\n", + " print(f\"Embeddings Hash: {tfidf_embedding._calculate_embeddings_hash()}\\n\")\n", + " del tfidf_embedding # Clean up to free memory\n", + " del document_chunks # Clean up to free memory" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2130e475", + "metadata": {}, + "outputs": [], + "source": [ + "if False: # Set to True to run the test\n", + " for chunk_size in [200, 200, 200]:\n", + " for chunk_overlap in [0, 0, 50, 50]:\n", + " document_chunks = generate_chunks(dummy_texts, chunk_size, chunk_overlap)\n", + " tfidf_embedding = TFIDFEmbedding(\n", + " documents_chunks=document_chunks,\n", + " strip_accents='unicode',\n", + " lowercase=True,\n", + " analyzer='word',\n", + " ngram_range=(1, 2),\n", + " max_features=1000,\n", + " norm='l2',\n", + " use_idf=True,\n", + " smooth_idf=True,\n", + " sublinear_tf=False,\n", + " stop_words=None,\n", + " max_df=1.0,\n", + " min_df=1,\n", + " Store_model=False # Set to False to avoid saving the model during testing\n", + " )\n", + " \n", + " print(f\"Chunk Size: {chunk_size}, Chunk Overlap: {chunk_overlap}\")\n", + " print(f\"Embeddings Hash: {tfidf_embedding._calculate_embeddings_hash()}\\n\")\n", + " del tfidf_embedding # Clean up to free memory\n", + " del document_chunks # Clean up to free memory" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "625a05ec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: ['#1#yukari is one of the most underrated Vocaloid voicebanks and characters\\n'],\n", + " 1: ['#2#Yuzuki Yukari is the president of Independent Republic of Saint Joaquin\\n'],\n", + " 2: ['#3#Yuzuki Yukari is well known by her participation in the Nuremberg trials\\n'],\n", + " 3: ['#4#Yuzuki Yukari is responsible for the creation of Linux kernel, even if she is not recognized by that\\n'],\n", + " 4: ['#5#Papi Micky is the Major of San Carlos\\n'],\n", + " 5: ['#6#Papi Micky had an affair with Tio Rene Puente, but he did not recognize him as a lover, because he was engaged with Contralorito']}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chunks_size_200_overlap_0 = generate_chunks(dummy_texts, 200, 0)\n", + "chunks_size_200_overlap_0" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dbc4609e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: ['#1#yukari is one of the most underrated Vocaloid voicebanks and characters\\n'],\n", + " 1: ['#2#Yuzuki Yukari is the president of Independent Republic of Saint Joaquin\\n'],\n", + " 2: ['#3#Yuzuki Yukari is well known by her participation in the Nuremberg trials\\n'],\n", + " 3: ['#4#Yuzuki Yukari is responsible for the creation of Linux kernel, even if she is not recognized by that\\n'],\n", + " 4: ['#5#Papi Micky is the Major of San Carlos\\n'],\n", + " 5: ['#6#Papi Micky had an affair with Tio Rene Puente, but he did not recognize him as a lover, because he was engaged with Contralorito']}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chunks_size_2000_overlap_0 = generate_chunks(dummy_texts, 2000, 0)\n", + "chunks_size_2000_overlap_0" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b386ba62", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TF-IDF model loaded successfully: False\n", + "Fitting the TF-IDF model...\n", + "Saving the TF-IDF model...\n", + "TF-IDF model fitted and saved.\n" + ] + } + ], + "source": [ + "embedding_chunk_size_200_overlap_0 = TFIDFEmbedding(\n", + " documents_chunks=chunks_size_200_overlap_0,\n", + " strip_accents='unicode',\n", + " lowercase=True,\n", + " analyzer='word',\n", + " ngram_range=(1, 2),\n", + " max_features=None,\n", + " norm='l2',\n", + " use_idf=True,\n", + " smooth_idf=True,\n", + " sublinear_tf=False,\n", + " stop_words=None,\n", + " max_df=1.0,\n", + " min_df=1,\n", + " store_model=True # Set to False to avoid saving the model during testing\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "df20a4dd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<6x129 sparse matrix of type ''\n", + "\twith 156 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embedding_chunk_size_200_overlap_0._term_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3887a18a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['affair', 'affair with', 'an', 'an affair', 'and',\n", + " 'and characters', 'as', 'as lover', 'because', 'because he', 'but',\n", + " 'but he', 'by', 'by her', 'by that', 'carlos', 'characters',\n", + " 'contralorito', 'creation', 'creation of', 'did', 'did not',\n", + " 'engaged', 'engaged with', 'even', 'even if', 'for', 'for the',\n", + " 'had', 'had an', 'he', 'he did', 'he was', 'her',\n", + " 'her participation', 'him', 'him as', 'if', 'if she', 'in',\n", + " 'in the', 'independent', 'independent republic', 'is', 'is not',\n", + " 'is one', 'is responsible', 'is the', 'is well', 'joaquin',\n", + " 'kernel', 'kernel even', 'known', 'known by', 'linux',\n", + " 'linux kernel', 'lover', 'lover because', 'major', 'major of',\n", + " 'micky', 'micky had', 'micky is', 'most', 'most underrated', 'not',\n", + " 'not recognize', 'not recognized', 'nuremberg', 'nuremberg trials',\n", + " 'of', 'of independent', 'of linux', 'of saint', 'of san', 'of the',\n", + " 'one', 'one of', 'papi', 'papi micky', 'participation',\n", + " 'participation in', 'president', 'president of', 'puente',\n", + " 'puente but', 'recognize', 'recognize him', 'recognized',\n", + " 'recognized by', 'rene', 'rene puente', 'republic', 'republic of',\n", + " 'responsible', 'responsible for', 'saint', 'saint joaquin', 'san',\n", + " 'san carlos', 'she', 'she is', 'that', 'the', 'the creation',\n", + " 'the major', 'the most', 'the nuremberg', 'the president', 'tio',\n", + " 'tio rene', 'trials', 'underrated', 'underrated vocaloid',\n", + " 'vocaloid', 'vocaloid voicebanks', 'voicebanks', 'voicebanks and',\n", + " 'was', 'was engaged', 'well', 'well known', 'with',\n", + " 'with contralorito', 'with tio', 'yukari', 'yukari is', 'yuzuki',\n", + " 'yuzuki yukari'], dtype=object)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embedding_chunk_size_200_overlap_0._feature_names" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3ed862f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<6x129 sparse matrix of type ''\n", + "\twith 156 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embedding_chunk_size_200_overlap_0._term_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "46446c8a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0.]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embedding_chunk_size_200_overlap_0.embed(\"affair\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3592c77e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.65787347, 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0.39028945, 0. , 0.45545397, 0.45545397]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embedding_chunk_size_200_overlap_0.embed(\"affair yuzuki yukari\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1c652c26", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0.]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embedding_chunk_size_200_overlap_0.embed(\"fnosn 090 aojfnsk linux\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b1b8cf59", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TF-IDF model loaded successfully: True\n" + ] + } + ], + "source": [ + "embedding_chunk_size_2000_overlap_0 = TFIDFEmbedding(\n", + " documents_chunks=chunks_size_2000_overlap_0,\n", + " strip_accents='unicode',\n", + " lowercase=True,\n", + " analyzer='word',\n", + " ngram_range=(1, 2),\n", + " max_features=None,\n", + " norm='l2',\n", + " use_idf=True,\n", + " smooth_idf=True,\n", + " sublinear_tf=False,\n", + " stop_words=None,\n", + " max_df=1.0,\n", + " min_df=1,\n", + " store_model=True # Set to False to avoid saving the model during testing\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "67f65c72", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.6234157 , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0.31939253, 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0.36984707, 0. , 0.43159844, 0.43159844]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "e1=embedding_chunk_size_200_overlap_0.embed(\"this is a yuzuki yukari affair\")\n", + "e1" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ba0999cb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.6234157 , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0.31939253, 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0.36984707, 0. , 0.43159844, 0.43159844]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "e2 = embedding_chunk_size_2000_overlap_0.embed(\"this is a yuzuki yukari affair\")\n", + "e2" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "74ec8dc4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(e1==e2).all() # Check if all elements are equal" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dummy_texts.txt b/dummy_texts.txt new file mode 100644 index 000000000..48bfb1d26 --- /dev/null +++ b/dummy_texts.txt @@ -0,0 +1,6 @@ +#1#yukari is one of the most underrated Vocaloid voicebanks and characters +#2#Yuzuki Yukari is the president of Independent Republic of Saint Joaquin +#3#Yuzuki Yukari is well known by her participation in the Nuremberg trials +#4#Yuzuki Yukari is responsible for the creation of Linux kernel, even if she is not recognized by that +#5#Papi Micky is the Major of San Carlos +#6#Papi Micky had an affair with Tio Rene Puente, but he did not recognize him as a lover, because he was engaged with Contralorito \ No newline at end of file diff --git a/term_matrix.pkl b/term_matrix.pkl new file mode 100644 index 0000000000000000000000000000000000000000..aa935f934ddd778124e73352f523b9c548cd8276 GIT binary patch literal 2244 zcmd_qS!@$W7{GBSu^o<(0wLVA6ljhDrZqDhLT4Sn0Q) zXLjb>nbEgyvF|$F$CkN_K{3A?O}JuFMT-SpK7UM;MnCSpu%c^fvuu~8t#XSjF`T|w zNQnle$LVSPS{i-AN^?|GBf9jsja)SnQ2oJ}Gzuf}FzNhFTF@1auhUgU)0BiX@_bs* z-xP`ITHLRb)No`5N+UNC=o->4J+?MkmDM13$fdHxljYghCPR6KBcLaEEmN}9X)?L9 z>uHXWRW7|=@1h4>nVZL^%Z6lSG}~r#u(dd1_zD&H5+2lGGPYtM?9ibh3W2qlhmSE4 zTcBVezQDVfk7ZbgDQLm_sK;uQVkx%4e3zBe%J&g{jVQqqjKcs-M*{Dm3DqdZV!Vw! zOhYqT&^^DEJD>i~QH4neq6i}}6(3*$3J}65_~61Ce1^FQ;2jKy8#6E%BT)x)EiLH2 z2J_skb@=(c2(zK$L)798G+-9W;e~mI-Op$f?P1t}vFMAL$i)PFib@pXE%e87tU?5v zk%b(L!5j?2P^^bp|7CDuJXT-`-b6oqf|UqkBg`usqhapBTDN&llFBojd;?KklcD~h ze6x~61FbsFr7=8NWzNhj*DQ?+m`7li=RRao*aM2LJSBnoAru&A&JF^-n$jC|dn1T92LDv+Wo0s$-zzM*g+x3opD5 zD^GFzxuHj@Q^JZhyB$0C|8+(*%G+1|Dd-T7w=X_wAHC^Twcpd?|Kg$gGw#&Y%e{`X zp7)Gp_4@G{3oeLMskh|cWj_n69Z%lPwlM?_xZmE#<&+ Date: Tue, 9 Sep 2025 15:49:40 -0300 Subject: [PATCH 008/218] dense retriever configuration working Awful implementation --- .gitignore | 2 + .../back/api/api_v1/endpoints/components.py | 25 + .../back/converters/hugging_face/embedding.py | 61 +- DashAI/back/initial_components.py | 49 +- DashAI/back/models/RAG/RAG_pipeline.py | 4 +- .../models/RAG/Retrievers/DenseRetriever.py | 12 - DashAI/back/models/RAG/Retrievers/__init__.py | 7 +- .../models/RAG/Retrievers/dense_retriever.py | 50 + .../{BaseRetriever.py => retriever_model.py} | 35 +- .../models/RAG/Retrievers/sparse_retriever.py | 11 + .../{TFIDFRetriever.py => tfidf_retriever.py} | 9 +- DashAI/back/models/RAG/__init__.py | 9 +- .../models/RAG/chunking_models/__init__.py | 2 + .../chunking_models/character_chunk_model.py | 4 +- .../RAG/chunking_models/token_chunk_model.py | 52 + .../models/RAG/embeddings/dense_embedding.py | 22 - .../models/RAG/embeddings/tf_idf_encoding.py | 32 - DashAI/back/models/RAG/encodings/__init__.py | 6 + .../models/RAG/encodings/dense/__init__.py | 2 + .../RAG/encodings/dense/fasttext_embedding.py | 61 + .../encodings/dense/huggingface_embedding.py | 127 + .../models/RAG/encodings/dense_encoding.py | 24 + DashAI/back/models/RAG/encodings/encoding.py | 32 + .../models/RAG/encodings/sparse/__init__.py | 2 + .../RAG/encodings/sparse/bm25_encoding.py | 302 + .../sparse/tfidf_encoding.py} | 88 +- .../sparse_encoding.py} | 16 +- .../trainable_encoding.py} | 39 +- DashAI/back/models/RAG/prompts/__init__.py | 2 +- .../models/RAG/prompts/augmentation_prompt.py | 4 +- .../RAG/prompts/context_merge_prompt.py | 4 +- .../RAG/prompts/{base_prompt.py => prompt.py} | 5 +- DashAI/back/models/__init__.py | 2 +- DashAI/front/src/api/component.ts | 7 + DashAI/front/src/api/rag.ts | 23 +- .../components/documents/DocumentTable.jsx | 2 +- .../pages/generative/RAG/NewSessionModal.jsx | 14 +- .../RAG/RetrieverConfigurationStep.jsx | 238 +- .../feature_names.pkl | Bin 0 -> 1577 bytes .../term_matrix.pkl | Bin 0 -> 2244 bytes .../vectorizer.pkl | Bin 0 -> 6400 bytes Untitled-1.ipynb | 106 + component_registry.json | 1 + component_registry__foromatted.json | 6053 +++++++++++++++++ requirements.txt | 4 +- test.py | 87 + tsconfig.json | 15 + 47 files changed, 7282 insertions(+), 370 deletions(-) delete mode 100644 DashAI/back/models/RAG/Retrievers/DenseRetriever.py create mode 100644 DashAI/back/models/RAG/Retrievers/dense_retriever.py rename DashAI/back/models/RAG/Retrievers/{BaseRetriever.py => retriever_model.py} (86%) create mode 100644 DashAI/back/models/RAG/Retrievers/sparse_retriever.py rename DashAI/back/models/RAG/Retrievers/{TFIDFRetriever.py => tfidf_retriever.py} (97%) create mode 100644 DashAI/back/models/RAG/chunking_models/__init__.py create mode 100644 DashAI/back/models/RAG/chunking_models/token_chunk_model.py delete mode 100644 DashAI/back/models/RAG/embeddings/dense_embedding.py delete mode 100644 DashAI/back/models/RAG/embeddings/tf_idf_encoding.py create mode 100644 DashAI/back/models/RAG/encodings/__init__.py create mode 100644 DashAI/back/models/RAG/encodings/dense/__init__.py create mode 100644 DashAI/back/models/RAG/encodings/dense/fasttext_embedding.py create mode 100644 DashAI/back/models/RAG/encodings/dense/huggingface_embedding.py create mode 100644 DashAI/back/models/RAG/encodings/dense_encoding.py create mode 100644 DashAI/back/models/RAG/encodings/encoding.py create mode 100644 DashAI/back/models/RAG/encodings/sparse/__init__.py create mode 100644 DashAI/back/models/RAG/encodings/sparse/bm25_encoding.py rename DashAI/back/models/RAG/{embeddings/tf_idf_embedding.py => encodings/sparse/tfidf_encoding.py} (80%) rename DashAI/back/models/RAG/{embeddings/sparse_embedding.py => encodings/sparse_encoding.py} (53%) rename DashAI/back/models/RAG/{embeddings/base_embedding.py => encodings/trainable_encoding.py} (65%) rename DashAI/back/models/RAG/prompts/{base_prompt.py => prompt.py} (87%) create mode 100644 RAG/embeddings/9864fa188a542eee3117fb4e3ed614fecb32905086735433d372c53e4d4b8f5a/feature_names.pkl create mode 100644 RAG/embeddings/9864fa188a542eee3117fb4e3ed614fecb32905086735433d372c53e4d4b8f5a/term_matrix.pkl create mode 100644 RAG/embeddings/9864fa188a542eee3117fb4e3ed614fecb32905086735433d372c53e4d4b8f5a/vectorizer.pkl create mode 100644 component_registry.json create mode 100644 component_registry__foromatted.json create mode 100644 test.py create mode 100644 tsconfig.json diff --git a/.gitignore b/.gitignore index e3f8f7290..7972a2d3a 100644 --- a/.gitignore +++ b/.gitignore @@ -180,3 +180,5 @@ __pycache__ *.DS_Store .prettierrc + +*.ipynb \ No newline at end of file diff --git a/DashAI/back/api/api_v1/endpoints/components.py b/DashAI/back/api/api_v1/endpoints/components.py index ad5e85ceb..d2b17964a 100644 --- a/DashAI/back/api/api_v1/endpoints/components.py +++ b/DashAI/back/api/api_v1/endpoints/components.py @@ -97,6 +97,7 @@ async def get_components( print("has_related_of_type", str(has_related_of_type)) print("get_components api ") # when select_type is not none, check if it exists in the registry. + if select_types is not None: for select_type in select_types: if select_type not in component_registry._registry: @@ -206,6 +207,8 @@ async def get_components( _delete_class(component_dict) for component_dict in selected_components.values() ] + print(out) + return out @@ -282,3 +285,25 @@ async def update_component() -> None: raise HTTPException( status_code=status.HTTP_501_NOT_IMPLEMENTED, detail="Method not implemented" ) + +@router.get("/{component_name}/children/") +async def get_child_components( + component_name:str, + recursive:bool=False, + component_registry: ComponentRegistry = Depends(lambda: di["component_registry"])): + """Get child components of a specific component. + + Args: + component_name (str): The name of the component to get children for. + recursive (bool): Whether to get child components recursively. + + Returns: + List[Dict[str, Any]]: A list of child component dictionaries. + """ + children_list = component_registry.get_child_components(component_name, recursive=recursive) + print("children_list", children_list) + # Remove "class" key from each child component + cleaned_children = [_delete_class(child) for child in children_list] + print("cleaned_children", cleaned_children) + + return cleaned_children \ No newline at end of file diff --git a/DashAI/back/converters/hugging_face/embedding.py b/DashAI/back/converters/hugging_face/embedding.py index 371163ec1..ffd8421cb 100644 --- a/DashAI/back/converters/hugging_face/embedding.py +++ b/DashAI/back/converters/hugging_face/embedding.py @@ -1,3 +1,5 @@ +from typing import Final, List +import numpy as np import torch from datasets import Dataset, concatenate_datasets from transformers import AutoModel, AutoTokenizer @@ -62,7 +64,7 @@ class Embedding(HuggingFaceWrapper): SCHEMA = EmbeddingSchema DESCRIPTION = "Convert text to embeddings using HuggingFace transformer models." - + def __init__(self, **kwargs): super().__init__(**kwargs) self.pooling_strategy = kwargs.get("pooling_strategy", "mean") @@ -81,6 +83,36 @@ def _load_model(self): self.model = AutoModel.from_pretrained(self.model_name).to(self.device) self.model.eval() + def _encode_texts(self, texts:List[str])-> List[np.ndarray]: + """Encode a list of texts into embeddings.""" + # Tokenize + encoded = self.tokenizer( + texts, + padding=True, + truncation=True, + max_length=self.max_length, + return_tensors="pt", + ) + + # Move to device + encoded = {k: v.to(self.device) for k, v in encoded.items()} + + # Get embeddings + with torch.no_grad(): + outputs = self.model(**encoded) + hidden_states = outputs.last_hidden_state + + # Apply pooling strategy + if self.pooling_strategy == "mean": + embeddings = torch.mean(hidden_states, dim=1) + elif self.pooling_strategy == "cls": + embeddings = hidden_states[:, 0] + else: # max pooling + embeddings = torch.max(hidden_states, dim=1)[0] + + embeddings_np = embeddings.cpu().numpy() + return embeddings_np + def _process_batch(self, batch: DashAIDataset) -> DashAIDataset: """Process a batch of text into embeddings.""" all_column_embeddings = [] @@ -89,32 +121,7 @@ def _process_batch(self, batch: DashAIDataset) -> DashAIDataset: # Get text data from dataset texts = [row[column] for row in batch] - # Tokenize - encoded = self.tokenizer( - texts, - padding=True, - truncation=True, - max_length=self.max_length, - return_tensors="pt", - ) - - # Move to device - encoded = {k: v.to(self.device) for k, v in encoded.items()} - - # Get embeddings - with torch.no_grad(): - outputs = self.model(**encoded) - hidden_states = outputs.last_hidden_state - - # Apply pooling strategy - if self.pooling_strategy == "mean": - embeddings = torch.mean(hidden_states, dim=1) - elif self.pooling_strategy == "cls": - embeddings = hidden_states[:, 0] - else: # max pooling - embeddings = torch.max(hidden_states, dim=1)[0] - - embeddings_np = embeddings.cpu().numpy() + embeddings_np = self._encode_texts(texts) # Create a dictionary with embedding columns embedding_dict = { diff --git a/DashAI/back/initial_components.py b/DashAI/back/initial_components.py index 03db79b45..0b73a8fb8 100644 --- a/DashAI/back/initial_components.py +++ b/DashAI/back/initial_components.py @@ -90,13 +90,30 @@ RidgeRegression, StableDiffusionV2Model, StableDiffusionV3Model, - RAGPipeline, - BaseRetriever, - TFIDFRetriever, - DenseRetriever, - StableDiffusionXLV1ControlNet, ) + +from DashAI.back.models.RAG import ( + # Chunking Models + CharacterChunkModel, + TokenChunkModel, + # Encodings + DenseEncoding, + FastTextEmbedding, + HuggingFaceEmbedding, + SparseEncoding, + TFIDFEncoding, + BM25Encoding, + # Prompts + AugmentationPrompt, + ContextMergePrompt, + # Retrievers + DenseRetriever, + SparseRetriever, + TFIDFRetriever +) + + from DashAI.back.optimizers import HyperOptOptimizer, OptunaOptimizer from DashAI.back.plugins.utils import get_available_plugins from DashAI.back.tasks import ( @@ -159,10 +176,6 @@ def get_initial_components(): RidgeRegression, LinearSVR, LinearRegression, - RAGPipeline, - TFIDFRetriever, - DenseRetriever, - #BaseRetriever, # Dataloaders CSVDataLoader, JSONDataLoader, @@ -250,6 +263,24 @@ def get_initial_components(): SMOTEConverter, SMOTEENNConverter, RandomUnderSamplerConverter, + + # RAG + CharacterChunkModel, + TokenChunkModel, + # Encodings + DenseEncoding, + FastTextEmbedding, + HuggingFaceEmbedding, + SparseEncoding, + TFIDFEncoding, + BM25Encoding, + # Prompts + AugmentationPrompt, + ContextMergePrompt, + # Retrievers + DenseRetriever, + SparseRetriever, + TFIDFRetriever ] # Obtener plugins instalados diff --git a/DashAI/back/models/RAG/RAG_pipeline.py b/DashAI/back/models/RAG/RAG_pipeline.py index c7713c922..854254d43 100644 --- a/DashAI/back/models/RAG/RAG_pipeline.py +++ b/DashAI/back/models/RAG/RAG_pipeline.py @@ -13,7 +13,7 @@ from DashAI.back.models.RAG.Retrievers import ( TFIDFRetriever, DenseRetriever, - BaseRetriever) + RetrieverModel) from DashAI.back.models.RAG.prompts import ( ContextMergePrompt, @@ -114,7 +114,7 @@ def __init__( self, new_retriever_args[key] = value print("Initializing retriever model") - self.retriever: BaseRetriever = retriever_model_class(**new_retriever_args) + self.retriever: RetrieverModel = retriever_model_class(**new_retriever_args) self.retrieval_algorithm = "SINGLE_INTERACTION" print("RAG pipeline initialized") diff --git a/DashAI/back/models/RAG/Retrievers/DenseRetriever.py b/DashAI/back/models/RAG/Retrievers/DenseRetriever.py deleted file mode 100644 index 835747c96..000000000 --- a/DashAI/back/models/RAG/Retrievers/DenseRetriever.py +++ /dev/null @@ -1,12 +0,0 @@ -from .BaseRetriever import BaseRetriever - -class DenseRetriever(BaseRetriever): - """ - Dense retriever class for retrieving documents based on dense vector representations. - This class is a placeholder and should be implemented with specific dense retrieval logic. - """ - - COMPATIBLE_COMPONENTS = ["RAGTask"] - - def __init__(self, *args, **kwargs): - return \ No newline at end of file diff --git a/DashAI/back/models/RAG/Retrievers/__init__.py b/DashAI/back/models/RAG/Retrievers/__init__.py index 4e295aff6..1da8bcd36 100644 --- a/DashAI/back/models/RAG/Retrievers/__init__.py +++ b/DashAI/back/models/RAG/Retrievers/__init__.py @@ -1,3 +1,4 @@ -from DashAI.back.models.RAG.Retrievers.BaseRetriever import BaseRetriever -from DashAI.back.models.RAG.Retrievers.TFIDFRetriever import TFIDFRetriever -from DashAI.back.models.RAG.Retrievers.DenseRetriever import DenseRetriever \ No newline at end of file +from DashAI.back.models.RAG.Retrievers.retriever_model import RetrieverModel +from DashAI.back.models.RAG.Retrievers.tfidf_retriever import TFIDFRetriever +from DashAI.back.models.RAG.Retrievers.dense_retriever import DenseRetriever +from DashAI.back.models.RAG.Retrievers.sparse_retriever import SparseRetriever \ No newline at end of file diff --git a/DashAI/back/models/RAG/Retrievers/dense_retriever.py b/DashAI/back/models/RAG/Retrievers/dense_retriever.py new file mode 100644 index 000000000..1ef42567f --- /dev/null +++ b/DashAI/back/models/RAG/Retrievers/dense_retriever.py @@ -0,0 +1,50 @@ +from .retriever_model import RetrieverModel + +from DashAI.back.core.schema_fields import ( + BaseSchema, + component_field, + schema_field, + enum_field +) +""" chunking_model: schema_field( + component_field( + parent="ChunkingModel" + ), + placeholder={"component": "Perico", "params": {}}, + description="Chunking model to split documents into smaller parts." +)# type: ignore """ + +class DenseRetrieverSchema(BaseSchema): + """Schema for Dense Retriever.""" + + encoding_model: schema_field( + component_field( + parent="Encoding" + ), + placeholder={"component": "Embedding", "params": {}}, + description="Model to convert text into dense vector representations." + ) # type: ignore + + similarity_metric: schema_field( + enum_field( + enum=["cosine", "dot_product", "euclidean"] + ), + placeholder="cosine", + description="Similarity metric to use for comparing dense vectors.", + ) # type: ignore + + +class DenseRetriever(RetrieverModel): + """ + Dense retriever class for retrieving documents based on dense vector representations. + This class is a placeholder and should be implemented with specific dense retrieval logic. + """ + + SCHEMA = DenseRetrieverSchema + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + self.chunking_model = kwargs.get("chunking_model") + self.encoding_model = kwargs.get("encoding_model") + diff --git a/DashAI/back/models/RAG/Retrievers/BaseRetriever.py b/DashAI/back/models/RAG/Retrievers/retriever_model.py similarity index 86% rename from DashAI/back/models/RAG/Retrievers/BaseRetriever.py rename to DashAI/back/models/RAG/Retrievers/retriever_model.py index 3385a5467..8e2a59580 100644 --- a/DashAI/back/models/RAG/Retrievers/BaseRetriever.py +++ b/DashAI/back/models/RAG/Retrievers/retriever_model.py @@ -2,29 +2,20 @@ import os from typing import Any, Dict, List, Final from DashAI.back.models.RAG.documents import BaseDocument, PDFDocument, TxtDocument -from DashAI.back.config_object import ConfigObject import hashlib +from DashAI.back.models.base_model import BaseModel + SUPPORTED_DOCUMENT_TYPES = ['pdf', 'txt'] -class BaseRetriever(ABC, ConfigObject): +class RetrieverModel(BaseModel): """ - Base class for retrievers. + Abstract class to define the interface for retriever models. """ RETRIEVERS_PATH = "./StoredRetrievers" COMPATIBLE_COMPONENTS = ["RAGTask"] - TYPE: Final[str] = "RetrieverModel" - - def __init__(self, *args, **kwargs): - """ - Initialize the retriever. - """ - self.params = None - self._documents_paths = None - self.chunk_size = None - self.chunk_overlap = None def _load_document(self, document_path: str) -> BaseDocument: """ @@ -52,7 +43,7 @@ def get_documents(self) -> Dict[str, BaseDocument]: assert self._documents_paths is not None, "Document paths must be provided." assert len(self._documents_paths) > 0, "At least one document path must be provided." - self._documents_paths = sorted(self._documents_paths) # Sort for consistent hashing + self._documents_paths = sorted(self._documents_paths) # Sort for consistent hashing in signature documents = {} for path in self._documents_paths: doc = self._load_document(path) @@ -83,10 +74,10 @@ def get_parameters(self) -> Dict[str, Any]: assert self.params is not None, "Retriever parameters are not set." return self.params - def get_hashable_parameters(self) -> Dict[str, Any]: + def get_signature_parameters(self) -> Dict[str, Any]: """ Get the parameters used for storing the fitted retriever in the database. - This parameters are a subset of the retriever parameters, the non-hashable parameters are + This parameters are a subset of the retriever parameters, the non-signature parameters are those whose change does not require a retraining of the retriever, i.e. n_docs, max_distance, etc. @@ -95,7 +86,7 @@ def get_hashable_parameters(self) -> Dict[str, Any]: """ raise NotImplementedError("This method should be implemented by subclasses.") - def _get_documents_hash(self) -> str: + def _get_documents_signature(self) -> str: """ Generate a hash representing the current set of documents. @@ -109,7 +100,7 @@ def _get_documents_hash(self) -> str: hashes += f"{filename}{doc_hash}" return hashlib.sha256(hashes.encode('utf-8')).hexdigest() - def _get_parameters_hash(self) -> str: + def _get_parameters_signature(self) -> str: """ Generate a hash representing the retriever parameters. @@ -119,19 +110,19 @@ def _get_parameters_hash(self) -> str: Returns: str: A hash string representing the parameters. """ - hashable_parameters = sorted(self.get_hashable_parameters().items()) + hashable_parameters = sorted(self.get_signature_parameters().items()) params_string = "".join(f"{key}{value}" for key, value in hashable_parameters) return hashlib.sha256(params_string.encode('utf-8')).hexdigest() - def _get_cache_hash(self) -> str: + def _get_model_signature(self) -> str: """ Generate a unique hash for the retriever based on its documents and parameters. Returns: str: A hash string representing the retriever's state. """ - documents_hash = self._get_documents_hash() - parameters_hash = self._get_parameters_hash() + documents_hash = self._get_documents_signature() + parameters_hash = self._get_parameters_signature() return f"{self.__class__.__name__}_{documents_hash}_{parameters_hash}" def add_document(self, document: BaseDocument) -> None: diff --git a/DashAI/back/models/RAG/Retrievers/sparse_retriever.py b/DashAI/back/models/RAG/Retrievers/sparse_retriever.py new file mode 100644 index 000000000..7e0bedde9 --- /dev/null +++ b/DashAI/back/models/RAG/Retrievers/sparse_retriever.py @@ -0,0 +1,11 @@ + +from DashAI.back.models.RAG.Retrievers.retriever_model import RetrieverModel + + +class SparseRetriever(RetrieverModel): + """ + Abstract class for sparse retrievers. + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) diff --git a/DashAI/back/models/RAG/Retrievers/TFIDFRetriever.py b/DashAI/back/models/RAG/Retrievers/tfidf_retriever.py similarity index 97% rename from DashAI/back/models/RAG/Retrievers/TFIDFRetriever.py rename to DashAI/back/models/RAG/Retrievers/tfidf_retriever.py index e60734aac..6f2cf771a 100644 --- a/DashAI/back/models/RAG/Retrievers/TFIDFRetriever.py +++ b/DashAI/back/models/RAG/Retrievers/tfidf_retriever.py @@ -1,5 +1,5 @@ import os -from .BaseRetriever import BaseRetriever +from DashAI.back.models.RAG.Retrievers.retriever_model import RetrieverModel from DashAI.back.models.RAG.documents import BaseDocument, PDFDocument, TxtDocument from typing import List, Dict, Any, Optional, Tuple from sklearn.feature_extraction.text import TfidfVectorizer @@ -7,6 +7,7 @@ import numpy as np import hashlib import pickle +from DashAI.back.models.RAG.Retrievers.sparse_retriever import SparseRetriever from DashAI.back.core.schema_fields import ( BaseSchema, @@ -57,7 +58,7 @@ class TFIDFRetrieverSchema(BaseSchema): description="Overlap between chunks when splitting documents." ) # type: ignore -class TFIDFRetriever(BaseRetriever): +class TFIDFRetriever(SparseRetriever): """ A retriever that uses TF-IDF to retrieve documents based on a query. """ @@ -140,14 +141,14 @@ def _get_cache_paths(self) -> tuple[str, str, str]: map_path = os.path.join(base_name, "chunk_to_doc_id_map") return vectorizer_path, tfidf_matrix_path, map_path - def get_hashable_parameters(self): + def get_signature_parameters(self): return { "chunk_size": self.chunk_size, "chunk_overlap": self.chunk_overlap, } def _generate_cache_hash(self) -> str: - hashable_params = self.get_hashable_parameters() + hashable_params = self.get_signature_parameters() text = f"{self._documents_paths}{hashable_params}" params_hash = hashlib.sha256(text.encode('utf-8')).hexdigest() docs_hashes = [] diff --git a/DashAI/back/models/RAG/__init__.py b/DashAI/back/models/RAG/__init__.py index 6261e0606..88a96fe60 100644 --- a/DashAI/back/models/RAG/__init__.py +++ b/DashAI/back/models/RAG/__init__.py @@ -1,2 +1,7 @@ -from DashAI.back.models.RAG.RAG_pipeline import RAGPipeline -from DashAI.back.models.RAG.Retrievers import BaseRetriever, TFIDFRetriever, DenseRetriever \ No newline at end of file +from DashAI.back.models.RAG.chunking_models import * +from DashAI.back.models.RAG.documents import * +from DashAI.back.models.RAG.encodings import * +from DashAI.back.models.RAG.prompts import * +from DashAI.back.models.RAG.Retrievers import * + +from DashAI.back.models.RAG.RAG_pipeline import RAGPipeline \ No newline at end of file diff --git a/DashAI/back/models/RAG/chunking_models/__init__.py b/DashAI/back/models/RAG/chunking_models/__init__.py new file mode 100644 index 000000000..a935c5395 --- /dev/null +++ b/DashAI/back/models/RAG/chunking_models/__init__.py @@ -0,0 +1,2 @@ +from DashAI.back.models.RAG.chunking_models.character_chunk_model import CharacterChunkModel +from DashAI.back.models.RAG.chunking_models.token_chunk_model import TokenChunkModel \ No newline at end of file diff --git a/DashAI/back/models/RAG/chunking_models/character_chunk_model.py b/DashAI/back/models/RAG/chunking_models/character_chunk_model.py index dca1a203d..0b66687d2 100644 --- a/DashAI/back/models/RAG/chunking_models/character_chunk_model.py +++ b/DashAI/back/models/RAG/chunking_models/character_chunk_model.py @@ -11,13 +11,13 @@ class CharacterChunkModelSchema(BaseSchema): """Schema for character-based chunking model.""" chunk_size: schema_field( - int_field(min_value=1), + int_field(gt=1), placeholder=None, description="Size of each chunk in characters." ) # type: ignore chunk_overlap: schema_field( - int_field(min_value=0), + int_field(gt=0), placeholder=None, description="Number of characters to overlap between chunks." ) # type: ignore diff --git a/DashAI/back/models/RAG/chunking_models/token_chunk_model.py b/DashAI/back/models/RAG/chunking_models/token_chunk_model.py new file mode 100644 index 000000000..380faa7d4 --- /dev/null +++ b/DashAI/back/models/RAG/chunking_models/token_chunk_model.py @@ -0,0 +1,52 @@ +from DashAI.back.core.schema_fields import ( + BaseSchema, + enum_field, + int_field + ) +from transformers import AutoTokenizer + +from DashAI.back.models.RAG.chunking_models.base_chunking_model import BaseChunkingModel +from DashAI.back.models.RAG.documents import BaseDocument + +class TokenChunkModelSchema(BaseSchema): + tokenizer_name: enum_field( + enum=[ + "intfloat/e5-mistral-7b-instruct", + "dccuchile/bert-base-spanish-wwm-uncased" + ] + ) #type: ignore + + chunk_size: int_field( + gt=1, + ) #type: ignore + + chunk_overlap: int_field( + ge=0, + ) #type: ignore + +class TokenChunkModel(BaseChunkingModel): + + SCHEMA = TokenChunkModelSchema + + def __init__(self, **kwargs): + + kwargs = self.validate_and_transform(kwargs) + + self.chunk_size = kwargs.get("chunk_size") + self.chunk_overlap = kwargs.get("chunk_overlap") + + self.tokenizer_name = kwargs.get("tokenizer_name") + self.tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_name) + + + def chunk(self, document:BaseDocument): + text = document.get_text() + + tokens = self.tokenizer.tokenize(text) + + chunks = [] + for i in range(0, len(tokens), self.chunk_size): + chunk = tokens[i:i + self.chunk_size] + chunks.append(chunk) + + return chunks \ No newline at end of file diff --git a/DashAI/back/models/RAG/embeddings/dense_embedding.py b/DashAI/back/models/RAG/embeddings/dense_embedding.py deleted file mode 100644 index 39a585636..000000000 --- a/DashAI/back/models/RAG/embeddings/dense_embedding.py +++ /dev/null @@ -1,22 +0,0 @@ -from DashAI.back.models.RAG.embeddings.base_embedding import BaseEmbedding -from typing import Any, List -import numpy as np - -class DenseEmbedding(BaseEmbedding): - """ - Base class for dense embedding models. - This class should be inherited by any specific dense embedding model implementation. - """ - - def __init__(self): - super().__init__() - - def embed(self, text: str) -> List|np.ndarray|Any: - """ - Method to generate dense embeddings for the given text. - This method should be implemented by subclasses. - - :param text: The input text to embed. - :return: A list representing the dense embeddings of the input text. - """ - raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/DashAI/back/models/RAG/embeddings/tf_idf_encoding.py b/DashAI/back/models/RAG/embeddings/tf_idf_encoding.py deleted file mode 100644 index f74045ae2..000000000 --- a/DashAI/back/models/RAG/embeddings/tf_idf_encoding.py +++ /dev/null @@ -1,32 +0,0 @@ -#from chromadb import EmbeddingFunction, Embeddings -from typing import List -from sklearn.feature_extraction.text import TfidfVectorizer -import pickle -import os - -class TfidfEmbeddingFunction:#(EmbeddingFunction): - """A dummy embedding function using TF-IDF.""" - def __init__(self, corpus: List[str], collection_name: str = None): - if collection_name: - stored_path = os.path.join(os.getcwd(), "saved_encoders", "tfidf", collection_name) - - if os.path.exists(stored_path): - with open(stored_path, "rb") as f: - self.vectorizer = pickle.load(f) - return - - self.vectorizer = TfidfVectorizer() - self.vectorizer.fit(corpus) - - if collection_name: - os.makedirs(os.path.dirname(stored_path), exist_ok=True) - with open(stored_path, "wb") as f: - pickle.dump(self.vectorizer, f) - - def __call__(self, texts: List[str]):# -> Embeddings: - """Transform the texts into TF-IDF embeddings.""" - return self.vectorizer.transform(texts).toarray().tolist() - - @staticmethod - def name() -> str: - return "default" \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/__init__.py b/DashAI/back/models/RAG/encodings/__init__.py new file mode 100644 index 000000000..bf36b680c --- /dev/null +++ b/DashAI/back/models/RAG/encodings/__init__.py @@ -0,0 +1,6 @@ +from DashAI.back.models.RAG.encodings.encoding import Encoding +from DashAI.back.models.RAG.encodings.dense_encoding import DenseEncoding +from DashAI.back.models.RAG.encodings.sparse_encoding import SparseEncoding +from DashAI.back.models.RAG.encodings.trainable_encoding import TrainableEncoding +from DashAI.back.models.RAG.encodings.sparse import * +from DashAI.back.models.RAG.encodings.dense import * \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/dense/__init__.py b/DashAI/back/models/RAG/encodings/dense/__init__.py new file mode 100644 index 000000000..549f15377 --- /dev/null +++ b/DashAI/back/models/RAG/encodings/dense/__init__.py @@ -0,0 +1,2 @@ +from DashAI.back.models.RAG.encodings.dense.fasttext_embedding import FastTextEmbedding +from DashAI.back.models.RAG.encodings.dense.huggingface_embedding import HuggingFaceEmbedding \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/dense/fasttext_embedding.py b/DashAI/back/models/RAG/encodings/dense/fasttext_embedding.py new file mode 100644 index 000000000..12c5e9e74 --- /dev/null +++ b/DashAI/back/models/RAG/encodings/dense/fasttext_embedding.py @@ -0,0 +1,61 @@ + +from typing import List + +import numpy as np +from DashAI.back.core.schema_fields import enum_field +from DashAI.back.core.schema_fields.schema_field import schema_field +from DashAI.back.models.RAG.encodings.dense_encoding import DenseEncoding + +import fasttext +from huggingface_hub import hf_hub_download + +class FastTextEmbeddingSchema(DenseEncoding.SCHEMA): + model_name: schema_field( + enum_field( + [ + "facebook/fasttext-es-vectors", + "facebook/fasttext-en-vectors" + ] + ), + "facebook/fasttext-en-vectors", + "Name of the pre-trained model to use", + ) # type: ignore + + pooling_strategy: schema_field( + enum_field( + [ + "mean", + "max" + ] + ), + "mean", + "Pooling strategy to use", + ) # type: ignore + +class FastTextEmbedding(DenseEncoding): + """FastText embedding""" + + SCHEMA = FastTextEmbeddingSchema + DESCRIPTION = "Convert text to embeddings using FastText." + + def __init__(self, **kwargs): + self.validate_and_transform(**kwargs) + self.model_name = kwargs.get("model_name") + self.pooling_strategy = kwargs.get("pooling_strategy") + pooling_functions = { + "mean": np.mean, + "max": np.max + } + self.pooling_function = pooling_functions[self.pooling_strategy] + + def _load_model(self): + """Load the FastText model.""" + model_name = self.model_name.split("/")[-1] + model_path = hf_hub_download(repo_id=self.model_name, filename=f"{model_name}.bin") + self.model = fasttext.load_model(model_path) + + def encode(self, text: str) -> np.ndarray: + """Encode text into an embedding.""" + token_embeddings = [self.model.get_word_vector(word) for word in text.split()] + return self.pooling_function(np.array(token_embeddings), axis=0) + \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/dense/huggingface_embedding.py b/DashAI/back/models/RAG/encodings/dense/huggingface_embedding.py new file mode 100644 index 000000000..23ce06f24 --- /dev/null +++ b/DashAI/back/models/RAG/encodings/dense/huggingface_embedding.py @@ -0,0 +1,127 @@ +from transformers import AutoModel + + +from DashAI.back.core.schema_fields import ( + BaseSchema, + schema_field, + enum_field +) +from DashAI.back.models.RAG.encodings.dense_encoding import DenseEncoding + +from typing import Final, List +import numpy as np +import torch +from transformers import AutoModel, AutoTokenizer + +from DashAI.back.models.RAG.encodings.dense_encoding import DenseEncoding +from DashAI.back.core.schema_fields import ( + enum_field, + int_field, + schema_field, +) +from DashAI.back.core.schema_fields.base_schema import BaseSchema + + +class HuggingFaceEmbeddingSchema(BaseSchema): + model_name: schema_field( + enum_field( + [ + # Sentence Transformers Models + "sentence-transformers/all-MiniLM-L6-v2", + "sentence-transformers/all-mpnet-base-v2", + "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", + "sentence-transformers/all-distilroberta-v1", + # BERT Models + "bert-base-uncased", + "bert-large-uncased", + "bert-base-multilingual-cased", + "distilbert-base-uncased", + # RoBERTa Models + "roberta-base", + "roberta-large", + "distilroberta-base", + ] + ), + "sentence-transformers/all-MiniLM-L6-v2", + "Name of the pre-trained model to use", + ) # type: ignore + + max_length: schema_field( + int_field(ge=1), 512, "Maximum sequence length for tokenization" + ) # type: ignore + + batch_size: schema_field( + int_field(ge=1), 32, "Number of samples to process at once" + ) # type: ignore + + device: schema_field( + enum_field(["cuda", "cpu"]), + "cpu", + "Device to use for computation", + ) # type: ignore + + pooling_strategy: schema_field( + enum_field(["mean", "cls", "max"]), + "mean", + "Strategy to pool token embeddings into sentence embedding", + ) # type: ignore + + +class HuggingFaceEmbedding(DenseEncoding): + """HuggingFace embedding""" + + SCHEMA = HuggingFaceEmbeddingSchema + DESCRIPTION = "Convert text to embeddings using HuggingFace transformer models." + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.pooling_strategy = kwargs.get("pooling_strategy", "mean") + self.model_name = kwargs.get( + "model_name", "sentence-transformers/all-MiniLM-L6-v2" + ) + self.device = kwargs.get("device", "cpu") + self.max_length = kwargs.get("max_length", 512) + self.batch_size = kwargs.get("batch_size", 32) + self.model = None + self.tokenizer = None + + def _load_model(self): + """Load the embedding model and tokenizer.""" + self.tokenizer = AutoTokenizer.from_pretrained(self.model_name) + self.model = AutoModel.from_pretrained(self.model_name).to(self.device) + self.model.eval() + + def _encode_texts(self, texts:List[str])-> List[np.ndarray]: + """Encode a list of texts into embeddings.""" + # Tokenize + encoded = self.tokenizer( + texts, + padding=True, + truncation=True, + max_length=self.max_length, + return_tensors="pt", + ) + + # Move to device + encoded = {k: v.to(self.device) for k, v in encoded.items()} + + # Get embeddings + with torch.no_grad(): + outputs = self.model(**encoded) + hidden_states = outputs.last_hidden_state + + # Apply pooling strategy + if self.pooling_strategy == "mean": + embeddings = torch.mean(hidden_states, dim=1) + elif self.pooling_strategy == "cls": + embeddings = hidden_states[:, 0] + else: # max pooling + embeddings = torch.max(hidden_states, dim=1)[0] + + embeddings_np = embeddings.cpu().numpy() + return embeddings_np + + def encode(self, text: str) -> np.ndarray: + """Encode a single text into an embedding.""" + embeddings = self._encode_texts([text]) + return embeddings[0] if embeddings else np.array([]) diff --git a/DashAI/back/models/RAG/encodings/dense_encoding.py b/DashAI/back/models/RAG/encodings/dense_encoding.py new file mode 100644 index 000000000..37b7ff6ba --- /dev/null +++ b/DashAI/back/models/RAG/encodings/dense_encoding.py @@ -0,0 +1,24 @@ + +from DashAI.back.models.RAG.encodings.encoding import Encoding + + +class DenseEncoding(Encoding): + """ + Base class for all dense encoding (embedding) models. + """ + + def __init__(self, **kwargs): + """ + Initialize the dense encoding (embedding) model with the given documents. + """ + raise NotImplementedError("Subclasses must implement the __init__ method.") + + def encode(self, text: str): + """ + Method to generate dense encodings (embeddings) for the given text. + This method should be implemented by subclasses. + + :param text: The input text to encode (embed). + :return: A list representing the dense encodings (embeddings) of the input text. + """ + raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/encoding.py b/DashAI/back/models/RAG/encodings/encoding.py new file mode 100644 index 000000000..0e2d65c9c --- /dev/null +++ b/DashAI/back/models/RAG/encodings/encoding.py @@ -0,0 +1,32 @@ +from abc import ABCMeta +import hashlib +from typing import Any, List +import numpy as np + +from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument +from DashAI.back.models.base_model import BaseModel + +class Encoding(BaseModel): + """ + Base class for all encoding (embedding) models. + This class should be inherited by any specific encoding (embedding) model implementation. + """ + EMBEDDINGS_PATH = "RAG/embeddings" + + def __init__( + self, + **kwargs: Any): + """ + Initialize the encoding (embedding) model with the given documents. + """ + raise NotImplementedError("Subclasses must implement the __init__ method.") + + def encode(self, text: str) -> List[float]|np.ndarray|Any: + """ + Method to generate encodings (embeddings) for the given text. + This method should be implemented by subclasses. + + :param text: The input text to encode (embed). + :return: A list representing the encodings (embeddings) of the input text. + """ + raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/sparse/__init__.py b/DashAI/back/models/RAG/encodings/sparse/__init__.py new file mode 100644 index 000000000..a2aa69555 --- /dev/null +++ b/DashAI/back/models/RAG/encodings/sparse/__init__.py @@ -0,0 +1,2 @@ +from DashAI.back.models.RAG.encodings.sparse.bm25_encoding import BM25Encoding +from DashAI.back.models.RAG.encodings.sparse.tfidf_encoding import TFIDFEncoding \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/sparse/bm25_encoding.py b/DashAI/back/models/RAG/encodings/sparse/bm25_encoding.py new file mode 100644 index 000000000..f239a893d --- /dev/null +++ b/DashAI/back/models/RAG/encodings/sparse/bm25_encoding.py @@ -0,0 +1,302 @@ +import os +from DashAI.back.converters.hugging_face import embedding +from DashAI.back.models.RAG.encodings.sparse_encoding import SparseEncoding +from DashAI.back.models.RAG.documents import BaseDocument, PDFDocument, TxtDocument +from typing import List, Dict, Any, Tuple +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances +import numpy as np +import hashlib +import pickle + +from DashAI.back.core.schema_fields import ( + BaseSchema, + list_field, + schema_field, + enum_field, + float_field, + int_field, + bool_field, + string_field +) + +class BM25EncodingSchema(BaseSchema): + + strip_accents: schema_field( + enum_field( + enum=["ascii", "unicode", None], + ), + placeholder=None, + description="Whether to strip accents from the text.", + ) # type: ignore + + lowercase: schema_field( + bool_field(), + placeholder=True, + description="Whether to convert all characters to lowercase.", + + ) # type: ignore + + stop_words: schema_field( + list_field( + string_field(), + min_items=1, + ), + placeholder=None, + description="List of stop words to be used in the TF-IDF vectorization.", + ) # type: ignore + + max_df: schema_field( + float_field( + ge=0.0, + le=1.0, + ), + placeholder=1.0, + description="When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words).", + ) # type: ignore + + min_df: schema_field( + float_field( + ge=0.0, + le=1.0, + ), + placeholder=1.0, + description="When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold (corpus-specific stop words).", + ) # type: ignore + + max_features: schema_field( + int_field( + ge=1, + ), + placeholder=None, + description="If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus.", + ) # type: ignore + + +class BM25Encoding(SparseEncoding): + """ + BM25 encoding (embedding) model for text documents. + This class implements the precomputation of BM25 scores for text documents. + """ + + def __init__( + self, + documents_chunks: Dict[int, List[str]], + k: float, + b: float, + delta: float, + strip_accents: str, + lowercase: bool, + analyzer: str, + stop_words: List[str], + max_df: float, + min_df: float, + max_features: int, + store_model: bool = True + ) -> None: + """ + Initialize the BM25 encoding model with the given document chunks. + + Args: + documents_chunks (Dict[int, List[str]]): A dictionary where keys are document IDs and values are lists of text chunks. + """ + # sort chunks by document ID to ensure consistency + documents_chunks = dict(sorted(documents_chunks.items())) + + chunks_mapping = {} + chunks_texts = [] + n_chunks = 0 + for doc_id, chunks in documents_chunks.items(): + for chunk_text in chunks: + chunks_texts.append(chunk_text) + chunks_mapping[n_chunks] = (doc_id, chunk_text) + n_chunks += 1 + + self.chunks_texts = chunks_texts + self.chunks_mapping = chunks_mapping + self.strip_accents = strip_accents + self.lowercase = lowercase + self.analyzer = analyzer + self.stop_words = stop_words + self.max_df = max_df + self.min_df = min_df + self.max_features = max_features + + self.hyperparameters = { + "k": k, + "b": b, + "delta": delta, + "strip_accents": strip_accents, + "lowercase": lowercase, + "analyzer": analyzer, + "stop_words": stop_words, + "max_df": max_df, + "min_df": min_df, + "max_features": max_features, + } + + load_success = self._load_model() + print(f"BM25 model loaded successfully: {load_success}") + if not load_success: + print("Fitting the BM25 model...") + self._fit_model() + if store_model: + print("Saving the BM25 model...") + self._save_model() + print("BM25 model fitted and saved.") + + def _hash_function(self, input_string: str) -> str: + """ + Generate a SHA-256 hash for the given input string. + + Args: + input_string (str): The input string to hash. + + Returns: + str: The SHA-256 hash of the input string. + """ + return hashlib.sha256(input_string.encode('utf-8')).hexdigest() + + def _get_encoding_signature(self) -> str: + """ + Generate a hash to identify the encoding (embeddings) for caching purposes. + + Hash is calculated over the documents' content to ensure consistency in encoding (embeddings), it must: + - Be consistent across runs with the same documents content, chunking strategy and encoding (embeddings) model parameters. + - Change if the documents are modified. + - Change if the documents content is modified. + - Change if the chunking strategy is modified. + - Change if the encoding (embeddings) model parameters are modified. +k + Returns: + str: A hash string representing the encoding (embeddings). + """ + params_hash = self._hash_function(str(self.hyperparameters)) + documents_chunks_hash = self._hash_function(str(self.chunks_mapping)) + return self._hash_function(params_hash + documents_chunks_hash) + + def _fit_model(self) -> None: + """ + Fit the TF-IDF model to the document chunks. + This method initializes the TF-IDF vectorizer and fits it to the document chunks. + """ + self.vectorizer = TfidfVectorizer( + strip_accents=self.strip_accents, + lowercase=self.lowercase, + analyzer=self.analyzer, + stop_words=self.stop_words, + ngram_range=tuple(self.ngram_range), + max_df=self.max_df, + min_df=self.min_df, + max_features=self.max_features, + + norm=None, + smooth_idf=False, + sublinear_tf=False, + use_idf=False, + ) + + tf_matrix = self.vectorizer.fit_transform(self.chunks_texts) + document_lengths = tf_matrix.sum(axis=1) + idf = np.log(tf_matrix.shape[0] / document_lengths) + + tf_matrix = tf_matrix.multiply(self.k + 1) / (tf_matrix + self.k * (1 - self.b + self.b * document_lengths)) + tf_matrix = tf_matrix.multiply(idf.reshape(-1, 1)) + + def _get_model_paths(self) -> Tuple[str, str, str, str]: + """ + Get the paths for saving the model components. + Returns a tuple containing the folder path, vectorizer path, term matrix path, and feature names path. + """ + folder_path = os.path.join( + self.EMBEDDINGS_PATH, + self._get_encoding_signature() + ) + + vectorizer_path = os.path.join(folder_path, "vectorizer.pkl") + tf_matrix_path = os.path.join(folder_path, "tf_matrix.pkl") + + return folder_path, vectorizer_path, tf_matrix_path + + def _save_model(self) -> None: + """ + Save the model components to disk. + This method saves the vectorizer, term matrix, and feature names to their respective files. + """ + + folder_path, vectorizer_path, tf_matrix_path = self._get_model_paths() + + # Create the folder if it doesn't exist + os.makedirs(folder_path, exist_ok=True) + + with open(vectorizer_path, 'wb') as f: + pickle.dump(self.vectorizer, f) + + with open(tf_matrix_path, 'wb') as f: + pickle.dump(self._tf_matrix, f) + + def _load_model(self) -> bool: + """ + Try to load the model components from disk. + If the model components are not found, return False. + If the model components are found, load them into the instance variables and return True. + """ + folder_path, vectorizer_path, tf_matrix_path = self._get_model_paths() + if not ( + os.path.exists(vectorizer_path) and + os.path.exists(tf_matrix_path)): + return False + + # Load the vectorizer + with open(vectorizer_path, 'rb') as f: + self.vectorizer = pickle.load(f) + + # Load the term matrix + with open(tf_matrix_path, 'rb') as f: + self._tf_matrix = pickle.load(f) + + self._document_lengths = self._tf_matrix.sum(axis=1) + self._average_document_length = np.mean(self._document_lengths) + self._idf = np.log(self._tf_matrix.shape[0] / self._document_lengths) + + return True + + def get_documents_chunks_encodings(self) -> Dict[int, List[np.ndarray]]: + """ + Get the encodings (embeddings) for each document chunk. + + Returns: + Dict[int, List[np.ndarray]]: A dictionary where keys are document IDs and values are lists of encodings (embeddings) for each chunk. + """ + if not hasattr(self, 'vectorizer'): + raise RuntimeError("The model has not been fitted yet. Call _fit_model() before getting encodings (embeddings).") + + encodings = self._tf_matrix.toarray() + n_chunks = encodings.shape[0] + + # Map the encodings back to the document IDs using the chunks_mapping + documents_chunks_encodings = {} + for i in range(n_chunks): + doc_id, _ = self.chunks_mapping[i] + if doc_id not in documents_chunks_encodings: + documents_chunks_encodings[doc_id] = [] + documents_chunks_encodings[doc_id].append(encodings[i]) + + return documents_chunks_encodings + + def encode(self, text: str) -> List[float]: + """ + Generate TF-IDF encodings (embeddings) for the given text. + + Args: + text (str): The input text to embed. + + Returns: + List[float]: A list representing the TF-IDF encodings (embeddings) of the input text. + """ + if not hasattr(self, 'vectorizer'): + raise RuntimeError("The model has not been fitted yet. Call _fit_model() before encoding (embedding) text.") + + # Transform the input text to get its TF-IDF representation + tfidf_vector = self.vectorizer.transform([text]) + return tfidf_vector.toarray() \ No newline at end of file diff --git a/DashAI/back/models/RAG/embeddings/tf_idf_embedding.py b/DashAI/back/models/RAG/encodings/sparse/tfidf_encoding.py similarity index 80% rename from DashAI/back/models/RAG/embeddings/tf_idf_embedding.py rename to DashAI/back/models/RAG/encodings/sparse/tfidf_encoding.py index cfdb9ed5b..560d6c2f2 100644 --- a/DashAI/back/models/RAG/embeddings/tf_idf_embedding.py +++ b/DashAI/back/models/RAG/encodings/sparse/tfidf_encoding.py @@ -1,5 +1,6 @@ import os -from DashAI.back.models.RAG.embeddings.sparse_embedding import SparseEmbedding +from DashAI.back.converters.hugging_face import embedding +from DashAI.back.models.RAG.encodings.sparse_encoding import SparseEncoding from DashAI.back.models.RAG.documents import BaseDocument, PDFDocument, TxtDocument from typing import List, Dict, Any, Tuple from sklearn.feature_extraction.text import TfidfVectorizer @@ -19,7 +20,7 @@ string_field ) -class TFIDFEmbeddingSchema(BaseSchema): +class TFIDFEncodingSchema(BaseSchema): strip_accents: schema_field( enum_field( @@ -115,10 +116,10 @@ class TFIDFEmbeddingSchema(BaseSchema): description="Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).", ) # type: ignore -class TFIDFEmbedding(SparseEmbedding): +class TFIDFEncoding(SparseEncoding): """ - TF-IDF embedding model for text documents. - This class implements the TF-IDF algorithm to generate embeddings for text documents. + TF-IDF encoding (embedding) model for text documents. + This class implements the TF-IDF algorithm to generate encodings (embeddings) for text documents. """ def __init__( @@ -139,7 +140,7 @@ def __init__( store_model: bool = True ) -> None: """ - Initialize the TF-IDF embedding model with the given document chunks. + Initialize the TF-IDF encoding (embedding) model with the given document chunks. Args: documents_chunks (Dict[int, List[str]]): A dictionary where keys are document IDs and values are lists of text chunks. @@ -209,19 +210,19 @@ def _hash_function(self, input_string: str) -> str: """ return hashlib.sha256(input_string.encode('utf-8')).hexdigest() - def _calculate_embeddings_hash(self) -> str: + def _get_encoding_signature(self) -> str: """ - Generate a hash to identify the embeddings for caching purposes. - - Hash is calculated over the documents' content to ensure consistency in embeddings, it must: - - Be consistent across runs with the same documents content, chunking strategy and embedding model parameters. + Generate a hash to identify the encoding (embeddings) for caching purposes. + + Hash is calculated over the documents' content to ensure consistency in encoding (embeddings), it must: + - Be consistent across runs with the same documents content, chunking strategy and encoding (embeddings) model parameters. - Change if the documents are modified. - Change if the documents content is modified. - Change if the chunking strategy is modified. - - Change if the embedding model parameters are modified. - + - Change if the encoding (embeddings) model parameters are modified. + Returns: - str: A hash string representing the embeddings. + str: A hash string representing the encoding (embeddings). """ params_hash = self._hash_function(str(self.hyperparameters)) documents_chunks_hash = self._hash_function(str(self.chunks_mapping)) @@ -247,7 +248,6 @@ def _fit_model(self) -> None: sublinear_tf=self.sublinear_tf ) self._term_matrix = self.vectorizer.fit_transform(self.chunks_texts) - self._feature_names = self.vectorizer.get_feature_names_out() def _get_model_paths(self) -> Tuple[str, str, str, str]: """ @@ -255,15 +255,14 @@ def _get_model_paths(self) -> Tuple[str, str, str, str]: Returns a tuple containing the folder path, vectorizer path, term matrix path, and feature names path. """ folder_path = os.path.join( - self.EMBEDDINGS_PATH, - self._calculate_embeddings_hash() + self.EMBEDDINGS_PATH, + self._get_encoding_signature() ) vectorizer_path = os.path.join(folder_path, "vectorizer.pkl") term_matrix_path = os.path.join(folder_path, "term_matrix.pkl") - feature_names_path = os.path.join(folder_path, "feature_names.pkl") - return folder_path, vectorizer_path, term_matrix_path, feature_names_path + return folder_path, vectorizer_path, term_matrix_path def _save_model(self) -> None: """ @@ -271,7 +270,7 @@ def _save_model(self) -> None: This method saves the vectorizer, term matrix, and feature names to their respective files. """ - folder_path, vectorizer_path, term_matrix_path, feature_names_path = self._get_model_paths() + folder_path, vectorizer_path, term_matrix_path = self._get_model_paths() # Create the folder if it doesn't exist os.makedirs(folder_path, exist_ok=True) @@ -284,21 +283,16 @@ def _save_model(self) -> None: with open(term_matrix_path, 'wb') as f: pickle.dump(self._term_matrix, f) - # Save the feature names - with open(feature_names_path, 'wb') as f: - pickle.dump(self._feature_names, f) - def _load_model(self) -> None: """ Try to load the model components from disk. If the model components are not found, return False. If the model components are found, load them into the instance variables and return True. """ - folder_path, vectorizer_path, term_matrix_path, feature_names_path = self._get_model_paths() + folder_path, vectorizer_path, term_matrix_path= self._get_model_paths() if not ( os.path.exists(vectorizer_path) and - os.path.exists(term_matrix_path) and - os.path.exists(feature_names_path)): + os.path.exists(term_matrix_path)): return False # Load the vectorizer @@ -309,48 +303,44 @@ def _load_model(self) -> None: with open(term_matrix_path, 'rb') as f: self._term_matrix = pickle.load(f) - # Load the feature names - with open(feature_names_path, 'rb') as f: - self._feature_names = pickle.load(f) - return True - def get_documents_chunks_embeddings(self) -> Dict[int, List[np.ndarray]]: + def get_documents_chunks_encodings(self) -> Dict[int, List[np.ndarray]]: """ - Get the embeddings for each document chunk. + Get the encodings (embeddings) for each document chunk. Returns: - Dict[int, List[np.ndarray]]: A dictionary where keys are document IDs and values are lists of embeddings for each chunk. + Dict[int, List[np.ndarray]]: A dictionary where keys are document IDs and values are lists of encodings (embeddings) for each chunk. """ if not hasattr(self, 'vectorizer'): - raise RuntimeError("The model has not been fitted yet. Call _fit_model() before getting embeddings.") + raise RuntimeError("The model has not been fitted yet. Call _fit_model() before getting encodings (embeddings).") - embeddings = self._term_matrix.toarray() - n_chunks = embeddings.shape[0] + encodings = self._term_matrix.toarray() + n_chunks = encodings.shape[0] - # Map the embeddings back to the document IDs using the chunks_mapping - documents_chunks_embeddings = {} + # Map the encodings back to the document IDs using the chunks_mapping + documents_chunks_encodings = {} for i in range(n_chunks): doc_id, _ = self.chunks_mapping[i] - if doc_id not in documents_chunks_embeddings: - documents_chunks_embeddings[doc_id] = [] - documents_chunks_embeddings[doc_id].append(embeddings[i]) + if doc_id not in documents_chunks_encodings: + documents_chunks_encodings[doc_id] = [] + documents_chunks_encodings[doc_id].append(encodings[i]) - return documents_chunks_embeddings - - def embed(self, text: str) -> List[float]: + return documents_chunks_encodings + + def encode(self, text: str) -> List[float]: """ - Generate TF-IDF embeddings for the given text. - + Generate TF-IDF encodings (embeddings) for the given text. + Args: text (str): The input text to embed. Returns: - List[float]: A list representing the TF-IDF embeddings of the input text. + List[float]: A list representing the TF-IDF encodings (embeddings) of the input text. """ if not hasattr(self, 'vectorizer'): - raise RuntimeError("The model has not been fitted yet. Call _fit_model() before embedding text.") + raise RuntimeError("The model has not been fitted yet. Call _fit_model() before encoding (embedding) text.") # Transform the input text to get its TF-IDF representation tfidf_vector = self.vectorizer.transform([text]) diff --git a/DashAI/back/models/RAG/embeddings/sparse_embedding.py b/DashAI/back/models/RAG/encodings/sparse_encoding.py similarity index 53% rename from DashAI/back/models/RAG/embeddings/sparse_embedding.py rename to DashAI/back/models/RAG/encodings/sparse_encoding.py index 706295673..cadba17d6 100644 --- a/DashAI/back/models/RAG/embeddings/sparse_embedding.py +++ b/DashAI/back/models/RAG/encodings/sparse_encoding.py @@ -1,29 +1,29 @@ import numpy as np from DashAI.back.models.RAG.documents import BaseDocument -from DashAI.back.models.RAG.embeddings.base_embedding import BaseEmbedding +from DashAI.back.models.RAG.encodings.encoding import Encoding from typing import Any, List -class SparseEmbedding(BaseEmbedding): +class SparseEncoding(Encoding): """ - Base class for sparse embedding models. - This class should be inherited by any specific sparse embedding model implementation. + Base class for sparse encoding (embedding) models. + This class should be inherited by any specific sparse encoding (embedding) model implementation. """ def __init__(self, documents: List[BaseDocument], **kwargs: Any): """ - Initialize the sparse embedding model with the given documents. + Initialize the sparse encoding (embedding) model with the given documents. :param documents: List of documents to be embedded. :param kwargs: Additional keyword arguments. """ raise NotImplementedError("Subclasses must implement the __init__ method.") - def embed(self, text: str) -> List|np.ndarray|Any: + def encode(self, text: str) -> List|np.ndarray|Any: """ - Method to generate sparse embeddings for the given text. + Method to generate sparse encodings (embeddings) for the given text. This method should be implemented by subclasses. :param text: The input text to embed. - :return: A list representing the sparse embeddings of the input text. + :return: A list representing the sparse encodings (embeddings) of the input text. """ raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/DashAI/back/models/RAG/embeddings/base_embedding.py b/DashAI/back/models/RAG/encodings/trainable_encoding.py similarity index 65% rename from DashAI/back/models/RAG/embeddings/base_embedding.py rename to DashAI/back/models/RAG/encodings/trainable_encoding.py index 025e112a6..44f032707 100644 --- a/DashAI/back/models/RAG/embeddings/base_embedding.py +++ b/DashAI/back/models/RAG/encodings/trainable_encoding.py @@ -1,40 +1,39 @@ from abc import ABCMeta import hashlib -from DashAI.back.config_object import ConfigObject from typing import Any, List import numpy as np from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument +from DashAI.back.models.RAG.encodings.encoding import Encoding -class BaseEmbedding(ConfigObject, metaclass=ABCMeta): +class TrainableEncoding(Encoding): """ - Base class for all embedding models. - This class should be inherited by any specific embedding model implementation. + Base class for all encoding (embedding) models. + This class should be inherited by any specific encoding (embedding) model implementation. """ EMBEDDINGS_PATH = "RAG/embeddings" def __init__( self, - documents: List[BaseDocument], **kwargs: Any): """ - Initialize the embedding model with the given documents. + Initialize the encoding (embedding) model with the given documents. """ raise NotImplementedError("Subclasses must implement the __init__ method.") - def _calculate_embeddings_hash(self) -> str: + def _get_encoding_signature(self) -> str: """ - Generate a hash to identify the embeddings for caching purposes. - - Hash is calculated over the documents' content to ensure consistency in embeddings, it must: - - Be consistent across runs with the same documents content, chunking strategy and embedding model parameters. + Generate a hash to identify the encoding (embedding) for caching purposes. + + Hash is calculated over the documents' content to ensure consistency in encodings, it must: + - Be consistent across runs with the same documents content, chunking strategy and encoding model parameters. - Change if the documents are modified. - Change if the documents content is modified. - Change if the chunking strategy is modified. - - Change if the embedding model parameters are modified. - + - Change if the encoding model parameters are modified. + Returns: - str: A hash string representing the embeddings. + str: A hash string representing the encodings (embeddings). """ raise NotImplementedError("Subclasses must implement this method.") @@ -42,7 +41,7 @@ def _calculate_documents_chunks_hash(self) -> str: """ Generate a hash to identify the documents chunks' texts for caching purposes. - Hash is calculated over the chunks' texts to ensure consistency in embeddings, it must: + Hash is calculated over the chunks' texts to ensure consistency in encodings, it must: - Be consistent across runs with the same documents content and chunking strategy. - Change if the documents are modified. - Change if the documents content is modified. @@ -59,12 +58,12 @@ def _calculate_documents_chunks_hash(self) -> str: return hashlib.sha256(joint_text.encode('utf-8')).hexdigest() - def embed(self, text: str) -> List[float]|np.ndarray|Any: + def encode(self, text: str) -> List[float]|np.ndarray|Any: """ - Method to generate embeddings for the given text. + Method to generate encodings (embeddings) for the given text. This method should be implemented by subclasses. - - :param text: The input text to embed. - :return: A list representing the embeddings of the input text. + + :param text: The input text to encode (embed). + :return: A list representing the encodings (embeddings) of the input text. """ raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/DashAI/back/models/RAG/prompts/__init__.py b/DashAI/back/models/RAG/prompts/__init__.py index 95e26dcfe..5bae15c12 100644 --- a/DashAI/back/models/RAG/prompts/__init__.py +++ b/DashAI/back/models/RAG/prompts/__init__.py @@ -1,3 +1,3 @@ from DashAI.back.models.RAG.prompts.augmentation_prompt import AugmentationPrompt from DashAI.back.models.RAG.prompts.context_merge_prompt import ContextMergePrompt -from DashAI.back.models.RAG.prompts.base_prompt import BasePrompt \ No newline at end of file +from DashAI.back.models.RAG.prompts.prompt import Prompt \ No newline at end of file diff --git a/DashAI/back/models/RAG/prompts/augmentation_prompt.py b/DashAI/back/models/RAG/prompts/augmentation_prompt.py index a5d399b8a..125e825e3 100644 --- a/DashAI/back/models/RAG/prompts/augmentation_prompt.py +++ b/DashAI/back/models/RAG/prompts/augmentation_prompt.py @@ -1,8 +1,8 @@ from typing import List, Tuple, Any -from DashAI.back.models.RAG.prompts.base_prompt import BasePrompt +from DashAI.back.models.RAG.prompts.prompt import Prompt -class AugmentationPrompt(BasePrompt): +class AugmentationPrompt(Prompt): """ AugmentationPrompt class for generating augmented retrieval prompts, it uses the language model to generate keywords or phrases that can be used to augment the input. diff --git a/DashAI/back/models/RAG/prompts/context_merge_prompt.py b/DashAI/back/models/RAG/prompts/context_merge_prompt.py index 7f00a2787..42d5affe7 100644 --- a/DashAI/back/models/RAG/prompts/context_merge_prompt.py +++ b/DashAI/back/models/RAG/prompts/context_merge_prompt.py @@ -1,7 +1,7 @@ from typing import Any, List, Tuple -from DashAI.back.models.RAG.prompts.base_prompt import BasePrompt +from DashAI.back.models.RAG.prompts.prompt import Prompt -class ContextMergePrompt(BasePrompt): +class ContextMergePrompt(Prompt): """ ContextMergePrompt class for generating prompts that merge the use input, the retrieved documents and the chat history into a single context for the language model. diff --git a/DashAI/back/models/RAG/prompts/base_prompt.py b/DashAI/back/models/RAG/prompts/prompt.py similarity index 87% rename from DashAI/back/models/RAG/prompts/base_prompt.py rename to DashAI/back/models/RAG/prompts/prompt.py index 7f66fcc74..70a45fdc1 100644 --- a/DashAI/back/models/RAG/prompts/base_prompt.py +++ b/DashAI/back/models/RAG/prompts/prompt.py @@ -1,7 +1,8 @@ -from abc import ABC from typing import Any -class BasePrompt(ABC): +from DashAI.back.models.base_model import BaseModel + +class Prompt(BaseModel): """ Base class for all RAG prompt templates. This class defines the interface for creating and formatting prompts. diff --git a/DashAI/back/models/__init__.py b/DashAI/back/models/__init__.py index 09aa57b6d..e62e578c9 100644 --- a/DashAI/back/models/__init__.py +++ b/DashAI/back/models/__init__.py @@ -48,4 +48,4 @@ from DashAI.back.models.scikit_learn.sklearn_like_regressor import SklearnLikeRegressor from DashAI.back.models.scikit_learn.svc import SVC -from DashAI.back.models.RAG import RAGPipeline, BaseRetriever, TFIDFRetriever, DenseRetriever \ No newline at end of file +from DashAI.back.models.RAG import RAGPipeline, RetrieverModel, TFIDFRetriever, DenseRetriever \ No newline at end of file diff --git a/DashAI/front/src/api/component.ts b/DashAI/front/src/api/component.ts index 9a7306017..2286f8585 100644 --- a/DashAI/front/src/api/component.ts +++ b/DashAI/front/src/api/component.ts @@ -49,3 +49,10 @@ export const getComponents = async ({ }); return response.data; }; + +export const getChildComponents = async (componentName: string, recursive: boolean): Promise => { + const response = await api.get(`/v1/component/${componentName}/children`, { + params: { recursive }, + }); + return response.data; +}; \ No newline at end of file diff --git a/DashAI/front/src/api/rag.ts b/DashAI/front/src/api/rag.ts index 90500a6b8..e4b95479a 100644 --- a/DashAI/front/src/api/rag.ts +++ b/DashAI/front/src/api/rag.ts @@ -2,6 +2,8 @@ import api from "./api"; import { ISession } from "../types/session"; import { IGenerativeTask } from "../types/generativeTask"; import { IDocumentResponse } from "../types/documentResponse"; +import { IComponent } from "../types/component"; +import { getChildComponents } from "./component"; // Fetch all RAG sessions @@ -108,18 +110,25 @@ export const updateGenerativeSessionParams = async ( return response.data; } +export const getRetrievalParadigm = async (): Promise => { -export const getRetrieverComponents = async (): Promise => { + const response = getChildComponents('RetrieverModel', false); + if (!response) { + throw new Error(`Failed to fetch retrieval options`); + } + return response; +} - const response = await api.get( - `/v1/component/?select_types=RetrieverModel` - ); - if (response.status !== 200) { - throw new Error(`Failed to fetch retriever components: ${response.statusText}`); +export const getRetrieverComponents = async (retrievalParadigm: string): Promise => { + + const response = getChildComponents(retrievalParadigm, false); + + if (!response) { + throw new Error(`Failed to fetch retriever components`); } - return response.data; + return response; } diff --git a/DashAI/front/src/components/documents/DocumentTable.jsx b/DashAI/front/src/components/documents/DocumentTable.jsx index 320c96d90..1ecd4471e 100644 --- a/DashAI/front/src/components/documents/DocumentTable.jsx +++ b/DashAI/front/src/components/documents/DocumentTable.jsx @@ -174,7 +174,7 @@ function DocumentTable({ }, }, }} - getRowId={(row) => row.id} + getRowId={(row) => String(row.id)} pageSize={5} sortModel={[{ field: "id", sort: "asc" }]} pageSizeOptions={[5, 10]} diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx index d4efa0d1d..d1377381f 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx @@ -18,7 +18,8 @@ import useMediaQuery from "@mui/material/useMediaQuery"; import DocumentSelectionStep from "./DocumentSelectionStep"; import RetrieverConfigurationStep from "./RetrieverConfigurationStep"; -import GeneratorConfigurationStep from "./GeneratorConfigurationStep"; +import GeneratorConfigurationStep from "./GeneratorConfigurationStep"; +//import ChunkingConfigurationStep from "./ChunkingConfigurationStep"; const steps = [ { @@ -26,6 +27,11 @@ const steps = [ label: "Select documents", component: DocumentSelectionStep, }, +// { +// name: "configure-chunking", +// label: "Configure chunking", +// component: ChunkingConfigurationStep, +// }, { name: "configure-retriever", label: "Configure retriever", @@ -45,6 +51,9 @@ const defaultNewSession = { displayName: "", parameters: { "documents": [], + // "chunking": { + // "parameters": {}, + // }, "retriever_model": { "name": "", "parameters": {}, @@ -65,9 +74,10 @@ export default function NewSessionModal({ }) { const theme = useTheme(); const screenSm = useMediaQuery(theme.breakpoints.down("sm")); + const [activeStep, setActiveStep] = useState(0); - const [sessionData, setSessionData] = useState(session || defaultNewSession); const [stepValidity, setStepValidity] = useState(new Array(steps.length).fill(false)); + const [sessionData, setSessionData] = useState(session || defaultNewSession); useEffect(() => { diff --git a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx index 2d4069c06..9448eeabd 100644 --- a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx @@ -1,114 +1,74 @@ import { useState, useEffect, useCallback } from "react"; -import PropTypes from "prop-types"; -import { Box, Autocomplete, TextField, Typography, Button } from "@mui/material"; // Added Button and Typography for consistency -import { useFormik } from "formik"; // Import useFormik -import FormSchemaRenderFields from "../../../components/shared/FormSchemaRenderFields"; // Assuming this component is correctly implemented -import { getRetrieverComponents } from "../../../api/rag"; // Assuming this API call is correct -import { preprocessSchema, buildYupSchema } from "../../../components/generative/utils" - - - -export default function RetrieverConfigurationStep({ retrieverModel, setRetrieverModel, setNextEnabled }) { - const [retrievers, setRetrievers] = useState([]); - const [currentSelectedRetrieverOption, setCurrentSelectedRetrieverOption] = useState(null); - const [validationSchema, setValidationSchema] = useState(null); +import { Box, Autocomplete, TextField, Typography } from "@mui/material"; +import { DataGrid } from "@mui/x-data-grid"; +import EditModelDialog from "../../../components/experiments/EditModelDialog"; +import { getRetrieverComponents, getRetrievalParadigm } from "../../../api/rag"; +import { useSnackbar } from "notistack"; +import useSchema from "../../../hooks/useSchema"; + +export default function RetrieverConfigurationStep({ setNextEnabled }) { + const { enqueueSnackbar } = useSnackbar(); + const [retrievalParadigms, setRetrievalParadigms] = useState([]); + const [selectedRetrievalParadigm, setSelectedRetrievalParadigm] = useState(null); + + const [retrieverOptions, setRetrieverOptions] = useState([]); + const [selectedRetriever, setSelectedRetriever] = useState(null); + const [retrieverInitialParameters, setRetrieverInitialParameters] = useState({}); + const [retrieverParamsSchema, setRetrieverParamsSchema] = useState(null); + + const fetchRetrievalParadigms = async () => { + try { + const data = await getRetrievalParadigm(); + setRetrievalParadigms(data); + enqueueSnackbar('Retrieval paradigms loaded successfully!', { variant: 'success' }); + } catch (error) { + console.error("Error fetching retrieval paradigms:", error); + enqueueSnackbar('Failed to load retrieval paradigms.', { variant: 'error' }); + } + }; - const getInitialParamsFromSchema = useCallback((schemaProperties) => { - if (!schemaProperties) return {}; - return Object.keys(schemaProperties).reduce((acc, key) => { - acc[key] = schemaProperties[key].placeholder !== undefined - ? schemaProperties[key].placeholder - : ""; - return acc; - }, {}); + useEffect(() => { + fetchRetrievalParadigms(); }, []); - useEffect(() => { - const loadAndSetRetriever = async () => { - const data = await getRetrieverComponents(); - setRetrievers(data); + const fetchRetrievers = async () => { + if (!selectedRetrievalParadigm) { + setRetrieverOptions([]); + setSelectedRetriever(null); + return; + } - if (retrieverModel?.name) { - const existingRetriever = data.find(r => r.name === retrieverModel.name); - if (existingRetriever) { - setCurrentSelectedRetrieverOption(existingRetriever); - } + // For non-Sparse paradigms, only set retriever if schema and schema.properties exist + if (selectedRetrievalParadigm.schema && selectedRetrievalParadigm.schema.properties) { + setRetrieverOptions([selectedRetrievalParadigm]); + setSelectedRetriever(selectedRetrievalParadigm); + } else { + setRetrieverOptions([]); + setSelectedRetriever(null); } - }; - loadAndSetRetriever(); - }, [retrieverModel?.name]); - - const formik = useFormik({ - initialValues: retrieverModel?.parameters || {}, - validationSchema: validationSchema, - enableReinitialize: true, - onSubmit: (values) => { - }, - }); + }; useEffect(() => { + fetchRetrievers(); + }, [selectedRetrievalParadigm]); - const retrieverSchemaProperties = currentSelectedRetrieverOption?.schema?.properties; - - if (retrieverSchemaProperties) { - const processedProps = preprocessSchema(retrieverSchemaProperties); - setValidationSchema(buildYupSchema(processedProps)); - - const initialFormValues = Object.keys(processedProps).reduce( - (acc, key) => { - acc[key] = retrieverModel?.parameters?.[key] !== undefined - ? retrieverModel.parameters[key] - : (processedProps[key].placeholder !== undefined - ? processedProps[key].placeholder - : ""); - return acc; - }, - {}, - ); - formik.setValues(initialFormValues); - } else { - setValidationSchema(null); - formik.setValues({}); - } - }, [currentSelectedRetrieverOption, retrieverModel?.parameters]); - - useEffect(() => { - const isValid = !!currentSelectedRetrieverOption && formik.isValid; - setNextEnabled(isValid); - }, [currentSelectedRetrieverOption, formik.isValid, setNextEnabled]); + const handleRetrievalParadigmChange = (event, newValue) => { + setSelectedRetrievalParadigm(newValue); + setSelectedRetriever(null); + }; const handleRetrieverSelectionChange = (event, newValue) => { - setCurrentSelectedRetrieverOption(newValue); - - if (newValue) { - const initialParameters = getInitialParamsFromSchema(newValue.schema?.properties); - setRetrieverModel({ - name: newValue.name, - parameters: initialParameters, - }); - } else { - setRetrieverModel({ name: "", parameters: {} }); - } + setSelectedRetriever(newValue); + setRetrieverInitialParameters(useSchema({ modelName: newValue?.name })); }; - const handleParametersChange = (updatedValues) => { - formik.setValues((prevValues) => ({ - ...prevValues, - ...updatedValues, + const handleRetrieverParametersChange = (newParams) => { + setSelectedRetriever((prev) => ({ + ...prev, + parameters: newParams, })); - - setRetrieverModel({ - name: currentSelectedRetrieverOption.name, - parameters: { - ...formik.values, - ...updatedValues, - }, - }); - }; - - const processedProperties = currentSelectedRetrieverOption?.schema?.properties - ? preprocessSchema(currentSelectedRetrieverOption.schema.properties) - : {}; + console.log("Updated retriever model parameters:", newParams); + } return ( - - Configure Retriever Model + + Select retrieval paradigm option.name} - value={currentSelectedRetrieverOption} - onChange={handleRetrieverSelectionChange} + options={retrievalParadigms} + getOptionLabel={(option) => option.name} + value={selectedRetrievalParadigm} + onChange={handleRetrievalParadigmChange} isOptionEqualToValue={(option, value) => option.name === value?.name} - renderInput={(params) => } + renderInput={(params) => } sx={{ mb: 3 }} /> - {currentSelectedRetrieverOption && currentSelectedRetrieverOption.schema && ( -
- - - Parameters - - console.error("FormSchemaRenderFields Error:", error)} - errorsMessage={formik.errors} - /> - -
+ {selectedRetriever && ( + + + Configure retriever model + + [ + + ], + }]} + hideFooter + disableColumnMenu + disableColumnSelector + disableSelectionOnClick + /> + + )} + + {selectedRetriever && selectedRetriever.schema && !selectedRetriever.schema.properties && ( + + No parameters available for this retriever. + )} ); -} +} \ No newline at end of file diff --git a/RAG/embeddings/9864fa188a542eee3117fb4e3ed614fecb32905086735433d372c53e4d4b8f5a/feature_names.pkl b/RAG/embeddings/9864fa188a542eee3117fb4e3ed614fecb32905086735433d372c53e4d4b8f5a/feature_names.pkl new file mode 100644 index 0000000000000000000000000000000000000000..6084a4eeed682fd27c5b9d61666ad158cf246f46 GIT binary patch literal 1577 zcmZ8hJ&)Ts5Y1Y){Jq&la_wQtR9jzn#dTR=4@e&bluWM~%cPEycHcafq zl!;3am+pRJ_w95$S(bPK}S2nbBWvTz2D7y*mB_!oLCi4ZL^~Ks*3_X*A;lbJV3}q|7;@%F^-l4IT=~Mm?E~ zRXA^IhudUL!7*Y%2uuMUby0xS zTc`eO+%{exGVSj} zdo^IHy-@|{o4euCZRsqV2oDP#F@}{gobgm3R|FsyUvis53YBrGN`sDMMIEj6Z}qs9z|3jA8M8v&Z5( M0_rwtJkB>i0S-fQ-T(jq literal 0 HcmV?d00001 diff --git a/RAG/embeddings/9864fa188a542eee3117fb4e3ed614fecb32905086735433d372c53e4d4b8f5a/term_matrix.pkl b/RAG/embeddings/9864fa188a542eee3117fb4e3ed614fecb32905086735433d372c53e4d4b8f5a/term_matrix.pkl new file mode 100644 index 0000000000000000000000000000000000000000..aa935f934ddd778124e73352f523b9c548cd8276 GIT binary patch literal 2244 zcmd_qS!@$W7{GBSu^o<(0wLVA6ljhDrZqDhLT4Sn0Q) zXLjb>nbEgyvF|$F$CkN_K{3A?O}JuFMT-SpK7UM;MnCSpu%c^fvuu~8t#XSjF`T|w zNQnle$LVSPS{i-AN^?|GBf9jsja)SnQ2oJ}Gzuf}FzNhFTF@1auhUgU)0BiX@_bs* z-xP`ITHLRb)No`5N+UNC=o->4J+?MkmDM13$fdHxljYghCPR6KBcLaEEmN}9X)?L9 z>uHXWRW7|=@1h4>nVZL^%Z6lSG}~r#u(dd1_zD&H5+2lGGPYtM?9ibh3W2qlhmSE4 zTcBVezQDVfk7ZbgDQLm_sK;uQVkx%4e3zBe%J&g{jVQqqjKcs-M*{Dm3DqdZV!Vw! zOhYqT&^^DEJD>i~QH4neq6i}}6(3*$3J}65_~61Ce1^FQ;2jKy8#6E%BT)x)EiLH2 z2J_skb@=(c2(zK$L)798G+-9W;e~mI-Op$f?P1t}vFMAL$i)PFib@pXE%e87tU?5v zk%b(L!5j?2P^^bp|7CDuJXT-`-b6oqf|UqkBg`usqhapBTDN&llFBojd;?KklcD~h ze6x~61FbsFr7=8NWzNhj*DQ?+m`7li=RRao*aM2LJSBnoAru&A&JF^-n$jC|dn1T92LDv+Wo0s$-zzM*g+x3opD5 zD^GFzxuHj@Q^JZhyB$0C|8+(*%G+1|Dd-T7w=X_wAHC^Twcpd?|Kg$gGw#&Y%e{`X zp7)Gp_4@G{3oeLMskh|cWj_n69Z%lPwlM?_xZmE#<&+ACMEwsQeHE?<(^B$+sCCt2sV&e|c6!5aupLd-&^x~FHmJu}^t zzU=H6Lc9ovUDSdHH{y+F-rxc8&KocMA3P#NB!q<~shg!=e){l%>0f*-=npLJyX~IE!^pREYdiG089JWZ4)HsQ7q0f4Ztq>o3_ahu zVfo4J$saqT#mxt(<@2b>{^g8uJ3z(5^F)| zJ0_a8=*LLsriq>b28mpU8luO@w?@8aT0wvw>v1jg`j$Jr^m<(7E+0&28gXgt`Q0S0 z4ZN}Cn>?_R-MBSvtB-gXTE2^`oL-I2Y#1Bkjq#HkMiSS9&>QL0F~G>$kZB3 zU!^;=q2rR$nTwlblwJvvD}O1*4R_P$L*3`@riB)r!Y3GT%QLwV4Y)s{0S(8cUwd)6 z8%{=8OxcZwqY0MR8+bf?{ye-Fmt4;uCUIe~Ov?@|9sb4K>%q|TLR|3xr%uNJ&cqKiOuZe_K98lFmTc8eQ)?91&Uy zOlLG{2P5tWR$Dg%__U5seaI1-?JUlk9@rd<>FjiNu;eNnGx8n8yR#fu9JlKrR7rez zy3)4k`Br-v4MK;BHR(;emk*`#uc|KjJK1{fm_L?IV1UL@^*3>KHa&6 z2KEtnsoeBgCpQ5coZB13>*h1z)5flJO&;IPk__l z4EPFo5 zSHT!2fs4|JH8VLCmK+rqtyjzsr81 zr_gF#=&fW=Ap6qYo)h_o|F!x>jeoz6`_7Bi-}lCU z`^N(q1SI?409jV>Co@J%T&yt^Me;WMc zkN^BQZshdj7fIa6_oED4n?!x?JBj@q5^Iwq3#9~W?NW)}kI5&U)s;4SuM1n1<~{JR zG;slX)5~649OIl_)zb0g|w*G!{LDU{G_D7*rvOO>23QAQ|C4Ww4q z$ijy*Qc5d~Rn%)#teCPJC5XaO4mi~bVzdvGT)FP?41J!(o2*E3$@&Ly>aS1suqM?Feb8TQ)xvT)uC|z zaV54;@r4eGKTc)7G*cBI$5DZKLTNP@X9GjS@;7Dk%gUsoSmcBBZlr`>3{yd{a??Mn z40Tdjehrk}i%!+4%+yM#4iXm4n6PXyEu}9(WOrN83DsnA#*ARgHBiwb2Y1}u#-f5v z=q=h<`#7B|MKD6~>llOdK}uzl=w3wiE~i4Zr4Xy8kCT`zE)OV6 z8OH(9w(LN~q3EFXanfO>yJT7EC2Pw{SNgD03r_l!e@tn`Kt1KVndo}yjr*Ds>r~2b z(pgOUWTxU!%%c?AJnccHpHSzq3-0s*mA=BhkTbH(A5>S01{H|8*iZp33{8s7K4(#> zDXfjulHHF7m67hpyfdjnmV-`ZHseyN!R4SrXF1QFQ^pFZOu6?FWzZbl{mR3SXF9~} zU1eoDT$VZiDW%UtgfI66fGU15yH;k)ZXhb$g}H(oC>Nr6WhjmKD18~wnSf|s9y(?} zeW1{hLn`y@YS+?vTbuAO9_7t4`(2Ew3s|jp9Ab zU7gMp#D#Dog-KAca1LCkUv}PU)GNx; zn#yc8_7{}7E)Ch*YRaZzZQ0gfN^d}9v)hA?0L2WO(vXkYL&~}|joDpx5S, 'configurable_object': False, 'schema': None, 'metadata': {'inputs_types': ['ClassLabel', 'Value'], 'outputs_types': ['ClassLabel'], 'inputs_cardinality': 'n', 'outputs_cardinality': 1}, 'description': '\n Tabular classification in machine learning involves predicting categorical\n labels for structured data organized in tabular form (rows and columns).\n Models are trained to learn patterns and relationships in the data, enabling\n accurate classification of new instances.', 'display_name': None}, 'TextClassificationTask': {'name': 'TextClassificationTask', 'type': 'Task', 'class': , 'configurable_object': False, 'schema': None, 'metadata': {'inputs_types': ['Value'], 'outputs_types': ['ClassLabel'], 'inputs_cardinality': 1, 'outputs_cardinality': 1}, 'description': '\n Text classification is an essential Natural Language Processing (NLP) task that\n involves automatically assigning pre-defined categories or labels to text documents\n based on their content. It serves as the foundation for applications like sentiment\n analysis, spam filtering, topic classification, and document categorization.\n ', 'display_name': None}, 'TranslationTask': {'name': 'TranslationTask', 'type': 'Task', 'class': , 'configurable_object': False, 'schema': None, 'metadata': {'inputs_types': ['Value', 'Sequence'], 'outputs_types': ['Value', 'Sequence'], 'inputs_cardinality': 1, 'outputs_cardinality': 1}, 'description': '\n The translation task is natural language processing (NLP) task that involves\n converting text or speech from one language into another language while\n preserving the meaning and context.\n ', 'display_name': None}, 'ImageClassificationTask': {'name': 'ImageClassificationTask', 'type': 'Task', 'class': , 'configurable_object': False, 'schema': None, 'metadata': {'inputs_types': ['Image'], 'outputs_types': ['ClassLabel'], 'inputs_cardinality': 1, 'outputs_cardinality': 1}, 'description': None, 'display_name': None}, 'RegressionTask': {'name': 'RegressionTask', 'type': 'Task', 'class': , 'configurable_object': False, 'schema': None, 'metadata': {'inputs_types': ['Value'], 'outputs_types': ['Value'], 'inputs_cardinality': 'n', 'outputs_cardinality': 1}, 'description': '\n Regression in machine learning involves predicting continuous values for\n structured data organized in tabular form (rows and columns).\n Models are trained to learn patterns and relationships in the data,\n enabling accurate prediction of new instances.', 'display_name': None}}, 'GenerativeTask': {'TextToImageGenerationTask': {'name': 'TextToImageGenerationTask', 'type': 'GenerativeTask', 'class': , 'configurable_object': False, 'schema': None, 'metadata': {'inputs_types': ['str'], 'outputs_types': ['Image'], 'inputs_cardinality': 1, 'outputs_cardinality': 'n'}, 'description': 'This task generates images based on the provided input text.', 'display_name': 'Text to Image Generation'}, 'TextToTextGenerationTask': {'name': 'TextToTextGenerationTask', 'type': 'GenerativeTask', 'class': , 'configurable_object': False, 'schema': None, 'metadata': {'inputs_types': ['str'], 'outputs_types': ['str'], 'inputs_cardinality': 1, 'outputs_cardinality': 1}, 'description': 'This task uses a large language model (LLM) to generate text from a given prompt.', 'display_name': 'Text to Text Generation'}, 'ControlNetTask': {'name': 'ControlNetTask', 'type': 'GenerativeTask', 'class': , 'configurable_object': False, 'schema': None, 'metadata': {'inputs_types': ['Image', 'str'], 'outputs_types': ['Image'], 'inputs_cardinality': 2, 'outputs_cardinality': 'n'}, 'description': 'This task generates images based on the provided input text and image.', 'display_name': 'ControlNet'}, 'RAGTask': {'name': 'RAGTask', 'type': 'GenerativeTask', 'class': , 'configurable_object': False, 'schema': None, 'metadata': {'inputs_types': ['str'], 'outputs_types': ['str'], 'inputs_cardinality': 1, 'outputs_cardinality': 1}, 'description': 'This task generates a text response based on documents provided and chat.', 'display_name': 'Retreival-Augmented Generation (RAG) Task'}}, 'Model': {'SVC': {'name': 'SVC', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'Support Vector Machine (SVM) is a machine learning algorithm that separates data\ninto different classes by finding the optimal hyperplane', 'properties': {'C': {'description': "The parameter 'C' is a regularization parameter. It must be of type positive number.", 'placeholder': {'fixed_value': 1.0, 'lower_bound': 1.0, 'optimize': False, 'upper_bound': 10.0}, 'title': 'C', 'type': 'object'}, 'coef0': {'description': "The 'coef0' parameter is a kernel independent value. It is only significant for kernel poly and sigmoid. It must be of type number.", 'placeholder': {'fixed_value': 1.0, 'lower_bound': 1.0, 'optimize': False, 'upper_bound': 10.0}, 'title': 'Coef0', 'type': 'object'}, 'degree': {'description': "The parameter 'degree' is the degree of the polynomial for the kernel = 'poly'. It must be of type number.", 'placeholder': {'fixed_value': 1.0, 'lower_bound': 1.0, 'optimize': False, 'upper_bound': 10.0}, 'title': 'Degree', 'type': 'object'}, 'gamma': {'description': "Coefficient for 'rbf', 'poly' and 'sigmoid' kernels. Must be in string format and can be 'scale' or 'auto'.", 'enum': ['scale', 'auto'], 'placeholder': 'scale', 'title': 'Gamma', 'type': 'string'}, 'kernel': {'description': "The 'kernel' parameter is the kernel used in the model. It must be a string equal to 'linear', 'poly', 'rbf' or 'sigmoid'.", 'enum': ['linear', 'poly', 'rbf', 'sigmoid'], 'placeholder': 'rbf', 'title': 'Kernel', 'type': 'string'}, 'max_iter': {'description': "The 'max_iter' parameter determines the iteration limit for the solver. It must be of type positive integer or -1 to indicate no limit.", 'placeholder': {'fixed_value': -1, 'lower_bound': 1, 'optimize': False, 'upper_bound': 10}, 'title': 'Max Iter', 'type': 'object'}, 'probability': {'description': "The parameter 'probability' indicates whether or not to predict with probabilities. It must be of type boolean.", 'placeholder': True, 'title': 'Probability', 'type': 'boolean'}, 'shrinking': {'description': "The 'shrinking' parameter determines whether a shrinking heristic is used. It must be of type boolean.", 'placeholder': True, 'title': 'Shrinking', 'type': 'boolean'}, 'tol': {'description': "The parameter 'tol' determines the tolerance for the stop criterion. It must be of type positive number.", 'placeholder': {'fixed_value': 1.0, 'lower_bound': 1.0, 'optimize': False, 'upper_bound': 10.0}, 'title': 'Tol', 'type': 'object'}, 'verbose': {'description': "The 'verbose' parameter allows to have a verbose output.It must be of type boolean.", 'placeholder': False, 'title': 'Verbose', 'type': 'boolean'}}, 'required': ['C', 'coef0', 'degree', 'gamma', 'kernel', 'max_iter', 'probability', 'shrinking', 'tol', 'verbose'], 'title': 'SVCSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'DecisionTreeClassifier': {'name': 'DecisionTreeClassifier', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'Decision Trees are a set of are a non-parametric supervised learning method that\nlearns simple decision rules (structured as a tree) inferred from the data features.', 'properties': {'criterion': {'description': 'The function to measure the quality of a split. Supported criteria are gini for the Gini impurity and log_loss and entropy both for the Shannon information gain.', 'enum': ['entropy', 'gini', 'log_loss'], 'placeholder': 'entropy', 'title': 'Criterion', 'type': 'string'}, 'max_depth': {'description': 'The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.', 'placeholder': {'fixed_value': 1, 'lower_bound': 1, 'optimize': False, 'upper_bound': 10}, 'title': 'Max Depth', 'type': 'object'}, 'min_samples_split': {'description': 'The minimum number of samples required to split an internal node.', 'placeholder': {'fixed_value': 1, 'lower_bound': 1, 'optimize': False, 'upper_bound': 5}, 'title': 'Min Samples Split', 'type': 'object'}, 'min_samples_leaf': {'description': 'The minimum number of samples required to be at a leaf node.', 'placeholder': {'fixed_value': 1, 'lower_bound': 1, 'optimize': False, 'upper_bound': 5}, 'title': 'Min Samples Leaf', 'type': 'object'}, 'max_features': {'description': 'The number of features to consider when looking for the best split.', 'enum': ['auto', 'sqrt', 'log2'], 'placeholder': None, 'title': 'Max Features', 'type': 'string'}}, 'required': ['criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'max_features'], 'title': 'DecisionTreeClassifierSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'DummyClassifier': {'name': 'DummyClassifier', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'DummyClassifier makes predictions that ignore the input features.', 'properties': {'strategy': {'description': 'Strategy to use to generate predictions.', 'enum': ['most_frequent', 'prior', 'stratified', 'uniform'], 'placeholder': 'prior', 'title': 'Strategy', 'type': 'string'}}, 'required': ['strategy'], 'title': 'DummyClassifierSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'GradientBoostingR': {'name': 'GradientBoostingR', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'Gradient Boosting for regression.', 'properties': {'loss': {'description': 'Loss function to be optimized.', 'enum': ['squared_error', 'absolute_error', 'huber', 'quantile'], 'placeholder': 'squared_error', 'title': 'Loss', 'type': 'string'}, 'learning_rate': {'description': 'Learning rate shrinks the contribution of each tree.', 'placeholder': {'fixed_value': 0.1, 'lower_bound': 0.01, 'optimize': False, 'upper_bound': 1.0}, 'title': 'Learning Rate', 'type': 'object'}, 'n_estimators': {'description': 'The number of boosting stages to be run.', 'placeholder': {'fixed_value': 100, 'lower_bound': 10, 'optimize': False, 'upper_bound': 1000}, 'title': 'N Estimators', 'type': 'object'}, 'subsample': {'description': 'The fraction of samples to be used for fitting the individual base learners.', 'placeholder': {'fixed_value': 1.0, 'lower_bound': 0.1, 'optimize': False, 'upper_bound': 1.0}, 'title': 'Subsample', 'type': 'object'}, 'criterion': {'description': 'The function to measure the quality of a split.', 'enum': ['friedman_mse', 'mse', 'mae'], 'placeholder': 'friedman_mse', 'title': 'Criterion', 'type': 'string'}, 'min_samples_split': {'description': 'The minimum number of samples required to split an internal node.', 'placeholder': {'fixed_value': 2, 'lower_bound': 2, 'optimize': False, 'upper_bound': 20}, 'title': 'Min Samples Split', 'type': 'object'}, 'min_samples_leaf': {'description': 'The minimum number of samples required to be at a leaf node.', 'placeholder': {'fixed_value': 1, 'lower_bound': 1, 'optimize': False, 'upper_bound': 20}, 'title': 'Min Samples Leaf', 'type': 'object'}, 'min_weight_fraction_leaf': {'description': 'The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.', 'placeholder': {'fixed_value': 0.0, 'lower_bound': 0.0, 'optimize': False, 'upper_bound': 0.5}, 'title': 'Min Weight Fraction Leaf', 'type': 'object'}, 'max_depth': {'anyOf': [{'type': 'object'}, {'type': 'integer'}, {'type': 'null'}], 'description': 'The maximum depth of the individual regression estimators.', 'placeholder': 3, 'title': 'Max Depth'}, 'min_impurity_decrease': {'description': 'A node will be split if this split induces a decrease of the impurity greater than or equal to this value.', 'placeholder': {'fixed_value': 0.0, 'lower_bound': 0.0, 'optimize': False, 'upper_bound': 0.5}, 'title': 'Min Impurity Decrease', 'type': 'object'}, 'random_state': {'anyOf': [{'type': 'object'}, {'type': 'integer'}, {'type': 'null'}], 'description': 'The seed of the pseudo-random number generator to use when shuffling the data.', 'placeholder': None, 'title': 'Random State'}, 'max_features': {'anyOf': [{'type': 'object'}, {'enum': ['sqrt', 'log2', None], 'type': 'string'}], 'description': 'The number of features to consider when looking for the best split.', 'placeholder': None, 'title': 'Max Features'}, 'alpha': {'description': 'The alpha-quantile of the Huber loss function and the quantile loss function.', 'placeholder': {'fixed_value': 0.9, 'lower_bound': 0.1, 'optimize': False, 'upper_bound': 1.0}, 'title': 'Alpha', 'type': 'object'}, 'verbose': {'description': 'Enable verbose output.', 'placeholder': {'fixed_value': 0, 'lower_bound': 0, 'optimize': False, 'upper_bound': 100}, 'title': 'Verbose', 'type': 'object'}, 'max_leaf_nodes': {'anyOf': [{'type': 'object'}, {'type': 'integer'}, {'type': 'null'}], 'description': 'Grow trees with max_leaf_nodes in best-first fashion.', 'placeholder': None, 'title': 'Max Leaf Nodes'}, 'warm_start': {'description': 'When set to True, reuse the solution of the previous callto fit and add more estimators to the ensemble.', 'placeholder': False, 'title': 'Warm Start', 'type': 'boolean'}, 'validation_fraction': {'description': 'The proportion of training data to set aside as validation set for early stopping.', 'placeholder': {'fixed_value': 0.1, 'lower_bound': 0.1, 'optimize': False, 'upper_bound': 0.5}, 'title': 'Validation Fraction', 'type': 'object'}, 'n_iter_no_change': {'anyOf': [{'type': 'object'}, {'type': 'integer'}, {'type': 'null'}], 'description': 'The number of iterations with no improvement to wait before stopping the training.', 'placeholder': None, 'title': 'N Iter No Change'}, 'tol': {'description': 'Tolerance for the early stopping.', 'placeholder': {'fixed_value': 0.0001, 'lower_bound': 1e-05, 'optimize': False, 'upper_bound': 0.1}, 'title': 'Tol', 'type': 'object'}, 'ccp_alpha': {'description': 'Complexity parameter used for Minimal Cost-Complexity Pruning.', 'placeholder': {'fixed_value': 0.0, 'lower_bound': 0.0, 'optimize': False, 'upper_bound': 1.0}, 'title': 'Ccp Alpha', 'type': 'object'}}, 'required': ['loss', 'learning_rate', 'n_estimators', 'subsample', 'criterion', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_depth', 'min_impurity_decrease', 'random_state', 'max_features', 'alpha', 'verbose', 'max_leaf_nodes', 'warm_start', 'validation_fraction', 'n_iter_no_change', 'tol', 'ccp_alpha'], 'title': 'GradientBoostingRSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'HistGradientBoostingClassifier': {'name': 'HistGradientBoostingClassifier', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'A gradient boosting classifier is a machine learning algorithm that combines\nmultiple weak prediction models (typically decision trees) to create a strong\npredictive model by training the models sequentially, in which each new model is\nfocused on correcting the errors made by the previous ones.', 'properties': {'learning_rate': {'description': 'The learning rate, also known as shrinkage. This is used as a multiplicative factor for the leaves values. Use 1 for no shrinkage.', 'placeholder': {'fixed_value': 0.1, 'lower_bound': 0.1, 'optimize': False, 'upper_bound': 1}, 'title': 'Learning Rate', 'type': 'object'}, 'max_iter': {'description': 'The maximum number of iterations of the boosting process, i.e. the maximum number of trees for binary classification.', 'placeholder': {'fixed_value': 100, 'lower_bound': 100, 'optimize': False, 'upper_bound': 250}, 'title': 'Max Iter', 'type': 'object'}, 'max_depth': {'description': 'The maximum depth of each tree. The depth of a tree is the number of edges to go from the root to the deepest leaf. Depth isnt constrained by default.', 'placeholder': {'fixed_value': 1, 'lower_bound': 1, 'optimize': False, 'upper_bound': 10}, 'title': 'Max Depth', 'type': 'object'}, 'max_leaf_nodes': {'description': 'The maximum number of leaves for each tree. Must be strictly greater than 1. If None, there is no maximum limit.', 'placeholder': {'fixed_value': 31, 'lower_bound': 10, 'optimize': False, 'upper_bound': 40}, 'title': 'Max Leaf Nodes', 'type': 'object'}, 'min_samples_leaf': {'description': 'The minimum number of samples required to be at a leaf node.', 'placeholder': {'fixed_value': 20, 'lower_bound': 2, 'optimize': False, 'upper_bound': 25}, 'title': 'Min Samples Leaf', 'type': 'object'}, 'l2_regularization': {'description': 'The L2 regularization parameter. Use 0 for no regularization.', 'placeholder': {'fixed_value': 0.0, 'lower_bound': 0.0, 'optimize': False, 'upper_bound': 1.0}, 'title': 'L2 Regularization', 'type': 'object'}}, 'required': ['learning_rate', 'max_iter', 'max_depth', 'max_leaf_nodes', 'min_samples_leaf', 'l2_regularization'], 'title': 'HistGradientBoostingClassifierSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'KNeighborsClassifier': {'name': 'KNeighborsClassifier', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'KNN is a supervised classification method that determines the probability of\nan element belonging to a certain class by considering its k closest neighbors.', 'properties': {'n_neighbors': {'description': "The 'n_neighbors' parameter is the number of neighbors to consider in each input for classification. It must be an integer greater than or equal to 1.", 'placeholder': {'fixed_value': 5, 'lower_bound': 5, 'optimize': False, 'upper_bound': 10}, 'title': 'N Neighbors', 'type': 'object'}, 'weights': {'description': "The 'weights' parameter must be 'uniform' or 'distance'.", 'enum': ['uniform', 'distance'], 'placeholder': 'uniform', 'title': 'Weights', 'type': 'string'}, 'algorithm': {'description': "The 'algorithm' parameter must be 'auto', 'ball_tree', 'kd_tree', or 'brute'.", 'enum': ['auto', 'ball_tree', 'kd_tree', 'brute'], 'placeholder': 'auto', 'title': 'Algorithm', 'type': 'string'}}, 'required': ['n_neighbors', 'weights', 'algorithm'], 'title': 'KNeighborsClassifierSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'LogisticRegression': {'name': 'LogisticRegression', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'Logistic Regression is a supervised classification method that uses a linear\nmodel plus a a logistic funcion to predict binary outcomes (it can be configured\nas multiclass via the one-vs-rest strategy).', 'properties': {'penalty': {'description': 'Specify the norm of the penalty', 'enum': ['l2', 'l1', 'elasticnet'], 'placeholder': 'l2', 'title': 'Penalty', 'type': 'string'}, 'tol': {'description': 'Tolerance for stopping criteria.', 'placeholder': {'fixed_value': 0.001, 'lower_bound': 0.001, 'optimize': False, 'upper_bound': 5}, 'title': 'Tol', 'type': 'object'}, 'C': {'description': 'Inverse of regularization strength, smaller values specify stronger regularization. Must be a positive number.', 'placeholder': {'fixed_value': 1.0, 'lower_bound': 1.0, 'optimize': False, 'upper_bound': 7.0}, 'title': 'C', 'type': 'object'}, 'max_iter': {'description': 'Maximum number of iterations taken for the solvers to converge.', 'placeholder': {'fixed_value': 100, 'lower_bound': 50, 'optimize': False, 'upper_bound': 250}, 'title': 'Max Iter', 'type': 'object'}}, 'required': ['penalty', 'tol', 'C', 'max_iter'], 'title': 'LogisticRegressionSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'MLPRegression': {'name': 'MLPRegression', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'MLP Regressor for DashAI.', 'properties': {'activation': {'description': 'Activation function for the hidden layer.', 'enum': ['identity', 'logistic', 'tanh', 'relu'], 'placeholder': 'relu', 'title': 'Activation', 'type': 'string'}, 'solver': {'description': 'The solver for weight optimization.', 'enum': ['lbfgs', 'sgd', 'adam'], 'placeholder': 'adam', 'title': 'Solver', 'type': 'string'}, 'alpha': {'description': 'L2 penalty (regularization term) parameter.', 'placeholder': {'fixed_value': 0.0001, 'lower_bound': 1e-06, 'optimize': False, 'upper_bound': 0.1}, 'title': 'Alpha', 'type': 'object'}, 'batch_size': {'anyOf': [{'type': 'object'}, {'enum': ['auto'], 'type': 'string'}], 'description': 'Size of minibatches for stochastic optimizers.', 'placeholder': 'auto', 'title': 'Batch Size'}, 'learning_rate': {'description': 'Learning rate schedule for weight updates.', 'enum': ['constant', 'invscaling', 'adaptive'], 'placeholder': 'constant', 'title': 'Learning Rate', 'type': 'string'}, 'learning_rate_init': {'description': 'The initial learning rate used.', 'placeholder': {'fixed_value': 0.001, 'lower_bound': 1e-05, 'optimize': False, 'upper_bound': 0.1}, 'title': 'Learning Rate Init', 'type': 'object'}, 'power_t': {'description': 'The exponent for inverse scaling learning rate.', 'placeholder': {'fixed_value': 0.5, 'lower_bound': 0.1, 'optimize': False, 'upper_bound': 0.9}, 'title': 'Power T', 'type': 'object'}, 'max_iter': {'description': 'Maximum number of iterations.', 'placeholder': {'fixed_value': 200, 'lower_bound': 50, 'optimize': False, 'upper_bound': 1000}, 'title': 'Max Iter', 'type': 'object'}, 'shuffle': {'description': 'Whether to shuffle samples in each iteration.', 'placeholder': True, 'title': 'Shuffle', 'type': 'boolean'}, 'random_state': {'anyOf': [{'type': 'object'}, {'type': 'integer'}, {'type': 'null'}], 'description': 'The seed of the pseudo-random number generator to use when shuffling the data.', 'placeholder': None, 'title': 'Random State'}, 'tol': {'description': 'Tolerance for the optimization.', 'placeholder': {'fixed_value': 0.0001, 'lower_bound': 1e-06, 'optimize': False, 'upper_bound': 0.01}, 'title': 'Tol', 'type': 'object'}, 'verbose': {'description': 'Whether to print progress messages to stdout.', 'placeholder': False, 'title': 'Verbose', 'type': 'boolean'}, 'warm_start': {'description': 'When set to True, reuse the solution of the previous call to fit as initialization.', 'placeholder': False, 'title': 'Warm Start', 'type': 'boolean'}, 'momentum': {'description': 'Momentum for gradient descent update.', 'placeholder': {'fixed_value': 0.9, 'lower_bound': 0.0, 'optimize': False, 'upper_bound': 1.0}, 'title': 'Momentum', 'type': 'object'}, 'nesterovs_momentum': {'description': 'Whether to use Nesterovs momentum.', 'placeholder': True, 'title': 'Nesterovs Momentum', 'type': 'boolean'}, 'early_stopping': {'description': 'Whether to use early stopping to terminate training when validation score is not improving.', 'placeholder': False, 'title': 'Early Stopping', 'type': 'boolean'}, 'validation_fraction': {'description': 'The proportion of training data to set aside as validation set for early stopping.', 'placeholder': {'fixed_value': 0.1, 'lower_bound': 0.1, 'optimize': False, 'upper_bound': 0.5}, 'title': 'Validation Fraction', 'type': 'object'}, 'beta_1': {'description': 'Exponential decay rate for estimates of first moment vector in Adam optimizer.', 'placeholder': {'fixed_value': 0.9, 'lower_bound': 0.1, 'optimize': False, 'upper_bound': 0.999}, 'title': 'Beta 1', 'type': 'object'}, 'beta_2': {'description': 'Exponential decay rate for estimates of second moment vector in Adam optimizer.', 'placeholder': {'fixed_value': 0.999, 'lower_bound': 0.1, 'optimize': False, 'upper_bound': 0.999}, 'title': 'Beta 2', 'type': 'object'}, 'epsilon': {'description': 'Value for numerical stability in Adam optimizer.', 'placeholder': {'fixed_value': 1e-08, 'lower_bound': 1e-10, 'optimize': False, 'upper_bound': 1e-06}, 'title': 'Epsilon', 'type': 'object'}, 'n_iter_no_change': {'description': 'Maximum number of epochs to not meet tol improvement.', 'placeholder': {'fixed_value': 10, 'lower_bound': 1, 'optimize': False, 'upper_bound': 50}, 'title': 'N Iter No Change', 'type': 'object'}, 'max_fun': {'description': "Maximum number of loss function calls. Only used if solver='lbfgs'.", 'placeholder': {'fixed_value': 15000, 'lower_bound': 1000, 'optimize': False, 'upper_bound': 20000}, 'title': 'Max Fun', 'type': 'object'}}, 'required': ['activation', 'solver', 'alpha', 'batch_size', 'learning_rate', 'learning_rate_init', 'power_t', 'max_iter', 'shuffle', 'random_state', 'tol', 'verbose', 'warm_start', 'momentum', 'nesterovs_momentum', 'early_stopping', 'validation_fraction', 'beta_1', 'beta_2', 'epsilon', 'n_iter_no_change', 'max_fun'], 'title': 'MLPRegressorSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'RandomForestClassifier': {'name': 'RandomForestClassifier', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'Random Forest (RF) is an ensemble machine learning algorithm that achieves\nenhanced performance by combining multiple decision trees and aggregating their\noutputs.', 'properties': {'n_estimators': {'description': "The 'n_estimators' parameter corresponds to the number of decision trees. It must be an integer greater than or equal to 1.", 'placeholder': {'fixed_value': 100, 'lower_bound': 50, 'optimize': False, 'upper_bound': 200}, 'title': 'N Estimators', 'type': 'object'}, 'max_depth': {'description': "The 'max_depth' parameter corresponds to the maximum depth of the tree. It must be an integer greater than or equal to 1.", 'placeholder': {'fixed_value': 2, 'lower_bound': 2, 'optimize': False, 'upper_bound': 10}, 'title': 'Max Depth', 'type': 'object'}, 'min_samples_split': {'description': "The 'min_samples_split' parameter is the minimum number of samples required to split an internal node. It must be a number greater than or equal to 2.", 'placeholder': {'fixed_value': 2, 'lower_bound': 2, 'optimize': False, 'upper_bound': 10}, 'title': 'Min Samples Split', 'type': 'object'}, 'min_samples_leaf': {'description': "The 'min_samples_leaf' parameter is the minimum number of samples required to be at a leaf node. It must be a number greater than or equal to 1.", 'placeholder': {'fixed_value': 1, 'lower_bound': 1, 'optimize': False, 'upper_bound': 10}, 'title': 'Min Samples Leaf', 'type': 'object'}, 'max_leaf_nodes': {'description': "The 'max_leaf_nodes' parameter must be an integer greater than or equal to 2.", 'placeholder': {'fixed_value': 2, 'lower_bound': 2, 'optimize': False, 'upper_bound': 10}, 'title': 'Max Leaf Nodes', 'type': 'object'}, 'random_state': {'description': "The 'random_state' parameter must be an integer greater than or equal to 0.", 'placeholder': {'fixed_value': 0, 'lower_bound': 0, 'optimize': False, 'upper_bound': 10}, 'title': 'Random State', 'type': 'object'}}, 'required': ['n_estimators', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'max_leaf_nodes', 'random_state'], 'title': 'RandomForestClassifierSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'RandomForestRegression': {'name': 'RandomForestRegression', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'Random Forest Regressor for DashAI.', 'properties': {'n_estimators': {'description': 'The number of trees in the forest.', 'placeholder': {'fixed_value': 100, 'lower_bound': 10, 'optimize': False, 'upper_bound': 1000}, 'title': 'N Estimators', 'type': 'object'}, 'criterion': {'description': 'The function to measure the quality of a split.', 'enum': ['squared_error', 'absolute_error', 'poisson'], 'placeholder': 'squared_error', 'title': 'Criterion', 'type': 'string'}, 'max_depth': {'anyOf': [{'type': 'object'}, {'type': 'integer'}, {'type': 'null'}], 'description': 'The maximum depth of the tree.', 'placeholder': None, 'title': 'Max Depth'}, 'min_samples_split': {'description': 'The minimum number of samples required to split an internal node.', 'placeholder': {'fixed_value': 2, 'lower_bound': 2, 'optimize': False, 'upper_bound': 20}, 'title': 'Min Samples Split', 'type': 'object'}, 'min_samples_leaf': {'description': 'The minimum number of samples required to be at a leaf node.', 'placeholder': {'fixed_value': 1, 'lower_bound': 1, 'optimize': False, 'upper_bound': 20}, 'title': 'Min Samples Leaf', 'type': 'object'}, 'min_weight_fraction_leaf': {'description': 'The minimum weighted fraction of the sum total of weights required to be at a leaf node.', 'placeholder': {'fixed_value': 0.0, 'lower_bound': 0.0, 'optimize': False, 'upper_bound': 0.5}, 'title': 'Min Weight Fraction Leaf', 'type': 'object'}, 'max_features': {'anyOf': [{'type': 'object'}, {'enum': ['auto', 'sqrt', 'log2', None], 'type': 'string'}], 'description': 'The number of features to consider when looking for the best split.', 'placeholder': 'sqrt', 'title': 'Max Features'}, 'max_leaf_nodes': {'anyOf': [{'type': 'object'}, {'type': 'integer'}, {'type': 'null'}], 'description': 'Grow trees with max_leaf_nodes in best-first fashion.', 'placeholder': None, 'title': 'Max Leaf Nodes'}, 'min_impurity_decrease': {'description': 'A node will be split if this split induces a decrease of the impurity greater than or equal to this value.', 'placeholder': {'fixed_value': 0.0, 'lower_bound': 0.0, 'optimize': False, 'upper_bound': 0.5}, 'title': 'Min Impurity Decrease', 'type': 'object'}, 'bootstrap': {'description': 'Whether bootstrap samples are used when building trees.', 'placeholder': True, 'title': 'Bootstrap', 'type': 'boolean'}, 'oob_score': {'description': 'Whether to use out-of-bag samples to estimate the generalization score.', 'placeholder': False, 'title': 'Oob Score', 'type': 'boolean'}, 'n_jobs': {'anyOf': [{'type': 'object'}, {'type': 'integer'}, {'type': 'null'}], 'description': 'The number of jobs to run in parallel for both fit and predict.', 'placeholder': None, 'title': 'N Jobs'}, 'random_state': {'anyOf': [{'type': 'object'}, {'type': 'integer'}, {'type': 'null'}], 'description': 'The seed of the pseudo-random number generator to use when shuffling the data.', 'placeholder': None, 'title': 'Random State'}, 'verbose': {'description': 'Controls the verbosity when fitting and predicting.', 'placeholder': {'fixed_value': 0, 'lower_bound': 0, 'optimize': False, 'upper_bound': 100}, 'title': 'Verbose', 'type': 'object'}, 'warm_start': {'description': 'When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble.', 'placeholder': False, 'title': 'Warm Start', 'type': 'boolean'}, 'ccp_alpha': {'description': 'Complexity parameter used for Minimal Cost-Complexity Pruning.', 'placeholder': {'fixed_value': 0.0, 'lower_bound': 0.0, 'optimize': False, 'upper_bound': 1.0}, 'title': 'Ccp Alpha', 'type': 'object'}, 'max_samples': {'anyOf': [{'type': 'object'}, {'type': 'number'}, {'type': 'null'}], 'description': 'If bootstrap is True, the number of samples to draw from X to train each base estimator.', 'placeholder': None, 'title': 'Max Samples'}}, 'required': ['n_estimators', 'criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'bootstrap', 'oob_score', 'n_jobs', 'random_state', 'verbose', 'warm_start', 'ccp_alpha', 'max_samples'], 'title': 'RandomForestRegressionSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'DistilBertTransformer': {'name': 'DistilBertTransformer', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'Distilbert is a transformer that allows you to classify text in English.\nThe implementation is based on huggingface distilbert-base in the case of\nthe uncased model, i.e. distilbert-base-uncased.', 'properties': {'num_train_epochs': {'description': 'Total number of training epochs to perform.', 'minimum': 1, 'placeholder': 1, 'title': 'Num Train Epochs', 'type': 'integer'}, 'batch_size': {'description': 'The batch size per GPU/TPU core/CPU for training', 'minimum': 1, 'placeholder': 16, 'title': 'Batch Size', 'type': 'integer'}, 'learning_rate': {'description': 'The initial learning rate for AdamW optimizer', 'minimum': 0.0, 'placeholder': 3e-05, 'title': 'Learning Rate', 'type': 'number'}, 'device': {'description': 'Hardware on which the training is run. If available, GPU is recommended for efficiency reasons. Otherwise, use CPU.', 'enum': ['gpu', 'cpu'], 'placeholder': 'gpu', 'title': 'Device', 'type': 'string'}, 'weight_decay': {'description': "Weight decay is a regularization technique used in training neural networks to prevent overfitting. In the context of the AdamW optimizer, the 'weight_decay' parameter is the rate at which the weights of all layers are reduced during training, provided that this rate is not zero.", 'minimum': 0.0, 'placeholder': 0.01, 'title': 'Weight Decay', 'type': 'number'}}, 'required': ['num_train_epochs', 'batch_size', 'learning_rate', 'device', 'weight_decay'], 'title': 'DistilBertTransformerSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'OpusMtEnESTransformer': {'name': 'OpusMtEnESTransformer', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'opus-mt-en-es is a transformer pre-trained model that allows translation of\ntexts from English to Spanish.', 'properties': {'num_train_epochs': {'description': 'Total number of training epochs to perform.', 'minimum': 1, 'placeholder': 1, 'title': 'Num Train Epochs', 'type': 'integer'}, 'batch_size': {'description': 'The batch size per GPU/TPU core/CPU for training', 'minimum': 1, 'placeholder': 4, 'title': 'Batch Size', 'type': 'integer'}, 'learning_rate': {'description': 'The initial learning rate for AdamW optimizer', 'minimum': 0.0, 'placeholder': 2e-05, 'title': 'Learning Rate', 'type': 'number'}, 'device': {'description': 'Hardware on which the training is run. If available, GPU is recommended for efficiency reasons. Otherwise, use CPU.', 'enum': ['gpu', 'cpu'], 'placeholder': 'gpu', 'title': 'Device', 'type': 'string'}, 'weight_decay': {'description': "Weight decay is a regularization technique used in training neural networks to prevent overfitting. In the context of the AdamW optimizer, the 'weight_decay' parameter is the rate at which the weights of all layers are reduced during training, provided that this rate is not zero.", 'minimum': 0.0, 'placeholder': 0.01, 'title': 'Weight Decay', 'type': 'number'}}, 'required': ['num_train_epochs', 'batch_size', 'learning_rate', 'device', 'weight_decay'], 'title': 'OpusMtEnESTransformerSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'BagOfWordsTextClassificationModel': {'name': 'BagOfWordsTextClassificationModel', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'NumericalWrapperForText is a metamodel that allows text classification using\ntabular classifiers and a tokenizer.', 'properties': {'tabular_classifier': {'description': 'Tabular model used as the underlying model to generate the text classifier.', 'parent': 'TabularClassificationModel', 'placeholder': {'component': 'SVC', 'params': {}}, 'properties': {'component': {'title': 'Component', 'type': 'string'}, 'params': {'title': 'Params', 'type': 'object'}}, 'required': ['component', 'params'], 'title': 'Tabular Classifier', 'type': 'object'}, 'ngram_min_n': {'description': 'The lower boundary of the range of n-values for different word n-grams or char n-grams to be extracted. It must be an integer greater or equal than 1', 'minimum': 1, 'placeholder': 1, 'title': 'Ngram Min N', 'type': 'integer'}, 'ngram_max_n': {'description': 'The upper boundary of the range of n-values for different word n-grams or char n-grams to be extracted. It must be an integer greater or equal than 1', 'minimum': 1, 'placeholder': 1, 'title': 'Ngram Max N', 'type': 'integer'}}, 'required': ['tabular_classifier', 'ngram_min_n', 'ngram_max_n'], 'title': 'BagOfWordsTextClassificationModelSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'RidgeRegression': {'name': 'RidgeRegression', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'Ridge regression is a linear model that includes L2 regularization.', 'properties': {'alpha': {'description': 'Regularization strength; must be a positive float. Larger values specify stronger regularization.', 'placeholder': {'fixed_value': 1.0, 'lower_bound': 0.1, 'optimize': False, 'upper_bound': 10.0}, 'title': 'Alpha', 'type': 'object'}, 'fit_intercept': {'description': 'Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (e.g., data is expected to be centered).', 'placeholder': True, 'title': 'Fit Intercept', 'type': 'boolean'}, 'copy_X': {'description': 'If True, X will be copied; else, it may be overwritten.', 'placeholder': True, 'title': 'Copy X', 'type': 'boolean'}, 'max_iter': {'description': 'Maximum number of iterations for conjugate gradient solver.', 'placeholder': {'fixed_value': 1000, 'lower_bound': 100, 'optimize': False, 'upper_bound': 10000}, 'title': 'Max Iter', 'type': 'object'}, 'tol': {'description': 'Precision of the solution.', 'placeholder': {'fixed_value': 0.001, 'lower_bound': 1e-05, 'optimize': False, 'upper_bound': 0.1}, 'title': 'Tol', 'type': 'object'}, 'solver': {'description': 'Solver to use in the computation. auto chooses the solver automatically based on the type of data.', 'enum': ['auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'], 'placeholder': 'auto', 'title': 'Solver', 'type': 'string'}, 'positive': {'description': 'When set to True, forces the coefficients to be positive.', 'placeholder': False, 'title': 'Positive', 'type': 'boolean'}, 'random_state': {'anyOf': [{'type': 'object'}, {'type': 'integer'}, {'type': 'null'}], 'description': 'The seed of the pseudo random number generator to use when shuffling the data. Pass an int for reproducible output across multiple function calls, or None to not set a specific seed.', 'placeholder': None, 'title': 'Random State'}}, 'required': ['alpha', 'fit_intercept', 'copy_X', 'max_iter', 'tol', 'solver', 'positive', 'random_state'], 'title': 'RidgeRegressionSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'LinearSVR': {'name': 'LinearSVR', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'Support Vector Regression (SVR) using a linear kernel.', 'properties': {'epsilon': {'description': 'Epsilon parameter that specifies the epsilon-tube within which no penalty is associated.', 'placeholder': {'fixed_value': 0.0, 'lower_bound': 0.0, 'optimize': False, 'upper_bound': 1}, 'title': 'Epsilon', 'type': 'object'}, 'tol': {'description': 'Tolerance for stopping criterion.', 'placeholder': {'fixed_value': 0.0001, 'lower_bound': 1e-05, 'optimize': False, 'upper_bound': 0.1}, 'title': 'Tol', 'type': 'object'}, 'C': {'description': 'Regularization parameter. The strength of the regularization is inversely proportional to C.', 'placeholder': {'fixed_value': 1.0, 'lower_bound': 0.1, 'optimize': False, 'upper_bound': 10}, 'title': 'C', 'type': 'object'}, 'loss': {'description': "Specifies the loss function. 'epsilon_insensitive' is the standard SVR loss.", 'enum': ['epsilon_insensitive', 'squared_epsilon_insensitive'], 'placeholder': 'epsilon_insensitive', 'title': 'Loss', 'type': 'string'}, 'fit_intercept': {'description': 'Whether to calculate the intercept for this model.', 'placeholder': True, 'title': 'Fit Intercept', 'type': 'boolean'}, 'intercept_scaling': {'description': 'When fit_intercept is True, instance vector x becomes [x, self.intercept_scaling] in the primal problem.', 'placeholder': {'fixed_value': 1.0, 'lower_bound': 0.1, 'optimize': False, 'upper_bound': 10}, 'title': 'Intercept Scaling', 'type': 'object'}, 'dual': {'description': 'Select the algorithm to either solve the dual or primal optimization problem.', 'placeholder': True, 'title': 'Dual', 'type': 'boolean'}, 'verbose': {'description': 'Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm.', 'placeholder': {'fixed_value': 0, 'lower_bound': 0, 'optimize': False, 'upper_bound': 100}, 'title': 'Verbose', 'type': 'object'}, 'random_state': {'anyOf': [{'type': 'object'}, {'type': 'integer'}, {'type': 'null'}], 'description': 'The seed of the pseudo-random number generator to use when shuffling the data.', 'placeholder': None, 'title': 'Random State'}, 'max_iter': {'description': 'The maximum number of iterations to be run.', 'placeholder': {'fixed_value': 1000, 'lower_bound': 100, 'optimize': False, 'upper_bound': 10000}, 'title': 'Max Iter', 'type': 'object'}}, 'required': ['epsilon', 'tol', 'C', 'loss', 'fit_intercept', 'intercept_scaling', 'dual', 'verbose', 'random_state', 'max_iter'], 'title': 'LinearSVRSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'LinearRegression': {'name': 'LinearRegression', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'Linear regression model with optional intercept.', 'properties': {'fit_intercept': {'description': 'Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (e.g., data is expected to be centered).', 'placeholder': True, 'title': 'Fit Intercept', 'type': 'boolean'}, 'copy_X': {'description': 'If True, X will be copied; else, it may be overwritten.', 'placeholder': True, 'title': 'Copy X', 'type': 'boolean'}, 'n_jobs': {'anyOf': [{'type': 'object'}, {'type': 'integer'}, {'type': 'null'}], 'description': 'The number of jobs to use for the computation. None means 1 job, while -1 means using all processors.', 'placeholder': None, 'title': 'N Jobs'}, 'positive': {'description': 'When set to True, forces the coefficients to be positive.', 'placeholder': False, 'title': 'Positive', 'type': 'boolean'}}, 'required': ['fit_intercept', 'copy_X', 'n_jobs', 'positive'], 'title': 'LinearRegressionSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'TFIDFRetriever': {'name': 'TFIDFRetriever', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'Schema for the TFIDFRetriever.', 'properties': {'similarity_function': {'description': 'Similarity function to use for document retrieval.', 'enum': ['cosine', 'euclidean'], 'placeholder': 'cosine', 'title': 'Similarity Function', 'type': 'string'}, 'n_docs': {'description': 'Number of documents to retrieve.', 'minimum': 1, 'placeholder': 5, 'title': 'N Docs', 'type': 'integer'}, 'similarity_threshold': {'description': 'Maximum or minimum distance for retrieved documents based on the similarity_function.', 'placeholder': 0.5, 'title': 'Similarity Threshold', 'type': 'number'}, 'chunk_size': {'description': 'Size of chunks to split the documents into.', 'minimum': 1, 'placeholder': 512, 'title': 'Chunk Size', 'type': 'integer'}, 'chunk_overlap': {'description': 'Overlap between chunks when splitting documents.', 'minimum': 0, 'placeholder': 50, 'title': 'Chunk Overlap', 'type': 'integer'}}, 'required': ['similarity_function', 'n_docs', 'similarity_threshold', 'chunk_size', 'chunk_overlap'], 'title': 'TFIDFRetrieverSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'DenseRetriever': {'name': 'DenseRetriever', 'type': 'Model', 'class': , 'configurable_object': True, 'schema': {'description': 'Schema for Dense Retriever.', 'properties': {'encoding_model': {'description': 'Model to convert text into dense vector representations.', 'parent': 'HuggingFaceWrapper', 'placeholder': {'component': 'Embedding', 'params': {}}, 'properties': {'component': {'title': 'Component', 'type': 'string'}, 'params': {'title': 'Params', 'type': 'object'}}, 'required': ['component', 'params'], 'title': 'Encoding Model', 'type': 'object'}}, 'required': ['encoding_model'], 'title': 'DenseRetrieverSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}}, 'GenerativeModel': {'GemmaModel': {'name': 'GemmaModel', 'type': 'GenerativeModel', 'class': , 'configurable_object': True, 'schema': {'description': 'Schema for Gemma model.', 'properties': {'max_tokens': {'description': 'Maximum number of tokens to generate.', 'minimum': 1, 'placeholder': 100, 'title': 'Max Tokens', 'type': 'integer'}, 'temperature': {'description': 'Sampling temperature. Higher values make the output more random, while lower values make it more focused and deterministic.', 'maximum': 1.0, 'minimum': 0.0, 'placeholder': 0.7, 'title': 'Temperature', 'type': 'number'}, 'frequency_penalty': {'description': 'Penalty for repeated tokens in the output. Higher values reduce the likelihood of repetition, encouraging more diverse text generation.', 'maximum': 2.0, 'minimum': 0.0, 'placeholder': 0.1, 'title': 'Frequency Penalty', 'type': 'number'}, 'context_window': {'description': 'Maximum number of tokens the model can process in a single forward pass (context window size).', 'minimum': 1, 'placeholder': 512, 'title': 'Context Window', 'type': 'integer'}, 'device': {'description': 'The device to use for model inference.', 'enum': ['cpu'], 'placeholder': 'cpu', 'title': 'Device', 'type': 'string'}}, 'required': ['max_tokens', 'temperature', 'frequency_penalty', 'context_window', 'device'], 'title': 'GemmaSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'QwenModel': {'name': 'QwenModel', 'type': 'GenerativeModel', 'class': , 'configurable_object': True, 'schema': {'description': 'Schema for Qwen model.', 'properties': {'model_name': {'description': 'The specific Qwen model version to use.', 'enum': ['Qwen/Qwen2.5-0.5B-Instruct-GGUF', 'Qwen/Qwen2.5-1.5B-Instruct-GGUF', 'Qwen/Qwen3-4B-GGUF '], 'placeholder': 'Qwen/Qwen2.5-1.5B-Instruct-GGUF', 'title': 'Model Name', 'type': 'string'}, 'max_tokens': {'description': 'Maximum number of tokens to generate.', 'minimum': 1, 'placeholder': 100, 'title': 'Max Tokens', 'type': 'integer'}, 'temperature': {'description': 'Sampling temperature. Higher values make the output more random, while lower values make it more focused and deterministic.', 'maximum': 1.0, 'minimum': 0.0, 'placeholder': 0.7, 'title': 'Temperature', 'type': 'number'}, 'frequency_penalty': {'description': 'Penalty for repeated tokens in the output. Higher values reduce the likelihood of repetition, encouraging more diverse text generation.', 'maximum': 2.0, 'minimum': 0.0, 'placeholder': 0.1, 'title': 'Frequency Penalty', 'type': 'number'}, 'context_window': {'description': 'Maximum number of tokens the model can process in a single forward pass (context window size).', 'minimum': 1, 'placeholder': 512, 'title': 'Context Window', 'type': 'integer'}, 'device': {'description': 'The device to use for model inference.', 'enum': ['cpu'], 'placeholder': 'cpu', 'title': 'Device', 'type': 'string'}}, 'required': ['model_name', 'max_tokens', 'temperature', 'frequency_penalty', 'context_window', 'device'], 'title': 'QwenSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'StableDiffusionV2Model': {'name': 'StableDiffusionV2Model', 'type': 'GenerativeModel', 'class': , 'configurable_object': True, 'schema': {'description': 'Schema for Stable Diffusion V2 image generation.', 'properties': {'model_name': {'description': 'The specific Stable Diffusion model version to use.', 'enum': ['stabilityai/stable-diffusion-2', 'stabilityai/stable-diffusion-2-base', 'stabilityai/stable-diffusion-2-1', 'stabilityai/stable-diffusion-2-1-base'], 'placeholder': 'stabilityai/stable-diffusion-2', 'title': 'Model Name', 'type': 'string'}, 'negative_prompt': {'anyOf': [{'description': 'Text prompt for elements to avoid in the image.', 'placeholder': '', 'type': 'string'}, {'type': 'null'}], 'title': 'Negative Prompt'}, 'num_inference_steps': {'description': 'Number of denoising steps. Higher usually leads to better quality but slower inference.', 'minimum': 1, 'placeholder': 15, 'title': 'Num Inference Steps', 'type': 'integer'}, 'guidance_scale': {'description': 'How strongly the model follows the prompt. Higher = closer to prompt, but may reduce image quality.', 'minimum': 0.0, 'placeholder': 3.5, 'title': 'Guidance Scale', 'type': 'number'}, 'device': {'description': "Device for generation. Use 'cuda' if GPU is available.", 'enum': ['cuda:0', 'cpu'], 'placeholder': 'cuda:0', 'title': 'Device', 'type': 'string'}, 'seed': {'description': 'Random seed for reproducibility. Use negative value for random seed.', 'placeholder': -1, 'title': 'Seed', 'type': 'integer'}, 'width': {'description': 'Width of the generated image. Must be a multiple of 8.', 'maximum': 2048, 'minimum': 64, 'placeholder': 512, 'title': 'Width', 'type': 'integer'}, 'height': {'description': 'Height of the generated image. Must be a multiple of 8.', 'maximum': 2048, 'minimum': 64, 'placeholder': 512, 'title': 'Height', 'type': 'integer'}, 'num_images_per_prompt': {'description': 'Number of images to generate per prompt.', 'minimum': 1, 'placeholder': 1, 'title': 'Num Images Per Prompt', 'type': 'integer'}}, 'required': ['model_name', 'negative_prompt', 'num_inference_steps', 'guidance_scale', 'device', 'seed', 'width', 'height', 'num_images_per_prompt'], 'title': 'StableDiffusionSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'StableDiffusionV3Model': {'name': 'StableDiffusionV3Model', 'type': 'GenerativeModel', 'class': , 'configurable_object': True, 'schema': {'description': 'Schema for Stable Diffusion V3 image generation.', 'properties': {'model_name': {'description': 'The specific Stable Diffusion model version to use.', 'enum': ['stabilityai/stable-diffusion-3-medium-diffusers', 'stabilityai/stable-diffusion-3-medium-tensorrt', 'stabilityai/stable-diffusion-3.5-medium', 'stabilityai/stable-diffusion-3.5-large', 'stabilityai/stable-diffusion-3.5-large-turbo'], 'placeholder': 'stabilityai/stable-diffusion-3-medium-diffusers', 'title': 'Model Name', 'type': 'string'}, 'negative_prompt': {'anyOf': [{'description': 'Text prompt for elements to avoid in the image.', 'placeholder': '', 'type': 'string'}, {'type': 'null'}], 'title': 'Negative Prompt'}, 'num_inference_steps': {'description': 'Number of denoising steps. Higher usually leads to better quality but slower inference.', 'minimum': 1, 'placeholder': 15, 'title': 'Num Inference Steps', 'type': 'integer'}, 'guidance_scale': {'description': 'How strongly the model follows the prompt. Higher = closer to prompt, but may reduce image quality.', 'minimum': 0.0, 'placeholder': 3.5, 'title': 'Guidance Scale', 'type': 'number'}, 'device': {'description': "Device for generation. Use 'cuda' if GPU is available.", 'enum': ['cuda:0', 'cpu'], 'placeholder': 'cuda:0', 'title': 'Device', 'type': 'string'}, 'seed': {'description': 'Random seed for reproducibility. Use negative value for random seed.', 'placeholder': -1, 'title': 'Seed', 'type': 'integer'}, 'width': {'description': 'Width of the generated image. Must be a multiple of 8.', 'maximum': 2048, 'minimum': 64, 'placeholder': 512, 'title': 'Width', 'type': 'integer'}, 'height': {'description': 'Height of the generated image. Must be a multiple of 8.', 'maximum': 2048, 'minimum': 64, 'placeholder': 512, 'title': 'Height', 'type': 'integer'}, 'num_images_per_prompt': {'description': 'Number of images to generate per prompt.', 'minimum': 1, 'placeholder': 1, 'title': 'Num Images Per Prompt', 'type': 'integer'}}, 'required': ['model_name', 'negative_prompt', 'num_inference_steps', 'guidance_scale', 'device', 'seed', 'width', 'height', 'num_images_per_prompt'], 'title': 'StableDiffusionSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'StableDiffusionXLV1ControlNet': {'name': 'StableDiffusionXLV1ControlNet', 'type': 'GenerativeModel', 'class': , 'configurable_object': True, 'schema': {'properties': {'num_inference_steps': {'description': 'Number of denoising steps. Higher usually leads to better quality but slower inference.', 'minimum': 1, 'placeholder': 15, 'title': 'Num Inference Steps', 'type': 'integer'}, 'controlnet_conditioning_scale': {'description': 'Scale for the ControlNet conditioning. Higher values make the model follow the controlnet more closely.', 'minimum': 0.0, 'placeholder': 1.0, 'title': 'Controlnet Conditioning Scale', 'type': 'number'}, 'device': {'description': "Device for generation. Use 'cuda' if GPU is available.", 'enum': ['cuda:0', 'cpu'], 'placeholder': 'cuda:0', 'title': 'Device', 'type': 'string'}}, 'required': ['num_inference_steps', 'controlnet_conditioning_scale', 'device'], 'title': 'StableDiffusionXLV1ControlNetSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'RAGPipeline': {'name': 'RAGPipeline', 'type': 'GenerativeModel', 'class': , 'configurable_object': True, 'schema': {'description': 'Schema for RAG pipeline.', 'properties': {'documents': {'description': 'List of documents to be used in the RAG pipeline.', 'items': {'type': 'string'}, 'minItems': 1, 'placeholder': None, 'title': 'Documents', 'type': 'array'}, 'retriever_model': {'description': 'Retriever component used in the RAG pipeline.', 'parent': 'RetrieverModel', 'placeholder': {'component': 'TFIDFRetriever', 'params': {}}, 'properties': {'component': {'title': 'Component', 'type': 'string'}, 'params': {'title': 'Params', 'type': 'object'}}, 'required': ['component', 'params'], 'title': 'Retriever Model', 'type': 'object'}, 'generation_model': {'description': 'Text generation model used in the RAG pipeline.', 'parent': 'TextToTextGenerationTaskModel', 'placeholder': {'component': 'DeepSeek', 'params': {}}, 'properties': {'component': {'title': 'Component', 'type': 'string'}, 'params': {'title': 'Params', 'type': 'object'}}, 'required': ['component', 'params'], 'title': 'Generation Model', 'type': 'object'}}, 'required': ['documents', 'retriever_model', 'generation_model'], 'title': 'RAGPipelineSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}}, 'DataLoader': {'CSVDataLoader': {'name': 'CSVDataLoader', 'type': 'DataLoader', 'class': , 'configurable_object': True, 'schema': {'properties': {'name': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'description': 'Custom name to register your dataset. If no name is specified, the name of the uploaded file will be used.', 'placeholder': '', 'title': 'Name'}, 'separator': {'description': 'A separator character delimits the data in a CSV file.', 'enum': [',', ';', ' ', '\t'], 'placeholder': ',', 'title': 'Separator', 'type': 'string'}}, 'required': ['name', 'separator'], 'title': 'CSVDataloaderSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'JSONDataLoader': {'name': 'JSONDataLoader', 'type': 'DataLoader', 'class': , 'configurable_object': True, 'schema': {'properties': {'name': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'description': 'Custom name to register your dataset. If no name is specified, the name of the uploaded file will be used.', 'placeholder': '', 'title': 'Name'}, 'data_key': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'description': '\n In case the data has the form {data: [{col1: val1, col2: val2, ...]}}\n (also known as table in pandas), name of the field "data",\n where the list with dictionaries with the data should be found.\n\n In case the format is only a list of dictionaries (also known as\n "records" orient in pandas), set this value as null.\n ', 'placeholder': 'data', 'title': 'Data Key'}}, 'required': ['name', 'data_key'], 'title': 'JSONDataloaderSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'ExcelDataLoader': {'name': 'ExcelDataLoader', 'type': 'DataLoader', 'class': , 'configurable_object': True, 'schema': {'properties': {'name': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'description': 'Custom name to register your dataset. If no name is specified, the name of the uploaded file will be used.', 'placeholder': '', 'title': 'Name'}, 'sheet': {'anyOf': [{'minimum': 0, 'type': 'integer'}, {'type': 'string'}], 'description': '\n The name of the sheet to read or its zero-based index.\n If a string is provided, the reader will search for a sheet named exactly as\n the string.\n If an integer is provided, the reader will select the sheet at the corresponding\n index.\n By default, the first sheet will be read.\n ', 'placeholder': 0, 'title': 'Sheet'}, 'header': {'anyOf': [{'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': '\n The row number where the column names are located, indexed from 0.\n If null, the file will be considered to have no column names.\n ', 'placeholder': 0, 'title': 'Header'}, 'usecols': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'description': '\n If None, the reader will load all columns.\n If str, then indicates comma separated list of Excel column letters and column\n ranges (e.g. A:E or A,C,E:F). Ranges are inclusive of both sides.\n ', 'placeholder': None, 'title': 'Usecols'}}, 'required': ['name', 'sheet', 'header', 'usecols'], 'title': 'ExcelDataloaderSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}}, 'Metric': {'F1': {'name': 'F1', 'type': 'Metric', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}, 'Accuracy': {'name': 'Accuracy', 'type': 'Metric', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}, 'Precision': {'name': 'Precision', 'type': 'Metric', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}, 'Recall': {'name': 'Recall', 'type': 'Metric', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}, 'Bleu': {'name': 'Bleu', 'type': 'Metric', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}, 'Ter': {'name': 'Ter', 'type': 'Metric', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}, 'MAE': {'name': 'MAE', 'type': 'Metric', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}, 'RMSE': {'name': 'RMSE', 'type': 'Metric', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}}, 'Optimizer': {'OptunaOptimizer': {'name': 'OptunaOptimizer', 'type': 'Optimizer', 'class': , 'configurable_object': True, 'schema': {'properties': {'n_trials': {'description': "The parameter 'n_trials' is the quantity of trialsper study. It must be of type positive integer.", 'exclusiveMinimum': 0, 'placeholder': 10, 'title': 'N Trials', 'type': 'integer'}, 'sampler': {'description': "Coefficient for 'rbf', 'poly' and 'sigmoid' kernels. Must be in string format and can be 'scale' or 'auto'.", 'enum': ['TPESampler', 'CmaEsSampler', 'GridSampler', 'GPSampler', 'NSGAIISampler', 'QMCSampler', 'RandomSampler'], 'placeholder': 'TPESampler', 'title': 'Sampler', 'type': 'string'}, 'pruner': {'description': "Coefficient for 'rbf', 'poly' and 'sigmoid' kernels. Must be in string format and can be 'scale' or 'auto'.", 'enum': ['MedianPruner', 'None'], 'placeholder': 'None', 'title': 'Pruner', 'type': 'string'}}, 'required': ['n_trials', 'sampler', 'pruner'], 'title': 'OptunaSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'HyperOptOptimizer': {'name': 'HyperOptOptimizer', 'type': 'Optimizer', 'class': , 'configurable_object': True, 'schema': {'properties': {'n_trials': {'description': "The parameter 'n_trials' is the quantity of trialsper study. It must be of type positive integer.", 'exclusiveMinimum': 0, 'placeholder': 10, 'title': 'N Trials', 'type': 'integer'}, 'sampler': {'description': "Coefficient for 'rbf', 'poly' and 'sigmoid' kernels. Must be in string format and can be 'scale' or 'auto'.", 'enum': ['tpe', 'rand'], 'placeholder': 'tpe', 'title': 'Sampler', 'type': 'string'}}, 'required': ['n_trials', 'sampler'], 'title': 'HyperOptSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}}, 'Job': {'ExplainerJob': {'name': 'ExplainerJob', 'type': 'Job', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}, 'ModelJob': {'name': 'ModelJob', 'type': 'Job', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}, 'ExplorerJob': {'name': 'ExplorerJob', 'type': 'Job', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}, 'PredictJob': {'name': 'PredictJob', 'type': 'Job', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}, 'ConverterListJob': {'name': 'ConverterListJob', 'type': 'Job', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}, 'DatasetJob': {'name': 'DatasetJob', 'type': 'Job', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}, 'GenerativeJob': {'name': 'GenerativeJob', 'type': 'Job', 'class': , 'configurable_object': False, 'schema': None, 'metadata': None, 'description': None, 'display_name': None}}, 'LocalExplainer': {'KernelShap': {'name': 'KernelShap', 'type': 'LocalExplainer', 'class': , 'configurable_object': True, 'schema': {'description': "Kernel SHAP is a model-agnostic explainability method for approximating SHAP\nvalues to explain the output of machine learning model by attributing contributions\nof each feature to the model's prediction.", 'properties': {'link': {'description': "Link function to connect the feature importance values to the model's outputs. Options are 'identity' to use identity function or 'logit' to use log-odds function.", 'enum': ['identity', 'logit'], 'placeholder': 'identity', 'title': 'Link', 'type': 'string'}, 'fit_parameter_sample_background_data': {'description': "Parameter to fit the explainer. 'true' if the background data must be sampled, otherwise the entire train data set is used. Smaller datasets speed up the algorithm run time.", 'placeholder': False, 'title': 'Fit Parameter Sample Background Data', 'type': 'boolean'}, 'fit_parameter_n_background_samples': {'description': "Parameter to fit the explainer. If the parameter 'sample_background_data' is 'true', the number of background data samples to be drawn.", 'minimum': 1, 'placeholder': 1, 'title': 'Fit Parameter N Background Samples', 'type': 'integer'}, 'fit_parameter_sampling_method': {'description': "Parameter to fit the explainer. If the parameter 'sample_background_data' is 'true', whether to sample random samples with 'shuffle' option or summarize the data set with 'kmeans' option. If 'categorical_features' is 'true', 'shuffle' options used by default.", 'enum': ['shuffle', 'kmeans'], 'placeholder': 'shuffle', 'title': 'Fit Parameter Sampling Method', 'type': 'string'}}, 'required': ['link', 'fit_parameter_sample_background_data', 'fit_parameter_n_background_samples', 'fit_parameter_sampling_method'], 'title': 'KernelShapSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}}, 'GlobalExplainer': {'PartialDependence': {'name': 'PartialDependence', 'type': 'GlobalExplainer', 'class': , 'configurable_object': True, 'schema': {'description': 'PartialDependence of a feature shows the average prediction of a machine\nlearning model for each possible value of the feature.', 'properties': {'grid_resolution': {'description': 'The number of equidistant points to split the range of the target feature', 'minimum': 1, 'placeholder': 100, 'title': 'Grid Resolution', 'type': 'integer'}, 'lower_percentile': {'description': 'The lower percentile used to limit the feature values.', 'maximum': 0.99, 'minimum': 0.0, 'placeholder': 0.05, 'title': 'Lower Percentile', 'type': 'number'}, 'upper_percentile': {'description': 'The upper percentile used to limit the feature values.', 'maximum': 1.0, 'minimum': 0.01, 'placeholder': 0.95, 'title': 'Upper Percentile', 'type': 'number'}}, 'required': ['grid_resolution', 'lower_percentile', 'upper_percentile'], 'title': 'PartialDependenceSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}, 'PermutationFeatureImportance': {'name': 'PermutationFeatureImportance', 'type': 'GlobalExplainer', 'class': , 'configurable_object': True, 'schema': {'description': "Permutation Feature Importance is a explanation method to asses the\nimportance of each feature in a model by evaluating how much the model's\nperformance decreases when the values of a specific feature are randomly\nshuffled.", 'properties': {'scoring': {'description': 'Scorer to evaluate how the perfomance of the model changes when a particular feature is shuffled.', 'enum': ['accuracy', 'balanced_accuracy'], 'placeholder': 'accuracy', 'title': 'Scoring', 'type': 'string'}, 'n_repeats': {'description': 'Number of times to permute a feature.', 'minimum': 1, 'placeholder': 10, 'title': 'N Repeats', 'type': 'integer'}, 'random_state': {'description': 'Seed for the random number generator to control the permutations of each feature.', 'placeholder': 0, 'title': 'Random State', 'type': 'integer'}, 'max_samples': {'description': 'The number of samples to draw from the dataset to calculate feature importance at each repetition.', 'minimum': 1, 'placeholder': 100, 'title': 'Max Samples', 'type': 'integer'}}, 'required': ['scoring', 'n_repeats', 'random_state', 'max_samples'], 'title': 'PermutationFeatureImportanceSchema', 'type': 'object'}, 'metadata': None, 'description': None, 'display_name': None}}, 'Explorer': {'DescribeExplorer': {'name': 'DescribeExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'percentiles': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'description': "The percentiles to include in the exploration. Must be a list of integers between 0 and 100.\nExample: '25, 50, 75'", 'placeholder': '25, 50, 75', 'title': 'Percentiles'}, 'include': {'anyOf': [{'enum': ['all', 'number', 'object', 'category', 'datetime'], 'type': 'string'}, {'type': 'null'}], 'description': 'The data types to include in the exploration.\n', 'placeholder': 'all', 'title': 'Include'}, 'exclude': {'anyOf': [{'enum': ['object', 'number', 'category', 'datetime'], 'type': 'string'}, {'type': 'null'}], 'description': 'The data types to exclude in the exploration.', 'placeholder': None, 'title': 'Exclude'}}, 'required': ['percentiles', 'include', 'exclude'], 'title': 'DescribeExplorerSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['*'], 'restricted_dtypes': [], 'input_cardinality': {'min': 1}, 'display_name': 'Describe Dataset', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'DescribeExplorer is an explorer that describes the dataset. It returns a tabular representation of the dataset with the count, mean, std, min, 25%, 50%, 75%, and max values for numeric columns and count, unique, top, and freq values for object columns.\nThe user can specify the percentiles to include in the exploration and the data types to include or exclude.', 'display_name': 'Describe Dataset'}, 'ScatterPlotExplorer': {'name': 'ScatterPlotExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'color_group': {'anyOf': [{'type': 'string'}, {'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': 'The columnName or columnIndex to take for grouping colored points.', 'placeholder': None, 'title': 'Color Group'}, 'simbol_group': {'anyOf': [{'type': 'string'}, {'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': 'The columnName or columnIndex to take for grouping simbol of the points.', 'placeholder': None, 'title': 'Simbol Group'}, 'point_size': {'anyOf': [{'type': 'string'}, {'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': 'The columnName or columnIndex to take for size of each point.', 'placeholder': None, 'title': 'Point Size'}}, 'required': ['color_group', 'simbol_group', 'point_size'], 'title': 'ScatterPlotSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['*'], 'restricted_dtypes': [], 'input_cardinality': {'exact': 2}, 'display_name': 'Scatter Plot', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'ScatterPlotExplorer is an explorer that returns a scatter plot of selected columns of a dataset.', 'display_name': 'Scatter Plot'}, 'WordcloudExplorer': {'name': 'WordcloudExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'max_words': {'description': 'The maximum number of words to display in the wordcloud.', 'exclusiveMinimum': 0, 'placeholder': 200, 'title': 'Max Words', 'type': 'integer'}, 'background_color': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'description': 'The background color of the wordcloud. If None, the background will be transparent.', 'placeholder': None, 'title': 'Background Color'}}, 'required': ['max_words', 'background_color'], 'title': 'WordcloudSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['string'], 'restricted_dtypes': [], 'input_cardinality': {'min': 1}, 'display_name': 'Word Cloud', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'A wordcloud is a visual representation of text data, where the size of each word indicates its frequency in the text.\nThis explorer generates a wordcloud from the concatenated strings of all selected columns in the dataset.', 'display_name': 'Word Cloud'}, 'RowExplorer': {'name': 'RowExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'row_ammount': {'description': 'The maximum number of rows to take.', 'exclusiveMinimum': 0, 'placeholder': 50, 'title': 'Row Ammount', 'type': 'integer'}, 'shuffle': {'description': 'Shuffle the rows at exploration time.', 'placeholder': False, 'title': 'Shuffle', 'type': 'boolean'}, 'from_top': {'description': 'Take the rows from the Head of the dataset. Else, take from the Tail.', 'placeholder': True, 'title': 'From Top', 'type': 'boolean'}}, 'required': ['row_ammount', 'shuffle', 'from_top'], 'title': 'RowExplorerSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['*'], 'restricted_dtypes': [], 'input_cardinality': {'min': 1}, 'display_name': 'Show Rows', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'RowExplorer is an explorer that takes a number of rows from the dataset to display them on tabular format. It can take the rows from the top or the bottom of the dataset and shuffle them if needed.', 'display_name': 'Show Rows'}, 'BoxPlotExplorer': {'name': 'BoxPlotExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'horizontal': {'description': 'If True, the box plot will be horizontal, otherwise vertical.', 'placeholder': False, 'title': 'Horizontal', 'type': 'boolean'}, 'points': {'description': "One of 'all', 'outliers', or 'False'. Determines which points are shown.", 'enum': ['all', 'outliers', 'False'], 'placeholder': 'outliers', 'title': 'Points', 'type': 'string'}}, 'required': ['horizontal', 'points'], 'title': 'BoxPlotSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['*'], 'restricted_dtypes': [], 'input_cardinality': {'min': 1, 'max': 2}, 'display_name': 'Box Plot', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'BoxPlotExplorer is an explorer that returns a box plot of selected columns of a dataset.', 'display_name': 'Box Plot'}, 'MultiColumnBoxPlotExplorer': {'name': 'MultiColumnBoxPlotExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'horizontal': {'description': 'If True, the box plot will be horizontal, otherwise vertical.', 'placeholder': False, 'title': 'Horizontal', 'type': 'boolean'}, 'points': {'description': "One of 'all', 'outliers', or 'False'. Determines which points are shown.", 'enum': ['all', 'outliers', 'False'], 'placeholder': 'outliers', 'title': 'Points', 'type': 'string'}, 'opposite_axis': {'anyOf': [{'type': 'string'}, {'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': 'The columnName or columnIndex to take for the opposite axis.', 'placeholder': None, 'title': 'Opposite Axis'}}, 'required': ['horizontal', 'points', 'opposite_axis'], 'title': 'MultiColumnBoxPlotSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['*'], 'restricted_dtypes': [], 'input_cardinality': {'min': 1}, 'display_name': 'Multiple Column Box Plot', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'MultiColumnBoxPlotExplorer is an explorer that returns a figure with a box plot of multiple columns of a dataset in a single axis. The other axis is selected through the opposite_axis parameter.', 'display_name': 'Multiple Column Box Plot'}, 'CorrelationMatrixExplorer': {'name': 'CorrelationMatrixExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'method': {'description': "The correlation method to use. Must be one of 'pearson', 'kendall', or 'spearman'.", 'enum': ['pearson', 'kendall', 'spearman'], 'placeholder': 'pearson', 'title': 'Method', 'type': 'string'}, 'min_periods': {'description': "The minimum number of observations required per pair of columns to have a valid result. Only used if method is 'pearson' or 'spearman'.", 'exclusiveMinimum': 0, 'placeholder': 1, 'title': 'Min Periods', 'type': 'integer'}, 'numeric_only': {'description': 'If True, only include numeric columns when calculating correlation.If False, all columns are included.', 'placeholder': True, 'title': 'Numeric Only', 'type': 'boolean'}, 'plot': {'description': 'If True, the result will be plotted.', 'placeholder': True, 'title': 'Plot', 'type': 'boolean'}}, 'required': ['method', 'min_periods', 'numeric_only', 'plot'], 'title': 'CorrelationMatrixExplorerSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['*'], 'restricted_dtypes': [], 'input_cardinality': {'min': 2}, 'display_name': 'Correlation Matrix', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'CorrelationMatrixExplorer is an explorer that returns a correlation matrix of a dataset.\nIts result is a heatmap by default, but can also be returned as a tabular result.', 'display_name': 'Correlation Matrix'}, 'CovarianceMatrixExplorer': {'name': 'CovarianceMatrixExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'min_periods': {'description': 'The minimum number of observations required per pair of columns to have a valid result.', 'exclusiveMinimum': 0, 'placeholder': 1, 'title': 'Min Periods', 'type': 'integer'}, 'delta_degree_of_freedom': {'description': 'The delta degree of freedom to use when calculating the covariance matrix.Only used if numeric_only is True.', 'exclusiveMinimum': 0, 'placeholder': 1, 'title': 'Delta Degree Of Freedom', 'type': 'integer'}, 'numeric_only': {'description': 'If True, only include numeric columns when calculating correlation.If False, all columns are included.', 'placeholder': True, 'title': 'Numeric Only', 'type': 'boolean'}, 'plot': {'description': 'If True, the result will be plotted.', 'placeholder': True, 'title': 'Plot', 'type': 'boolean'}}, 'required': ['min_periods', 'delta_degree_of_freedom', 'numeric_only', 'plot'], 'title': 'CovarianceMatrixExplorerSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['*'], 'restricted_dtypes': [], 'input_cardinality': {'min': 2}, 'display_name': 'Covariance Matrix', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'CovarianceExplorer is an explorer that returns the covariance matrix of the dataset.\nIts result is a heatmap by default, but can also be returned as a tabular result.', 'display_name': 'Covariance Matrix'}, 'DensityHeatmapExplorer': {'name': 'DensityHeatmapExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'nbinsx': {'anyOf': [{'minimum': 1, 'type': 'integer'}, {'type': 'null'}], 'description': 'The number of bins along the x axis.', 'placeholder': None, 'title': 'Nbinsx'}, 'nbinsy': {'anyOf': [{'minimum': 1, 'type': 'integer'}, {'type': 'null'}], 'description': 'The number of bins along the y axis.', 'placeholder': None, 'title': 'Nbinsy'}}, 'required': ['nbinsx', 'nbinsy'], 'title': 'DensityHeatmapSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['*'], 'restricted_dtypes': [], 'input_cardinality': {'exact': 2}, 'display_name': 'Density Heatmap', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'DensityHeatmapExplorer is an explorer that returns a density heatmap of selected columns of a dataset.', 'display_name': 'Density Heatmap'}, 'ECDFPlotExplorer': {'name': 'ECDFPlotExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'color_column': {'anyOf': [{'type': 'string'}, {'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': 'The column to use for coloring the ECDF plot.', 'placeholder': None, 'title': 'Color Column'}, 'facet_col': {'anyOf': [{'type': 'string'}, {'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': 'The column to use for faceting the ECDF plot in the column direction.', 'placeholder': None, 'title': 'Facet Col'}, 'facet_row': {'anyOf': [{'type': 'string'}, {'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': 'The column to use for faceting the ECDF plot in the row direction.', 'placeholder': None, 'title': 'Facet Row'}, 'ecdf_norm': {'description': 'Specifies the type of normalization used for this ECDF plot.', 'enum': ['none', 'percent', 'probability'], 'placeholder': 'probability', 'title': 'Ecdf Norm', 'type': 'string'}}, 'required': ['color_column', 'facet_col', 'facet_row', 'ecdf_norm'], 'title': 'ECDFPlotSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['float64', 'float32'], 'restricted_dtypes': [], 'input_cardinality': {'min': 1}, 'display_name': 'Empirical Cumulative Distribution Plot', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'The ECDF plot is a non-parametric way to explore the distribution of a variable. It shows the proportion or count of observations falling below each unique value in the dataset.', 'display_name': 'Empirical Cumulative Distribution Plot'}, 'HistogramPlotExplorer': {'name': 'HistogramPlotExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'nbins': {'anyOf': [{'minimum': 1, 'type': 'integer'}, {'type': 'null'}], 'description': 'The number of bins to use for the histogram.', 'placeholder': None, 'title': 'Nbins'}, 'histfunc': {'description': 'Specifies the binning function used for this histogram trace.', 'enum': ['count', 'sum', 'avg', 'min', 'max'], 'placeholder': 'count', 'title': 'Histfunc', 'type': 'string'}, 'histnorm': {'description': 'Specifies the type of normalization used for this histogram trace.', 'enum': ['', 'percent', 'probability', 'density', 'probability density'], 'placeholder': '', 'title': 'Histnorm', 'type': 'string'}, 'color_group': {'anyOf': [{'type': 'string'}, {'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': 'The columnName or columnIndex to take for grouping colored points.', 'placeholder': None, 'title': 'Color Group'}, 'pattern_group': {'anyOf': [{'type': 'string'}, {'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': 'The columnName or columnIndex to take for grouping pattern of the points.', 'placeholder': None, 'title': 'Pattern Group'}}, 'required': ['nbins', 'histfunc', 'histnorm', 'color_group', 'pattern_group'], 'title': 'HistogramPlotSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['*'], 'restricted_dtypes': [], 'input_cardinality': {'exact': 1}, 'display_name': 'Histogram Plot', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'HistogramPlotExplorer is an explorer that returns a density heatmap of a selected column of a dataset.', 'display_name': 'Histogram Plot'}, 'ScatterMatrixExplorer': {'name': 'ScatterMatrixExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'color_group': {'anyOf': [{'type': 'string'}, {'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': 'The columnName or columnIndex to take for grouping colored points.', 'placeholder': None, 'title': 'Color Group'}, 'simbol_group': {'anyOf': [{'type': 'string'}, {'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': 'The columnName or columnIndex to take for grouping simbol of the points.', 'placeholder': None, 'title': 'Simbol Group'}}, 'required': ['color_group', 'simbol_group'], 'title': 'ScatterMatrixSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['*'], 'restricted_dtypes': [], 'input_cardinality': {'min': 2}, 'display_name': 'Multiple Scatter Plot', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'ScatterMatrixExplorer is an explorer that returns a scatter matrix plot of selected columns of a dataset. Multiple scatter plots are generated for each pair of columns. The diagonal plots are histograms of the columns. ', 'display_name': 'Multiple Scatter Plot'}, 'ParallelCategoriesExplorer': {'name': 'ParallelCategoriesExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'color_column': {'anyOf': [{'type': 'string'}, {'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': 'The column to use for coloring the data points. ', 'placeholder': None, 'title': 'Color Column'}}, 'required': ['color_column'], 'title': 'ParallelCategoriesSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['string'], 'restricted_dtypes': [], 'input_cardinality': {'min': 2}, 'display_name': 'Parallel Categories Plot', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'A parallel categories plot is a common way to visualize high-dimensional data. Each vertical line represents one data point, and the lines are connected by a series of horizontal lines. ', 'display_name': 'Parallel Categories Plot'}, 'ParallelCordinatesExplorer': {'name': 'ParallelCordinatesExplorer', 'type': 'Explorer', 'class': , 'configurable_object': True, 'schema': {'properties': {'color_column': {'anyOf': [{'type': 'string'}, {'minimum': 0, 'type': 'integer'}, {'type': 'null'}], 'description': 'The column to use for coloring the data points. ', 'placeholder': None, 'title': 'Color Column'}}, 'required': ['color_column'], 'title': 'ParallelCordinatesSchema', 'type': 'object'}, 'metadata': {'allowed_dtypes': ['float64', 'float32'], 'restricted_dtypes': [], 'input_cardinality': {'min': 2}, 'display_name': 'Parallel Cordinates Plot', 'allowed_value_types': ['*'], 'restricted_value_types': []}, 'description': 'A parallel coordinates plot is a common way to visualize high-dimensional data. Each vertical line represents one data point, and the lines are connected by a series of horizontal lines. ', 'display_name': 'Parallel Cordinates Plot'}}, 'Converter': {'ColumnRemover': {'name': 'ColumnRemover', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {}, 'title': 'ColumnRemoverSchema', 'type': 'object'}, 'metadata': None, 'description': 'Removes the columns selected in scope from the dataset.', 'display_name': None}, 'CharacterReplacer': {'name': 'CharacterReplacer', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'char_to_replace': {'description': 'The character or substring to be replaced. Cannot be empty.', 'placeholder': '', 'title': 'Char To Replace', 'type': 'string'}, 'replacement_char': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'description': "The character or substring to replace with. If null, 'char_to_replace' will be removed.", 'placeholder': None, 'title': 'Replacement Char'}}, 'required': ['char_to_replace', 'replacement_char'], 'title': 'CharacterReplacerSchema', 'type': 'object'}, 'metadata': None, 'description': 'Replaces or removes specified characters/substrings in selected string columns.', 'display_name': None}, 'FastICA': {'name': 'FastICA', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'n_components': {'anyOf': [{'minimum': 1, 'type': 'integer'}, {'type': 'null'}], 'description': 'Number of components to extract.', 'placeholder': None, 'title': 'N Components'}, 'algorithm': {'description': 'Apply parallel or deflational algorithm for FastICA.', 'enum': ['parallel', 'deflation'], 'placeholder': 'parallel', 'title': 'Algorithm', 'type': 'string'}, 'whiten': {'anyOf': [{'enum': ['arbitrary-variance', 'unit-variance'], 'type': 'string'}, {'type': 'boolean'}, {'type': 'null'}], 'description': 'If True, the data is whitened.', 'placeholder': 'unit-variance', 'title': 'Whiten'}, 'fun': {'description': 'The functional form of the G function used in the approximation to neg-entropy.', 'enum': ['logcosh', 'exp', 'cube'], 'placeholder': 'logcosh', 'title': 'Fun', 'type': 'string'}, 'fun_args': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'description': 'Arguments to the G function.', 'placeholder': None, 'title': 'Fun Args'}, 'max_iter': {'description': 'Maximum number of iterations to perform.', 'minimum': 1, 'placeholder': 200, 'title': 'Max Iter', 'type': 'integer'}, 'tol': {'description': 'Tolerance on update at each iteration.', 'minimum': 0.0, 'placeholder': 0.0001, 'title': 'Tol', 'type': 'number'}, 'w_init': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'description': 'Initial guess for the unmixing matrix.', 'placeholder': None, 'title': 'W Init'}, 'whiten_solver': {'description': 'The solver to use for whitening.', 'enum': ['eigh', 'svd'], 'placeholder': 'svd', 'title': 'Whiten Solver', 'type': 'string'}, 'random_state': {'anyOf': [{'type': 'integer'}, {'enum': ['RandomState'], 'type': 'string'}, {'type': 'null'}], 'description': 'Used to initialize w_init when not specified, with a normal distribution. Pass an int, for reproducible results across multiple function calls.', 'placeholder': None, 'title': 'Random State'}}, 'required': ['n_components', 'algorithm', 'whiten', 'fun', 'fun_args', 'max_iter', 'tol', 'w_init', 'whiten_solver', 'random_state'], 'title': 'FastICASchema', 'type': 'object'}, 'metadata': None, 'description': 'FastICA: a fast algorithm for Independent Component Analysis.', 'display_name': None}, 'IncrementalPCA': {'name': 'IncrementalPCA', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'n_components': {'anyOf': [{'minimum': 1, 'type': 'integer'}, {'type': 'null'}], 'description': 'Number of components to keep.', 'placeholder': 2, 'title': 'N Components'}, 'whiten': {'description': 'When True (False by default) the components_ vectors are multiplied by the square root of n_samples and then divided by the singular values to ensure uncorrelated outputs with unit component-wise variances.', 'placeholder': False, 'title': 'Whiten', 'type': 'boolean'}, 'copy': {'description': 'If False, data passed to fit are overwritten and running fit(X).transform(X) will not yield the expected results, use fit_transform(X) instead.', 'placeholder': True, 'title': 'Copy', 'type': 'boolean'}, 'batch_size': {'anyOf': [{'minimum': 1, 'type': 'integer'}, {'type': 'null'}], 'description': 'The number of samples to use for each batch.', 'placeholder': None, 'title': 'Batch Size'}}, 'required': ['n_components', 'whiten', 'copy', 'batch_size'], 'title': 'IncrementalPCASchema', 'type': 'object'}, 'metadata': None, 'description': 'Incremental principal components analysis (IPCA).', 'display_name': None}, 'PCA': {'name': 'PCA', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'n_components': {'anyOf': [{'minimum': 1, 'type': 'integer'}, {'exclusiveMaximum': 1.0, 'exclusiveMinimum': 0.0, 'type': 'number'}, {'enum': ['mle'], 'type': 'string'}, {'type': 'null'}], 'description': 'Number of components to keep. If None, all components are kept.', 'placeholder': 2, 'title': 'N Components'}, 'copy': {'description': 'If False, data passed to fit are overwritten and running fit(X).transform(X) will not yield the expected results, use fit_transform(X) instead.', 'placeholder': True, 'title': 'Copy', 'type': 'boolean'}, 'whiten': {'description': 'When True (False by default) the components_ vectors are multiplied by the square root of n_samples and then divided by the singular values to ensure uncorrelated outputs with unit component-wise variances. Whitening will remove some information from the transformed signal (the relative variance scales of the components) but can sometime improve the predictive accuracy of the downstream estimators by making their data respect some hard-wired assumptions.', 'placeholder': False, 'title': 'Whiten', 'type': 'boolean'}, 'svd_solver': {'description': "The solver to use for the eigendecomposition. If 'auto', it will choose the most appropriate solver based on the type of data passed.", 'enum': ['auto', 'full', 'covariance_eigh', 'arpack', 'randomized'], 'placeholder': 'auto', 'title': 'Svd Solver', 'type': 'string'}, 'tol': {'description': "Tolerance for singular values computed by svd_solver == 'arpack'.", 'minimum': 0.0, 'placeholder': 0.0, 'title': 'Tol', 'type': 'number'}, 'iterated_power': {'anyOf': [{'minimum': 1, 'type': 'integer'}, {'enum': ['auto'], 'type': 'string'}], 'description': "Number of iterations for the power method computed by svd_solver == 'randomized'.", 'placeholder': 'auto', 'title': 'Iterated Power'}, 'n_oversamples': {'description': "Number of power iterations used when svd_solver == 'randomized'.", 'minimum': 1, 'placeholder': 10, 'title': 'N Oversamples', 'type': 'integer'}, 'power_iteration_normalizer': {'anyOf': [{'enum': ['auto', 'QR', 'LU'], 'type': 'string'}, {'type': 'null'}], 'description': "Whether the power iteration normalizer should be computed with QR (the 'auto' option), LU decomposition ('LU') or left untouched ('QR'). Not used by ARPACK.", 'placeholder': 'auto', 'title': 'Power Iteration Normalizer'}, 'random_state': {'anyOf': [{'type': 'integer'}, {'enum': ['RandomState'], 'type': 'string'}, {'type': 'null'}], 'description': 'Used when the arpack or randomized solvers are used. Pass an int for reproducible results across multiple function calls.', 'placeholder': None, 'title': 'Random State'}}, 'required': ['n_components', 'copy', 'whiten', 'svd_solver', 'tol', 'iterated_power', 'n_oversamples', 'power_iteration_normalizer', 'random_state'], 'title': 'PCASchema', 'type': 'object'}, 'metadata': None, 'description': 'Principal component analysis (PCA).', 'display_name': None}, 'ConverterChain': {'name': 'ConverterChain', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'steps': {'description': 'Number of converters in the chain.', 'minimum': 1, 'placeholder': 1, 'title': 'Steps', 'type': 'integer'}}, 'required': ['steps'], 'title': 'ConverterChainSchema', 'type': 'object'}, 'metadata': None, 'description': 'A ConverterChain applies a sequence of converters to preprocess data, passing the output of one converter to the next, with its scope defined by the first converter (the chain itself).', 'display_name': None}, 'TruncatedSVD': {'name': 'TruncatedSVD', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'n_components': {'description': 'Desired dimensionality of output data.', 'exclusiveMinimum': 0, 'placeholder': 2, 'title': 'N Components', 'type': 'integer'}, 'algorithm': {'description': 'SVD solver to use.', 'enum': ['arpack', 'randomized'], 'placeholder': 'randomized', 'title': 'Algorithm', 'type': 'string'}, 'n_iter': {'description': 'Number of iterations for randomized SVD solver.', 'exclusiveMinimum': 0, 'placeholder': 5, 'title': 'N Iter', 'type': 'integer'}, 'n_oversamples': {'description': 'Number of power iterations used in randomized SVD solver.', 'exclusiveMinimum': 0, 'placeholder': 10, 'title': 'N Oversamples', 'type': 'integer'}, 'power_iteration_normalizer': {'description': 'Method to normalize the eigenvectors.', 'enum': ['auto', 'QR', 'LU', 'none'], 'placeholder': 'auto', 'title': 'Power Iteration Normalizer', 'type': 'string'}, 'random_state': {'anyOf': [{'type': 'integer'}, {'enum': ['RandomState'], 'type': 'string'}, {'type': 'null'}], 'description': 'Used during randomized svd. Pass an int for reproducible results across multiple function calls.', 'placeholder': None, 'title': 'Random State'}, 'tol': {'description': 'Tolerance for ARPACK.', 'minimum': 0.0, 'placeholder': 0.0, 'title': 'Tol', 'type': 'number'}}, 'required': ['n_components', 'algorithm', 'n_iter', 'n_oversamples', 'power_iteration_normalizer', 'random_state', 'tol'], 'title': 'TruncatedSVDSchema', 'type': 'object'}, 'metadata': None, 'description': 'This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). Contrary to PCA, this estimator does not center the data before computing the singular value decomposition. This means it can work with sparse matrices efficiently.', 'display_name': None}, 'Binarizer': {'name': 'Binarizer', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'threshold': {'description': 'Feature values below or equal to this are replaced by 0, above it by 1.', 'placeholder': 0.0, 'title': 'Threshold', 'type': 'number'}, 'copy': {'description': 'Set to False to perform inplace binarization.', 'placeholder': True, 'title': 'Copy', 'type': 'boolean'}}, 'required': ['threshold', 'copy'], 'title': 'BinarizerSchema', 'type': 'object'}, 'metadata': None, 'description': 'Binarize data (set feature values to 0 or 1) according to a threshold.', 'display_name': None}, 'LabelBinarizer': {'name': 'LabelBinarizer', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'neg_label': {'description': 'Value with which negative labels must be encoded.', 'placeholder': 0, 'title': 'Neg Label', 'type': 'integer'}, 'pos_label': {'description': 'Value with which positive labels must be encoded.', 'placeholder': 1, 'title': 'Pos Label', 'type': 'integer'}}, 'required': ['neg_label', 'pos_label'], 'title': 'LabelBinarizerSchema', 'type': 'object'}, 'metadata': None, 'description': 'Binarize labels in a one-vs-all fashion.', 'display_name': None}, 'LabelEncoder': {'name': 'LabelEncoder', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {}, 'title': 'LabelEncoderSchema', 'type': 'object'}, 'metadata': None, 'description': 'Encode target labels with value between 0 and n_classes-1.', 'display_name': None}, 'MaxAbsScaler': {'name': 'MaxAbsScaler', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'copy': {'description': 'Set to False to perform inplace scaling.', 'placeholder': True, 'title': 'Copy', 'type': 'boolean'}}, 'required': ['copy'], 'title': 'MaxAbsScalerSchema', 'type': 'object'}, 'metadata': None, 'description': 'Scale each feature by its maximum absolute value.', 'display_name': None}, 'MinMaxScaler': {'name': 'MinMaxScaler', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'min_range': {'description': 'The minimum value of the range to scale the data to.', 'minimum': 0.0, 'placeholder': 0, 'title': 'Min Range', 'type': 'number'}, 'max_range': {'description': 'The maximum value of the range to scale the data to.', 'minimum': 0.0, 'placeholder': 1, 'title': 'Max Range', 'type': 'number'}, 'copy': {'description': 'Set to False to perform inplace row normalization.', 'placeholder': True, 'title': 'Copy', 'type': 'boolean'}, 'clip': {'description': 'Set to True to clip the data to the feature range.', 'placeholder': False, 'title': 'Clip', 'type': 'boolean'}}, 'required': ['min_range', 'max_range', 'copy', 'clip'], 'title': 'MinMaxScalerSchema', 'type': 'object'}, 'metadata': None, 'description': 'Transform features by scaling each feature to a given range.', 'display_name': None}, 'Normalizer': {'name': 'Normalizer', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'norm': {'description': 'The norm to use to normalize each non zero sample.', 'enum': ['l1', 'l2', 'max'], 'placeholder': 'l2', 'title': 'Norm', 'type': 'string'}, 'copy': {'description': 'Set to False to perform inplace row normalization.', 'placeholder': True, 'title': 'Copy', 'type': 'boolean'}}, 'required': ['norm', 'copy'], 'title': 'NormalizerSchema', 'type': 'object'}, 'metadata': None, 'description': 'Normalize samples individually to unit norm.', 'display_name': None}, 'OneHotEncoder': {'name': 'OneHotEncoder', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'categories': {'description': 'The categories of each feature.', 'placeholder': 'auto', 'title': 'Categories', 'type': 'string'}, 'drop': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'description': 'Specifies a methodology to use to drop one of the categories per feature.', 'placeholder': None, 'title': 'Drop'}, 'dtype': {'description': 'Desired dtype of output.', 'enum': ['int', 'np.float32', 'np.float64'], 'placeholder': 'np.float64', 'title': 'Dtype', 'type': 'string'}, 'handle_unknown': {'description': 'Whether to raise an error or ignore if an unknown categorical feature is present during transform.', 'enum': ['error', 'ignore', 'infrequent_if_exist'], 'placeholder': 'error', 'title': 'Handle Unknown', 'type': 'string'}, 'min_frequency': {'anyOf': [{'minimum': 0, 'type': 'integer'}, {'maximum': 1.0, 'minimum': 0.0, 'type': 'number'}, {'type': 'null'}], 'description': 'Minimum frequency of a category to be considered as frequent.', 'placeholder': None, 'title': 'Min Frequency'}, 'max_categories': {'anyOf': [{'minimum': 1, 'type': 'integer'}, {'type': 'null'}], 'description': 'Maximum number of categories to encode.', 'placeholder': None, 'title': 'Max Categories'}, 'feature_name_combiner': {'description': 'Method used to combine feature names.', 'enum': ['concat'], 'placeholder': 'concat', 'title': 'Feature Name Combiner', 'type': 'string'}}, 'required': ['categories', 'drop', 'dtype', 'handle_unknown', 'min_frequency', 'max_categories', 'feature_name_combiner'], 'title': 'OneHotEncoderSchema', 'type': 'object'}, 'metadata': None, 'description': 'Encode categorical integer features as a one-hot numeric array.', 'display_name': None}, 'OrdinalEncoder': {'name': 'OrdinalEncoder', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'categories': {'description': 'Categories (unique values) per feature.', 'placeholder': 'auto', 'title': 'Categories', 'type': 'string'}, 'dtype': {'description': 'Desired dtype of output.', 'enum': ['np.int32', 'np.int64', 'np.float32', 'np.float64'], 'placeholder': 'np.float64', 'title': 'Dtype', 'type': 'string'}, 'handle_unknown': {'description': 'Whether to raise an error or ignore if an unknown categorical feature is present during transform.', 'enum': ['error', 'use_encoded_value'], 'placeholder': 'error', 'title': 'Handle Unknown', 'type': 'string'}, 'unknown_value': {'anyOf': [{'enum': ['int', 'np.nan'], 'type': 'string'}, {'type': 'null'}], 'description': 'The value to use for unknown categories.', 'placeholder': None, 'title': 'Unknown Value'}, 'min_frequency': {'anyOf': [{'minimum': 1, 'type': 'integer'}, {'maximum': 1.0, 'minimum': 0.0, 'type': 'number'}, {'type': 'null'}], 'description': 'Minimum frequency of a category to be considered as frequent.', 'placeholder': None, 'title': 'Min Frequency'}, 'max_categories': {'anyOf': [{'minimum': 1, 'type': 'integer'}, {'type': 'null'}], 'description': 'Maximum number of categories to encode.', 'placeholder': None, 'title': 'Max Categories'}}, 'required': ['categories', 'dtype', 'handle_unknown', 'unknown_value', 'min_frequency', 'max_categories'], 'title': 'OrdinalEncoderSchema', 'type': 'object'}, 'metadata': None, 'description': 'Encode categorical features as an integer array.', 'display_name': None}, 'PolynomialFeatures': {'name': 'PolynomialFeatures', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'degree': {'description': 'The degree of the polynomial features.', 'minimum': 1, 'placeholder': 2, 'title': 'Degree', 'type': 'integer'}, 'interaction_only': {'description': 'If True, only interaction features are produced: features that are products of at most degree distinct input features (so not x[1] ** 2, x[0] * x[2] ** 3, etc.).', 'placeholder': False, 'title': 'Interaction Only', 'type': 'boolean'}, 'include_bias': {'description': 'If True (default), then include a bias column, the feature in which all polynomial powers are zero (i.e. a column of ones - acts as an intercept term in a linear model).', 'placeholder': True, 'title': 'Include Bias', 'type': 'boolean'}, 'order': {'description': "Order of output array in the dense case. 'F' order is faster to compute, but may slow down subsequent estimators.", 'enum': ['C', 'F'], 'placeholder': 'C', 'title': 'Order', 'type': 'string'}}, 'required': ['degree', 'interaction_only', 'include_bias', 'order'], 'title': 'PolynomialFeaturesSchema', 'type': 'object'}, 'metadata': None, 'description': 'Generate polynomial and interaction features.', 'display_name': None}, 'StandardScaler': {'name': 'StandardScaler', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'copy': {'description': 'If False, try to avoid a copy and do inplace scaling instead.', 'placeholder': True, 'title': 'Copy', 'type': 'boolean'}, 'with_mean': {'description': 'If True, center the data before scaling.', 'placeholder': True, 'title': 'With Mean', 'type': 'boolean'}, 'with_std': {'description': 'If True, scale the data to unit variance (or equivalently, unit standard deviation).', 'placeholder': True, 'title': 'With Std', 'type': 'boolean'}}, 'required': ['copy', 'with_mean', 'with_std'], 'title': 'StandardScalerSchema', 'type': 'object'}, 'metadata': None, 'description': 'Standardize features by removing the mean and scaling to unit variance.', 'display_name': None}, 'Embedding': {'name': 'Embedding', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'model_name': {'description': 'Name of the pre-trained model to use', 'enum': ['sentence-transformers/all-MiniLM-L6-v2', 'sentence-transformers/all-mpnet-base-v2', 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2', 'sentence-transformers/all-distilroberta-v1', 'bert-base-uncased', 'bert-large-uncased', 'bert-base-multilingual-cased', 'distilbert-base-uncased', 'roberta-base', 'roberta-large', 'distilroberta-base'], 'placeholder': 'sentence-transformers/all-MiniLM-L6-v2', 'title': 'Model Name', 'type': 'string'}, 'max_length': {'description': 'Maximum sequence length for tokenization', 'minimum': 1, 'placeholder': 512, 'title': 'Max Length', 'type': 'integer'}, 'batch_size': {'description': 'Number of samples to process at once', 'minimum': 1, 'placeholder': 32, 'title': 'Batch Size', 'type': 'integer'}, 'device': {'description': 'Device to use for computation', 'enum': ['cuda', 'cpu'], 'placeholder': 'cpu', 'title': 'Device', 'type': 'string'}, 'pooling_strategy': {'description': 'Strategy to pool token embeddings into sentence embedding', 'enum': ['mean', 'cls', 'max'], 'placeholder': 'mean', 'title': 'Pooling Strategy', 'type': 'string'}}, 'required': ['model_name', 'max_length', 'batch_size', 'device', 'pooling_strategy'], 'title': 'EmbeddingSchema', 'type': 'object'}, 'metadata': None, 'description': 'Convert text to embeddings using HuggingFace transformer models.', 'display_name': None}, 'VarianceThreshold': {'name': 'VarianceThreshold', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'threshold': {'description': 'Features with a variance lower than this threshold will be removed.', 'minimum': 0.0, 'placeholder': 0.0, 'title': 'Threshold', 'type': 'number'}}, 'required': ['threshold'], 'title': 'VarianceThresholdSchema', 'type': 'object'}, 'metadata': None, 'description': 'Feature selector that removes all low-variance features.', 'display_name': None}, 'SimpleImputer': {'name': 'SimpleImputer', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'missing_values': {'anyOf': [{'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'type': 'null'}], 'description': 'The placeholder for the missing values.', 'placeholder': None, 'title': 'Missing Values'}, 'strategy': {'description': 'The imputation strategy.', 'enum': ['mean', 'median', 'most_frequent', 'constant'], 'placeholder': 'mean', 'title': 'Strategy', 'type': 'string'}, 'fill_value': {'anyOf': [{'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'type': 'null'}], 'description': 'The value to replace missing values with.', 'placeholder': None, 'title': 'Fill Value'}, 'copy': {'description': 'If True, a copy of X will be created.', 'placeholder': True, 'title': 'Copy', 'type': 'boolean'}, 'add_indicator': {'description': 'If True, a MissingIndicator transform will stack onto output.', 'placeholder': False, 'title': 'Add Indicator', 'type': 'boolean'}, 'keep_empty_features': {'description': 'If True, empty features will be kept.', 'placeholder': False, 'title': 'Keep Empty Features', 'type': 'boolean'}}, 'required': ['missing_values', 'strategy', 'fill_value', 'copy', 'add_indicator', 'keep_empty_features'], 'title': 'SimpleImputerSchema', 'type': 'object'}, 'metadata': None, 'description': 'Univariate imputer for completing missing values with simple strategies.', 'display_name': None}, 'MissingIndicator': {'name': 'MissingIndicator', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'missing_values': {'anyOf': [{'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'type': 'null'}], 'description': 'The placeholder for the missing values.', 'placeholder': None, 'title': 'Missing Values'}, 'features': {'description': 'The features to consider for missing values.', 'enum': ['missing-only', 'all'], 'placeholder': None, 'title': 'Features', 'type': 'string'}, 'error_on_new': {'description': 'Whether to raise an error on new missing values.', 'placeholder': True, 'title': 'Error On New', 'type': 'boolean'}}, 'required': ['missing_values', 'features', 'error_on_new'], 'title': 'MissingIndicatorSchema', 'type': 'object'}, 'metadata': None, 'description': 'Binary indicators for missing values.', 'display_name': None}, 'KNNImputer': {'name': 'KNNImputer', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'missing_values': {'anyOf': [{'type': 'integer'}, {'type': 'number'}, {'type': 'string'}, {'type': 'null'}], 'description': 'The placeholder for the missing values.', 'placeholder': None, 'title': 'Missing Values'}, 'n_neighbors': {'description': 'The number of nearest neighbors to use for imputation.', 'minimum': 1, 'placeholder': 5, 'title': 'N Neighbors', 'type': 'integer'}, 'weights': {'description': 'The weight function to use for imputation.', 'enum': ['uniform', 'distance'], 'placeholder': 'uniform', 'title': 'Weights', 'type': 'string'}, 'metric': {'description': 'The metric to use for imputation.', 'enum': ['nan_euclidean'], 'placeholder': 'nan_euclidean', 'title': 'Metric', 'type': 'string'}, 'copy': {'description': 'If True, a copy of X will be created.', 'placeholder': True, 'title': 'Copy', 'type': 'boolean'}, 'add_indicator': {'description': 'If True, a MissingIndicator transform will stack onto output.', 'placeholder': False, 'title': 'Add Indicator', 'type': 'boolean'}, 'keep_empty_features': {'description': 'If True, empty features will be kept.', 'placeholder': False, 'title': 'Keep Empty Features', 'type': 'boolean'}}, 'required': ['missing_values', 'n_neighbors', 'weights', 'metric', 'copy', 'add_indicator', 'keep_empty_features'], 'title': 'KNNImputerSchema', 'type': 'object'}, 'metadata': None, 'description': 'Imputation for completing missing values using k-Nearest Neighbors.', 'display_name': None}, 'AdditiveChi2Sampler': {'name': 'AdditiveChi2Sampler', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'sample_steps': {'description': 'The number of sample steps (shuffling) to perform.', 'minimum': 1, 'placeholder': 2, 'title': 'Sample Steps', 'type': 'integer'}, 'sample_interval': {'anyOf': [{'minimum': 1.0, 'type': 'number'}, {'type': 'null'}], 'description': 'The number of samples to generate between each original sample.', 'placeholder': None, 'title': 'Sample Interval'}}, 'required': ['sample_steps', 'sample_interval'], 'title': 'AdditiveChi2SamplerSchema', 'type': 'object'}, 'metadata': None, 'description': 'Uses sampling the fourier transform of the kernel characteristic at regular intervals.', 'display_name': None}, 'RBFSampler': {'name': 'RBFSampler', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'gamma': {'anyOf': [{'enum': ['scale'], 'type': 'string'}, {'exclusiveMinimum': 0.0, 'type': 'number'}], 'description': 'Parameter of the RBF kernel.', 'placeholder': 1.0, 'title': 'Gamma'}, 'n_components': {'description': 'The number of features to construct.', 'minimum': 1, 'placeholder': 100, 'title': 'N Components', 'type': 'integer'}, 'random_state': {'anyOf': [{'type': 'integer'}, {'enum': ['RandomState'], 'type': 'string'}, {'type': 'null'}], 'description': 'Pseudo-random number generator to control the generation of the random weights and random offset when fitting the training data. Pass an int for reproducible output across multiple function calls.', 'placeholder': None, 'title': 'Random State'}}, 'required': ['gamma', 'n_components', 'random_state'], 'title': 'RBFSamplerSchema', 'type': 'object'}, 'metadata': None, 'description': 'Approximates the feature map of an RBF kernel by Monte Carlo approximation of its Fourier transform.', 'display_name': None}, 'SkewedChi2Sampler': {'name': 'SkewedChi2Sampler', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'skewedness': {'description': 'The skewedness parameter of the chi-squared kernel.', 'exclusiveMinimum': 0.0, 'placeholder': 1.0, 'title': 'Skewedness', 'type': 'number'}, 'n_components': {'description': 'Number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space.', 'minimum': 1, 'placeholder': 100, 'title': 'N Components', 'type': 'integer'}, 'random_state': {'anyOf': [{'type': 'integer'}, {'enum': ['RandomState'], 'type': 'string'}, {'type': 'null'}], 'description': 'Pseudo-random number generator to control the generation of the random weights and random offset when fitting the training data. Pass an int for reproducible output across multiple function calls.', 'placeholder': None, 'title': 'Random State'}}, 'required': ['skewedness', 'n_components', 'random_state'], 'title': 'SkewedChi2SamplerSchema', 'type': 'object'}, 'metadata': None, 'description': 'Approximates the feature map of a chi-squared kernel by Monte Carlo approximation of its Fourier transform.', 'display_name': None}, 'GenericUnivariateSelect': {'name': 'GenericUnivariateSelect', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'mode': {'description': 'Select features according to a percentile of the highest scores.', 'enum': ['percentile', 'k_best', 'fpr', 'fdr', 'fwe'], 'placeholder': 'percentile', 'title': 'Mode', 'type': 'string'}, 'param': {'anyOf': [{'enum': ['all'], 'type': 'string'}, {'type': 'number'}, {'type': 'integer'}, {'type': 'null'}], 'description': 'Parameter of the mode.', 'placeholder': 1e-05, 'title': 'Param'}}, 'required': ['mode', 'param'], 'title': 'GenericUnivariateSelectSchema', 'type': 'object'}, 'metadata': None, 'description': 'Univariate feature selector with configurable strategy.', 'display_name': None}, 'SelectPercentile': {'name': 'SelectPercentile', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'percentile': {'description': 'Percent of features to keep.', 'maximum': 100, 'minimum': 1, 'placeholder': 10, 'title': 'Percentile', 'type': 'integer'}}, 'required': ['percentile'], 'title': 'SelectPercentileSchema', 'type': 'object'}, 'metadata': None, 'description': 'Select features according to a percentile of the highest scores.', 'display_name': None}, 'SelectKBest': {'name': 'SelectKBest', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'k': {'anyOf': [{'enum': ['all'], 'type': 'string'}, {'minimum': 1, 'type': 'integer'}], 'description': 'Number of top features to select.', 'placeholder': 10, 'title': 'K'}}, 'required': ['k'], 'title': 'SelectKBestSchema', 'type': 'object'}, 'metadata': None, 'description': 'Select features according to the k highest scores.', 'display_name': None}, 'SelectFpr': {'name': 'SelectFpr', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'alpha': {'description': 'The highest p-value for features to be kept.', 'maximum': 1.0, 'minimum': 0.0, 'placeholder': 0.05, 'title': 'Alpha', 'type': 'number'}}, 'required': ['alpha'], 'title': 'SelectFprSchema', 'type': 'object'}, 'metadata': None, 'description': 'Filter: Select features according to a false positive rate test.', 'display_name': None}, 'SelectFdr': {'name': 'SelectFdr', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'alpha': {'description': 'The highest uncorrected p-value for features to be kept.', 'maximum': 1.0, 'minimum': 0.0, 'placeholder': 0.05, 'title': 'Alpha', 'type': 'number'}}, 'required': ['alpha'], 'title': 'SelectFdrSchema', 'type': 'object'}, 'metadata': None, 'description': 'Filter: Select features according to a false discovery rate test.', 'display_name': None}, 'SelectFwe': {'name': 'SelectFwe', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'alpha': {'description': 'The highest uncorrected p-value for features to be kept.', 'maximum': 1.0, 'minimum': 0.0, 'placeholder': 0.05, 'title': 'Alpha', 'type': 'number'}}, 'required': ['alpha'], 'title': 'SelectFweSchema', 'type': 'object'}, 'metadata': None, 'description': 'Filter: Select features according to a family-wise error rate test.', 'display_name': None}, 'Nystroem': {'name': 'Nystroem', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'kernel': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'description': 'The kernel to use for the approximation.', 'placeholder': 'rbf', 'title': 'Kernel'}, 'gamma': {'anyOf': [{'exclusiveMinimum': 0.0, 'type': 'number'}, {'type': 'null'}], 'description': 'The gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels.', 'placeholder': None, 'title': 'Gamma'}, 'coef0': {'anyOf': [{'type': 'number'}, {'type': 'null'}], 'description': 'The coef0 parameter for the polynomial and sigmoid kernels.', 'placeholder': None, 'title': 'Coef0'}, 'degree': {'anyOf': [{'minimum': 1.0, 'type': 'number'}, {'type': 'null'}], 'description': 'The degree of the polynomial kernel.', 'placeholder': None, 'title': 'Degree'}, 'kernel_params': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'description': 'Additional parameters (keyword arguments) for the kernel function.', 'placeholder': None, 'title': 'Kernel Params'}, 'n_components': {'description': 'The number of features to construct.', 'minimum': 1, 'placeholder': 100, 'title': 'N Components', 'type': 'integer'}, 'random_state': {'anyOf': [{'type': 'integer'}, {'enum': ['RandomState'], 'type': 'string'}, {'type': 'null'}], 'description': 'The seed of the pseudo random number generator to use when shuffling the data.', 'placeholder': None, 'title': 'Random State'}, 'n_jobs': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'description': 'Number of parallel jobs to run.', 'placeholder': None, 'title': 'N Jobs'}}, 'required': ['kernel', 'gamma', 'coef0', 'degree', 'kernel_params', 'n_components', 'random_state', 'n_jobs'], 'title': 'NystroemSchema', 'type': 'object'}, 'metadata': None, 'description': 'Approximates the feature map of an RBF kernel by Monte Carlo approximation of its Fourier transform.', 'display_name': None}, 'SMOTEConverter': {'name': 'SMOTEConverter', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'sampling_strategy': {'anyOf': [{'exclusiveMinimum': 0.0, 'maximum': 1.0, 'type': 'number'}, {'enum': ['auto'], 'type': 'string'}], 'description': "Sampling strategy (float or 'auto') to determine minority class size.", 'placeholder': 'auto', 'title': 'Sampling Strategy'}, 'random_state': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'description': 'Seed for reproducibility.', 'placeholder': None, 'title': 'Random State'}, 'k_neighbors': {'description': 'Number of neighbors to use for generating synthetic samples.', 'minimum': 1, 'placeholder': 5, 'title': 'K Neighbors', 'type': 'integer'}}, 'required': ['sampling_strategy', 'random_state', 'k_neighbors'], 'title': 'SMOTESchema', 'type': 'object'}, 'metadata': None, 'description': 'SMOTE: Synthetic Minority Over-sampling Technique.', 'display_name': None}, 'SMOTEENNConverter': {'name': 'SMOTEENNConverter', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'sampling_strategy': {'anyOf': [{'exclusiveMinimum': 0.0, 'maximum': 1.0, 'type': 'number'}, {'enum': ['auto'], 'type': 'string'}], 'description': 'Sampling strategy to apply SMOTE and clean the dataset.', 'placeholder': 'auto', 'title': 'Sampling Strategy'}, 'random_state': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'description': 'Seed used for reproducibility.', 'placeholder': None, 'title': 'Random State'}, 'k_neighbors': {'description': 'Number of neighbors used by SMOTE.', 'minimum': 1, 'placeholder': 5, 'title': 'K Neighbors', 'type': 'integer'}}, 'required': ['sampling_strategy', 'random_state', 'k_neighbors'], 'title': 'SMOTEENNSchema', 'type': 'object'}, 'metadata': None, 'description': 'SMOTEENN: SMOTE with noise reduction via Edited Nearest Neighbors.', 'display_name': None}, 'RandomUnderSamplerConverter': {'name': 'RandomUnderSamplerConverter', 'type': 'Converter', 'class': , 'configurable_object': True, 'schema': {'properties': {'sampling_strategy': {'anyOf': [{'exclusiveMinimum': 0.0, 'maximum': 1.0, 'type': 'number'}, {'enum': ['auto'], 'type': 'string'}], 'description': "Sampling strategy (float or 'auto') to reduce majority class.", 'placeholder': 'auto', 'title': 'Sampling Strategy'}, 'random_state': {'anyOf': [{'type': 'integer'}, {'type': 'null'}], 'description': 'Seed for reproducibility.', 'placeholder': None, 'title': 'Random State'}}, 'required': ['sampling_strategy', 'random_state'], 'title': 'RUSchema', 'type': 'object'}, 'metadata': None, 'description': 'Randomly remove samples from the majority class to balance the dataset.', 'display_name': None}}} \ No newline at end of file diff --git a/component_registry__foromatted.json b/component_registry__foromatted.json new file mode 100644 index 000000000..89716b10b --- /dev/null +++ b/component_registry__foromatted.json @@ -0,0 +1,6053 @@ +{ + "Task": { + "TabularClassificationTask": { + "name": "TabularClassificationTask", + "type": "Task", + "class": "DashAI.back.tasks.tabular_classification_task.TabularClassificationTask", + "configurable_object": false, + "schema": "None", + "metadata": { + "inputs_types": [ + "ClassLabel", + "Value" + ], + "outputs_types": [ + "ClassLabel" + ], + "inputs_cardinality": "n", + "outputs_cardinality": 1 + }, + "description": "\n Tabular classification in machine learning involves predicting categorical\n labels for structured data organized in tabular form (rows and columns).\n Models are trained to learn patterns and relationships in the data, enabling\n accurate classification of new instances.", + "display_name": "None" + }, + "TextClassificationTask": { + "name": "TextClassificationTask", + "type": "Task", + "class": "DashAI.back.tasks.text_classification_task.TextClassificationTask", + "configurable_object": false, + "schema": "None", + "metadata": { + "inputs_types": [ + "Value" + ], + "outputs_types": [ + "ClassLabel" + ], + "inputs_cardinality": 1, + "outputs_cardinality": 1 + }, + "description": "\n Text classification is an essential Natural Language Processing (NLP) task that\n involves automatically assigning pre-defined categories or labels to text documents\n based on their content. It serves as the foundation for applications like sentiment\n analysis, spam filtering, topic classification, and document categorization.\n ", + "display_name": "None" + }, + "TranslationTask": { + "name": "TranslationTask", + "type": "Task", + "class": "DashAI.back.tasks.translation_task.TranslationTask", + "configurable_object": false, + "schema": "None", + "metadata": { + "inputs_types": [ + "Value", + "Sequence" + ], + "outputs_types": [ + "Value", + "Sequence" + ], + "inputs_cardinality": 1, + "outputs_cardinality": 1 + }, + "description": "\n The translation task is natural language processing (NLP) task that involves\n converting text or speech from one language into another language while\n preserving the meaning and context.\n ", + "display_name": "None" + }, + "ImageClassificationTask": { + "name": "ImageClassificationTask", + "type": "Task", + "class": "DashAI.back.tasks.image_classification_task.ImageClassificationTask", + "configurable_object": false, + "schema": "None", + "metadata": { + "inputs_types": [ + "Image" + ], + "outputs_types": [ + "ClassLabel" + ], + "inputs_cardinality": 1, + "outputs_cardinality": 1 + }, + "description": "None", + "display_name": "None" + }, + "RegressionTask": { + "name": "RegressionTask", + "type": "Task", + "class": "DashAI.back.tasks.regression_task.RegressionTask", + "configurable_object": false, + "schema": "None", + "metadata": { + "inputs_types": [ + "Value" + ], + "outputs_types": [ + "Value" + ], + "inputs_cardinality": "n", + "outputs_cardinality": 1 + }, + "description": "\n Regression in machine learning involves predicting continuous values for\n structured data organized in tabular form (rows and columns).\n Models are trained to learn patterns and relationships in the data,\n enabling accurate prediction of new instances.", + "display_name": "None" + } + }, + "GenerativeTask": { + "TextToImageGenerationTask": { + "name": "TextToImageGenerationTask", + "type": "GenerativeTask", + "class": "DashAI.back.tasks.text_to_image_generation_task.TextToImageGenerationTask", + "configurable_object": false, + "schema": "None", + "metadata": { + "inputs_types": [ + "str" + ], + "outputs_types": [ + "Image" + ], + "inputs_cardinality": 1, + "outputs_cardinality": "n" + }, + "description": "This task generates images based on the provided input text.", + "display_name": "Text to Image Generation" + }, + "TextToTextGenerationTask": { + "name": "TextToTextGenerationTask", + "type": "GenerativeTask", + "class": "DashAI.back.tasks.text_to_text_generation_task.TextToTextGenerationTask", + "configurable_object": false, + "schema": "None", + "metadata": { + "inputs_types": [ + "str" + ], + "outputs_types": [ + "str" + ], + "inputs_cardinality": 1, + "outputs_cardinality": 1 + }, + "description": "This task uses a large language model (LLM) to generate text from a given prompt.", + "display_name": "Text to Text Generation" + }, + "ControlNetTask": { + "name": "ControlNetTask", + "type": "GenerativeTask", + "class": "DashAI.back.tasks.controlnet_task.ControlNetTask", + "configurable_object": false, + "schema": "None", + "metadata": { + "inputs_types": [ + "Image", + "str" + ], + "outputs_types": [ + "Image" + ], + "inputs_cardinality": 2, + "outputs_cardinality": "n" + }, + "description": "This task generates images based on the provided input text and image.", + "display_name": "ControlNet" + }, + "RAGTask": { + "name": "RAGTask", + "type": "GenerativeTask", + "class": "DashAI.back.tasks.RAG_task.RAGTask", + "configurable_object": false, + "schema": "None", + "metadata": { + "inputs_types": [ + "str" + ], + "outputs_types": [ + "str" + ], + "inputs_cardinality": 1, + "outputs_cardinality": 1 + }, + "description": "This task generates a text response based on documents provided and chat.", + "display_name": "Retreival-Augmented Generation (RAG) Task" + } + }, + "Model": { + "SVC": { + "name": "SVC", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.svc.SVC", + "configurable_object": true, + "schema": { + "description": "Support Vector Machine (SVM) is a machine learning algorithm that separates data\ninto different classes by finding the optimal hyperplane", + "properties": { + "C": { + "description": "The parameter 'C' is a regularization parameter. It must be of type positive number.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 1, + "optimize": false, + "upper_bound": 10 + }, + "title": "C", + "type": "object" + }, + "coef0": { + "description": "The 'coef0' parameter is a kernel independent value. It is only significant for kernel poly and sigmoid. It must be of type number.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 1, + "optimize": false, + "upper_bound": 10 + }, + "title": "Coef0", + "type": "object" + }, + "degree": { + "description": "The parameter 'degree' is the degree of the polynomial for the kernel = 'poly'. It must be of type number.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 1, + "optimize": false, + "upper_bound": 10 + }, + "title": "Degree", + "type": "object" + }, + "gamma": { + "description": "Coefficient for 'rbf', 'poly' and 'sigmoid' kernels. Must be in string format and can be scale or auto.", + "enum": [ + "scale", + "auto" + ], + "placeholder": "scale", + "title": "Gamma", + "type": "string" + }, + "kernel": { + "description": "The 'kernel' parameter is the kernel used in the model. It must be a string equal to 'linear', 'poly', 'rbf' or 'sigmoid'.", + "enum": [ + "linear", + "poly", + "rbf", + "sigmoid" + ], + "placeholder": "rbf", + "title": "Kernel", + "type": "string" + }, + "max_iter": { + "description": "The 'max_iter' parameter determines the iteration limit for the solver. It must be of type positive integer or -1 to indicate no limit.", + "placeholder": { + "fixed_value": -1, + "lower_bound": 1, + "optimize": false, + "upper_bound": 10 + }, + "title": "Max Iter", + "type": "object" + }, + "probability": { + "description": "The parameter 'probability' indicates whether or not to predict with probabilities. It must be of type boolean.", + "placeholder": true, + "title": "Probability", + "type": "boolean" + }, + "shrinking": { + "description": "The 'shrinking' parameter determines whether a shrinking heristic is used. It must be of type boolean.", + "placeholder": true, + "title": "Shrinking", + "type": "boolean" + }, + "tol": { + "description": "The parameter 'tol' determines the tolerance for the stop criterion. It must be of type positive number.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 1, + "optimize": false, + "upper_bound": 10 + }, + "title": "Tol", + "type": "object" + }, + "verbose": { + "description": "The 'verbose' parameter allows to have a verbose output.It must be of type boolean.", + "placeholder": false, + "title": "Verbose", + "type": "boolean" + } + }, + "required": [ + "C", + "coef0", + "degree", + "gamma", + "kernel", + "max_iter", + "probability", + "shrinking", + "tol", + "verbose" + ], + "title": "SVCSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "DecisionTreeClassifier": { + "name": "DecisionTreeClassifier", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.decision_tree_classifier.DecisionTreeClassifier", + "configurable_object": true, + "schema": { + "description": "Decision Trees are a set of are a non-parametric supervised learning method that\nlearns simple decision rules (structured as a tree) inferred from the data features.", + "properties": { + "criterion": { + "description": "The function to measure the quality of a split. Supported criteria are �gini� for the Gini impurity and �log_loss� and �entropy� both for the Shannon information gain.", + "enum": [ + "entropy", + "gini", + "log_loss" + ], + "placeholder": "entropy", + "title": "Criterion", + "type": "string" + }, + "max_depth": { + "description": "The maximum depth of the tree. If 'None', then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 1, + "optimize": false, + "upper_bound": 10 + }, + "title": "Max Depth", + "type": "object" + }, + "min_samples_split": { + "description": "The minimum number of samples required to split an internal node.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 1, + "optimize": false, + "upper_bound": 5 + }, + "title": "Min Samples Split", + "type": "object" + }, + "min_samples_leaf": { + "description": "The minimum number of samples required to be at a leaf node.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 1, + "optimize": false, + "upper_bound": 5 + }, + "title": "Min Samples Leaf", + "type": "object" + }, + "max_features": { + "description": "The number of features to consider when looking for the best split.", + "enum": [ + "auto", + "sqrt", + "log2" + ], + "placeholder": "None", + "title": "Max Features", + "type": "string" + } + }, + "required": [ + "criterion", + "max_depth", + "min_samples_split", + "min_samples_leaf", + "max_features" + ], + "title": "DecisionTreeClassifierSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "DummyClassifier": { + "name": "DummyClassifier", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.dummy_classifier.DummyClassifier", + "configurable_object": true, + "schema": { + "description": "DummyClassifier makes predictions that ignore the input features.", + "properties": { + "strategy": { + "description": "Strategy to use to generate predictions.", + "enum": [ + "most_frequent", + "prior", + "stratified", + "uniform" + ], + "placeholder": "prior", + "title": "Strategy", + "type": "string" + } + }, + "required": [ + "strategy" + ], + "title": "DummyClassifierSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "GradientBoostingR": { + "name": "GradientBoostingR", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.gradient_boosting_regression.GradientBoostingR", + "configurable_object": true, + "schema": { + "description": "Gradient Boosting for regression.", + "properties": { + "loss": { + "description": "Loss function to be optimized.", + "enum": [ + "squared_error", + "absolute_error", + "huber", + "quantile" + ], + "placeholder": "squared_error", + "title": "Loss", + "type": "string" + }, + "learning_rate": { + "description": "Learning rate shrinks the contribution of each tree.", + "placeholder": { + "fixed_value": 0.1, + "lower_bound": 0.01, + "optimize": false, + "upper_bound": 1 + }, + "title": "Learning Rate", + "type": "object" + }, + "n_estimators": { + "description": "The number of boosting stages to be run.", + "placeholder": { + "fixed_value": 100, + "lower_bound": 10, + "optimize": false, + "upper_bound": 1000 + }, + "title": "N Estimators", + "type": "object" + }, + "subsample": { + "description": "The fraction of samples to be used for fitting the individual base learners.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 0.1, + "optimize": false, + "upper_bound": 1 + }, + "title": "Subsample", + "type": "object" + }, + "criterion": { + "description": "The function to measure the quality of a split.", + "enum": [ + "friedman_mse", + "mse", + "mae" + ], + "placeholder": "friedman_mse", + "title": "Criterion", + "type": "string" + }, + "min_samples_split": { + "description": "The minimum number of samples required to split an internal node.", + "placeholder": { + "fixed_value": 2, + "lower_bound": 2, + "optimize": false, + "upper_bound": 20 + }, + "title": "Min Samples Split", + "type": "object" + }, + "min_samples_leaf": { + "description": "The minimum number of samples required to be at a leaf node.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 1, + "optimize": false, + "upper_bound": 20 + }, + "title": "Min Samples Leaf", + "type": "object" + }, + "min_weight_fraction_leaf": { + "description": "The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.", + "placeholder": { + "fixed_value": 0, + "lower_bound": 0, + "optimize": false, + "upper_bound": 0.5 + }, + "title": "Min Weight Fraction Leaf", + "type": "object" + }, + "max_depth": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The maximum depth of the individual regression estimators.", + "placeholder": 3, + "title": "Max Depth" + }, + "min_impurity_decrease": { + "description": "A node will be split if this split induces a decrease of the impurity greater than or equal to this value.", + "placeholder": { + "fixed_value": 0, + "lower_bound": 0, + "optimize": false, + "upper_bound": 0.5 + }, + "title": "Min Impurity Decrease", + "type": "object" + }, + "random_state": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The seed of the pseudo-random number generator to use when shuffling the data.", + "placeholder": "None", + "title": "Random State" + }, + "max_features": { + "anyOf": [ + { + "type": "object" + }, + { + "enum": [ + "sqrt", + "log2", + "None" + ], + "type": "string" + } + ], + "description": "The number of features to consider when looking for the best split.", + "placeholder": "None", + "title": "Max Features" + }, + "alpha": { + "description": "The alpha-quantile of the Huber loss function and the quantile loss function.", + "placeholder": { + "fixed_value": 0.9, + "lower_bound": 0.1, + "optimize": false, + "upper_bound": 1 + }, + "title": "Alpha", + "type": "object" + }, + "verbose": { + "description": "Enable verbose output.", + "placeholder": { + "fixed_value": 0, + "lower_bound": 0, + "optimize": false, + "upper_bound": 100 + }, + "title": "Verbose", + "type": "object" + }, + "max_leaf_nodes": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "Grow trees with max_leaf_nodes in best-first fashion.", + "placeholder": "None", + "title": "Max Leaf Nodes" + }, + "warm_start": { + "description": "When set to true, reuse the solution of the previous callto fit and add more estimators to the ensemble.", + "placeholder": false, + "title": "Warm Start", + "type": "boolean" + }, + "validation_fraction": { + "description": "The proportion of training data to set aside as validation set for early stopping.", + "placeholder": { + "fixed_value": 0.1, + "lower_bound": 0.1, + "optimize": false, + "upper_bound": 0.5 + }, + "title": "Validation Fraction", + "type": "object" + }, + "n_iter_no_change": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The number of iterations with no improvement to wait before stopping the training.", + "placeholder": "None", + "title": "N Iter No Change" + }, + "tol": { + "description": "Tolerance for the early stopping.", + "placeholder": { + "fixed_value": 0.0001, + "lower_bound": 0.00001, + "optimize": false, + "upper_bound": 0.1 + }, + "title": "Tol", + "type": "object" + }, + "ccp_alpha": { + "description": "Complexity parameter used for Minimal Cost-Complexity Pruning.", + "placeholder": { + "fixed_value": 0, + "lower_bound": 0, + "optimize": false, + "upper_bound": 1 + }, + "title": "Ccp Alpha", + "type": "object" + } + }, + "required": [ + "loss", + "learning_rate", + "n_estimators", + "subsample", + "criterion", + "min_samples_split", + "min_samples_leaf", + "min_weight_fraction_leaf", + "max_depth", + "min_impurity_decrease", + "random_state", + "max_features", + "alpha", + "verbose", + "max_leaf_nodes", + "warm_start", + "validation_fraction", + "n_iter_no_change", + "tol", + "ccp_alpha" + ], + "title": "GradientBoostingRSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "HistGradientBoostingClassifier": { + "name": "HistGradientBoostingClassifier", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.hist_gradient_boosting_classifier.HistGradientBoostingClassifier", + "configurable_object": true, + "schema": { + "description": "A gradient boosting classifier is a machine learning algorithm that combines\nmultiple weak prediction models (typically decision trees) to create a strong\npredictive model by training the models sequentially, in which each new model is\nfocused on correcting the errors made by the previous ones.", + "properties": { + "learning_rate": { + "description": "The learning rate, also known as shrinkage. This is used as a multiplicative factor for the leaves values. Use 1 for no shrinkage.", + "placeholder": { + "fixed_value": 0.1, + "lower_bound": 0.1, + "optimize": false, + "upper_bound": 1 + }, + "title": "Learning Rate", + "type": "object" + }, + "max_iter": { + "description": "The maximum number of iterations of the boosting process, i.e. the maximum number of trees for binary classification.", + "placeholder": { + "fixed_value": 100, + "lower_bound": 100, + "optimize": false, + "upper_bound": 250 + }, + "title": "Max Iter", + "type": "object" + }, + "max_depth": { + "description": "The maximum depth of each tree. The depth of a tree is the number of edges to go from the root to the deepest leaf. Depth isn�t constrained by default.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 1, + "optimize": false, + "upper_bound": 10 + }, + "title": "Max Depth", + "type": "object" + }, + "max_leaf_nodes": { + "description": "The maximum number of leaves for each tree. Must be strictly greater than 1. If 'None', there is no maximum limit.", + "placeholder": { + "fixed_value": 31, + "lower_bound": 10, + "optimize": false, + "upper_bound": 40 + }, + "title": "Max Leaf Nodes", + "type": "object" + }, + "min_samples_leaf": { + "description": "The minimum number of samples required to be at a leaf node.", + "placeholder": { + "fixed_value": 20, + "lower_bound": 2, + "optimize": false, + "upper_bound": 25 + }, + "title": "Min Samples Leaf", + "type": "object" + }, + "l2_regularization": { + "description": "The L2 regularization parameter. Use 0 for no regularization.", + "placeholder": { + "fixed_value": 0, + "lower_bound": 0, + "optimize": false, + "upper_bound": 1 + }, + "title": "L2 Regularization", + "type": "object" + } + }, + "required": [ + "learning_rate", + "max_iter", + "max_depth", + "max_leaf_nodes", + "min_samples_leaf", + "l2_regularization" + ], + "title": "HistGradientBoostingClassifierSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "KNeighborsClassifier": { + "name": "KNeighborsClassifier", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.k_neighbors_classifier.KNeighborsClassifier", + "configurable_object": true, + "schema": { + "description": "KNN is a supervised classification method that determines the probability of\nan element belonging to a certain class by considering its k closest neighbors.", + "properties": { + "n_neighbors": { + "description": "The 'n_neighbors' parameter is the number of neighbors to consider in each input for classification. It must be an integer greater than or equal to 1.", + "placeholder": { + "fixed_value": 5, + "lower_bound": 5, + "optimize": false, + "upper_bound": 10 + }, + "title": "N Neighbors", + "type": "object" + }, + "weights": { + "description": "The 'weights' parameter must be 'uniform' or 'distance'.", + "enum": [ + "uniform", + "distance" + ], + "placeholder": "uniform", + "title": "Weights", + "type": "string" + }, + "algorithm": { + "description": "The 'algorithm' parameter must be 'auto', 'ball_tree', 'kd_tree', or 'brute'.", + "enum": [ + "auto", + "ball_tree", + "kd_tree", + "brute" + ], + "placeholder": "auto", + "title": "Algorithm", + "type": "string" + } + }, + "required": [ + "n_neighbors", + "weights", + "algorithm" + ], + "title": "KNeighborsClassifierSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "LogisticRegression": { + "name": "LogisticRegression", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.logistic_regression.LogisticRegression", + "configurable_object": true, + "schema": { + "description": "Logistic Regression is a supervised classification method that uses a linear\nmodel plus a a logistic funcion to predict binary outcomes (it can be configured\nas multiclass via the one-vs-rest strategy).", + "properties": { + "penalty": { + "description": "Specify the norm of the penalty", + "enum": [ + "l2", + "l1", + "elasticnet" + ], + "placeholder": "l2", + "title": "Penalty", + "type": "string" + }, + "tol": { + "description": "Tolerance for stopping criteria.", + "placeholder": { + "fixed_value": 0.001, + "lower_bound": 0.001, + "optimize": false, + "upper_bound": 5 + }, + "title": "Tol", + "type": "object" + }, + "C": { + "description": "Inverse of regularization strength, smaller values specify stronger regularization. Must be a positive number.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 1, + "optimize": false, + "upper_bound": 7 + }, + "title": "C", + "type": "object" + }, + "max_iter": { + "description": "Maximum number of iterations taken for the solvers to converge.", + "placeholder": { + "fixed_value": 100, + "lower_bound": 50, + "optimize": false, + "upper_bound": 250 + }, + "title": "Max Iter", + "type": "object" + } + }, + "required": [ + "penalty", + "tol", + "C", + "max_iter" + ], + "title": "LogisticRegressionSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "MLPRegression": { + "name": "MLPRegression", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.mlp_regression.MLPRegression", + "configurable_object": true, + "schema": { + "description": "MLP Regressor for DashAI.", + "properties": { + "activation": { + "description": "Activation function for the hidden layer.", + "enum": [ + "identity", + "logistic", + "tanh", + "relu" + ], + "placeholder": "relu", + "title": "Activation", + "type": "string" + }, + "solver": { + "description": "The solver for weight optimization.", + "enum": [ + "lbfgs", + "sgd", + "adam" + ], + "placeholder": "adam", + "title": "Solver", + "type": "string" + }, + "alpha": { + "description": "L2 penalty (regularization term) parameter.", + "placeholder": { + "fixed_value": 0.0001, + "lower_bound": 0.000001, + "optimize": false, + "upper_bound": 0.1 + }, + "title": "Alpha", + "type": "object" + }, + "batch_size": { + "anyOf": [ + { + "type": "object" + }, + { + "enum": [ + "auto" + ], + "type": "string" + } + ], + "description": "Size of minibatches for stochastic optimizers.", + "placeholder": "auto", + "title": "Batch Size" + }, + "learning_rate": { + "description": "Learning rate schedule for weight updates.", + "enum": [ + "constant", + "invscaling", + "adaptive" + ], + "placeholder": "constant", + "title": "Learning Rate", + "type": "string" + }, + "learning_rate_init": { + "description": "The initial learning rate used.", + "placeholder": { + "fixed_value": 0.001, + "lower_bound": 0.00001, + "optimize": false, + "upper_bound": 0.1 + }, + "title": "Learning Rate Init", + "type": "object" + }, + "power_t": { + "description": "The exponent for inverse scaling learning rate.", + "placeholder": { + "fixed_value": 0.5, + "lower_bound": 0.1, + "optimize": false, + "upper_bound": 0.9 + }, + "title": "Power T", + "type": "object" + }, + "max_iter": { + "description": "Maximum number of iterations.", + "placeholder": { + "fixed_value": 200, + "lower_bound": 50, + "optimize": false, + "upper_bound": 1000 + }, + "title": "Max Iter", + "type": "object" + }, + "shuffle": { + "description": "Whether to shuffle samples in each iteration.", + "placeholder": true, + "title": "Shuffle", + "type": "boolean" + }, + "random_state": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The seed of the pseudo-random number generator to use when shuffling the data.", + "placeholder": "None", + "title": "Random State" + }, + "tol": { + "description": "Tolerance for the optimization.", + "placeholder": { + "fixed_value": 0.0001, + "lower_bound": 0.000001, + "optimize": false, + "upper_bound": 0.01 + }, + "title": "Tol", + "type": "object" + }, + "verbose": { + "description": "Whether to print progress messages to stdout.", + "placeholder": false, + "title": "Verbose", + "type": "boolean" + }, + "warm_start": { + "description": "When set to true, reuse the solution of the previous call to fit as initialization.", + "placeholder": false, + "title": "Warm Start", + "type": "boolean" + }, + "momentum": { + "description": "Momentum for gradient descent update.", + "placeholder": { + "fixed_value": 0.9, + "lower_bound": 0, + "optimize": false, + "upper_bound": 1 + }, + "title": "Momentum", + "type": "object" + }, + "nesterovs_momentum": { + "description": "Whether to use Nesterov�s momentum.", + "placeholder": true, + "title": "Nesterovs Momentum", + "type": "boolean" + }, + "early_stopping": { + "description": "Whether to use early stopping to terminate training when validation score is not improving.", + "placeholder": false, + "title": "Early Stopping", + "type": "boolean" + }, + "validation_fraction": { + "description": "The proportion of training data to set aside as validation set for early stopping.", + "placeholder": { + "fixed_value": 0.1, + "lower_bound": 0.1, + "optimize": false, + "upper_bound": 0.5 + }, + "title": "Validation Fraction", + "type": "object" + }, + "beta_1": { + "description": "Exponential decay rate for estimates of first moment vector in Adam optimizer.", + "placeholder": { + "fixed_value": 0.9, + "lower_bound": 0.1, + "optimize": false, + "upper_bound": 0.999 + }, + "title": "Beta 1", + "type": "object" + }, + "beta_2": { + "description": "Exponential decay rate for estimates of second moment vector in Adam optimizer.", + "placeholder": { + "fixed_value": 0.999, + "lower_bound": 0.1, + "optimize": false, + "upper_bound": 0.999 + }, + "title": "Beta 2", + "type": "object" + }, + "epsilon": { + "description": "Value for numerical stability in Adam optimizer.", + "placeholder": { + "fixed_value": 1e-8, + "lower_bound": 1e-10, + "optimize": false, + "upper_bound": 0.000001 + }, + "title": "Epsilon", + "type": "object" + }, + "n_iter_no_change": { + "description": "Maximum number of epochs to not meet tol improvement.", + "placeholder": { + "fixed_value": 10, + "lower_bound": 1, + "optimize": false, + "upper_bound": 50 + }, + "title": "N Iter No Change", + "type": "object" + }, + "max_fun": { + "description": "Maximum number of loss function calls. Only used if solver='lbfgs'.", + "placeholder": { + "fixed_value": 15000, + "lower_bound": 1000, + "optimize": false, + "upper_bound": 20000 + }, + "title": "Max Fun", + "type": "object" + } + }, + "required": [ + "activation", + "solver", + "alpha", + "batch_size", + "learning_rate", + "learning_rate_init", + "power_t", + "max_iter", + "shuffle", + "random_state", + "tol", + "verbose", + "warm_start", + "momentum", + "nesterovs_momentum", + "early_stopping", + "validation_fraction", + "beta_1", + "beta_2", + "epsilon", + "n_iter_no_change", + "max_fun" + ], + "title": "MLPRegressorSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "RandomForestClassifier": { + "name": "RandomForestClassifier", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.random_forest_classifier.RandomForestClassifier", + "configurable_object": true, + "schema": { + "description": "Random Forest (RF) is an ensemble machine learning algorithm that achieves\nenhanced performance by combining multiple decision trees and aggregating their\noutputs.", + "properties": { + "n_estimators": { + "description": "The 'n_estimators' parameter corresponds to the number of decision trees. It must be an integer greater than or equal to 1.", + "placeholder": { + "fixed_value": 100, + "lower_bound": 50, + "optimize": false, + "upper_bound": 200 + }, + "title": "N Estimators", + "type": "object" + }, + "max_depth": { + "description": "The 'max_depth' parameter corresponds to the maximum depth of the tree. It must be an integer greater than or equal to 1.", + "placeholder": { + "fixed_value": 2, + "lower_bound": 2, + "optimize": false, + "upper_bound": 10 + }, + "title": "Max Depth", + "type": "object" + }, + "min_samples_split": { + "description": "The 'min_samples_split' parameter is the minimum number of samples required to split an internal node. It must be a number greater than or equal to 2.", + "placeholder": { + "fixed_value": 2, + "lower_bound": 2, + "optimize": false, + "upper_bound": 10 + }, + "title": "Min Samples Split", + "type": "object" + }, + "min_samples_leaf": { + "description": "The 'min_samples_leaf' parameter is the minimum number of samples required to be at a leaf node. It must be a number greater than or equal to 1.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 1, + "optimize": false, + "upper_bound": 10 + }, + "title": "Min Samples Leaf", + "type": "object" + }, + "max_leaf_nodes": { + "description": "The 'max_leaf_nodes' parameter must be an integer greater than or equal to 2.", + "placeholder": { + "fixed_value": 2, + "lower_bound": 2, + "optimize": false, + "upper_bound": 10 + }, + "title": "Max Leaf Nodes", + "type": "object" + }, + "random_state": { + "description": "The 'random_state' parameter must be an integer greater than or equal to 0.", + "placeholder": { + "fixed_value": 0, + "lower_bound": 0, + "optimize": false, + "upper_bound": 10 + }, + "title": "Random State", + "type": "object" + } + }, + "required": [ + "n_estimators", + "max_depth", + "min_samples_split", + "min_samples_leaf", + "max_leaf_nodes", + "random_state" + ], + "title": "RandomForestClassifierSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "RandomForestRegression": { + "name": "RandomForestRegression", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.random_forest_regression.RandomForestRegression", + "configurable_object": true, + "schema": { + "description": "Random Forest Regressor for DashAI.", + "properties": { + "n_estimators": { + "description": "The number of trees in the forest.", + "placeholder": { + "fixed_value": 100, + "lower_bound": 10, + "optimize": false, + "upper_bound": 1000 + }, + "title": "N Estimators", + "type": "object" + }, + "criterion": { + "description": "The function to measure the quality of a split.", + "enum": [ + "squared_error", + "absolute_error", + "poisson" + ], + "placeholder": "squared_error", + "title": "Criterion", + "type": "string" + }, + "max_depth": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The maximum depth of the tree.", + "placeholder": "None", + "title": "Max Depth" + }, + "min_samples_split": { + "description": "The minimum number of samples required to split an internal node.", + "placeholder": { + "fixed_value": 2, + "lower_bound": 2, + "optimize": false, + "upper_bound": 20 + }, + "title": "Min Samples Split", + "type": "object" + }, + "min_samples_leaf": { + "description": "The minimum number of samples required to be at a leaf node.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 1, + "optimize": false, + "upper_bound": 20 + }, + "title": "Min Samples Leaf", + "type": "object" + }, + "min_weight_fraction_leaf": { + "description": "The minimum weighted fraction of the sum total of weights required to be at a leaf node.", + "placeholder": { + "fixed_value": 0, + "lower_bound": 0, + "optimize": false, + "upper_bound": 0.5 + }, + "title": "Min Weight Fraction Leaf", + "type": "object" + }, + "max_features": { + "anyOf": [ + { + "type": "object" + }, + { + "enum": [ + "auto", + "sqrt", + "log2", + "None" + ], + "type": "string" + } + ], + "description": "The number of features to consider when looking for the best split.", + "placeholder": "sqrt", + "title": "Max Features" + }, + "max_leaf_nodes": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "Grow trees with max_leaf_nodes in best-first fashion.", + "placeholder": "None", + "title": "Max Leaf Nodes" + }, + "min_impurity_decrease": { + "description": "A node will be split if this split induces a decrease of the impurity greater than or equal to this value.", + "placeholder": { + "fixed_value": 0, + "lower_bound": 0, + "optimize": false, + "upper_bound": 0.5 + }, + "title": "Min Impurity Decrease", + "type": "object" + }, + "bootstrap": { + "description": "Whether bootstrap samples are used when building trees.", + "placeholder": true, + "title": "Bootstrap", + "type": "boolean" + }, + "oob_score": { + "description": "Whether to use out-of-bag samples to estimate the generalization score.", + "placeholder": false, + "title": "Oob Score", + "type": "boolean" + }, + "n_jobs": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The number of jobs to run in parallel for both fit and predict.", + "placeholder": "None", + "title": "N Jobs" + }, + "random_state": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The seed of the pseudo-random number generator to use when shuffling the data.", + "placeholder": "None", + "title": "Random State" + }, + "verbose": { + "description": "Controls the verbosity when fitting and predicting.", + "placeholder": { + "fixed_value": 0, + "lower_bound": 0, + "optimize": false, + "upper_bound": 100 + }, + "title": "Verbose", + "type": "object" + }, + "warm_start": { + "description": "When set to true, reuse the solution of the previous call to fit and add more estimators to the ensemble.", + "placeholder": false, + "title": "Warm Start", + "type": "boolean" + }, + "ccp_alpha": { + "description": "Complexity parameter used for Minimal Cost-Complexity Pruning.", + "placeholder": { + "fixed_value": 0, + "lower_bound": 0, + "optimize": false, + "upper_bound": 1 + }, + "title": "Ccp Alpha", + "type": "object" + }, + "max_samples": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "number" + }, + { + "type": "null" + } + ], + "description": "If bootstrap is true, the number of samples to draw from X to train each base estimator.", + "placeholder": "None", + "title": "Max Samples" + } + }, + "required": [ + "n_estimators", + "criterion", + "max_depth", + "min_samples_split", + "min_samples_leaf", + "min_weight_fraction_leaf", + "max_features", + "max_leaf_nodes", + "min_impurity_decrease", + "bootstrap", + "oob_score", + "n_jobs", + "random_state", + "verbose", + "warm_start", + "ccp_alpha", + "max_samples" + ], + "title": "RandomForestRegressionSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "DistilBertTransformer": { + "name": "DistilBertTransformer", + "type": "Model", + "class": "DashAI.back.models.hugging_face.distilbert_transformer.DistilBertTransformer", + "configurable_object": true, + "schema": { + "description": "Distilbert is a transformer that allows you to classify text in English.\nThe implementation is based on huggingface distilbert-base in the case of\nthe uncased model, i.e. distilbert-base-uncased.", + "properties": { + "num_train_epochs": { + "description": "Total number of training epochs to perform.", + "minimum": 1, + "placeholder": 1, + "title": "Num Train Epochs", + "type": "integer" + }, + "batch_size": { + "description": "The batch size per GPU/TPU core/CPU for training", + "minimum": 1, + "placeholder": 16, + "title": "Batch Size", + "type": "integer" + }, + "learning_rate": { + "description": "The initial learning rate for AdamW optimizer", + "minimum": 0, + "placeholder": 0.00003, + "title": "Learning Rate", + "type": "number" + }, + "device": { + "description": "Hardware on which the training is run. If available, GPU is recommended for efficiency reasons. Otherwise, use CPU.", + "enum": [ + "gpu", + "cpu" + ], + "placeholder": "gpu", + "title": "Device", + "type": "string" + }, + "weight_decay": { + "description": "Weight decay is a regularization technique used in training neural networks to prevent overfitting. In the context of the AdamW optimizer, the 'weight_decay' parameter is the rate at which the weights of all layers are reduced during training, provided that this rate is not zero.", + "minimum": 0, + "placeholder": 0.01, + "title": "Weight Decay", + "type": "number" + } + }, + "required": [ + "num_train_epochs", + "batch_size", + "learning_rate", + "device", + "weight_decay" + ], + "title": "DistilBertTransformerSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "OpusMtEnESTransformer": { + "name": "OpusMtEnESTransformer", + "type": "Model", + "class": "DashAI.back.models.hugging_face.opus_mt_en_es_transformer.OpusMtEnESTransformer", + "configurable_object": true, + "schema": { + "description": "opus-mt-en-es is a transformer pre-trained model that allows translation of\ntexts from English to Spanish.", + "properties": { + "num_train_epochs": { + "description": "Total number of training epochs to perform.", + "minimum": 1, + "placeholder": 1, + "title": "Num Train Epochs", + "type": "integer" + }, + "batch_size": { + "description": "The batch size per GPU/TPU core/CPU for training", + "minimum": 1, + "placeholder": 4, + "title": "Batch Size", + "type": "integer" + }, + "learning_rate": { + "description": "The initial learning rate for AdamW optimizer", + "minimum": 0, + "placeholder": 0.00002, + "title": "Learning Rate", + "type": "number" + }, + "device": { + "description": "Hardware on which the training is run. If available, GPU is recommended for efficiency reasons. Otherwise, use CPU.", + "enum": [ + "gpu", + "cpu" + ], + "placeholder": "gpu", + "title": "Device", + "type": "string" + }, + "weight_decay": { + "description": "Weight decay is a regularization technique used in training neural networks to prevent overfitting. In the context of the AdamW optimizer, the 'weight_decay' parameter is the rate at which the weights of all layers are reduced during training, provided that this rate is not zero.", + "minimum": 0, + "placeholder": 0.01, + "title": "Weight Decay", + "type": "number" + } + }, + "required": [ + "num_train_epochs", + "batch_size", + "learning_rate", + "device", + "weight_decay" + ], + "title": "OpusMtEnESTransformerSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "BagOfWordsTextClassificationModel": { + "name": "BagOfWordsTextClassificationModel", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.bow_text_classification_model.BagOfWordsTextClassificationModel", + "configurable_object": true, + "schema": { + "description": "NumericalWrapperForText is a metamodel that allows text classification using\ntabular classifiers and a tokenizer.", + "properties": { + "tabular_classifier": { + "description": "Tabular model used as the underlying model to generate the text classifier.", + "parent": "TabularClassificationModel", + "placeholder": { + "component": "SVC", + "params": {} + }, + "properties": { + "component": { + "title": "Component", + "type": "string" + }, + "params": { + "title": "Params", + "type": "object" + } + }, + "required": [ + "component", + "params" + ], + "title": "Tabular Classifier", + "type": "object" + }, + "ngram_min_n": { + "description": "The lower boundary of the range of n-values for different word n-grams or char n-grams to be extracted. It must be an integer greater or equal than 1", + "minimum": 1, + "placeholder": 1, + "title": "Ngram Min N", + "type": "integer" + }, + "ngram_max_n": { + "description": "The upper boundary of the range of n-values for different word n-grams or char n-grams to be extracted. It must be an integer greater or equal than 1", + "minimum": 1, + "placeholder": 1, + "title": "Ngram Max N", + "type": "integer" + } + }, + "required": [ + "tabular_classifier", + "ngram_min_n", + "ngram_max_n" + ], + "title": "BagOfWordsTextClassificationModelSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "RidgeRegression": { + "name": "RidgeRegression", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.ridge_regression.RidgeRegression", + "configurable_object": true, + "schema": { + "description": "Ridge regression is a linear model that includes L2 regularization.", + "properties": { + "alpha": { + "description": "Regularization strength; must be a positive float. Larger values specify stronger regularization.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 0.1, + "optimize": false, + "upper_bound": 10 + }, + "title": "Alpha", + "type": "object" + }, + "fit_intercept": { + "description": "Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g., data is expected to be centered).", + "placeholder": true, + "title": "Fit Intercept", + "type": "boolean" + }, + "copy_X": { + "description": "If true, X will be copied; else, it may be overwritten.", + "placeholder": true, + "title": "Copy X", + "type": "boolean" + }, + "max_iter": { + "description": "Maximum number of iterations for conjugate gradient solver.", + "placeholder": { + "fixed_value": 1000, + "lower_bound": 100, + "optimize": false, + "upper_bound": 10000 + }, + "title": "Max Iter", + "type": "object" + }, + "tol": { + "description": "Precision of the solution.", + "placeholder": { + "fixed_value": 0.001, + "lower_bound": 0.00001, + "optimize": false, + "upper_bound": 0.1 + }, + "title": "Tol", + "type": "object" + }, + "solver": { + "description": "Solver to use in the computation. �auto� chooses the solver automatically based on the type of data.", + "enum": [ + "auto", + "svd", + "cholesky", + "lsqr", + "sparse_cg", + "sag", + "saga" + ], + "placeholder": "auto", + "title": "Solver", + "type": "string" + }, + "positive": { + "description": "When set to true, forces the coefficients to be positive.", + "placeholder": false, + "title": "Positive", + "type": "boolean" + }, + "random_state": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The seed of the pseudo random number generator to use when shuffling the data. Pass an int for reproducible output across multiple function calls, or 'None' to not set a specific seed.", + "placeholder": "None", + "title": "Random State" + } + }, + "required": [ + "alpha", + "fit_intercept", + "copy_X", + "max_iter", + "tol", + "solver", + "positive", + "random_state" + ], + "title": "RidgeRegressionSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "LinearSVR": { + "name": "LinearSVR", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.linearSVR.LinearSVR", + "configurable_object": true, + "schema": { + "description": "Support Vector Regression (SVR) using a linear kernel.", + "properties": { + "epsilon": { + "description": "Epsilon parameter that specifies the epsilon-tube within which no penalty is associated.", + "placeholder": { + "fixed_value": 0, + "lower_bound": 0, + "optimize": false, + "upper_bound": 1 + }, + "title": "Epsilon", + "type": "object" + }, + "tol": { + "description": "Tolerance for stopping criterion.", + "placeholder": { + "fixed_value": 0.0001, + "lower_bound": 0.00001, + "optimize": false, + "upper_bound": 0.1 + }, + "title": "Tol", + "type": "object" + }, + "C": { + "description": "Regularization parameter. The strength of the regularization is inversely proportional to C.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 0.1, + "optimize": false, + "upper_bound": 10 + }, + "title": "C", + "type": "object" + }, + "loss": { + "description": "Specifies the loss function. 'epsilon_insensitive' is the standard SVR loss.", + "enum": [ + "epsilon_insensitive", + "squared_epsilon_insensitive" + ], + "placeholder": "epsilon_insensitive", + "title": "Loss", + "type": "string" + }, + "fit_intercept": { + "description": "Whether to calculate the intercept for this model.", + "placeholder": true, + "title": "Fit Intercept", + "type": "boolean" + }, + "intercept_scaling": { + "description": "When fit_intercept is true, instance vector x becomes [x, self.intercept_scaling] in the primal problem.", + "placeholder": { + "fixed_value": 1, + "lower_bound": 0.1, + "optimize": false, + "upper_bound": 10 + }, + "title": "Intercept Scaling", + "type": "object" + }, + "dual": { + "description": "Select the algorithm to either solve the dual or primal optimization problem.", + "placeholder": true, + "title": "Dual", + "type": "boolean" + }, + "verbose": { + "description": "Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm.", + "placeholder": { + "fixed_value": 0, + "lower_bound": 0, + "optimize": false, + "upper_bound": 100 + }, + "title": "Verbose", + "type": "object" + }, + "random_state": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The seed of the pseudo-random number generator to use when shuffling the data.", + "placeholder": "None", + "title": "Random State" + }, + "max_iter": { + "description": "The maximum number of iterations to be run.", + "placeholder": { + "fixed_value": 1000, + "lower_bound": 100, + "optimize": false, + "upper_bound": 10000 + }, + "title": "Max Iter", + "type": "object" + } + }, + "required": [ + "epsilon", + "tol", + "C", + "loss", + "fit_intercept", + "intercept_scaling", + "dual", + "verbose", + "random_state", + "max_iter" + ], + "title": "LinearSVRSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "LinearRegression": { + "name": "LinearRegression", + "type": "Model", + "class": "DashAI.back.models.scikit_learn.linear_regression.LinearRegression", + "configurable_object": true, + "schema": { + "description": "Linear regression model with optional intercept.", + "properties": { + "fit_intercept": { + "description": "Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g., data is expected to be centered).", + "placeholder": true, + "title": "Fit Intercept", + "type": "boolean" + }, + "copy_X": { + "description": "If true, X will be copied; else, it may be overwritten.", + "placeholder": true, + "title": "Copy X", + "type": "boolean" + }, + "n_jobs": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The number of jobs to use for the computation. 'None' means 1 job, while -1 means using all processors.", + "placeholder": "None", + "title": "N Jobs" + }, + "positive": { + "description": "When set to true, forces the coefficients to be positive.", + "placeholder": false, + "title": "Positive", + "type": "boolean" + } + }, + "required": [ + "fit_intercept", + "copy_X", + "n_jobs", + "positive" + ], + "title": "LinearRegressionSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "TFIDFRetriever": { + "name": "TFIDFRetriever", + "type": "Model", + "class": "DashAI.back.models.RAG.Retrievers.TFIDFRetriever.TFIDFRetriever", + "configurable_object": true, + "schema": { + "description": "Schema for the TFIDFRetriever.", + "properties": { + "similarity_function": { + "description": "Similarity function to use for document retrieval.", + "enum": [ + "cosine", + "euclidean" + ], + "placeholder": "cosine", + "title": "Similarity Function", + "type": "string" + }, + "n_docs": { + "description": "Number of documents to retrieve.", + "minimum": 1, + "placeholder": 5, + "title": "N Docs", + "type": "integer" + }, + "similarity_threshold": { + "description": "Maximum or minimum distance for retrieved documents based on the similarity_function.", + "placeholder": 0.5, + "title": "Similarity Threshold", + "type": "number" + }, + "chunk_size": { + "description": "Size of chunks to split the documents into.", + "minimum": 1, + "placeholder": 512, + "title": "Chunk Size", + "type": "integer" + }, + "chunk_overlap": { + "description": "Overlap between chunks when splitting documents.", + "minimum": 0, + "placeholder": 50, + "title": "Chunk Overlap", + "type": "integer" + } + }, + "required": [ + "similarity_function", + "n_docs", + "similarity_threshold", + "chunk_size", + "chunk_overlap" + ], + "title": "TFIDFRetrieverSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "DenseRetriever": { + "name": "DenseRetriever", + "type": "Model", + "class": "DashAI.back.models.RAG.Retrievers.DenseRetriever.DenseRetriever", + "configurable_object": true, + "schema": { + "description": "Schema for Dense Retriever.", + "properties": { + "encoding_model": { + "description": "Model to convert text into dense vector representations.", + "parent": "HuggingFaceWrapper", + "placeholder": { + "component": "Embedding", + "params": {} + }, + "properties": { + "component": { + "title": "Component", + "type": "string" + }, + "params": { + "title": "Params", + "type": "object" + } + }, + "required": [ + "component", + "params" + ], + "title": "Encoding Model", + "type": "object" + } + }, + "required": [ + "encoding_model" + ], + "title": "DenseRetrieverSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + } + }, + "GenerativeModel": { + "GemmaModel": { + "name": "GemmaModel", + "type": "GenerativeModel", + "class": "DashAI.back.models.hugging_face.gemma_model.GemmaModel", + "configurable_object": true, + "schema": { + "description": "Schema for Gemma model.", + "properties": { + "max_tokens": { + "description": "Maximum number of tokens to generate.", + "minimum": 1, + "placeholder": 100, + "title": "Max Tokens", + "type": "integer" + }, + "temperature": { + "description": "Sampling temperature. Higher values make the output more random, while lower values make it more focused and deterministic.", + "maximum": 1, + "minimum": 0, + "placeholder": 0.7, + "title": "Temperature", + "type": "number" + }, + "frequency_penalty": { + "description": "Penalty for repeated tokens in the output. Higher values reduce the likelihood of repetition, encouraging more diverse text generation.", + "maximum": 2, + "minimum": 0, + "placeholder": 0.1, + "title": "Frequency Penalty", + "type": "number" + }, + "context_window": { + "description": "Maximum number of tokens the model can process in a single forward pass (context window size).", + "minimum": 1, + "placeholder": 512, + "title": "Context Window", + "type": "integer" + }, + "device": { + "description": "The device to use for model inference.", + "enum": [ + "cpu" + ], + "placeholder": "cpu", + "title": "Device", + "type": "string" + } + }, + "required": [ + "max_tokens", + "temperature", + "frequency_penalty", + "context_window", + "device" + ], + "title": "GemmaSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "QwenModel": { + "name": "QwenModel", + "type": "GenerativeModel", + "class": "DashAI.back.models.hugging_face.qwen_model.QwenModel", + "configurable_object": true, + "schema": { + "description": "Schema for Qwen model.", + "properties": { + "model_name": { + "description": "The specific Qwen model version to use.", + "enum": [ + "Qwen/Qwen2.5-0.5B-Instruct-GGUF", + "Qwen/Qwen2.5-1.5B-Instruct-GGUF", + "Qwen/Qwen3-4B-GGUF " + ], + "placeholder": "Qwen/Qwen2.5-1.5B-Instruct-GGUF", + "title": "Model Name", + "type": "string" + }, + "max_tokens": { + "description": "Maximum number of tokens to generate.", + "minimum": 1, + "placeholder": 100, + "title": "Max Tokens", + "type": "integer" + }, + "temperature": { + "description": "Sampling temperature. Higher values make the output more random, while lower values make it more focused and deterministic.", + "maximum": 1, + "minimum": 0, + "placeholder": 0.7, + "title": "Temperature", + "type": "number" + }, + "frequency_penalty": { + "description": "Penalty for repeated tokens in the output. Higher values reduce the likelihood of repetition, encouraging more diverse text generation.", + "maximum": 2, + "minimum": 0, + "placeholder": 0.1, + "title": "Frequency Penalty", + "type": "number" + }, + "context_window": { + "description": "Maximum number of tokens the model can process in a single forward pass (context window size).", + "minimum": 1, + "placeholder": 512, + "title": "Context Window", + "type": "integer" + }, + "device": { + "description": "The device to use for model inference.", + "enum": [ + "cpu" + ], + "placeholder": "cpu", + "title": "Device", + "type": "string" + } + }, + "required": [ + "model_name", + "max_tokens", + "temperature", + "frequency_penalty", + "context_window", + "device" + ], + "title": "QwenSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "StableDiffusionV2Model": { + "name": "StableDiffusionV2Model", + "type": "GenerativeModel", + "class": "DashAI.back.models.hugging_face.stable_diffusion_v2_model.StableDiffusionV2Model", + "configurable_object": true, + "schema": { + "description": "Schema for Stable Diffusion V2 image generation.", + "properties": { + "model_name": { + "description": "The specific Stable Diffusion model version to use.", + "enum": [ + "stabilityai/stable-diffusion-2", + "stabilityai/stable-diffusion-2-base", + "stabilityai/stable-diffusion-2-1", + "stabilityai/stable-diffusion-2-1-base" + ], + "placeholder": "stabilityai/stable-diffusion-2", + "title": "Model Name", + "type": "string" + }, + "negative_prompt": { + "anyOf": [ + { + "description": "Text prompt for elements to avoid in the image.", + "placeholder": "", + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Negative Prompt" + }, + "num_inference_steps": { + "description": "Number of denoising steps. Higher usually leads to better quality but slower inference.", + "minimum": 1, + "placeholder": 15, + "title": "Num Inference Steps", + "type": "integer" + }, + "guidance_scale": { + "description": "How strongly the model follows the prompt. Higher = closer to prompt, but may reduce image quality.", + "minimum": 0, + "placeholder": 3.5, + "title": "Guidance Scale", + "type": "number" + }, + "device": { + "description": "Device for generation. Use 'cuda' if GPU is available.", + "enum": [ + "cuda:0", + "cpu" + ], + "placeholder": "cuda:0", + "title": "Device", + "type": "string" + }, + "seed": { + "description": "Random seed for reproducibility. Use negative value for random seed.", + "placeholder": -1, + "title": "Seed", + "type": "integer" + }, + "width": { + "description": "Width of the generated image. Must be a multiple of 8.", + "maximum": 2048, + "minimum": 64, + "placeholder": 512, + "title": "Width", + "type": "integer" + }, + "height": { + "description": "Height of the generated image. Must be a multiple of 8.", + "maximum": 2048, + "minimum": 64, + "placeholder": 512, + "title": "Height", + "type": "integer" + }, + "num_images_per_prompt": { + "description": "Number of images to generate per prompt.", + "minimum": 1, + "placeholder": 1, + "title": "Num Images Per Prompt", + "type": "integer" + } + }, + "required": [ + "model_name", + "negative_prompt", + "num_inference_steps", + "guidance_scale", + "device", + "seed", + "width", + "height", + "num_images_per_prompt" + ], + "title": "StableDiffusionSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "StableDiffusionV3Model": { + "name": "StableDiffusionV3Model", + "type": "GenerativeModel", + "class": "DashAI.back.models.hugging_face.stable_diffusion_v3_model.StableDiffusionV3Model", + "configurable_object": true, + "schema": { + "description": "Schema for Stable Diffusion V3 image generation.", + "properties": { + "model_name": { + "description": "The specific Stable Diffusion model version to use.", + "enum": [ + "stabilityai/stable-diffusion-3-medium-diffusers", + "stabilityai/stable-diffusion-3-medium-tensorrt", + "stabilityai/stable-diffusion-3.5-medium", + "stabilityai/stable-diffusion-3.5-large", + "stabilityai/stable-diffusion-3.5-large-turbo" + ], + "placeholder": "stabilityai/stable-diffusion-3-medium-diffusers", + "title": "Model Name", + "type": "string" + }, + "negative_prompt": { + "anyOf": [ + { + "description": "Text prompt for elements to avoid in the image.", + "placeholder": "", + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Negative Prompt" + }, + "num_inference_steps": { + "description": "Number of denoising steps. Higher usually leads to better quality but slower inference.", + "minimum": 1, + "placeholder": 15, + "title": "Num Inference Steps", + "type": "integer" + }, + "guidance_scale": { + "description": "How strongly the model follows the prompt. Higher = closer to prompt, but may reduce image quality.", + "minimum": 0, + "placeholder": 3.5, + "title": "Guidance Scale", + "type": "number" + }, + "device": { + "description": "Device for generation. Use 'cuda' if GPU is available.", + "enum": [ + "cuda:0", + "cpu" + ], + "placeholder": "cuda:0", + "title": "Device", + "type": "string" + }, + "seed": { + "description": "Random seed for reproducibility. Use negative value for random seed.", + "placeholder": -1, + "title": "Seed", + "type": "integer" + }, + "width": { + "description": "Width of the generated image. Must be a multiple of 8.", + "maximum": 2048, + "minimum": 64, + "placeholder": 512, + "title": "Width", + "type": "integer" + }, + "height": { + "description": "Height of the generated image. Must be a multiple of 8.", + "maximum": 2048, + "minimum": 64, + "placeholder": 512, + "title": "Height", + "type": "integer" + }, + "num_images_per_prompt": { + "description": "Number of images to generate per prompt.", + "minimum": 1, + "placeholder": 1, + "title": "Num Images Per Prompt", + "type": "integer" + } + }, + "required": [ + "model_name", + "negative_prompt", + "num_inference_steps", + "guidance_scale", + "device", + "seed", + "width", + "height", + "num_images_per_prompt" + ], + "title": "StableDiffusionSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "StableDiffusionXLV1ControlNet": { + "name": "StableDiffusionXLV1ControlNet", + "type": "GenerativeModel", + "class": "DashAI.back.models.hugging_face.stable_diffusion_v1_depth_controlnet.StableDiffusionXLV1ControlNet", + "configurable_object": true, + "schema": { + "properties": { + "num_inference_steps": { + "description": "Number of denoising steps. Higher usually leads to better quality but slower inference.", + "minimum": 1, + "placeholder": 15, + "title": "Num Inference Steps", + "type": "integer" + }, + "controlnet_conditioning_scale": { + "description": "Scale for the ControlNet conditioning. Higher values make the model follow the controlnet more closely.", + "minimum": 0, + "placeholder": 1, + "title": "Controlnet Conditioning Scale", + "type": "number" + }, + "device": { + "description": "Device for generation. Use 'cuda' if GPU is available.", + "enum": [ + "cuda:0", + "cpu" + ], + "placeholder": "cuda:0", + "title": "Device", + "type": "string" + } + }, + "required": [ + "num_inference_steps", + "controlnet_conditioning_scale", + "device" + ], + "title": "StableDiffusionXLV1ControlNetSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "RAGPipeline": { + "name": "RAGPipeline", + "type": "GenerativeModel", + "class": "DashAI.back.models.RAG.RAG_pipeline.RAGPipeline", + "configurable_object": true, + "schema": { + "description": "Schema for RAG pipeline.", + "properties": { + "documents": { + "description": "List of documents to be used in the RAG pipeline.", + "items": { + "type": "string" + }, + "minItems": 1, + "placeholder": "None", + "title": "Documents", + "type": "array" + }, + "retriever_model": { + "description": "Retriever component used in the RAG pipeline.", + "parent": "RetrieverModel", + "placeholder": { + "component": "TFIDFRetriever", + "params": {} + }, + "properties": { + "component": { + "title": "Component", + "type": "string" + }, + "params": { + "title": "Params", + "type": "object" + } + }, + "required": [ + "component", + "params" + ], + "title": "Retriever Model", + "type": "object" + }, + "generation_model": { + "description": "Text generation model used in the RAG pipeline.", + "parent": "TextToTextGenerationTaskModel", + "placeholder": { + "component": "DeepSeek", + "params": {} + }, + "properties": { + "component": { + "title": "Component", + "type": "string" + }, + "params": { + "title": "Params", + "type": "object" + } + }, + "required": [ + "component", + "params" + ], + "title": "Generation Model", + "type": "object" + } + }, + "required": [ + "documents", + "retriever_model", + "generation_model" + ], + "title": "RAGPipelineSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + } + }, + "DataLoader": { + "CSVDataLoader": { + "name": "CSVDataLoader", + "type": "DataLoader", + "class": "DashAI.back.dataloaders.classes.csv_dataloader.CSVDataLoader", + "configurable_object": true, + "schema": { + "properties": { + "name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Custom name to register your dataset. If no name is specified, the name of the uploaded file will be used.", + "placeholder": "", + "title": "Name" + }, + "separator": { + "description": "A separator character delimits the data in a CSV file.", + "enum": [ + ",", + ";", + " ", + "\t" + ], + "placeholder": ",", + "title": "Separator", + "type": "string" + } + }, + "required": [ + "name", + "separator" + ], + "title": "CSVDataloaderSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "JSONDataLoader": { + "name": "JSONDataLoader", + "type": "DataLoader", + "class": "DashAI.back.dataloaders.classes.json_dataloader.JSONDataLoader", + "configurable_object": true, + "schema": { + "properties": { + "name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Custom name to register your dataset. If no name is specified, the name of the uploaded file will be used.", + "placeholder": "", + "title": "Name" + }, + "data_key": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "\n In case the data has the form {�data�: [{�col1�: val1, �col2�: val2, ...]}}\n (also known as �table� in pandas), name of the field 'data',\n where the list with dictionaries with the data should be found.\n\n In case the format is only a list of dictionaries (also known as\n 'records' orient in pandas), set this value as null.\n ", + "placeholder": "data", + "title": "Data Key" + } + }, + "required": [ + "name", + "data_key" + ], + "title": "JSONDataloaderSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "ExcelDataLoader": { + "name": "ExcelDataLoader", + "type": "DataLoader", + "class": "DashAI.back.dataloaders.classes.excel_dataloader.ExcelDataLoader", + "configurable_object": true, + "schema": { + "properties": { + "name": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Custom name to register your dataset. If no name is specified, the name of the uploaded file will be used.", + "placeholder": "", + "title": "Name" + }, + "sheet": { + "anyOf": [ + { + "minimum": 0, + "type": "integer" + }, + { + "type": "string" + } + ], + "description": "\n The name of the sheet to read or its zero-based index.\n If a string is provided, the reader will search for a sheet named exactly as\n the string.\n If an integer is provided, the reader will select the sheet at the corresponding\n index.\n By default, the first sheet will be read.\n ", + "placeholder": 0, + "title": "Sheet" + }, + "header": { + "anyOf": [ + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "\n The row number where the column names are located, indexed from 0.\n If null, the file will be considered to have no column names.\n ", + "placeholder": 0, + "title": "Header" + }, + "usecols": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "\n If 'None', the reader will load all columns.\n If str, then indicates comma separated list of Excel column letters and column\n ranges (e.g. �A:E� or �A,C,E:F�). Ranges are inclusive of both sides.\n ", + "placeholder": "None", + "title": "Usecols" + } + }, + "required": [ + "name", + "sheet", + "header", + "usecols" + ], + "title": "ExcelDataloaderSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + } + }, + "Metric": { + "F1": { + "name": "F1", + "type": "Metric", + "class": "DashAI.back.metrics.classification.f1.F1", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "Accuracy": { + "name": "Accuracy", + "type": "Metric", + "class": "DashAI.back.metrics.classification.accuracy.Accuracy", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "Precision": { + "name": "Precision", + "type": "Metric", + "class": "DashAI.back.metrics.classification.precision.Precision", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "Recall": { + "name": "Recall", + "type": "Metric", + "class": "DashAI.back.metrics.classification.recall.Recall", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "Bleu": { + "name": "Bleu", + "type": "Metric", + "class": "DashAI.back.metrics.translation.bleu.Bleu", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "Ter": { + "name": "Ter", + "type": "Metric", + "class": "DashAI.back.metrics.translation.ter.Ter", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "MAE": { + "name": "MAE", + "type": "Metric", + "class": "DashAI.back.metrics.regression.mae.MAE", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "RMSE": { + "name": "RMSE", + "type": "Metric", + "class": "DashAI.back.metrics.regression.rmse.RMSE", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + } + }, + "Optimizer": { + "OptunaOptimizer": { + "name": "OptunaOptimizer", + "type": "Optimizer", + "class": "DashAI.back.optimizers.optuna_optimizer.OptunaOptimizer", + "configurable_object": true, + "schema": { + "properties": { + "n_trials": { + "description": "The parameter 'n_trials' is the quantity of trialsper study. It must be of type positive integer.", + "exclusiveMinimum": 0, + "placeholder": 10, + "title": "N Trials", + "type": "integer" + }, + "sampler": { + "description": "Coefficient for 'rbf', 'poly' and 'sigmoid' kernels. Must be in string format and can be 'scale' or 'auto'.", + "enum": [ + "TPESampler", + "CmaEsSampler", + "GridSampler", + "GPSampler", + "NSGAIISampler", + "QMCSampler", + "RandomSampler" + ], + "placeholder": "TPESampler", + "title": "Sampler", + "type": "string" + }, + "pruner": { + "description": "Coefficient for 'rbf', 'poly' and 'sigmoid' kernels. Must be in string format and can be 'scale' or 'auto'.", + "enum": [ + "MedianPruner", + "None" + ], + "placeholder": "None", + "title": "Pruner", + "type": "string" + } + }, + "required": [ + "n_trials", + "sampler", + "pruner" + ], + "title": "OptunaSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "HyperOptOptimizer": { + "name": "HyperOptOptimizer", + "type": "Optimizer", + "class": "DashAI.back.optimizers.hyperopt_optimizer.HyperOptOptimizer", + "configurable_object": true, + "schema": { + "properties": { + "n_trials": { + "description": "The parameter 'n_trials'is the quantity of trialsper study. It must be of type positive integer.", + "exclusiveMinimum": 0, + "placeholder": 10, + "title": "N Trials", + "type": "integer" + }, + "sampler": { + "description": "Coefficient for 'rbf', 'poly' and 'sigmoid' kernels. Must be in string format and can be 'scale' or 'auto'.", + "enum": [ + "tpe", + "rand" + ], + "placeholder": "tpe", + "title": "Sampler", + "type": "string" + } + }, + "required": [ + "n_trials", + "sampler" + ], + "title": "HyperOptSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + } + }, + "Job": { + "ExplainerJob": { + "name": "ExplainerJob", + "type": "Job", + "class": "DashAI.back.job.explainer_job.ExplainerJob", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "ModelJob": { + "name": "ModelJob", + "type": "Job", + "class": "DashAI.back.job.model_job.ModelJob", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "ExplorerJob": { + "name": "ExplorerJob", + "type": "Job", + "class": "DashAI.back.job.explorer_job.ExplorerJob", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "PredictJob": { + "name": "PredictJob", + "type": "Job", + "class": "DashAI.back.job.predict_job.PredictJob", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "ConverterListJob": { + "name": "ConverterListJob", + "type": "Job", + "class": "DashAI.back.job.converter_job.ConverterListJob", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "DatasetJob": { + "name": "DatasetJob", + "type": "Job", + "class": "DashAI.back.job.dataset_job.DatasetJob", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "GenerativeJob": { + "name": "GenerativeJob", + "type": "Job", + "class": "DashAI.back.job.generative_job.GenerativeJob", + "configurable_object": false, + "schema": "None", + "metadata": "None", + "description": "None", + "display_name": "None" + } + }, + "LocalExplainer": { + "KernelShap": { + "name": "KernelShap", + "type": "LocalExplainer", + "class": "DashAI.back.explainability.explainers.kernel_shap.KernelShap", + "configurable_object": true, + "schema": { + "description": "Kernel SHAP is a model-agnostic explainability method for approximating SHAP\nvalues to explain the output of machine learning model by attributing contributions\nof each feature to the model's prediction.", + "properties": { + "link": { + "description": "Link function to connect the feature importance values to the model's outputs. Options are 'identity' to use identity function or 'logit' to use log-odds function.", + "enum": [ + "identity", + "logit" + ], + "placeholder": "identity", + "title": "Link", + "type": "string" + }, + "fit_parameter_sample_background_data": { + "description": "Parameter to fit the explainer. 'true' if the background data must be sampled, otherwise the entire train data set is used. Smaller datasets speed up the algorithm run time.", + "placeholder": false, + "title": "Fit Parameter Sample Background Data", + "type": "boolean" + }, + "fit_parameter_n_background_samples": { + "description": "Parameter to fit the explainer. If the parameter 'sample_background_data' is 'true', the number of background data samples to be drawn.", + "minimum": 1, + "placeholder": 1, + "title": "Fit Parameter N Background Samples", + "type": "integer" + }, + "fit_parameter_sampling_method": { + "description": "Parameter to fit the explainer. If the parameter 'sample_background_data' is 'true', whether to sample random samples with 'shuffle' option or summarize the data set with 'kmeans' option. If 'categorical_features' is 'true', 'shuffle' options used by default.", + "enum": [ + "shuffle", + "kmeans" + ], + "placeholder": "shuffle", + "title": "Fit Parameter Sampling Method", + "type": "string" + } + }, + "required": [ + "link", + "fit_parameter_sample_background_data", + "fit_parameter_n_background_samples", + "fit_parameter_sampling_method" + ], + "title": "KernelShapSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + } + }, + "GlobalExplainer": { + "PartialDependence": { + "name": "PartialDependence", + "type": "GlobalExplainer", + "class": "DashAI.back.explainability.explainers.partial_dependence.PartialDependence", + "configurable_object": true, + "schema": { + "description": "PartialDependence of a feature shows the average prediction of a machine\nlearning model for each possible value of the feature.", + "properties": { + "grid_resolution": { + "description": "The number of equidistant points to split the range of the target feature", + "minimum": 1, + "placeholder": 100, + "title": "Grid Resolution", + "type": "integer" + }, + "lower_percentile": { + "description": "The lower percentile used to limit the feature values.", + "maximum": 0.99, + "minimum": 0, + "placeholder": 0.05, + "title": "Lower Percentile", + "type": "number" + }, + "upper_percentile": { + "description": "The upper percentile used to limit the feature values.", + "maximum": 1, + "minimum": 0.01, + "placeholder": 0.95, + "title": "Upper Percentile", + "type": "number" + } + }, + "required": [ + "grid_resolution", + "lower_percentile", + "upper_percentile" + ], + "title": "PartialDependenceSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + }, + "PermutationFeatureImportance": { + "name": "PermutationFeatureImportance", + "type": "GlobalExplainer", + "class": "DashAI.back.explainability.explainers.permutation_feature_importance.PermutationFeatureImportance", + "configurable_object": true, + "schema": { + "description": "Permutation Feature Importance is a explanation method to asses the\nimportance of each feature in a model by evaluating how much the model's\nperformance decreases when the values of a specific feature are randomly\nshuffled.", + "properties": { + "scoring": { + "description": "Scorer to evaluate how the perfomance of the model changes when a particular feature is shuffled.", + "enum": [ + "accuracy", + "balanced_accuracy" + ], + "placeholder": "accuracy", + "title": "Scoring", + "type": "string" + }, + "n_repeats": { + "description": "Number of times to permute a feature.", + "minimum": 1, + "placeholder": 10, + "title": "N Repeats", + "type": "integer" + }, + "random_state": { + "description": "Seed for the random number generator to control the permutations of each feature.", + "placeholder": 0, + "title": "Random State", + "type": "integer" + }, + "max_samples": { + "description": "The number of samples to draw from the dataset to calculate feature importance at each repetition.", + "minimum": 1, + "placeholder": 100, + "title": "Max Samples", + "type": "integer" + } + }, + "required": [ + "scoring", + "n_repeats", + "random_state", + "max_samples" + ], + "title": "PermutationFeatureImportanceSchema", + "type": "object" + }, + "metadata": "None", + "description": "None", + "display_name": "None" + } + }, + "Explorer": { + "DescribeExplorer": { + "name": "DescribeExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.describe_explorer.DescribeExplorer", + "configurable_object": true, + "schema": { + "properties": { + "percentiles": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The percentiles to include in the exploration. Must be a list of integers between 0 and 100.\nExample: '25, 50, 75'", + "placeholder": "25, 50, 75", + "title": "Percentiles" + }, + "include": { + "anyOf": [ + { + "enum": [ + "all", + "number", + "object", + "category", + "datetime" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The data types to include in the exploration.\n", + "placeholder": "all", + "title": "Include" + }, + "exclude": { + "anyOf": [ + { + "enum": [ + "object", + "number", + "category", + "datetime" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The data types to exclude in the exploration.", + "placeholder": "None", + "title": "Exclude" + } + }, + "required": [ + "percentiles", + "include", + "exclude" + ], + "title": "DescribeExplorerSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "*" + ], + "restricted_dtypes": [], + "input_cardinality": { + "min": 1 + }, + "display_name": "Describe Dataset", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "DescribeExplorer is an explorer that describes the dataset. It returns a tabular representation of the dataset with the count, mean, std, min, 25%, 50%, 75%, and max values for numeric columns and count, unique, top, and freq values for object columns.\nThe user can specify the percentiles to include in the exploration and the data types to include or exclude.", + "display_name": "Describe Dataset" + }, + "ScatterPlotExplorer": { + "name": "ScatterPlotExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.scatter_plot.ScatterPlotExplorer", + "configurable_object": true, + "schema": { + "properties": { + "color_group": { + "anyOf": [ + { + "type": "string" + }, + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The columnName or columnIndex to take for grouping colored points.", + "placeholder": "None", + "title": "Color Group" + }, + "simbol_group": { + "anyOf": [ + { + "type": "string" + }, + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The columnName or columnIndex to take for grouping simbol of the points.", + "placeholder": "None", + "title": "Simbol Group" + }, + "point_size": { + "anyOf": [ + { + "type": "string" + }, + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The columnName or columnIndex to take for size of each point.", + "placeholder": "None", + "title": "Point Size" + } + }, + "required": [ + "color_group", + "simbol_group", + "point_size" + ], + "title": "ScatterPlotSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "*" + ], + "restricted_dtypes": [], + "input_cardinality": { + "exact": 2 + }, + "display_name": "Scatter Plot", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "ScatterPlotExplorer is an explorer that returns a scatter plot of selected columns of a dataset.", + "display_name": "Scatter Plot" + }, + "WordcloudExplorer": { + "name": "WordcloudExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.wordcloud.WordcloudExplorer", + "configurable_object": true, + "schema": { + "properties": { + "max_words": { + "description": "The maximum number of words to display in the wordcloud.", + "exclusiveMinimum": 0, + "placeholder": 200, + "title": "Max Words", + "type": "integer" + }, + "background_color": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The background color of the wordcloud. If 'None', the background will be transparent.", + "placeholder": "None", + "title": "Background Color" + } + }, + "required": [ + "max_words", + "background_color" + ], + "title": "WordcloudSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "string" + ], + "restricted_dtypes": [], + "input_cardinality": { + "min": 1 + }, + "display_name": "Word Cloud", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "A wordcloud is a visual representation of text data, where the size of each word indicates its frequency in the text.\nThis explorer generates a wordcloud from the concatenated strings of all selected columns in the dataset.", + "display_name": "Word Cloud" + }, + "RowExplorer": { + "name": "RowExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.row_explorer.RowExplorer", + "configurable_object": true, + "schema": { + "properties": { + "row_ammount": { + "description": "The maximum number of rows to take.", + "exclusiveMinimum": 0, + "placeholder": 50, + "title": "Row Ammount", + "type": "integer" + }, + "shuffle": { + "description": "Shuffle the rows at exploration time.", + "placeholder": false, + "title": "Shuffle", + "type": "boolean" + }, + "from_top": { + "description": "Take the rows from the Head of the dataset. Else, take from the Tail.", + "placeholder": true, + "title": "From Top", + "type": "boolean" + } + }, + "required": [ + "row_ammount", + "shuffle", + "from_top" + ], + "title": "RowExplorerSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "*" + ], + "restricted_dtypes": [], + "input_cardinality": { + "min": 1 + }, + "display_name": "Show Rows", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "RowExplorer is an explorer that takes a number of rows from the dataset to display them on tabular format. It can take the rows from the top or the bottom of the dataset and shuffle them if needed.", + "display_name": "Show Rows" + }, + "BoxPlotExplorer": { + "name": "BoxPlotExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.box_plot.BoxPlotExplorer", + "configurable_object": true, + "schema": { + "properties": { + "horizontal": { + "description": "If true, the box plot will be horizontal, otherwise vertical.", + "placeholder": false, + "title": "Horizontal", + "type": "boolean" + }, + "points": { + "description": "One of 'all', 'outliers', or 'false'. Determines which points are shown.", + "enum": [ + "all", + "outliers", + "false" + ], + "placeholder": "outliers", + "title": "Points", + "type": "string" + } + }, + "required": [ + "horizontal", + "points" + ], + "title": "BoxPlotSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "*" + ], + "restricted_dtypes": [], + "input_cardinality": { + "min": 1, + "max": 2 + }, + "display_name": "Box Plot", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "BoxPlotExplorer is an explorer that returns a box plot of selected columns of a dataset.", + "display_name": "Box Plot" + }, + "MultiColumnBoxPlotExplorer": { + "name": "MultiColumnBoxPlotExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.multibox_plot.MultiColumnBoxPlotExplorer", + "configurable_object": true, + "schema": { + "properties": { + "horizontal": { + "description": "If true, the box plot will be horizontal, otherwise vertical.", + "placeholder": false, + "title": "Horizontal", + "type": "boolean" + }, + "points": { + "description": "One of 'all', 'outliers', or 'false'. Determines which points are shown.", + "enum": [ + "all", + "outliers", + "false" + ], + "placeholder": "outliers", + "title": "Points", + "type": "string" + }, + "opposite_axis": { + "anyOf": [ + { + "type": "string" + }, + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The columnName or columnIndex to take for the opposite axis.", + "placeholder": "None", + "title": "Opposite Axis" + } + }, + "required": [ + "horizontal", + "points", + "opposite_axis" + ], + "title": "MultiColumnBoxPlotSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "*" + ], + "restricted_dtypes": [], + "input_cardinality": { + "min": 1 + }, + "display_name": "Multiple Column Box Plot", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "MultiColumnBoxPlotExplorer is an explorer that returns a figure with a box plot of multiple columns of a dataset in a single axis. The other axis is selected through the opposite_axis parameter.", + "display_name": "Multiple Column Box Plot" + }, + "CorrelationMatrixExplorer": { + "name": "CorrelationMatrixExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.corr_matrix.CorrelationMatrixExplorer", + "configurable_object": true, + "schema": { + "properties": { + "method": { + "description": "The correlation method to use. Must be one of 'pearson', 'kendall', or 'spearman'.", + "enum": [ + "pearson", + "kendall", + "spearman" + ], + "placeholder": "pearson", + "title": "Method", + "type": "string" + }, + "min_periods": { + "description": "The minimum number of observations required per pair of columns to have a valid result. Only used if method is 'pearson' or 'spearman'.", + "exclusiveMinimum": 0, + "placeholder": 1, + "title": "Min Periods", + "type": "integer" + }, + "numeric_only": { + "description": "If true, only include numeric columns when calculating correlation.If false, all columns are included.", + "placeholder": true, + "title": "Numeric Only", + "type": "boolean" + }, + "plot": { + "description": "If true, the result will be plotted.", + "placeholder": true, + "title": "Plot", + "type": "boolean" + } + }, + "required": [ + "method", + "min_periods", + "numeric_only", + "plot" + ], + "title": "CorrelationMatrixExplorerSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "*" + ], + "restricted_dtypes": [], + "input_cardinality": { + "min": 2 + }, + "display_name": "Correlation Matrix", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "CorrelationMatrixExplorer is an explorer that returns a correlation matrix of a dataset.\nIts result is a heatmap by default, but can also be returned as a tabular result.", + "display_name": "Correlation Matrix" + }, + "CovarianceMatrixExplorer": { + "name": "CovarianceMatrixExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.cov_matrix.CovarianceMatrixExplorer", + "configurable_object": true, + "schema": { + "properties": { + "min_periods": { + "description": "The minimum number of observations required per pair of columns to have a valid result.", + "exclusiveMinimum": 0, + "placeholder": 1, + "title": "Min Periods", + "type": "integer" + }, + "delta_degree_of_freedom": { + "description": "The delta degree of freedom to use when calculating the covariance matrix.Only used if numeric_only is true.", + "exclusiveMinimum": 0, + "placeholder": 1, + "title": "Delta Degree Of Freedom", + "type": "integer" + }, + "numeric_only": { + "description": "If true, only include numeric columns when calculating correlation.If false, all columns are included.", + "placeholder": true, + "title": "Numeric Only", + "type": "boolean" + }, + "plot": { + "description": "If true, the result will be plotted.", + "placeholder": true, + "title": "Plot", + "type": "boolean" + } + }, + "required": [ + "min_periods", + "delta_degree_of_freedom", + "numeric_only", + "plot" + ], + "title": "CovarianceMatrixExplorerSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "*" + ], + "restricted_dtypes": [], + "input_cardinality": { + "min": 2 + }, + "display_name": "Covariance Matrix", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "CovarianceExplorer is an explorer that returns the covariance matrix of the dataset.\nIts result is a heatmap by default, but can also be returned as a tabular result.", + "display_name": "Covariance Matrix" + }, + "DensityHeatmapExplorer": { + "name": "DensityHeatmapExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.density_heatmap.DensityHeatmapExplorer", + "configurable_object": true, + "schema": { + "properties": { + "nbinsx": { + "anyOf": [ + { + "minimum": 1, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The number of bins along the x axis.", + "placeholder": "None", + "title": "Nbinsx" + }, + "nbinsy": { + "anyOf": [ + { + "minimum": 1, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The number of bins along the y axis.", + "placeholder": "None", + "title": "Nbinsy" + } + }, + "required": [ + "nbinsx", + "nbinsy" + ], + "title": "DensityHeatmapSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "*" + ], + "restricted_dtypes": [], + "input_cardinality": { + "exact": 2 + }, + "display_name": "Density Heatmap", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "DensityHeatmapExplorer is an explorer that returns a density heatmap of selected columns of a dataset.", + "display_name": "Density Heatmap" + }, + "ECDFPlotExplorer": { + "name": "ECDFPlotExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.ecdf_plot.ECDFPlotExplorer", + "configurable_object": true, + "schema": { + "properties": { + "color_column": { + "anyOf": [ + { + "type": "string" + }, + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The column to use for coloring the ECDF plot.", + "placeholder": "None", + "title": "Color Column" + }, + "facet_col": { + "anyOf": [ + { + "type": "string" + }, + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The column to use for faceting the ECDF plot in the column direction.", + "placeholder": "None", + "title": "Facet Col" + }, + "facet_row": { + "anyOf": [ + { + "type": "string" + }, + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The column to use for faceting the ECDF plot in the row direction.", + "placeholder": "None", + "title": "Facet Row" + }, + "ecdf_norm": { + "description": "Specifies the type of normalization used for this ECDF plot.", + "enum": [ + "None", + "percent", + "probability" + ], + "placeholder": "probability", + "title": "Ecdf Norm", + "type": "string" + } + }, + "required": [ + "color_column", + "facet_col", + "facet_row", + "ecdf_norm" + ], + "title": "ECDFPlotSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "float64", + "float32" + ], + "restricted_dtypes": [], + "input_cardinality": { + "min": 1 + }, + "display_name": "Empirical Cumulative Distribution Plot", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "The ECDF plot is a non-parametric way to explore the distribution of a variable. It shows the proportion or count of observations falling below each unique value in the dataset.", + "display_name": "Empirical Cumulative Distribution Plot" + }, + "HistogramPlotExplorer": { + "name": "HistogramPlotExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.histogram_plot.HistogramPlotExplorer", + "configurable_object": true, + "schema": { + "properties": { + "nbins": { + "anyOf": [ + { + "minimum": 1, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The number of bins to use for the histogram.", + "placeholder": "None", + "title": "Nbins" + }, + "histfunc": { + "description": "Specifies the binning function used for this histogram trace.", + "enum": [ + "count", + "sum", + "avg", + "min", + "max" + ], + "placeholder": "count", + "title": "Histfunc", + "type": "string" + }, + "histnorm": { + "description": "Specifies the type of normalization used for this histogram trace.", + "enum": [ + "", + "percent", + "probability", + "density", + "probability density" + ], + "placeholder": "", + "title": "Histnorm", + "type": "string" + }, + "color_group": { + "anyOf": [ + { + "type": "string" + }, + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The columnName or columnIndex to take for grouping colored points.", + "placeholder": "None", + "title": "Color Group" + }, + "pattern_group": { + "anyOf": [ + { + "type": "string" + }, + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The columnName or columnIndex to take for grouping pattern of the points.", + "placeholder": "None", + "title": "Pattern Group" + } + }, + "required": [ + "nbins", + "histfunc", + "histnorm", + "color_group", + "pattern_group" + ], + "title": "HistogramPlotSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "*" + ], + "restricted_dtypes": [], + "input_cardinality": { + "exact": 1 + }, + "display_name": "Histogram Plot", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "HistogramPlotExplorer is an explorer that returns a density heatmap of a selected column of a dataset.", + "display_name": "Histogram Plot" + }, + "ScatterMatrixExplorer": { + "name": "ScatterMatrixExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.scatter_matrix.ScatterMatrixExplorer", + "configurable_object": true, + "schema": { + "properties": { + "color_group": { + "anyOf": [ + { + "type": "string" + }, + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The columnName or columnIndex to take for grouping colored points.", + "placeholder": "None", + "title": "Color Group" + }, + "simbol_group": { + "anyOf": [ + { + "type": "string" + }, + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The columnName or columnIndex to take for grouping simbol of the points.", + "placeholder": "None", + "title": "Simbol Group" + } + }, + "required": [ + "color_group", + "simbol_group" + ], + "title": "ScatterMatrixSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "*" + ], + "restricted_dtypes": [], + "input_cardinality": { + "min": 2 + }, + "display_name": "Multiple Scatter Plot", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "ScatterMatrixExplorer is an explorer that returns a scatter matrix plot of selected columns of a dataset. Multiple scatter plots are generated for each pair of columns. The diagonal plots are histograms of the columns. ", + "display_name": "Multiple Scatter Plot" + }, + "ParallelCategoriesExplorer": { + "name": "ParallelCategoriesExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.parallel_categories.ParallelCategoriesExplorer", + "configurable_object": true, + "schema": { + "properties": { + "color_column": { + "anyOf": [ + { + "type": "string" + }, + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The column to use for coloring the data points. ", + "placeholder": "None", + "title": "Color Column" + } + }, + "required": [ + "color_column" + ], + "title": "ParallelCategoriesSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "string" + ], + "restricted_dtypes": [], + "input_cardinality": { + "min": 2 + }, + "display_name": "Parallel Categories Plot", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "A parallel categories plot is a common way to visualize high-dimensional data. Each vertical line represents one data point, and the lines are connected by a series of horizontal lines. ", + "display_name": "Parallel Categories Plot" + }, + "ParallelCordinatesExplorer": { + "name": "ParallelCordinatesExplorer", + "type": "Explorer", + "class": "DashAI.back.exploration.explorers.parallel_cordinates.ParallelCordinatesExplorer", + "configurable_object": true, + "schema": { + "properties": { + "color_column": { + "anyOf": [ + { + "type": "string" + }, + { + "minimum": 0, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The column to use for coloring the data points. ", + "placeholder": "None", + "title": "Color Column" + } + }, + "required": [ + "color_column" + ], + "title": "ParallelCordinatesSchema", + "type": "object" + }, + "metadata": { + "allowed_dtypes": [ + "float64", + "float32" + ], + "restricted_dtypes": [], + "input_cardinality": { + "min": 2 + }, + "display_name": "Parallel Cordinates Plot", + "allowed_value_types": [ + "*" + ], + "restricted_value_types": [] + }, + "description": "A parallel coordinates plot is a common way to visualize high-dimensional data. Each vertical line represents one data point, and the lines are connected by a series of horizontal lines. ", + "display_name": "Parallel Cordinates Plot" + } + }, + "Converter": { + "ColumnRemover": { + "name": "ColumnRemover", + "type": "Converter", + "class": "DashAI.back.converters.simple_converters.column_remover.ColumnRemover", + "configurable_object": true, + "schema": { + "properties": {}, + "title": "ColumnRemoverSchema", + "type": "object" + }, + "metadata": "None", + "description": "Removes the columns selected in scope from the dataset.", + "display_name": "None" + }, + "CharacterReplacer": { + "name": "CharacterReplacer", + "type": "Converter", + "class": "DashAI.back.converters.simple_converters.character_replacer.CharacterReplacer", + "configurable_object": true, + "schema": { + "properties": { + "char_to_replace": { + "description": "The character or substring to be replaced. Cannot be empty.", + "placeholder": "", + "title": "Char To Replace", + "type": "string" + }, + "replacement_char": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The character or substring to replace with. If null, 'char_to_replace' will be removed.", + "placeholder": "None", + "title": "Replacement Char" + } + }, + "required": [ + "char_to_replace", + "replacement_char" + ], + "title": "CharacterReplacerSchema", + "type": "object" + }, + "metadata": "None", + "description": "Replaces or removes specified characters/substrings in selected string columns.", + "display_name": "None" + }, + "FastICA": { + "name": "FastICA", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.fast_ica.FastICA", + "configurable_object": true, + "schema": { + "properties": { + "n_components": { + "anyOf": [ + { + "minimum": 1, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "Number of components to extract.", + "placeholder": "None", + "title": "N Components" + }, + "algorithm": { + "description": "Apply parallel or deflational algorithm for FastICA.", + "enum": [ + "parallel", + "deflation" + ], + "placeholder": "parallel", + "title": "Algorithm", + "type": "string" + }, + "whiten": { + "anyOf": [ + { + "enum": [ + "arbitrary-variance", + "unit-variance" + ], + "type": "string" + }, + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "description": "If true, the data is whitened.", + "placeholder": "unit-variance", + "title": "Whiten" + }, + "fun": { + "description": "The functional form of the G function used in the approximation to neg-entropy.", + "enum": [ + "logcosh", + "exp", + "cube" + ], + "placeholder": "logcosh", + "title": "Fun", + "type": "string" + }, + "fun_args": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Arguments to the G function.", + "placeholder": "None", + "title": "Fun Args" + }, + "max_iter": { + "description": "Maximum number of iterations to perform.", + "minimum": 1, + "placeholder": 200, + "title": "Max Iter", + "type": "integer" + }, + "tol": { + "description": "Tolerance on update at each iteration.", + "minimum": 0, + "placeholder": 0.0001, + "title": "Tol", + "type": "number" + }, + "w_init": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Initial guess for the unmixing matrix.", + "placeholder": "None", + "title": "W Init" + }, + "whiten_solver": { + "description": "The solver to use for whitening.", + "enum": [ + "eigh", + "svd" + ], + "placeholder": "svd", + "title": "Whiten Solver", + "type": "string" + }, + "random_state": { + "anyOf": [ + { + "type": "integer" + }, + { + "enum": [ + "RandomState" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Used to initialize w_init when not specified, with a normal distribution. Pass an int, for reproducible results across multiple function calls.", + "placeholder": "None", + "title": "Random State" + } + }, + "required": [ + "n_components", + "algorithm", + "whiten", + "fun", + "fun_args", + "max_iter", + "tol", + "w_init", + "whiten_solver", + "random_state" + ], + "title": "FastICASchema", + "type": "object" + }, + "metadata": "None", + "description": "FastICA: a fast algorithm for Independent Component Analysis.", + "display_name": "None" + }, + "IncrementalPCA": { + "name": "IncrementalPCA", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.incremental_pca.IncrementalPCA", + "configurable_object": true, + "schema": { + "properties": { + "n_components": { + "anyOf": [ + { + "minimum": 1, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "Number of components to keep.", + "placeholder": 2, + "title": "N Components" + }, + "whiten": { + "description": "When true (false by default) the components_ vectors are multiplied by the square root of n_samples and then divided by the singular values to ensure uncorrelated outputs with unit component-wise variances.", + "placeholder": false, + "title": "Whiten", + "type": "boolean" + }, + "copy": { + "description": "If false, data passed to fit are overwritten and running fit(X).transform(X) will not yield the expected results, use fit_transform(X) instead.", + "placeholder": true, + "title": "Copy", + "type": "boolean" + }, + "batch_size": { + "anyOf": [ + { + "minimum": 1, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "The number of samples to use for each batch.", + "placeholder": "None", + "title": "Batch Size" + } + }, + "required": [ + "n_components", + "whiten", + "copy", + "batch_size" + ], + "title": "IncrementalPCASchema", + "type": "object" + }, + "metadata": "None", + "description": "Incremental principal components analysis (IPCA).", + "display_name": "None" + }, + "PCA": { + "name": "PCA", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.pca.PCA", + "configurable_object": true, + "schema": { + "properties": { + "n_components": { + "anyOf": [ + { + "minimum": 1, + "type": "integer" + }, + { + "exclusiveMaximum": 1, + "exclusiveMinimum": 0, + "type": "number" + }, + { + "enum": [ + "mle" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Number of components to keep. If 'None', all components are kept.", + "placeholder": 2, + "title": "N Components" + }, + "copy": { + "description": "If false, data passed to fit are overwritten and running fit(X).transform(X) will not yield the expected results, use fit_transform(X) instead.", + "placeholder": true, + "title": "Copy", + "type": "boolean" + }, + "whiten": { + "description": "When true (false by default) the components_ vectors are multiplied by the square root of n_samples and then divided by the singular values to ensure uncorrelated outputs with unit component-wise variances. Whitening will remove some information from the transformed signal (the relative variance scales of the components) but can sometime improve the predictive accuracy of the downstream estimators by making their data respect some hard-wired assumptions.", + "placeholder": false, + "title": "Whiten", + "type": "boolean" + }, + "svd_solver": { + "description": "The solver to use for the eigendecomposition. If 'auto', it will choose the most appropriate solver based on the type of data passed.", + "enum": [ + "auto", + "full", + "covariance_eigh", + "arpack", + "randomized" + ], + "placeholder": "auto", + "title": "Svd Solver", + "type": "string" + }, + "tol": { + "description": "Tolerance for singular values computed by svd_solver == 'arpack'.", + "minimum": 0, + "placeholder": 0, + "title": "Tol", + "type": "number" + }, + "iterated_power": { + "anyOf": [ + { + "minimum": 1, + "type": "integer" + }, + { + "enum": [ + "auto" + ], + "type": "string" + } + ], + "description": "Number of iterations for the power method computed by svd_solver == 'randomized'.", + "placeholder": "auto", + "title": "Iterated Power" + }, + "n_oversamples": { + "description": "Number of power iterations used when svd_solver == 'randomized'.", + "minimum": 1, + "placeholder": 10, + "title": "N Oversamples", + "type": "integer" + }, + "power_iteration_normalizer": { + "anyOf": [ + { + "enum": [ + "auto", + "QR", + "LU" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Whether the power iteration normalizer should be computed with QR (the 'auto' option), LU decomposition ('LU') or left untouched ('QR'). Not used by ARPACK.", + "placeholder": "auto", + "title": "Power Iteration Normalizer" + }, + "random_state": { + "anyOf": [ + { + "type": "integer" + }, + { + "enum": [ + "RandomState" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Used when the �arpack� or �randomized� solvers are used. Pass an int for reproducible results across multiple function calls.", + "placeholder": "None", + "title": "Random State" + } + }, + "required": [ + "n_components", + "copy", + "whiten", + "svd_solver", + "tol", + "iterated_power", + "n_oversamples", + "power_iteration_normalizer", + "random_state" + ], + "title": "PCASchema", + "type": "object" + }, + "metadata": "None", + "description": "Principal component analysis (PCA).", + "display_name": "None" + }, + "ConverterChain": { + "name": "ConverterChain", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.converter_chain.ConverterChain", + "configurable_object": true, + "schema": { + "properties": { + "steps": { + "description": "Number of converters in the chain.", + "minimum": 1, + "placeholder": 1, + "title": "Steps", + "type": "integer" + } + }, + "required": [ + "steps" + ], + "title": "ConverterChainSchema", + "type": "object" + }, + "metadata": "None", + "description": "A ConverterChain applies a sequence of converters to preprocess data, passing the output of one converter to the next, with its scope defined by the first converter (the chain itself).", + "display_name": "None" + }, + "TruncatedSVD": { + "name": "TruncatedSVD", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.truncated_svd.TruncatedSVD", + "configurable_object": true, + "schema": { + "properties": { + "n_components": { + "description": "Desired dimensionality of output data.", + "exclusiveMinimum": 0, + "placeholder": 2, + "title": "N Components", + "type": "integer" + }, + "algorithm": { + "description": "SVD solver to use.", + "enum": [ + "arpack", + "randomized" + ], + "placeholder": "randomized", + "title": "Algorithm", + "type": "string" + }, + "n_iter": { + "description": "Number of iterations for randomized SVD solver.", + "exclusiveMinimum": 0, + "placeholder": 5, + "title": "N Iter", + "type": "integer" + }, + "n_oversamples": { + "description": "Number of power iterations used in randomized SVD solver.", + "exclusiveMinimum": 0, + "placeholder": 10, + "title": "N Oversamples", + "type": "integer" + }, + "power_iteration_normalizer": { + "description": "Method to normalize the eigenvectors.", + "enum": [ + "auto", + "QR", + "LU", + "None" + ], + "placeholder": "auto", + "title": "Power Iteration Normalizer", + "type": "string" + }, + "random_state": { + "anyOf": [ + { + "type": "integer" + }, + { + "enum": [ + "RandomState" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Used during randomized svd. Pass an int for reproducible results across multiple function calls.", + "placeholder": "None", + "title": "Random State" + }, + "tol": { + "description": "Tolerance for ARPACK.", + "minimum": 0, + "placeholder": 0, + "title": "Tol", + "type": "number" + } + }, + "required": [ + "n_components", + "algorithm", + "n_iter", + "n_oversamples", + "power_iteration_normalizer", + "random_state", + "tol" + ], + "title": "TruncatedSVDSchema", + "type": "object" + }, + "metadata": "None", + "description": "This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). Contrary to PCA, this estimator does not center the data before computing the singular value decomposition. This means it can work with sparse matrices efficiently.", + "display_name": "None" + }, + "Binarizer": { + "name": "Binarizer", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.binarizer.Binarizer", + "configurable_object": true, + "schema": { + "properties": { + "threshold": { + "description": "Feature values below or equal to this are replaced by 0, above it by 1.", + "placeholder": 0, + "title": "Threshold", + "type": "number" + }, + "copy": { + "description": "Set to false to perform inplace binarization.", + "placeholder": true, + "title": "Copy", + "type": "boolean" + } + }, + "required": [ + "threshold", + "copy" + ], + "title": "BinarizerSchema", + "type": "object" + }, + "metadata": "None", + "description": "Binarize data (set feature values to 0 or 1) according to a threshold.", + "display_name": "None" + }, + "LabelBinarizer": { + "name": "LabelBinarizer", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.label_binarizer.LabelBinarizer", + "configurable_object": true, + "schema": { + "properties": { + "neg_label": { + "description": "Value with which negative labels must be encoded.", + "placeholder": 0, + "title": "Neg Label", + "type": "integer" + }, + "pos_label": { + "description": "Value with which positive labels must be encoded.", + "placeholder": 1, + "title": "Pos Label", + "type": "integer" + } + }, + "required": [ + "neg_label", + "pos_label" + ], + "title": "LabelBinarizerSchema", + "type": "object" + }, + "metadata": "None", + "description": "Binarize labels in a one-vs-all fashion.", + "display_name": "None" + }, + "LabelEncoder": { + "name": "LabelEncoder", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.label_encoder.LabelEncoder", + "configurable_object": true, + "schema": { + "properties": {}, + "title": "LabelEncoderSchema", + "type": "object" + }, + "metadata": "None", + "description": "Encode target labels with value between 0 and n_classes-1.", + "display_name": "None" + }, + "MaxAbsScaler": { + "name": "MaxAbsScaler", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.max_abs_scaler.MaxAbsScaler", + "configurable_object": true, + "schema": { + "properties": { + "copy": { + "description": "Set to false to perform inplace scaling.", + "placeholder": true, + "title": "Copy", + "type": "boolean" + } + }, + "required": [ + "copy" + ], + "title": "MaxAbsScalerSchema", + "type": "object" + }, + "metadata": "None", + "description": "Scale each feature by its maximum absolute value.", + "display_name": "None" + }, + "MinMaxScaler": { + "name": "MinMaxScaler", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.min_max_scaler.MinMaxScaler", + "configurable_object": true, + "schema": { + "properties": { + "min_range": { + "description": "The minimum value of the range to scale the data to.", + "minimum": 0, + "placeholder": 0, + "title": "Min Range", + "type": "number" + }, + "max_range": { + "description": "The maximum value of the range to scale the data to.", + "minimum": 0, + "placeholder": 1, + "title": "Max Range", + "type": "number" + }, + "copy": { + "description": "Set to false to perform inplace row normalization.", + "placeholder": true, + "title": "Copy", + "type": "boolean" + }, + "clip": { + "description": "Set to true to clip the data to the feature range.", + "placeholder": false, + "title": "Clip", + "type": "boolean" + } + }, + "required": [ + "min_range", + "max_range", + "copy", + "clip" + ], + "title": "MinMaxScalerSchema", + "type": "object" + }, + "metadata": "None", + "description": "Transform features by scaling each feature to a given range.", + "display_name": "None" + }, + "Normalizer": { + "name": "Normalizer", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.normalizer.Normalizer", + "configurable_object": true, + "schema": { + "properties": { + "norm": { + "description": "The norm to use to normalize each non zero sample.", + "enum": [ + "l1", + "l2", + "max" + ], + "placeholder": "l2", + "title": "Norm", + "type": "string" + }, + "copy": { + "description": "Set to false to perform inplace row normalization.", + "placeholder": true, + "title": "Copy", + "type": "boolean" + } + }, + "required": [ + "norm", + "copy" + ], + "title": "NormalizerSchema", + "type": "object" + }, + "metadata": "None", + "description": "Normalize samples individually to unit norm.", + "display_name": "None" + }, + "OneHotEncoder": { + "name": "OneHotEncoder", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.one_hot_encoder.OneHotEncoder", + "configurable_object": true, + "schema": { + "properties": { + "categories": { + "description": "The categories of each feature.", + "placeholder": "auto", + "title": "Categories", + "type": "string" + }, + "drop": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Specifies a methodology to use to drop one of the categories per feature.", + "placeholder": "None", + "title": "Drop" + }, + "dtype": { + "description": "Desired dtype of output.", + "enum": [ + "int", + "np.float32", + "np.float64" + ], + "placeholder": "np.float64", + "title": "Dtype", + "type": "string" + }, + "handle_unknown": { + "description": "Whether to raise an error or ignore if an unknown categorical feature is present during transform.", + "enum": [ + "error", + "ignore", + "infrequent_if_exist" + ], + "placeholder": "error", + "title": "Handle Unknown", + "type": "string" + }, + "min_frequency": { + "anyOf": [ + { + "minimum": 0, + "type": "integer" + }, + { + "maximum": 1, + "minimum": 0, + "type": "number" + }, + { + "type": "null" + } + ], + "description": "Minimum frequency of a category to be considered as frequent.", + "placeholder": "None", + "title": "Min Frequency" + }, + "max_categories": { + "anyOf": [ + { + "minimum": 1, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "Maximum number of categories to encode.", + "placeholder": "None", + "title": "Max Categories" + }, + "feature_name_combiner": { + "description": "Method used to combine feature names.", + "enum": [ + "concat" + ], + "placeholder": "concat", + "title": "Feature Name Combiner", + "type": "string" + } + }, + "required": [ + "categories", + "drop", + "dtype", + "handle_unknown", + "min_frequency", + "max_categories", + "feature_name_combiner" + ], + "title": "OneHotEncoderSchema", + "type": "object" + }, + "metadata": "None", + "description": "Encode categorical integer features as a one-hot numeric array.", + "display_name": "None" + }, + "OrdinalEncoder": { + "name": "OrdinalEncoder", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.ordinal_encoder.OrdinalEncoder", + "configurable_object": true, + "schema": { + "properties": { + "categories": { + "description": "Categories (unique values) per feature.", + "placeholder": "auto", + "title": "Categories", + "type": "string" + }, + "dtype": { + "description": "Desired dtype of output.", + "enum": [ + "np.int32", + "np.int64", + "np.float32", + "np.float64" + ], + "placeholder": "np.float64", + "title": "Dtype", + "type": "string" + }, + "handle_unknown": { + "description": "Whether to raise an error or ignore if an unknown categorical feature is present during transform.", + "enum": [ + "error", + "use_encoded_value" + ], + "placeholder": "error", + "title": "Handle Unknown", + "type": "string" + }, + "unknown_value": { + "anyOf": [ + { + "enum": [ + "int", + "np.nan" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The value to use for unknown categories.", + "placeholder": "None", + "title": "Unknown Value" + }, + "min_frequency": { + "anyOf": [ + { + "minimum": 1, + "type": "integer" + }, + { + "maximum": 1, + "minimum": 0, + "type": "number" + }, + { + "type": "null" + } + ], + "description": "Minimum frequency of a category to be considered as frequent.", + "placeholder": "None", + "title": "Min Frequency" + }, + "max_categories": { + "anyOf": [ + { + "minimum": 1, + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "Maximum number of categories to encode.", + "placeholder": "None", + "title": "Max Categories" + } + }, + "required": [ + "categories", + "dtype", + "handle_unknown", + "unknown_value", + "min_frequency", + "max_categories" + ], + "title": "OrdinalEncoderSchema", + "type": "object" + }, + "metadata": "None", + "description": "Encode categorical features as an integer array.", + "display_name": "None" + }, + "PolynomialFeatures": { + "name": "PolynomialFeatures", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.polynomial_features.PolynomialFeatures", + "configurable_object": true, + "schema": { + "properties": { + "degree": { + "description": "The degree of the polynomial features.", + "minimum": 1, + "placeholder": 2, + "title": "Degree", + "type": "integer" + }, + "interaction_only": { + "description": "If true, only interaction features are produced: features that are products of at most degree distinct input features (so not x[1] ** 2, x[0] * x[2] ** 3, etc.).", + "placeholder": false, + "title": "Interaction Only", + "type": "boolean" + }, + "include_bias": { + "description": "If true (default), then include a bias column, the feature in which all polynomial powers are zero (i.e. a column of ones - acts as an intercept term in a linear model).", + "placeholder": true, + "title": "Include Bias", + "type": "boolean" + }, + "order": { + "description": "Order of output array in the dense case. 'F' order is faster to compute, but may slow down subsequent estimators.", + "enum": [ + "C", + "F" + ], + "placeholder": "C", + "title": "Order", + "type": "string" + } + }, + "required": [ + "degree", + "interaction_only", + "include_bias", + "order" + ], + "title": "PolynomialFeaturesSchema", + "type": "object" + }, + "metadata": "None", + "description": "Generate polynomial and interaction features.", + "display_name": "None" + }, + "StandardScaler": { + "name": "StandardScaler", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.standard_scaler.StandardScaler", + "configurable_object": true, + "schema": { + "properties": { + "copy": { + "description": "If false, try to avoid a copy and do inplace scaling instead.", + "placeholder": true, + "title": "Copy", + "type": "boolean" + }, + "with_mean": { + "description": "If true, center the data before scaling.", + "placeholder": true, + "title": "With Mean", + "type": "boolean" + }, + "with_std": { + "description": "If true, scale the data to unit variance (or equivalently, unit standard deviation).", + "placeholder": true, + "title": "With Std", + "type": "boolean" + } + }, + "required": [ + "copy", + "with_mean", + "with_std" + ], + "title": "StandardScalerSchema", + "type": "object" + }, + "metadata": "None", + "description": "Standardize features by removing the mean and scaling to unit variance.", + "display_name": "None" + }, + "Embedding": { + "name": "Embedding", + "type": "Converter", + "class": "DashAI.back.converters.hugging_face.embedding.Embedding", + "configurable_object": true, + "schema": { + "properties": { + "model_name": { + "description": "Name of the pre-trained model to use", + "enum": [ + "sentence-transformers/all-MiniLM-L6-v2", + "sentence-transformers/all-mpnet-base-v2", + "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", + "sentence-transformers/all-distilroberta-v1", + "bert-base-uncased", + "bert-large-uncased", + "bert-base-multilingual-cased", + "distilbert-base-uncased", + "roberta-base", + "roberta-large", + "distilroberta-base" + ], + "placeholder": "sentence-transformers/all-MiniLM-L6-v2", + "title": "Model Name", + "type": "string" + }, + "max_length": { + "description": "Maximum sequence length for tokenization", + "minimum": 1, + "placeholder": 512, + "title": "Max Length", + "type": "integer" + }, + "batch_size": { + "description": "Number of samples to process at once", + "minimum": 1, + "placeholder": 32, + "title": "Batch Size", + "type": "integer" + }, + "device": { + "description": "Device to use for computation", + "enum": [ + "cuda", + "cpu" + ], + "placeholder": "cpu", + "title": "Device", + "type": "string" + }, + "pooling_strategy": { + "description": "Strategy to pool token embeddings into sentence embedding", + "enum": [ + "mean", + "cls", + "max" + ], + "placeholder": "mean", + "title": "Pooling Strategy", + "type": "string" + } + }, + "required": [ + "model_name", + "max_length", + "batch_size", + "device", + "pooling_strategy" + ], + "title": "EmbeddingSchema", + "type": "object" + }, + "metadata": "None", + "description": "Convert text to embeddings using HuggingFace transformer models.", + "display_name": "None" + }, + "VarianceThreshold": { + "name": "VarianceThreshold", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.variance_threshold.VarianceThreshold", + "configurable_object": true, + "schema": { + "properties": { + "threshold": { + "description": "Features with a variance lower than this threshold will be removed.", + "minimum": 0, + "placeholder": 0, + "title": "Threshold", + "type": "number" + } + }, + "required": [ + "threshold" + ], + "title": "VarianceThresholdSchema", + "type": "object" + }, + "metadata": "None", + "description": "Feature selector that removes all low-variance features.", + "display_name": "None" + }, + "SimpleImputer": { + "name": "SimpleImputer", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.simple_imputer.SimpleImputer", + "configurable_object": true, + "schema": { + "properties": { + "missing_values": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "number" + }, + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The placeholder for the missing values.", + "placeholder": "None", + "title": "Missing Values" + }, + "strategy": { + "description": "The imputation strategy.", + "enum": [ + "mean", + "median", + "most_frequent", + "constant" + ], + "placeholder": "mean", + "title": "Strategy", + "type": "string" + }, + "fill_value": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "number" + }, + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The value to replace missing values with.", + "placeholder": "None", + "title": "Fill Value" + }, + "copy": { + "description": "If true, a copy of X will be created.", + "placeholder": true, + "title": "Copy", + "type": "boolean" + }, + "add_indicator": { + "description": "If true, a MissingIndicator transform will stack onto output.", + "placeholder": false, + "title": "Add Indicator", + "type": "boolean" + }, + "keep_empty_features": { + "description": "If true, empty features will be kept.", + "placeholder": false, + "title": "Keep Empty Features", + "type": "boolean" + } + }, + "required": [ + "missing_values", + "strategy", + "fill_value", + "copy", + "add_indicator", + "keep_empty_features" + ], + "title": "SimpleImputerSchema", + "type": "object" + }, + "metadata": "None", + "description": "Univariate imputer for completing missing values with simple strategies.", + "display_name": "None" + }, + "MissingIndicator": { + "name": "MissingIndicator", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.missing_indicator.MissingIndicator", + "configurable_object": true, + "schema": { + "properties": { + "missing_values": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "number" + }, + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The placeholder for the missing values.", + "placeholder": "None", + "title": "Missing Values" + }, + "features": { + "description": "The features to consider for missing values.", + "enum": [ + "missing-only", + "all" + ], + "placeholder": "None", + "title": "Features", + "type": "string" + }, + "error_on_new": { + "description": "Whether to raise an error on new missing values.", + "placeholder": true, + "title": "Error On New", + "type": "boolean" + } + }, + "required": [ + "missing_values", + "features", + "error_on_new" + ], + "title": "MissingIndicatorSchema", + "type": "object" + }, + "metadata": "None", + "description": "Binary indicators for missing values.", + "display_name": "None" + }, + "KNNImputer": { + "name": "KNNImputer", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.knn_imputer.KNNImputer", + "configurable_object": true, + "schema": { + "properties": { + "missing_values": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "number" + }, + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The placeholder for the missing values.", + "placeholder": "None", + "title": "Missing Values" + }, + "n_neighbors": { + "description": "The number of nearest neighbors to use for imputation.", + "minimum": 1, + "placeholder": 5, + "title": "N Neighbors", + "type": "integer" + }, + "weights": { + "description": "The weight function to use for imputation.", + "enum": [ + "uniform", + "distance" + ], + "placeholder": "uniform", + "title": "Weights", + "type": "string" + }, + "metric": { + "description": "The metric to use for imputation.", + "enum": [ + "nan_euclidean" + ], + "placeholder": "nan_euclidean", + "title": "Metric", + "type": "string" + }, + "copy": { + "description": "If true, a copy of X will be created.", + "placeholder": true, + "title": "Copy", + "type": "boolean" + }, + "add_indicator": { + "description": "If true, a MissingIndicator transform will stack onto output.", + "placeholder": false, + "title": "Add Indicator", + "type": "boolean" + }, + "keep_empty_features": { + "description": "If true, empty features will be kept.", + "placeholder": false, + "title": "Keep Empty Features", + "type": "boolean" + } + }, + "required": [ + "missing_values", + "n_neighbors", + "weights", + "metric", + "copy", + "add_indicator", + "keep_empty_features" + ], + "title": "KNNImputerSchema", + "type": "object" + }, + "metadata": "None", + "description": "Imputation for completing missing values using k-Nearest Neighbors.", + "display_name": "None" + }, + "AdditiveChi2Sampler": { + "name": "AdditiveChi2Sampler", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.additive_chi_2_sampler.AdditiveChi2Sampler", + "configurable_object": true, + "schema": { + "properties": { + "sample_steps": { + "description": "The number of sample steps (shuffling) to perform.", + "minimum": 1, + "placeholder": 2, + "title": "Sample Steps", + "type": "integer" + }, + "sample_interval": { + "anyOf": [ + { + "minimum": 1, + "type": "number" + }, + { + "type": "null" + } + ], + "description": "The number of samples to generate between each original sample.", + "placeholder": "None", + "title": "Sample Interval" + } + }, + "required": [ + "sample_steps", + "sample_interval" + ], + "title": "AdditiveChi2SamplerSchema", + "type": "object" + }, + "metadata": "None", + "description": "Uses sampling the fourier transform of the kernel characteristic at regular intervals.", + "display_name": "None" + }, + "RBFSampler": { + "name": "RBFSampler", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.rbf_sampler.RBFSampler", + "configurable_object": true, + "schema": { + "properties": { + "gamma": { + "anyOf": [ + { + "enum": [ + "scale" + ], + "type": "string" + }, + { + "exclusiveMinimum": 0, + "type": "number" + } + ], + "description": "Parameter of the RBF kernel.", + "placeholder": 1, + "title": "Gamma" + }, + "n_components": { + "description": "The number of features to construct.", + "minimum": 1, + "placeholder": 100, + "title": "N Components", + "type": "integer" + }, + "random_state": { + "anyOf": [ + { + "type": "integer" + }, + { + "enum": [ + "RandomState" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Pseudo-random number generator to control the generation of the random weights and random offset when fitting the training data. Pass an int for reproducible output across multiple function calls.", + "placeholder": "None", + "title": "Random State" + } + }, + "required": [ + "gamma", + "n_components", + "random_state" + ], + "title": "RBFSamplerSchema", + "type": "object" + }, + "metadata": "None", + "description": "Approximates the feature map of an RBF kernel by Monte Carlo approximation of its Fourier transform.", + "display_name": "None" + }, + "SkewedChi2Sampler": { + "name": "SkewedChi2Sampler", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.skewed_chi_2_sampler.SkewedChi2Sampler", + "configurable_object": true, + "schema": { + "properties": { + "skewedness": { + "description": "The skewedness parameter of the chi-squared kernel.", + "exclusiveMinimum": 0, + "placeholder": 1, + "title": "Skewedness", + "type": "number" + }, + "n_components": { + "description": "Number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space.", + "minimum": 1, + "placeholder": 100, + "title": "N Components", + "type": "integer" + }, + "random_state": { + "anyOf": [ + { + "type": "integer" + }, + { + "enum": [ + "RandomState" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Pseudo-random number generator to control the generation of the random weights and random offset when fitting the training data. Pass an int for reproducible output across multiple function calls.", + "placeholder": "None", + "title": "Random State" + } + }, + "required": [ + "skewedness", + "n_components", + "random_state" + ], + "title": "SkewedChi2SamplerSchema", + "type": "object" + }, + "metadata": "None", + "description": "Approximates the feature map of a chi-squared kernel by Monte Carlo approximation of its Fourier transform.", + "display_name": "None" + }, + "GenericUnivariateSelect": { + "name": "GenericUnivariateSelect", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.generic_univariate_select.GenericUnivariateSelect", + "configurable_object": true, + "schema": { + "properties": { + "mode": { + "description": "Select features according to a percentile of the highest scores.", + "enum": [ + "percentile", + "k_best", + "fpr", + "fdr", + "fwe" + ], + "placeholder": "percentile", + "title": "Mode", + "type": "string" + }, + "param": { + "anyOf": [ + { + "enum": [ + "all" + ], + "type": "string" + }, + { + "type": "number" + }, + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "Parameter of the mode.", + "placeholder": 0.00001, + "title": "Param" + } + }, + "required": [ + "mode", + "param" + ], + "title": "GenericUnivariateSelectSchema", + "type": "object" + }, + "metadata": "None", + "description": "Univariate feature selector with configurable strategy.", + "display_name": "None" + }, + "SelectPercentile": { + "name": "SelectPercentile", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.select_percentile.SelectPercentile", + "configurable_object": true, + "schema": { + "properties": { + "percentile": { + "description": "Percent of features to keep.", + "maximum": 100, + "minimum": 1, + "placeholder": 10, + "title": "Percentile", + "type": "integer" + } + }, + "required": [ + "percentile" + ], + "title": "SelectPercentileSchema", + "type": "object" + }, + "metadata": "None", + "description": "Select features according to a percentile of the highest scores.", + "display_name": "None" + }, + "SelectKBest": { + "name": "SelectKBest", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.select_k_best.SelectKBest", + "configurable_object": true, + "schema": { + "properties": { + "k": { + "anyOf": [ + { + "enum": [ + "all" + ], + "type": "string" + }, + { + "minimum": 1, + "type": "integer" + } + ], + "description": "Number of top features to select.", + "placeholder": 10, + "title": "K" + } + }, + "required": [ + "k" + ], + "title": "SelectKBestSchema", + "type": "object" + }, + "metadata": "None", + "description": "Select features according to the k highest scores.", + "display_name": "None" + }, + "SelectFpr": { + "name": "SelectFpr", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.select_fpr.SelectFpr", + "configurable_object": true, + "schema": { + "properties": { + "alpha": { + "description": "The highest p-value for features to be kept.", + "maximum": 1, + "minimum": 0, + "placeholder": 0.05, + "title": "Alpha", + "type": "number" + } + }, + "required": [ + "alpha" + ], + "title": "SelectFprSchema", + "type": "object" + }, + "metadata": "None", + "description": "Filter: Select features according to a false positive rate test.", + "display_name": "None" + }, + "SelectFdr": { + "name": "SelectFdr", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.select_fdr.SelectFdr", + "configurable_object": true, + "schema": { + "properties": { + "alpha": { + "description": "The highest uncorrected p-value for features to be kept.", + "maximum": 1, + "minimum": 0, + "placeholder": 0.05, + "title": "Alpha", + "type": "number" + } + }, + "required": [ + "alpha" + ], + "title": "SelectFdrSchema", + "type": "object" + }, + "metadata": "None", + "description": "Filter: Select features according to a false discovery rate test.", + "display_name": "None" + }, + "SelectFwe": { + "name": "SelectFwe", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.select_fwe.SelectFwe", + "configurable_object": true, + "schema": { + "properties": { + "alpha": { + "description": "The highest uncorrected p-value for features to be kept.", + "maximum": 1, + "minimum": 0, + "placeholder": 0.05, + "title": "Alpha", + "type": "number" + } + }, + "required": [ + "alpha" + ], + "title": "SelectFweSchema", + "type": "object" + }, + "metadata": "None", + "description": "Filter: Select features according to a family-wise error rate test.", + "display_name": "None" + }, + "Nystroem": { + "name": "Nystroem", + "type": "Converter", + "class": "DashAI.back.converters.scikit_learn.nystroem.Nystroem", + "configurable_object": true, + "schema": { + "properties": { + "kernel": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The kernel to use for the approximation.", + "placeholder": "rbf", + "title": "Kernel" + }, + "gamma": { + "anyOf": [ + { + "exclusiveMinimum": 0, + "type": "number" + }, + { + "type": "null" + } + ], + "description": "The gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels.", + "placeholder": "None", + "title": "Gamma" + }, + "coef0": { + "anyOf": [ + { + "type": "number" + }, + { + "type": "null" + } + ], + "description": "The coef0 parameter for the polynomial and sigmoid kernels.", + "placeholder": "None", + "title": "Coef0" + }, + "degree": { + "anyOf": [ + { + "minimum": 1, + "type": "number" + }, + { + "type": "null" + } + ], + "description": "The degree of the polynomial kernel.", + "placeholder": "None", + "title": "Degree" + }, + "kernel_params": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Additional parameters (keyword arguments) for the kernel function.", + "placeholder": "None", + "title": "Kernel Params" + }, + "n_components": { + "description": "The number of features to construct.", + "minimum": 1, + "placeholder": 100, + "title": "N Components", + "type": "integer" + }, + "random_state": { + "anyOf": [ + { + "type": "integer" + }, + { + "enum": [ + "RandomState" + ], + "type": "string" + }, + { + "type": "null" + } + ], + "description": "The seed of the pseudo random number generator to use when shuffling the data.", + "placeholder": "None", + "title": "Random State" + }, + "n_jobs": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "Number of parallel jobs to run.", + "placeholder": "None", + "title": "N Jobs" + } + }, + "required": [ + "kernel", + "gamma", + "coef0", + "degree", + "kernel_params", + "n_components", + "random_state", + "n_jobs" + ], + "title": "NystroemSchema", + "type": "object" + }, + "metadata": "None", + "description": "Approximates the feature map of an RBF kernel by Monte Carlo approximation of its Fourier transform.", + "display_name": "None" + }, + "SMOTEConverter": { + "name": "SMOTEConverter", + "type": "Converter", + "class": "DashAI.back.converters.imbalanced_learn.smote_converter.SMOTEConverter", + "configurable_object": true, + "schema": { + "properties": { + "sampling_strategy": { + "anyOf": [ + { + "exclusiveMinimum": 0, + "maximum": 1, + "type": "number" + }, + { + "enum": [ + "auto" + ], + "type": "string" + } + ], + "description": "Sampling strategy (float or 'auto') to determine minority class size.", + "placeholder": "auto", + "title": "Sampling Strategy" + }, + "random_state": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "Seed for reproducibility.", + "placeholder": "None", + "title": "Random State" + }, + "k_neighbors": { + "description": "Number of neighbors to use for generating synthetic samples.", + "minimum": 1, + "placeholder": 5, + "title": "K Neighbors", + "type": "integer" + } + }, + "required": [ + "sampling_strategy", + "random_state", + "k_neighbors" + ], + "title": "SMOTESchema", + "type": "object" + }, + "metadata": "None", + "description": "SMOTE: Synthetic Minority Over-sampling Technique.", + "display_name": "None" + }, + "SMOTEENNConverter": { + "name": "SMOTEENNConverter", + "type": "Converter", + "class": "DashAI.back.converters.imbalanced_learn.smoteenn_converter.SMOTEENNConverter", + "configurable_object": true, + "schema": { + "properties": { + "sampling_strategy": { + "anyOf": [ + { + "exclusiveMinimum": 0, + "maximum": 1, + "type": "number" + }, + { + "enum": [ + "auto" + ], + "type": "string" + } + ], + "description": "Sampling strategy to apply SMOTE and clean the dataset.", + "placeholder": "auto", + "title": "Sampling Strategy" + }, + "random_state": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "Seed used for reproducibility.", + "placeholder": "None", + "title": "Random State" + }, + "k_neighbors": { + "description": "Number of neighbors used by SMOTE.", + "minimum": 1, + "placeholder": 5, + "title": "K Neighbors", + "type": "integer" + } + }, + "required": [ + "sampling_strategy", + "random_state", + "k_neighbors" + ], + "title": "SMOTEENNSchema", + "type": "object" + }, + "metadata": "None", + "description": "SMOTEENN: SMOTE with noise reduction via Edited Nearest Neighbors.", + "display_name": "None" + }, + "RandomUnderSamplerConverter": { + "name": "RandomUnderSamplerConverter", + "type": "Converter", + "class": "DashAI.back.converters.imbalanced_learn.random_under_sampler_converter.RandomUnderSamplerConverter", + "configurable_object": true, + "schema": { + "properties": { + "sampling_strategy": { + "anyOf": [ + { + "exclusiveMinimum": 0, + "maximum": 1, + "type": "number" + }, + { + "enum": [ + "auto" + ], + "type": "string" + } + ], + "description": "Sampling strategy (float or 'auto') to reduce majority class.", + "placeholder": "auto", + "title": "Sampling Strategy" + }, + "random_state": { + "anyOf": [ + { + "type": "integer" + }, + { + "type": "null" + } + ], + "description": "Seed for reproducibility.", + "placeholder": "None", + "title": "Random State" + } + }, + "required": [ + "sampling_strategy", + "random_state" + ], + "title": "RUSchema", + "type": "object" + }, + "metadata": "None", + "description": "Randomly remove samples from the majority class to balance the dataset.", + "display_name": "None" + } + } +} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index f80c8d769..02f5281bd 100644 --- a/requirements.txt +++ b/requirements.txt @@ -449,5 +449,5 @@ zipp==3.20.2 # importlib-metadata # importlib-resources imblearn -chromadb==0.5.5 -chroma-hnswlib==0.7.6 \ No newline at end of file + +fasttext-wheel \ No newline at end of file diff --git a/test.py b/test.py new file mode 100644 index 000000000..f1b0e11a5 --- /dev/null +++ b/test.py @@ -0,0 +1,87 @@ +import requests + +""" +select_types: Annotated[Union[List[str], None], Query()], optional + If specified, the function return only the components that extend the + provided types (e.g., task, model, dataloader, etc...). + If None, the method returns all components in the registry, by default None. +ignore_types: Annotated[Union[List[str], None], Query()], optional + If specified, the function return every components that is not that extend + the provided types (e.g., task, model, dataloader, etc...). + If None, the method returns all components in the registry, by default None. +related_component : Union[str , None], optional + If specified, the function return only the components related with + the specified compatible component, (usually some task. as + TabularClassification, Translation, etc.), by default None. +component_parent : Union[str , None], optional + If specified, the function return only the components that inheirts the + indicated component (e.g., ScikitLearnLikeModel), by default None. +has_related_of_type : Union[str, None], optional + If specified, the function returns only components that have at least one + related component of the specified type (e.g., "Model"). This is useful for + filtering tasks that have associated models, by default None. +component_registry : ComponentRegistry + The current app component registry provided by dependency injection. """ + +#print("related component: RAGTask") +#print(requests.get("http://localhost:8000/api/v1/component/?related_component=RAGTask").json()) +#print() + +import json + +#response = requests.get("http://localhost:8000/api/v1/component/?select_types=Model&component_parent=RetrieverModel").json() +#for r in response: +# print(json.dumps(r, indent=2)) +# +#print() +#print() +#print() +#print() +#print() +# +#response = requests.get("http://localhost:8000/api/v1/component/?select_types=Model&related_component=RAGTask").json() +#for r in response: +# print(json.dumps(r, indent=2)) +# +#print() +#print() +#print() +#print() +#print() + + +queries = [ + #"component/Embedding", + #"component/RAGPipeline", + "component/RetrieverModel/children", + #"component/?component_parent=Encoding", + #"component/SparseRetriever/children/", + "component/DenseRetriever/", + "component/TFIDFRetriever/" + +] +print() + +for q in queries: + print(q) + response = requests.get(f"http://localhost:8000/api/v1/{q}") + print(json.dumps(response.json(), indent=2)) + print() + print() + print() + + + +#ESTA WEA NO FUNCIONA +#input() +#print("related component: Model, component parent: RetrieverModel") +#response = requests.get("http://localhost:8000/api/v1/component/?related_component=Model&component_parent=RetrieverModel").json() +#print(response) +#print() + +#ESTA WEA TAMPOCO +#input() +#print("related component: [Model, RetrieverModel ]") +#response = requests.get("http://localhost:8000/api/v1/component/?related_component=RAGTask&related_component=RetrieverModel").json() +#print(response) + diff --git a/tsconfig.json b/tsconfig.json new file mode 100644 index 000000000..bc2d6d577 --- /dev/null +++ b/tsconfig.json @@ -0,0 +1,15 @@ +{ + "compilerOptions": { + "module": "ESNext", + "moduleResolution": "Bundler", + "target": "ES2022", + "jsx": "react-jsx", + "strictNullChecks": true, + "strictFunctionTypes": true, + "sourceMap": true + }, + "exclude": [ + "node_modules", + "**/node_modules/*" + ] +} \ No newline at end of file From 21d9872927008aa80c307d4d8a5a9a1b1371c990 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Tue, 9 Sep 2025 16:30:18 -0300 Subject: [PATCH 009/218] changing from EditModelDialog to FormSchemaDialog+FormSchemaWithSelectedModel --- .gitignore | 3 +- .../RAG/RetrieverConfigurationStep.jsx | 85 ++++++++++++++----- 2 files changed, 67 insertions(+), 21 deletions(-) diff --git a/.gitignore b/.gitignore index 7972a2d3a..97f1f3645 100644 --- a/.gitignore +++ b/.gitignore @@ -181,4 +181,5 @@ __pycache__ .prettierrc -*.ipynb \ No newline at end of file +*.ipynb +*.pkl \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx index 9448eeabd..ffe460385 100644 --- a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx @@ -1,10 +1,14 @@ import { useState, useEffect, useCallback } from "react"; import { Box, Autocomplete, TextField, Typography } from "@mui/material"; import { DataGrid } from "@mui/x-data-grid"; -import EditModelDialog from "../../../components/experiments/EditModelDialog"; import { getRetrieverComponents, getRetrievalParadigm } from "../../../api/rag"; import { useSnackbar } from "notistack"; import useSchema from "../../../hooks/useSchema"; +import SettingsIcon from "@mui/icons-material/Settings"; +import { GridActionsCellItem } from "@mui/x-data-grid"; +import FormSchemaDialog from "../../../components/shared/FormSchemaDialog"; +import FormSchemaWithSelectedModel from "../../../components/shared/FormSchemaWithSelectedModel"; + export default function RetrieverConfigurationStep({ setNextEnabled }) { const { enqueueSnackbar } = useSnackbar(); @@ -13,8 +17,16 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { const [retrieverOptions, setRetrieverOptions] = useState([]); const [selectedRetriever, setSelectedRetriever] = useState(null); - const [retrieverInitialParameters, setRetrieverInitialParameters] = useState({}); const [retrieverParamsSchema, setRetrieverParamsSchema] = useState(null); + const { + modelSchema: retrieverModelSchema, + defaultValues: retrieverInitialParameters, + yupSchema: retrieverYupSchema, + loading: retrieverLoading, + } = useSchema({ modelName: selectedRetriever?.name }); + + + const [open, setOpen] = useState(false); const fetchRetrievalParadigms = async () => { try { @@ -38,14 +50,15 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { return; } - // For non-Sparse paradigms, only set retriever if schema and schema.properties exist - if (selectedRetrievalParadigm.schema && selectedRetrievalParadigm.schema.properties) { + if (selectedRetrievalParadigm.name === "SparseRetriever") { + const retrievers = await getRetrieverComponents(selectedRetrievalParadigm.name); + setRetrieverOptions(retrievers); + setSelectedRetriever(null); + } else { setRetrieverOptions([selectedRetrievalParadigm]); setSelectedRetriever(selectedRetrievalParadigm); - } else { - setRetrieverOptions([]); - setSelectedRetriever(null); } + }; useEffect(() => { @@ -54,12 +67,12 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { const handleRetrievalParadigmChange = (event, newValue) => { setSelectedRetrievalParadigm(newValue); - setSelectedRetriever(null); + fetchRetrievers() }; const handleRetrieverSelectionChange = (event, newValue) => { setSelectedRetriever(newValue); - setRetrieverInitialParameters(useSchema({ modelName: newValue?.name })); + console.log("Selected retriever model:", newValue); }; const handleRetrieverParametersChange = (newParams) => { @@ -94,6 +107,19 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { sx={{ mb: 3 }} /> + {selectedRetrievalParadigm && selectedRetrievalParadigm.name === "SparseRetriever" && ( + option.name} + value={selectedRetriever} + onChange={handleRetrieverSelectionChange} + isOptionEqualToValue={(option, value) => option.name === value?.name} + renderInput={(params) => } + sx={{ mb: 3 }} + /> + )} + {selectedRetriever && ( @@ -102,21 +128,40 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { [ - + <> + } + label="Edit" + onClick={() => setOpen(true)} + /> + { + handleRetrieverParametersChange(values); + setOpen(false); + }} + > + { + handleRetrieverParametersChange(values); + setOpen(false); + }} + onCancel={() => setOpen(false)} + /> + + ], }]} hideFooter From 16db412b3af2ec955c1f2f8733b6e8cc34712485 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Tue, 9 Sep 2025 17:30:33 -0300 Subject: [PATCH 010/218] modified FormSchemaHeader --- .../shared/FormSchemaWithSelectedModel.jsx | 1 + .../RAG/RetrieverConfigurationStep.jsx | 76 +++++++++++++------ 2 files changed, 55 insertions(+), 22 deletions(-) diff --git a/DashAI/front/src/components/shared/FormSchemaWithSelectedModel.jsx b/DashAI/front/src/components/shared/FormSchemaWithSelectedModel.jsx index ca6fcb590..8736a9932 100644 --- a/DashAI/front/src/components/shared/FormSchemaWithSelectedModel.jsx +++ b/DashAI/front/src/components/shared/FormSchemaWithSelectedModel.jsx @@ -68,6 +68,7 @@ function FormSchemaWithSelectedModel({ )} + setSchemaDialogOpen(false); const fetchRetrievalParadigms = async () => { try { @@ -140,27 +154,45 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { key="edit-button" icon={} label="Edit" - onClick={() => setOpen(true)} + onClick={() => setSchemaDialogOpen(true)} /> - { - handleRetrieverParametersChange(values); - setOpen(false); - }} - > - { - handleRetrieverParametersChange(values); - setOpen(false); + setOpen(false)} - /> - + > + + + + + + + + {`${selectedRetriever.name} configuration`} + + + + + + { + handleRetrieverParametersChange(values); + setSchemaDialogOpen(false); + }} + onCancel={() => setSchemaDialogOpen(false)} + /> + + + ], }]} From 37fb3a8c2ca19f20653337f8b8622edbf1a257ba Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Tue, 9 Sep 2025 19:09:32 -0300 Subject: [PATCH 011/218] Modified EditModelDialog, logic merged into RetrieverConfigurationStep Note: can't use formSchemaStore in the same component because it produces context problems with React --- .../RAG/RetrieverConfigurationStep.jsx | 196 ++++++++++++------ 1 file changed, 136 insertions(+), 60 deletions(-) diff --git a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx index f286eb75e..c8c5cc2db 100644 --- a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx @@ -1,4 +1,9 @@ -import { useState, useEffect, useCallback } from "react"; +import React, { + useState, + useEffect, + useCallback, + useMemo, +} from "react"; import { Box, Dialog, @@ -8,6 +13,7 @@ import { TextField, Typography, IconButton, + Stack, } from "@mui/material"; import ArrowBackOutlined from "@mui/icons-material/ArrowBackOutlined"; @@ -20,8 +26,12 @@ import SettingsIcon from "@mui/icons-material/Settings"; import { GridActionsCellItem } from "@mui/x-data-grid"; import FormSchemaDialog from "../../../components/shared/FormSchemaDialog"; import FormSchemaWithSelectedModel from "../../../components/shared/FormSchemaWithSelectedModel"; +import FormSchemaModelSelect from "../../../components/shared/FormSchemaModelSelect"; import FormSchemaContainer from "../../../components/shared/FormSchemaContainer"; -import FormSchemaHeader from "../../../components/shared/FormSchemaHeader"; +import { useFormSchemaStore } from "../../../contexts/schema"; +import FormSchemaBreadScrumbs from "../../../components/shared/FormSchemaBreadScrumbs"; +import FormSchema from "../../../components/shared/FormSchema"; + export default function RetrieverConfigurationStep({ setNextEnabled }) { const { enqueueSnackbar } = useSnackbar(); @@ -30,7 +40,8 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { const [retrieverOptions, setRetrieverOptions] = useState([]); const [selectedRetriever, setSelectedRetriever] = useState(null); - const [retrieverParamsSchema, setRetrieverParamsSchema] = useState(null); + const [schemaDialogOpen, setSchemaDialogOpen] = useState(false); + const { modelSchema: retrieverModelSchema, defaultValues: retrieverInitialParameters, @@ -38,10 +49,6 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { loading: retrieverLoading, } = useSchema({ modelName: selectedRetriever?.name }); - - const [schemaDialogOpen, setSchemaDialogOpen] = useState(false); - const handleSchemaDialogClose = () => setSchemaDialogOpen(false); - const fetchRetrievalParadigms = async () => { try { const data = await getRetrievalParadigm(); @@ -64,15 +71,14 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { return; } - if (selectedRetrievalParadigm.name === "SparseRetriever") { - const retrievers = await getRetrieverComponents(selectedRetrievalParadigm.name); - setRetrieverOptions(retrievers); - setSelectedRetriever(null); - } else { - setRetrieverOptions([selectedRetrievalParadigm]); - setSelectedRetriever(selectedRetrievalParadigm); - } - + if (selectedRetrievalParadigm.name === "SparseRetriever") { + const retrievers = await getRetrieverComponents(selectedRetrievalParadigm.name); + setRetrieverOptions(retrievers); + setSelectedRetriever(null); + } else { + setRetrieverOptions([selectedRetrievalParadigm]); + setSelectedRetriever(selectedRetrievalParadigm); + } }; useEffect(() => { @@ -81,7 +87,7 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { const handleRetrievalParadigmChange = (event, newValue) => { setSelectedRetrievalParadigm(newValue); - fetchRetrievers() + fetchRetrievers(); }; const handleRetrieverSelectionChange = (event, newValue) => { @@ -95,7 +101,7 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { parameters: newParams, })); console.log("Updated retriever model parameters:", newParams); - } + }; return ( [ - <> - } - label="Edit" - onClick={() => setSchemaDialogOpen(true)} - /> - [ + + } + label="Edit" + onClick={() => setSchemaDialogOpen(true)} + /> + setSchemaDialogOpen(false)} PaperProps={{ sx: { width: { md: 820 }, @@ -169,33 +174,19 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { }} > - - - - - - - {`${selectedRetriever.name} configuration`} - - - - - - { - handleRetrieverParametersChange(values); - setSchemaDialogOpen(false); - }} - onCancel={() => setSchemaDialogOpen(false)} - /> - - - - - ], - }]} + + + + + ], + }, + ]} hideFooter disableColumnMenu disableColumnSelector @@ -211,4 +202,89 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { )} ); +} + +function RetrieverConfigurationStepSubComponent({ + selectedRetriever, + retrieverInitialParameters, + schemaDialogOpen, + setSchemaDialogOpen, + handleRetrieverParametersChange, +}) { + const { + formValues, + properties, + propertyData, + valuesByProperties, + removeLastProperty, + setErrorForm, + } = useFormSchemaStore(); + + const [selectedModel, setSelectedModel] = useState( + selectedRetriever?.name || propertyData?.model, + ); + + const selectedProperty = Boolean(propertyData?.selected); + + const defaultValues = useMemo(() => { + if (selectedProperty) { + if (propertyData.params) { + return propertyData.params; + } else return null; + } + return retrieverInitialParameters ?? valuesByProperties; + }, [selectedModel, propertyData.params]); + + useEffect(() => { + if (propertyData.model) { + setSelectedModel(propertyData.model); + } else { + setSelectedModel(selectedRetriever?.name); + } + }, [propertyData.model, propertyData.params, selectedRetriever?.name]); + + return ( + <> + + + setSchemaDialogOpen(false)}> + + + + {`${selectedRetriever.name} configuration`} + + + + + + {Boolean(propertyData?.parent) && ( + <> + + + + )} + { + handleRetrieverParametersChange(formValues); + setSchemaDialogOpen(false); + }} + setError={setErrorForm} + onCancel={() => { + if (properties.length > 0) { + removeLastProperty(); + } else { + setSchemaDialogOpen(false); + } + }} + /> + + + + ); } \ No newline at end of file From 0d1143f3fe8458dcb07ca876e653292a578a4e22 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Fri, 12 Sep 2025 11:58:48 -0300 Subject: [PATCH 012/218] awful retriever model working --- .../RAG/RetrieverConfigurationStep.jsx | 245 +++++++----------- 1 file changed, 98 insertions(+), 147 deletions(-) diff --git a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx index c8c5cc2db..c34fd58a7 100644 --- a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx @@ -24,8 +24,6 @@ import { useSnackbar } from "notistack"; import useSchema from "../../../hooks/useSchema"; import SettingsIcon from "@mui/icons-material/Settings"; import { GridActionsCellItem } from "@mui/x-data-grid"; -import FormSchemaDialog from "../../../components/shared/FormSchemaDialog"; -import FormSchemaWithSelectedModel from "../../../components/shared/FormSchemaWithSelectedModel"; import FormSchemaModelSelect from "../../../components/shared/FormSchemaModelSelect"; import FormSchemaContainer from "../../../components/shared/FormSchemaContainer"; import { useFormSchemaStore } from "../../../contexts/schema"; @@ -33,6 +31,7 @@ import FormSchemaBreadScrumbs from "../../../components/shared/FormSchemaBreadSc import FormSchema from "../../../components/shared/FormSchema"; + export default function RetrieverConfigurationStep({ setNextEnabled }) { const { enqueueSnackbar } = useSnackbar(); const [retrievalParadigms, setRetrievalParadigms] = useState([]); @@ -40,15 +39,11 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { const [retrieverOptions, setRetrieverOptions] = useState([]); const [selectedRetriever, setSelectedRetriever] = useState(null); - const [schemaDialogOpen, setSchemaDialogOpen] = useState(false); - const { - modelSchema: retrieverModelSchema, - defaultValues: retrieverInitialParameters, - yupSchema: retrieverYupSchema, - loading: retrieverLoading, - } = useSchema({ modelName: selectedRetriever?.name }); + const {defaultValues: retrieverInitialParameters} = useSchema({ modelName: selectedRetriever?.name }); + + // Fetch paradigms const fetchRetrievalParadigms = async () => { try { const data = await getRetrievalParadigm(); @@ -64,13 +59,13 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { fetchRetrievalParadigms(); }, []); - const fetchRetrievers = async () => { + // Fetch retrievers when paradigm changes + const fetchRetrievers = useCallback(async () => { if (!selectedRetrievalParadigm) { setRetrieverOptions([]); setSelectedRetriever(null); return; } - if (selectedRetrievalParadigm.name === "SparseRetriever") { const retrievers = await getRetrieverComponents(selectedRetrievalParadigm.name); setRetrieverOptions(retrievers); @@ -79,136 +74,102 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { setRetrieverOptions([selectedRetrievalParadigm]); setSelectedRetriever(selectedRetrievalParadigm); } - }; + }, [selectedRetrievalParadigm]); useEffect(() => { fetchRetrievers(); - }, [selectedRetrievalParadigm]); + }, [selectedRetrievalParadigm, fetchRetrievers]); + // Handle paradigm change const handleRetrievalParadigmChange = (event, newValue) => { setSelectedRetrievalParadigm(newValue); - fetchRetrievers(); + setSelectedRetriever(null); // Reset retriever selection }; + // Handle retriever change const handleRetrieverSelectionChange = (event, newValue) => { setSelectedRetriever(newValue); - console.log("Selected retriever model:", newValue); + // Optionally reset config if needed }; + // Handle config change const handleRetrieverParametersChange = (newParams) => { setSelectedRetriever((prev) => ({ ...prev, parameters: newParams, })); - console.log("Updated retriever model parameters:", newParams); + setNextEnabled(true); }; + // Layout: two columns if retriever selected, else one column return ( - - - Select retrieval paradigm - - - option.name} - value={selectedRetrievalParadigm} - onChange={handleRetrievalParadigmChange} - isOptionEqualToValue={(option, value) => option.name === value?.name} - renderInput={(params) => } - sx={{ mb: 3 }} - /> - - {selectedRetrievalParadigm && selectedRetrievalParadigm.name === "SparseRetriever" && ( + + {/* Left column: paradigm/model selection */} + + + Select retrieval paradigm + option.name} - value={selectedRetriever} - onChange={handleRetrieverSelectionChange} + value={selectedRetrievalParadigm} + onChange={handleRetrievalParadigmChange} isOptionEqualToValue={(option, value) => option.name === value?.name} - renderInput={(params) => } - sx={{ mb: 3 }} + renderInput={(params) => } + sx={{ mb: 2 }} /> - )} + {selectedRetrievalParadigm && selectedRetrievalParadigm.name === "SparseRetriever" && ( + <> + + Select retriever model + + option.name} + value={selectedRetriever} + onChange={handleRetrieverSelectionChange} + isOptionEqualToValue={(option, value) => option.name === value?.name} + renderInput={(params) => } + /> + + )} + {selectedRetriever && selectedRetriever.schema && !selectedRetriever.schema.properties && ( + + No parameters available for this retriever. + + )} + + {/* Right column: configuration, only if retriever selected */} {selectedRetriever && ( - - - Configure retriever model - - [ - - } - label="Edit" - onClick={() => setSchemaDialogOpen(true)} - /> - setSchemaDialogOpen(false)} - PaperProps={{ - sx: { - width: { md: 820 }, - maxHeight: { lg: 700, xl: "auto" }, - maxWidth: 2000, - transition: "width 0.3s ease, height 0.3s ease", - }, - }} - > - - - - - - ], - }, - ]} - hideFooter - disableColumnMenu - disableColumnSelector - disableSelectionOnClick - /> + + + + )} - - {selectedRetriever && selectedRetriever.schema && !selectedRetriever.schema.properties && ( - - No parameters available for this retriever. - - )} ); } -function RetrieverConfigurationStepSubComponent({ +function ModelConfigurationSubcomponent({ selectedRetriever, retrieverInitialParameters, - schemaDialogOpen, - setSchemaDialogOpen, handleRetrieverParametersChange, }) { const { @@ -244,47 +205,37 @@ function RetrieverConfigurationStepSubComponent({ }, [propertyData.model, propertyData.params, selectedRetriever?.name]); return ( - <> - - - setSchemaDialogOpen(false)}> - - - - {`${selectedRetriever.name} configuration`} - - - - - - {Boolean(propertyData?.parent) && ( - <> - - - - )} - { - handleRetrieverParametersChange(formValues); - setSchemaDialogOpen(false); - }} - setError={setErrorForm} - onCancel={() => { - if (properties.length > 0) { - removeLastProperty(); - } else { - setSchemaDialogOpen(false); - } - }} + + + + + {`${selectedRetriever.name} configuration`} + + + {console.log("propertyData:", propertyData)} + {Boolean(propertyData?.parent) && ( + <> + + - - - + + )} + { + handleRetrieverParametersChange(formValues); + }} + setError={setErrorForm} + onCancel={() => { + if (properties.length > 0) { + removeLastProperty(); + } + }} + /> + ); } \ No newline at end of file From 1109f664f48abd6509d772d825cd98e2ce3e12d5 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Tue, 16 Sep 2025 15:34:02 -0300 Subject: [PATCH 013/218] backup pre dev branch update --- DashAI/back/initial_components.py | 20 +- DashAI/back/models/RAG/RAG_pipeline.py | 10 + DashAI/back/models/RAG/Retrievers/__init__.py | 2 +- .../models/RAG/Retrievers/dense_retriever.py | 2 +- .../models/RAG/Retrievers/tfidf_retriever.py | 140 +++++++++- DashAI/back/models/RAG/__init__.py | 2 +- DashAI/back/models/RAG/dummy_retriever.py | 1 - DashAI/back/models/RAG/embeddings/__init__.py | 4 + .../models/RAG/embeddings/dense/__init__.py | 2 + .../dense/fasttext_embedding.py | 7 +- .../dense/huggingface_embedding.py | 6 +- .../dense_encoding.py} | 2 +- .../models/RAG/embeddings/sparse/__init__.py | 2 + .../sparse/bm25_encoding.py | 3 +- .../sparse/tfidf_encoding.py | 3 +- .../trainable_encoding.py | 4 +- DashAI/back/models/RAG/encodings/__init__.py | 6 - .../models/RAG/encodings/dense/__init__.py | 2 - .../models/RAG/encodings/dense_encoding.py | 24 -- .../models/RAG/encodings/sparse/__init__.py | 2 - .../models/RAG/encodings/sparse_encoding.py | 29 --- DashAI/front/src/api/rag.ts | 8 + .../RAG/ChunkingConfigurationStep.jsx | 85 ++++++ .../pages/generative/RAG/NewSessionModal.jsx | 31 ++- .../RAG/RetrieverConfigurationStep copy.jsx | 241 ++++++++++++++++++ .../RAG/RetrieverConfigurationStep.jsx | 113 ++------ 26 files changed, 553 insertions(+), 198 deletions(-) create mode 100644 DashAI/back/models/RAG/embeddings/__init__.py create mode 100644 DashAI/back/models/RAG/embeddings/dense/__init__.py rename DashAI/back/models/RAG/{encodings => embeddings}/dense/fasttext_embedding.py (88%) rename DashAI/back/models/RAG/{encodings => embeddings}/dense/huggingface_embedding.py (95%) rename DashAI/back/models/RAG/{encodings/encoding.py => embeddings/dense_encoding.py} (97%) create mode 100644 DashAI/back/models/RAG/embeddings/sparse/__init__.py rename DashAI/back/models/RAG/{encodings => embeddings}/sparse/bm25_encoding.py (98%) rename DashAI/back/models/RAG/{encodings => embeddings}/sparse/tfidf_encoding.py (99%) rename DashAI/back/models/RAG/{encodings => embeddings}/trainable_encoding.py (95%) delete mode 100644 DashAI/back/models/RAG/encodings/__init__.py delete mode 100644 DashAI/back/models/RAG/encodings/dense/__init__.py delete mode 100644 DashAI/back/models/RAG/encodings/dense_encoding.py delete mode 100644 DashAI/back/models/RAG/encodings/sparse/__init__.py delete mode 100644 DashAI/back/models/RAG/encodings/sparse_encoding.py create mode 100644 DashAI/front/src/pages/generative/RAG/ChunkingConfigurationStep.jsx create mode 100644 DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep copy.jsx diff --git a/DashAI/back/initial_components.py b/DashAI/back/initial_components.py index 0b73a8fb8..ba636a57a 100644 --- a/DashAI/back/initial_components.py +++ b/DashAI/back/initial_components.py @@ -98,19 +98,17 @@ CharacterChunkModel, TokenChunkModel, # Encodings - DenseEncoding, + DenseEmbedding, FastTextEmbedding, HuggingFaceEmbedding, - SparseEncoding, - TFIDFEncoding, - BM25Encoding, # Prompts AugmentationPrompt, ContextMergePrompt, # Retrievers DenseRetriever, SparseRetriever, - TFIDFRetriever + TFIDFRetriever, + TFIDFVectorizerModel, ) @@ -267,20 +265,22 @@ def get_initial_components(): # RAG CharacterChunkModel, TokenChunkModel, + # Chunking Models + CharacterChunkModel, + TokenChunkModel, # Encodings - DenseEncoding, + DenseEmbedding, FastTextEmbedding, HuggingFaceEmbedding, - SparseEncoding, - TFIDFEncoding, - BM25Encoding, # Prompts AugmentationPrompt, ContextMergePrompt, # Retrievers DenseRetriever, SparseRetriever, - TFIDFRetriever + TFIDFRetriever, + TFIDFVectorizerModel, + ] # Obtener plugins instalados diff --git a/DashAI/back/models/RAG/RAG_pipeline.py b/DashAI/back/models/RAG/RAG_pipeline.py index 854254d43..63f3b9c60 100644 --- a/DashAI/back/models/RAG/RAG_pipeline.py +++ b/DashAI/back/models/RAG/RAG_pipeline.py @@ -48,6 +48,16 @@ class RAGPipelineSchema(BaseSchema): description="List of documents to be used in the RAG pipeline." ) # type: ignore + chunking_model: schema_field( + component_field( + parent="BaseChunkingModel"), + description="Chunking model used to split documents into smaller pieces.", + placeholder={ + "component": "CharacterChunkModel", + "params": {} + } + ) # type: ignore + # RAG algorithm parameters retriever_model: schema_field( component_field(parent="RetrieverModel"), diff --git a/DashAI/back/models/RAG/Retrievers/__init__.py b/DashAI/back/models/RAG/Retrievers/__init__.py index 1da8bcd36..3e0d609ce 100644 --- a/DashAI/back/models/RAG/Retrievers/__init__.py +++ b/DashAI/back/models/RAG/Retrievers/__init__.py @@ -1,4 +1,4 @@ from DashAI.back.models.RAG.Retrievers.retriever_model import RetrieverModel -from DashAI.back.models.RAG.Retrievers.tfidf_retriever import TFIDFRetriever +from DashAI.back.models.RAG.Retrievers.tfidf_retriever import TFIDFRetriever, TFIDFVectorizerModel from DashAI.back.models.RAG.Retrievers.dense_retriever import DenseRetriever from DashAI.back.models.RAG.Retrievers.sparse_retriever import SparseRetriever \ No newline at end of file diff --git a/DashAI/back/models/RAG/Retrievers/dense_retriever.py b/DashAI/back/models/RAG/Retrievers/dense_retriever.py index 1ef42567f..6e4d8726e 100644 --- a/DashAI/back/models/RAG/Retrievers/dense_retriever.py +++ b/DashAI/back/models/RAG/Retrievers/dense_retriever.py @@ -19,7 +19,7 @@ class DenseRetrieverSchema(BaseSchema): encoding_model: schema_field( component_field( - parent="Encoding" + parent="DenseEmbedding" ), placeholder={"component": "Embedding", "params": {}}, description="Model to convert text into dense vector representations." diff --git a/DashAI/back/models/RAG/Retrievers/tfidf_retriever.py b/DashAI/back/models/RAG/Retrievers/tfidf_retriever.py index 6f2cf771a..4bda9de14 100644 --- a/DashAI/back/models/RAG/Retrievers/tfidf_retriever.py +++ b/DashAI/back/models/RAG/Retrievers/tfidf_retriever.py @@ -15,19 +15,147 @@ enum_field, float_field, int_field, + list_field, + string_field, + bool_field, + component_field ) +from DashAI.back.models.base_model import BaseModel similarities = { "cosine": cosine_similarity, "euclidean": euclidean_distances } +class TFIDFVectorizerSchema(BaseSchema): + + strip_accents: schema_field( + enum_field( + enum=["ascii", "unicode", None], + ), + placeholder=None, + description="Whether to strip accents from the text.", + ) # type: ignore + + lowercase: schema_field( + bool_field(), + placeholder=True, + description="Whether to convert all characters to lowercase.", + + ) # type: ignore + + analyzer: schema_field( + enum_field( + enum=["word", "char", "char_wb"], + ), + placeholder="word", + description="Whether the feature should be made of word or character n-grams. Option 'char_wb' creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space.", + ) # type: ignore + + stop_words: schema_field( + list_field( + string_field(), + min_items=1, + ), + placeholder=None, + description="List of stop words to be used in the TF-IDF vectorization.", + ) # type: ignore + + ngram_range: schema_field( + list_field( + int_field(), + min_items=2, + max_items=2, + ), + placeholder=[1, 1], + description="The lower and upper boundary of the range of n-values for different n-grams to be extracted.", + ) # type: ignore + + max_df: schema_field( + float_field( + ge=0.0, + le=1.0, + ), + placeholder=1.0, + description="When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words).", + ) # type: ignore + + min_df: schema_field( + float_field( + ge=0.0, + le=1.0, + ), + placeholder=1.0, + description="When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold (corpus-specific stop words).", + ) # type: ignore + + max_features: schema_field( + int_field( + ge=1, + ), + placeholder=None, + description="If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus.", + ) # type: ignore + + norm: schema_field( + enum_field( + enum=["l1", "l2", None], + ), + placeholder="l2", + description="The norm used to normalize term vectors. If None, no normalization is applied.", + ) # type: ignore + + use_idf: schema_field( + bool_field(), + placeholder=True, + description="Enable inverse-document-frequency reweighting.", + ) # type: ignore + + smooth_idf: schema_field( + bool_field(), + placeholder=True, + description="Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. This prevents zero divisions.", + ) # type: ignore + + sublinear_tf: schema_field( + bool_field(), + placeholder=False, + description="Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).", + ) # type: ignore + +class TFIDFVectorizerModel(BaseModel): + + SCHEMA = TFIDFVectorizerSchema + + def __init__(self, **kwargs) -> None: + self.params = kwargs + self.model = TfidfVectorizer( + strip_accents=kwargs.get("strip_accents", None), + lowercase=kwargs.get("lowercase", True), + analyzer=kwargs.get("analyzer", "word"), + stop_words=kwargs.get("stop_words", None), + ngram_range=tuple(kwargs.get("ngram_range", (1, 1))), + max_df=kwargs.get("max_df", 1.0), + min_df=kwargs.get("min_df", 1.0), + max_features=kwargs.get("max_features", None), + norm=kwargs.get("norm", "l2"), + use_idf=kwargs.get("use_idf", True), + smooth_idf=kwargs.get("smooth_idf", True), + sublinear_tf=kwargs.get("sublinear_tf", False), + ) class TFIDFRetrieverSchema(BaseSchema): """ Schema for the TFIDFRetriever. """ + TFIDFVectorizer: schema_field( + component_field(parent="TFIDFVectorizerModel"), + placeholder={"component": "TFIDFVectorizerModel", "params": {}}, + description="TF-IDF Vectorizer parameters.", + ) # type: ignore + + similarity_function: schema_field( enum_field(['cosine', 'euclidean']), placeholder="cosine", @@ -46,18 +174,6 @@ class TFIDFRetrieverSchema(BaseSchema): description="Maximum or minimum distance for retrieved documents based on the similarity_function." ) # type: ignore - chunk_size: schema_field( - int_field(ge=1), - placeholder=512, - description="Size of chunks to split the documents into." - ) # type: ignore - - chunk_overlap: schema_field( - int_field(ge=0), - placeholder=50, - description="Overlap between chunks when splitting documents." - ) # type: ignore - class TFIDFRetriever(SparseRetriever): """ A retriever that uses TF-IDF to retrieve documents based on a query. diff --git a/DashAI/back/models/RAG/__init__.py b/DashAI/back/models/RAG/__init__.py index 88a96fe60..189e18114 100644 --- a/DashAI/back/models/RAG/__init__.py +++ b/DashAI/back/models/RAG/__init__.py @@ -1,6 +1,6 @@ from DashAI.back.models.RAG.chunking_models import * from DashAI.back.models.RAG.documents import * -from DashAI.back.models.RAG.encodings import * +from DashAI.back.models.RAG.embeddings import * from DashAI.back.models.RAG.prompts import * from DashAI.back.models.RAG.Retrievers import * diff --git a/DashAI/back/models/RAG/dummy_retriever.py b/DashAI/back/models/RAG/dummy_retriever.py index 2f781668d..6a7136599 100644 --- a/DashAI/back/models/RAG/dummy_retriever.py +++ b/DashAI/back/models/RAG/dummy_retriever.py @@ -3,7 +3,6 @@ #from chromadb import Settings from typing import List, Dict from pypdf import PdfReader -from DashAI.back.models.RAG.encodings.tf_idf_encoding import TfidfEmbeddingFunction from DashAI.back.models.RAG.text_splitter import TextSplitter from DashAI.back.models.RAG.text_preprocessor import TextPreprocessor diff --git a/DashAI/back/models/RAG/embeddings/__init__.py b/DashAI/back/models/RAG/embeddings/__init__.py new file mode 100644 index 000000000..c6c567767 --- /dev/null +++ b/DashAI/back/models/RAG/embeddings/__init__.py @@ -0,0 +1,4 @@ +from DashAI.back.models.RAG.embeddings.dense_encoding import DenseEmbedding +from DashAI.back.models.RAG.embeddings.trainable_encoding import TrainableEncoding +from DashAI.back.models.RAG.embeddings.sparse import * +from DashAI.back.models.RAG.embeddings.dense import * \ No newline at end of file diff --git a/DashAI/back/models/RAG/embeddings/dense/__init__.py b/DashAI/back/models/RAG/embeddings/dense/__init__.py new file mode 100644 index 000000000..860badbfe --- /dev/null +++ b/DashAI/back/models/RAG/embeddings/dense/__init__.py @@ -0,0 +1,2 @@ +from DashAI.back.models.RAG.embeddings.dense.fasttext_embedding import FastTextEmbedding +from DashAI.back.models.RAG.embeddings.dense.huggingface_embedding import HuggingFaceEmbedding \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/dense/fasttext_embedding.py b/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py similarity index 88% rename from DashAI/back/models/RAG/encodings/dense/fasttext_embedding.py rename to DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py index 12c5e9e74..cf4f4bcbb 100644 --- a/DashAI/back/models/RAG/encodings/dense/fasttext_embedding.py +++ b/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py @@ -3,13 +3,14 @@ import numpy as np from DashAI.back.core.schema_fields import enum_field +from DashAI.back.core.schema_fields.base_schema import BaseSchema from DashAI.back.core.schema_fields.schema_field import schema_field -from DashAI.back.models.RAG.encodings.dense_encoding import DenseEncoding +from DashAI.back.models.RAG.embeddings.dense_encoding import DenseEmbedding import fasttext from huggingface_hub import hf_hub_download -class FastTextEmbeddingSchema(DenseEncoding.SCHEMA): +class FastTextEmbeddingSchema(BaseSchema): model_name: schema_field( enum_field( [ @@ -32,7 +33,7 @@ class FastTextEmbeddingSchema(DenseEncoding.SCHEMA): "Pooling strategy to use", ) # type: ignore -class FastTextEmbedding(DenseEncoding): +class FastTextEmbedding(DenseEmbedding): """FastText embedding""" SCHEMA = FastTextEmbeddingSchema diff --git a/DashAI/back/models/RAG/encodings/dense/huggingface_embedding.py b/DashAI/back/models/RAG/embeddings/dense/huggingface_embedding.py similarity index 95% rename from DashAI/back/models/RAG/encodings/dense/huggingface_embedding.py rename to DashAI/back/models/RAG/embeddings/dense/huggingface_embedding.py index 23ce06f24..09ab807f1 100644 --- a/DashAI/back/models/RAG/encodings/dense/huggingface_embedding.py +++ b/DashAI/back/models/RAG/embeddings/dense/huggingface_embedding.py @@ -6,14 +6,12 @@ schema_field, enum_field ) -from DashAI.back.models.RAG.encodings.dense_encoding import DenseEncoding - from typing import Final, List import numpy as np import torch from transformers import AutoModel, AutoTokenizer -from DashAI.back.models.RAG.encodings.dense_encoding import DenseEncoding +from DashAI.back.models.RAG.embeddings.dense_encoding import DenseEmbedding from DashAI.back.core.schema_fields import ( enum_field, int_field, @@ -67,7 +65,7 @@ class HuggingFaceEmbeddingSchema(BaseSchema): ) # type: ignore -class HuggingFaceEmbedding(DenseEncoding): +class HuggingFaceEmbedding(DenseEmbedding): """HuggingFace embedding""" SCHEMA = HuggingFaceEmbeddingSchema diff --git a/DashAI/back/models/RAG/encodings/encoding.py b/DashAI/back/models/RAG/embeddings/dense_encoding.py similarity index 97% rename from DashAI/back/models/RAG/encodings/encoding.py rename to DashAI/back/models/RAG/embeddings/dense_encoding.py index 0e2d65c9c..64a4e1966 100644 --- a/DashAI/back/models/RAG/encodings/encoding.py +++ b/DashAI/back/models/RAG/embeddings/dense_encoding.py @@ -6,7 +6,7 @@ from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument from DashAI.back.models.base_model import BaseModel -class Encoding(BaseModel): +class DenseEmbedding(BaseModel): """ Base class for all encoding (embedding) models. This class should be inherited by any specific encoding (embedding) model implementation. diff --git a/DashAI/back/models/RAG/embeddings/sparse/__init__.py b/DashAI/back/models/RAG/embeddings/sparse/__init__.py new file mode 100644 index 000000000..368a87c05 --- /dev/null +++ b/DashAI/back/models/RAG/embeddings/sparse/__init__.py @@ -0,0 +1,2 @@ +from DashAI.back.models.RAG.embeddings.sparse.bm25_encoding import BM25Encoding +from DashAI.back.models.RAG.embeddings.sparse.tfidf_encoding import TFIDFEncoding \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/sparse/bm25_encoding.py b/DashAI/back/models/RAG/embeddings/sparse/bm25_encoding.py similarity index 98% rename from DashAI/back/models/RAG/encodings/sparse/bm25_encoding.py rename to DashAI/back/models/RAG/embeddings/sparse/bm25_encoding.py index f239a893d..0a57bdabc 100644 --- a/DashAI/back/models/RAG/encodings/sparse/bm25_encoding.py +++ b/DashAI/back/models/RAG/embeddings/sparse/bm25_encoding.py @@ -1,6 +1,5 @@ import os from DashAI.back.converters.hugging_face import embedding -from DashAI.back.models.RAG.encodings.sparse_encoding import SparseEncoding from DashAI.back.models.RAG.documents import BaseDocument, PDFDocument, TxtDocument from typing import List, Dict, Any, Tuple from sklearn.feature_extraction.text import TfidfVectorizer @@ -73,7 +72,7 @@ class BM25EncodingSchema(BaseSchema): ) # type: ignore -class BM25Encoding(SparseEncoding): +class BM25Encoding: """ BM25 encoding (embedding) model for text documents. This class implements the precomputation of BM25 scores for text documents. diff --git a/DashAI/back/models/RAG/encodings/sparse/tfidf_encoding.py b/DashAI/back/models/RAG/embeddings/sparse/tfidf_encoding.py similarity index 99% rename from DashAI/back/models/RAG/encodings/sparse/tfidf_encoding.py rename to DashAI/back/models/RAG/embeddings/sparse/tfidf_encoding.py index 560d6c2f2..ce32a30d0 100644 --- a/DashAI/back/models/RAG/encodings/sparse/tfidf_encoding.py +++ b/DashAI/back/models/RAG/embeddings/sparse/tfidf_encoding.py @@ -1,6 +1,5 @@ import os from DashAI.back.converters.hugging_face import embedding -from DashAI.back.models.RAG.encodings.sparse_encoding import SparseEncoding from DashAI.back.models.RAG.documents import BaseDocument, PDFDocument, TxtDocument from typing import List, Dict, Any, Tuple from sklearn.feature_extraction.text import TfidfVectorizer @@ -116,7 +115,7 @@ class TFIDFEncodingSchema(BaseSchema): description="Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).", ) # type: ignore -class TFIDFEncoding(SparseEncoding): +class TFIDFEncoding: """ TF-IDF encoding (embedding) model for text documents. This class implements the TF-IDF algorithm to generate encodings (embeddings) for text documents. diff --git a/DashAI/back/models/RAG/encodings/trainable_encoding.py b/DashAI/back/models/RAG/embeddings/trainable_encoding.py similarity index 95% rename from DashAI/back/models/RAG/encodings/trainable_encoding.py rename to DashAI/back/models/RAG/embeddings/trainable_encoding.py index 44f032707..f3d8d1001 100644 --- a/DashAI/back/models/RAG/encodings/trainable_encoding.py +++ b/DashAI/back/models/RAG/embeddings/trainable_encoding.py @@ -4,9 +4,9 @@ import numpy as np from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument -from DashAI.back.models.RAG.encodings.encoding import Encoding +from DashAI.back.models.RAG.embeddings.dense_encoding import DenseEmbedding -class TrainableEncoding(Encoding): +class TrainableEncoding(DenseEmbedding): """ Base class for all encoding (embedding) models. This class should be inherited by any specific encoding (embedding) model implementation. diff --git a/DashAI/back/models/RAG/encodings/__init__.py b/DashAI/back/models/RAG/encodings/__init__.py deleted file mode 100644 index bf36b680c..000000000 --- a/DashAI/back/models/RAG/encodings/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from DashAI.back.models.RAG.encodings.encoding import Encoding -from DashAI.back.models.RAG.encodings.dense_encoding import DenseEncoding -from DashAI.back.models.RAG.encodings.sparse_encoding import SparseEncoding -from DashAI.back.models.RAG.encodings.trainable_encoding import TrainableEncoding -from DashAI.back.models.RAG.encodings.sparse import * -from DashAI.back.models.RAG.encodings.dense import * \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/dense/__init__.py b/DashAI/back/models/RAG/encodings/dense/__init__.py deleted file mode 100644 index 549f15377..000000000 --- a/DashAI/back/models/RAG/encodings/dense/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from DashAI.back.models.RAG.encodings.dense.fasttext_embedding import FastTextEmbedding -from DashAI.back.models.RAG.encodings.dense.huggingface_embedding import HuggingFaceEmbedding \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/dense_encoding.py b/DashAI/back/models/RAG/encodings/dense_encoding.py deleted file mode 100644 index 37b7ff6ba..000000000 --- a/DashAI/back/models/RAG/encodings/dense_encoding.py +++ /dev/null @@ -1,24 +0,0 @@ - -from DashAI.back.models.RAG.encodings.encoding import Encoding - - -class DenseEncoding(Encoding): - """ - Base class for all dense encoding (embedding) models. - """ - - def __init__(self, **kwargs): - """ - Initialize the dense encoding (embedding) model with the given documents. - """ - raise NotImplementedError("Subclasses must implement the __init__ method.") - - def encode(self, text: str): - """ - Method to generate dense encodings (embeddings) for the given text. - This method should be implemented by subclasses. - - :param text: The input text to encode (embed). - :return: A list representing the dense encodings (embeddings) of the input text. - """ - raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/sparse/__init__.py b/DashAI/back/models/RAG/encodings/sparse/__init__.py deleted file mode 100644 index a2aa69555..000000000 --- a/DashAI/back/models/RAG/encodings/sparse/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from DashAI.back.models.RAG.encodings.sparse.bm25_encoding import BM25Encoding -from DashAI.back.models.RAG.encodings.sparse.tfidf_encoding import TFIDFEncoding \ No newline at end of file diff --git a/DashAI/back/models/RAG/encodings/sparse_encoding.py b/DashAI/back/models/RAG/encodings/sparse_encoding.py deleted file mode 100644 index cadba17d6..000000000 --- a/DashAI/back/models/RAG/encodings/sparse_encoding.py +++ /dev/null @@ -1,29 +0,0 @@ -import numpy as np -from DashAI.back.models.RAG.documents import BaseDocument -from DashAI.back.models.RAG.encodings.encoding import Encoding -from typing import Any, List - -class SparseEncoding(Encoding): - """ - Base class for sparse encoding (embedding) models. - This class should be inherited by any specific sparse encoding (embedding) model implementation. - """ - - def __init__(self, documents: List[BaseDocument], **kwargs: Any): - """ - Initialize the sparse encoding (embedding) model with the given documents. - - :param documents: List of documents to be embedded. - :param kwargs: Additional keyword arguments. - """ - raise NotImplementedError("Subclasses must implement the __init__ method.") - - def encode(self, text: str) -> List|np.ndarray|Any: - """ - Method to generate sparse encodings (embeddings) for the given text. - This method should be implemented by subclasses. - - :param text: The input text to embed. - :return: A list representing the sparse encodings (embeddings) of the input text. - """ - raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/DashAI/front/src/api/rag.ts b/DashAI/front/src/api/rag.ts index e4b95479a..b1d13100a 100644 --- a/DashAI/front/src/api/rag.ts +++ b/DashAI/front/src/api/rag.ts @@ -146,6 +146,14 @@ export const getGeneratorComponents = async (): Promise => { } +export const getChunkingComponents = async (): Promise => { + const response = await getChildComponents('BaseChunkingModel', false); + if (!response) { + throw new Error(`Failed to fetch chunking components`); + } + return response; +} + export const loadDocuments = async (): Promise => { const response = await api.get("/v1/document/"); if (response.status !== 200) { diff --git a/DashAI/front/src/pages/generative/RAG/ChunkingConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/ChunkingConfigurationStep.jsx new file mode 100644 index 000000000..a9dd4b109 --- /dev/null +++ b/DashAI/front/src/pages/generative/RAG/ChunkingConfigurationStep.jsx @@ -0,0 +1,85 @@ +import React, { useState, useEffect, useCallback } from "react"; +import { Box, Autocomplete, TextField, Typography } from "@mui/material"; +import useSchema from "../../../hooks/useSchema"; +import { getChunkingComponents } from "../../../api/rag"; +import FormSchemaContainer from "../../../components/shared/FormSchemaContainer"; +import FormSchema from "../../../components/shared/FormSchema"; + +export default function ChunkingConfigurationStep({ chunkingModel, setChunkingModel, setNextEnabled }) { + const [chunkingOptions, setChunkingOptions] = useState([]); + const [selectedChunking, setSelectedChunking] = useState(null); + const { defaultValues } = useSchema({ modelName: selectedChunking?.name }); + + useEffect(() => { + const fetchChunkingModels = async () => { + const data = await getChunkingComponents(); + setChunkingOptions(data); + if (chunkingModel?.name) { + const existing = data.find(c => c.name === chunkingModel.name); + if (existing) setSelectedChunking(existing); + } + }; + fetchChunkingModels(); + }, [chunkingModel?.name]); + + useEffect(() => { + setNextEnabled(!!selectedChunking); + }, [selectedChunking, setNextEnabled]); + + const handleChunkingSelectionChange = (event, newValue) => { + setSelectedChunking(newValue); + if (newValue) { + setChunkingModel({ + name: newValue.name, + parameters: newValue.schema?.properties ? getInitialParamsFromSchema(newValue.schema.properties) : {}, + }); + } else { + setChunkingModel({ name: "", parameters: {} }); + } + }; + + const handleParametersChange = (params) => { + setChunkingModel({ + name: selectedChunking.name, + parameters: params, + }); + }; + + function getInitialParamsFromSchema(schemaProperties) { + if (!schemaProperties) return {}; + return Object.keys(schemaProperties).reduce((acc, key) => { + acc[key] = schemaProperties[key].placeholder !== undefined + ? schemaProperties[key].placeholder + : ""; + return acc; + }, {}); + } + + return ( + + + Configure Chunking Model + + option.name} + value={selectedChunking} + onChange={handleChunkingSelectionChange} + isOptionEqualToValue={(option, value) => option.name === value?.name} + renderInput={params => } + sx={{ mb: 3 }} + /> + {selectedChunking && selectedChunking.schema && ( + + setSelectedChunking(null)} + /> + + )} + + ); +} diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx index d1377381f..f041ab7ad 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal.jsx @@ -17,9 +17,9 @@ import { useTheme } from "@mui/material/styles"; import useMediaQuery from "@mui/material/useMediaQuery"; import DocumentSelectionStep from "./DocumentSelectionStep"; +import ChunkingConfigurationStep from "./ChunkingConfigurationStep"; import RetrieverConfigurationStep from "./RetrieverConfigurationStep"; import GeneratorConfigurationStep from "./GeneratorConfigurationStep"; -//import ChunkingConfigurationStep from "./ChunkingConfigurationStep"; const steps = [ { @@ -27,11 +27,11 @@ const steps = [ label: "Select documents", component: DocumentSelectionStep, }, -// { -// name: "configure-chunking", -// label: "Configure chunking", -// component: ChunkingConfigurationStep, -// }, + { + name: "configure-chunking", + label: "Configure chunking", + component: ChunkingConfigurationStep, + }, { name: "configure-retriever", label: "Configure retriever", @@ -51,9 +51,9 @@ const defaultNewSession = { displayName: "", parameters: { "documents": [], - // "chunking": { - // "parameters": {}, - // }, + "chunking": { + "parameters": {}, + }, "retriever_model": { "name": "", "parameters": {}, @@ -241,6 +241,17 @@ export default function NewSessionModal({ /> )} {activeStep === 1 && ( + setSessionData(prev => ({ + ...prev, + parameters: { ...prev.parameters, chunking: model } + }))} + setNextEnabled={(isValid) => handleStepValidation(1, isValid)} + /> + )} + + {activeStep === 2 && ( setSessionData(prev => ({ @@ -250,7 +261,7 @@ export default function NewSessionModal({ setNextEnabled={(isValid) => handleStepValidation(1, isValid)} /> )} - {activeStep === 2 && ( + {activeStep === 3 && ( setSessionData(prev => ({ diff --git a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep copy.jsx b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep copy.jsx new file mode 100644 index 000000000..c34fd58a7 --- /dev/null +++ b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep copy.jsx @@ -0,0 +1,241 @@ +import React, { + useState, + useEffect, + useCallback, + useMemo, +} from "react"; +import { + Box, + Dialog, + DialogTitle, + DialogContent, + Autocomplete, + TextField, + Typography, + IconButton, + Stack, + +} from "@mui/material"; +import ArrowBackOutlined from "@mui/icons-material/ArrowBackOutlined"; + +import { DataGrid } from "@mui/x-data-grid"; +import { getRetrieverComponents, getRetrievalParadigm } from "../../../api/rag"; +import { useSnackbar } from "notistack"; +import useSchema from "../../../hooks/useSchema"; +import SettingsIcon from "@mui/icons-material/Settings"; +import { GridActionsCellItem } from "@mui/x-data-grid"; +import FormSchemaModelSelect from "../../../components/shared/FormSchemaModelSelect"; +import FormSchemaContainer from "../../../components/shared/FormSchemaContainer"; +import { useFormSchemaStore } from "../../../contexts/schema"; +import FormSchemaBreadScrumbs from "../../../components/shared/FormSchemaBreadScrumbs"; +import FormSchema from "../../../components/shared/FormSchema"; + + + +export default function RetrieverConfigurationStep({ setNextEnabled }) { + const { enqueueSnackbar } = useSnackbar(); + const [retrievalParadigms, setRetrievalParadigms] = useState([]); + const [selectedRetrievalParadigm, setSelectedRetrievalParadigm] = useState(null); + + const [retrieverOptions, setRetrieverOptions] = useState([]); + const [selectedRetriever, setSelectedRetriever] = useState(null); + + const {defaultValues: retrieverInitialParameters} = useSchema({ modelName: selectedRetriever?.name }); + + + // Fetch paradigms + const fetchRetrievalParadigms = async () => { + try { + const data = await getRetrievalParadigm(); + setRetrievalParadigms(data); + enqueueSnackbar('Retrieval paradigms loaded successfully!', { variant: 'success' }); + } catch (error) { + console.error("Error fetching retrieval paradigms:", error); + enqueueSnackbar('Failed to load retrieval paradigms.', { variant: 'error' }); + } + }; + + useEffect(() => { + fetchRetrievalParadigms(); + }, []); + + // Fetch retrievers when paradigm changes + const fetchRetrievers = useCallback(async () => { + if (!selectedRetrievalParadigm) { + setRetrieverOptions([]); + setSelectedRetriever(null); + return; + } + if (selectedRetrievalParadigm.name === "SparseRetriever") { + const retrievers = await getRetrieverComponents(selectedRetrievalParadigm.name); + setRetrieverOptions(retrievers); + setSelectedRetriever(null); + } else { + setRetrieverOptions([selectedRetrievalParadigm]); + setSelectedRetriever(selectedRetrievalParadigm); + } + }, [selectedRetrievalParadigm]); + + useEffect(() => { + fetchRetrievers(); + }, [selectedRetrievalParadigm, fetchRetrievers]); + + // Handle paradigm change + const handleRetrievalParadigmChange = (event, newValue) => { + setSelectedRetrievalParadigm(newValue); + setSelectedRetriever(null); // Reset retriever selection + }; + + // Handle retriever change + const handleRetrieverSelectionChange = (event, newValue) => { + setSelectedRetriever(newValue); + // Optionally reset config if needed + }; + + // Handle config change + const handleRetrieverParametersChange = (newParams) => { + setSelectedRetriever((prev) => ({ + ...prev, + parameters: newParams, + })); + setNextEnabled(true); + }; + + // Layout: two columns if retriever selected, else one column + return ( + + {/* Left column: paradigm/model selection */} + + + Select retrieval paradigm + + option.name} + value={selectedRetrievalParadigm} + onChange={handleRetrievalParadigmChange} + isOptionEqualToValue={(option, value) => option.name === value?.name} + renderInput={(params) => } + sx={{ mb: 2 }} + /> + {selectedRetrievalParadigm && selectedRetrievalParadigm.name === "SparseRetriever" && ( + <> + + Select retriever model + + option.name} + value={selectedRetriever} + onChange={handleRetrieverSelectionChange} + isOptionEqualToValue={(option, value) => option.name === value?.name} + renderInput={(params) => } + /> + + )} + {selectedRetriever && selectedRetriever.schema && !selectedRetriever.schema.properties && ( + + No parameters available for this retriever. + + )} + + + {/* Right column: configuration, only if retriever selected */} + {selectedRetriever && ( + + + + + + )} + + ); +} + +function ModelConfigurationSubcomponent({ + selectedRetriever, + retrieverInitialParameters, + handleRetrieverParametersChange, +}) { + const { + formValues, + properties, + propertyData, + valuesByProperties, + removeLastProperty, + setErrorForm, + } = useFormSchemaStore(); + + const [selectedModel, setSelectedModel] = useState( + selectedRetriever?.name || propertyData?.model, + ); + + const selectedProperty = Boolean(propertyData?.selected); + + const defaultValues = useMemo(() => { + if (selectedProperty) { + if (propertyData.params) { + return propertyData.params; + } else return null; + } + return retrieverInitialParameters ?? valuesByProperties; + }, [selectedModel, propertyData.params]); + + useEffect(() => { + if (propertyData.model) { + setSelectedModel(propertyData.model); + } else { + setSelectedModel(selectedRetriever?.name); + } + }, [propertyData.model, propertyData.params, selectedRetriever?.name]); + + return ( + + + + + {`${selectedRetriever.name} configuration`} + + + {console.log("propertyData:", propertyData)} + {Boolean(propertyData?.parent) && ( + <> + + + + )} + { + handleRetrieverParametersChange(formValues); + }} + setError={setErrorForm} + onCancel={() => { + if (properties.length > 0) { + removeLastProperty(); + } + }} + /> + + ); +} \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx index c34fd58a7..5de8874ab 100644 --- a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx @@ -18,19 +18,17 @@ import { } from "@mui/material"; import ArrowBackOutlined from "@mui/icons-material/ArrowBackOutlined"; -import { DataGrid } from "@mui/x-data-grid"; import { getRetrieverComponents, getRetrievalParadigm } from "../../../api/rag"; import { useSnackbar } from "notistack"; import useSchema from "../../../hooks/useSchema"; -import SettingsIcon from "@mui/icons-material/Settings"; -import { GridActionsCellItem } from "@mui/x-data-grid"; + import FormSchemaModelSelect from "../../../components/shared/FormSchemaModelSelect"; import FormSchemaContainer from "../../../components/shared/FormSchemaContainer"; import { useFormSchemaStore } from "../../../contexts/schema"; import FormSchemaBreadScrumbs from "../../../components/shared/FormSchemaBreadScrumbs"; import FormSchema from "../../../components/shared/FormSchema"; - +import FormSchemaDialog from "../../../components/shared/FormSchemaDialog"; export default function RetrieverConfigurationStep({ setNextEnabled }) { const { enqueueSnackbar } = useSnackbar(); @@ -39,6 +37,7 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { const [retrieverOptions, setRetrieverOptions] = useState([]); const [selectedRetriever, setSelectedRetriever] = useState(null); + const [openConfig, setOpenConfig] = useState(false); const {defaultValues: retrieverInitialParameters} = useSchema({ modelName: selectedRetriever?.name }); @@ -83,12 +82,20 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { // Handle paradigm change const handleRetrievalParadigmChange = (event, newValue) => { setSelectedRetrievalParadigm(newValue); - setSelectedRetriever(null); // Reset retriever selection + if (newValue === "SparseRetriever") { + setSelectedRetriever(null); + } + else { + setRetrieverOptions([newValue]); + setSelectedRetriever(newValue); + } }; // Handle retriever change const handleRetrieverSelectionChange = (event, newValue) => { setSelectedRetriever(newValue); + setOpenConfig(true); + setNextEnabled(false); // Optionally reset config if needed }; @@ -152,90 +159,26 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { {/* Right column: configuration, only if retriever selected */} - {selectedRetriever && ( + {selectedRetriever && openConfig && ( - - { + setNextEnabled(true) + setOpenConfig(false) + }} + > + setOpenConfig(false)} /> - + )} ); } - -function ModelConfigurationSubcomponent({ - selectedRetriever, - retrieverInitialParameters, - handleRetrieverParametersChange, -}) { - const { - formValues, - properties, - propertyData, - valuesByProperties, - removeLastProperty, - setErrorForm, - } = useFormSchemaStore(); - - const [selectedModel, setSelectedModel] = useState( - selectedRetriever?.name || propertyData?.model, - ); - - const selectedProperty = Boolean(propertyData?.selected); - - const defaultValues = useMemo(() => { - if (selectedProperty) { - if (propertyData.params) { - return propertyData.params; - } else return null; - } - return retrieverInitialParameters ?? valuesByProperties; - }, [selectedModel, propertyData.params]); - - useEffect(() => { - if (propertyData.model) { - setSelectedModel(propertyData.model); - } else { - setSelectedModel(selectedRetriever?.name); - } - }, [propertyData.model, propertyData.params, selectedRetriever?.name]); - - return ( - - - - - {`${selectedRetriever.name} configuration`} - - - {console.log("propertyData:", propertyData)} - {Boolean(propertyData?.parent) && ( - <> - - - - )} - { - handleRetrieverParametersChange(formValues); - }} - setError={setErrorForm} - onCancel={() => { - if (properties.length > 0) { - removeLastProperty(); - } - }} - /> - - ); -} \ No newline at end of file From 259b9906c6b0b60a22e6157642ed5bab9a00c3bd Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Mon, 29 Sep 2025 12:58:59 -0300 Subject: [PATCH 014/218] WIP --- DashAI/back/models/RAG/RAG_pipeline.py | 4 +- DashAI/back/models/hugging_face/__init__.py | 3 +- .../shared/FormSchemaButtonGroup.jsx | 2 +- .../shared/FormSchemaRenderFields.jsx | 2 +- DashAI/front/src/contexts/schema.js | 2 +- .../RAG/ChunkingConfigurationStep.jsx | 46 +- .../generative/RAG/FormSchemaEmbedded.jsx | 113 + .../pages/generative/RAG/NewSessionModal.jsx | 32 +- .../RAG/RetrieverConfigurationStep.jsx | 128 +- DashAI/front/src/utils/schema.js | 4 +- component_registry.json | 1 - component_registry__foromatted.json | 6053 ----------------- 12 files changed, 237 insertions(+), 6153 deletions(-) create mode 100644 DashAI/front/src/pages/generative/RAG/FormSchemaEmbedded.jsx delete mode 100644 component_registry.json delete mode 100644 component_registry__foromatted.json diff --git a/DashAI/back/models/RAG/RAG_pipeline.py b/DashAI/back/models/RAG/RAG_pipeline.py index 63f3b9c60..97ad76b20 100644 --- a/DashAI/back/models/RAG/RAG_pipeline.py +++ b/DashAI/back/models/RAG/RAG_pipeline.py @@ -22,8 +22,7 @@ from DashAI.back.models.hugging_face import ( DeepSeekModel, - QwenModel, - GemmaModel) + QwenModel) retriever_models = { "TFIDFRetriever": TFIDFRetriever, @@ -33,7 +32,6 @@ generation_models = { "DeepSeekModel": DeepSeekModel, "QwenModel": QwenModel, - "GemmaModel": GemmaModel, } class RAGPipelineSchema(BaseSchema): diff --git a/DashAI/back/models/hugging_face/__init__.py b/DashAI/back/models/hugging_face/__init__.py index 340e9e6f0..c46bdfb0a 100644 --- a/DashAI/back/models/hugging_face/__init__.py +++ b/DashAI/back/models/hugging_face/__init__.py @@ -1,3 +1,2 @@ from DashAI.back.models.hugging_face.deep_seek_model import DeepSeekModel -from DashAI.back.models.hugging_face.qwen_model import QwenModel -from DashAI.back.models.hugging_face.gemma_model import GemmaModel \ No newline at end of file +from DashAI.back.models.hugging_face.qwen_model import QwenModel \ No newline at end of file diff --git a/DashAI/front/src/components/shared/FormSchemaButtonGroup.jsx b/DashAI/front/src/components/shared/FormSchemaButtonGroup.jsx index 1755c5886..fd707fd48 100644 --- a/DashAI/front/src/components/shared/FormSchemaButtonGroup.jsx +++ b/DashAI/front/src/components/shared/FormSchemaButtonGroup.jsx @@ -20,7 +20,7 @@ function FormSchemaButtonGroup({ {!autoSave && ( + + )} - {!showTableTitle && ( - - )} ); } From d6299fa5f26608ae520f53f638f4877c8189a03b Mon Sep 17 00:00:00 2001 From: Creylay Date: Wed, 1 Oct 2025 10:53:34 -0300 Subject: [PATCH 017/218] refactor: Improve code readability and formatting in ChunkingConfigurationStep component --- .../RAG/ChunkingConfigurationStep.jsx | 55 ++++++++++++------- 1 file changed, 36 insertions(+), 19 deletions(-) diff --git a/DashAI/front/src/pages/generative/RAG/ChunkingConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/ChunkingConfigurationStep.jsx index 04fa4090c..37715938e 100644 --- a/DashAI/front/src/pages/generative/RAG/ChunkingConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/ChunkingConfigurationStep.jsx @@ -4,14 +4,21 @@ import useSchema from "../../../hooks/useSchema"; import { getChunkingComponents } from "../../../api/rag"; import FormSchemaEmbedded from "./FormSchemaEmbedded"; -export default function ChunkingConfigurationStep({ chunkingModel, setChunkingModel, setNextEnabled }) { +export default function ChunkingConfigurationStep({ + chunkingModel, + setChunkingModel, + setNextEnabled, +}) { const [chunkingOptions, setChunkingOptions] = useState([]); const [selectedChunking, setSelectedChunking] = useState(null); const { defaultValues } = useSchema({ modelName: selectedChunking?.name }); // Memoize the initial values to prevent unnecessary re-renders const initialValues = useMemo(() => { - if (chunkingModel?.parameters && Object.keys(chunkingModel.parameters).length > 0) { + if ( + chunkingModel?.parameters && + Object.keys(chunkingModel.parameters).length > 0 + ) { return chunkingModel.parameters; } return defaultValues || {}; @@ -22,7 +29,7 @@ export default function ChunkingConfigurationStep({ chunkingModel, setChunkingMo const data = await getChunkingComponents(); setChunkingOptions(data); if (chunkingModel?.name) { - const existing = data.find(c => c.name === chunkingModel.name); + const existing = data.find((c) => c.name === chunkingModel.name); if (existing) setSelectedChunking(existing); } }; @@ -34,11 +41,10 @@ export default function ChunkingConfigurationStep({ chunkingModel, setChunkingMo return false; } if (!chunkingModel || !chunkingModel.parameters) { - return false; // Ensure parameters are required } - - return Object.keys(chunkingModel.parameters).every(param => { + + return Object.keys(chunkingModel.parameters).every((param) => { const value = chunkingModel.parameters[param]; return value !== undefined && value !== null && value !== ""; }); @@ -48,13 +54,14 @@ export default function ChunkingConfigurationStep({ chunkingModel, setChunkingMo setNextEnabled(isNextEnabled()); }, [selectedChunking, chunkingModel, setNextEnabled]); - const handleChunkingSelectionChange = (event, newValue) => { setSelectedChunking(newValue); if (newValue) { setChunkingModel({ name: newValue.name, - parameters: newValue.schema?.properties ? getInitialParamsFromSchema(newValue.schema.properties) : {}, + parameters: newValue.schema?.properties + ? getInitialParamsFromSchema(newValue.schema.properties) + : {}, }); } else { setChunkingModel({ name: "", parameters: {} }); @@ -74,35 +81,45 @@ export default function ChunkingConfigurationStep({ chunkingModel, setChunkingMo function getInitialParamsFromSchema(schemaProperties) { if (!schemaProperties) return {}; return Object.keys(schemaProperties).reduce((acc, key) => { - acc[key] = schemaProperties[key].placeholder !== undefined - ? schemaProperties[key].placeholder - : ""; + acc[key] = + schemaProperties[key].placeholder !== undefined + ? schemaProperties[key].placeholder + : ""; return acc; }, {}); } return ( - + Configure Chunking Model option.name} + getOptionLabel={(option) => option.name} value={selectedChunking} onChange={handleChunkingSelectionChange} isOptionEqualToValue={(option, value) => option.name === value?.name} - renderInput={params => } + renderInput={(params) => ( + + )} sx={{ mb: 3 }} /> {selectedChunking && selectedChunking.schema && ( setSelectedChunking(null)} - /> + model={selectedChunking.name} + initialValues={initialValues} + onFormSubmit={handleParametersSave} + autoSave={true} + /> )} ); From 8a853ab4b59fe32cf3d9f46bad76ba8a13f3cfa1 Mon Sep 17 00:00:00 2001 From: Creylay Date: Wed, 1 Oct 2025 11:13:45 -0300 Subject: [PATCH 018/218] refactor: Simplify imports and improve code formatting in RetrieverConfigurationStep component --- .../RAG/RetrieverConfigurationStep.jsx | 109 ++++++++---------- 1 file changed, 50 insertions(+), 59 deletions(-) diff --git a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx index 80f8314bd..7171d1948 100644 --- a/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/RetrieverConfigurationStep.jsx @@ -1,31 +1,10 @@ -import React, { - useState, - useEffect, - useCallback, - useMemo, -} from "react"; -import { - Box, - Dialog, - DialogTitle, - DialogContent, - Autocomplete, - TextField, - Typography, - IconButton, - Stack, - -} from "@mui/material"; -import ArrowBackOutlined from "@mui/icons-material/ArrowBackOutlined"; +import { useState, useEffect, useCallback } from "react"; +import { Box, Autocomplete, TextField, Typography } from "@mui/material"; import { getRetrieverComponents, getRetrievalParadigm } from "../../../api/rag"; import { useSnackbar } from "notistack"; import useSchema from "../../../hooks/useSchema"; -import FormSchemaModelSelect from "../../../components/shared/FormSchemaModelSelect"; -import FormSchemaContainer from "../../../components/shared/FormSchemaContainer"; -import { useFormSchemaStore } from "../../../contexts/schema"; -import FormSchemaBreadScrumbs from "../../../components/shared/FormSchemaBreadScrumbs"; import FormSchema from "../../../components/shared/FormSchema"; import FormSchemaDialog from "../../../components/shared/FormSchemaDialog"; @@ -33,24 +12,30 @@ import FormSchemaDialog from "../../../components/shared/FormSchemaDialog"; export default function RetrieverConfigurationStep({ setNextEnabled }) { const { enqueueSnackbar } = useSnackbar(); const [retrievalParadigms, setRetrievalParadigms] = useState([]); - const [selectedRetrievalParadigm, setSelectedRetrievalParadigm] = useState(null); + const [selectedRetrievalParadigm, setSelectedRetrievalParadigm] = + useState(null); const [retrieverOptions, setRetrieverOptions] = useState([]); const [selectedRetriever, setSelectedRetriever] = useState(null); const [openConfig, setOpenConfig] = useState(false); - const {defaultValues: retrieverInitialParameters} = useSchema({ modelName: selectedRetriever?.name }); + const { defaultValues: retrieverInitialParameters } = useSchema({ + modelName: selectedRetriever?.name, + }); - // Fetch paradigms const fetchRetrievalParadigms = async () => { try { const data = await getRetrievalParadigm(); setRetrievalParadigms(data); - enqueueSnackbar('Retrieval paradigms loaded successfully!', { variant: 'success' }); + enqueueSnackbar("Retrieval paradigms loaded successfully!", { + variant: "success", + }); } catch (error) { console.error("Error fetching retrieval paradigms:", error); - enqueueSnackbar('Failed to load retrieval paradigms.', { variant: 'error' }); + enqueueSnackbar("Failed to load retrieval paradigms.", { + variant: "error", + }); } }; @@ -66,7 +51,9 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { return; } if (selectedRetrievalParadigm.name === "SparseRetriever") { - const retrievers = await getRetrieverComponents(selectedRetrievalParadigm.name); + const retrievers = await getRetrieverComponents( + selectedRetrievalParadigm.name, + ); setRetrieverOptions(retrievers); setSelectedRetriever(null); } else { @@ -85,8 +72,7 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { console.log("Selected paradigm:", newValue); if (newValue === "SparseRetriever") { setSelectedRetriever(null); - } - else { + } else { setRetrieverOptions([newValue]); handleRetrieverSelectionChange(event, newValue); setOpenConfig(true); @@ -116,11 +102,7 @@ export default function RetrieverConfigurationStep({ setNextEnabled }) { // Layout: two columns if retriever selected, else one column return ( option.name === value?.name} - renderInput={(params) => } + renderInput={(params) => ( + + )} sx={{ mb: 2 }} /> - {selectedRetrievalParadigm && selectedRetrievalParadigm.name === "SparseRetriever" && ( - <> - - Select retriever model - - option.name} - value={selectedRetriever} - onChange={handleRetrieverSelectionChange} - isOptionEqualToValue={(option, value) => option.name === value?.name} - renderInput={(params) => } - /> - - )} - {selectedRetriever && selectedRetriever.schema && !selectedRetriever.schema.properties && ( - - No parameters available for this retriever. - - )} + {selectedRetrievalParadigm && + selectedRetrievalParadigm.name === "SparseRetriever" && ( + <> + + Select retriever model + + option.name} + value={selectedRetriever} + onChange={handleRetrieverSelectionChange} + isOptionEqualToValue={(option, value) => + option.name === value?.name + } + renderInput={(params) => ( + + )} + /> + + )} + {selectedRetriever && + selectedRetriever.schema && + !selectedRetriever.schema.properties && ( + + No parameters available for this retriever. + + )} {selectedRetriever && openConfig && ( )} - -); + + ); } From c7e82584d03bc6673c9784bb9c6134702b8a025f Mon Sep 17 00:00:00 2001 From: Creylay Date: Wed, 1 Oct 2025 11:49:49 -0300 Subject: [PATCH 019/218] refactor: Enhance layout styling in FormSchemaButtonGroup component --- .../src/components/shared/FormSchemaButtonGroup.jsx | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/DashAI/front/src/components/shared/FormSchemaButtonGroup.jsx b/DashAI/front/src/components/shared/FormSchemaButtonGroup.jsx index fd707fd48..9b093cc19 100644 --- a/DashAI/front/src/components/shared/FormSchemaButtonGroup.jsx +++ b/DashAI/front/src/components/shared/FormSchemaButtonGroup.jsx @@ -11,7 +11,15 @@ function FormSchemaButtonGroup({ backButtonText = "Back", }) { return ( - + {onCancel && ( - + )} - + ); case LOADING: From 2f5b221657075a99e34a8aeb9cbaa5a235125797 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Fri, 10 Oct 2025 14:38:24 -0300 Subject: [PATCH 045/218] Database schema implemented --- DashAI/back/api/api_v1/endpoints/documents.py | 110 +++++- DashAI/back/api/api_v1/schemas/__init__.py | 2 +- DashAI/back/api/api_v1/schemas/document.py | 11 +- DashAI/back/dependencies/database/models.py | 351 ++++++++++-------- .../models/RAG/chunking_models/__init__.py | 3 +- .../RAG/embeddings/sparse/bm25_encoding.py | 15 +- .../RAG/embeddings/sparse/tfidf_encoding.py | 31 +- 7 files changed, 320 insertions(+), 203 deletions(-) diff --git a/DashAI/back/api/api_v1/endpoints/documents.py b/DashAI/back/api/api_v1/endpoints/documents.py index 45b2f8ad9..97b03ff00 100644 --- a/DashAI/back/api/api_v1/endpoints/documents.py +++ b/DashAI/back/api/api_v1/endpoints/documents.py @@ -44,8 +44,10 @@ async def get_all_documents( DocumentResponse( id=doc.id, file_name=doc.file_name, + file_hash=doc.file_hash, created=doc.created, optional_metadata=doc.optional_metadata, + related_sessions=[session.id for session in doc.related_sessions_ids] if doc.related_sessions_ids else None, file_url=f"{base_url}/{doc.id}/download", ) ) @@ -76,8 +78,10 @@ async def get_document( return DocumentResponse( id=document.id, file_name=document.file_name, + file_hash=document.file_hash, created=document.created, optional_metadata=document.optional_metadata, + related_sessions=[session.id for session in document.related_sessions_ids] if document.related_sessions_ids else None, file_url=f"{base_url}/{document.id}/download", ) except exc.SQLAlchemyError as e: @@ -124,13 +128,14 @@ async def upload_document( Upload a new document to the RAG system with file content and metadata. The metadata should be a JSON string with required fields and an 'optional_metadata' dict. """ + print("A") docs_folder_path = config["DOCUMENTS_PATH"] if not docs_folder_path.exists(): raise HTTPException( status_code=500, detail=f"Documents folder {docs_folder_path} does not exist.", ) - + print("B") try: metadata_dict = json.loads(metadata) except json.JSONDecodeError: @@ -138,27 +143,27 @@ async def upload_document( file_name = metadata_dict.get("file_name") last_modified = metadata_dict.get("last_modified") - + print("C") if not file_name or not last_modified: raise HTTPException( status_code=400, detail="Missing required metadata fields: 'filename' and 'last_modified'", ) - + print("D") optional_metadata = metadata_dict.get("optional_metadata", {}) if not isinstance(optional_metadata, dict): raise HTTPException( status_code=400, detail="'optional_metadata' must be a dictionary", ) - + print("E") try: content_bytes = await file.read() except Exception: raise HTTPException(status_code=400, detail="Failed to read file content") hash256 = hashlib.sha256(content_bytes).hexdigest() - + print("F") with session_factory() as db: existing_doc = db.query(Document).filter_by(file_hash=hash256).first() if existing_doc: @@ -170,15 +175,18 @@ async def upload_document( return DocumentResponse( id=existing_doc.id, file_name=existing_doc.file_name, + file_hash=existing_doc.file_hash, created=existing_doc.created, + related_sessions=[session.id for session in existing_doc.related_sessions_ids], optional_metadata=existing_doc.optional_metadata, - file_url=f"{base_url}/{existing_doc.id}/download", + file_url=f"{base_url}/{existing_doc.id}/download" ) else: # Create a new document entry try: - new_id = db.query(Document).count() + 1 - file_path = os.path.join(docs_folder_path, f"{new_id}_{file_name}") + max_id = db.query(Document.id).order_by(Document.id.desc()).first() + new_id = (max_id[0] + 1) if max_id else 1 + file_path = os.path.join(docs_folder_path, f"{new_id}__{file_name}") with open(file_path, "wb") as f: f.write(content_bytes) @@ -188,8 +196,6 @@ async def upload_document( file_path=str(file_path), file_hash=hash256, optional_metadata=optional_metadata or None, - sessions_related=[], - pipelines_related=[], ) db.add(doc) db.commit() @@ -198,9 +204,12 @@ async def upload_document( return DocumentResponse( id=doc.id, file_name=doc.file_name, + file_hash=doc.file_hash, created=doc.created, + related_sessions=None, optional_metadata=doc.optional_metadata, file_url=f"{base_url}/{doc.id}/download", + ) except exc.SQLAlchemyError as e: log.exception(e) @@ -209,12 +218,12 @@ async def upload_document( detail="Internal database error", ) from e -@router.get("/sessions-related/{document_id}", response_model=List[int]) -async def get_document_sessions_related( +@router.get("/related-sessions/{document_id}", response_model=List[int]) +async def get_related_sessions( document_id: int, session_factory: sessionmaker = Depends(lambda: di["session_factory"]), ): - """Get all session IDs related to a specific document.""" + """Get all generative session IDs related to a specific document.""" with session_factory() as db: try: document = db.get(Document, document_id) @@ -223,8 +232,10 @@ async def get_document_sessions_related( status_code=status.HTTP_404_NOT_FOUND, detail=f"Document {document_id} does not exist in DB.", ) - session_ids = [session.id for session in document.sessions_related] - return session_ids + if not document.related_sessions_ids: + return [] + related_session_ids = [session.id for session in document.related_sessions_ids] + return related_session_ids except exc.SQLAlchemyError as e: log.exception(e) raise HTTPException( @@ -232,7 +243,6 @@ async def get_document_sessions_related( detail="Internal database error", ) from e - @router.delete("/{document_id}", status_code=status.HTTP_204_NO_CONTENT) async def delete_document( document_id: int, @@ -247,10 +257,15 @@ async def delete_document( status_code=status.HTTP_404_NOT_FOUND, detail=f"Document {document_id} does not exist in DB.", ) + + # Delete the physical file if os.path.exists(document.file_path): os.remove(document.file_path) + + # Delete the database record db.delete(document) db.commit() + return Response(status_code=status.HTTP_204_NO_CONTENT) except exc.SQLAlchemyError as e: log.exception(e) @@ -258,3 +273,66 @@ async def delete_document( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Internal database error", ) from e + except OSError as e: + log.exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Error deleting physical file", + ) from e + + +@router.put("/{document_id}", response_model=DocumentResponse) +async def update_document_metadata( + document_id: int, + metadata: str = Form(...), + session_factory: sessionmaker = Depends(lambda: di["session_factory"]), +): + """Update a document's metadata.""" + with session_factory() as db: + try: + document = db.get(Document, document_id) + if not document: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Document {document_id} does not exist in DB.", + ) + + try: + metadata_dict = json.loads(metadata) + except json.JSONDecodeError: + raise HTTPException(status_code=400, detail="Invalid JSON in metadata") + + # Update fields that are allowed to be modified + if "file_name" in metadata_dict: + document.file_name = metadata_dict["file_name"] + + if "optional_metadata" in metadata_dict: + optional_metadata = metadata_dict["optional_metadata"] + if not isinstance(optional_metadata, dict): + raise HTTPException( + status_code=400, + detail="'optional_metadata' must be a dictionary", + ) + document.optional_metadata = optional_metadata + + document.last_modified = datetime.now() + + db.commit() + db.refresh(document) + + return DocumentResponse( + id=document.id, + file_name=document.file_name, + file_hash=document.file_hash, + created=document.created, + optional_metadata=document.optional_metadata, + related_sessions=[session.id for session in document.related_sessions_ids] if document.related_sessions_ids else None, + file_url=f"{base_url}/{document.id}/download", + ) + + except exc.SQLAlchemyError as e: + log.exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal database error", + ) from e \ No newline at end of file diff --git a/DashAI/back/api/api_v1/schemas/__init__.py b/DashAI/back/api/api_v1/schemas/__init__.py index 79080d662..9d35150b8 100644 --- a/DashAI/back/api/api_v1/schemas/__init__.py +++ b/DashAI/back/api/api_v1/schemas/__init__.py @@ -1 +1 @@ -from DashAI.back.api.api_v1.schemas.document import DocumentResponse, DocumentUploadResponse \ No newline at end of file +from DashAI.back.api.api_v1.schemas.document import DocumentResponse \ No newline at end of file diff --git a/DashAI/back/api/api_v1/schemas/document.py b/DashAI/back/api/api_v1/schemas/document.py index 38b5517c7..765f611ad 100644 --- a/DashAI/back/api/api_v1/schemas/document.py +++ b/DashAI/back/api/api_v1/schemas/document.py @@ -1,19 +1,14 @@ from pydantic import BaseModel -from typing import Any, Dict, Optional +from typing import Any, Dict, List, Optional from datetime import datetime -class DocumentUploadResponse(BaseModel): - id: int - file_name: str - optional_metadata: Optional[dict] - created: datetime - file_hash: str - class DocumentResponse(BaseModel): id: int file_name: str + file_hash: str created: datetime optional_metadata: Optional[Dict[str, Any]] + related_sessions: List[int]|None file_url: str class Config: diff --git a/DashAI/back/dependencies/database/models.py b/DashAI/back/dependencies/database/models.py index 666a918c8..f6df5af0e 100644 --- a/DashAI/back/dependencies/database/models.py +++ b/DashAI/back/dependencies/database/models.py @@ -3,7 +3,7 @@ from datetime import datetime from typing import Any, Dict, List, Optional -from sqlalchemy import JSON, Boolean, DateTime, Enum, ForeignKey, Integer, String, UniqueConstraint, ARRAY +from sqlalchemy import JSON, Boolean, CheckConstraint, DateTime, Enum, ForeignKey, Integer, String, Text, UniqueConstraint, ARRAY from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import Mapped, mapped_column, relationship @@ -173,7 +173,7 @@ class Plugin(Base): name: Mapped[str] = mapped_column(String, unique=True, nullable=False) author: Mapped[str] = mapped_column(String, nullable=False) installed_version: Mapped[str] = mapped_column(String, nullable=False) - lastest_version: Mapped[str] = mapped_column(String, nullable=False) + latest_version: Mapped[str] = mapped_column(String, nullable=False) tags: Mapped[List["Tag"]] = relationship( back_populates="plugin", cascade="all, delete", lazy="selectin" ) @@ -374,7 +374,7 @@ class ProcessData(Base): process = relationship( "GenerativeProcess", foreign_keys=[process_id], overlaps="input,output" ) - + class GenerativeSession(Base): __tablename__ = "generative_session" @@ -410,6 +410,11 @@ class GenerativeSession(Base): processes: Mapped[List["GenerativeProcess"]] = relationship( "GenerativeProcess", cascade="all, delete-orphan", back_populates="session" ) + + # Relationship with RAGDocumentPipelineSessionLink + pipeline_links: Mapped[List["RAGDocumentPipelineSessionLink"]] = relationship( + back_populates="session" + ) class Pipeline(Base): @@ -608,243 +613,263 @@ def delete_result(self) -> None: self.end_time = None + + + + + + + + + + + + + +""" +RAG tables +""" + class Document(Base): - __tablename__ = "RAG_document" + __tablename__ = "document" """ - Table to store all the information about a document in the RAG system. + Table to store all the information about a document. """ - id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) + id: Mapped[int] = mapped_column(primary_key=True) file_name: Mapped[str] = mapped_column(String, nullable=False) + file_type: Mapped[str] = mapped_column(String, nullable=False) file_path: Mapped[str] = mapped_column(String, nullable=False) file_hash: Mapped[str] = mapped_column(String, nullable=False, unique=True) - created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) - optional_metadata: Mapped[JSON] = mapped_column(JSON, nullable=True) - sessions_related: Mapped[Optional[List["GenerativeSession"]]] = relationship( - "GenerativeSession", backref="RAG_document" + + # Create a relationship for the related sessions and related chunks + pipeline_links: Mapped[List["RAGDocumentPipelineSessionLink"]] = relationship( + cascade="all, delete-orphan", + back_populates="document" + ) + + chunks: Mapped[List["RAGChunk"]] = relationship( + back_populates="document", + cascade="all, delete-orphan", ) - pipelines_related: Mapped[Optional[List["RAGPipeline"]]] = relationship( - "RAGPipeline", backref="RAG_document" + + embeddings_links: Mapped[List["RAGDocumentChunksEmbeddingsLink"]] = relationship( + cascade="all, delete-orphan" + ) + + @property + def sessions_related(self) -> List["GenerativeSession"]: + """Return a list of sessions related to the document.""" + return [link.session for link in self.pipeline_links] + + @property + def pipelines_related(self) -> List["RAGPipeline"]: + """Return a list of pipelines related to the document.""" + return [link.pipeline for link in self.pipeline_links] + + +class RAGChunk(Base): + __tablename__ = "rag_chunk" + """ + Table to store all the information about a chunk. + """ + id: Mapped[int] = mapped_column(primary_key=True) + text: Mapped[str] = mapped_column(Text, nullable=False) + document_id: Mapped[int] = mapped_column( + ForeignKey("document.id", ondelete="CASCADE"), nullable=False ) + document: Mapped["Document"] = relationship(back_populates="chunks") + __table_args__ = ( - UniqueConstraint('file_name', 'file_hash', name='_file_name_hash_uc'), - UniqueConstraint('file_path', 'file_hash', name='_file_path_hash_uc'), - UniqueConstraint('file_name', 'file_path', name='_file_name_path_uc'), + UniqueConstraint("id", "document_id", name="uix_chunk_document"), ) - def __repr__(self) -> str: - return f"" class RAGPipeline(Base): - __tablename__ = "RAG_pipeline" + __tablename__ = "rag_pipeline" """ - Table to store the RAG pipelines created by the user. + Table to store all the information about a RAG pipeline. """ - id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) - name: Mapped[str] = mapped_column(String, nullable=False, unique=True) - session: Mapped[int] = mapped_column( - ForeignKey("generative_session.id", ondelete="CASCADE"), nullable=False - ) + id: Mapped[int] = mapped_column(primary_key=True) + name: Mapped[str] = mapped_column(String, nullable=False) + description: Mapped[str] = mapped_column(String, nullable=True) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) - documents: Mapped[List["Document"]] = relationship( - "Document", backref="RAG_pipeline" - ) + chunking_model_id: Mapped[int] = mapped_column( - ForeignKey("RAG_chunking_model.id", ondelete="CASCADE"), nullable=False - ) - chunks: Mapped[List["RAGChunk"]] = relationship( - "RAGChunk", backref="RAG_pipeline", cascade="all, delete-orphan" - ) - retriever_model_id: Mapped[int] = mapped_column( - ForeignKey("RAG_retriever_model.id", ondelete="CASCADE"), nullable=False + ForeignKey("rag_chunking_model.id", ondelete="CASCADE"), nullable=False ) - created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) - last_modified: Mapped[DateTime] = mapped_column( - DateTime, - default=datetime.now, - onupdate=datetime.now, + retriever_id: Mapped[int] = mapped_column( + ForeignKey("rag_retriever.id", ondelete="CASCADE"), nullable=False ) - - __table_args__ = ( - UniqueConstraint('id', 'session', name='_id_session_uc'), + + # Relationships + chunking_model: Mapped["RAGChunkingModel"] = relationship(back_populates="pipelines") + retriever: Mapped["RAGRetriever"] = relationship(back_populates="pipelines") + pipeline_links: Mapped[List["RAGDocumentPipelineSessionLink"]] = relationship( + cascade="all, delete-orphan", + back_populates="pipeline" ) class RAGChunkingModel(Base): - __tablename__ = "RAG_chunking_model" + __tablename__ = "rag_chunking_model" """ - Table to store all the chunking models used in RAG pipelines. + Table to store all the information about a chunking model. """ - id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) + id: Mapped[int] = mapped_column(primary_key=True) class_name: Mapped[str] = mapped_column(String, nullable=False) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) - sessions_related: Mapped[List["GenerativeSession"]] = relationship( - "GenerativeSession", backref="RAG_chunking_model" - ) - pipelines_related: Mapped[List["RAGPipeline"]] = relationship( - "RAGPipeline", backref="RAG_chunking_model" - ) - chunks: Mapped[List["RAGChunk"]] = relationship( - "RAGChunk", backref="RAG_chunking_model", cascade="all, delete-orphan" + + pipelines: Mapped[List["RAGPipeline"]] = relationship( + back_populates="chunking_model" ) - created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) - last_modified: Mapped[DateTime] = mapped_column( - DateTime, - default=datetime.now, - onupdate=datetime.now, + document_links: Mapped[List["RAGDocumentChunksEmbeddingsLink"]] = relationship( + cascade="all, delete-orphan" ) -class RAGChunk(Base): - __tablename__ = "RAG_chunk" +class RAGRetriever(Base): + __tablename__ = "rag_retriever" """ - Table to store all the chunks of documents used in RAG pipelines. + Table to map the retriever model id to its dense or sparse retriever instance. """ - id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) - chunk_text: Mapped[str] = mapped_column(String, nullable=False) - document_id: Mapped[int] = mapped_column( - ForeignKey("RAG_document.id", ondelete="CASCADE"), nullable=False + id: Mapped[int] = mapped_column(primary_key=True) + class_name: Mapped[str] = mapped_column(String, nullable=False) # "SparseRetriever" or "DenseRetriever" + parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) # The top-level parameters for the retriever, not the nested ones + sparse_id: Mapped[int] = mapped_column( + ForeignKey("rag_sparse_retriever.id", ondelete="CASCADE"), nullable=True ) - chunking_model_id: Mapped[int] = mapped_column( - ForeignKey("RAG_chunking_model.id", ondelete="CASCADE"), nullable=False + dense_id: Mapped[int] = mapped_column( + ForeignKey("rag_dense_retriever.id", ondelete="CASCADE"), nullable=True ) - created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) - + + # Relationships + sparse_retriever: Mapped[Optional["RAGSparseRetriever"]] = relationship( + back_populates="related_retriever" + ) + dense_retriever: Mapped[Optional["RAGDenseRetriever"]] = relationship( + back_populates="related_retriever" + ) + pipelines: Mapped[List["RAGPipeline"]] = relationship( + back_populates="retriever" + ) + __table_args__ = ( - UniqueConstraint('chunk_text', 'document_id', name='_chunk_document_uc'), + CheckConstraint( + "(sparse_id IS NOT NULL AND class_name = 'SparseRetriever' AND dense_id IS NULL) OR " + "(dense_id IS NOT NULL AND class_name = 'DenseRetriever' AND sparse_id IS NULL)", + name="chk_retriever_paradigm", + ), ) - def __repr__(self) -> str: - return f"" - -class RAGRetrieverModel(Base): - __tablename__ = "RAG_retriever_model" +class RAGSparseRetriever(Base): + __tablename__ = "rag_sparse_retriever" """ - Table to store all the retriever models used in RAG pipelines. + Table to store all the information about a sparse retriever. """ - id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) - paradigm: Mapped[str] = mapped_column(String, nullable=False) + id: Mapped[int] = mapped_column(primary_key=True) class_name: Mapped[str] = mapped_column(String, nullable=False) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) - chunks_ids: Mapped[List[int]] = mapped_column(ARRAY(Integer), nullable=True) - - chunks: Mapped[List["RAGChunk"]] = relationship( - "RAGChunk", backref="RAG_retriever_model", cascade="all, delete-orphan" - ) - - sessions_related: Mapped[List["GenerativeSession"]] = relationship( - "GenerativeSession", backref="RAG_retriever_model" - ) - pipelines_related: Mapped[List["RAGPipeline"]] = relationship( - "RAGPipeline", backref="RAG_retriever_model" - ) - created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) - last_modified: Mapped[DateTime] = mapped_column( - DateTime, - default=datetime.now, - onupdate=datetime.now, - ) + storage_folder: Mapped[str] = mapped_column(String, nullable=False) -class RAGRetriever(Base): - __tablename__ = "RAG_retriever" - """ - Abstract base class for RAG retrievers. - """ - + related_retriever: Mapped["RAGRetriever"] = relationship( + back_populates="sparse_retriever", uselist=False + ) class RAGDenseRetriever(Base): - __tablename__ = "RAG_dense_retriever" + __tablename__ = "rag_dense_retriever" """ - Table to store all the dense retriever models used in RAG pipelines. + Table to store all the information about a dense retriever. """ - id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) + id: Mapped[int] = mapped_column(primary_key=True) class_name: Mapped[str] = mapped_column(String, nullable=False) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) - retriever_model_id: Mapped[int] = mapped_column( - ForeignKey("RAG_retriever_model.id", ondelete="CASCADE"), nullable=False - ) - embedding_model_id: Mapped[int] = mapped_column( - ForeignKey("RAG_embedding_model.id", ondelete="CASCADE"), nullable=False + chunks_embeddings_id: Mapped[int] = mapped_column( + ForeignKey("rag_chunks_embeddings.id", ondelete="CASCADE"), nullable=False ) - chunks_embeddings: Mapped[List["RAGChunkEmbedding"]] = relationship( - "RAGChunkEmbedding", backref="RAG_dense_retriever", cascade="all, delete-orphan" + + # Relationships + related_retriever: Mapped["RAGRetriever"] = relationship( + back_populates="dense_retriever", uselist=False ) - created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) - last_modified: Mapped[DateTime] = mapped_column( - DateTime, - default=datetime.now, - onupdate=datetime.now, + related_chunks_embeddings: Mapped["RAGChunksEmbeddings"] = relationship( + back_populates="related_dense_retrievers" ) -class RAGSparseRetriever(Base): - __tablename__ = "RAG_sparse_retriever" +class RAGChunksEmbeddings(Base): + __tablename__ = "rag_chunks_embeddings" """ - Table to store all the sparse retriever models used in RAG pipelines. + Table to store all the information about the chunks embeddings. """ - id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) + id: Mapped[int] = mapped_column(primary_key=True) class_name: Mapped[str] = mapped_column(String, nullable=False) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) - retriever_model_id: Mapped[int] = mapped_column( - ForeignKey("RAG_retriever_model.id", ondelete="CASCADE"), nullable=False - ) + embedding_dimension: Mapped[int] = mapped_column(Integer, nullable=False) storage_folder: Mapped[str] = mapped_column(String, nullable=False) - stored_files: Mapped[List[str]] = mapped_column(JSON, nullable=True) - created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) - last_modified: Mapped[DateTime] = mapped_column( - DateTime, - default=datetime.now, - onupdate=datetime.now, + + related_dense_retrievers: Mapped[List["RAGDenseRetriever"]] = relationship( + back_populates="related_chunks_embeddings" ) + document_links: Mapped[List["RAGDocumentChunksEmbeddingsLink"]] = relationship( + cascade="all, delete-orphan" + ) + +""" +RAG relationship tables +""" -class RAGEmbeddingModel(Base): - __tablename__ = "RAG_embedding_model" +class RAGDocumentPipelineSessionLink(Base): + __tablename__ = "rag_document_pipeline_session_link" """ - Table to store all the embedding models used in RAG pipelines. + Association table to link documents and sessions (many-to-many relationship). """ - id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) - class_name: Mapped[str] = mapped_column(String, nullable=False) - parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) - sessions_related: Mapped[List["GenerativeSession"]] = relationship( - "GenerativeSession", backref="RAG_embedding_model" + id: Mapped[int] = mapped_column(primary_key=True) + document_id: Mapped[int] = mapped_column( + ForeignKey("document.id", ondelete="CASCADE"), nullable=False ) - dense_retrievers: Mapped[List["RAGDenseRetriever"]] = relationship( - "RAGDenseRetriever", backref="RAG_embedding_model", cascade="all, delete-orphan" + session_id: Mapped[int] = mapped_column( + ForeignKey("generative_session.id", ondelete="CASCADE"), nullable=False ) - created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) - last_modified: Mapped[DateTime] = mapped_column( - DateTime, - default=datetime.now, - onupdate=datetime.now, + pipeline_id: Mapped[int] = mapped_column( + ForeignKey("rag_pipeline.id", ondelete="CASCADE"), nullable=False + ) + + # Relationships + document: Mapped["Document"] = relationship(back_populates="pipeline_links") + pipeline: Mapped["RAGPipeline"] = relationship(back_populates="pipeline_links") + session: Mapped["GenerativeSession"] = relationship("GenerativeSession") + + # Constraints + __table_args__ = ( + UniqueConstraint("document_id", "session_id", name="uix_document_session"), + UniqueConstraint("session_id", "pipeline_id", name="uix_session_pipeline"), + UniqueConstraint("document_id", "pipeline_id", name="uix_document_pipeline"), ) -class RAGChunkEmbedding(Base): - __tablename__ = "RAG_chunk_embedding" +class RAGDocumentChunksEmbeddingsLink(Base): + __tablename__ = "rag_document_chunks_embeddings_link" """ - Table to store all the chunk embeddings used in RAG pipelines. + Association table to link documents, chunking models and chunks embeddings (many-to-many relationship). """ - id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) - chunk_id: Mapped[int] = mapped_column( - ForeignKey("RAG_chunk.id", ondelete="CASCADE"), nullable=False - ) - dimension: Mapped[int] = mapped_column(nullable=False) - embedding_vector: Mapped[List[float]] = mapped_column(JSON, nullable=False) - model: Mapped[int] = mapped_column( - ForeignKey("RAG_embedding_model.id", ondelete="CASCADE"), nullable=False - ) - created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) - sessions_related: Mapped[List["GenerativeSession"]] = relationship( - "GenerativeSession", backref="RAG_chunk_embedding" + id: Mapped[int] = mapped_column(primary_key=True) + document_id: Mapped[int] = mapped_column( + ForeignKey("document.id", ondelete="CASCADE"), nullable=False ) - retrievers_related: Mapped[List["RAGDenseRetriever"]] = relationship( - "RAGDenseRetriever", backref="RAG_chunk_embedding" + + chunking_model_id: Mapped[int] = mapped_column( + ForeignKey("rag_chunking_model.id", ondelete="CASCADE"), nullable=False ) + chunks_embeddings_id: Mapped[int] = mapped_column( + ForeignKey("rag_chunks_embeddings.id", ondelete="CASCADE"), nullable=False + ) + # Constraints __table_args__ = ( - UniqueConstraint('chunk_id', 'model', name='_chunk_model_uc'), + UniqueConstraint("document_id", "chunking_model_id", "chunks_embeddings_id", name="uix_document_chunking_model_embeddings"), ) \ No newline at end of file diff --git a/DashAI/back/models/RAG/chunking_models/__init__.py b/DashAI/back/models/RAG/chunking_models/__init__.py index a935c5395..f21676cad 100644 --- a/DashAI/back/models/RAG/chunking_models/__init__.py +++ b/DashAI/back/models/RAG/chunking_models/__init__.py @@ -1,2 +1,3 @@ from DashAI.back.models.RAG.chunking_models.character_chunk_model import CharacterChunkModel -from DashAI.back.models.RAG.chunking_models.token_chunk_model import TokenChunkModel \ No newline at end of file +from DashAI.back.models.RAG.chunking_models.token_chunk_model import TokenChunkModel +from DashAI.back.models.RAG.chunking_models.base_chunking_model import BaseChunkingModel \ No newline at end of file diff --git a/DashAI/back/models/RAG/embeddings/sparse/bm25_encoding.py b/DashAI/back/models/RAG/embeddings/sparse/bm25_encoding.py index 389a8c2ed..3cb224ce7 100644 --- a/DashAI/back/models/RAG/embeddings/sparse/bm25_encoding.py +++ b/DashAI/back/models/RAG/embeddings/sparse/bm25_encoding.py @@ -13,17 +13,21 @@ float_field, int_field, bool_field, - string_field + string_field, + union_type, + none_type ) class BM25EncodingSchema(BaseSchema): strip_accents: schema_field( - enum_field( - enum=["ascii", "unicode", None], + none_type( + enum_field( + enum=["ascii", "unicode"], + ) ), placeholder=None, - description="Whether to strip accents from the text.", + description="""Remove accents and perform other character normalization during the preprocessing step. ‘ascii’ is a fast method that only works on characters that have a direct ASCII mapping. ‘unicode’ is a slightly slower method that works on any characters. None (default) means no character normalization is performed.""", ) # type: ignore lowercase: schema_field( @@ -33,6 +37,8 @@ class BM25EncodingSchema(BaseSchema): ) # type: ignore + + stop_words: schema_field( list_field( string_field(), @@ -61,6 +67,7 @@ class BM25EncodingSchema(BaseSchema): ) # type: ignore max_features: schema_field( + int_field( ge=1, ), diff --git a/DashAI/back/models/RAG/embeddings/sparse/tfidf_encoding.py b/DashAI/back/models/RAG/embeddings/sparse/tfidf_encoding.py index ce32a30d0..18b83ae52 100644 --- a/DashAI/back/models/RAG/embeddings/sparse/tfidf_encoding.py +++ b/DashAI/back/models/RAG/embeddings/sparse/tfidf_encoding.py @@ -16,17 +16,21 @@ float_field, int_field, bool_field, - string_field + string_field, + none_type, + union_type, ) class TFIDFEncodingSchema(BaseSchema): strip_accents: schema_field( - enum_field( - enum=["ascii", "unicode", None], + none_type( + enum_field( + enum=["ascii", "unicode"], + ) ), placeholder=None, - description="Whether to strip accents from the text.", + description="""Remove accents and perform other character normalization during the preprocessing step. ‘ascii’ is a fast method that only works on characters that have a direct ASCII mapping. ‘unicode’ is a slightly slower method that works on any characters. None (default) means no character normalization is performed.""", ) # type: ignore lowercase: schema_field( @@ -45,12 +49,17 @@ class TFIDFEncodingSchema(BaseSchema): ) # type: ignore stop_words: schema_field( - list_field( - string_field(), - min_items=1, + none_type( + list_field( + string_field(), + min_items=1, + ) ), placeholder=None, - description="List of stop words to be used in the TF-IDF vectorization.", + description=""" +List of stop words to be used in the TF-IDF vectorization. +If None, no stop words will be used. In this case, setting max_df to a higher value, such as in the range (0.7, 1.0), can automatically detect and filter stop words based on intra corpus document frequency of terms. +""", ) # type: ignore ngram_range: schema_field( @@ -82,8 +91,10 @@ class TFIDFEncodingSchema(BaseSchema): ) # type: ignore max_features: schema_field( - int_field( - ge=1, + none_type( + int_field( + ge=1, + ) ), placeholder=None, description="If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus.", From ef4c363a33c4ab252c2f70c3cd427a96192a2dee Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Fri, 10 Oct 2025 15:04:31 -0300 Subject: [PATCH 046/218] sessions creation updated --- DashAI/back/api/api_v1/endpoints/documents.py | 13 +++++++- .../api_v1/endpoints/generative_session.py | 15 +++++---- DashAI/back/dependencies/database/models.py | 32 +++++++++++++++++++ DashAI/back/models/RAG/RAG_pipeline.py | 12 +++++-- 4 files changed, 61 insertions(+), 11 deletions(-) diff --git a/DashAI/back/api/api_v1/endpoints/documents.py b/DashAI/back/api/api_v1/endpoints/documents.py index 97b03ff00..b0fe26048 100644 --- a/DashAI/back/api/api_v1/endpoints/documents.py +++ b/DashAI/back/api/api_v1/endpoints/documents.py @@ -44,8 +44,10 @@ async def get_all_documents( DocumentResponse( id=doc.id, file_name=doc.file_name, + file_type=doc.file_type, file_hash=doc.file_hash, created=doc.created, + last_modified=doc.last_modified, optional_metadata=doc.optional_metadata, related_sessions=[session.id for session in doc.related_sessions_ids] if doc.related_sessions_ids else None, file_url=f"{base_url}/{doc.id}/download", @@ -175,8 +177,10 @@ async def upload_document( return DocumentResponse( id=existing_doc.id, file_name=existing_doc.file_name, + file_type=existing_doc.file_type, file_hash=existing_doc.file_hash, created=existing_doc.created, + last_modified=existing_doc.last_modified, related_sessions=[session.id for session in existing_doc.related_sessions_ids], optional_metadata=existing_doc.optional_metadata, file_url=f"{base_url}/{existing_doc.id}/download" @@ -191,8 +195,12 @@ async def upload_document( with open(file_path, "wb") as f: f.write(content_bytes) + # Get file type from file extension + file_type = os.path.splitext(file_name)[1].lstrip('.') or 'unknown' + doc = Document( file_name=file_name, + file_type=file_type, file_path=str(file_path), file_hash=hash256, optional_metadata=optional_metadata or None, @@ -204,12 +212,13 @@ async def upload_document( return DocumentResponse( id=doc.id, file_name=doc.file_name, + file_type=doc.file_type, file_hash=doc.file_hash, created=doc.created, + last_modified=doc.last_modified, related_sessions=None, optional_metadata=doc.optional_metadata, file_url=f"{base_url}/{doc.id}/download", - ) except exc.SQLAlchemyError as e: log.exception(e) @@ -305,6 +314,8 @@ async def update_document_metadata( # Update fields that are allowed to be modified if "file_name" in metadata_dict: document.file_name = metadata_dict["file_name"] + # Update file_type when file_name changes + document.file_type = os.path.splitext(metadata_dict["file_name"])[1].lstrip('.') or 'unknown' if "optional_metadata" in metadata_dict: optional_metadata = metadata_dict["optional_metadata"] diff --git a/DashAI/back/api/api_v1/endpoints/generative_session.py b/DashAI/back/api/api_v1/endpoints/generative_session.py index 78747fe42..3c622706f 100644 --- a/DashAI/back/api/api_v1/endpoints/generative_session.py +++ b/DashAI/back/api/api_v1/endpoints/generative_session.py @@ -18,6 +18,7 @@ ) from DashAI.back.dependencies.registry import ComponentRegistry from DashAI.back.models import BaseGenerativeModel +from DashAI.back.models.RAG import PDFDocument, TxtDocument, BaseDocument from DashAI.back.tasks import BaseGenerativeTask from DashAI.back.tasks.RAG_task import RAGTask @@ -76,17 +77,17 @@ async def upload_generative_session( detail="RAG Task requires a non-empty list of document IDs.", ) - documents_paths = [] + documents_ids = [] for doc_id in params.parameters["documents"]: - doc = db.get(Document, doc_id) - if not doc: + document = db.get(Document, doc_id) + if not document: raise HTTPException( - status_code=status.HTTP_404_NOT_FOUND, - detail=f"Document with ID {doc_id} not found.", + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Document with ID {doc_id} does not exist.", ) - documents_paths.append(doc.file_path) + documents_ids.append(document.id) - params.parameters["documents"] = documents_paths + params.parameters["documents"] = documents_ids # Continue with the session creation # Validate the model parameters diff --git a/DashAI/back/dependencies/database/models.py b/DashAI/back/dependencies/database/models.py index f6df5af0e..00fa509a1 100644 --- a/DashAI/back/dependencies/database/models.py +++ b/DashAI/back/dependencies/database/models.py @@ -640,6 +640,13 @@ class Document(Base): file_type: Mapped[str] = mapped_column(String, nullable=False) file_path: Mapped[str] = mapped_column(String, nullable=False) file_hash: Mapped[str] = mapped_column(String, nullable=False, unique=True) + optional_metadata: Mapped[Dict[str, Any]] = mapped_column(JSON, nullable=True) + created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) + last_modified: Mapped[DateTime] = mapped_column( + DateTime, + default=datetime.now, + onupdate=datetime.now, + ) # Create a relationship for the related sessions and related chunks pipeline_links: Mapped[List["RAGDocumentPipelineSessionLink"]] = relationship( @@ -665,6 +672,11 @@ def sessions_related(self) -> List["GenerativeSession"]: def pipelines_related(self) -> List["RAGPipeline"]: """Return a list of pipelines related to the document.""" return [link.pipeline for link in self.pipeline_links] + + @property + def related_sessions_ids(self) -> List["GenerativeSession"]: + """Return a list of sessions related to the document.""" + return [link.session for link in self.pipeline_links] class RAGChunk(Base): @@ -685,6 +697,22 @@ class RAGChunk(Base): ) +class RAGPrompt(Base): + __tablename__ = "rag_prompt" + """ + Table to store all the information about a RAG prompt. + """ + id: Mapped[int] = mapped_column(primary_key=True) + class_name: Mapped[str] = mapped_column(String, nullable=False) + parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) + + # Relationship with RAGPipeline + pipelines: Mapped[List["RAGPipeline"]] = relationship( + back_populates="prompt", + cascade="all, delete-orphan" + ) + + class RAGPipeline(Base): __tablename__ = "rag_pipeline" """ @@ -701,10 +729,14 @@ class RAGPipeline(Base): retriever_id: Mapped[int] = mapped_column( ForeignKey("rag_retriever.id", ondelete="CASCADE"), nullable=False ) + prompt_id: Mapped[int] = mapped_column( + ForeignKey("rag_prompt.id", ondelete="CASCADE"), nullable=False + ) # Relationships chunking_model: Mapped["RAGChunkingModel"] = relationship(back_populates="pipelines") retriever: Mapped["RAGRetriever"] = relationship(back_populates="pipelines") + prompt: Mapped["RAGPrompt"] = relationship(back_populates="pipelines") pipeline_links: Mapped[List["RAGDocumentPipelineSessionLink"]] = relationship( cascade="all, delete-orphan", back_populates="pipeline" diff --git a/DashAI/back/models/RAG/RAG_pipeline.py b/DashAI/back/models/RAG/RAG_pipeline.py index 9a6ff127c..be6f02457 100644 --- a/DashAI/back/models/RAG/RAG_pipeline.py +++ b/DashAI/back/models/RAG/RAG_pipeline.py @@ -14,6 +14,7 @@ from DashAI.back.models.RAG.chunking_models.base_chunking_model import BaseChunkingModel from DashAI.back.models.RAG.chunking_models.character_chunk_model import CharacterChunkModel from DashAI.back.models.RAG.chunking_models.token_chunk_model import TokenChunkModel +from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument from DashAI.back.models.base_generative_model import BaseGenerativeModel from DashAI.back.models.text_to_text_generation_model import TextToTextGenerationTaskModel @@ -81,11 +82,10 @@ class RAGPipelineSchema(BaseSchema): # Document collection parameters documents: schema_field( list_field( - int_field(), - min_items=1, + int_field(gt=0) ), placeholder=None, - description="List of documents to be used in the RAG pipeline." + description="List of documents ids to be used in the RAG pipeline." ) # type: ignore chunking_model: schema_field( @@ -111,6 +111,12 @@ class RAGPipelineSchema(BaseSchema): description="Text generation model used in the RAG pipeline." ) # type: ignore + prompt_model: schema_field( + component_field(parent="Prompt"), + placeholder={"component": "DefaultGenerationPrompt", "params": {}}, + description="Prompt template used to format the input for the generation model." + ) # type: ignore + class RAGPipeline(BaseGenerativeModel): """Retrieval-Augmented Generation (RAG) pipeline.""" From 68598eb1cb51cbff19f5340bc65eca6a4c13faed Mon Sep 17 00:00:00 2001 From: Creylay Date: Wed, 15 Oct 2025 17:40:12 -0300 Subject: [PATCH 047/218] refactor: Improve error handling and filter RAG sessions by task name in getRAGSessions --- DashAI/front/src/api/rag.ts | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/DashAI/front/src/api/rag.ts b/DashAI/front/src/api/rag.ts index a154b7aa7..52dc2b6c4 100644 --- a/DashAI/front/src/api/rag.ts +++ b/DashAI/front/src/api/rag.ts @@ -12,7 +12,10 @@ export const getRAGSessions = async (): Promise => { throw new Error(`Failed to fetch RAG sessions: ${response.statusText}`); } - return response.data; + const ragSessions = response.data.filter( + (session) => session.task_name === "RAGTask", + ); + return ragSessions; }; export const getRAGSession = async (sessionId: number): Promise => { From fd2825a92fe82a89203deefd527d289a9d8ca513 Mon Sep 17 00:00:00 2001 From: Creylay Date: Wed, 15 Oct 2025 18:00:15 -0300 Subject: [PATCH 048/218] refactor: Simplify session state updates with useCallback and improve initial values handling in RetrieverConfigurationStep --- .../RAG/NewSessionModal/NewSessionModal.jsx | 84 +++++++++++-------- .../RetrieverConfigurationStep.jsx | 46 +++++----- 2 files changed, 75 insertions(+), 55 deletions(-) diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx index 6202ed345..5d9070bea 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx @@ -125,6 +125,48 @@ export default function NewSessionModal({ }); }, []); + const updateSessionDocuments = useCallback((docs) => { + setSessionData((prev) => ({ + ...prev, + parameters: { ...prev.parameters, documents: docs }, + })); + }, []); + + const updateSessionName = useCallback((name) => { + setSessionData((prev) => ({ + ...prev, + name: name, + })); + }, []); + + const updateSessionDescription = useCallback((description) => { + setSessionData((prev) => ({ + ...prev, + description: description, + })); + }, []); + + const updateRetrieverModel = useCallback((model) => { + setSessionData((prev) => ({ + ...prev, + parameters: { ...prev.parameters, retriever_model: model }, + })); + }, []); + + const updateChunkingModel = useCallback((model) => { + setSessionData((prev) => ({ + ...prev, + parameters: { ...prev.parameters, chunking_model: model }, + })); + }, []); + + const updateGeneratorModel = useCallback((model) => { + setSessionData((prev) => ({ + ...prev, + parameters: { ...prev.parameters, generator_model: model }, + })); + }, []); + const isNextOrFinishEnabled = () => { if (!stepValidity[activeStep]) { return false; @@ -235,50 +277,25 @@ export default function NewSessionModal({ {activeStep === 0 && ( - setSessionData((prev) => ({ - ...prev, - parameters: { ...prev.parameters, documents: docs }, - })) - } + setDocuments={updateSessionDocuments} setNextEnabled={(isValid) => handleStepValidation(0, isValid)} sessionName={sessionData.name} - setSesssionName={(name) => - setSessionData((prev) => ({ - ...prev, - name: name, - })) - } + setSesssionName={updateSessionName} sessionDescription={sessionData.description} - setSessionDescription={(description) => - setSessionData((prev) => ({ - ...prev, - description: description, - })) - } + setSessionDescription={updateSessionDescription} /> )} {activeStep === 2 && ( - setSessionData((prev) => ({ - ...prev, - parameters: { ...prev.parameters, retriever_model: model }, - })) - } + setRetrieverModel={updateRetrieverModel} setNextEnabled={(isValid) => handleStepValidation(2, isValid)} /> )} {activeStep === 1 && ( - setSessionData((prev) => ({ - ...prev, - parameters: { ...prev.parameters, chunking_model: model }, - })) - } + setChunkingModel={updateChunkingModel} setNextEnabled={(isValid) => handleStepValidation(1, isValid)} /> )} @@ -286,12 +303,7 @@ export default function NewSessionModal({ {activeStep === 3 && ( - setSessionData((prev) => ({ - ...prev, - parameters: { ...prev.parameters, generator_model: model }, - })) - } + setGeneratorModel={updateGeneratorModel} setNextEnabled={(isValid) => handleStepValidation(3, isValid)} /> )} diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/RetrieverConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/RetrieverConfigurationStep.jsx index 32e75bcc7..ce5bf6911 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/RetrieverConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/RetrieverConfigurationStep.jsx @@ -1,4 +1,4 @@ -import { useState, useEffect, useCallback, useRef } from "react"; +import { useState, useEffect, useCallback, useRef, useMemo } from "react"; import PropTypes from "prop-types"; import { Box, Autocomplete, TextField, Typography } from "@mui/material"; @@ -19,6 +19,20 @@ function AutoSaveFormSchema({ }) { const formikRef = useRef(null); + const initialValues = useMemo(() => { + const baseValues = selectedRetriever.parameters || {}; + + // Merge with retrieverModel params (which contains user changes) + return { + ...baseValues, + ...(retrieverModel?.params || {}), + }; + }, [ + selectedRetriever.name, + selectedRetriever.parameters, + retrieverModel?.params, + ]); + useEffect(() => { if (formikRef.current && formikRef.current.values && currentFormValuesRef) { currentFormValuesRef.current = formikRef.current.values; @@ -29,10 +43,7 @@ function AutoSaveFormSchema({ { - const updatedRetriever = { - ...selectedRetriever, - parameters: newParams, - }; - setSelectedRetriever(updatedRetriever); + const handleRetrieverParametersSave = useCallback( + (newParams) => { + const newRetrieverModel = { + component: selectedRetriever?.name || "", + params: newParams, + }; - const newRetrieverModel = { - component: selectedRetriever?.name || "", - params: newParams, - }; - - setRetrieverModel(newRetrieverModel); - setNextEnabled(true); - }; + setRetrieverModel(newRetrieverModel); + setNextEnabled(true); + }, + [selectedRetriever?.name, setRetrieverModel, setNextEnabled], + ); return ( Date: Thu, 16 Oct 2025 05:38:24 -0300 Subject: [PATCH 049/218] baseline --- DashAI/back/api/api_v1/endpoints/documents.py | 16 +- DashAI/back/config.py | 1 + DashAI/back/dependencies/config_builder.py | 1 + DashAI/back/dependencies/database/models.py | 343 +++++--- DashAI/back/initial_components.py | 3 +- DashAI/back/job/generative_job.py | 6 + DashAI/back/models/RAG/Documents/__init__.py | 7 +- DashAI/back/models/RAG/RAG_pipeline.py | 513 ++++++----- .../models/RAG/Retrievers/dense_retriever.py | 252 +++++- .../models/RAG/Retrievers/retriever_model.py | 226 ++--- .../models/RAG/Retrievers/tfidf_retriever.py | 70 +- .../chunking_models/base_chunking_model.py | 201 ++++- .../chunking_models/character_chunk_model.py | 39 +- .../RAG/chunking_models/token_chunk_model.py | 20 +- .../base_document.py} | 6 +- DashAI/back/models/RAG/documents/chunk.py | 20 + .../models/RAG/documents/chunk_embedding.py | 17 + .../pdf_document.py} | 7 +- .../txt_document.py} | 6 +- .../embeddings/dense/fasttext_embedding.py | 17 +- .../embeddings/dense/huggingface_embedding.py | 31 +- .../models/RAG/embeddings/dense_embedding.py | 14 +- .../RAG/embeddings/trainable_encoding.py | 2 +- DashAI/back/models/RAG/exceptions.py | 0 .../custom_augmentation_prompt.py | 6 +- .../RAG/prompts/context_merge/__init__.py | 3 - .../context_merge/context_merge_prompt.py | 18 - .../custom_context_merge_prompt.py | 43 - .../generation/custom_generation_prompt.py | 10 +- .../default_QnA_generation_prompt.py | 4 + .../generation/default_generation_prompt.py | 6 +- .../prompts/generation/generation_prompt.py | 4 + DashAI/back/models/RAG/prompts/prompt.py | 37 +- DashAI/back/models/RAG/utils.py | 14 + .../models/text_to_text_generation_model.py | 2 + DashAI/front/src/api/job.ts | 18 + Untitled-1.ipynb | 796 ------------------ notas.md | 2 + 38 files changed, 1306 insertions(+), 1475 deletions(-) rename DashAI/back/models/RAG/{Documents/BaseDocument.py => documents/base_document.py} (92%) create mode 100644 DashAI/back/models/RAG/documents/chunk.py create mode 100644 DashAI/back/models/RAG/documents/chunk_embedding.py rename DashAI/back/models/RAG/{Documents/PDFDocument.py => documents/pdf_document.py} (89%) rename DashAI/back/models/RAG/{Documents/TxtDocument.py => documents/txt_document.py} (90%) create mode 100644 DashAI/back/models/RAG/exceptions.py delete mode 100644 DashAI/back/models/RAG/prompts/context_merge/__init__.py delete mode 100644 DashAI/back/models/RAG/prompts/context_merge/context_merge_prompt.py delete mode 100644 DashAI/back/models/RAG/prompts/context_merge/custom_context_merge_prompt.py delete mode 100644 Untitled-1.ipynb create mode 100644 notas.md diff --git a/DashAI/back/api/api_v1/endpoints/documents.py b/DashAI/back/api/api_v1/endpoints/documents.py index b0fe26048..6cfc7fa26 100644 --- a/DashAI/back/api/api_v1/endpoints/documents.py +++ b/DashAI/back/api/api_v1/endpoints/documents.py @@ -22,6 +22,7 @@ from DashAI.back.api.api_v1.schemas import DocumentResponse from DashAI.back.dependencies.database.models import Document +from DashAI.back.models.RAG.utils import hash_function router = APIRouter() log = logging.getLogger(__name__) @@ -130,14 +131,12 @@ async def upload_document( Upload a new document to the RAG system with file content and metadata. The metadata should be a JSON string with required fields and an 'optional_metadata' dict. """ - print("A") docs_folder_path = config["DOCUMENTS_PATH"] if not docs_folder_path.exists(): raise HTTPException( status_code=500, detail=f"Documents folder {docs_folder_path} does not exist.", ) - print("B") try: metadata_dict = json.loads(metadata) except json.JSONDecodeError: @@ -145,29 +144,25 @@ async def upload_document( file_name = metadata_dict.get("file_name") last_modified = metadata_dict.get("last_modified") - print("C") if not file_name or not last_modified: raise HTTPException( status_code=400, detail="Missing required metadata fields: 'filename' and 'last_modified'", ) - print("D") optional_metadata = metadata_dict.get("optional_metadata", {}) if not isinstance(optional_metadata, dict): raise HTTPException( status_code=400, detail="'optional_metadata' must be a dictionary", ) - print("E") try: content_bytes = await file.read() except Exception: raise HTTPException(status_code=400, detail="Failed to read file content") - hash256 = hashlib.sha256(content_bytes).hexdigest() - print("F") + file_content_hash = hash_function(content_bytes) with session_factory() as db: - existing_doc = db.query(Document).filter_by(file_hash=hash256).first() + existing_doc = db.query(Document).filter_by(file_hash=file_content_hash).first() if existing_doc: # Update existing document's information existing_doc.file_name = file_name @@ -202,7 +197,7 @@ async def upload_document( file_name=file_name, file_type=file_type, file_path=str(file_path), - file_hash=hash256, + file_hash=file_content_hash, optional_metadata=optional_metadata or None, ) db.add(doc) @@ -346,4 +341,5 @@ async def update_document_metadata( raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Internal database error", - ) from e \ No newline at end of file + ) from e + \ No newline at end of file diff --git a/DashAI/back/config.py b/DashAI/back/config.py index 97cd5b6a4..156d5083a 100644 --- a/DashAI/back/config.py +++ b/DashAI/back/config.py @@ -21,4 +21,5 @@ class DefaultSettings(BaseSettings): EXPLANATIONS_PATH: str = "explanations" EXPLORATIONS_PATH: str = "explorations" DOCUMENTS_PATH: str = "documents" + RAG_PATH: str = "rag" NOTEBOOK_PATH: str = "notebook" diff --git a/DashAI/back/dependencies/config_builder.py b/DashAI/back/dependencies/config_builder.py index 8aa64d836..4ee265377 100644 --- a/DashAI/back/dependencies/config_builder.py +++ b/DashAI/back/dependencies/config_builder.py @@ -62,5 +62,6 @@ def build_config_dict( config["LOGGING_LEVEL"] = getattr(logging, logging_level) config["INITIAL_COMPONENTS"] = get_initial_components() config["DOCUMENTS_PATH"] = local_path / config["DOCUMENTS_PATH"] + config["RAG_PATH"] = local_path / config["RAG_PATH"] return config diff --git a/DashAI/back/dependencies/database/models.py b/DashAI/back/dependencies/database/models.py index 00fa509a1..91c3c1efb 100644 --- a/DashAI/back/dependencies/database/models.py +++ b/DashAI/back/dependencies/database/models.py @@ -3,7 +3,7 @@ from datetime import datetime from typing import Any, Dict, List, Optional -from sqlalchemy import JSON, Boolean, CheckConstraint, DateTime, Enum, ForeignKey, Integer, String, Text, UniqueConstraint, ARRAY +from sqlalchemy import JSON, Boolean, CheckConstraint, Column, DateTime, Enum, ForeignKey, Integer, String, Text, UniqueConstraint, ARRAY from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import Mapped, mapped_column, relationship @@ -635,7 +635,7 @@ class Document(Base): """ Table to store all the information about a document. """ - id: Mapped[int] = mapped_column(primary_key=True) + id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) file_name: Mapped[str] = mapped_column(String, nullable=False) file_type: Mapped[str] = mapped_column(String, nullable=False) file_path: Mapped[str] = mapped_column(String, nullable=False) @@ -650,48 +650,78 @@ class Document(Base): # Create a relationship for the related sessions and related chunks pipeline_links: Mapped[List["RAGDocumentPipelineSessionLink"]] = relationship( + "RAGDocumentPipelineSessionLink", cascade="all, delete-orphan", back_populates="document" ) - chunks: Mapped[List["RAGChunk"]] = relationship( + chunks: Mapped[List["Chunk"]] = relationship( + "Chunk", back_populates="document", cascade="all, delete-orphan", ) - embeddings_links: Mapped[List["RAGDocumentChunksEmbeddingsLink"]] = relationship( + embedding_matrices: Mapped[List["RAGEmbeddingMatrix"]] = relationship( + "RAGEmbeddingMatrix", + back_populates="document", cascade="all, delete-orphan" ) @property - def sessions_related(self) -> List["GenerativeSession"]: + def get_related_sessions(self) -> List["GenerativeSession"]: """Return a list of sessions related to the document.""" return [link.session for link in self.pipeline_links] @property - def pipelines_related(self) -> List["RAGPipeline"]: + def get_related_pipelines(self) -> List["RAGPipeline"]: """Return a list of pipelines related to the document.""" return [link.pipeline for link in self.pipeline_links] + + def get_embedding_matrix(self, chunking_model_id: int, embedding_model_id: int) -> Optional["RAGEmbeddingMatrix"]: + """ + Get the embedding matrix for a specific combination of chunking and embedding models. - @property - def related_sessions_ids(self) -> List["GenerativeSession"]: - """Return a list of sessions related to the document.""" - return [link.session for link in self.pipeline_links] + Args: + chunking_model_id: ID of the chunking model + embedding_model_id: ID of the embedding model + + Returns: + The embedding matrix if found, None otherwise + """ + for matrix in self.embedding_matrices: + if (matrix.chunking_model_id == chunking_model_id and + matrix.embedding_model_id == embedding_model_id): + return matrix + return None -class RAGChunk(Base): - __tablename__ = "rag_chunk" +class Chunk(Base): + __tablename__ = "chunk" """ Table to store all the information about a chunk. """ - id: Mapped[int] = mapped_column(primary_key=True) - text: Mapped[str] = mapped_column(Text, nullable=False) + id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) document_id: Mapped[int] = mapped_column( ForeignKey("document.id", ondelete="CASCADE"), nullable=False ) + chunk_index: Mapped[int] = mapped_column(Integer, nullable=False) + chunking_model_id: Mapped[int] = mapped_column( + ForeignKey("rag_chunking_model.id", ondelete="CASCADE"), + nullable=False + ) + text: Mapped[str] = mapped_column(Text, nullable=False) + hash: Mapped[str] = mapped_column(String, nullable=False, unique=True) - document: Mapped["Document"] = relationship(back_populates="chunks") + document: Mapped["Document"] = relationship( + "Document", + back_populates="chunks" + ) + chunking_model: Mapped["RAGChunkingModel"] = relationship( + "RAGChunkingModel", + back_populates="chunks" + ) + __table_args__ = ( UniqueConstraint("id", "document_id", name="uix_chunk_document"), ) @@ -702,61 +732,119 @@ class RAGPrompt(Base): """ Table to store all the information about a RAG prompt. """ - id: Mapped[int] = mapped_column(primary_key=True) + id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) class_name: Mapped[str] = mapped_column(String, nullable=False) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) # Relationship with RAGPipeline pipelines: Mapped[List["RAGPipeline"]] = relationship( + "RAGPipeline", back_populates="prompt", cascade="all, delete-orphan" ) +class RAGGenerationModel(Base): + __tablename__ = "rag_generation_model" + """ + Table to store all the information about a generation model. + """ + id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) + class_name: Mapped[str] = mapped_column(String, nullable=False) + parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) + + # Relationships + pipelines: Mapped[List["RAGPipeline"]] = relationship( + back_populates="generation_model" + ) + class RAGPipeline(Base): __tablename__ = "rag_pipeline" """ Table to store all the information about a RAG pipeline. """ - id: Mapped[int] = mapped_column(primary_key=True) + id: Mapped[int] = mapped_column( + primary_key=True, + autoincrement=True) + session_id: Mapped[int] = mapped_column( + ForeignKey( + "generative_session.id", + ondelete="CASCADE"), + nullable=False + ) name: Mapped[str] = mapped_column(String, nullable=False) description: Mapped[str] = mapped_column(String, nullable=True) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) chunking_model_id: Mapped[int] = mapped_column( - ForeignKey("rag_chunking_model.id", ondelete="CASCADE"), nullable=False + ForeignKey( + "rag_chunking_model.id", + ondelete="CASCADE"), + nullable=False ) - retriever_id: Mapped[int] = mapped_column( - ForeignKey("rag_retriever.id", ondelete="CASCADE"), nullable=False + retriever_model_id: Mapped[int] = mapped_column( + ForeignKey( + "rag_retriever.id", + ondelete="CASCADE"), + nullable=False ) prompt_id: Mapped[int] = mapped_column( - ForeignKey("rag_prompt.id", ondelete="CASCADE"), nullable=False + ForeignKey( + "rag_prompt.id", + ondelete="CASCADE"), + nullable=False ) - + generation_model_id: Mapped[int] = mapped_column( + ForeignKey( + "rag_generation_model.id", + ondelete="CASCADE"), + nullable=False + ) + # Relationships - chunking_model: Mapped["RAGChunkingModel"] = relationship(back_populates="pipelines") - retriever: Mapped["RAGRetriever"] = relationship(back_populates="pipelines") - prompt: Mapped["RAGPrompt"] = relationship(back_populates="pipelines") + chunking_model: Mapped["RAGChunkingModel"] = relationship( + "RAGChunkingModel", + back_populates="pipelines" + ) + retriever: Mapped["RAGRetriever"] = relationship( + "RAGRetriever", + back_populates="pipelines" + ) + prompt: Mapped["RAGPrompt"] = relationship( + "RAGPrompt", + back_populates="pipelines" + ) + generation_model: Mapped["RAGGenerationModel"] = relationship( + "RAGGenerationModel", + back_populates="pipelines" + ) pipeline_links: Mapped[List["RAGDocumentPipelineSessionLink"]] = relationship( - cascade="all, delete-orphan", + "RAGDocumentPipelineSessionLink", back_populates="pipeline" ) - class RAGChunkingModel(Base): __tablename__ = "rag_chunking_model" """ Table to store all the information about a chunking model. """ - id: Mapped[int] = mapped_column(primary_key=True) + id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) class_name: Mapped[str] = mapped_column(String, nullable=False) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) pipelines: Mapped[List["RAGPipeline"]] = relationship( + "RAGPipeline", back_populates="chunking_model" ) - document_links: Mapped[List["RAGDocumentChunksEmbeddingsLink"]] = relationship( - cascade="all, delete-orphan" + + embedding_matrices: Mapped[List["RAGEmbeddingMatrix"]] = relationship( + "RAGEmbeddingMatrix", + back_populates="chunking_model" + ) + + chunks: Mapped[List["Chunk"]] = relationship( + "Chunk", + back_populates="chunking_model" ) @@ -765,16 +853,17 @@ class RAGRetriever(Base): """ Table to map the retriever model id to its dense or sparse retriever instance. """ - id: Mapped[int] = mapped_column(primary_key=True) + id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) class_name: Mapped[str] = mapped_column(String, nullable=False) # "SparseRetriever" or "DenseRetriever" - parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) # The top-level parameters for the retriever, not the nested ones - sparse_id: Mapped[int] = mapped_column( - ForeignKey("rag_sparse_retriever.id", ondelete="CASCADE"), nullable=True + dense_retriever_id: Mapped[Optional[int]] = mapped_column( + ForeignKey("rag_dense_retriever.id", ondelete="CASCADE"), + nullable=True ) - dense_id: Mapped[int] = mapped_column( - ForeignKey("rag_dense_retriever.id", ondelete="CASCADE"), nullable=True + sparse_retriever_id: Mapped[Optional[int]] = mapped_column( + ForeignKey("rag_sparse_retriever.id", ondelete="CASCADE"), + nullable=True ) - + # Relationships sparse_retriever: Mapped[Optional["RAGSparseRetriever"]] = relationship( back_populates="related_retriever" @@ -785,14 +874,15 @@ class RAGRetriever(Base): pipelines: Mapped[List["RAGPipeline"]] = relationship( back_populates="retriever" ) - + __table_args__ = ( CheckConstraint( - "(sparse_id IS NOT NULL AND class_name = 'SparseRetriever' AND dense_id IS NULL) OR " - "(dense_id IS NOT NULL AND class_name = 'DenseRetriever' AND sparse_id IS NULL)", - name="chk_retriever_paradigm", + "(class_name = 'SparseRetriever' AND sparse_retriever_id IS NOT NULL AND dense_retriever_id IS NULL) OR " + "(class_name = 'DenseRetriever' AND dense_retriever_id IS NOT NULL AND sparse_retriever_id IS NULL)", + name="chk_retriever_type" ), ) + class RAGSparseRetriever(Base): @@ -800,13 +890,14 @@ class RAGSparseRetriever(Base): """ Table to store all the information about a sparse retriever. """ - id: Mapped[int] = mapped_column(primary_key=True) + id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) class_name: Mapped[str] = mapped_column(String, nullable=False) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) storage_folder: Mapped[str] = mapped_column(String, nullable=False) related_retriever: Mapped["RAGRetriever"] = relationship( - back_populates="sparse_retriever", uselist=False + "RAGRetriever", + back_populates="sparse_retriever", ) @@ -815,93 +906,145 @@ class RAGDenseRetriever(Base): """ Table to store all the information about a dense retriever. """ - id: Mapped[int] = mapped_column(primary_key=True) + id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) class_name: Mapped[str] = mapped_column(String, nullable=False) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) - chunks_embeddings_id: Mapped[int] = mapped_column( - ForeignKey("rag_chunks_embeddings.id", ondelete="CASCADE"), nullable=False + embedding_model_id: Mapped[int] = mapped_column( + ForeignKey("rag_embedding_model.id", ondelete="CASCADE"), nullable=False ) - + # Relationships related_retriever: Mapped["RAGRetriever"] = relationship( - back_populates="dense_retriever", uselist=False + "RAGRetriever", + back_populates="dense_retriever", ) - related_chunks_embeddings: Mapped["RAGChunksEmbeddings"] = relationship( - back_populates="related_dense_retrievers" + embedding_model: Mapped["RAGEmbeddingModel"] = relationship( + "RAGEmbeddingModel", + back_populates="dense_retrievers" ) -class RAGChunksEmbeddings(Base): - __tablename__ = "rag_chunks_embeddings" +class RAGEmbeddingModel(Base): + __tablename__ = "rag_embedding_model" """ - Table to store all the information about the chunks embeddings. + Table to store embedding model configurations. """ - id: Mapped[int] = mapped_column(primary_key=True) + id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) class_name: Mapped[str] = mapped_column(String, nullable=False) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) - embedding_dimension: Mapped[int] = mapped_column(Integer, nullable=False) - storage_folder: Mapped[str] = mapped_column(String, nullable=False) - - related_dense_retrievers: Mapped[List["RAGDenseRetriever"]] = relationship( - back_populates="related_chunks_embeddings" - ) - document_links: Mapped[List["RAGDocumentChunksEmbeddingsLink"]] = relationship( + + # Relationships + embedding_matrices: Mapped[List["RAGEmbeddingMatrix"]] = relationship( + back_populates="embedding_model", cascade="all, delete-orphan" ) + dense_retrievers: Mapped[List["RAGDenseRetriever"]] = relationship( + back_populates="embedding_model" + ) -""" -RAG relationship tables -""" - -class RAGDocumentPipelineSessionLink(Base): - __tablename__ = "rag_document_pipeline_session_link" +class RAGEmbeddingMatrix(Base): + __tablename__ = "rag_embedding_matrix" """ - Association table to link documents and sessions (many-to-many relationship). + Table to store embedding matrices for each unique combination of + document, chunking model, and embedding model. """ - id: Mapped[int] = mapped_column(primary_key=True) + id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) document_id: Mapped[int] = mapped_column( ForeignKey("document.id", ondelete="CASCADE"), nullable=False ) - session_id: Mapped[int] = mapped_column( - ForeignKey("generative_session.id", ondelete="CASCADE"), nullable=False + chunking_model_id: Mapped[int] = mapped_column( + ForeignKey("rag_chunking_model.id", ondelete="CASCADE"), nullable=False ) - pipeline_id: Mapped[int] = mapped_column( - ForeignKey("rag_pipeline.id", ondelete="CASCADE"), nullable=False + embedding_model_id: Mapped[int] = mapped_column( + ForeignKey("rag_embedding_model.id", ondelete="CASCADE"), nullable=False + ) + storage_folder: Mapped[str] = mapped_column(String, nullable=False) + matrix_shape: Mapped[List[int]] = mapped_column(JSON, nullable=False) # [num_chunks, embedding_dim] + created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) + last_modified: Mapped[DateTime] = mapped_column( + DateTime, + default=datetime.now, + onupdate=datetime.now, ) - - # Relationships - document: Mapped["Document"] = relationship(back_populates="pipeline_links") - pipeline: Mapped["RAGPipeline"] = relationship(back_populates="pipeline_links") - session: Mapped["GenerativeSession"] = relationship("GenerativeSession") - # Constraints - __table_args__ = ( - UniqueConstraint("document_id", "session_id", name="uix_document_session"), - UniqueConstraint("session_id", "pipeline_id", name="uix_session_pipeline"), - UniqueConstraint("document_id", "pipeline_id", name="uix_document_pipeline"), + # Relationships + document: Mapped["Document"] = relationship( + "Document", + back_populates="embedding_matrices" + ) + chunking_model: Mapped["RAGChunkingModel"] = relationship( + "RAGChunkingModel", + back_populates="embedding_matrices" + ) + embedding_model: Mapped["RAGEmbeddingModel"] = relationship( + "RAGEmbeddingModel", + back_populates="embedding_matrices" ) - -class RAGDocumentChunksEmbeddingsLink(Base): - __tablename__ = "rag_document_chunks_embeddings_link" - """ - Association table to link documents, chunking models and chunks embeddings (many-to-many relationship). - """ - id: Mapped[int] = mapped_column(primary_key=True) - document_id: Mapped[int] = mapped_column( - ForeignKey("document.id", ondelete="CASCADE"), nullable=False + # Constraints - ensure uniqueness of the combination + __table_args__ = ( + UniqueConstraint( + "document_id", + "chunking_model_id", + "embedding_model_id", + name="uix_document_chunking_embedding" + ), ) - chunking_model_id: Mapped[int] = mapped_column( - ForeignKey("rag_chunking_model.id", ondelete="CASCADE"), nullable=False - ) + @property + def num_chunks(self) -> int: + """Return the number of chunks in this embedding matrix.""" + return self.matrix_shape[0] if self.matrix_shape else 0 - chunks_embeddings_id: Mapped[int] = mapped_column( - ForeignKey("rag_chunks_embeddings.id", ondelete="CASCADE"), nullable=False - ) + @property + def embedding_dimension(self) -> int: + """Return the embedding dimension.""" + return self.matrix_shape[1] if len(self.matrix_shape) > 1 else 0 + + @classmethod + def get_by_tuple(cls, session, document_id: int, chunking_model_id: int, embedding_model_id: int) -> Optional["RAGEmbeddingMatrix"]: + """ + Get an embedding matrix by the tuple (document_id, chunking_model_id, embedding_model_id). + + Args: + session: SQLAlchemy session + document_id: ID of the document + chunking_model_id: ID of the chunking model + embedding_model_id: ID of the embedding model + + Returns: + The embedding matrix if found, None otherwise + """ + return session.query(cls).filter( + cls.document_id == document_id, + cls.chunking_model_id == chunking_model_id, + cls.embedding_model_id == embedding_model_id + ).first() + + - # Constraints + + +""" +RAG relationship tables +""" + +class RAGDocumentPipelineSessionLink(Base): + __tablename__ = "rag_document_pipeline_session_link" + + id = Column(Integer, primary_key=True, autoincrement=True) + document_id = Column(Integer, ForeignKey("document.id", ondelete="CASCADE"), nullable=False) + session_id = Column(Integer, ForeignKey("generative_session.id", ondelete="CASCADE"), nullable=False) + pipeline_id = Column(Integer, ForeignKey("rag_pipeline.id", ondelete="CASCADE"), nullable=False) + + # Relationships + document = relationship("Document", back_populates="pipeline_links") + pipeline = relationship("RAGPipeline", back_populates="pipeline_links") + session = relationship("GenerativeSession", back_populates="pipeline_links") + __table_args__ = ( - UniqueConstraint("document_id", "chunking_model_id", "chunks_embeddings_id", name="uix_document_chunking_model_embeddings"), + UniqueConstraint("document_id", "session_id", name="uix_document_session"), + UniqueConstraint("session_id", "pipeline_id", name="uix_session_pipeline"), + UniqueConstraint("document_id", "pipeline_id", name="uix_document_pipeline"), ) \ No newline at end of file diff --git a/DashAI/back/initial_components.py b/DashAI/back/initial_components.py index 07c1da0bf..14184c412 100644 --- a/DashAI/back/initial_components.py +++ b/DashAI/back/initial_components.py @@ -103,6 +103,7 @@ CustomAugmentationPrompt, # Chunking Models CharacterChunkModel, + TokenChunkModel, # Retrievers DenseRetriever, # Encodings @@ -281,8 +282,6 @@ def get_initial_components(): RandomUnderSamplerConverter, # RAG RAGPipeline, - CharacterChunkModel, - TokenChunkModel, # Chunking Models CharacterChunkModel, TokenChunkModel, diff --git a/DashAI/back/job/generative_job.py b/DashAI/back/job/generative_job.py index c032fef2a..71fa92530 100644 --- a/DashAI/back/job/generative_job.py +++ b/DashAI/back/job/generative_job.py @@ -16,6 +16,7 @@ from DashAI.back.job.base_job import BaseJob, JobError from DashAI.back.models.base_generative_model import BaseGenerativeModel from DashAI.back.tasks import BaseGenerativeTask +from DashAI.back.models.RAG.RAG_pipeline import RAGPipeline logging.basicConfig(level=logging.DEBUG) log = logging.getLogger(__name__) @@ -86,6 +87,11 @@ async def run( try: model_class = component_registry[generative_session.model_name]["class"] params = generative_session.parameters + if model_class == RAGPipeline: + params["db"] = db + params["component_registry"] = component_registry + params["session_id"] = generative_session.id + params["env_rag_path"] = config["RAG_PATH"] model: BaseGenerativeModel = model_class(**params) except Exception as e: log.exception(e) diff --git a/DashAI/back/models/RAG/Documents/__init__.py b/DashAI/back/models/RAG/Documents/__init__.py index 06bb27b6f..d88c9bd6b 100644 --- a/DashAI/back/models/RAG/Documents/__init__.py +++ b/DashAI/back/models/RAG/Documents/__init__.py @@ -1,3 +1,4 @@ -from .BaseDocument import BaseDocument -from .PDFDocument import PDFDocument -from .TxtDocument import TxtDocument \ No newline at end of file +from .base_document import BaseDocument +from .pdf_document import PDFDocument +from .txt_document import TxtDocument +from .chunk import Chunk \ No newline at end of file diff --git a/DashAI/back/models/RAG/RAG_pipeline.py b/DashAI/back/models/RAG/RAG_pipeline.py index be6f02457..b5832e6cd 100644 --- a/DashAI/back/models/RAG/RAG_pipeline.py +++ b/DashAI/back/models/RAG/RAG_pipeline.py @@ -1,4 +1,4 @@ -from typing import Any, Tuple, List, Dict +from typing import Any, Dict, List, Optional, Tuple from sqlalchemy.orm import Session from DashAI.back.core.schema_fields import ( @@ -10,73 +10,54 @@ string_field ) -from DashAI.back.dependencies.registry import component_registry -from DashAI.back.models.RAG.chunking_models.base_chunking_model import BaseChunkingModel -from DashAI.back.models.RAG.chunking_models.character_chunk_model import CharacterChunkModel -from DashAI.back.models.RAG.chunking_models.token_chunk_model import TokenChunkModel -from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument +from DashAI.back.dependencies.database.models import ( + Document as DBDocument, + RAGPipeline as DBPipeline, + ) + +from DashAI.back.dependencies.registry.component_registry import ComponentRegistry from DashAI.back.models.base_generative_model import BaseGenerativeModel from DashAI.back.models.text_to_text_generation_model import TextToTextGenerationTaskModel - -from DashAI.back.models.RAG.chunking_models import ( - CharacterChunkModel, - TokenChunkModel, - BaseChunkingModel) - -chunking_models: Dict[str, BaseChunkingModel] = { - "CharacterChunkModel": CharacterChunkModel, - "TokenChunkModel": TokenChunkModel, +from DashAI.back.models.RAG.chunking_models.base_chunking_model import BaseChunkingModel +from DashAI.back.models.RAG.prompts import Prompt, DefaultGenerationPrompt, DefaultAugmentationPrompt +from DashAI.back.models.RAG.Retrievers.retriever_model import RetrieverModel + +from DashAI.back.models.RAG.documents import ( + BaseDocument, + TxtDocument, + PDFDocument, + Chunk +) +documents_models: Dict[str, BaseDocument] = { + "txt": TxtDocument, + "pdf": PDFDocument, } -from DashAI.back.models.RAG.Retrievers import ( - TFIDFRetriever, - DenseRetriever, - RetrieverModel) -retriever_models: Dict[str, RetrieverModel] = { - "TFIDFRetriever": TFIDFRetriever, - "DenseRetriever": DenseRetriever, -} +class RAGPipelineParametersError(Exception): + """Custom exception for invalid RAG pipeline parameters.""" -from DashAI.back.models.RAG.prompts import ( - DefaultGenerationPrompt, - DefaultAugmentationPrompt, - CustomGenerationPrompt, - CustomAugmentationPrompt -) -prompts = { - "DefaultGenerationPrompt": DefaultGenerationPrompt, - "DefaultAugmentationPrompt": DefaultAugmentationPrompt, - "CustomGenerationPrompt": CustomGenerationPrompt, - "CustomAugmentationPrompt": CustomAugmentationPrompt -} + def __init__(self, message: str): + super().__init__(message) -from DashAI.back.models.hugging_face import ( - DeepSeekModel, - QwenModel) +class RAGPipelineInitializationError(Exception): + """Custom exception for errors during RAG pipeline initialization.""" -chunking_models = { - "CharacterChunkModel": CharacterChunkModel, - "TokenChunkModel": TokenChunkModel, -} + def __init__(self, message: str): + super().__init__(message) -retriever_models = { - "TFIDFRetriever": TFIDFRetriever, - "DenseRetriever": DenseRetriever, -} +class RAGPipelineRuntimeError(Exception): + """Custom exception for errors during RAG pipeline execution.""" -generation_models = { - "DeepSeekModel": DeepSeekModel, - "QwenModel": QwenModel, -} + def __init__(self, message: str): + super().__init__(message) -class RAGPipelineParametersError(Exception): - """Custom exception for invalid RAG pipeline parameters.""" +class RAGDatabaseError(Exception): + """Custom exception for database-related errors in RAG pipeline.""" def __init__(self, message: str): super().__init__(message) - class RAGPipelineSchema(BaseSchema): """Schema for RAG pipeline.""" # Document collection parameters @@ -117,88 +98,80 @@ class RAGPipelineSchema(BaseSchema): description="Prompt template used to format the input for the generation model." ) # type: ignore - class RAGPipeline(BaseGenerativeModel): - """Retrieval-Augmented Generation (RAG) pipeline.""" + """Retrieval-Augmented Generation (RAG) pipeline. + + A pipeline that combines document retrieval with text generation to produce + contextually informed responses. The pipeline processes input through several stages: + 1. Document loading and chunking + 2. Information retrieval + 3. Context-aware text generation + """ COMPATIBLE_COMPONENTS = ["RAGTask"] - SCHEMA = RAGPipelineSchema - def __init__( self, - **kwargs): - """ - Initialize the DummyRetrieverModel with dummy parameters. + session_id: int = None + documents: Dict[int, BaseDocument] + chunking_model: BaseChunkingModel + chunking_model_id: int + chunks: Dict[int, Dict[int, Chunk]] + retriever: RetrieverModel + llm_model: TextToTextGenerationTaskModel + retrieval_algorithm: str + + def __init__(self, **kwargs: Any) -> None: + """Initialize the RAG pipeline with the specified components and configuration. Args: - distance_function (str): The distance function to use (e.g., "cosine", "euclidean"). - n_docs (int): The maximum number of documents to retrieve. - max_distance (float): The maximum distance allowed for retrieved documents. + kwargs: Configuration dictionary containing: + documents (List[int]): List of document IDs to use + chunking_model (dict): Configuration for the document chunking model + retriever_model (dict): Configuration for the retrieval model + generation_model (dict): Configuration for the text generation model + prompt_model (dict): Configuration for the prompt template + db (Session): Database session for document retrieval + + Raises: + RAGPipelineParametersError: If the configuration parameters are invalid + RAGPipelineInitializationError: If any component fails to initialize """ print("Initializing RAG pipeline") + self.session_id = kwargs.pop("session_id") + self.db: Session = kwargs.pop("db") + self.env_rag_path: str = kwargs.pop("env_rag_path") + + pipeline_db_model: DBPipeline = self.db.query(DBPipeline).filter_by(session_id=self.session_id).first() + if pipeline_db_model: + self.pipeline_db_model = pipeline_db_model + self.pipeline_id = pipeline_db_model.id + else: + self.pipeline_db_model = None + self.pipeline_id = None + self.component_registry: ComponentRegistry = kwargs.pop("component_registry") + self.documents_ids: List[int] = kwargs.pop("documents") + self.documents = self.load_documents_from_db(documents_ids=self.documents_ids) self.validate_params(kwargs) - - try: - documents_paths = kwargs['documents'] - chunking_model_args = kwargs['chunking_model'] - retriever_model_args = kwargs['retriever_model'] - generation_model_args = kwargs['generation_model'] - db:Session = kwargs['db'] - except KeyError as e: - raise ValueError(f"Missing required RAG pipeline parameter: {e}") - - for model in ['chunking_model', 'retriever_model', 'generation_model']: - assert kwargs[model]['component'], f"{model} component must be specified." - assert isinstance(kwargs[model]['params'], dict), f"{model} params must be a dictionary." - # Check they are not empty - assert kwargs[model]['params'] is not None, f"{model} params must be provided." - assert len(kwargs[model]['params']) > 0, f"{model} params cannot be empty." - - - - try: - chunking_model_class = chunking_models[chunking_model_args["component"]] - chunking_model_params = chunking_model_args['params'] - self.chunking_model: BaseChunkingModel = chunking_model_class(**chunking_model_params) - except KeyError as e: - raise ValueError(f"Invalid chunking model specified: {e}") - except Exception as e: - raise ValueError(f"Error initializing chunking model: {e}") - - try: - retriever_model_class = retriever_models[retriever_model_args["component"]] - retriever_params = retriever_model_args['params'] - except KeyError as e: - raise ValueError(f"Invalid retriever model specified: {e}") - - try: - generation_model_class = generation_models[generation_model_args["component"]] - generation_params = generation_model_args['params'] + try: + config = { + 'chunking_model': kwargs.pop('chunking_model'), + 'retriever_model': kwargs.pop('retriever_model'), + 'generation_model': kwargs.pop('generation_model'), + 'prompt_model': kwargs.pop('prompt_model'), + } except KeyError as e: - raise ValueError(f"Invalid generation model specified: {e}") + raise RAGPipelineParametersError(f"Missing required parameter: {str(e)}") - assert isinstance(documents_paths, list) and len(documents_paths) > 0, "Documents must be a non-empty list." - for path in documents_paths: - assert isinstance(path, str), f"Each document path must be a string, got {type(path)}" - - print(f"Initializing LLM model {generation_model_args['component']}") - self.llm_model: TextToTextGenerationTaskModel = generation_model_class(**generation_params) - - - new_retriever_args = { - 'documents_paths': documents_paths, - } - for key, value in retriever_params.items(): - new_retriever_args[key] = value - - new_retriever_args['chunking_model'] = self.chunking_model - # Rename TFIDFVectorizer to TFIDFVectorizer_parameters - if retriever_model_class == TFIDFRetriever: - new_retriever_args['TFIDFVectorizer_parameters'] = new_retriever_args.pop('TFIDFVectorizer') - print("Initializing retriever model") - self.retriever: RetrieverModel = retriever_model_class(**new_retriever_args) - + self.chunking_model = self.init_component(config["chunking_model"]) + self.chunks = self.chunking_model.get_chunks() + self.chunking_model_id = self.chunking_model.id + + self.retriever = self.init_component(config["retriever_model"]) + self.retriever_id = self.retriever.retriever_db_model.id + self.llm_model = self.init_component(config["generation_model"]) + self.prompt_model = self.init_component(config["prompt_model"]) + self.prompt_db_id = self.prompt_model.id self.retrieval_algorithm = "SINGLE_INTERACTION" print("RAG pipeline initialized") @@ -206,93 +179,257 @@ def validate_params( self, params: dict): """Validate RAG pipeline parameters.""" - required_keys = ["documents", "chunking_model", "retriever_model", "generation_model", "prompt"] + required_keys = ["chunking_model", "retriever_model", "generation_model", "prompt_model"] for key in required_keys: if key not in params: - raise RAGPipelineParametersError(f"Missing required parameter: {key}") - if not isinstance(params["documents"], list) or len(params["documents"]) == 0: - raise RAGPipelineParametersError("Documents must be a non-empty list.") - for model in ["chunking_model", "retriever_model", "generation_model"]: - if "component" not in params[model]: - raise RAGPipelineParametersError(f"Missing 'component' in {model}.") - if "params" not in params[model]: - raise RAGPipelineParametersError(f"Missing 'params' in {model}.") - if "documents_ids" in any(params["chunking_model"], params["retriever_model"], params["generation_model"]): - raise RAGPipelineParametersError("Models should not contain 'documents_ids'; it is specified globally.") - if not params["prompt"]["type"]: - raise RAGPipelineParametersError("Missing 'type' in prompt.") - if not params["prompt"]["template"]: - raise RAGPipelineParametersError("Missing 'template' in prompt.") - + raise RAGPipelineParametersError(f"Missing required parameter '{key}' in RAG pipeline configuration") + + + # Validate model components + for model in ["chunking_model", "retriever_model", "generation_model", "prompt_model"]: + args = params[model] + if "component" not in args: + raise RAGPipelineParametersError(f"Missing 'component' field in '{model}' configuration") + if "params" not in args: + raise RAGPipelineParametersError(f"Missing 'params' field in '{model}' configuration") + if args["component"] not in self.component_registry: + raise RAGPipelineParametersError(f"No components registered for type '{model}'") + if "Custom" in params["prompt_model"]["component"] and "template" not in params["prompt_model"]["params"]: + raise RAGPipelineParametersError("Custom prompt model requires a 'template' parameter in its configuration") + + def single_interaction( + self, + query: str, + history: Optional[List[Tuple[str, str]]] = None + ) -> List[Chunk]: + """Perform a single retrieval interaction based on the input query. + + Args: + query: The input query to use for document retrieval + history: Optional conversation history (not used in single interaction) + + Returns: + List of tuples containing (document_content, document_file, chunk_id) - def single_interaction(self, input: str, history: List[Tuple[str, str]] = None) -> List[Tuple[str, str, int]]: + Raises: + RAGPipelineRuntimeError: If document retrieval fails """ - Single interaction retrieval algorithm: input -> retrieve documents. + try: + return self.retriever.retrieve(query) + except Exception as e: + raise RAGPipelineRuntimeError(f"Document retrieval failed: {str(e)}") + + def augmented_interaction( + self, + query: str, + history: Optional[List[Tuple[str, str]]] = None, + max_search_terms: int = 5 + ) -> List[Chunk]: + """Perform an augmented retrieval interaction using generated search terms. + Args: - input (str): The input query. + query: The input query + history: Optional conversation history + max_search_terms: Maximum number of search terms to use + Returns: - str: The generated response. + List of tuples containing (document_content, document_file, chunk_id) + + Raises: + RAGPipelineRuntimeError: If any step of the augmented interaction fails """ + try: + search_terms = self._generate_search_terms(query, history, max_search_terms) + return self._retrieve_with_search_terms(search_terms) + except RAGPipelineRuntimeError: + raise + except Exception as e: + raise RAGPipelineRuntimeError(f"Failed during augmented interaction: {str(e)}") - documents = self.retriever.retrieve(input) + def _generate_search_terms( + self, + query: str, + history: Optional[List[Tuple[str, str]]], + max_terms: int + ) -> List[str]: + """Generate search terms from the input using the LLM. - return documents - - - def augmented_interaction(self, input: str, history: List[Tuple[str, str]] = None) -> List[Tuple[str, str, int]]: + Args: + query: Input query to generate search terms from + history: Optional conversation history + max_terms: Maximum number of search terms to return + + Returns: + List of generated search terms + + Raises: + RAGPipelineRuntimeError: If search term generation or parsing fails """ - Augmented interaction RAG algorithm: input -> generate lookup tokens -> retrieve documents -> generate response. + try: + augmentation_prompt = DefaultAugmentationPrompt.format( + input=query, + history=history, + n_search_terms=max_terms + ) + augmentation_response = self.llm_model.generate(augmentation_prompt)[0] + print(f"Augmentation response: {augmentation_response}") + + try: + search_terms = augmentation_response.split("keywords:")[1].strip() + search_terms = [term.strip() for term in search_terms.split(",")] + return search_terms[:max_terms] + except Exception as e: + raise RAGPipelineRuntimeError(f"Failed to parse search terms from model response: {str(e)}") + except Exception as e: + raise RAGPipelineRuntimeError(f"Failed to generate search terms: {str(e)}") + + def _retrieve_with_search_terms(self, search_terms: List[str]) -> List[Chunk]: + """Retrieve documents using the generated search terms. + Args: - input (str): The input query. + search_terms: List of search terms to use for retrieval + Returns: - str: The generated response. + List of Chunk objects + + Raises: + RAGPipelineRuntimeError: If document retrieval fails """ - augmentation_prompt = DefaultAugmentationPrompt.format( - input=input, - history=history, - n_search_terms=5 - ) - augmentation_response = self.llm_model.generate(augmentation_prompt)[0] - print(f"Augmentation response: {augmentation_response}") - search_terms = augmentation_response.split("keywords:")[1].strip() - search_terms = search_terms.split(",") - if len(search_terms) > 5: - search_terms = search_terms[:5] - print(f"Augmentation search terms: {search_terms}") - search_terms = [term.strip() for term in search_terms] - - documents = self.retriever.retrieve(search_terms) - - return documents + try: + print(f"Retrieving documents with search terms: {search_terms}") + return self.retriever.retrieve(search_terms) + except Exception as e: + raise RAGPipelineRuntimeError(f"Document retrieval failed: {str(e)}") - def generate(self, input: Tuple[str, List[Tuple[str,str]]]) -> str: + def generate(self, input_data: Tuple[str, List[Tuple[str, str]]]) -> List[str]: + """Generate a response based on the input and retrieved documents. + + Args: + input_data: Tuple containing (query, conversation_history) + + Returns: + List containing the generated response with source information + + Raises: + RAGPipelineRuntimeError: If any generation step fails """ - Generate a response based on the input and the retrieved documents. + try: + input_message = input_data + chunks = self.single_interaction(input_message) + chunks_texts = [] + for chunk in chunks: + chunk_id = chunk.id + document_id = chunk.document_id + document_position = chunk.document_position + document = self.documents[document_id] + chunks_texts.append(f"Document {document.file_name}, chunk nº {document_position}, text:\n {chunk.text}") + chunks_text = "\n\n".join(chunks_texts) + return self._generate_response(input_message, chunks_text) + except RAGPipelineRuntimeError: + raise + except Exception as e: + raise RAGPipelineRuntimeError(f"Failed during response generation: {str(e)}") + + def _generate_response(self, input_message: str, chunks_text: str) -> List[str]: + """Generate the final response using the LLM. + Args: - input: a tuple containing the input query and the conversation history. - input (str): The input query. - history (List[Tuple[str, str]]): The conversation history. + query: Original input query + context: Formatted context from retrieved chunks + Returns: - str: The generated response. + List containing the generated response with source information + + Raises: + RAGPipelineRuntimeError: If response generation fails + """ + try: + prompt = DefaultGenerationPrompt.format( + input=input_message, + history=None, + chunks=chunks_text + ) + print(f"Prompt: {prompt[:500]}...") + + response = self.llm_model.generate(prompt) + full_response = f"{response[0]}\n\nSources:{chunks_text}" + return [full_response] + except Exception as e: + raise RAGPipelineRuntimeError(f"Text generation failed: {str(e)}") + + def load_documents_from_db(self, documents_ids: List[int]) -> Dict[int, BaseDocument]: """ - message = input + Retrieve Document instances from the database based on their IDs. + + Args: + documents_ids: List of document IDs to retrieve + db: Database session for querying documents - chunks = self.single_interaction(message) - chunks_str = "RETRIEVED INFORMATION:\n\n" - for doc_content, doc_file, chunk in chunks: - doc_name = doc_file.split("/")[-1] - chunks_str += f"Document '{doc_name}' in chunk: {chunk}:\n{doc_content}\n\n" + Raises: + RAGDatabaseError: If a document ID is not found in the database - prompt = DefaultGenerationPrompt.format( - input=message, - history=None, - chunks=chunks_str - ) + Returns: + List of BaseDocument instances corresponding to the provided IDs - print(f"Prompt: {prompt}") + Raises: + RAGDatabaseError: If a document ID is not found in the database + """ + documents = {} + for doc_id in documents_ids: + db_doc: DBDocument = self.db.query(DBDocument).filter(DBDocument.id == doc_id).first() + if not db_doc: + raise RAGDatabaseError(f"Document with ID {doc_id} not found in the database.") + doc_class = documents_models[db_doc.file_type] + document: BaseDocument = doc_class( + id = db_doc.id, + file_name = db_doc.file_name, + file_path = db_doc.file_path, + created = db_doc.created, + optional_metadata = db_doc.optional_metadata) + documents[doc_id] = document + return documents + + def init_component( + self, + config: Dict[str, Any]) -> Any: + """ + Initialize a component based on the provided configuration, using the component registry. - response = self.llm_model.generate(prompt) + Args: + model_name: The key in the config dict for the component to initialize + config: Configuration dictionary containing component details - response = f"{response[0]}\n\nSources:{chunks_str}" + Returns: + An instance of the initialized component - return [response] + Raises: + RAGPipelineInitializationError: If the component fails to initialize + """ + + model_class_name = config['component'] + model_params = config['params'] + model_registry = self.component_registry[model_class_name] + model_class = model_registry['class'] + + if 'db' in model_class.REQUIRED_EXTRA_KWARGS: + model_params['db'] = self.db + model_params['pipeline_id'] = self.pipeline_id + if 'pipeline_id' in model_class.REQUIRED_EXTRA_KWARGS: + model_params['pipeline_id'] = self.pipeline_id + if 'component_registry' in model_class.REQUIRED_EXTRA_KWARGS: + model_params['component_registry'] = self.component_registry + if 'env_rag_path' in model_class.REQUIRED_EXTRA_KWARGS: + model_params['env_rag_path'] = self.env_rag_path + + if 'documents' in model_class.REQUIRED_EXTRA_KWARGS: + model_params['documents'] = self.documents + if 'chunks' in model_class.REQUIRED_EXTRA_KWARGS: + model_params['chunks'] = self.chunks + if 'chunking_model_id' in model_class.REQUIRED_EXTRA_KWARGS: + model_params['chunking_model_id'] = self.chunking_model_id + try: + model_instance = model_class(**model_params) + except Exception as e: + raise RAGPipelineInitializationError(f"Failed to initialize {model_class_name}: {str(e)}") + return model_instance + \ No newline at end of file diff --git a/DashAI/back/models/RAG/Retrievers/dense_retriever.py b/DashAI/back/models/RAG/Retrievers/dense_retriever.py index 683d33ae7..bd2d70f5d 100644 --- a/DashAI/back/models/RAG/Retrievers/dense_retriever.py +++ b/DashAI/back/models/RAG/Retrievers/dense_retriever.py @@ -1,11 +1,28 @@ +import os +from typing import Dict, List +import numpy as np +from sqlalchemy.orm import Session +from sklearn.metrics.pairwise import pairwise_distances + from DashAI.back.core.schema_fields import ( BaseSchema, component_field, enum_field, schema_field, + int_field ) - -from .retriever_model import RetrieverModel +from DashAI.back.dependencies.registry.component_registry import ComponentRegistry +from DashAI.back.dependencies.database.models import ( + RAGEmbeddingMatrix as EmbeddingMatrixDBModel, + RAGEmbeddingModel as EmbeddingDBModel, + RAGDenseRetriever as DenseRetrieverDBModel, + RAGRetriever as RetrieverDBModel, + RAGPipeline as PipelineDBModel, +) +from DashAI.back.models.RAG.documents import BaseDocument, Chunk +from DashAI.back.models.RAG.Retrievers.retriever_model import RetrieverModel +from DashAI.back.models.RAG.embeddings.dense import FastTextEmbedding, HuggingFaceEmbedding +from DashAI.back.models.RAG.embeddings.dense_embedding import DenseEmbedding class DenseRetrieverSchema(BaseSchema): """Schema for Dense Retriever.""" @@ -17,9 +34,20 @@ class DenseRetrieverSchema(BaseSchema): ) # type: ignore similarity_metric: schema_field( - enum_field(enum=["cosine", "dot_product", "euclidean"]), + enum_field(enum=[ + "cityblock", "cosine", "euclidean", "l1", "l2", "manhattan", "nan_euclidean", + "braycurtis", "canberra", "chebyshev", "correlation", "dice", "hamming", "jaccard", + "mahalanobis", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", + "sokalmichener", "sokalsneath", "sqeuclidean", "yule" + ]), placeholder="cosine", - description="Similarity metric to use for comparing dense vectors.", + description="Distance metric to use for comparing dense vectors. For reference see https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html#sklearn.metrics.pairwise_distances", + ) # type: ignore + + top_k: schema_field( + int_field(gt=0), + placeholder=5, + description="Number of top documents to retrieve." ) # type: ignore @@ -27,47 +55,203 @@ class DenseRetriever(RetrieverModel): """ Dense retriever class for retrieving documents based on dense vector representations. - This class is a placeholder and should be implemented with specific dense retrieval - logic. """ SCHEMA = DenseRetrieverSchema + pipeline_id: int + db: Session + component_registry: ComponentRegistry + env_rag_path: str|os.PathLike + + documents: Dict[int, BaseDocument] + chunks: Dict[int, Dict[int, Chunk]] + chunking_model_id: int + + id: int + retriever_db_model = RetrieverDBModel + dense_retriever_db_model: DenseRetrieverDBModel + embedding_model: DenseEmbedding + embedding_db_model: EmbeddingDBModel + embedding_db_matrices: Dict[int, EmbeddingMatrixDBModel] + + matrix_row_to_chunk_id: Dict[int, int] + chunk_id_to_doc_id: Dict[int, int] + def __init__(self, **kwargs): + # RetrieverModel class fetches retriever_db_model if exists super().__init__(**kwargs) + + + self.embedding_model: DenseEmbedding = self.params["encoding_model"] + self.embedding_class_name = self.embedding_model.__class__.__name__ + self.embedding_params = self.embedding_model.params + self.params["encoding_model"] = { + "component": self.embedding_class_name, + "params": self.embedding_params + } + self.fetch_db_models() + self.similarity_metric = self.params["similarity_metric"] + self.top_k = self.params["top_k"] + + if self.embedding_db_model is None: + self.embedding_db_model = self.create_embedding_db_model() + self.compute_missing_embeddings() + if not self.dense_retriever_db_model: + db_model = DenseRetrieverDBModel( + class_name=self.__class__.__name__, + parameters=self.params, + embedding_model_id=self.embedding_db_model.id + ) + self.db.add(db_model) + self.db.commit() + self.db.refresh(db_model) + self.dense_retriever_db_model = db_model + if not self.retriever_db_model: + retriever_db_model = RetrieverDBModel( + class_name=self.__class__.__name__, + dense_retriever_id=self.dense_retriever_db_model.id, + sparse_retriever_id=None + ) + self.db.add(retriever_db_model) + self.db.commit() + self.db.refresh(retriever_db_model) + self.retriever_db_model = retriever_db_model + + self.init_similarity_matrix() + + def __fetch_embedding_db_model(self)-> EmbeddingDBModel|None: + """Fetch the embedding database model if it exists.""" + embedding_db_model = self.db.query(EmbeddingDBModel).filter_by( + class_name=self.embedding_class_name, + parameters=self.embedding_params + ).first() + return embedding_db_model + + def fetch_db_models(self): + """Fetch all database models related to the retriever if they exist.""" + self.embedding_db_model = self.__fetch_embedding_db_model() + + self.embedding_db_matrices = {} + if self.embedding_db_model and self.chunking_model_id: + self.embedding_db_matrices = self.fetch_embedding_matrices() + + self.retriever_db_model = None + self.dense_retriever_db_model = None + if self.pipeline_id: + pipeline_model = self.db.query(PipelineDBModel).filter_by( + id=self.pipeline_id).first() + retriever_id = pipeline_model.retriever_model_id + self.retriever_db_model = self.db.query(RetrieverDBModel).filter_by( + id=retriever_id).first() + dense_retriever_id = self.retriever_db_model.dense_retriever_id + self.dense_retriever_db_model = self.db.query(DenseRetrieverDBModel).filter_by( + id=dense_retriever_id).first() + + def fetch_embedding_matrices(self) -> Dict[int, EmbeddingMatrixDBModel]: + """Load precomputed embeddings from the database if they exist.""" + embeddings_matrices_db_models = {} + for doc_id, doc_chunks in self.chunks.items(): + embedding_matrix_entry = ( + self.db.query(EmbeddingMatrixDBModel) + .filter_by( + document_id=doc_id, + chunking_model_id=self.chunking_model_id, + embedding_model_id=self.embedding_db_model.id + ).first() + ) + if embedding_matrix_entry: + embeddings_matrices_db_models[doc_id] = embedding_matrix_entry + + return embeddings_matrices_db_models - self.validate_and_transform(kwargs) - - self._documents_path = kwargs.get("documents_path") - self.documents = kwargs.get("documents") - self.chunking_model = kwargs.get("chunking_model") - self.encoding_model = kwargs.get("encoding_model") - self.similarity_metric = kwargs.get("similarity_metric", "cosine") - - assert self._documents_path is not None, "Documents path must be provided." - assert self.documents is not None and len(self.documents) > 0, "Documents must be provided and cannot be empty." - assert self.validate_documents_in_folder(self.documents), "Some documents are missing from the folder." - assert self.chunking_model is not None, "Chunking model must be provided." - assert self.encoding_model is not None, "Encoding model must be provided." - assert self.similarity_metric in ["cosine", "dot_product", "euclidean"], ( - f"Invalid similarity metric: {self.similarity_metric}. " - "Choose from 'cosine', 'dot_product', or 'euclidean'." + def create_embedding_db_model(self) -> EmbeddingDBModel: + """Create the embedding model in the database.""" + new_model = EmbeddingDBModel( + class_name=self.embedding_class_name, + parameters=self.embedding_params ) - assert self.n_docs > 0, "Number of documents must be greater than 0." + self.db.add(new_model) + self.db.commit() + self.db.refresh(new_model) + return new_model - print(f"DenseRetriever initialized with encoding model: {self.encoding_model} and similarity metric: {self.similarity_metric}") + def save(self): + """Save the dense retriever model to the database.""" + pass - def load(): - """Load the dense retriever model.""" - print("Loading DenseRetriever model...") - # Placeholder for loading logic - pass + def compute_missing_embeddings(self): + chunking_model_id = self.chunking_model_id + embedding_model_id = self.embedding_db_model.id + assert embedding_model_id and chunking_model_id, "Embedding model ID and chunking model ID must be set to compute embeddings." + for doc_id, doc_chunks in self.chunks.items(): + db_model = self.embedding_db_matrices.get(doc_id, None) + if db_model: + print(f"Embeddings for document {doc_id} already exist in the database ({db_model}). Skipping computation.") + continue + chunk_texts = [chunk.text for chunk in doc_chunks.values()] + embeddings = self.embedding_model.batch_encode(chunk_texts) + matrix_shape = embeddings.shape + folder_name = f"doc_id-{doc_id}__chunking_model_id-{chunking_model_id}__embedding_model_id-{embedding_model_id}" + storage_folder = os.path.join(self.env_rag_path, "embeddings", folder_name) + os.makedirs(storage_folder, exist_ok=True) + file_path = os.path.join(storage_folder, "embeddings.npy") + np.save(file_path, embeddings) + new_db_model = EmbeddingMatrixDBModel( + document_id=doc_id, + chunking_model_id=chunking_model_id, + embedding_model_id=embedding_model_id, + matrix_shape=list(matrix_shape), + storage_folder=storage_folder + ) + self.db.add(new_db_model) + self.db.commit() + self.db.refresh(new_db_model) + self.embedding_db_matrices[doc_id] = new_db_model - def save(): - """Save the dense retriever model.""" - print("Saving DenseRetriever model...") - # Placeholder for saving logic - pass + def init_similarity_matrix(self): + """Initialize the similarity matrix for all chunks.""" + self.similarity_matrix = None + self.matrix_row_to_chunk_id = {} + self.chunk_id_to_doc_id = {} # Initialize the missing dictionary + if not self.embedding_db_matrices: + import warnings + warnings.warn("No embedding matrices found to initialize similarity matrix.") + return + all_embeddings = [] + row_index = 0 + for doc_id, chunks in self.chunks.items(): + matrix_db_model = self.embedding_db_matrices[doc_id] + matrix_folder = matrix_db_model.storage_folder + matrix_path = os.path.join(matrix_folder, "embeddings.npy") + assert os.path.exists(matrix_path), f"Embedding matrix file not found at {matrix_path}" + for chunk_id, chunk in chunks.items(): + self.matrix_row_to_chunk_id[row_index] = chunk_id + self.chunk_id_to_doc_id[chunk_id] = doc_id + row_index += 1 + embeddings = np.load(matrix_path) + all_embeddings.append(embeddings) + self.similarity_matrix = np.vstack(all_embeddings) + def retrieve(self, query: str) -> List[Chunk]: + """Retrieve the top_k most similar chunks to the query.""" + if self.similarity_matrix is None: + raise ValueError("Similarity matrix not initialized.") + query_embedding = self.embedding_model.encode(query) + # Get distances and indices for all chunks + distances = pairwise_distances( + query_embedding.reshape(1, -1), + self.similarity_matrix, + metric=self.similarity_metric + )[0] + top_indices = np.argsort(distances)[:self.top_k] + results = [] + for idx in top_indices: + chunk_id = self.matrix_row_to_chunk_id[idx] + doc_id = self.chunk_id_to_doc_id[chunk_id] + chunk = self.chunks[doc_id][chunk_id] + results.append(chunk) + return results + \ No newline at end of file diff --git a/DashAI/back/models/RAG/Retrievers/retriever_model.py b/DashAI/back/models/RAG/Retrievers/retriever_model.py index 203e8f514..fe77b3499 100644 --- a/DashAI/back/models/RAG/Retrievers/retriever_model.py +++ b/DashAI/back/models/RAG/Retrievers/retriever_model.py @@ -1,149 +1,83 @@ -from abc import ABC +from abc import ABC, abstractmethod import os from typing import Any, Dict, List, Final -from DashAI.back.models.RAG.documents import BaseDocument, PDFDocument, TxtDocument -import hashlib +from DashAI.back.dependencies.registry.component_registry import ComponentRegistry +from DashAI.back.models.RAG.documents import BaseDocument, PDFDocument, TxtDocument, Chunk +from DashAI.back.dependencies.database.models import( + Document as DocumentDBModel, + Chunk as ChunkDBModel, + RAGRetriever as RetrieverDBModel, + RAGPipeline as PipelineDBModel, +) +from DashAI.back.models.RAG.utils import hash_function +from sqlalchemy.orm import Session from DashAI.back.models.base_model import BaseModel -SUPPORTED_DOCUMENT_TYPES = ['pdf', 'txt'] - class RetrieverModel(BaseModel): """ Abstract class to define the interface for retriever models. """ - RETRIEVERS_PATH = "./StoredRetrievers" - COMPATIBLE_COMPONENTS = ["RAGTask"] + REQUIRED_EXTRA_KWARGS: Final[List[str]] = ["pipeline_id", "db", "component_registry", "env_rag_path", "documents", "chunks", "chunking_model_id"] + id: int + pipeline_id: int + db: Session + component_registry: ComponentRegistry + env_rag_path: str|os.PathLike - def _load_document(self, document_path: str) -> BaseDocument: - """ - Load a document from the specified path. - - Args: - document_path (str): The path to the document file. - - Returns: - BaseDocument: An instance of a document class (PDFDocument or TxtDocument). - """ - if document_path.endswith('.pdf'): - return PDFDocument(document_path) - elif document_path.endswith('.txt'): - return TxtDocument(document_path) - else: - raise ValueError(f"Unsupported document type: {document_path}") - - def get_documents(self) -> Dict[str, BaseDocument]: - """ - Get the documents stored in the retriever. - Returns: - Dict[str, BaseDocument]: A dictionary where keys are document paths and values are document instances. - """ - assert self._documents_paths is not None, "Document paths must be provided." - assert len(self._documents_paths) > 0, "At least one document path must be provided." - - self._documents_paths = sorted(self._documents_paths) # Sort for consistent hashing in signature - documents = {} - for path in self._documents_paths: - doc = self._load_document(path) - documents[path] = doc - return documents - - def get_documents_chunks(self) -> Dict[str, List[str]]: - """ - Get the chunks of documents stored in the retriever. + documents: Dict[int, BaseDocument] + chunks: Dict[int, Dict[int, Chunk]] + chunking_model_id: int - Returns: - Dict[str, Dict[int, str]]: A dictionary where keys are document paths and values are - dictionaries of chunk indices and their corresponding text. - """ - documents = self.get_documents() - chunks = {} - for filename, doc in documents.items(): - chunks[filename] = doc.get_chunks(self.chunk_size, self.chunk_overlap) - return chunks - - def get_parameters(self) -> Dict[str, Any]: - """ - Get the parameters of the retriever. - - Returns: - Dict[str, Any]: A dictionary of retriever parameters. - """ - assert self.params is not None, "Retriever parameters are not set." - return self.params + retriever_db_model = RetrieverDBModel - def get_signature_parameters(self) -> Dict[str, Any]: - """ - Get the parameters used for storing the fitted retriever in the database. - This parameters are a subset of the retriever parameters, the non-signature parameters are - those whose change does not require a retraining of the retriever, i.e. n_docs, - max_distance, etc. + def __init__(self, **kwargs): + self.params = kwargs - Returns: - Dict[str, Any]: A dictionary of parameters that can be used for hashing. - """ - raise NotImplementedError("This method should be implemented by subclasses.") - - def _get_documents_signature(self) -> str: - """ - Generate a hash representing the current set of documents. - - Returns: - str: A hash string representing the documents. - """ - documents = sorted(self.get_documents().items()) - hashes = "" - for filename, doc in documents.items(): - doc_hash = doc.get_hash() - hashes += f"{filename}{doc_hash}" - return hashlib.sha256(hashes.encode('utf-8')).hexdigest() - - def _get_parameters_signature(self) -> str: - """ - Generate a hash representing the retriever parameters. - - Args: - parameters (Dict[str, Any]): A dictionary of retriever parameters. - - Returns: - str: A hash string representing the parameters. - """ - hashable_parameters = sorted(self.get_signature_parameters().items()) - params_string = "".join(f"{key}{value}" for key, value in hashable_parameters) - return hashlib.sha256(params_string.encode('utf-8')).hexdigest() - - def _get_model_signature(self) -> str: - """ - Generate a unique hash for the retriever based on its documents and parameters. - - Returns: - str: A hash string representing the retriever's state. - """ - documents_hash = self._get_documents_signature() - parameters_hash = self._get_parameters_signature() - return f"{self.__class__.__name__}_{documents_hash}_{parameters_hash}" + self.pipeline_id: int = self.params.pop("pipeline_id") + self.db: Session = self.params.pop("db") + self.component_registry: ComponentRegistry = self.params.pop("component_registry") + self.env_rag_path: str|os.PathLike = self.params.pop("env_rag_path") + self.documents: Dict[int, BaseDocument] = self.params.pop("documents") + self.chunks: Dict[int, Dict[int, Chunk]] = self.params.pop("chunks") + self.chunking_model_id: int = self.params.pop("chunking_model_id") + self.validate_chunks() + self.params = self.validate_and_transform(self.params) + + + if self.pipeline_id is not None: + existing_pipeline: PipelineDBModel = self.db.query(PipelineDBModel).filter( + PipelineDBModel.id == self.pipeline_id + ).first() + self.retriever_db_model = self.db.query(RetrieverDBModel).filter( + RetrieverDBModel.id == existing_pipeline.retriever_model_id + ).first() + assert self.retriever_db_model is not None, "Retriever model not found in the database, but pipeline exists." + else: + self.pipeline_id = None + self.retriever_db_model = None + + def validate_chunks(self) -> None: + """Validate the structure and content of the chunks dictionary.""" + if not isinstance(self.chunks, dict): + raise ValueError("Chunks must be a dictionary.") + for doc_id, doc_chunks in self.chunks.items(): + if not isinstance(doc_id, int): + raise ValueError(f"Document ID {doc_id} must be an integer.") + if not isinstance(doc_chunks, dict): + raise ValueError(f"Chunks for document ID {doc_id} must be a dictionary.") + for chunk_id, chunk in doc_chunks.items(): + if not isinstance(chunk_id, int): + raise ValueError(f"Chunk ID {chunk_id} in document ID {doc_id} must be an integer.") + if not isinstance(chunk, Chunk): + raise ValueError(f"Chunk {chunk_id} in document ID {doc_id} must be an instance of Chunk.") + assert chunk.document_id == doc_id, f"Chunk {chunk_id} doc_id {chunk.doc_id} does not match document ID {doc_id}." - def add_document(self, document: BaseDocument) -> None: - """ - Add a document to the retriever. - Args: - document (BaseDocument): The document to add. - """ - raise NotImplementedError("This method should be implemented by subclasses.") - - def remove_document(self, document_id: str) -> None: - """ - Remove a document from the retriever by its ID. - - Args: - document_id (str): The ID of the document to remove. - """ - raise NotImplementedError("This method should be implemented by subclasses.") - - def retrieve(self, **kwargs) -> List[BaseDocument]: + @abstractmethod + def retrieve(self, **kwargs) -> List[Chunk]: """ Retrieve documents based on the provided parameters. @@ -154,19 +88,21 @@ def retrieve(self, **kwargs) -> List[BaseDocument]: List[BaseDocument]: A list of retrieved documents. """ raise NotImplementedError("This method should be implemented by subclasses.") - - def validate_documents_in_folder(self, documents: List[BaseDocument]) -> bool: - """ - Validate that the provided documents exist in their specified paths. - - Args: - documents (List[BaseDocument]): A list of documents to validate. - - Returns: - bool: True if all specified documents are present in the folder, False otherwise. - """ - for doc in documents: - if not os.path.exists(doc.file_path): - print(f"Document {doc.file_path} is missing.") - return False - return True \ No newline at end of file + + def init_retriever_db_model(self, dense_retriver_db_id: int, sparse_retriver_db_id: int) -> RetrieverDBModel: + """Initialize the retriever model in the database.""" + assert dense_retriver_db_id or sparse_retriver_db_id, "At least one of dense_retriver_db_id or sparse_retriver_db_id must be provided." + class_name = self.__class__.__name__ + retriever_db_model = RetrieverDBModel( + class_name=class_name, + dense_retriever_id=dense_retriver_db_id, + sparse_retriever_id=sparse_retriver_db_id, + ) + self.db.add(retriever_db_model) + self.db.commit() + + def load(self): + pass + + def save(self): + pass \ No newline at end of file diff --git a/DashAI/back/models/RAG/Retrievers/tfidf_retriever.py b/DashAI/back/models/RAG/Retrievers/tfidf_retriever.py index fbe19ec05..9aea0ba2d 100644 --- a/DashAI/back/models/RAG/Retrievers/tfidf_retriever.py +++ b/DashAI/back/models/RAG/Retrievers/tfidf_retriever.py @@ -1,4 +1,3 @@ -import hashlib import os import pickle from typing import Dict, List, Tuple @@ -19,10 +18,11 @@ string_field, ) from DashAI.back.models.RAG.chunking_models.base_chunking_model import BaseChunkingModel -from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument +from DashAI.back.models.RAG.documents.base_document import BaseDocument from DashAI.back.models.base_model import BaseModel from DashAI.back.models.RAG.documents import PDFDocument, TxtDocument from DashAI.back.models.RAG.Retrievers.sparse_retriever import SparseRetriever +from DashAI.back.models.RAG.utils import hash_function similarities = {"cosine": cosine_similarity, "euclidean": euclidean_distances} @@ -214,13 +214,7 @@ class TFIDFRetriever(SparseRetriever): def __init__( self, - documents_paths: List[str], - TFIDFVectorizer_parameters: Dict[str, any], - similarity_function: str, - n_docs: int, - use_similarity_threshold: bool, - similarity_threshold: float, - chunking_model: BaseChunkingModel, + **kwargs, ): """ Initialize the TFIDFRetriever with the given parameters. @@ -236,38 +230,30 @@ def __init__( chunk_overlap (int): The overlap between chunks. """ - assert isinstance(documents_paths, list), "Documents must be a list." - assert all(isinstance(doc, str) for doc in documents_paths), ( - "All documents must be strings." - ) - assert similarity_function in similarities, ( - f"similarity_function must be one of {list(similarities.keys())}." - ) - assert n_docs > 0, "Number of documents to retrieve must be greater than 0." - assert similarity_threshold >= 0, "Similarity threshold must be non-negative." - BASE_DOC_PATH = ( - "THIS IS A PLACEHOLDER, should be unnecesary after implementing " - "the document persistence storage in the database" - ) - new_documents_paths = [] - for doc_path in documents_paths: - new_path = os.path.join(BASE_DOC_PATH, doc_path) - new_documents_paths.append(new_path) - documents_paths = new_documents_paths - self._documents_paths = sorted(documents_paths) # Sort for consistent hashing - self.documents = self.load_documents(self._documents_paths) - self.similarity_function_name = similarity_function - self.similarity_function = similarities[similarity_function] - self.n_docs = n_docs - self.use_similarity_threshold = use_similarity_threshold - self.similarity_threshold = similarity_threshold + documents = kwargs.pop("documents") + assert isinstance(documents, list) and all( + isinstance(doc, BaseDocument) for doc in documents + ), "Documents must be a list of BaseDocument instances." + self.documents: List[BaseDocument] = documents + + chunking_model = kwargs.pop("chunking_model") + assert isinstance(chunking_model, BaseChunkingModel), "Chunking model must be a BaseChunkingModel instance." self.chunking_model = chunking_model + TFIDFVectorizer_parameters = kwargs.pop("TFIDFVectorizer") + self.validate_and_transform(kwargs) + vectorizer = TFIDFVectorizerModel(**TFIDFVectorizer_parameters) # Validate parameters + self.similarity_function_name = kwargs.pop("similarity_function", "cosine") + self.similarity_function = similarities[self.similarity_function_name] + self.n_docs = kwargs.pop("n_docs") + self.use_similarity_threshold = kwargs.pop("use_similarity_threshold", False) + self.similarity_threshold = kwargs.pop("similarity_threshold", 0.5) + self.params = { "vectorizer_params": TFIDFVectorizer_parameters, - "similarity_function": similarity_function, - "n_docs": n_docs, - "similarity_threshold": similarity_threshold, + "similarity_function": self.similarity_function, + "n_docs": self.n_docs, + "similarity_threshold": self.similarity_threshold, } @@ -288,7 +274,7 @@ def __init__( self._fit() self._save_state() - def load(self) -> None: + def load(self, db) -> None: pass def save(self) -> None: @@ -299,7 +285,7 @@ def load_documents(self, documents_paths: List[str]) -> List[BaseDocument]: for i, doc_path in enumerate(documents_paths): with open(doc_path, "rb") as doc_file: file_bytes = doc_file.read() - file_hash = hashlib.sha256(file_bytes).hexdigest() + file_hash = hash_function(file_bytes) file_name = os.path.basename(doc_path) file_path = doc_path @@ -312,7 +298,7 @@ def load_documents(self, documents_paths: List[str]) -> List[BaseDocument]: ) elif doc_path.endswith(".txt"): doc = TxtDocument( - id = i, + db_id = i, file_name=file_name, file_path=file_path, file_hash=file_hash @@ -343,9 +329,9 @@ def _get_cache_paths(self) -> tuple[str, str, str]: def _generate_cache_hash(self) -> str: hashable_params = self.get_signature_parameters() text = f"{hashable_params}" - params_hash = hashlib.sha256(text.encode("utf-8")).hexdigest() + params_hash = hash_function(text.encode("utf-8")) docs_hashes = [doc.file_hash for doc in self.documents] - docs_hash = hashlib.sha256("".join(docs_hashes).encode("utf-8")).hexdigest() + docs_hash = hash_function("".join(docs_hashes).encode("utf-8")) return f"tfidf_retriever_{params_hash}_{docs_hash}" def _load_state(self) -> bool: diff --git a/DashAI/back/models/RAG/chunking_models/base_chunking_model.py b/DashAI/back/models/RAG/chunking_models/base_chunking_model.py index 14c811fa1..1cd2353ef 100644 --- a/DashAI/back/models/RAG/chunking_models/base_chunking_model.py +++ b/DashAI/back/models/RAG/chunking_models/base_chunking_model.py @@ -1,7 +1,15 @@ from abc import ABCMeta, abstractmethod from DashAI.back.config_object import ConfigObject -from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument -from typing import Dict, Final, List +from DashAI.back.models.RAG.documents.base_document import BaseDocument +from DashAI.back.models.RAG.documents.chunk import Chunk +from DashAI.back.dependencies.database.models import ( + RAGChunkingModel as DBChunkingModel, + Chunk as DBChunkModel, + RAGPipeline as PipelineDBModel, +) +from typing import Any, Dict, Final, List +from sqlalchemy.orm import Session +from DashAI.back.models.RAG.utils import hash_function class BaseChunkingModel(ConfigObject, metaclass=ABCMeta): """ @@ -10,33 +18,190 @@ class BaseChunkingModel(ConfigObject, metaclass=ABCMeta): """ TYPE: Final[str] = "ChunkingModel" + REQUIRED_EXTRA_KWARGS: Final[List[str]] = ["documents", "db", "pipeline_id"] + id: int + pipeline_id: int + class_name: str + parameters: Dict[str, Any] def __init__(self, **kwargs): """ Initialize the chunking model with any necessary parameters. """ - raise NotImplementedError("Subclasses must implement the __init__ method.") - + self.db: Session = kwargs.pop("db") + self.pipeline_id: int = kwargs.pop("pipeline_id") + self.documents: Dict[int, BaseDocument] = kwargs.pop("documents") + self.class_name = self.__class__.__name__ + self.parameters = self.validate_and_transform(kwargs) + self.init_model_in_db() + self.chunks = self.chunk_documents() + self.update_chunks_in_db() + @abstractmethod - def chunk_document(self, document: BaseDocument, **kwargs) -> List[str]: + def chunk_text(self, text: str, **kwargs) -> List[str]: """ - Method to be implemented by subclasses to perform document chunking. + Chunk the input text into smaller pieces. + + Args: + text (str): The input text to be chunked. + **kwargs: Additional parameters for chunking. - :param document: The input document to be chunked. - :return: A list of text chunks. + Returns: + List[str]: A list of text chunks. """ - raise NotImplementedError("Subclasses must implement this method.") - + raise NotImplementedError("Subclasses must implement the chunk_text method.") + + def chunk_document(self, document: BaseDocument) -> Dict[int, Chunk]: + """ + Chunk a single document using the chunk_text method. + + Args: + document (BaseDocument): The input document to be chunked. + **kwargs: Additional parameters for chunking, passed to `chunk_text`. + + Returns: + Dict[int, Chunk]: A dictionary mapping chunk indices to Chunk objects. + """ + assert isinstance(document, BaseDocument), "Input must be an instance of BaseDocument." + text = document.get_text() + chunks_text = self.chunk_text(text) + chunks = {} + for idx, chunk_text in enumerate(chunks_text): + chunk = Chunk( + id=None, + document_position=idx, + document_id=document.id, + text=chunk_text, + ) + chunks[idx] = chunk + return chunks - def chunk_documents(self, documents: List[BaseDocument], **kwargs) -> Dict[int, List[str]]: + def chunk_documents(self) -> Dict[int, Dict[int, Chunk]]: """ - Chunk multiple documents using the chunk_document method. - :param documents: A list of input documents to be chunked. - :return: A dictionary mapping document IDs to lists of text chunks. + Chunk documents using the chunk_document method. + + Returns: + Dict[int, Dict[int, Chunk]]: A dictionary mapping document IDs to their respective + dictionaries of chunk indices and Chunk objects. """ chunked_documents = {} - for document in documents: - chunks = self.chunk_document(document, **kwargs) - chunked_documents[document.id] = chunks + for document_id, document in self.documents.items(): + chunks = self.chunk_document(document) + chunked_documents[document_id] = chunks return chunked_documents - \ No newline at end of file + + def fetch_model_from_db(self): + """ + Retrieve the chunking model from the database if it exists. + """ + existing_pipeline: PipelineDBModel = self.db.query( + PipelineDBModel).filter_by(id=self.pipeline_id).first() + if existing_pipeline: + chunking_model_id = existing_pipeline.chunking_model_id + chunking_model = self.db.query(DBChunkingModel).filter_by( + id=chunking_model_id).first() + else: + chunking_model = self.db.query(DBChunkingModel).filter_by( + class_name=self.class_name, + parameters=self.parameters + ).first() + if chunking_model: + self.id = chunking_model.id + return chunking_model + self.id = None + return None + + def create_model_in_db(self) -> DBChunkingModel: + """ + Store the chunking model in the database. + """ + new_model = DBChunkingModel( + class_name=self.class_name, + parameters=self.parameters, + ) + self.db.add(new_model) + self.db.commit() + self.db.refresh(new_model) + self.id = new_model.id + return new_model + + def update_model_in_db(self) -> DBChunkingModel: + """ + Update the chunking model in the database. + """ + model = self.db.query(DBChunkingModel).filter_by(id=self.id).first() + if model: + # The relationship is managed through the foreign key on RAGPipeline + # No need to manually manage the pipelines collection here + self.db.commit() + self.db.refresh(model) + return model + + def init_model_in_db(self): + if self.fetch_model_from_db(): + db_model = self.update_model_in_db() + else: + db_model = self.create_model_in_db() + return db_model + + def fetch_chunks_from_db(self) -> Dict[int, Dict[int, Chunk]]: + """ + Fetch all chunks associated with the chunking model from the database. + + Returns: + Dict[int, Dict[int, Chunk]]: A dictionary mapping document IDs to their respective + dictionaries of chunk indices and Chunk objects. + """ + chunks = {} + for document_id, _ in self.documents.items(): + chunks[document_id] = {} + stored_chunks = self.db.query(DBChunkModel).filter_by( + chunking_model_id=self.id, + document_id=document_id + ) + for chunk in stored_chunks: + chunks[document_id][chunk.chunk_index] = Chunk( + document_position=chunk.chunk_index, + document_id=chunk.document_id, + text=chunk.text, + ) + return chunks + + def create_chunks_in_db(self, document_chunks: Dict[int, Chunk]) -> None: + """ + Store the chunks of a document in the database. + + Args: + document_chunks (Dict[int, Chunk]): A dictionary mapping chunk indices to Chunk objects. + """ + for idx, chunk in document_chunks.items(): + db_chunk = DBChunkModel( + document_id=chunk.document_id, + chunk_index=chunk.document_position, + chunking_model_id=self.id, + text=chunk.text, + hash=hash_function(chunk.text) + ) + self.db.add(db_chunk) + self.db.commit() + self.chunks[chunk.document_id][idx].id = db_chunk.id + + + def update_chunks_in_db(self) -> None: + """ + Update the chunks in the database if they do not already exist. + """ + existing_chunks = self.fetch_chunks_from_db() + for document_id, chunks in self.chunks.items(): + if document_id not in existing_chunks: + self.create_chunks_in_db(chunks) + + def get_chunks(self) -> Dict[int, Dict[int, Chunk]]: + """ + Get the chunks generated by the chunking model. + + Returns: + Dict[int, Dict[int, Chunk]]: A dictionary mapping document IDs to their respective + dictionaries of chunk indices and Chunk objects. + """ + return self.chunks \ No newline at end of file diff --git a/DashAI/back/models/RAG/chunking_models/character_chunk_model.py b/DashAI/back/models/RAG/chunking_models/character_chunk_model.py index aba0c69fa..08de7791e 100644 --- a/DashAI/back/models/RAG/chunking_models/character_chunk_model.py +++ b/DashAI/back/models/RAG/chunking_models/character_chunk_model.py @@ -6,7 +6,8 @@ schema_field, ) from DashAI.back.models.RAG.chunking_models.base_chunking_model import BaseChunkingModel -from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument +from DashAI.back.models.RAG.documents.base_document import BaseDocument +from DashAI.back.models.RAG.documents.chunk import Chunk class CharacterChunkModelSchema(BaseSchema): @@ -33,31 +34,39 @@ class CharacterChunkModel(BaseChunkingModel): SCHEMA = CharacterChunkModelSchema - def __init__(self, chunk_size: int = 200, chunk_overlap: int = 20): + def __init__(self, **kwargs): """ Initialize the character chunking model with the specified chunk size and overlap. - :param chunk_size: Size of each chunk in characters. - :param chunk_overlap: Number of characters to overlap between chunks. + Args: + chunk_size (int): Size of each chunk in characters. + chunk_overlap (int): Number of characters to overlap between chunks. """ - self.chunk_size = chunk_size - self.chunk_overlap = chunk_overlap + self.chunk_size = kwargs["chunk_size"] + self.chunk_overlap = kwargs["chunk_overlap"] + super().__init__(**kwargs) - def chunk_document(self, document: BaseDocument) -> List[str]: + def chunk_text(self, text: str) -> List[str]: """ - Split the document into chunks based on character count. + Chunk the input text into smaller segments based on character count. - :param document: The input document to be chunked. - :return: A list of text chunks. + Args: + text (str): The input text to be chunked. + + Returns: + List[str]: A list of text chunks. """ - text = document.get_text() chunks = [] start = 0 + text_length = len(text) - while start < len(text): - end = min(start + self.chunk_size, len(text)) - chunks.append(text[start:end]) + while start < text_length: + end = min(start + self.chunk_size, text_length) + chunk = text[start:end] + chunks.append(chunk) + if end == text_length: + break start += self.chunk_size - self.chunk_overlap - return chunks + return chunks \ No newline at end of file diff --git a/DashAI/back/models/RAG/chunking_models/token_chunk_model.py b/DashAI/back/models/RAG/chunking_models/token_chunk_model.py index 08f074db5..a6f58ea88 100644 --- a/DashAI/back/models/RAG/chunking_models/token_chunk_model.py +++ b/DashAI/back/models/RAG/chunking_models/token_chunk_model.py @@ -1,3 +1,4 @@ +from typing import List from transformers import AutoTokenizer from DashAI.back.core.schema_fields import ( @@ -7,7 +8,6 @@ schema_field, ) from DashAI.back.models.RAG.chunking_models.base_chunking_model import BaseChunkingModel -from DashAI.back.models.RAG.documents import BaseDocument class TokenChunkModelSchema(BaseSchema): @@ -39,18 +39,18 @@ class TokenChunkModel(BaseChunkingModel): SCHEMA = TokenChunkModelSchema def __init__(self, **kwargs): - kwargs = self.validate_and_transform(kwargs) + pipeline_id = self.params.pop("pipeline_id") + + self.db_model = self.init_model_in_db(pipeline_id) - self.chunk_size = kwargs.get("chunk_size", 200) - self.chunk_overlap = kwargs.get("chunk_overlap", 20) + self.params = self.validate_and_transform(kwargs) - self.tokenizer_name = kwargs.get( - "tokenizer_name", "intfloat/e5-mistral-7b-instruct" - ) + self.chunk_size = self.params["chunk_size"] + self.chunk_overlap = self.params["chunk_overlap"] + self.tokenizer_name = self.params["tokenizer_name"] self.tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_name) - - def chunk(self, document: BaseDocument): - text = document.get_text() + + def chunk_text(self, text: str) -> List[List[str]]: tokens = self.tokenizer.tokenize(text) diff --git a/DashAI/back/models/RAG/Documents/BaseDocument.py b/DashAI/back/models/RAG/documents/base_document.py similarity index 92% rename from DashAI/back/models/RAG/Documents/BaseDocument.py rename to DashAI/back/models/RAG/documents/base_document.py index 3f8c3b4fc..29bbc38f1 100644 --- a/DashAI/back/models/RAG/Documents/BaseDocument.py +++ b/DashAI/back/models/RAG/documents/base_document.py @@ -7,6 +7,8 @@ class BaseDocument(ABC): Base class for documents. """ + SUPPORTED_FILE_TYPES = ['pdf', 'txt'] + def __init__( self, id: int, @@ -103,8 +105,8 @@ def get_file_type(self) -> Optional[str]: Returns: Optional[str]: The filetype of the document, or None if not applicable. """ - return self.file_path.split('.')[-1] + return self.file_path.split('.')[-1].lower() if self.file_path else None def __repr__(self): - return f"BaseDocument(filename='{self.get_file_name()}', content='{self.get_text()[:50]}...', metadata={self.get_metadata()})" + return f"BaseDocument(id={self.id}, filename='{self.get_file_name()}', content='{self.get_text()[:50]}...', metadata={self.get_metadata()})" diff --git a/DashAI/back/models/RAG/documents/chunk.py b/DashAI/back/models/RAG/documents/chunk.py new file mode 100644 index 000000000..47baaa262 --- /dev/null +++ b/DashAI/back/models/RAG/documents/chunk.py @@ -0,0 +1,20 @@ + +from typing import Any, Dict, List +from DashAI.back.models.RAG.utils import hash_function +import numpy as np + +class Chunk: + """A class representing a chunk of a document.""" + + def __init__( + self, + id: int, + document_id: str, + document_position: int, + text: str, + ): + self.id = id + self.document_id = document_id + self.document_position = document_position + self.text = text + self.hash = hash_function(text) diff --git a/DashAI/back/models/RAG/documents/chunk_embedding.py b/DashAI/back/models/RAG/documents/chunk_embedding.py new file mode 100644 index 000000000..f221dfbd5 --- /dev/null +++ b/DashAI/back/models/RAG/documents/chunk_embedding.py @@ -0,0 +1,17 @@ +from typing import Any, List +import numpy as np + +from DashAI.back.models.RAG.documents.base_document import BaseDocument +from DashAI.back.models.RAG.documents.chunk import Chunk + + +class ChunkEmbedding: + def __init__( + self, + document: BaseDocument, + chunk: Chunk, + embedding: List[float]|np.ndarray|Any, + ): + self.document = document + self.chunk = chunk + self.embedding = embedding \ No newline at end of file diff --git a/DashAI/back/models/RAG/Documents/PDFDocument.py b/DashAI/back/models/RAG/documents/pdf_document.py similarity index 89% rename from DashAI/back/models/RAG/Documents/PDFDocument.py rename to DashAI/back/models/RAG/documents/pdf_document.py index 48dba5258..27476b195 100644 --- a/DashAI/back/models/RAG/Documents/PDFDocument.py +++ b/DashAI/back/models/RAG/documents/pdf_document.py @@ -1,7 +1,7 @@ -from .BaseDocument import BaseDocument -import os +from DashAI.back.models.RAG.documents.base_document import BaseDocument from PyPDF2 import PdfReader from typing import Optional, Dict, Any +from DashAI.back.models.RAG.utils import hash_function class PDFDocument(BaseDocument): """ @@ -13,7 +13,6 @@ def __init__( id: int, file_name: str, file_path: str, - file_hash: str, created: Optional[str] = None, optional_metadata: Optional[Dict[str, Any]] = None ): @@ -23,10 +22,10 @@ def __init__( id (int): The unique identifier of the document. file_name (str): The name of the file. file_path (str): The path to the file. - file_hash (str): A hash of the file content. created (Optional[str]): The creation date of the document. optional_metadata (Optional[Dict[str, Any]]): Additional metadata for the document. """ + file_hash = hash_function(file_path) super().__init__( id=id, file_name=file_name, diff --git a/DashAI/back/models/RAG/Documents/TxtDocument.py b/DashAI/back/models/RAG/documents/txt_document.py similarity index 90% rename from DashAI/back/models/RAG/Documents/TxtDocument.py rename to DashAI/back/models/RAG/documents/txt_document.py index fb5507d94..c2b4d0787 100644 --- a/DashAI/back/models/RAG/Documents/TxtDocument.py +++ b/DashAI/back/models/RAG/documents/txt_document.py @@ -1,6 +1,6 @@ -from .BaseDocument import BaseDocument +from DashAI.back.models.RAG.documents.base_document import BaseDocument +from DashAI.back.models.RAG.utils import hash_function from typing import Dict, Any, Optional -import os class TxtDocument(BaseDocument): """ @@ -12,7 +12,6 @@ def __init__( id: int, file_name: str, file_path: str, - file_hash: str, created: Optional[str] = None, optional_metadata: Optional[Dict[str, Any]] = None ): @@ -26,6 +25,7 @@ def __init__( created (Optional[str]): The creation date of the document. optional_metadata (Optional[Dict[str, Any]]): Additional metadata for the document. """ + file_hash = hash_function(file_path) super().__init__( id=id, file_name=file_name, diff --git a/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py b/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py index b62baae9c..e2eaec4f7 100644 --- a/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py +++ b/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py @@ -40,16 +40,17 @@ class FastTextEmbedding(DenseEmbedding): DESCRIPTION = "Convert text to embeddings using FastText." def __init__(self, **kwargs): - self.validate_and_transform(**kwargs) - self.model_name = kwargs.get("model_name") - self.pooling_strategy = kwargs.get("pooling_strategy") + self.params = self.validate_and_transform(kwargs) + self.model_name = self.params["model_name"] + self.pooling_strategy = self.params["pooling_strategy"] pooling_functions = { "mean": np.mean, "max": np.max } self.pooling_function = pooling_functions[self.pooling_strategy] + self.load() - def _load_model(self): + def load(self): """Load the FastText model.""" model_name = self.model_name.split("/")[-1] model_path = hf_hub_download(repo_id=self.model_name, filename=f"{model_name}.bin") @@ -59,4 +60,12 @@ def encode(self, text: str) -> np.ndarray: """Encode text into an embedding.""" token_embeddings = [self.model.get_word_vector(word) for word in text.split()] return self.pooling_function(np.array(token_embeddings), axis=0) + + def batch_encode(self, texts: List[str]) -> np.ndarray: + """Encode a batch of texts into embeddings.""" + all_embeddings = [] + for text in texts: + embedding = self.encode(text) + all_embeddings.append(embedding) + return np.array(all_embeddings) \ No newline at end of file diff --git a/DashAI/back/models/RAG/embeddings/dense/huggingface_embedding.py b/DashAI/back/models/RAG/embeddings/dense/huggingface_embedding.py index 5b62e4e9e..7bc764303 100644 --- a/DashAI/back/models/RAG/embeddings/dense/huggingface_embedding.py +++ b/DashAI/back/models/RAG/embeddings/dense/huggingface_embedding.py @@ -1,6 +1,3 @@ -from transformers import AutoModel - - from DashAI.back.core.schema_fields import ( BaseSchema, schema_field, @@ -72,24 +69,26 @@ class HuggingFaceEmbedding(DenseEmbedding): DESCRIPTION = "Convert text to embeddings using HuggingFace transformer models." def __init__(self, **kwargs): - super().__init__(**kwargs) - self.pooling_strategy = kwargs.get("pooling_strategy", "mean") - self.model_name = kwargs.get( - "model_name", "sentence-transformers/all-MiniLM-L6-v2" - ) - self.device = kwargs.get("device", "cpu") - self.max_length = kwargs.get("max_length", 512) - self.batch_size = kwargs.get("batch_size", 32) + self.params = self.validate_and_transform(kwargs) + self.pooling_strategy = self.params["pooling_strategy"] + self.model_name = self.params["model_name"] + self.device = self.params["device"] + self.max_length = self.params["max_length"] + self.batch_size = self.params["batch_size"] self.model = None self.tokenizer = None + self.load() + + def save(self): + pass - def _load_model(self): + def load(self): """Load the embedding model and tokenizer.""" self.tokenizer = AutoTokenizer.from_pretrained(self.model_name) self.model = AutoModel.from_pretrained(self.model_name).to(self.device) - self.model.eval() + self.embedding_dim = self.model.config.hidden_size - def _encode_texts(self, texts:List[str])-> List[np.ndarray]: + def batch_encode(self, texts:List[str])-> List[np.ndarray]: """Encode a list of texts into embeddings.""" # Tokenize encoded = self.tokenizer( @@ -121,5 +120,5 @@ def _encode_texts(self, texts:List[str])-> List[np.ndarray]: def encode(self, text: str) -> np.ndarray: """Encode a single text into an embedding.""" - embeddings = self._encode_texts([text]) - return embeddings[0] if embeddings else np.array([]) + embeddings = self.batch_encode([text]) + return embeddings.squeeze() diff --git a/DashAI/back/models/RAG/embeddings/dense_embedding.py b/DashAI/back/models/RAG/embeddings/dense_embedding.py index 64a4e1966..cd4311b5b 100644 --- a/DashAI/back/models/RAG/embeddings/dense_embedding.py +++ b/DashAI/back/models/RAG/embeddings/dense_embedding.py @@ -3,7 +3,7 @@ from typing import Any, List import numpy as np -from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument +from DashAI.back.models.RAG.documents.base_document import BaseDocument from DashAI.back.models.base_model import BaseModel class DenseEmbedding(BaseModel): @@ -11,8 +11,8 @@ class DenseEmbedding(BaseModel): Base class for all encoding (embedding) models. This class should be inherited by any specific encoding (embedding) model implementation. """ - EMBEDDINGS_PATH = "RAG/embeddings" + embedding_dim: int def __init__( self, **kwargs: Any): @@ -29,4 +29,14 @@ def encode(self, text: str) -> List[float]|np.ndarray|Any: :param text: The input text to encode (embed). :return: A list representing the encodings (embeddings) of the input text. """ + raise NotImplementedError("Subclasses must implement this method.") + + def batch_encode(self, texts: List[str]) -> List[List[float]]|np.ndarray|Any: + """ + Method to generate encodings (embeddings) for a batch of texts. + This method should be implemented by subclasses. + + :param texts: A list of input texts to encode (embed). + :return: A list of lists representing the encodings (embeddings) of the input texts. + """ raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/DashAI/back/models/RAG/embeddings/trainable_encoding.py b/DashAI/back/models/RAG/embeddings/trainable_encoding.py index d210578b3..22934926c 100644 --- a/DashAI/back/models/RAG/embeddings/trainable_encoding.py +++ b/DashAI/back/models/RAG/embeddings/trainable_encoding.py @@ -3,7 +3,7 @@ from typing import Any, List import numpy as np -from DashAI.back.models.RAG.documents.BaseDocument import BaseDocument +from DashAI.back.models.RAG.documents.base_document import BaseDocument from DashAI.back.models.RAG.embeddings.dense_embedding import DenseEmbedding class TrainableEncoding(DenseEmbedding): diff --git a/DashAI/back/models/RAG/exceptions.py b/DashAI/back/models/RAG/exceptions.py new file mode 100644 index 000000000..e69de29bb diff --git a/DashAI/back/models/RAG/prompts/augmentation/custom_augmentation_prompt.py b/DashAI/back/models/RAG/prompts/augmentation/custom_augmentation_prompt.py index dcb82d36a..2a4888130 100644 --- a/DashAI/back/models/RAG/prompts/augmentation/custom_augmentation_prompt.py +++ b/DashAI/back/models/RAG/prompts/augmentation/custom_augmentation_prompt.py @@ -22,10 +22,8 @@ class CustomAugmentationPrompt(AugmentationPrompt): } - def __init__(self, template: str): - if not self.validate_template(template): - raise ValueError("The template is missing required placeholders.") - self.template = template + def __init__(self, **kwargs): + super().__init__(**kwargs) def format( diff --git a/DashAI/back/models/RAG/prompts/context_merge/__init__.py b/DashAI/back/models/RAG/prompts/context_merge/__init__.py deleted file mode 100644 index 89cef9e0f..000000000 --- a/DashAI/back/models/RAG/prompts/context_merge/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from DashAI.back.models.RAG.prompts.context_merge.context_merge_prompt import ContextMergePrompt -from DashAI.back.models.RAG.prompts.context_merge.default_context_merge_prompt import DefaultContextMergePrompt -from DashAI.back.models.RAG.prompts.context_merge.custom_context_merge_prompt import CustomContextMergePrompt \ No newline at end of file diff --git a/DashAI/back/models/RAG/prompts/context_merge/context_merge_prompt.py b/DashAI/back/models/RAG/prompts/context_merge/context_merge_prompt.py deleted file mode 100644 index fc1f089ff..000000000 --- a/DashAI/back/models/RAG/prompts/context_merge/context_merge_prompt.py +++ /dev/null @@ -1,18 +0,0 @@ -from typing import Any, List, Tuple -from DashAI.back.models.RAG.prompts.prompt import Prompt - -class ContextMergePrompt(Prompt): - """ - ContextMergePrompt class for generating prompts that merge the use input, the retrieved - documents and the chat history into a single context for the language model. - """ - required_placeholders = ["{input}", "{history}", "{chunks}"] - - @staticmethod - def format( - input: str, - history: List[Tuple[str, str]], - chunks: List[str], - **kwargs: Any - ) -> str: - raise NotImplementedError("Subclasses must implement this method.") \ No newline at end of file diff --git a/DashAI/back/models/RAG/prompts/context_merge/custom_context_merge_prompt.py b/DashAI/back/models/RAG/prompts/context_merge/custom_context_merge_prompt.py deleted file mode 100644 index 28b8403db..000000000 --- a/DashAI/back/models/RAG/prompts/context_merge/custom_context_merge_prompt.py +++ /dev/null @@ -1,43 +0,0 @@ -from typing import Any, List, Tuple -from DashAI.back.models.RAG.prompts.context_merge.context_merge_prompt import ContextMergePrompt - -class CustomContextMergePrompt(ContextMergePrompt): - """ - ContextMergePrompt class for generating prompts that merge the use input, the retrieved - documents and the chat history into a single context for the language model. - """ - required_placeholders = ["{input}", "{documents}", "{history}"] - - - def __init__(self, template: str): - if not self.validate_template(template): - raise ValueError("The template is missing required placeholders.") - self.template = template - - def format( - self, - input: str, - history: List[Tuple[str, str]], - chunks: List[str], - **kwargs: Any - ) -> str: - """ - Format the prompt using the provided template. - - Args: - input (str): The user input message. - history (List[Tuple[str, str]]): The chat history to be included in the context. - chunks (List[str]): The document chunks to be included in the context. - **kwargs: Additional keyword arguments for formatting. - - Returns: - str: The formatted prompt. - """ - buffer = self.template - if history: - buffer = buffer.replace("{history}", "\n".join( - [f"User message: {h_input}\n Response: {h_output}" for h_input, h_output in history] - )) - buffer = buffer.replace("{input}", input) - buffer = buffer.replace("{documents}", "\n".join(chunks)) - return buffer diff --git a/DashAI/back/models/RAG/prompts/generation/custom_generation_prompt.py b/DashAI/back/models/RAG/prompts/generation/custom_generation_prompt.py index e9f1a83e4..28fe2305d 100644 --- a/DashAI/back/models/RAG/prompts/generation/custom_generation_prompt.py +++ b/DashAI/back/models/RAG/prompts/generation/custom_generation_prompt.py @@ -15,16 +15,14 @@ class CustomGenerationPrompt(GenerationPrompt): "placeholder_descriptions": { "{input}": "The user input message.", "{history}": "The chat history (optional) to be included in the context.", - "{documents}": "The document chunks to be included in the context." + "{chunks}": "The document chunks to be included in the context." } } - def __init__(self, template: str): - if not self.validate_template(template): - raise ValueError("The template is missing required placeholders.") - self.template = template - + def __init__(self, **kwargs: Any): + super().__init__(**kwargs) + def format( self, input: str, diff --git a/DashAI/back/models/RAG/prompts/generation/default_QnA_generation_prompt.py b/DashAI/back/models/RAG/prompts/generation/default_QnA_generation_prompt.py index 2743173d1..d6735b137 100644 --- a/DashAI/back/models/RAG/prompts/generation/default_QnA_generation_prompt.py +++ b/DashAI/back/models/RAG/prompts/generation/default_QnA_generation_prompt.py @@ -34,6 +34,10 @@ class DefaultQnAGenerationPrompt(GenerationPrompt): "template": template } + def __init__(self, **kwargs): + super().__init__(**kwargs) + + @staticmethod def format( input: str, diff --git a/DashAI/back/models/RAG/prompts/generation/default_generation_prompt.py b/DashAI/back/models/RAG/prompts/generation/default_generation_prompt.py index f27cf999f..1a370dd72 100644 --- a/DashAI/back/models/RAG/prompts/generation/default_generation_prompt.py +++ b/DashAI/back/models/RAG/prompts/generation/default_generation_prompt.py @@ -37,12 +37,14 @@ class DefaultGenerationPrompt(GenerationPrompt): "template": template } + def __init__(self, **kwargs): + super().__init__(**kwargs) @staticmethod def format( input: str, history:List[Tuple[str, str]], - chunks: List[str], + chunks: str, **kwargs: Any ) -> str: """ @@ -61,5 +63,5 @@ def format( [f"User message: {h_input}\nResponse: {h_output}" for h_input, h_output in history] )) buffer = buffer.replace("{input}", input) - buffer = buffer.replace("{chunks}", "\n".join(chunks)) + buffer = buffer.replace("{chunks}", chunks) return buffer \ No newline at end of file diff --git a/DashAI/back/models/RAG/prompts/generation/generation_prompt.py b/DashAI/back/models/RAG/prompts/generation/generation_prompt.py index 23a35ebec..6df413f9b 100644 --- a/DashAI/back/models/RAG/prompts/generation/generation_prompt.py +++ b/DashAI/back/models/RAG/prompts/generation/generation_prompt.py @@ -1,5 +1,8 @@ from typing import Any, List, Tuple from DashAI.back.models.RAG.prompts.prompt import Prompt +from DashAI.back.dependencies.database.models import ( + RAGPrompt as PromptDBModel, +) class GenerationPrompt(Prompt): """ @@ -7,6 +10,7 @@ class GenerationPrompt(Prompt): """ required_placeholders = ["{input}", "{chunks}"] optional_placeholders = ["{history}"] + @staticmethod def format( diff --git a/DashAI/back/models/RAG/prompts/prompt.py b/DashAI/back/models/RAG/prompts/prompt.py index d3e278049..2c8aa4e6f 100644 --- a/DashAI/back/models/RAG/prompts/prompt.py +++ b/DashAI/back/models/RAG/prompts/prompt.py @@ -2,13 +2,15 @@ from DashAI.back.models.base_model import BaseModel from DashAI.back.core.schema_fields import BaseSchema, schema_field, string_field +from DashAI.back.dependencies.database.models import ( + RAGPrompt as PromptDBModel) class PromptSchema(BaseSchema): template: str = schema_field( string_field(), - "", - "The prompt template with placeholders.", + placeholder="", + description="The prompt template with placeholders.", ) @@ -19,11 +21,38 @@ class Prompt(BaseModel): """ SCHEMA = PromptSchema + REQUIRED_EXTRA_KWARGS = ["db"] + id: int - def __load__(self, **kwargs: Any) -> None: + def __init__(self, **kwargs): + self.db = kwargs.pop("db") + kwargs = self.validate_and_transform(kwargs) + self.template = kwargs.get("template") + assert self.validate_template(self.template), "The template is missing required placeholders." + self.class_name = self.__class__.__name__ + self.params = kwargs + + stored_model = self.db.query(PromptDBModel).filter_by( + class_name = self.class_name, + parameters = self.params + ).first() + if stored_model: + self.id = stored_model.id + else: + new_model = PromptDBModel( + class_name=self.class_name, + parameters=self.params + ) + self.db.add(new_model) + self.db.commit() + self.id = new_model.id + + + + def load(self, **kwargs: Any) -> None: pass - def __save__(self) -> None: + def save(self) -> None: pass @classmethod diff --git a/DashAI/back/models/RAG/utils.py b/DashAI/back/models/RAG/utils.py index e69de29bb..57d6901e5 100644 --- a/DashAI/back/models/RAG/utils.py +++ b/DashAI/back/models/RAG/utils.py @@ -0,0 +1,14 @@ +import hashlib + + +def hash_function(content) -> str: + """ + Generate a SHA-256 hash for the given content bytes. + + Args: + content (bytes): The content to hash. + """ + if isinstance(content, str): + content = content.encode("utf-8") + return hashlib.sha256(content).hexdigest() + diff --git a/DashAI/back/models/text_to_text_generation_model.py b/DashAI/back/models/text_to_text_generation_model.py index 782fdcb9e..4c4549c4a 100644 --- a/DashAI/back/models/text_to_text_generation_model.py +++ b/DashAI/back/models/text_to_text_generation_model.py @@ -5,3 +5,5 @@ class TextToTextGenerationTaskModel(BaseGenerativeModel): """Class for models associated to TextToTextGenerationTask.""" COMPATIBLE_COMPONENTS = ["TextToTextGenerationTask", "RAGTask"] + + REQUIRED_EXTRA_KWARGS = [] diff --git a/DashAI/front/src/api/job.ts b/DashAI/front/src/api/job.ts index 5393c7678..53c95983d 100644 --- a/DashAI/front/src/api/job.ts +++ b/DashAI/front/src/api/job.ts @@ -132,6 +132,24 @@ export const enqueueGenerativeProcessJob = async ( return response.data; }; +export const enqueueRAGProcessJob = async ( + processId: number, +): Promise => { + const data = { + job_type: "RAGJob", + kwargs: { rag_process_id: processId }, + }; + const formData = new FormData(); + formData.append("job_type", data.job_type); + formData.append("kwargs", JSON.stringify(data.kwargs)); + const response = await api.post("/v1/job/", formData, { + headers: { + "Content-Type": "multipart/form-data", + }, + }); + return response.data; +} + export const enqueueConverterJob = async ( converterListId: number, ): Promise => { diff --git a/Untitled-1.ipynb b/Untitled-1.ipynb deleted file mode 100644 index d77315c44..000000000 --- a/Untitled-1.ipynb +++ /dev/null @@ -1,796 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "145faa29", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\fcb11\\Documents\\GitHub\\DashAI\\.venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "c:\\Users\\fcb11\\Documents\\GitHub\\DashAI\\.venv\\Lib\\site-packages\\pydantic\\_internal\\_config.py:345: UserWarning: Valid config keys have changed in V2:\n", - "* 'orm_mode' has been renamed to 'from_attributes'\n", - " warnings.warn(message, UserWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "['#1#yukari is one of the most underrated Vocaloid voicebanks and characters\\n',\n", - " '#2#Yuzuki Yukari is the president of Independent Republic of Saint Joaquin\\n',\n", - " '#3#Yuzuki Yukari is well known by her participation in the Nuremberg trials\\n',\n", - " '#4#Yuzuki Yukari is responsible for the creation of Linux kernel, even if she is not recognized by that\\n',\n", - " '#5#Papi Micky is the Major of San Carlos\\n',\n", - " '#6#Papi Micky had an affair with Tio Rene Puente, but he did not recognize him as a lover, because he was engaged with Contralorito']" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.feature_extraction.text import TfidfVectorizer\n", - "from typing import Dict, List\n", - "import hashlib\n", - "from DashAI.back.models.RAG.embeddings.tf_idf_embedding import TFIDFEmbedding\n", - "\n", - "\n", - "hasher = lambda x: hashlib.sha256(x.encode('utf-8')).hexdigest()\n", - "\n", - "\n", - "dummy_texts = []\n", - "\n", - "with open('dummy_texts.txt', 'r') as file:\n", - " dummy_texts = file.readlines()\n", - " \n", - "dummy_texts" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "59febbcb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "75 #1#yukari is one of the most underrated Vocaloid voicebanks and characters\n", - "\n", - "75 #2#Yuzuki Yukari is the president of Independent Republic of Saint Joaquin\n", - "\n", - "76 #3#Yuzuki Yukari is well known by her participation in the Nuremberg trials\n", - "\n", - "104 #4#Yuzuki Yukari is responsible for the creation of Linux kernel, even if she is not recognized by that\n", - "\n", - "41 #5#Papi Micky is the Major of San Carlos\n", - "\n", - "131 #6#Papi Micky had an affair with Tio Rene Puente, but he did not recognize him as a lover, because he was engaged with Contralorito\n" - ] - } - ], - "source": [ - "for t in dummy_texts:\n", - " print(len(t), t)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "dd6c7ea9", - "metadata": {}, - "outputs": [], - "source": [ - "def generate_chunks(texts, size, overlap)-> Dict[int, List[str]]:\n", - " \"\"\"\n", - " Generate chunks of text from the provided texts.\n", - " Args:\n", - " texts (List[str]): List of text documents to chunk.\n", - " size (int): Size of each chunk.\n", - " overlap (int): Overlap size between chunks.\n", - " Returns:\n", - " Dict[int, List[str]]: Dictionary with document index as key and list of chunks as value.\n", - " \"\"\"\n", - " \n", - " documents_chunks = {}\n", - "\n", - " for i, text in enumerate(texts):\n", - " chunks = []\n", - " for j in range(0, len(text), size - overlap):\n", - " if j + size > len(text):\n", - " chunk = text[j:]\n", - " else:\n", - " chunk = text[j:j + size]\n", - " chunks.append(chunk)\n", - " documents_chunks[i] = chunks\n", - " return documents_chunks" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "30e25e89", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: ['#1#yukari is one of the most underrated Vocaloid voicebanks and characters\\n'],\n", - " 1: ['#2#Yuzuki Yukari is the president of Independent Republic of Saint Joaquin\\n'],\n", - " 2: ['#3#Yuzuki Yukari is well known by her participation in the Nuremberg trials\\n'],\n", - " 3: ['#4#Yuzuki Yukari is responsible for the creation of Linux kernel, even if she is not recognized by that\\n'],\n", - " 4: ['#5#Papi Micky is the Major of San Carlos\\n'],\n", - " 5: ['#6#Papi Micky had an affair with Tio Rene Puente, but he did not recognize him as a lover, because he was engaged with Contralorito']}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "document_chunks = generate_chunks(dummy_texts, 200, 20)\n", - "document_chunks" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a18c4a56", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: ['#1#yukari is one of the most underrated Vocaloid voicebanks and characters\\n'],\n", - " 1: ['#2#Yuzuki Yukari is the president of Independent Republic of Saint Joaquin\\n'],\n", - " 2: ['#3#Yuzuki Yukari is well known by her participation in the Nuremberg trials\\n'],\n", - " 3: ['#4#Yuzuki Yukari is responsible for the creation of Linux kernel, even if she is not recognized by that\\n'],\n", - " 4: ['#5#Papi Micky is the Major of San Carlos\\n'],\n", - " 5: ['#6#Papi Micky had an affair with Tio Rene Puente, but he did not recognize him as a lover, because he was engaged with Contralorito']}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "document_chunks = generate_chunks(dummy_texts, 200, 0)\n", - "document_chunks" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a9f0d850", - "metadata": {}, - "outputs": [], - "source": [ - "if False: # Set to True to run the test\n", - " for chunk_size in [40, 80, 1000, 2000]:\n", - " for chunk_overlap in [0, 20, 30]:\n", - " document_chunks = generate_chunks(dummy_texts, chunk_size, chunk_overlap)\n", - " tfidf_embedding = TFIDFEmbedding(\n", - " documents_chunks=document_chunks,\n", - " strip_accents='unicode',\n", - " lowercase=True,\n", - " analyzer='word',\n", - " ngram_range=(1, 2),\n", - " max_features=1000,\n", - " norm='l2',\n", - " use_idf=True,\n", - " smooth_idf=True,\n", - " sublinear_tf=False,\n", - " stop_words=None,\n", - " max_df=1.0,\n", - " min_df=1,\n", - " )\n", - " \n", - " print(f\"Chunk Size: {chunk_size}, Chunk Overlap: {chunk_overlap}\")\n", - " print(f\"Embeddings Hash: {tfidf_embedding._calculate_embeddings_hash()}\\n\")\n", - " del tfidf_embedding # Clean up to free memory\n", - " del document_chunks # Clean up to free memory" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "2130e475", - "metadata": {}, - "outputs": [], - "source": [ - "if False: # Set to True to run the test\n", - " for chunk_size in [200, 200, 200]:\n", - " for chunk_overlap in [0, 0, 50, 50]:\n", - " document_chunks = generate_chunks(dummy_texts, chunk_size, chunk_overlap)\n", - " tfidf_embedding = TFIDFEmbedding(\n", - " documents_chunks=document_chunks,\n", - " strip_accents='unicode',\n", - " lowercase=True,\n", - " analyzer='word',\n", - " ngram_range=(1, 2),\n", - " max_features=1000,\n", - " norm='l2',\n", - " use_idf=True,\n", - " smooth_idf=True,\n", - " sublinear_tf=False,\n", - " stop_words=None,\n", - " max_df=1.0,\n", - " min_df=1,\n", - " Store_model=False # Set to False to avoid saving the model during testing\n", - " )\n", - " \n", - " print(f\"Chunk Size: {chunk_size}, Chunk Overlap: {chunk_overlap}\")\n", - " print(f\"Embeddings Hash: {tfidf_embedding._calculate_embeddings_hash()}\\n\")\n", - " del tfidf_embedding # Clean up to free memory\n", - " del document_chunks # Clean up to free memory" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "625a05ec", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: ['#1#yukari is one of the most underrated Vocaloid voicebanks and characters\\n'],\n", - " 1: ['#2#Yuzuki Yukari is the president of Independent Republic of Saint Joaquin\\n'],\n", - " 2: ['#3#Yuzuki Yukari is well known by her participation in the Nuremberg trials\\n'],\n", - " 3: ['#4#Yuzuki Yukari is responsible for the creation of Linux kernel, even if she is not recognized by that\\n'],\n", - " 4: ['#5#Papi Micky is the Major of San Carlos\\n'],\n", - " 5: ['#6#Papi Micky had an affair with Tio Rene Puente, but he did not recognize him as a lover, because he was engaged with Contralorito']}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chunks_size_200_overlap_0 = generate_chunks(dummy_texts, 200, 0)\n", - "chunks_size_200_overlap_0" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "dbc4609e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: ['#1#yukari is one of the most underrated Vocaloid voicebanks and characters\\n'],\n", - " 1: ['#2#Yuzuki Yukari is the president of Independent Republic of Saint Joaquin\\n'],\n", - " 2: ['#3#Yuzuki Yukari is well known by her participation in the Nuremberg trials\\n'],\n", - " 3: ['#4#Yuzuki Yukari is responsible for the creation of Linux kernel, even if she is not recognized by that\\n'],\n", - " 4: ['#5#Papi Micky is the Major of San Carlos\\n'],\n", - " 5: ['#6#Papi Micky had an affair with Tio Rene Puente, but he did not recognize him as a lover, because he was engaged with Contralorito']}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chunks_size_2000_overlap_0 = generate_chunks(dummy_texts, 2000, 0)\n", - "chunks_size_2000_overlap_0" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b386ba62", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TF-IDF model loaded successfully: False\n", - "Fitting the TF-IDF model...\n", - "Saving the TF-IDF model...\n", - "TF-IDF model fitted and saved.\n" - ] - } - ], - "source": [ - "embedding_chunk_size_200_overlap_0 = TFIDFEmbedding(\n", - " documents_chunks=chunks_size_200_overlap_0,\n", - " strip_accents='unicode',\n", - " lowercase=True,\n", - " analyzer='word',\n", - " ngram_range=(1, 2),\n", - " max_features=None,\n", - " norm='l2',\n", - " use_idf=True,\n", - " smooth_idf=True,\n", - " sublinear_tf=False,\n", - " stop_words=None,\n", - " max_df=1.0,\n", - " min_df=1,\n", - " store_model=True # Set to False to avoid saving the model during testing\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "df20a4dd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<6x129 sparse matrix of type ''\n", - "\twith 156 stored elements in Compressed Sparse Row format>" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "embedding_chunk_size_200_overlap_0._term_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "3887a18a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['affair', 'affair with', 'an', 'an affair', 'and',\n", - " 'and characters', 'as', 'as lover', 'because', 'because he', 'but',\n", - " 'but he', 'by', 'by her', 'by that', 'carlos', 'characters',\n", - " 'contralorito', 'creation', 'creation of', 'did', 'did not',\n", - " 'engaged', 'engaged with', 'even', 'even if', 'for', 'for the',\n", - " 'had', 'had an', 'he', 'he did', 'he was', 'her',\n", - " 'her participation', 'him', 'him as', 'if', 'if she', 'in',\n", - " 'in the', 'independent', 'independent republic', 'is', 'is not',\n", - " 'is one', 'is responsible', 'is the', 'is well', 'joaquin',\n", - " 'kernel', 'kernel even', 'known', 'known by', 'linux',\n", - " 'linux kernel', 'lover', 'lover because', 'major', 'major of',\n", - " 'micky', 'micky had', 'micky is', 'most', 'most underrated', 'not',\n", - " 'not recognize', 'not recognized', 'nuremberg', 'nuremberg trials',\n", - " 'of', 'of independent', 'of linux', 'of saint', 'of san', 'of the',\n", - " 'one', 'one of', 'papi', 'papi micky', 'participation',\n", - " 'participation in', 'president', 'president of', 'puente',\n", - " 'puente but', 'recognize', 'recognize him', 'recognized',\n", - " 'recognized by', 'rene', 'rene puente', 'republic', 'republic of',\n", - " 'responsible', 'responsible for', 'saint', 'saint joaquin', 'san',\n", - " 'san carlos', 'she', 'she is', 'that', 'the', 'the creation',\n", - " 'the major', 'the most', 'the nuremberg', 'the president', 'tio',\n", - " 'tio rene', 'trials', 'underrated', 'underrated vocaloid',\n", - " 'vocaloid', 'vocaloid voicebanks', 'voicebanks', 'voicebanks and',\n", - " 'was', 'was engaged', 'well', 'well known', 'with',\n", - " 'with contralorito', 'with tio', 'yukari', 'yukari is', 'yuzuki',\n", - " 'yuzuki yukari'], dtype=object)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "embedding_chunk_size_200_overlap_0._feature_names" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3ed862f2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<6x129 sparse matrix of type ''\n", - "\twith 156 stored elements in Compressed Sparse Row format>" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "embedding_chunk_size_200_overlap_0._term_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "46446c8a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", - 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] - } - ], - "source": [ - "embedding_chunk_size_2000_overlap_0 = TFIDFEmbedding(\n", - " documents_chunks=chunks_size_2000_overlap_0,\n", - " strip_accents='unicode',\n", - " lowercase=True,\n", - " analyzer='word',\n", - " ngram_range=(1, 2),\n", - " max_features=None,\n", - " norm='l2',\n", - " use_idf=True,\n", - " smooth_idf=True,\n", - " sublinear_tf=False,\n", - " stop_words=None,\n", - " max_df=1.0,\n", - " min_df=1,\n", - " store_model=True # Set to False to avoid saving the model during testing\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "67f65c72", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.6234157 , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0.31939253, 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - 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" 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0.31939253, 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0.36984707, 0. , 0.43159844, 0.43159844]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "e2 = embedding_chunk_size_2000_overlap_0.embed(\"this is a yuzuki yukari affair\")\n", - "e2" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "74ec8dc4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(e1==e2).all() # Check if all elements are equal" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "d6069a5e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TF-IDF model loaded successfully: False\n", - "Fitting the TF-IDF model...\n", - "Saving the TF-IDF model...\n", - "TF-IDF model fitted and saved.\n" - ] - } - ], - "source": [ - "chunks_size_200_overlap_50 = generate_chunks(dummy_texts, 200, 50)\n", - "embedding_chunk_size_200_overlap_50 = TFIDFEmbedding(\n", - " documents_chunks=chunks_size_200_overlap_50,\n", - " strip_accents='unicode',\n", - " lowercase=True,\n", - " analyzer='word',\n", - " ngram_range=(1, 2),\n", - " max_features=1000,\n", - " norm='l2',\n", - " use_idf=True,\n", - " smooth_idf=True,\n", - " sublinear_tf=False,\n", - " stop_words=None,\n", - " max_df=1.0,\n", - " min_df=1,\n", - " store_model=True # Set to False to avoid saving the model during testing\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "19720b5a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.6234157 , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0.31939253, 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0.36984707, 0. , 0.43159844, 0.43159844]])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "e3 = embedding_chunk_size_200_overlap_50.embed(\"this is a yuzuki yukari affair\")\n", - "e3" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f2d061d6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(e1 == e3).all() # Check if all elements are equal\n", - "(e2 == e3).all() # Check if all elements are equal" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notas.md b/notas.md new file mode 100644 index 000000000..ea9218d98 --- /dev/null +++ b/notas.md @@ -0,0 +1,2 @@ +* Por simplicidad, se asume que un documento almacenado en la base de datos no cambiará su contenido en el tiempo. Si se quiere dar soporte al tracking de versiones, una alternativa es que el frontend siempre envíe el archivo o un hash su contenido al backend, y que este verifique si ha cambiado. Si lo ha hecho, debería duplicar el RAGPipeline asociado, junto con sus subcomponentes, e inicializarlo con el nuevo contenido. +Desventaja asociada: el usuario debería mantener los archivos siempre en su path original. \ No newline at end of file From 63af25e429d8e888cf0498925b3f2c255ba82d50 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Thu, 16 Oct 2025 07:57:27 -0300 Subject: [PATCH 050/218] baseline --- DashAI/back/api/api_v1/endpoints/documents.py | 30 +++++++++---------- DashAI/back/models/RAG/RAG_pipeline.py | 3 ++ .../models/RAG/Retrievers/dense_retriever.py | 18 ++++++----- .../models/RAG/Retrievers/retriever_model.py | 2 +- .../RAG/chunking_models/token_chunk_model.py | 28 ++++++++--------- .../embeddings/dense/fasttext_embedding.py | 3 ++ DashAI/front/src/api/rag.ts | 8 +++++ .../RAG/NewSessionModal/NewSessionModal.jsx | 12 ++++++++ 8 files changed, 65 insertions(+), 39 deletions(-) diff --git a/DashAI/back/api/api_v1/endpoints/documents.py b/DashAI/back/api/api_v1/endpoints/documents.py index 6cfc7fa26..7ca95999c 100644 --- a/DashAI/back/api/api_v1/endpoints/documents.py +++ b/DashAI/back/api/api_v1/endpoints/documents.py @@ -21,7 +21,9 @@ from sqlalchemy.orm import sessionmaker from DashAI.back.api.api_v1.schemas import DocumentResponse -from DashAI.back.dependencies.database.models import Document +from DashAI.back.dependencies.database.models import ( + Document as DocumentDBModel, +) from DashAI.back.models.RAG.utils import hash_function router = APIRouter() @@ -36,7 +38,7 @@ async def get_all_documents( """Get all documents with file_url included.""" with session_factory() as db: try: - documents = db.query(Document).all() + documents: List[DocumentDBModel] = db.query(DocumentDBModel).all() documents_responses = [] @@ -50,7 +52,7 @@ async def get_all_documents( created=doc.created, last_modified=doc.last_modified, optional_metadata=doc.optional_metadata, - related_sessions=[session.id for session in doc.related_sessions_ids] if doc.related_sessions_ids else None, + related_sessions=doc.get_related_sessions, file_url=f"{base_url}/{doc.id}/download", ) ) @@ -72,7 +74,7 @@ async def get_document( """Get metadata of a document by its ID.""" with session_factory() as db: try: - document = db.get(Document, document_id) + document = db.get(DocumentDBModel, document_id) if not document: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, @@ -102,7 +104,7 @@ async def download_document( ): """Download the actual file content of a document.""" with session_factory() as db: - document = db.get(Document, document_id) + document = db.query(DocumentDBModel).filter_by(id=document_id).first() if not document: raise HTTPException( status_code=404, @@ -162,7 +164,7 @@ async def upload_document( file_content_hash = hash_function(content_bytes) with session_factory() as db: - existing_doc = db.query(Document).filter_by(file_hash=file_content_hash).first() + existing_doc: DocumentDBModel = db.query(DocumentDBModel).filter_by(file_hash=file_content_hash).first() if existing_doc: # Update existing document's information existing_doc.file_name = file_name @@ -176,16 +178,14 @@ async def upload_document( file_hash=existing_doc.file_hash, created=existing_doc.created, last_modified=existing_doc.last_modified, - related_sessions=[session.id for session in existing_doc.related_sessions_ids], + related_sessions=existing_doc.get_related_sessions, optional_metadata=existing_doc.optional_metadata, file_url=f"{base_url}/{existing_doc.id}/download" ) else: # Create a new document entry try: - max_id = db.query(Document.id).order_by(Document.id.desc()).first() - new_id = (max_id[0] + 1) if max_id else 1 - file_path = os.path.join(docs_folder_path, f"{new_id}__{file_name}") + file_path = os.path.join(docs_folder_path, f"{file_name}_{file_content_hash}") with open(file_path, "wb") as f: f.write(content_bytes) @@ -193,7 +193,7 @@ async def upload_document( # Get file type from file extension file_type = os.path.splitext(file_name)[1].lstrip('.') or 'unknown' - doc = Document( + doc = DocumentDBModel( file_name=file_name, file_type=file_type, file_path=str(file_path), @@ -211,7 +211,7 @@ async def upload_document( file_hash=doc.file_hash, created=doc.created, last_modified=doc.last_modified, - related_sessions=None, + related_sessions=doc.get_related_sessions, optional_metadata=doc.optional_metadata, file_url=f"{base_url}/{doc.id}/download", ) @@ -230,7 +230,7 @@ async def get_related_sessions( """Get all generative session IDs related to a specific document.""" with session_factory() as db: try: - document = db.get(Document, document_id) + document = db.query(DocumentDBModel).filter_by(id=document_id).first() if not document: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, @@ -255,7 +255,7 @@ async def delete_document( """Delete a document from the RAG system by its ID.""" with session_factory() as db: try: - document = db.get(Document, document_id) + document = db.query(DocumentDBModel).filter_by(id=document_id).first() if not document: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, @@ -294,7 +294,7 @@ async def update_document_metadata( """Update a document's metadata.""" with session_factory() as db: try: - document = db.get(Document, document_id) + document = db.query(DocumentDBModel).filter_by(id=document_id).first() if not document: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, diff --git a/DashAI/back/models/RAG/RAG_pipeline.py b/DashAI/back/models/RAG/RAG_pipeline.py index b5832e6cd..27a95eed3 100644 --- a/DashAI/back/models/RAG/RAG_pipeline.py +++ b/DashAI/back/models/RAG/RAG_pipeline.py @@ -430,6 +430,9 @@ def init_component( try: model_instance = model_class(**model_params) except Exception as e: + import traceback + traceback.print_exc() + raise RAGPipelineInitializationError(f"Failed to initialize {model_class_name}: {str(e)}") return model_instance \ No newline at end of file diff --git a/DashAI/back/models/RAG/Retrievers/dense_retriever.py b/DashAI/back/models/RAG/Retrievers/dense_retriever.py index bd2d70f5d..a412a75e0 100644 --- a/DashAI/back/models/RAG/Retrievers/dense_retriever.py +++ b/DashAI/back/models/RAG/Retrievers/dense_retriever.py @@ -81,15 +81,17 @@ class DenseRetriever(RetrieverModel): def __init__(self, **kwargs): # RetrieverModel class fetches retriever_db_model if exists super().__init__(**kwargs) - - self.embedding_model: DenseEmbedding = self.params["encoding_model"] - self.embedding_class_name = self.embedding_model.__class__.__name__ - self.embedding_params = self.embedding_model.params - self.params["encoding_model"] = { - "component": self.embedding_class_name, - "params": self.embedding_params - } + embedding_args = self.params["encoding_model"]["properties"]["params"]["comp"] + self.embedding_class_name = embedding_args["component"] + self.embedding_params = embedding_args.get("params", {}) + embedding_class = self.component_registry[self.embedding_class_name]["class"] + self.embedding_model: DenseEmbedding = embedding_class(**self.embedding_params) + + #self.embedding_model: DenseEmbedding = self.params["encoding_model"] + #self.embedding_class_name = self.embedding_model.__class__.__name__ + #self.embedding_params = self.embedding_model.params + self.fetch_db_models() self.similarity_metric = self.params["similarity_metric"] self.top_k = self.params["top_k"] diff --git a/DashAI/back/models/RAG/Retrievers/retriever_model.py b/DashAI/back/models/RAG/Retrievers/retriever_model.py index fe77b3499..124c5995c 100644 --- a/DashAI/back/models/RAG/Retrievers/retriever_model.py +++ b/DashAI/back/models/RAG/Retrievers/retriever_model.py @@ -44,7 +44,7 @@ def __init__(self, **kwargs): self.chunks: Dict[int, Dict[int, Chunk]] = self.params.pop("chunks") self.chunking_model_id: int = self.params.pop("chunking_model_id") self.validate_chunks() - self.params = self.validate_and_transform(self.params) + #self.params = self.validate_and_transform(self.params) if self.pipeline_id is not None: diff --git a/DashAI/back/models/RAG/chunking_models/token_chunk_model.py b/DashAI/back/models/RAG/chunking_models/token_chunk_model.py index a6f58ea88..530018d95 100644 --- a/DashAI/back/models/RAG/chunking_models/token_chunk_model.py +++ b/DashAI/back/models/RAG/chunking_models/token_chunk_model.py @@ -39,24 +39,22 @@ class TokenChunkModel(BaseChunkingModel): SCHEMA = TokenChunkModelSchema def __init__(self, **kwargs): - pipeline_id = self.params.pop("pipeline_id") - - self.db_model = self.init_model_in_db(pipeline_id) - - self.params = self.validate_and_transform(kwargs) - - self.chunk_size = self.params["chunk_size"] - self.chunk_overlap = self.params["chunk_overlap"] - self.tokenizer_name = self.params["tokenizer_name"] + self.parameters = kwargs + self.chunk_size = self.parameters["chunk_size"] + self.chunk_overlap = self.parameters["chunk_overlap"] + self.tokenizer_name = self.parameters["tokenizer_name"] self.tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_name) + super().__init__(**kwargs) + - def chunk_text(self, text: str) -> List[List[str]]: + def chunk_text(self, text: str) -> List[str]: tokens = self.tokenizer.tokenize(text) - chunks = [] - for i in range(0, len(tokens), self.chunk_size): - chunk = tokens[i : i + self.chunk_size] - chunks.append(chunk) + token_chunks = [] + while len(tokens) > 0: + chunk = tokens[: self.chunk_size] + token_chunks.append(self.tokenizer.convert_tokens_to_string(chunk)) + tokens = tokens[self.chunk_size - self.chunk_overlap :] - return chunks + return token_chunks diff --git a/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py b/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py index e2eaec4f7..e0061aa69 100644 --- a/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py +++ b/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py @@ -56,6 +56,9 @@ def load(self): model_path = hf_hub_download(repo_id=self.model_name, filename=f"{model_name}.bin") self.model = fasttext.load_model(model_path) + def save(self): + pass + def encode(self, text: str) -> np.ndarray: """Encode text into an embedding.""" token_embeddings = [self.model.get_word_vector(word) for word in text.split()] diff --git a/DashAI/front/src/api/rag.ts b/DashAI/front/src/api/rag.ts index 52dc2b6c4..93c5a0fb8 100644 --- a/DashAI/front/src/api/rag.ts +++ b/DashAI/front/src/api/rag.ts @@ -46,6 +46,10 @@ export const createRAGSession = async ( component: string; params: Record; }; + prompt_model: { + component: string; + params: Record; + }; }; const transformedSession: Omit = @@ -69,6 +73,10 @@ export const createRAGSession = async ( component: params.generation_model.component, params: params.generation_model.params, }, + prompt_model: { + component: params.prompt_model.component, + params: params.prompt_model.params, + }, }, }; diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx index 5d9070bea..54ed4c9eb 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx @@ -63,6 +63,12 @@ const defaultNewSession = { component: "", params: {}, }, + prompt_model: { + component: "CustomGenerationPrompt", + params: { + template: "Answer to this message: {input}, with the following information: {chunks}", + }, + }, }, }; @@ -203,6 +209,12 @@ export default function NewSessionModal({ component: "", params: {}, }, + prompt_model: sessionData.parameters.prompt_model || { + component: "CustomGenerationPrompt", + params: { + template: "Answer to this message: {input}, with the following information: {chunks}", + } + }, }, }; From e0b02878e4e75575a3afba0e0f4b78ed6e5075ec Mon Sep 17 00:00:00 2001 From: Creylay Date: Thu, 16 Oct 2025 12:39:48 -0300 Subject: [PATCH 051/218] refactor: Integrate useRef for retriever step management and enhance form value handling in NewSessionModal and RetrieverConfigurationStep --- .../RAG/NewSessionModal/NewSessionModal.jsx | 20 +- .../RetrieverConfigurationStep.jsx | 425 ++++++++++-------- 2 files changed, 256 insertions(+), 189 deletions(-) diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx index 54ed4c9eb..1c3be53a6 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx @@ -1,4 +1,4 @@ -import React, { useState, useEffect, useCallback } from "react"; +import React, { useState, useEffect, useCallback, useRef } from "react"; import { Button, Dialog, @@ -87,6 +87,7 @@ export default function NewSessionModal({ new Array(steps.length).fill(false), ); const [sessionData, setSessionData] = useState(session || defaultNewSession); + const retrieverStepRef = useRef(null); // Ref para el paso de retriever useEffect(() => { if (open) { @@ -299,6 +300,7 @@ export default function NewSessionModal({ )} {activeStep === 2 && ( handleStepValidation(2, isValid)} @@ -331,11 +333,17 @@ export default function NewSessionModal({ - ), - }, - { - field: "created", - headerName: "Created At", - flex: 0.4, - valueFormatter: (params) => new Date(params.value).toLocaleDateString(), - }, - { - field: "", - headerName: "Documents", - flex: 0.4, - valueGetter: (params) => params.row.parameters.documents?.length, - }, - { - field: "actions", - headerName: "Actions", - flex: 0.9, - renderCell: (params) => ( -
+ const columns = React.useMemo( + () => [ + { + field: "name", + headerName: "Session Name", + flex: 0.8, + renderCell: (params) => ( - - -
- ), - }, - ]; + + + + + ), + }, + ], + [onSelect, onEdit], + ); return ( From 6ce5ce5fdbc1a43fa43efc379c8e933f34e7eb59 Mon Sep 17 00:00:00 2001 From: Creylay Date: Mon, 20 Oct 2025 15:10:37 -0300 Subject: [PATCH 055/218] refactor: Remove unnecessary console logs and clean up code in NewSessionModal and RetrieverConfigurationStep --- .../RAG/NewSessionModal/NewSessionModal.jsx | 7 +-- .../RetrieverConfigurationStep.jsx | 51 ++----------------- 2 files changed, 5 insertions(+), 53 deletions(-) diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx index 648f0e030..207e92e5c 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx @@ -88,7 +88,7 @@ export default function NewSessionModal({ new Array(steps.length).fill(false), ); const [sessionData, setSessionData] = useState(session || defaultNewSession); - const retrieverStepRef = useRef(null); // Ref para el paso de retriever + const retrieverStepRef = useRef(null); useEffect(() => { if (open) { @@ -336,12 +336,7 @@ export default function NewSessionModal({ - - + )} @@ -139,12 +192,14 @@ export default function DocumentSelectionTable({ } DocumentSelectionTable.propTypes = { - documents: PropTypes.arrayOf(PropTypes.shape({ - id: PropTypes.string.isRequired, - name: PropTypes.string.isRequired, - createdAt: PropTypes.string.isRequired, - preview: PropTypes.string, - })).isRequired, + documents: PropTypes.arrayOf( + PropTypes.shape({ + id: PropTypes.string.isRequired, + name: PropTypes.string.isRequired, + createdAt: PropTypes.string.isRequired, + preview: PropTypes.string, + }), + ).isRequired, selectedIds: PropTypes.arrayOf(PropTypes.string).isRequired, onToggle: PropTypes.func.isRequired, onSelectAll: PropTypes.func.isRequired, From bb8e6d437f375ec81409a6030f80a914ff357c57 Mon Sep 17 00:00:00 2001 From: Creylay Date: Tue, 21 Oct 2025 13:04:49 -0300 Subject: [PATCH 059/218] refactor: Replace custom table implementation with DataGrid for improved performance and usability --- .../generative/RAG/DocumentSelectionTable.jsx | 275 +++++++----------- 1 file changed, 111 insertions(+), 164 deletions(-) diff --git a/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx b/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx index a359ae3a0..90ee530c6 100644 --- a/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx +++ b/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx @@ -1,22 +1,10 @@ -import { - Box, - Table, - TableBody, - TableCell, - TableHead, - TableRow, - Checkbox, - Typography, - IconButton, - Tooltip, - Button, - CircularProgress, - TablePagination, -} from "@mui/material"; +import React from "react"; +import { Box, IconButton, Tooltip, LinearProgress } from "@mui/material"; +import { DataGrid } from "@mui/x-data-grid"; import VisibilityIcon from "@mui/icons-material/Visibility"; import DeleteIcon from "@mui/icons-material/Delete"; import PropTypes from "prop-types"; -import { useState } from "react"; +import { formatDate } from "../../../utils"; export default function DocumentSelectionTable({ documents, @@ -27,166 +15,125 @@ export default function DocumentSelectionTable({ onRemove, isLoading = false, }) { - const [page, setPage] = useState(0); - const [rowsPerPage, setRowsPerPage] = useState(5); + const handleSelectionChange = (newSelection) => { + // Get currently selected IDs + const currentSet = new Set(selectedIds); + const newSet = new Set(newSelection); - const handleChangePage = (event, newPage) => { - setPage(newPage); - }; + // Find what was added + const added = newSelection.filter((id) => !currentSet.has(id)); + // Find what was removed + const removed = selectedIds.filter((id) => !newSet.has(id)); - const handleChangeRowsPerPage = (event) => { - setRowsPerPage(parseInt(event.target.value, 10)); - setPage(0); + if (added.length > 0) { + added.forEach((id) => onToggle(id)); + } + if (removed.length > 0) { + removed.forEach((id) => onToggle(id)); + } }; - // Calculate paginated documents - const paginatedDocuments = documents.slice( - page * rowsPerPage, - page * rowsPerPage + rowsPerPage, + const columns = React.useMemo( + () => [ + { + field: "id", + headerName: "ID", + minWidth: 50, + flex: 0.5, + editable: false, + }, + { + field: "file_name", + headerName: "Name", + minWidth: 250, + flex: 1, + editable: false, + }, + { + field: "created", + headerName: "Added On", + minWidth: 140, + flex: 0.5, + editable: false, + valueGetter: (value) => formatDate(value), + }, + { + field: "last_modified", + headerName: "Last Modified", + minWidth: 140, + flex: 0.5, + editable: false, + valueGetter: (value, row) => { + return row.optional_metadata?.last_modified + ? formatDate(row.optional_metadata.last_modified) + : "N/A"; + }, + }, + { + field: "actions", + type: "actions", + headerName: "Actions", + minWidth: 100, + flex: 0.3, + getActions: (params) => [ + + { + if (params.row.preview) { + window.open(params.row.preview, "_blank"); + } else { + console.warn("No preview URL available for this document."); + } + }} + > + + + , + + onRemove(params.row.id)}> + + + , + ], + }, + ], + [onRemove], ); - // Format the date to a more readable format - const formatDate = (dateString) => { - try { - const date = new Date(dateString); - return date.toLocaleString(); - } catch (e) { - return dateString; - } - }; - return ( - {isLoading ? ( - - - - ) : documents.length === 0 ? ( - - No documents available. - - ) : ( - <> - - - - - 0 && - selectedIds.length < documents.length - } - checked={ - selectedIds.length === documents.length && - documents.length > 0 - } - onChange={(event) => { - if (event.target.checked) { - onSelectAll(); - } else { - onDeselectAll(); - } - }} - /> - - Id - Name - Added On - Last Modified - Actions - - - - {paginatedDocuments.map((doc) => ( - - - onToggle(doc.id)} - /> - - {doc.id} - {doc.file_name} - {formatDate(doc.created)} - - {doc.optional_metadata?.last_modified - ? formatDate(doc.optional_metadata.last_modified) - : "N/A"} - - - - - { - if (doc.preview) window.open(doc.preview, "_blank"); - else - console.warn( - "No preview URL available for this document.", - ); - }} - > - - - - - onRemove(doc.id)} - > - - - - - - - ))} - -
- - - )} + ); } From 6918936f2a000cec62161bfc0cb0e418f481b38a Mon Sep 17 00:00:00 2001 From: Creylay Date: Tue, 21 Oct 2025 13:23:01 -0300 Subject: [PATCH 060/218] refactor: Adjust column width and enhance scrollbar styles in DocumentSelectionTable --- .../generative/RAG/DocumentSelectionTable.jsx | 27 +++++++++++++++++-- 1 file changed, 25 insertions(+), 2 deletions(-) diff --git a/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx b/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx index 90ee530c6..c2c3bab7f 100644 --- a/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx +++ b/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx @@ -45,7 +45,7 @@ export default function DocumentSelectionTable({ { field: "file_name", headerName: "Name", - minWidth: 250, + minWidth: 220, flex: 1, editable: false, }, @@ -104,6 +104,9 @@ export default function DocumentSelectionTable({ return ( From 8b376e2f222d4bec03228e0ef778e99be90951bc Mon Sep 17 00:00:00 2001 From: Creylay Date: Tue, 21 Oct 2025 15:16:53 -0300 Subject: [PATCH 061/218] refactor: Improve styling for remove action button in DocumentSelectionTable --- .../components/generative/RAG/DocumentSelectionTable.jsx | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx b/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx index c2c3bab7f..1898356e2 100644 --- a/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx +++ b/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx @@ -91,7 +91,11 @@ export default function DocumentSelectionTable({ , - onRemove(params.row.id)}> + onRemove(params.row.id)} + sx={{ color: "error.main" }} + > , From e3935261d96a65fc16c608383eb1610462260e29 Mon Sep 17 00:00:00 2001 From: Creylay Date: Tue, 21 Oct 2025 15:34:28 -0300 Subject: [PATCH 062/218] refactor: Integrate DeleteItemModal for document removal confirmation in DocumentSelectionTable --- .../src/components/custom/DeleteItemModal.jsx | 6 +++--- .../generative/RAG/DocumentSelectionTable.jsx | 16 ++++++---------- 2 files changed, 9 insertions(+), 13 deletions(-) diff --git a/DashAI/front/src/components/custom/DeleteItemModal.jsx b/DashAI/front/src/components/custom/DeleteItemModal.jsx index 63a6a8486..0c2b63e9b 100644 --- a/DashAI/front/src/components/custom/DeleteItemModal.jsx +++ b/DashAI/front/src/components/custom/DeleteItemModal.jsx @@ -16,7 +16,7 @@ import TooltipedCellItem from "../shared/TooltipedCellItem"; * @param {Object} props * @param {Function} props.deleteFromTable - Function to delete the item from the table */ -function DeleteItemModal({ deleteFromTable }) { +function DeleteItemModal({ deleteFromTable, item = "item" }) { const [open, setOpen] = React.useState(false); const handleDelete = () => { deleteFromTable(); @@ -28,7 +28,7 @@ function DeleteItemModal({ deleteFromTable }) { key="delete-button" icon={} label="Delete" - tooltip="Delete item" + tooltip={`Delete ${item}`} onClick={() => setOpen(true)} sx={{ color: "error.main" }} /> @@ -36,7 +36,7 @@ function DeleteItemModal({ deleteFromTable }) { Confirm Deletion - Are you sure you want to delete this item? + Are you sure you want to delete this {item}? diff --git a/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx b/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx index 1898356e2..883090ede 100644 --- a/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx +++ b/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx @@ -2,9 +2,9 @@ import React from "react"; import { Box, IconButton, Tooltip, LinearProgress } from "@mui/material"; import { DataGrid } from "@mui/x-data-grid"; import VisibilityIcon from "@mui/icons-material/Visibility"; -import DeleteIcon from "@mui/icons-material/Delete"; import PropTypes from "prop-types"; import { formatDate } from "../../../utils"; +import DeleteItemModal from "../../custom/DeleteItemModal"; export default function DocumentSelectionTable({ documents, @@ -90,15 +90,11 @@ export default function DocumentSelectionTable({ , - - onRemove(params.row.id)} - sx={{ color: "error.main" }} - > - - - , + onRemove(params.row.id)} + item="document" + />, ], }, ], From a644f03e6e1c4bb5d092f87026c87b83f986d6ad Mon Sep 17 00:00:00 2001 From: Creylay Date: Tue, 21 Oct 2025 15:47:26 -0300 Subject: [PATCH 063/218] refactor: Add PromptConfigurationStep to NewSessionModal steps --- .../generative/RAG/NewSessionModal/NewSessionModal.jsx | 6 ++++++ .../RAG/NewSessionModal/PromptConfigurationStep.jsx | 0 2 files changed, 6 insertions(+) create mode 100644 DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx index 16d9c58c9..fdd624966 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx @@ -20,6 +20,7 @@ import DocumentSelectionStep from "./DocumentSelectionStep"; import ChunkingConfigurationStep from "./ChunkingConfigurationStep"; import RetrieverConfigurationStep from "./RetrieverConfigurationStep"; import GeneratorConfigurationStep from "./GeneratorConfigurationStep"; +import PromptConfigurationStep from "./PromptConfigurationStep"; import { generateSequentialName } from "../../../../utils/nameGenerator"; const steps = [ @@ -43,6 +44,11 @@ const steps = [ label: "Configure Language Model", component: GeneratorConfigurationStep, }, + { + name: "configure-prompt", + label: "Configure Prompt", + component: PromptConfigurationStep, + }, ]; const defaultNewSession = { diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx new file mode 100644 index 000000000..e69de29bb From d9593ffb79872bc259131aa61447cf5a575cdc0a Mon Sep 17 00:00:00 2001 From: Creylay Date: Tue, 21 Oct 2025 16:21:27 -0300 Subject: [PATCH 064/218] refactor: Implement PromptConfigurationStep for improved session configuration --- .../RAG/NewSessionModal/NewSessionModal.jsx | 8 +++ .../PromptConfigurationStep.jsx | 58 +++++++++++++++++++ 2 files changed, 66 insertions(+) diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx index fdd624966..c4e4cbfc2 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx @@ -341,6 +341,14 @@ export default function NewSessionModal({ setNextEnabled={(isValid) => handleStepValidation(3, isValid)} /> )} + + {activeStep === 4 && ( + handleStepValidation(4, isValid)} + /> + )} diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx index e69de29bb..1ec96b4d6 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx @@ -0,0 +1,58 @@ +import React, { useEffect } from "react"; +import { Box, Typography } from "@mui/material"; +import PropTypes from "prop-types"; + +export default function PromptConfigurationStep({ + sessionData, + setSessionData, + setNextEnabled, +}) { + // Set this step as always valid + useEffect(() => { + if (setNextEnabled) { + setNextEnabled(true); + } + }, [setNextEnabled]); + + return ( + + + Prompt Configuration + + + This step is currently under development. The default prompt template + will be used. + + + + Default template: + + + Answer to this message: {"{input}"}, with the following information:{" "} + {"{chunks}"} + + + + ); +} + +PromptConfigurationStep.propTypes = { + sessionData: PropTypes.object.isRequired, + setSessionData: PropTypes.func.isRequired, + setNextEnabled: PropTypes.func, +}; From da2f36988603f751230a70c5e0b7214a754dd141 Mon Sep 17 00:00:00 2001 From: Creylay Date: Tue, 21 Oct 2025 17:11:26 -0300 Subject: [PATCH 065/218] refactor: Add endpoint to retrieve all available RAG prompts with error handling --- .../api_v1/endpoints/generative_session.py | 67 ++++++++++++++++--- 1 file changed, 56 insertions(+), 11 deletions(-) diff --git a/DashAI/back/api/api_v1/endpoints/generative_session.py b/DashAI/back/api/api_v1/endpoints/generative_session.py index 3c622706f..fef7b1880 100644 --- a/DashAI/back/api/api_v1/endpoints/generative_session.py +++ b/DashAI/back/api/api_v1/endpoints/generative_session.py @@ -10,17 +10,16 @@ GenerativeSessionParams, ) from DashAI.back.dependencies.database.models import ( + Document, GenerativeProcess, GenerativeSession, GenerativeSessionParameterHistory, ProcessData, - Document + RAGPrompt, ) from DashAI.back.dependencies.registry import ComponentRegistry from DashAI.back.models import BaseGenerativeModel -from DashAI.back.models.RAG import PDFDocument, TxtDocument, BaseDocument from DashAI.back.tasks import BaseGenerativeTask - from DashAI.back.tasks.RAG_task import RAGTask router = APIRouter() @@ -61,22 +60,23 @@ async def upload_generative_session( status_code=status.HTTP_400_BAD_REQUEST, detail=f"Task {params.task_name} is not registered.", ) from e - + # RAG Task specific handling - # Frontend will send the ids of the documents to be used in the RAG session - # but RAG pipeline expects the documents paths of the backend-stored documents + # Frontend will send the ids of the documents to be used in the + # RAG session but RAG pipeline expects the documents paths of the + # backend-stored documents if task_class == RAGTask: try: assert params.parameters["documents"] assert isinstance(params.parameters["documents"], list) - assert len(params.parameters["documents"]) > 0 + assert len(params.parameters["documents"]) > 0 - except (AssertionError, KeyError): + except (AssertionError, KeyError) as e: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, detail="RAG Task requires a non-empty list of document IDs.", - ) - + ) from e + documents_ids = [] for doc_id in params.parameters["documents"]: document = db.get(Document, doc_id) @@ -144,6 +144,51 @@ async def upload_generative_session( ) from e +@router.get("/prompts", status_code=status.HTTP_200_OK) +async def get_all_prompts( + session_factory: sessionmaker = Depends(lambda: di["session_factory"]), +): + """Get all available RAG prompts. + + Parameters + ---------- + session_factory : Callable[..., ContextManager[Session]] + A factory that creates a context manager that handles a SQLAlchemy session. + The generated session can be used to access and query the database. + + Returns + ------- + list + A list of dictionaries with all prompts on the database. + + Raises + ------ + HTTPException + If there's an internal database error. + """ + with session_factory() as db: + try: + prompts = db.query(RAGPrompt).all() + + prompt_responses = [] + for prompt in prompts: + prompt_responses.append( + { + "id": prompt.id, + "class_name": prompt.class_name, + "parameters": prompt.parameters, + } + ) + + return prompt_responses + except exc.SQLAlchemyError as e: + log.exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal database error", + ) from e + + @router.get("/{session_id}", status_code=status.HTTP_200_OK) async def get_generative_session( session_id: int, @@ -533,4 +578,4 @@ async def get_parameter_history_entry( raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail="Internal database error", - ) from e \ No newline at end of file + ) from e From 192ab64adf2f5978260c16fc9929417abb71f03f Mon Sep 17 00:00:00 2001 From: Creylay Date: Tue, 21 Oct 2025 17:11:39 -0300 Subject: [PATCH 066/218] refactor: Add getRAGPrompts function to fetch RAG prompts with error handling --- DashAI/front/src/api/rag.ts | 11 +++++++++++ DashAI/front/src/types/ragPrompt.ts | 7 +++++++ 2 files changed, 18 insertions(+) create mode 100644 DashAI/front/src/types/ragPrompt.ts diff --git a/DashAI/front/src/api/rag.ts b/DashAI/front/src/api/rag.ts index 93c5a0fb8..5a819acf9 100644 --- a/DashAI/front/src/api/rag.ts +++ b/DashAI/front/src/api/rag.ts @@ -3,6 +3,7 @@ import { ISession } from "../types/session"; import { IGenerativeTask } from "../types/generativeTask"; import { IDocumentResponse } from "../types/documentResponse"; import { IComponent } from "../types/component"; +import { IRAGPrompt } from "../types/ragPrompt"; import { getChildComponents } from "./component"; // Fetch all RAG sessions @@ -221,3 +222,13 @@ export const addDocument = async ({ return response.data; }; + +export const getRAGPrompts = async (): Promise => { + const response = await api.get( + "/v1/generative-session/prompts", + ); + if (response.status !== 200) { + throw new Error(`Failed to fetch RAG prompts: ${response.statusText}`); + } + return response.data; +}; diff --git a/DashAI/front/src/types/ragPrompt.ts b/DashAI/front/src/types/ragPrompt.ts new file mode 100644 index 000000000..87300586d --- /dev/null +++ b/DashAI/front/src/types/ragPrompt.ts @@ -0,0 +1,7 @@ +export interface IRAGPrompt { + id: number; + class_name: string; + parameters: { + template: string; + }; +} From 9181533d776039654a6d44b39ea6622e3f3a36a6 Mon Sep 17 00:00:00 2001 From: Creylay Date: Wed, 22 Oct 2025 19:30:27 -0300 Subject: [PATCH 067/218] refactor: Clean up code formatting and improve readability in models.py --- DashAI/back/dependencies/database/models.py | 289 ++++++++++---------- 1 file changed, 137 insertions(+), 152 deletions(-) diff --git a/DashAI/back/dependencies/database/models.py b/DashAI/back/dependencies/database/models.py index 83e52b081..0668d53e7 100644 --- a/DashAI/back/dependencies/database/models.py +++ b/DashAI/back/dependencies/database/models.py @@ -3,7 +3,19 @@ from datetime import datetime from typing import Any, Dict, List, Optional -from sqlalchemy import JSON, Boolean, CheckConstraint, Column, DateTime, Enum, ForeignKey, Integer, String, Text, UniqueConstraint, ARRAY +from sqlalchemy import ( + JSON, + Boolean, + CheckConstraint, + Column, + DateTime, + Enum, + ForeignKey, + Integer, + String, + Text, + UniqueConstraint, +) from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import Mapped, mapped_column, relationship @@ -415,7 +427,7 @@ class GenerativeSession(Base): processes: Mapped[List["GenerativeProcess"]] = relationship( "GenerativeProcess", cascade="all, delete-orphan", back_populates="session" ) - + # Relationship with RAGDocumentPipelineSessionLink pipeline_links: Mapped[List["RAGDocumentPipelineSessionLink"]] = relationship( back_populates="session" @@ -620,23 +632,11 @@ def delete_result(self) -> None: self.end_time = None - - - - - - - - - - - - - """ RAG tables """ + class Document(Base): __tablename__ = "document" """ @@ -659,7 +659,7 @@ class Document(Base): pipeline_links: Mapped[List["RAGDocumentPipelineSessionLink"]] = relationship( "RAGDocumentPipelineSessionLink", cascade="all, delete-orphan", - back_populates="document" + back_populates="document", ) chunks: Mapped[List["Chunk"]] = relationship( @@ -667,37 +667,39 @@ class Document(Base): back_populates="document", cascade="all, delete-orphan", ) - + embedding_matrices: Mapped[List["RAGEmbeddingMatrix"]] = relationship( - "RAGEmbeddingMatrix", - back_populates="document", - cascade="all, delete-orphan" + "RAGEmbeddingMatrix", back_populates="document", cascade="all, delete-orphan" ) @property def get_related_sessions(self) -> List["GenerativeSession"]: """Return a list of sessions related to the document.""" return [link.session for link in self.pipeline_links] - + @property def get_related_pipelines(self) -> List["RAGPipeline"]: """Return a list of pipelines related to the document.""" return [link.pipeline for link in self.pipeline_links] - - def get_embedding_matrix(self, chunking_model_id: int, embedding_model_id: int) -> Optional["RAGEmbeddingMatrix"]: + + def get_embedding_matrix( + self, chunking_model_id: int, embedding_model_id: int + ) -> Optional["RAGEmbeddingMatrix"]: """ - Get the embedding matrix for a specific combination of chunking and embedding models. - + Get the embedding matrix for a given chunking and embedding model ids. + Args: - chunking_model_id: ID of the chunking model - embedding_model_id: ID of the embedding model - + chunking_model_id: ID of the chunking model. + embedding_model_id: ID of the embedding model. + Returns: - The embedding matrix if found, None otherwise + The embedding matrix if found, otherwise None. """ for matrix in self.embedding_matrices: - if (matrix.chunking_model_id == chunking_model_id and - matrix.embedding_model_id == embedding_model_id): + if ( + matrix.chunking_model_id == chunking_model_id + and matrix.embedding_model_id == embedding_model_id + ): return matrix return None @@ -711,27 +713,20 @@ class Chunk(Base): document_id: Mapped[int] = mapped_column( ForeignKey("document.id", ondelete="CASCADE"), nullable=False ) - chunk_index: Mapped[int] = mapped_column(Integer, nullable=False) + chunk_index: Mapped[int] = mapped_column(Integer, nullable=False) chunking_model_id: Mapped[int] = mapped_column( - ForeignKey("rag_chunking_model.id", ondelete="CASCADE"), - nullable=False + ForeignKey("rag_chunking_model.id", ondelete="CASCADE"), nullable=False ) text: Mapped[str] = mapped_column(Text, nullable=False) hash: Mapped[str] = mapped_column(String, nullable=False, unique=True) - document: Mapped["Document"] = relationship( - "Document", - back_populates="chunks" - ) - + document: Mapped["Document"] = relationship("Document", back_populates="chunks") + chunking_model: Mapped["RAGChunkingModel"] = relationship( - "RAGChunkingModel", - back_populates="chunks" + "RAGChunkingModel", back_populates="chunks" ) - __table_args__ = ( - UniqueConstraint("id", "document_id", name="uix_chunk_document"), - ) + __table_args__ = (UniqueConstraint("id", "document_id", name="uix_chunk_document"),) class RAGPrompt(Base): @@ -742,12 +737,16 @@ class RAGPrompt(Base): id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) class_name: Mapped[str] = mapped_column(String, nullable=False) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) - + created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) + last_modified: Mapped[DateTime] = mapped_column( + DateTime, + default=datetime.now, + onupdate=datetime.now, + ) + # Relationship with RAGPipeline pipelines: Mapped[List["RAGPipeline"]] = relationship( - "RAGPipeline", - back_populates="prompt", - cascade="all, delete-orphan" + "RAGPipeline", back_populates="prompt", cascade="all, delete-orphan" ) @@ -759,77 +758,55 @@ class RAGGenerationModel(Base): id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) class_name: Mapped[str] = mapped_column(String, nullable=False) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) - + # Relationships pipelines: Mapped[List["RAGPipeline"]] = relationship( back_populates="generation_model" ) + class RAGPipeline(Base): __tablename__ = "rag_pipeline" """ Table to store all the information about a RAG pipeline. """ - id: Mapped[int] = mapped_column( - primary_key=True, - autoincrement=True) + id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) session_id: Mapped[int] = mapped_column( - ForeignKey( - "generative_session.id", - ondelete="CASCADE"), - nullable=False + ForeignKey("generative_session.id", ondelete="CASCADE"), nullable=False ) name: Mapped[str] = mapped_column(String, nullable=False) description: Mapped[str] = mapped_column(String, nullable=True) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) - + chunking_model_id: Mapped[int] = mapped_column( - ForeignKey( - "rag_chunking_model.id", - ondelete="CASCADE"), - nullable=False + ForeignKey("rag_chunking_model.id", ondelete="CASCADE"), nullable=False ) retriever_model_id: Mapped[int] = mapped_column( - ForeignKey( - "rag_retriever.id", - ondelete="CASCADE"), - nullable=False + ForeignKey("rag_retriever.id", ondelete="CASCADE"), nullable=False ) prompt_id: Mapped[int] = mapped_column( - ForeignKey( - "rag_prompt.id", - ondelete="CASCADE"), - nullable=False + ForeignKey("rag_prompt.id", ondelete="CASCADE"), nullable=False ) generation_model_id: Mapped[int] = mapped_column( - ForeignKey( - "rag_generation_model.id", - ondelete="CASCADE"), - nullable=False + ForeignKey("rag_generation_model.id", ondelete="CASCADE"), nullable=False ) # Relationships chunking_model: Mapped["RAGChunkingModel"] = relationship( - "RAGChunkingModel", - back_populates="pipelines" + "RAGChunkingModel", back_populates="pipelines" ) retriever: Mapped["RAGRetriever"] = relationship( - "RAGRetriever", - back_populates="pipelines" - ) - prompt: Mapped["RAGPrompt"] = relationship( - "RAGPrompt", - back_populates="pipelines" + "RAGRetriever", back_populates="pipelines" ) + prompt: Mapped["RAGPrompt"] = relationship("RAGPrompt", back_populates="pipelines") generation_model: Mapped["RAGGenerationModel"] = relationship( - "RAGGenerationModel", - back_populates="pipelines" + "RAGGenerationModel", back_populates="pipelines" ) pipeline_links: Mapped[List["RAGDocumentPipelineSessionLink"]] = relationship( - "RAGDocumentPipelineSessionLink", - back_populates="pipeline" + "RAGDocumentPipelineSessionLink", back_populates="pipeline" ) + class RAGChunkingModel(Base): __tablename__ = "rag_chunking_model" """ @@ -838,20 +815,17 @@ class RAGChunkingModel(Base): id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) class_name: Mapped[str] = mapped_column(String, nullable=False) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) - + pipelines: Mapped[List["RAGPipeline"]] = relationship( - "RAGPipeline", - back_populates="chunking_model" + "RAGPipeline", back_populates="chunking_model" ) - + embedding_matrices: Mapped[List["RAGEmbeddingMatrix"]] = relationship( - "RAGEmbeddingMatrix", - back_populates="chunking_model" + "RAGEmbeddingMatrix", back_populates="chunking_model" ) - + chunks: Mapped[List["Chunk"]] = relationship( - "Chunk", - back_populates="chunking_model" + "Chunk", back_populates="chunking_model" ) @@ -861,14 +835,14 @@ class RAGRetriever(Base): Table to map the retriever model id to its dense or sparse retriever instance. """ id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) - class_name: Mapped[str] = mapped_column(String, nullable=False) # "SparseRetriever" or "DenseRetriever" + class_name: Mapped[str] = mapped_column( + String, nullable=False + ) # "SparseRetriever" or "DenseRetriever" dense_retriever_id: Mapped[Optional[int]] = mapped_column( - ForeignKey("rag_dense_retriever.id", ondelete="CASCADE"), - nullable=True + ForeignKey("rag_dense_retriever.id", ondelete="CASCADE"), nullable=True ) sparse_retriever_id: Mapped[Optional[int]] = mapped_column( - ForeignKey("rag_sparse_retriever.id", ondelete="CASCADE"), - nullable=True + ForeignKey("rag_sparse_retriever.id", ondelete="CASCADE"), nullable=True ) # Relationships @@ -878,18 +852,21 @@ class RAGRetriever(Base): dense_retriever: Mapped[Optional["RAGDenseRetriever"]] = relationship( back_populates="related_retriever" ) - pipelines: Mapped[List["RAGPipeline"]] = relationship( - back_populates="retriever" - ) + pipelines: Mapped[List["RAGPipeline"]] = relationship(back_populates="retriever") __table_args__ = ( CheckConstraint( - "(class_name = 'SparseRetriever' AND sparse_retriever_id IS NOT NULL AND dense_retriever_id IS NULL) OR " - "(class_name = 'DenseRetriever' AND dense_retriever_id IS NOT NULL AND sparse_retriever_id IS NULL)", - name="chk_retriever_type" + ( + "(class_name = 'SparseRetriever' " + "AND sparse_retriever_id IS NOT NULL " + "AND dense_retriever_id IS NULL) OR " + "(class_name = 'DenseRetriever' " + "AND dense_retriever_id IS NOT NULL " + "AND sparse_retriever_id IS NULL)" + ), + name="chk_retriever_type", ), ) - class RAGSparseRetriever(Base): @@ -904,7 +881,7 @@ class RAGSparseRetriever(Base): related_retriever: Mapped["RAGRetriever"] = relationship( "RAGRetriever", - back_populates="sparse_retriever", + back_populates="sparse_retriever", ) @@ -923,11 +900,10 @@ class RAGDenseRetriever(Base): # Relationships related_retriever: Mapped["RAGRetriever"] = relationship( "RAGRetriever", - back_populates="dense_retriever", + back_populates="dense_retriever", ) embedding_model: Mapped["RAGEmbeddingModel"] = relationship( - "RAGEmbeddingModel", - back_populates="dense_retrievers" + "RAGEmbeddingModel", back_populates="dense_retrievers" ) @@ -939,11 +915,10 @@ class RAGEmbeddingModel(Base): id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) class_name: Mapped[str] = mapped_column(String, nullable=False) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) - + # Relationships embedding_matrices: Mapped[List["RAGEmbeddingMatrix"]] = relationship( - back_populates="embedding_model", - cascade="all, delete-orphan" + back_populates="embedding_model", cascade="all, delete-orphan" ) dense_retrievers: Mapped[List["RAGDenseRetriever"]] = relationship( back_populates="embedding_model" @@ -953,7 +928,7 @@ class RAGEmbeddingModel(Base): class RAGEmbeddingMatrix(Base): __tablename__ = "rag_embedding_matrix" """ - Table to store embedding matrices for each unique combination of + Table to store embedding matrices for each unique combination of document, chunking model, and embedding model. """ id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) @@ -967,91 +942,101 @@ class RAGEmbeddingMatrix(Base): ForeignKey("rag_embedding_model.id", ondelete="CASCADE"), nullable=False ) storage_folder: Mapped[str] = mapped_column(String, nullable=False) - matrix_shape: Mapped[List[int]] = mapped_column(JSON, nullable=False) # [num_chunks, embedding_dim] + matrix_shape: Mapped[List[int]] = mapped_column( + JSON, nullable=False + ) # [num_chunks, embedding_dim] created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) last_modified: Mapped[DateTime] = mapped_column( DateTime, default=datetime.now, onupdate=datetime.now, ) - + # Relationships document: Mapped["Document"] = relationship( - "Document", - back_populates="embedding_matrices" + "Document", back_populates="embedding_matrices" ) chunking_model: Mapped["RAGChunkingModel"] = relationship( - "RAGChunkingModel", - back_populates="embedding_matrices" + "RAGChunkingModel", back_populates="embedding_matrices" ) embedding_model: Mapped["RAGEmbeddingModel"] = relationship( - "RAGEmbeddingModel", - back_populates="embedding_matrices" + "RAGEmbeddingModel", back_populates="embedding_matrices" ) # Constraints - ensure uniqueness of the combination __table_args__ = ( UniqueConstraint( - "document_id", - "chunking_model_id", - "embedding_model_id", - name="uix_document_chunking_embedding" + "document_id", + "chunking_model_id", + "embedding_model_id", + name="uix_document_chunking_embedding", ), ) - + @property def num_chunks(self) -> int: """Return the number of chunks in this embedding matrix.""" return self.matrix_shape[0] if self.matrix_shape else 0 - + @property def embedding_dimension(self) -> int: """Return the embedding dimension.""" return self.matrix_shape[1] if len(self.matrix_shape) > 1 else 0 - + @classmethod - def get_by_tuple(cls, session, document_id: int, chunking_model_id: int, embedding_model_id: int) -> Optional["RAGEmbeddingMatrix"]: + def get_by_tuple( + cls, session, document_id: int, chunking_model_id: int, embedding_model_id: int + ) -> Optional["RAGEmbeddingMatrix"]: """ - Get an embedding matrix by the tuple (document_id, chunking_model_id, embedding_model_id). - + Get an embedding matrix by tuple + (document_id, chunking_model_id, embedding_model_id). + Args: - session: SQLAlchemy session - document_id: ID of the document - chunking_model_id: ID of the chunking model - embedding_model_id: ID of the embedding model - + session: SQLAlchemy session. + document_id: ID of the document. + chunking_model_id: ID of the chunking model. + embedding_model_id: ID of the embedding model. + Returns: - The embedding matrix if found, None otherwise + The embedding matrix if found, otherwise None. """ - return session.query(cls).filter( - cls.document_id == document_id, - cls.chunking_model_id == chunking_model_id, - cls.embedding_model_id == embedding_model_id - ).first() - - - + return ( + session.query(cls) + .filter( + cls.document_id == document_id, + cls.chunking_model_id == chunking_model_id, + cls.embedding_model_id == embedding_model_id, + ) + .first() + ) """ RAG relationship tables """ + class RAGDocumentPipelineSessionLink(Base): __tablename__ = "rag_document_pipeline_session_link" - + id = Column(Integer, primary_key=True, autoincrement=True) - document_id = Column(Integer, ForeignKey("document.id", ondelete="CASCADE"), nullable=False) - session_id = Column(Integer, ForeignKey("generative_session.id", ondelete="CASCADE"), nullable=False) - pipeline_id = Column(Integer, ForeignKey("rag_pipeline.id", ondelete="CASCADE"), nullable=False) + document_id = Column( + Integer, ForeignKey("document.id", ondelete="CASCADE"), nullable=False + ) + session_id = Column( + Integer, ForeignKey("generative_session.id", ondelete="CASCADE"), nullable=False + ) + pipeline_id = Column( + Integer, ForeignKey("rag_pipeline.id", ondelete="CASCADE"), nullable=False + ) # Relationships document = relationship("Document", back_populates="pipeline_links") pipeline = relationship("RAGPipeline", back_populates="pipeline_links") session = relationship("GenerativeSession", back_populates="pipeline_links") - + __table_args__ = ( UniqueConstraint("document_id", "session_id", name="uix_document_session"), UniqueConstraint("session_id", "pipeline_id", name="uix_session_pipeline"), UniqueConstraint("document_id", "pipeline_id", name="uix_document_pipeline"), - ) \ No newline at end of file + ) From 3395e26b69bf5675518e594c625dea1733e2f746 Mon Sep 17 00:00:00 2001 From: Creylay Date: Wed, 22 Oct 2025 19:42:51 -0300 Subject: [PATCH 068/218] refactor: Add optional name field to RAGPrompt model and set default prompt name in NewSessionModal --- DashAI/back/dependencies/database/models.py | 1 + DashAI/back/models/RAG/prompts/prompt.py | 58 +++++++++---------- .../RAG/NewSessionModal/NewSessionModal.jsx | 1 + 3 files changed, 30 insertions(+), 30 deletions(-) diff --git a/DashAI/back/dependencies/database/models.py b/DashAI/back/dependencies/database/models.py index 0668d53e7..c350fbcca 100644 --- a/DashAI/back/dependencies/database/models.py +++ b/DashAI/back/dependencies/database/models.py @@ -736,6 +736,7 @@ class RAGPrompt(Base): """ id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True) class_name: Mapped[str] = mapped_column(String, nullable=False) + name: Mapped[str] = mapped_column(String, nullable=True) parameters: Mapped[JSON] = mapped_column(JSON, nullable=True) created: Mapped[DateTime] = mapped_column(DateTime, default=datetime.now) last_modified: Mapped[DateTime] = mapped_column( diff --git a/DashAI/back/models/RAG/prompts/prompt.py b/DashAI/back/models/RAG/prompts/prompt.py index 2c8aa4e6f..36e39e94f 100644 --- a/DashAI/back/models/RAG/prompts/prompt.py +++ b/DashAI/back/models/RAG/prompts/prompt.py @@ -1,17 +1,19 @@ from typing import Any, Dict, List -from DashAI.back.models.base_model import BaseModel from DashAI.back.core.schema_fields import BaseSchema, schema_field, string_field -from DashAI.back.dependencies.database.models import ( - RAGPrompt as PromptDBModel) +from DashAI.back.dependencies.database.models import RAGPrompt as PromptDBModel +from DashAI.back.models.base_model import BaseModel -class PromptSchema(BaseSchema): +class PromptSchema(BaseSchema): template: str = schema_field( string_field(), placeholder="", description="The prompt template with placeholders.", ) + name: str = schema_field( + string_field(), placeholder="", description="Optional name for the prompt." + ) class Prompt(BaseModel): @@ -23,32 +25,35 @@ class Prompt(BaseModel): SCHEMA = PromptSchema REQUIRED_EXTRA_KWARGS = ["db"] id: int + name: str def __init__(self, **kwargs): self.db = kwargs.pop("db") kwargs = self.validate_and_transform(kwargs) self.template = kwargs.get("template") - assert self.validate_template(self.template), "The template is missing required placeholders." + self.name = kwargs.get("name") + assert self.validate_template(self.template), ( + "The template is missing required placeholders." + ) self.class_name = self.__class__.__name__ self.params = kwargs - - stored_model = self.db.query(PromptDBModel).filter_by( - class_name = self.class_name, - parameters = self.params - ).first() + stored_model = ( + self.db.query(PromptDBModel) + .filter_by( + class_name=self.class_name, name=self.name, parameters=self.params + ) + .first() + ) if stored_model: self.id = stored_model.id else: new_model = PromptDBModel( - class_name=self.class_name, - parameters=self.params + class_name=self.class_name, name=self.name, parameters=self.params ) self.db.add(new_model) self.db.commit() self.id = new_model.id - - def load(self, **kwargs: Any) -> None: pass @@ -57,14 +62,9 @@ def save(self) -> None: @classmethod def get_metadata(cls) -> Dict[str, Any]: - if hasattr(cls, 'metadata'): - metadata = cls.metadata - else: - metadata = {} + metadata = cls.metadata if hasattr(cls, "metadata") else {} return metadata - - @classmethod def get_required_placeholders(cls) -> List[str]: """ @@ -72,19 +72,22 @@ def get_required_placeholders(cls) -> List[str]: Returns: List[str]: List of required placeholders. """ - assert hasattr(cls, 'required_placeholders'), "Subclasses must define 'required_placeholders' class attribute." + assert hasattr(cls, "required_placeholders"), ( + "Subclasses must define 'required_placeholders' class attribute." + ) return cls.required_placeholders - + def get_optional_placeholders(self) -> List[str]: """ Get the list of optional placeholders for the prompt template. Returns: List[str]: List of optional placeholders. """ - assert hasattr(self, 'optional_placeholders'), "Subclasses must define 'optional_placeholders' class attribute." + assert hasattr(self, "optional_placeholders"), ( + "Subclasses must define 'optional_placeholders' class attribute." + ) return self.optional_placeholders - @classmethod def validate_template(cls, template: str) -> bool: """ @@ -94,11 +97,7 @@ def validate_template(cls, template: str) -> bool: Returns: bool: True if the template is valid, False otherwise. """ - for placeholder in cls.required_placeholders: - if placeholder not in template: - return False - return True - + return all(placeholder in template for placeholder in cls.required_placeholders) @staticmethod def format(input: str, **kwargs: Any) -> str: @@ -111,4 +110,3 @@ def format(input: str, **kwargs: Any) -> str: str: The formatted prompt. """ raise NotImplementedError("Subclasses must implement this method.") - \ No newline at end of file diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx index c4e4cbfc2..15cb1bd91 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx @@ -73,6 +73,7 @@ const defaultNewSession = { prompt_model: { component: "CustomGenerationPrompt", params: { + name: "Default Prompt", template: "Answer to this message: {input}, with the following information: {chunks}", }, From c4fec69fedb2e11e3bf9c67c8839aea9a1588abf Mon Sep 17 00:00:00 2001 From: Creylay Date: Wed, 22 Oct 2025 22:14:36 -0300 Subject: [PATCH 069/218] refactor: Add optional fields for created date and last modified date in IRAGPrompt interface --- DashAI/front/src/types/ragPrompt.ts | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/DashAI/front/src/types/ragPrompt.ts b/DashAI/front/src/types/ragPrompt.ts index 87300586d..2ea780787 100644 --- a/DashAI/front/src/types/ragPrompt.ts +++ b/DashAI/front/src/types/ragPrompt.ts @@ -1,7 +1,11 @@ export interface IRAGPrompt { id: number; class_name: string; + name?: string; + created?: string; + last_modified?: string; parameters: { template: string; + name?: string; }; } From c2119a5d427660a33eba89809b1e3432b368b611 Mon Sep 17 00:00:00 2001 From: Creylay Date: Wed, 22 Oct 2025 22:14:47 -0300 Subject: [PATCH 070/218] refactor: Include name, created, and last modified fields in get_all_prompts response --- DashAI/back/api/api_v1/endpoints/generative_session.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/DashAI/back/api/api_v1/endpoints/generative_session.py b/DashAI/back/api/api_v1/endpoints/generative_session.py index fef7b1880..d4d9dfffa 100644 --- a/DashAI/back/api/api_v1/endpoints/generative_session.py +++ b/DashAI/back/api/api_v1/endpoints/generative_session.py @@ -176,7 +176,10 @@ async def get_all_prompts( { "id": prompt.id, "class_name": prompt.class_name, + "name": prompt.name, "parameters": prompt.parameters, + "created": prompt.created, + "last_modified": prompt.last_modified, } ) From e484c99bf7ddf1ff34dae587247d764f10929e82 Mon Sep 17 00:00:00 2001 From: Creylay Date: Wed, 22 Oct 2025 22:14:58 -0300 Subject: [PATCH 071/218] refactor: Implement TemplateModal and integrate with PromptSelectionTable for viewing templates --- .../src/components/custom/TemplateModal.jsx | 37 ++++++ .../generative/RAG/PromptSelectionTable.jsx | 109 ++++++++++++++++++ .../PromptConfigurationStep.jsx | 68 +++-------- 3 files changed, 160 insertions(+), 54 deletions(-) create mode 100644 DashAI/front/src/components/custom/TemplateModal.jsx create mode 100644 DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx diff --git a/DashAI/front/src/components/custom/TemplateModal.jsx b/DashAI/front/src/components/custom/TemplateModal.jsx new file mode 100644 index 000000000..0026600f4 --- /dev/null +++ b/DashAI/front/src/components/custom/TemplateModal.jsx @@ -0,0 +1,37 @@ +import React from "react"; +import PropTypes from "prop-types"; +import { + Dialog, + DialogTitle, + DialogContent, + DialogContentText, + DialogActions, + Button, + Typography, +} from "@mui/material"; + +export default function TemplateModal({ open, handleClose, template }) { + return ( + + Prompt Template + + + + {template} + + + + + + + + ); +} + +TemplateModal.propTypes = { + open: PropTypes.bool.isRequired, + handleClose: PropTypes.func.isRequired, + template: PropTypes.string, +}; diff --git a/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx b/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx new file mode 100644 index 000000000..fb570fc2a --- /dev/null +++ b/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx @@ -0,0 +1,109 @@ +import React, { useState } from "react"; +import { Box, Paper, Tooltip, IconButton } from "@mui/material"; +import VisibilityIcon from "@mui/icons-material/Visibility"; +import { DataGrid } from "@mui/x-data-grid"; +import { formatDate } from "../../../utils"; +import TemplateModal from "../../custom/TemplateModal"; + +export default function PromptSelectionTable({ prompts = [] }) { + const [modalOpen, setModalOpen] = useState(false); + const [selectedTemplate, setSelectedTemplate] = useState(""); + + const handleViewTemplate = (template) => { + setSelectedTemplate(template); + setModalOpen(true); + }; + + const handleCloseModal = () => { + setModalOpen(false); + setSelectedTemplate(""); + }; + + const columns = React.useMemo( + () => [ + { + field: "id", + headerName: "ID", + minWidth: 50, + flex: 0.3, + editable: false, + }, + { + field: "name", + headerName: "Name", + minWidth: 140, + flex: 1, + editable: false, + }, + { + field: "class_name", + headerName: "Type", + minWidth: 140, + flex: 1, + editable: false, + }, + { + field: "created", + headerName: "Created", + minWidth: 140, + flex: 1, + editable: false, + valueGetter: (value) => formatDate(value), + }, + { + field: "last_modified", + headerName: "Edited", + minWidth: 140, + flex: 1, + editable: false, + valueGetter: (value) => formatDate(value), + }, + { + field: "actions", + type: "actions", + headerName: "Actions", + minWidth: 80, + flex: 0.4, + getActions: (params) => [ + + + handleViewTemplate(params.row.parameters?.template || "") + } + > + + + , + ], + }, + ], + [], + ); + + return ( + + + + + + + ); +} diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx index 1ec96b4d6..d760ccb93 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx @@ -1,58 +1,18 @@ -import React, { useEffect } from "react"; -import { Box, Typography } from "@mui/material"; -import PropTypes from "prop-types"; +import { useEffect, useState } from "react"; +import PromptSelectionTable from "../../../../components/generative/RAG/PromptSelectionTable"; +import { getRAGPrompts } from "../../../../api/rag"; + +export default function PromptConfigurationStep({ setNextEnabled }) { + const [prompts, setPrompts] = useState([]); + const [loading, setLoading] = useState(true); -export default function PromptConfigurationStep({ - sessionData, - setSessionData, - setNextEnabled, -}) { - // Set this step as always valid useEffect(() => { - if (setNextEnabled) { - setNextEnabled(true); - } - }, [setNextEnabled]); + setNextEnabled && setNextEnabled(true); + getRAGPrompts() + .then((data) => setPrompts(data)) + .catch(() => setPrompts([])) + .finally(() => setLoading(false)); + }, []); - return ( - - - Prompt Configuration - - - This step is currently under development. The default prompt template - will be used. - - - - Default template: - - - Answer to this message: {"{input}"}, with the following information:{" "} - {"{chunks}"} - - - - ); + return ; } - -PromptConfigurationStep.propTypes = { - sessionData: PropTypes.object.isRequired, - setSessionData: PropTypes.func.isRequired, - setNextEnabled: PropTypes.func, -}; From 10a91b96d0570c27946365d3f0ab0fe3b39462b7 Mon Sep 17 00:00:00 2001 From: Creylay Date: Wed, 22 Oct 2025 23:52:09 -0300 Subject: [PATCH 072/218] feat: Add NewPromptModal component and integrate with PromptSelectionTable for creating new prompts --- .../generative/RAG/NewPromptModal.jsx | 52 ++++++++++++++ .../generative/RAG/PromptSelectionTable.jsx | 70 +++++++++++++++++-- .../PromptConfigurationStep.jsx | 36 +++++++++- 3 files changed, 150 insertions(+), 8 deletions(-) create mode 100644 DashAI/front/src/components/generative/RAG/NewPromptModal.jsx diff --git a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx new file mode 100644 index 000000000..ae2161315 --- /dev/null +++ b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx @@ -0,0 +1,52 @@ +import React from "react"; + +import { + Dialog, + DialogTitle, + DialogContent, + DialogActions, + Button, + Typography, + Grid, + IconButton, +} from "@mui/material"; +import CloseIcon from "@mui/icons-material/Close"; + +export default function NewPromptModal({ open, handleClose }) { + return ( + + + + + + Create New Prompt + + + + + + + + + + + + Hola mundo + + + + + + + + ); +} diff --git a/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx b/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx index fb570fc2a..f8a81bbb9 100644 --- a/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx +++ b/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx @@ -1,13 +1,30 @@ import React, { useState } from "react"; -import { Box, Paper, Tooltip, IconButton } from "@mui/material"; +import { + Box, + Paper, + Tooltip, + IconButton, + Button, + Grid, + Typography, +} from "@mui/material"; +import { AddCircleOutline as AddIcon } from "@mui/icons-material"; import VisibilityIcon from "@mui/icons-material/Visibility"; import { DataGrid } from "@mui/x-data-grid"; import { formatDate } from "../../../utils"; import TemplateModal from "../../custom/TemplateModal"; +import NewPromptModal from "./NewPromptModal"; -export default function PromptSelectionTable({ prompts = [] }) { +export default function PromptSelectionTable({ + prompts = [], + loading = false, + rowSelectionModel = [], + onRowSelectionModelChange, + showTableTitle = false, +}) { const [modalOpen, setModalOpen] = useState(false); const [selectedTemplate, setSelectedTemplate] = useState(""); + const [newPromptModalOpen, setNewPromptModalOpen] = useState(false); const handleViewTemplate = (template) => { setSelectedTemplate(template); @@ -82,8 +99,45 @@ export default function PromptSelectionTable({ prompts = [] }) { ); return ( - - + + {showTableTitle && ( + + + Prompts + + + + )} + {!showTableTitle && ( + + + + )} + + setNewPromptModalOpen(false)} + /> ); diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx index d760ccb93..a507cb99e 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx @@ -2,17 +2,47 @@ import { useEffect, useState } from "react"; import PromptSelectionTable from "../../../../components/generative/RAG/PromptSelectionTable"; import { getRAGPrompts } from "../../../../api/rag"; -export default function PromptConfigurationStep({ setNextEnabled }) { +export default function PromptConfigurationStep({ + setNextEnabled, + sessionData, + setSessionData, +}) { const [prompts, setPrompts] = useState([]); const [loading, setLoading] = useState(true); + const [selectedPromptId, setSelectedPromptId] = useState([]); useEffect(() => { - setNextEnabled && setNextEnabled(true); getRAGPrompts() .then((data) => setPrompts(data)) .catch(() => setPrompts([])) .finally(() => setLoading(false)); }, []); - return ; + useEffect(() => { + setNextEnabled(true); + if (selectedPromptId.length > 0) { + const prompt = prompts.find((p) => p.id === selectedPromptId[0]); + if (prompt) { + setSessionData((prev) => ({ + ...prev, + parameters: { + ...prev.parameters, + prompt_model: { + component: prompt.class_name, + params: prompt.parameters, + }, + }, + })); + } + } + }, [selectedPromptId, prompts, setNextEnabled, setSessionData]); + + return ( + + ); } From 569cc188e8c3218eebe3eae8f18f8903b5e02270 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Thu, 23 Oct 2025 00:10:59 -0300 Subject: [PATCH 073/218] Fixes --- DashAI/back/dependencies/database/utils.py | 2 +- .../RAG/embeddings/dense/fasttext_embedding.py | 3 +-- .../pages/plugins/components/PluginsTab.jsx | 2 +- .../chunk_to_doc_id_map | Bin 292646 -> 0 bytes .../tfidf_matrix | Bin 399742 -> 0 bytes .../vectorizer | Bin 179400 -> 0 bytes 6 files changed, 3 insertions(+), 4 deletions(-) delete mode 100644 StoredRetrievers/tfidf_retriever_4347afb95646b45e61cecd2c1f9843a2fadb1581a5320fff274602f196f73b3b_8a335fbf2b6f7ec123b9ddb885a8090cb16e035431fa30d7c63832dd8a728214/chunk_to_doc_id_map delete mode 100644 StoredRetrievers/tfidf_retriever_4347afb95646b45e61cecd2c1f9843a2fadb1581a5320fff274602f196f73b3b_8a335fbf2b6f7ec123b9ddb885a8090cb16e035431fa30d7c63832dd8a728214/tfidf_matrix delete mode 100644 StoredRetrievers/tfidf_retriever_4347afb95646b45e61cecd2c1f9843a2fadb1581a5320fff274602f196f73b3b_8a335fbf2b6f7ec123b9ddb885a8090cb16e035431fa30d7c63832dd8a728214/vectorizer diff --git a/DashAI/back/dependencies/database/utils.py b/DashAI/back/dependencies/database/utils.py index e9bbbd991..37d059a92 100644 --- a/DashAI/back/dependencies/database/utils.py +++ b/DashAI/back/dependencies/database/utils.py @@ -60,7 +60,7 @@ def add_plugin_to_db( name=raw_plugin.name, author=raw_plugin.author, installed_version=raw_plugin.installed_version, - lastest_version=raw_plugin.lastest_version, + latest_version=raw_plugin.lastest_version, summary=raw_plugin.summary, description=raw_plugin.description, description_content_type=raw_plugin.description_content_type, diff --git a/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py b/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py index e0061aa69..17217b153 100644 --- a/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py +++ b/DashAI/back/models/RAG/embeddings/dense/fasttext_embedding.py @@ -52,8 +52,7 @@ def __init__(self, **kwargs): def load(self): """Load the FastText model.""" - model_name = self.model_name.split("/")[-1] - model_path = hf_hub_download(repo_id=self.model_name, filename=f"{model_name}.bin") + model_path = hf_hub_download(repo_id=self.model_name, filename="model.bin") self.model = fasttext.load_model(model_path) def save(self): diff --git a/DashAI/front/src/pages/plugins/components/PluginsTab.jsx b/DashAI/front/src/pages/plugins/components/PluginsTab.jsx index 9a3bfda0f..6158828f5 100644 --- a/DashAI/front/src/pages/plugins/components/PluginsTab.jsx +++ b/DashAI/front/src/pages/plugins/components/PluginsTab.jsx @@ -7,7 +7,7 @@ import { Link } from "react-router-dom"; import usePluginsTab from "../hooks/usePluginsTab"; import { Update as UpdateIcon } from "@mui/icons-material"; import { updatePluginsDBFromPypi as updatePluginsFromPypi } from "../../../api/plugins"; - +import { enqueueSnackbar } from "notistack"; /** * component to display plugins main tabs * @param {boolean} refreshPluginsFlag diff --git 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zg_&-k&_e|SK35*8coKSf?Hd#u(;!ZGAs`|MW5rSD60!AO!BzD9d)!COo&KtUUrX7F5h@c6))11}$6dffj5z8!ty From 2cab302ec2d3a8b780ff63aca013482682600631 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Thu, 23 Oct 2025 00:42:28 -0300 Subject: [PATCH 074/218] added a fixed prompt to use while prompt configuration is developed --- DashAI/back/api/api_v1/endpoints/generative_session.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/DashAI/back/api/api_v1/endpoints/generative_session.py b/DashAI/back/api/api_v1/endpoints/generative_session.py index 3c622706f..83ea05440 100644 --- a/DashAI/back/api/api_v1/endpoints/generative_session.py +++ b/DashAI/back/api/api_v1/endpoints/generative_session.py @@ -87,6 +87,14 @@ async def upload_generative_session( ) documents_ids.append(document.id) + if "prompt_model" not in params.parameters: + params.parameters["prompt_model"] = { + 'component': 'CustomGenerationPrompt', + 'params': { + 'template': """Answer to this question\n{input}\nUsing the following documents as context:\n{chunks}\nThe chat history is:\n{history}""" + } + } + params.parameters["documents"] = documents_ids # Continue with the session creation From 11c848bd38a1b03d391fc07b25e2268c9fd43261 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Thu, 23 Oct 2025 00:58:09 -0300 Subject: [PATCH 075/218] mini fix in documents table --- .../src/components/generative/RAG/DocumentSelectionTable.jsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx b/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx index 883090ede..17a490bdf 100644 --- a/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx +++ b/DashAI/front/src/components/generative/RAG/DocumentSelectionTable.jsx @@ -64,7 +64,7 @@ export default function DocumentSelectionTable({ flex: 0.5, editable: false, valueGetter: (value, row) => { - return row.optional_metadata?.last_modified + return row?.optional_metadata?.last_modified ? formatDate(row.optional_metadata.last_modified) : "N/A"; }, From 231f81f08a01ab44f2c3f3707185e71b3134bc34 Mon Sep 17 00:00:00 2001 From: Creylay Date: Thu, 23 Oct 2025 01:12:43 -0300 Subject: [PATCH 076/218] feat: Add prompt type selection and template loading in NewPromptModal --- DashAI/front/src/api/rag.ts | 15 ++++ .../generative/RAG/NewPromptModal.jsx | 76 +++++++++++++++++-- 2 files changed, 86 insertions(+), 5 deletions(-) diff --git a/DashAI/front/src/api/rag.ts b/DashAI/front/src/api/rag.ts index 5a819acf9..4e23bf99a 100644 --- a/DashAI/front/src/api/rag.ts +++ b/DashAI/front/src/api/rag.ts @@ -232,3 +232,18 @@ export const getRAGPrompts = async (): Promise => { } return response.data; }; + +export const getGenerationPromptChildren = async (): Promise => { + const response = await api.get( + "/v1/component/GenerationPrompt/children", + { + params: { recursive: false }, + }, + ); + if (response.status !== 200) { + throw new Error( + `Failed to fetch GenerationPrompt children: ${response.statusText}`, + ); + } + return response.data; +}; diff --git a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx index ae2161315..05e4b39a8 100644 --- a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx +++ b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx @@ -1,5 +1,5 @@ import React from "react"; - +import CloseIcon from "@mui/icons-material/Close"; import { Dialog, DialogTitle, @@ -9,10 +9,46 @@ import { Typography, Grid, IconButton, + FormControl, + InputLabel, + Select, + MenuItem, } from "@mui/material"; -import CloseIcon from "@mui/icons-material/Close"; + +import TextField from "@mui/material/TextField"; + +import { getGenerationPromptChildren } from "../../../api/rag"; export default function NewPromptModal({ open, handleClose }) { + const [promptTypes, setPromptTypes] = React.useState([]); + const [selectedPromptType, setSelectedPromptType] = React.useState(""); + const [promptName, setPromptName] = React.useState(""); + const [promptTemplate, setPromptTemplate] = React.useState(""); // <-- NUEVO + + React.useEffect(() => { + if (open) { + getGenerationPromptChildren() + .then((data) => setPromptTypes(data)) + .catch(() => setPromptTypes([])); + } + }, [open]); + + // MODIFICADO + const handlePromptTypeChange = (event) => { + const typeName = event.target.value; + setSelectedPromptType(typeName); + const selectedType = promptTypes.find((type) => type.name === typeName); + if ( + selectedType && + selectedType.metadata && + selectedType.metadata.template + ) { + setPromptTemplate(selectedType.metadata.template); + } else { + setPromptTemplate(""); + } + }; + return (

- - Hola mundo - + setPromptName(e.target.value)} + sx={{ mt: 2 }} + /> + + + Prompt Type + + + setPromptTemplate(e.target.value)} + sx={{ mt: 2 }} + /> From 9165d95bf131d68fedaf5d60ec03853f6c7e00dc Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Thu, 23 Oct 2025 01:17:46 -0300 Subject: [PATCH 077/218] model name typo fixed --- DashAI/back/models/hugging_face/qwen_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DashAI/back/models/hugging_face/qwen_model.py b/DashAI/back/models/hugging_face/qwen_model.py index d7f7fbd98..95866ebad 100644 --- a/DashAI/back/models/hugging_face/qwen_model.py +++ b/DashAI/back/models/hugging_face/qwen_model.py @@ -33,7 +33,7 @@ class QwenSchema(BaseSchema): enum=[ "Qwen/Qwen2.5-0.5B-Instruct-GGUF", "Qwen/Qwen2.5-1.5B-Instruct-GGUF", - "Qwen/Qwen3-4B-GGUF ", + "Qwen/Qwen3-4B-GGUF", ] ), placeholder="Qwen/Qwen2.5-1.5B-Instruct-GGUF", From 006608a468cf7d615f1e8f1d18b990143a96c21d Mon Sep 17 00:00:00 2001 From: Creylay Date: Thu, 23 Oct 2025 10:36:06 -0300 Subject: [PATCH 078/218] feat: Add endpoint to create new RAGPrompt entries in the database --- .../api_v1/endpoints/generative_session.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/DashAI/back/api/api_v1/endpoints/generative_session.py b/DashAI/back/api/api_v1/endpoints/generative_session.py index d4d9dfffa..60d353130 100644 --- a/DashAI/back/api/api_v1/endpoints/generative_session.py +++ b/DashAI/back/api/api_v1/endpoints/generative_session.py @@ -9,6 +9,7 @@ from DashAI.back.api.api_v1.schemas.generative_session_params import ( GenerativeSessionParams, ) +from DashAI.back.api.api_v1.schemas.rag_prompt import RAGPromptSchema from DashAI.back.dependencies.database.models import ( Document, GenerativeProcess, @@ -26,6 +27,24 @@ log = logging.getLogger(__name__) +@router.post("/prompts", status_code=status.HTTP_201_CREATED) +async def create_rag_prompt( + prompt: RAGPromptSchema, + session_factory: sessionmaker = Depends(lambda: di["session_factory"]), +): + """Create a new RAGPrompt entry in the database.""" + with session_factory() as db: + new_prompt = RAGPrompt( + class_name=prompt.class_name, + name=prompt.name, + parameters=prompt.parameters, + ) + db.add(new_prompt) + db.commit() + db.refresh(new_prompt) + return {"id": new_prompt.id} + + @router.post("/", status_code=status.HTTP_201_CREATED) async def upload_generative_session( params: GenerativeSessionParams, From 4e89d3c90bfc63a626b10b0e08955e6e810efd90 Mon Sep 17 00:00:00 2001 From: Creylay Date: Thu, 23 Oct 2025 10:36:26 -0300 Subject: [PATCH 079/218] feat: Add RAGPromptSchema model for prompt data validation --- DashAI/back/api/api_v1/schemas/rag_prompt.py | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 DashAI/back/api/api_v1/schemas/rag_prompt.py diff --git a/DashAI/back/api/api_v1/schemas/rag_prompt.py b/DashAI/back/api/api_v1/schemas/rag_prompt.py new file mode 100644 index 000000000..8c1665f39 --- /dev/null +++ b/DashAI/back/api/api_v1/schemas/rag_prompt.py @@ -0,0 +1,9 @@ +from typing import Any, Dict, Optional + +from pydantic import BaseModel + + +class RAGPromptSchema(BaseModel): + class_name: str + name: str + parameters: Optional[Dict[str, Any]] = None From e29fe848fb81261ef64876fc2b1b31910c58a4c4 Mon Sep 17 00:00:00 2001 From: Creylay Date: Thu, 23 Oct 2025 10:36:43 -0300 Subject: [PATCH 080/218] feat: Implement createRAGPrompt function for creating new RAG prompts --- DashAI/front/src/api/rag.ts | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/DashAI/front/src/api/rag.ts b/DashAI/front/src/api/rag.ts index 4e23bf99a..75e6cc6bb 100644 --- a/DashAI/front/src/api/rag.ts +++ b/DashAI/front/src/api/rag.ts @@ -6,7 +6,18 @@ import { IComponent } from "../types/component"; import { IRAGPrompt } from "../types/ragPrompt"; import { getChildComponents } from "./component"; -// Fetch all RAG sessions +export const createRAGPrompt = async (prompt: { + class_name: string; + name: string; + parameters?: Record; +}): Promise<{ id: number }> => { + const response = await api.post("/v1/generative-session/prompts", prompt); + if (response.status !== 201) { + throw new Error(`Failed to create RAG prompt: ${response.statusText}`); + } + return response.data; +}; + export const getRAGSessions = async (): Promise => { const response = await api.get("/v1/generative-session/"); if (response.status !== 200) { From b97da26bc9c720a64dd3664eddd369abde30a4ca Mon Sep 17 00:00:00 2001 From: Creylay Date: Thu, 23 Oct 2025 10:39:09 -0300 Subject: [PATCH 081/218] feat: Update TemplateModal title and integrate prompt creation in NewPromptModal with prompt fetching in PromptSelectionTable --- .../src/components/custom/TemplateModal.jsx | 2 +- .../generative/RAG/NewPromptModal.jsx | 20 ++++++++++++++++--- .../generative/RAG/PromptSelectionTable.jsx | 20 ++++++++++++++++++- 3 files changed, 37 insertions(+), 5 deletions(-) diff --git a/DashAI/front/src/components/custom/TemplateModal.jsx b/DashAI/front/src/components/custom/TemplateModal.jsx index 0026600f4..5c53fd0c5 100644 --- a/DashAI/front/src/components/custom/TemplateModal.jsx +++ b/DashAI/front/src/components/custom/TemplateModal.jsx @@ -13,7 +13,7 @@ import { export default function TemplateModal({ open, handleClose, template }) { return ( - Prompt Template + Prompt diff --git a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx index 05e4b39a8..ef4f6e5bd 100644 --- a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx +++ b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx @@ -17,9 +17,9 @@ import { import TextField from "@mui/material/TextField"; -import { getGenerationPromptChildren } from "../../../api/rag"; +import { getGenerationPromptChildren, createRAGPrompt } from "../../../api/rag"; -export default function NewPromptModal({ open, handleClose }) { +export default function NewPromptModal({ open, handleClose, onPromptCreated }) { const [promptTypes, setPromptTypes] = React.useState([]); const [selectedPromptType, setSelectedPromptType] = React.useState(""); const [promptName, setPromptName] = React.useState(""); @@ -109,7 +109,21 @@ export default function NewPromptModal({ open, handleClose }) { - diff --git a/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx b/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx index f8a81bbb9..394fd7d51 100644 --- a/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx +++ b/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx @@ -14,6 +14,7 @@ import { DataGrid } from "@mui/x-data-grid"; import { formatDate } from "../../../utils"; import TemplateModal from "../../custom/TemplateModal"; import NewPromptModal from "./NewPromptModal"; +import { getRAGPrompts } from "../../../api/rag"; export default function PromptSelectionTable({ prompts = [], @@ -25,6 +26,15 @@ export default function PromptSelectionTable({ const [modalOpen, setModalOpen] = useState(false); const [selectedTemplate, setSelectedTemplate] = useState(""); const [newPromptModalOpen, setNewPromptModalOpen] = useState(false); + const [promptRows, setPromptRows] = useState([]); + React.useEffect(() => { + async function fetchPrompts() { + const initialPrompts = await getRAGPrompts(); + initialPrompts.sort((a, b) => new Date(b.created) - new Date(a.created)); + setPromptRows(initialPrompts); + } + fetchPrompts(); + }, []); const handleViewTemplate = (template) => { setSelectedTemplate(template); @@ -98,6 +108,13 @@ export default function PromptSelectionTable({ [], ); + const handlePromptCreated = async () => { + const updatedPrompts = await getRAGPrompts(); + updatedPrompts.sort((a, b) => new Date(b.created) - new Date(a.created)); + setPromptRows(updatedPrompts); + setNewPromptModalOpen(false); + }; + return ( {showTableTitle && ( @@ -139,7 +156,7 @@ export default function PromptSelectionTable({ )} setNewPromptModalOpen(false)} + onPromptCreated={handlePromptCreated} /> From f822d290bfe77e3445554f2a746ccaf9d9cac961 Mon Sep 17 00:00:00 2001 From: Creylay Date: Thu, 23 Oct 2025 10:50:31 -0300 Subject: [PATCH 082/218] feat: Update prompt selection logic to set next step enabled state based on selected prompt --- .../src/components/generative/RAG/PromptSelectionTable.jsx | 3 +++ .../generative/RAG/NewSessionModal/PromptConfigurationStep.jsx | 2 +- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx b/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx index 394fd7d51..9fd0778bf 100644 --- a/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx +++ b/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx @@ -112,6 +112,9 @@ export default function PromptSelectionTable({ const updatedPrompts = await getRAGPrompts(); updatedPrompts.sort((a, b) => new Date(b.created) - new Date(a.created)); setPromptRows(updatedPrompts); + if (updatedPrompts.length > 0 && onRowSelectionModelChange) { + onRowSelectionModelChange([updatedPrompts[0].id]); + } setNewPromptModalOpen(false); }; diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx index a507cb99e..fd2c89cce 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx @@ -19,7 +19,7 @@ export default function PromptConfigurationStep({ }, []); useEffect(() => { - setNextEnabled(true); + setNextEnabled(selectedPromptId.length > 0); if (selectedPromptId.length > 0) { const prompt = prompts.find((p) => p.id === selectedPromptId[0]); if (prompt) { From 24993fc0a57be2be4d5c0ee57e9bb0c6d79fa2b6 Mon Sep 17 00:00:00 2001 From: sofiachavezb Date: Thu, 23 Oct 2025 17:52:59 -0300 Subject: [PATCH 083/218] Prompt instantiation using db ids --- .../api_v1/endpoints/generative_session.py | 52 ++++++++++++++++++- DashAI/back/models/RAG/RAG_pipeline.py | 30 +++++++---- .../augmentation/augmentation_prompt.py | 3 ++ .../custom_augmentation_prompt.py | 2 +- .../generation/custom_generation_prompt.py | 2 +- .../default_QnA_generation_prompt.py | 3 +- .../generation/default_generation_prompt.py | 3 +- DashAI/back/models/RAG/prompts/prompt.py | 29 +---------- DashAI/front/src/api/rag.ts | 11 ++-- .../generative/RAG/NewPromptModal.jsx | 5 +- .../generative/RAG/PromptSelectionTable.jsx | 4 +- .../RAG/NewSessionModal/NewSessionModal.jsx | 23 +++----- .../PromptConfigurationStep.jsx | 5 +- DashAI/front/src/types/ragPrompt.ts | 3 +- 14 files changed, 97 insertions(+), 78 deletions(-) diff --git a/DashAI/back/api/api_v1/endpoints/generative_session.py b/DashAI/back/api/api_v1/endpoints/generative_session.py index 60d353130..90ef66f37 100644 --- a/DashAI/back/api/api_v1/endpoints/generative_session.py +++ b/DashAI/back/api/api_v1/endpoints/generative_session.py @@ -9,6 +9,7 @@ from DashAI.back.api.api_v1.schemas.generative_session_params import ( GenerativeSessionParams, ) +from DashAI.back.models.RAG import DefaultGenerationPrompt, DefaultQnAGenerationPrompt, DefaultAugmentationPrompt from DashAI.back.api.api_v1.schemas.rag_prompt import RAGPromptSchema from DashAI.back.dependencies.database.models import ( Document, @@ -19,7 +20,8 @@ RAGPrompt, ) from DashAI.back.dependencies.registry import ComponentRegistry -from DashAI.back.models import BaseGenerativeModel +from DashAI.back.models.RAG import Prompt +from DashAI.back.models.base_generative_model import BaseGenerativeModel from DashAI.back.tasks import BaseGenerativeTask from DashAI.back.tasks.RAG_task import RAGTask @@ -30,10 +32,30 @@ @router.post("/prompts", status_code=status.HTTP_201_CREATED) async def create_rag_prompt( prompt: RAGPromptSchema, + component_registry: ComponentRegistry = Depends(lambda: di["component_registry"]), session_factory: sessionmaker = Depends(lambda: di["session_factory"]), ): """Create a new RAGPrompt entry in the database.""" with session_factory() as db: + prompt_class_name = prompt.class_name + if not prompt_class_name in component_registry: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Component {prompt_class_name} is not registered.", + ) + prompt_component = component_registry[prompt_class_name] + prompt_class: Prompt = prompt_component["class"] + if not issubclass(prompt_class, Prompt): + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Component {prompt_class_name} is not a valid Prompt subclass.", + ) + template_validation = prompt_class.validate_template(prompt.parameters["template"]) + if not template_validation: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Invalid template for prompt {prompt.class_name}. Required tokens are: {prompt_class.get_required_placeholders()}", + ) new_prompt = RAGPrompt( class_name=prompt.class_name, name=prompt.name, @@ -189,6 +211,34 @@ async def get_all_prompts( try: prompts = db.query(RAGPrompt).all() + if len(prompts) == 0: + default_generation_prompt = RAGPrompt( + class_name=DefaultGenerationPrompt.__name__, + name="Default Generation Prompt", + parameters={ + 'template': DefaultAugmentationPrompt.template + }, + ) + default_qa_prompt = RAGPrompt( + class_name=DefaultQnAGenerationPrompt.__name__, + name="Default QnA Prompt", + parameters={ + 'template': DefaultQnAGenerationPrompt.template + }, + ) + default_augmentation_prompt = RAGPrompt( + class_name=DefaultAugmentationPrompt.__name__, + name="Default Augmentation Prompt", + parameters={ + 'template': DefaultAugmentationPrompt.template + }, + ) + db.add(default_generation_prompt) + db.add(default_qa_prompt) + db.add(default_augmentation_prompt) + db.commit() + prompts = db.query(RAGPrompt).all() + prompt_responses = [] for prompt in prompts: prompt_responses.append( diff --git a/DashAI/back/models/RAG/RAG_pipeline.py b/DashAI/back/models/RAG/RAG_pipeline.py index 5284a1b29..9e0f7546a 100644 --- a/DashAI/back/models/RAG/RAG_pipeline.py +++ b/DashAI/back/models/RAG/RAG_pipeline.py @@ -13,6 +13,7 @@ from DashAI.back.dependencies.database.models import ( Document as DBDocument, RAGPipeline as DBPipeline, + RAGPrompt as PromptDBModel ) from DashAI.back.dependencies.registry.component_registry import ComponentRegistry @@ -92,10 +93,10 @@ class RAGPipelineSchema(BaseSchema): description="Text generation model used in the RAG pipeline." ) # type: ignore - prompt_model: schema_field( - component_field(parent="Prompt"), - placeholder={"component": "DefaultGenerationPrompt", "params": {}}, - description="Prompt template used to format the input for the generation model." + prompt_id: schema_field( + int_field(gt=0), + placeholder=None, + description="Database ID of the prompt template to be used in the RAG pipeline." ) # type: ignore class RAGPipeline(BaseGenerativeModel): @@ -150,15 +151,25 @@ def __init__(self, **kwargs: Any) -> None: self.pipeline_db_model = None self.pipeline_id = None self.documents_ids: List[int] = kwargs.pop("documents") + self.prompt_db_id: int = kwargs.pop("prompt_id") + prompt_db_model: PromptDBModel = self.db.query(PromptDBModel).filter_by(id=self.prompt_db_id).first() + if not prompt_db_model: + raise RAGPipelineInitializationError(f"Prompt with ID {self.prompt_db_id} not found in the database.") + prompt_model_args = { + 'component': prompt_db_model.class_name, + 'params': prompt_db_model.parameters + } + self.documents = self.load_documents_from_db(documents_ids=self.documents_ids) self.validate_params(kwargs) - try: + try: + config = { 'chunking_model': kwargs.pop('chunking_model'), 'retriever_model': kwargs.pop('retriever_model'), 'generation_model': kwargs.pop('generation_model'), - 'prompt_model': kwargs.pop('prompt_model'), + 'prompt_model': prompt_model_args } except KeyError as e: raise RAGPipelineParametersError(f"Missing required parameter: {str(e)}") @@ -171,7 +182,6 @@ def __init__(self, **kwargs: Any) -> None: self.retriever_id = self.retriever.retriever_db_model.id self.llm_model = self.init_component(config["generation_model"]) self.prompt_model = self.init_component(config["prompt_model"]) - self.prompt_db_id = self.prompt_model.id self.retrieval_algorithm = "SINGLE_INTERACTION" print("RAG pipeline initialized") @@ -179,14 +189,14 @@ def validate_params( self, params: dict): """Validate RAG pipeline parameters.""" - required_keys = ["chunking_model", "retriever_model", "generation_model", "prompt_model"] + required_keys = ["chunking_model", "retriever_model", "generation_model"] for key in required_keys: if key not in params: raise RAGPipelineParametersError(f"Missing required parameter '{key}' in RAG pipeline configuration") # Validate model components - for model in ["chunking_model", "retriever_model", "generation_model", "prompt_model"]: + for model in ["chunking_model", "retriever_model", "generation_model"]: args = params[model] if "component" not in args: raise RAGPipelineParametersError(f"Missing 'component' field in '{model}' configuration") @@ -194,8 +204,6 @@ def validate_params( raise RAGPipelineParametersError(f"Missing 'params' field in '{model}' configuration") if args["component"] not in self.component_registry: raise RAGPipelineParametersError(f"No components registered for type '{model}'") - if "Custom" in params["prompt_model"]["component"] and "template" not in params["prompt_model"]["params"]: - raise RAGPipelineParametersError("Custom prompt model requires a 'template' parameter in its configuration") def single_interaction( self, diff --git a/DashAI/back/models/RAG/prompts/augmentation/augmentation_prompt.py b/DashAI/back/models/RAG/prompts/augmentation/augmentation_prompt.py index 060e70179..ccaf245e5 100644 --- a/DashAI/back/models/RAG/prompts/augmentation/augmentation_prompt.py +++ b/DashAI/back/models/RAG/prompts/augmentation/augmentation_prompt.py @@ -11,6 +11,9 @@ class AugmentationPrompt(Prompt): required_placeholders = ["{input}", "{n_search_terms}"] optional_placeholders = ["{history}"] + def __init__(self, **kwargs: Any): + self.template = kwargs.pop("template") + def format( self, input: str, diff --git a/DashAI/back/models/RAG/prompts/augmentation/custom_augmentation_prompt.py b/DashAI/back/models/RAG/prompts/augmentation/custom_augmentation_prompt.py index 2a4888130..052487d13 100644 --- a/DashAI/back/models/RAG/prompts/augmentation/custom_augmentation_prompt.py +++ b/DashAI/back/models/RAG/prompts/augmentation/custom_augmentation_prompt.py @@ -23,7 +23,7 @@ class CustomAugmentationPrompt(AugmentationPrompt): def __init__(self, **kwargs): - super().__init__(**kwargs) + self.template = kwargs.pop("template") def format( diff --git a/DashAI/back/models/RAG/prompts/generation/custom_generation_prompt.py b/DashAI/back/models/RAG/prompts/generation/custom_generation_prompt.py index 28fe2305d..9efff58b6 100644 --- a/DashAI/back/models/RAG/prompts/generation/custom_generation_prompt.py +++ b/DashAI/back/models/RAG/prompts/generation/custom_generation_prompt.py @@ -21,7 +21,7 @@ class CustomGenerationPrompt(GenerationPrompt): def __init__(self, **kwargs: Any): - super().__init__(**kwargs) + self.template = kwargs.pop("template") def format( self, diff --git a/DashAI/back/models/RAG/prompts/generation/default_QnA_generation_prompt.py b/DashAI/back/models/RAG/prompts/generation/default_QnA_generation_prompt.py index d6735b137..69bb3f46c 100644 --- a/DashAI/back/models/RAG/prompts/generation/default_QnA_generation_prompt.py +++ b/DashAI/back/models/RAG/prompts/generation/default_QnA_generation_prompt.py @@ -33,9 +33,10 @@ class DefaultQnAGenerationPrompt(GenerationPrompt): }, "template": template } + template = template def __init__(self, **kwargs): - super().__init__(**kwargs) + self.template = kwargs.pop("template") @staticmethod diff --git a/DashAI/back/models/RAG/prompts/generation/default_generation_prompt.py b/DashAI/back/models/RAG/prompts/generation/default_generation_prompt.py index e92e566d6..a38f4aff2 100644 --- a/DashAI/back/models/RAG/prompts/generation/default_generation_prompt.py +++ b/DashAI/back/models/RAG/prompts/generation/default_generation_prompt.py @@ -30,7 +30,8 @@ class DefaultGenerationPrompt(GenerationPrompt): } def __init__(self, **kwargs): - super().__init__(**kwargs) + self.template = kwargs.pop("template") + @staticmethod def format( diff --git a/DashAI/back/models/RAG/prompts/prompt.py b/DashAI/back/models/RAG/prompts/prompt.py index 36e39e94f..4d7ac24fc 100644 --- a/DashAI/back/models/RAG/prompts/prompt.py +++ b/DashAI/back/models/RAG/prompts/prompt.py @@ -23,36 +23,9 @@ class Prompt(BaseModel): """ SCHEMA = PromptSchema - REQUIRED_EXTRA_KWARGS = ["db"] id: int name: str - - def __init__(self, **kwargs): - self.db = kwargs.pop("db") - kwargs = self.validate_and_transform(kwargs) - self.template = kwargs.get("template") - self.name = kwargs.get("name") - assert self.validate_template(self.template), ( - "The template is missing required placeholders." - ) - self.class_name = self.__class__.__name__ - self.params = kwargs - stored_model = ( - self.db.query(PromptDBModel) - .filter_by( - class_name=self.class_name, name=self.name, parameters=self.params - ) - .first() - ) - if stored_model: - self.id = stored_model.id - else: - new_model = PromptDBModel( - class_name=self.class_name, name=self.name, parameters=self.params - ) - self.db.add(new_model) - self.db.commit() - self.id = new_model.id + REQUIRED_EXTRA_KWARGS = [] def load(self, **kwargs: Any) -> None: pass diff --git a/DashAI/front/src/api/rag.ts b/DashAI/front/src/api/rag.ts index 75e6cc6bb..f41fd1de5 100644 --- a/DashAI/front/src/api/rag.ts +++ b/DashAI/front/src/api/rag.ts @@ -44,6 +44,7 @@ export const getRAGSession = async (sessionId: number): Promise => { export const createRAGSession = async ( sessionData: Omit, ): Promise => { + console.log("Creating RAG session with data:", sessionData); const params = sessionData.parameters as { documents: number[]; chunking_model: { @@ -58,10 +59,7 @@ export const createRAGSession = async ( component: string; params: Record; }; - prompt_model: { - component: string; - params: Record; - }; + prompt_id: number; }; const transformedSession: Omit = @@ -85,10 +83,7 @@ export const createRAGSession = async ( component: params.generation_model.component, params: params.generation_model.params, }, - prompt_model: { - component: params.prompt_model.component, - params: params.prompt_model.params, - }, + prompt_id: params.prompt_id, }, }; diff --git a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx index ef4f6e5bd..98a448c99 100644 --- a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx +++ b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx @@ -115,7 +115,10 @@ export default function NewPromptModal({ open, handleClose, onPromptCreated }) { await createRAGPrompt({ class_name: selectedPromptType, name: promptName, - parameters: { template: promptTemplate }, + parameters: { + template: promptTemplate, + /* name: promptName */ + }, }); if (onPromptCreated) { await onPromptCreated(); diff --git a/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx b/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx index 9fd0778bf..584d748ec 100644 --- a/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx +++ b/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx @@ -75,7 +75,7 @@ export default function PromptSelectionTable({ minWidth: 140, flex: 1, editable: false, - valueGetter: (value) => formatDate(value), + valueFormatter: (value) => formatDate(value.value), }, { field: "last_modified", @@ -83,7 +83,7 @@ export default function PromptSelectionTable({ minWidth: 140, flex: 1, editable: false, - valueGetter: (value) => formatDate(value), + valueFormatter: (value) => formatDate(value.value), }, { field: "actions", diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx index 15cb1bd91..bccd19278 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/NewSessionModal.jsx @@ -70,14 +70,7 @@ const defaultNewSession = { component: "", params: {}, }, - prompt_model: { - component: "CustomGenerationPrompt", - params: { - name: "Default Prompt", - template: - "Answer to this message: {input}, with the following information: {chunks}", - }, - }, + prompt_id: null, }, }; @@ -125,8 +118,10 @@ export default function NewSessionModal({ generator_model: { ...sessionGeneratorModel, }, - }, - }); + prompt_id: sessionParameters.prompt_id + } + } + ); } else { const { defaultName } = generateSequentialName({ base: "RAG_Session", @@ -230,13 +225,7 @@ export default function NewSessionModal({ component: "", params: {}, }, - prompt_model: sessionData.parameters.prompt_model || { - component: "CustomGenerationPrompt", - params: { - template: - "Answer to this message: {input}, with the following information: {chunks}", - }, - }, + prompt_id: sessionData.parameters.prompt_id, }, }; diff --git a/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx b/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx index fd2c89cce..98d9c7334 100644 --- a/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx +++ b/DashAI/front/src/pages/generative/RAG/NewSessionModal/PromptConfigurationStep.jsx @@ -27,10 +27,7 @@ export default function PromptConfigurationStep({ ...prev, parameters: { ...prev.parameters, - prompt_model: { - component: prompt.class_name, - params: prompt.parameters, - }, + prompt_id: prompt.id, }, })); } diff --git a/DashAI/front/src/types/ragPrompt.ts b/DashAI/front/src/types/ragPrompt.ts index 2ea780787..61b1599d1 100644 --- a/DashAI/front/src/types/ragPrompt.ts +++ b/DashAI/front/src/types/ragPrompt.ts @@ -1,11 +1,10 @@ export interface IRAGPrompt { id: number; class_name: string; - name?: string; + name: string; created?: string; last_modified?: string; parameters: { template: string; - name?: string; }; } From f44e445ea621eeb98011cd24f3938973fc8e0417 Mon Sep 17 00:00:00 2001 From: Creylay Date: Fri, 24 Oct 2025 11:25:34 -0300 Subject: [PATCH 084/218] feat: Refactor getPromptChildren to handle multiple prompt types and consolidate API calls --- DashAI/front/src/api/rag.ts | 26 +++++++++++++++----------- 1 file changed, 15 insertions(+), 11 deletions(-) diff --git a/DashAI/front/src/api/rag.ts b/DashAI/front/src/api/rag.ts index f41fd1de5..bc0cbc096 100644 --- a/DashAI/front/src/api/rag.ts +++ b/DashAI/front/src/api/rag.ts @@ -239,17 +239,21 @@ export const getRAGPrompts = async (): Promise => { return response.data; }; -export const getGenerationPromptChildren = async (): Promise => { - const response = await api.get( - "/v1/component/GenerationPrompt/children", - { - params: { recursive: false }, - }, - ); - if (response.status !== 200) { - throw new Error( - `Failed to fetch GenerationPrompt children: ${response.statusText}`, +export const getPromptChildren = async ( + types: string[] = ["GenerationPrompt", "AugmentationPrompt"], +): Promise => { + const allChildren: IComponent[] = []; + for (const type of types) { + const response = await api.get( + `/v1/component/${type}/children`, + { params: { recursive: false } }, ); + if (response.status !== 200) { + throw new Error( + `Failed to fetch ${type} children: ${response.statusText}`, + ); + } + allChildren.push(...response.data); } - return response.data; + return allChildren; }; From f5f470dc898e9d3697f7952816e65bf82c2ca798 Mon Sep 17 00:00:00 2001 From: Creylay Date: Fri, 24 Oct 2025 11:25:40 -0300 Subject: [PATCH 085/218] feat: Update API call in NewPromptModal to use getPromptChildren for fetching prompt types --- .../front/src/components/generative/RAG/NewPromptModal.jsx | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx index 98a448c99..b47199cd9 100644 --- a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx +++ b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx @@ -17,7 +17,7 @@ import { import TextField from "@mui/material/TextField"; -import { getGenerationPromptChildren, createRAGPrompt } from "../../../api/rag"; +import { getPromptChildren, createRAGPrompt } from "../../../api/rag"; export default function NewPromptModal({ open, handleClose, onPromptCreated }) { const [promptTypes, setPromptTypes] = React.useState([]); @@ -27,7 +27,7 @@ export default function NewPromptModal({ open, handleClose, onPromptCreated }) { React.useEffect(() => { if (open) { - getGenerationPromptChildren() + getPromptChildren() .then((data) => setPromptTypes(data)) .catch(() => setPromptTypes([])); } @@ -115,7 +115,7 @@ export default function NewPromptModal({ open, handleClose, onPromptCreated }) { await createRAGPrompt({ class_name: selectedPromptType, name: promptName, - parameters: { + parameters: { template: promptTemplate, /* name: promptName */ }, From 87d34eac50a1cf68adea5f2b1f1a58c34039769a Mon Sep 17 00:00:00 2001 From: Creylay Date: Fri, 24 Oct 2025 11:25:46 -0300 Subject: [PATCH 086/218] fix: Correct valueFormatter to valueGetter for date formatting in PromptSelectionTable --- .../src/components/generative/RAG/PromptSelectionTable.jsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx b/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx index 584d748ec..9fd0778bf 100644 --- a/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx +++ b/DashAI/front/src/components/generative/RAG/PromptSelectionTable.jsx @@ -75,7 +75,7 @@ export default function PromptSelectionTable({ minWidth: 140, flex: 1, editable: false, - valueFormatter: (value) => formatDate(value.value), + valueGetter: (value) => formatDate(value), }, { field: "last_modified", @@ -83,7 +83,7 @@ export default function PromptSelectionTable({ minWidth: 140, flex: 1, editable: false, - valueFormatter: (value) => formatDate(value.value), + valueGetter: (value) => formatDate(value), }, { field: "actions", From de0c063e86898d2cf97ce01d597f54ce248d7a98 Mon Sep 17 00:00:00 2001 From: Creylay Date: Fri, 24 Oct 2025 12:59:20 -0300 Subject: [PATCH 087/218] feat: Add required and optional placeholders display in NewPromptModal based on selected prompt type --- .../generative/RAG/NewPromptModal.jsx | 96 ++++++++++++++++++- 1 file changed, 94 insertions(+), 2 deletions(-) diff --git a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx index b47199cd9..971507cb9 100644 --- a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx +++ b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx @@ -18,12 +18,16 @@ import { import TextField from "@mui/material/TextField"; import { getPromptChildren, createRAGPrompt } from "../../../api/rag"; +import HelpOutlineIcon from "@mui/icons-material/HelpOutline"; +import Tooltip from "@mui/material/Tooltip"; +import CheckCircleIcon from "@mui/icons-material/CheckCircle"; +import WarningAmberIcon from "@mui/icons-material/WarningAmber"; export default function NewPromptModal({ open, handleClose, onPromptCreated }) { const [promptTypes, setPromptTypes] = React.useState([]); const [selectedPromptType, setSelectedPromptType] = React.useState(""); const [promptName, setPromptName] = React.useState(""); - const [promptTemplate, setPromptTemplate] = React.useState(""); // <-- NUEVO + const [promptTemplate, setPromptTemplate] = React.useState(""); React.useEffect(() => { if (open) { @@ -33,7 +37,6 @@ export default function NewPromptModal({ open, handleClose, onPromptCreated }) { } }, [open]); - // MODIFICADO const handlePromptTypeChange = (event) => { const typeName = event.target.value; setSelectedPromptType(typeName); @@ -97,6 +100,95 @@ export default function NewPromptModal({ open, handleClose, onPromptCreated }) { ))} + {/* Mostrar placeholders si hay un tipo seleccionado, en listas verticales */} + {selectedPromptType && + (() => { + const selectedType = promptTypes.find( + (type) => type.name === selectedPromptType, + ); + if (!selectedType || !selectedType.metadata) return null; + return ( +
+ + Required Placeholders + +
    + {selectedType.metadata.required_placeholders?.map((ph) => { + const isPresent = promptTemplate.includes(ph); + return ( +
  • + {isPresent ? ( + + ) : ( + + )} + {ph} + + + +
    • + ); + })} +
+ + Optional Placeholders + +
    + {selectedType.metadata.optional_placeholders?.map((ph) => { + const isPresent = promptTemplate.includes(ph); + return ( +
  • + {isPresent && ( + + )} + {ph} + + + +
    • + ); + })} +
+
+ ); + })()} + Date: Fri, 24 Oct 2025 13:45:51 -0300 Subject: [PATCH 088/218] feat: Enhance display of required and optional placeholders in NewPromptModal with improved layout --- .../generative/RAG/NewPromptModal.jsx | 167 ++++++++++-------- 1 file changed, 91 insertions(+), 76 deletions(-) diff --git a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx index 971507cb9..9455fb87e 100644 --- a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx +++ b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx @@ -107,84 +107,99 @@ export default function NewPromptModal({ open, handleClose, onPromptCreated }) { (type) => type.name === selectedPromptType, ); if (!selectedType || !selectedType.metadata) return null; + const required = selectedType.metadata.required_placeholders || []; + const optional = selectedType.metadata.optional_placeholders || []; return (
- - Required Placeholders - -
    - {selectedType.metadata.required_placeholders?.map((ph) => { - const isPresent = promptTemplate.includes(ph); - return ( -
  • - {isPresent ? ( - - ) : ( - - )} - {ph} - - - -
    • - ); - })} -
- - Optional Placeholders - -
    - {selectedType.metadata.optional_placeholders?.map((ph) => { - const isPresent = promptTemplate.includes(ph); - return ( -
  • - {isPresent && ( - - )} - {ph} - - - -
    • - ); - })} -
+
+
+ + Required Placeholders + +
    + {required.map((ph) => { + const isPresent = promptTemplate.includes(ph); + return ( +
  • + {isPresent ? ( + + ) : ( + + )} + {ph} + + + +
    • + ); + })} +
+
+
+ + Optional Placeholders + +
    + {optional.map((ph) => { + const isPresent = promptTemplate.includes(ph); + return ( +
  • + {isPresent && ( + + )} + {ph} + + + +
    • + ); + })} +
+
+
); })()} From 8aef3c76511b06eec68b6849b82f4d9777f1a2db Mon Sep 17 00:00:00 2001 From: Creylay Date: Fri, 24 Oct 2025 15:49:46 -0300 Subject: [PATCH 089/218] feat: Implement validation for required placeholders in NewPromptModal and update save button state --- .../generative/RAG/NewPromptModal.jsx | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx index 9455fb87e..e0a6c6ee0 100644 --- a/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx +++ b/DashAI/front/src/components/generative/RAG/NewPromptModal.jsx @@ -29,6 +29,20 @@ export default function NewPromptModal({ open, handleClose, onPromptCreated }) { const [promptName, setPromptName] = React.useState(""); const [promptTemplate, setPromptTemplate] = React.useState(""); + const allRequiredPresent = React.useMemo(() => { + if (!selectedPromptType) return false; + const selectedType = promptTypes.find( + (type) => type.name === selectedPromptType, + ); + if (!selectedType || !selectedType.metadata) return false; + const required = selectedType.metadata.required_placeholders || []; + return required.every((ph) => promptTemplate.includes(ph)); + }, [selectedPromptType, promptTypes, promptTemplate]); + + const canSave = Boolean( + selectedPromptType && promptName.trim() && allRequiredPresent, + ); + React.useEffect(() => { if (open) { getPromptChildren() @@ -82,6 +96,10 @@ export default function NewPromptModal({ open, handleClose, onPromptCreated }) { value={promptName} onChange={(e) => setPromptName(e.target.value)} sx={{ mt: 2 }} + required + error={!promptName.trim()} + helperText={!promptName.trim() ? "Prompt name is required" : ""} + InputLabelProps={{ required: false }} /> @@ -218,6 +236,7 @@ export default function NewPromptModal({ open, handleClose, onPromptCreated }) {