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fix: share ML model instances to reduce startup time #229

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53 changes: 53 additions & 0 deletions backend/src/agents/retriever_tools.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,15 @@
import os
import logging
from typing import Tuple, Optional, Union
from dotenv import load_dotenv

from langchain_core.tools import tool
from langchain.retrievers import EnsembleRetriever
from langchain.retrievers import ContextualCompressionRetriever
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_google_vertexai import VertexAIEmbeddings
from langchain_community.cross_encoders import HuggingFaceCrossEncoder

from ..chains.hybrid_retriever_chain import HybridRetrieverChain
from ..tools.format_docs import format_docs
Expand Down Expand Up @@ -39,13 +44,49 @@ def __init__(self) -> None:
]
tool_descriptions: str = ""

@staticmethod
def _create_embedding_model(
embeddings_config: dict[str, str],
use_cuda: bool = False,
) -> Union[HuggingFaceEmbeddings, GoogleGenerativeAIEmbeddings, VertexAIEmbeddings]:
embeddings_type = embeddings_config["type"]
embeddings_model_name = embeddings_config["name"]

if embeddings_type == "GOOGLE_GENAI":
logging.info("Using Google GenerativeAI embeddings...")
return GoogleGenerativeAIEmbeddings(
model=embeddings_model_name,
task_type="retrieval_document",
)
elif embeddings_type == "GOOGLE_VERTEXAI":
logging.info("Using Google VertexAI embeddings...")
return VertexAIEmbeddings(model_name=embeddings_model_name)
elif embeddings_type == "HF":
logging.info("Using HuggingFace embeddings...")
model_kwargs = {"device": "cuda"} if use_cuda else {"device": "cpu"}
return HuggingFaceEmbeddings(
model_name=embeddings_model_name,
multi_process=False,
encode_kwargs={"normalize_embeddings": True},
model_kwargs=model_kwargs,
)
else:
raise ValueError("Invalid embeddings type specified.")

def initialize(
self,
embeddings_config: dict[str, str],
reranking_model_name: str,
use_cuda: bool = False,
fast_mode: bool = False,
) -> None:
# Create shared model instances once
embedding_model = self._create_embedding_model(embeddings_config, use_cuda)
logging.info("Shared embedding model created.")

reranker_model = HuggingFaceCrossEncoder(model_name=reranking_model_name)
logging.info("Shared reranker model created.")

markdown_docs_map = {
"general": [
"./data/markdown/OR_docs",
Expand Down Expand Up @@ -100,6 +141,8 @@ def initialize(
contextual_rerank=True,
search_k=search_k,
chunk_size=chunk_size,
embedding_model=embedding_model,
reranker_model=reranker_model,
)
general_retriever_chain.create_hybrid_retriever()
RetrieverTools.general_retriever = general_retriever_chain.retriever
Expand All @@ -115,6 +158,8 @@ def initialize(
contextual_rerank=True,
search_k=search_k,
chunk_size=chunk_size,
embedding_model=embedding_model,
reranker_model=reranker_model,
)
install_retriever_chain.create_hybrid_retriever()
RetrieverTools.install_retriever = install_retriever_chain.retriever
Expand All @@ -131,6 +176,8 @@ def initialize(
contextual_rerank=True,
search_k=search_k,
chunk_size=chunk_size,
embedding_model=embedding_model,
reranker_model=reranker_model,
)
commands_retriever_chain.create_hybrid_retriever()
RetrieverTools.commands_retriever = commands_retriever_chain.retriever
Expand All @@ -146,6 +193,8 @@ def initialize(
contextual_rerank=True,
search_k=search_k,
chunk_size=chunk_size,
embedding_model=embedding_model,
reranker_model=reranker_model,
)
yosys_rtdocs_retriever_chain.create_hybrid_retriever()
RetrieverTools.yosys_rtdocs_retriever = yosys_rtdocs_retriever_chain.retriever
Expand All @@ -161,6 +210,8 @@ def initialize(
contextual_rerank=True,
search_k=search_k,
chunk_size=chunk_size,
embedding_model=embedding_model,
reranker_model=reranker_model,
)
klayout_retriever_chain.create_hybrid_retriever()
RetrieverTools.klayout_retriever = klayout_retriever_chain.retriever
Expand All @@ -176,6 +227,8 @@ def initialize(
contextual_rerank=True,
search_k=search_k,
chunk_size=chunk_size,
embedding_model=embedding_model,
reranker_model=reranker_model,
)
errinfo_retriever_chain.create_hybrid_retriever()
RetrieverTools.errinfo_retriever = errinfo_retriever_chain.retriever
Expand Down
27 changes: 24 additions & 3 deletions backend/src/chains/hybrid_retriever_chain.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,9 @@
from langchain.retrievers import ContextualCompressionRetriever
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_community.cross_encoders import HuggingFaceCrossEncoder
from langchain_google_vertexai import ChatVertexAI
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_google_vertexai import ChatVertexAI, VertexAIEmbeddings
from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
from langchain_ollama import ChatOllama
from langchain.retrievers.document_compressors.cross_encoder_rerank import (
CrossEncoderReranker,
Expand Down Expand Up @@ -38,6 +39,14 @@ def __init__(
weights: list[float] = [0.33, 0.33, 0.33],
chunk_size: int = 500,
contextual_rerank: bool = False,
embedding_model: Optional[
Union[
HuggingFaceEmbeddings,
GoogleGenerativeAIEmbeddings,
VertexAIEmbeddings,
]
] = None,
reranker_model: Optional[HuggingFaceCrossEncoder] = None,
):
super().__init__(
llm_model=llm_model,
Expand All @@ -48,6 +57,14 @@ def __init__(

self.reranking_model_name: Optional[str] = reranking_model_name
self.use_cuda: bool = use_cuda
self.embedding_model: Optional[
Union[
HuggingFaceEmbeddings,
GoogleGenerativeAIEmbeddings,
VertexAIEmbeddings,
]
] = embedding_model
self.reranker_model: Optional[HuggingFaceCrossEncoder] = reranker_model

self.search_k: int = search_k
self.weights: list[float] = weights
Expand All @@ -74,6 +91,7 @@ def create_hybrid_retriever(self) -> None:
html_docs_path=self.html_docs_path,
chunk_size=self.chunk_size,
use_cuda=self.use_cuda,
embedding_model=self.embedding_model,
)
if self.vector_db is None:
cur_path = os.path.abspath(__file__)
Expand Down Expand Up @@ -121,8 +139,11 @@ def create_hybrid_retriever(self) -> None:
)

if self.contextual_rerank:
reranker = self.reranker_model or HuggingFaceCrossEncoder(
model_name=self.reranking_model_name
)
compressor = CrossEncoderReranker(
model=HuggingFaceCrossEncoder(model_name=self.reranking_model_name),
model=reranker,
top_n=self.search_k,
)
self.retriever = ContextualCompressionRetriever(
Expand Down
20 changes: 18 additions & 2 deletions backend/src/chains/similarity_retriever_chain.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,8 +3,9 @@

from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain.docstore.document import Document
from langchain_google_vertexai import ChatVertexAI
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
from langchain_google_vertexai import ChatVertexAI, VertexAIEmbeddings
from langchain_ollama import ChatOllama

from ..vectorstores.faiss import FAISSVectorDatabase
Expand All @@ -28,6 +29,13 @@ def __init__(
embeddings_config: Optional[dict[str, str]] = None,
use_cuda: bool = False,
chunk_size: int = 500,
embedding_model: Optional[
Union[
HuggingFaceEmbeddings,
GoogleGenerativeAIEmbeddings,
VertexAIEmbeddings,
]
] = None,
):
super().__init__(
llm_model=llm_model,
Expand All @@ -40,6 +48,13 @@ def __init__(

self.embeddings_config: Optional[dict[str, str]] = embeddings_config
self.use_cuda: bool = use_cuda
self.embedding_model: Optional[
Union[
HuggingFaceEmbeddings,
GoogleGenerativeAIEmbeddings,
VertexAIEmbeddings,
]
] = embedding_model

self.markdown_docs_path: Optional[list[str]] = markdown_docs_path
self.other_docs_path: Optional[list[str]] = other_docs_path
Expand Down Expand Up @@ -125,6 +140,7 @@ def create_vector_db(self) -> None:
embeddings_model_name=self.embeddings_config["name"],
embeddings_type=self.embeddings_config["type"],
use_cuda=self.use_cuda,
embedding_model=self.embedding_model,
)
else:
raise ValueError("Embeddings model config not provided correctly.")
Expand Down
12 changes: 9 additions & 3 deletions backend/src/vectorstores/faiss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,16 +28,21 @@ def __init__(
distance_strategy: DistanceStrategy = DistanceStrategy.COSINE,
debug: bool = False,
use_cuda: bool = False,
embedding_model: Optional[
Union[
HuggingFaceEmbeddings, GoogleGenerativeAIEmbeddings, VertexAIEmbeddings
]
] = None,
):
self.embeddings_model_name = embeddings_model_name

model_kwargs = {"device": "cuda"} if use_cuda else {"device": "cpu"}

self.embedding_model: Union[
HuggingFaceEmbeddings, GoogleGenerativeAIEmbeddings, VertexAIEmbeddings
]

if embeddings_type == "GOOGLE_GENAI":
if embedding_model is not None:
self.embedding_model = embedding_model
elif embeddings_type == "GOOGLE_GENAI":
self.embedding_model = GoogleGenerativeAIEmbeddings(
model=self.embeddings_model_name,
task_type="retrieval_document",
Expand All @@ -51,6 +56,7 @@ def __init__(
logging.info("Using Google VertexAI embeddings...")

elif embeddings_type == "HF":
model_kwargs = {"device": "cuda"} if use_cuda else {"device": "cpu"}
self.embedding_model = HuggingFaceEmbeddings(
model_name=self.embeddings_model_name,
multi_process=False,
Expand Down
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