diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/.gitignore b/agents/community/langgraph-agentic-rag-multisource-retrieval/.gitignore new file mode 100644 index 00000000..954edc78 --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/.gitignore @@ -0,0 +1,7 @@ +.env +.vscode +__pycache__ +.pytest_cache +.venv +dist +config.toml \ No newline at end of file diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/CHANGES.rst b/agents/community/langgraph-agentic-rag-multisource-retrieval/CHANGES.rst new file mode 100644 index 00000000..c49d269f --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/CHANGES.rst @@ -0,0 +1,4 @@ +Version 0.1.0 +------------- + +Base AI service LangGraph template, prepared using `ai-service-template `_ from `IBM/watson-machine-learning-samples` repository. \ No newline at end of file diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/LICENSE b/agents/community/langgraph-agentic-rag-multisource-retrieval/LICENSE new file mode 100644 index 00000000..d8ca6d9e --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 IBM + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/README.md b/agents/community/langgraph-agentic-rag-multisource-retrieval/README.md new file mode 100644 index 00000000..1f0bfea7 --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/README.md @@ -0,0 +1,214 @@ +# A LangGraph Agentic RAG template with multi-source retrieval examples + +Table of contents: +* [Introduction](#introduction) +* [Directory structure and file descriptions](#directory-structure-and-file-descriptions) +* [Prerequisites](#prerequisites) +* [Cloning and setting up the template](#cloning-and-setting-up-the-template) +* [Modifying and configuring the template](#modifying-and-configuring-the-template) +* [Running unit tests for the template](#running-unit-tests-for-the-template) +* [Running the application locally](#running-the-application-locally) +* [Deploying on Cloud](#deploying-on-ibm-cloud) +* [Inferencing the deployment](#inferencing-the-deployment) + + +## Introduction + +This repository provides an Agentic RAG template for LLM apps built using LangGraph framework. It also makes it easy to deploy them as an AI service as part of IBM watsonx.ai for IBM Cloud[^1]. +An AI service is a deployable unit of code that captures the logic of your generative AI use case. For and in-depth description of the topic please refer to the [IBM watsonx.ai documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ai-services-templates.html?context=wx&audience=wdp). + +[^1]: _IBM watsonx.ai for IBM Cloud_ is a full and proper name of the component we're using in this template and only a part of the whole suite of products offered in the SaaS model within IBM Cloud environment. Throughout this README, for the sake of simplicity, we'll be calling it just an **IBM Cloud**. + +The template builds an application with IBM watsonx Utility Agent Tool for addressing RAG use case. The structure of RAG graph is as follows + +![alt text](multi_source_graph.png "LangGraph Agentic RAG") + +> [!NOTE] +> The template uses predefined `Vector Index Asset` as a source of base knowledge for RAG. For more details about `Vector Index` see https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-prompt-data-index-create.html?context=wx&audience=wdp . To run following Agentic RAG app you should set `tool_config_projectId` and `tool_config_vectorIndexId` in section `deployment.online.parameters` in `config.toml`. Moreover, to help the Agent correctly choose whether or not to use the retriever tool, a description of the underlying knowledge contained in the Vector Index Asset can also be provided in field `base_knowledge_description`. + +Traditional RAG has the pain points "I have a lot of data scattered everywhere, I don’t know which data source the answer is in". This pattern allows the retrieval agent to make educated guess which data retrieval tool to use according to the description, and if another data source should be tried if the answer is not relevant. + +The three example data sources (made available as tools in `tools.py`) are: + +1) A SQL database +2) A vector database +3) Web search + +A retrieval flow looks like: + +![alt text](multi-source-diagram.png "LangGraph Agentic RAG multi-source") + +- Agent select a source to query +- Check answer relevance +- If relevant, generate answer +- If irrelevant, try a different data source for answer until all data sources have been tried + + +--- +Authors: +- Sherry Yu (shuang.yu@ibm.com) +- Mateusz Świtała (mateusz.switala@ibm.com) + +Date: 16 April 2025 + + +## Directory structure and file descriptions + +The high level structure of the repository is as follows: + +langgraph-agentic-rag + ┣ src + ┃ ┗ langgraph_agentic_rag + ┃ ┣ agent.py + ┃ ┗ tools.py + ┣ schema + ┣ ai_service.py + ┣ config.toml.example + ┣ pyproject.toml + +- `langgraph-agentic-rag-multisource-retrieval` folder: Contains auxiliary files used by the deployed function. They provide various framework specific definitions and extensions. This folder is packaged and sent to IBM Cloud during deployment as a [package extension](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-create-custom-software-spec.html?context=wx&audience=wdp#custom-wml). +- `schema` folder: Contains request and response schemas for the `/ai_service` endpoint queries. +- `ai_service.py` file: Contains the function to be deployed as an AI service defining the application's logic +- `config.toml.example` file: A configuration file with placeholders that stores the deployment metadata. After downloading the template repository, copy the contents of the `config.toml.example` file to the `config.toml` file and fill in the required fields. `config.toml` file can also be used to tweak the model for your use case. + +## Prerequisites + +- [Poetry](https://python-poetry.org/) package manager, +- [Pipx](https://github.com/pypa/pipx) due to Poetry's recommended [installation procedure](https://python-poetry.org/docs/#installation) + + +## Cloning and setting up the template locally + + +### Step 1: Clone the repository + +In order not to clone the whole `IBM/watsonx-developer-hub` repository we'll use git's shallow and sparse cloning feature to checkout only the template's directory: + +```sh +git clone --no-tags --depth 1 --single-branch --filter=tree:0 --sparse https://github.com/IBM/watsonx-developer-hub.git +cd watsonx-developer-hub +git sparse-checkout add agents/community/langgraph-agentic-rag-multisource-retrieval +``` + +Move to the directory with the agent template: + +```sh +cd agents/community/langgraph-agentic-rag-multisource-retrieval/ +``` + +> [!NOTE] +> From now on it'll be considered that the working directory is `watsonx-developer-hub/agents/community/langgraph-agentic-rag-multisource-retrieval/` + + +### Step 2: Install poetry + +```sh +pipx install --python 3.11 poetry +``` + +### Step 3: Install the template + +Running the below commands will install the repository in a separate virtual environment + +```sh +poetry install +``` + +### Step 4 (OPTIONAL): Activate the virtual environment + +```sh +source $(poetry -q env use 3.11 && poetry env info --path)/bin/activate +``` + +### Step 5: Export PYTHONPATH + +Adding working directory to PYTHONPATH is necessary for the next steps. In your terminal execute: +```sh +export PYTHONPATH=$(pwd):${PYTHONPATH} +``` + +## Modifying and configuring the template + +[config.toml](config.toml) file should be filled in before either deploying the template on IBM Cloud or executing it locally. +Possible config parameters are given in the provided file and explained using comments (when necessary). + + +The template can also be extended to provide additional key-value data to the application. Create a special asset from within your deployment space called _Parameter Sets_. Use the _watsonx.ai_ library to instantiate it and later reference it from the code. +For detailed description and API please refer to the [IBM watsonx.ai Parameter Set's docs](https://ibm.github.io/watsonx-ai-python-sdk/core_api.html#parameter-sets) + + +Sensitive data should not be passed unencrypted, e.g. in the configuration file. The recommended way to handle them is to make use of the [IBM Cloud® Secrets Manager](https://cloud.ibm.com/apidocs/secrets-manager/secrets-manager-v2). The approach to integrating the Secrets Manager's API with the app is for the user to decide on. + + +The [agent.py](src/langgraph/agent.py) file builds app the graph consisting of nodes and edges. The former define the logic for agents while the latter control the logic flow in the whole graph. +For detailed info on how to modify the graph object please refer to [LangGraph's official docs](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/multi-agent-collaboration/#create-graph) + + +The [ai_service.py](ai_service.py) file encompasses the core logic of the app alongside the way of authenticating the user to the IBM Cloud. +For a detailed breakdown of the ai-service's implementation please refer the [IBM Cloud docs](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ai-services-create.html?context=wx) + + +[tools.py](src/langgraph_agentic_rag/tools.py) file stores the definition for tools enhancing the chat model's capabilities. +In order to add new tool create a new function, wrap it with the `@tool` decorator and add to the `TOOLS` list in the `extensions` module's [__init__.py](src/langgraph_agentic_rag/__init__.py) + +For more sophisticated use cases (like async tools), please refer to the [langchain docs](https://python.langchain.com/docs/how_to/custom_tools/#creating-tools-from-runnables). + +## Testing the template + +The `tests/` directory's structure resembles the repository. Adding new tests should follow this convention. +For exemplary purposes only the tools and some general utility functions are covered with unit tests. + +Running the below command will run the complete tests suite: +```sh +pytest -r 'fEsxX' tests/ +``` + +## Running the application locally + +It is possible to run (or even debug) the ai-service locally, however it still requires creating the connection to the IBM Cloud. + +### Step 1: Fill in the `config` file + +Enter the necessary credentials in the `config.toml` file. + +### Step 2: Run the script for local AI service execution + +```sh +python examples/execute_ai_service_locally.py +``` + +### Step 3: Ask the model + +Choose from some pre-defined questions or ask the model your own. + + +## Deploying on IBM Cloud + +Follow these steps to deploy the model on IBM Cloud. + +### Step 1: Fill in the `config` file + +Enter the necessary credentials in the `config.toml` file. + +### Step 2: Run the deployment script + +```sh +python scripts/deploy.py +``` + +Successfully completed script will print on stdout the `deployment_id` which is necessary to locally test the deployment. For further info please refer [to the next section](#querying-the-deployment) + +## Querying the deployment + +Follow these steps to inference your deployment. The [query_existing_deployment.py](examples/query_existing_deployment.py) file shows how to test the existing deployment using `watsonx.ai` library. + +### Step 1: Initialize the deployment ID + +Initialize the `deployment_id` variable in the [query_existing_deployment.py](examples/query_existing_deployment.py) file. +The _deployment_id_ of your deployment can be obtained from [the previous section](#deploying-on-ibm-cloud) by running [scripts/deploy.sh](scripts/deploy.py) + +### Step 2: Run the script for querying the deployment + +```sh +python examples/query_existing_deployment.py +``` diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/ai_service.py b/agents/community/langgraph-agentic-rag-multisource-retrieval/ai_service.py new file mode 100644 index 00000000..966c678e --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/ai_service.py @@ -0,0 +1,242 @@ +def deployable_ai_service(context, url, model_id, space_id, thread_id, tool_config_projectId, tool_config_vectorIndexId, base_knowledge_description = None): + from typing import Generator + + from langgraph_agentic_rag.agent import get_graph_closure + from ibm_watsonx_ai import APIClient, Credentials + from langchain_core.messages import ( + BaseMessage, + HumanMessage, + AIMessage, + SystemMessage, + ) + + client = APIClient( + credentials=Credentials(url=url, token=context.generate_token()), + space_id=space_id, + ) + + graph = get_graph_closure( + client, + model_id, + tool_config={ + "projectId": tool_config_projectId, + "vectorIndexId": tool_config_vectorIndexId, + }, + base_knowledge_description=base_knowledge_description, + ) + + def get_formatted_message( + resp: BaseMessage, is_assistant: bool = False + ) -> dict | None: + role = resp.type + + if resp.content: + if role in {"AIMessageChunk", "ai"}: + return {"role": "assistant", "content": resp.content} + elif role == "tool": + if is_assistant: + return { + "role": "assistant", + "step_details": { + "type": "tool_response", + "id": resp.id, + "tool_call_id": resp.tool_call_id, + "name": resp.name, + "content": resp.content, + }, + } + else: + return { + "role": role, + "id": resp.id, + "tool_call_id": resp.tool_call_id, + "name": resp.name, + "content": resp.content, + } + elif role == "ai": # this implies resp.additional_kwargs + if additional_kw := resp.additional_kwargs: + tool_call = additional_kw["tool_calls"][0] + if is_assistant: + return { + "role": "assistant", + "step_details": { + "type": "tool_calls", + "tool_calls": [ + { + "id": tool_call["id"], + "name": tool_call["function"]["name"], + "args": tool_call["function"]["arguments"], + } + ], + }, + } + else: + return { + "role": "assistant", + "tool_calls": [ + { + "id": tool_call["id"], + "type": "function", + "function": { + "name": tool_call["function"]["name"], + "arguments": tool_call["function"]["arguments"], + }, + } + ], + } + + def convert_dict_to_message(_dict: dict) -> BaseMessage: + """Convert user message in dict to langchain_core.messages.BaseMessage""" + + if _dict["role"] == "assistant": + return AIMessage(content=_dict["content"]) + elif _dict["role"] == "system": + return SystemMessage(content=_dict["content"]) + else: + return HumanMessage(content=_dict["content"]) + + def generate(context) -> dict: + """ + The `generate` function handles the REST call to the inference endpoint + POST /ml/v4/deployments/{id_or_name}/ai_service + + The generate function should return a dict + + A JSON body sent to the above endpoint should follow the format: + { + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant that uses tools to answer questions in detail.", + }, + { + "role": "user", + "content": "Hello!", + }, + ] + } + Please note that the `system message` MUST be placed first in the list of messages! + """ + + client.set_token(context.get_token()) + + payload = context.get_json() + raw_messages = payload.get("messages", []) + messages = [convert_dict_to_message(_dict) for _dict in raw_messages] + + if messages and messages[0].type == "system": + agent = graph(messages[0]) + del messages[0] + else: + agent = graph() + + config = { + "configurable": {"thread_id": thread_id} + } # Checkpointer configuration + + # Invoke agent + generated_response = agent.invoke({"messages": messages}, config) + + choices = [] + execute_response = { + "headers": {"Content-Type": "application/json"}, + "body": {"choices": choices}, + } + + choices.append( + { + "index": 0, + "message": get_formatted_message(generated_response["messages"][-1]), + } + ) + + return execute_response + + def generate_stream(context) -> Generator[dict, ..., ...]: + """ + The `generate_stream` function handles the REST call to the Server-Sent Events (SSE) inference endpoint + POST /ml/v4/deployments/{id_or_name}/ai_service_stream + + The generate function should return a dict + + A JSON body sent to the above endpoint should follow the format: + { + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant that uses tools to answer questions in detail.", + }, + { + "role": "user", + "content": "Hello!", + }, + ] + } + Please note that the `system message` MUST be placed first in the list of messages! + """ + try: + headers = context.get_headers() + is_assistant = headers.get("X-Ai-Interface") == "assistant" + + client.set_token(context.get_token()) + + payload = context.get_json() + raw_messages = payload.get("messages", []) + messages = [convert_dict_to_message(_dict) for _dict in raw_messages] + + if messages and messages[0].type == "system": + agent = graph(messages[0]) + del messages[0] + else: + agent = graph() + + + # Checkpointer configuration + config = {"configurable": {"thread_id": thread_id}} + response_stream = agent.stream( + {"messages": messages}, config, stream_mode=["updates", "messages"] + ) + + for chunk_type, data in response_stream: + # print("[data]", data) + if chunk_type == "messages": + msg_obj = data[0] + elif chunk_type == "updates": + if agent := data.get("agent"): + msg_obj = agent["messages"][0] + if msg_obj.response_metadata.get("finish_reason") == "stop": + continue + elif tool := data.get("tools"): + msg_obj = tool["messages"][0] + else: + continue + else: + continue + + if ( + message := get_formatted_message(msg_obj, is_assistant=is_assistant) + ) is not None: + # print(message) + chunk_response = { + "choices": [ + { + "index": 0, + "delta": message, + "finish_reason": msg_obj.response_metadata.get( + "finish_reason" + ), + } + ] + } + yield chunk_response + except Exception as e: + yield { + "choices": [ + { + "index": 0, + "delta": {"role":"assistant", "content":str(e)}, + } + ] + } + + return generate, generate_stream diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/config.toml.example b/agents/community/langgraph-agentic-rag-multisource-retrieval/config.toml.example new file mode 100644 index 00000000..d698827b --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/config.toml.example @@ -0,0 +1,53 @@ +[cli.options] + # If true, cli `invoke` command is trying to use `ai_service.generate_stream` function for local tests, and `ai_service.generate` otherwise. + # Default: true + stream = true + + # Path to json file with a complete payload that will be send to proper AI service generate function. + # Note that, the payload file will be only used when no `query` is provided when running `invoke ` command + # Default: None + payload_path = "" + +[deployment] + # One of the below is required. + # To determine your `api_key`, refer to `IBM Cloud console API keys `_. + watsonx_apikey = "" + watsonx_token = "" + + # should follow the format: `https://{REGION}.ml.cloud.ibm.com` + watsonx_url = "https://us-south.ml.cloud.ibm.com" + + # Deployment space id is required to create deployment with AI service content. + space_id = "01c559cf-a66e-487e-b927-fef28f2961a7" + + # variable, that is populated with last created deployment_id every time when command `watsonx-ai service new` finish successfully + deployment_id = "" + +[deployment.online.parameters] + # during creation of deployment additional parameters can be provided inside `ONLINE` object for further referencing + # please refer to the API docs: https://cloud.ibm.com/apidocs/machine-learning-cp#deployments-create + # model_id = "meta-llama/llama-3-3-70b-instruct" # underlying model of WatsonxChat + model_id = "mistralai/mistral-large" + thread_id = "thread-1" # More info here: https://langchain-ai.github.io/langgraph/how-tos/persistence/ + space_id = "" + url = "https://us-south.ml.cloud.ibm.com" # should follow the format: `https://{REGION}.ml.cloud.ibm.com` + + # RAGQuery Tool configuration + # Required: + tool_config_projectId = "" + tool_config_vectorIndexId = "" + + # Optional: + base_knowledge_description = "" + +[deployment.software_specification] + # Name for derived software specification. If not provided, default one is used that will be build based on the package name: "{pkg_name}-sw-spec" + name = "" + + # Whether to overwrite (delete existing and create new with the same name) watsonx derived software specification + # Default: false + overwrite = false + + # The base software specification used to deploy the AI service. The template dependencies will be installed based on the packages included in the selected base software specification + # Default: "runtime-24.1-py3.11" + base_sw_spec = "runtime-24.1-py3.11" \ No newline at end of file diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/examples/_interactive_chat.py b/agents/community/langgraph-agentic-rag-multisource-retrieval/examples/_interactive_chat.py new file mode 100644 index 00000000..31b5b028 --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/examples/_interactive_chat.py @@ -0,0 +1,128 @@ +import textwrap +import json +from collections.abc import Generator +from typing import Callable + + +class InteractiveChat: + def __init__( + self, + ai_service_invoke: Callable, + questions: tuple[str] | None = None, + stream: bool = False, + verbose: bool = True, + ) -> None: + self.ai_service_invoke = ai_service_invoke + self._ordered_list = lambda seq_: "\n".join( + f"\t{i}) {k}" for i, k in enumerate(seq_, 1) + ) + self._delta_start = False + self.verbose = verbose + self.stream = stream + + self.questions = ("Hi! How are you?",) if questions is None else questions + + self._help_message = textwrap.dedent( + """ + The following commands are supported: + --> help | h : prints this help message + --> quit | q : exits the prompt and ends the program + --> list_questions : prints a list of available questions + """ + ) + + @property + def questions(self) -> tuple: + return self._questions + + @questions.setter + def questions(self, q: tuple) -> None: + self._questions = q + self._questions_prompt = ( + f"\tQuestions:\n{self._ordered_list(self._questions)}\n" + ) + + def _user_input_loop(self) -> Generator[tuple[str, str], None, None]: + print(self._help_message) + + while True: + q = input("\nChoose a question or ask one of your own.\n --> ") + + _ = yield q, "question" + + def _print_message(self, choice: dict) -> None: + if delta := choice.get("delta"): + if not self._delta_start: + header = f" {delta['role'].capitalize()} Message ".center(80, "=") + print("\n", header) + self._delta_start = ( + True + and (choice.get("finish_reason") is None) + and delta["role"] != "tool" + ) + print(delta.get("content") or delta.get("tool_calls"), flush=True, end="") + else: + header = f" {choice['message']['role'].capitalize()} Message ".center( + 80, "=" + ) + print("\n", header) + print(f"{choice['message'].get('content', choice['message'])}") + + def run(self) -> None: + # TODO implement signal handling (especially Ctrl-C) + while True: + try: + q = None + + user_loop = self._user_input_loop() + + for action, stage in user_loop: # unsupported command support! + + if action == "h" or action == "help": + print(self._help_message) + elif action == "quit" or action == "q": + raise EOFError + + elif action == "list_questions": + print(self._questions_prompt) + + elif stage == "question": + user_message = {} + # small caveat -- if user answers to the chat a single digit we'll treat it as trying to choose on of our self.questions + if not action.isdigit(): # user defined question + user_message["content"] = action.strip() + else: + number = int(action) + + print(f"you chose QUESTION {number}\n") + if number > len(self.questions) or number < 0: + print( + "provided numbers have to match the available numbers" + ) + else: + user_message["content"] = self.questions[number - 1] + + request_payload_json = { + "messages": [{"role": "user", **user_message}] + } + + resp = self.ai_service_invoke(request_payload_json) + + if self.stream: + for r in resp: + if type(r) == str: + r = json.loads(r) + for c in r["choices"]: + self._print_message(c) + self._delta_start = False + else: + resp_choices = resp.get("body", resp)["choices"] + choices = ( + resp_choices if self.verbose else resp_choices[-1:] + ) + + for c in choices: + self._print_message(c) + + except EOFError: + break diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/examples/execute_ai_service_locally.py b/agents/community/langgraph-agentic-rag-multisource-retrieval/examples/execute_ai_service_locally.py new file mode 100644 index 00000000..2d479e08 --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/examples/execute_ai_service_locally.py @@ -0,0 +1,31 @@ +from ibm_watsonx_ai import APIClient, Credentials +from ibm_watsonx_ai.deployments import RuntimeContext + +from ai_service import deployable_ai_service +from utils import load_config +from examples._interactive_chat import InteractiveChat + +stream = True +config = load_config() +dep_config = config["deployment"] +online_parameters = dep_config["online"]["parameters"] + +client = APIClient( + credentials=Credentials( + url=dep_config["watsonx_url"], api_key=dep_config["watsonx_apikey"] + ) +) + +context = RuntimeContext(api_client=client) +ai_service_resp_func = deployable_ai_service(context=context, **online_parameters)[ + stream +] + + +def ai_service_invoke(payload): + context.request_payload_json = payload + return ai_service_resp_func(context) + + +chat = InteractiveChat(ai_service_invoke, stream=stream) +chat.run() diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/examples/query_existing_deployment.py b/agents/community/langgraph-agentic-rag-multisource-retrieval/examples/query_existing_deployment.py new file mode 100644 index 00000000..3548ac51 --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/examples/query_existing_deployment.py @@ -0,0 +1,41 @@ +import ibm_watsonx_ai + +from utils import load_config +from examples._interactive_chat import InteractiveChat + +deployment_id = "8af73a87-a451-4d64-8dd7-f5fea7abd8c0" +stream = True +config = load_config("deployment") + +client = ibm_watsonx_ai.APIClient( + credentials=ibm_watsonx_ai.Credentials( + url=config["watsonx_url"], api_key=config["watsonx_apikey"] + ), + space_id=config["space_id"], +) + +# Executing deployed AI service with provided scoring data +# if stream: +# ai_service_invoke = lambda payload: client.deployments.run_ai_service_stream( +# deployment_id, payload +# ) +# else: +# ai_service_invoke = lambda payload: client.deployments.run_ai_service( +# deployment_id, payload +# ) + +# chat = InteractiveChat(ai_service_invoke, stream=stream) +# chat.run() + +payload = { + "messages": [ + { + "role": "user", + "content": "what's ibm's carbon emission" + } + ] + } + +for chunk in client.deployments.run_ai_service_stream(deployment_id, payload): + print(chunk) + diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/multi-source-diagram.png b/agents/community/langgraph-agentic-rag-multisource-retrieval/multi-source-diagram.png new file mode 100644 index 00000000..0e09225a Binary files /dev/null and b/agents/community/langgraph-agentic-rag-multisource-retrieval/multi-source-diagram.png differ diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/multi_source_graph.png 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ai_service. This particular example focuses on a WatsonX chatbot enhanced with watsonx Utility Agent Tool 'RAGQuery'." +authors = ["Your Name "] +license = "MIT" +readme = "README.md" +packages = [ + { include = "langgraph_agentic_rag", from = "src" } +] + + +[tool.poetry.dependencies] +python = ">=3.10,<3.13" +langchain-ibm = "^0.3.9" +python-dotenv = "^1.0.1" +ibm-watsonx-ai = { version = ">=1.1.22", python = ">=3.11,<3.13" } +langgraph = ">0.2,<0.3" +langchain = "^0.3.21" +pytest = "^8.3.3" +langchain-community = "0.3.16" +psycopg2-binary = "2.9.10" +ibm-secrets-manager-sdk = "^2.1.9" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/schema/request.json b/agents/community/langgraph-agentic-rag-multisource-retrieval/schema/request.json new file mode 100644 index 00000000..03b90bb4 --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/schema/request.json @@ -0,0 +1,37 @@ +{ + "application/json": { + "$schema": "http://json-schema.org/draft-07/schema#", + "type": "object", + "properties": { + "messages": { + "title": "The messages for this chat session.", + "type": "array", + "items": { + "type": "object", + "properties": { + "role": { + "title": "The role of the message author.", + "type": "string", + "enum": [ + "user", + "assistant", + "system" + ] + }, + "content": { + "title": "The contents of the message.", + "type": "string" + } + }, + "required": [ + "role", + "content" + ] + } + } + }, + "required": [ + "messages" + ] + } +} \ No newline at end of file diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/schema/response.json b/agents/community/langgraph-agentic-rag-multisource-retrieval/schema/response.json new file mode 100644 index 00000000..9fb5d30e --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/schema/response.json @@ -0,0 +1,86 @@ +{ + "application/json": { + "oneOf": [ + { + "$schema": "http://json-schema.org/draft-07/schema#", + "type": "object", + "description": "AI Service response for /ai_service_stream", + "properties": { + "choices": { + "description": "A list of chat completion choices.", + "type": "array", + "items": { + "type": "object", + "properties": { + "index": { + "type": "integer", + "title": "The index of this result." + }, + "delta": { + "description": "A message result.", + "type": "object", + "properties": { + "content": { + "description": "The contents of the message.", + "type": "string" + }, + "role": { + "description": "The role of the author of this message.", + "type": "string" + } + }, + "required": [ + "role" + ] + } + } + } + } + }, + "required": [ + "choices" + ] + }, + { + "$schema": "http://json-schema.org/draft-07/schema#", + "type": "object", + "description": "AI Service response for /ai_service", + "properties": { + "choices": { + "description": "A list of chat completion choices", + "type": "array", + "items": { + "type": "object", + "properties": { + "index": { + "type": "integer", + "description": "The index of this result." + }, + "message": { + "description": "A message result.", + "type": "object", + "properties": { + "role": { + "description": "The role of the author of this message.", + "type": "string" + }, + "content": { + "title": "Message content.", + "type": "string" + } + }, + "required": [ + "role" + ] + } + } + } + } + }, + "required": [ + "choices" + ] + } + ] + } +} \ No newline at end of file diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/scripts/build_package.py b/agents/community/langgraph-agentic-rag-multisource-retrieval/scripts/build_package.py new file mode 100644 index 00000000..fe8625bd --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/scripts/build_package.py @@ -0,0 +1,58 @@ +import shutil +import subprocess +import tomllib +from pathlib import Path + + +def get_package_name_and_version(pyproject_path: str) -> tuple[str, str]: + """ + Parse pyproject.toml to get the package name and version. + + :param pyproject_path: Path to pyproject.toml + :type pyproject_path: str + """ + with open(pyproject_path, "rb") as f: + pyproject_data = tomllib.load(f) + tool_poetry = pyproject_data.get("tool", {}).get("poetry", {}) + package_name = tool_poetry.get("name") + package_version = tool_poetry.get("version") + if not package_name or not package_version: + raise ValueError("Package name or version is missing in pyproject.toml.") + return package_name, package_version + + +def build_zip_sc(sc_dir: Path) -> None: + """ + Build and package a source distribution as a ZIP archive. + + This function performs the following steps: + 1. Builds a source distribution using Poetry. + 2. Extracts the built archive. + 3. Normalizes file timestamps to fix ZIP timestamp issues. + 4. Creates a ZIP archive of the source directory. + + :param sc_dir: Path to the source directory for building and packaging. + :type sc_dir: Path + """ + subprocess.run( + ["poetry", "build", f"--output={sc_dir.parent}", "--format=sdist"], check=True + ) + shutil.unpack_archive(sc_dir.with_suffix(".tar.gz"), sc_dir.parent) + + for file in sc_dir.parent.rglob("*"): + if file.is_file(): + file.touch() + + zip_dir = str(sc_dir.with_suffix("")) + shutil.make_archive(zip_dir, "zip", zip_dir) + + +if __name__ == "__main__": + pkg_name, pkg_version = get_package_name_and_version("../pyproject.toml") + pkg_ext_sc = ( + Path(__file__).parent + / ".." + / "dist" + / f"{pkg_name.replace('-', '_')}-{pkg_version}.zip" + ) + build_zip_sc(pkg_ext_sc) diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/scripts/deploy.py b/agents/community/langgraph-agentic-rag-multisource-retrieval/scripts/deploy.py new file mode 100644 index 00000000..fb343c88 --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/scripts/deploy.py @@ -0,0 +1,112 @@ +import json +import logging +from pathlib import Path +import json + +import ibm_watsonx_ai + +from ai_service import deployable_ai_service +from scripts.build_package import build_zip_sc, get_package_name_and_version +from utils import load_config + +logging.basicConfig() +logger = logging.getLogger(__name__) + +config = load_config() +dep_config = config["deployment"] + +client = ibm_watsonx_ai.APIClient( + credentials=ibm_watsonx_ai.Credentials( + url=dep_config["watsonx_url"], api_key=dep_config["watsonx_apikey"] + ), + space_id=dep_config["space_id"], +) + +root_dir = Path(__file__).parents[1] +pyproject_path = root_dir / "pyproject.toml" +pkg_name, pkg_version = get_package_name_and_version(str(pyproject_path)) + +# Create package extension +pkg_ext_metadata = { + client.package_extensions.ConfigurationMetaNames.NAME: pkg_name, + client.package_extensions.ConfigurationMetaNames.TYPE: "pip_zip", +} + +pkg_ext_sc = root_dir / "dist" / f"{pkg_name.replace('-', '_')}-{pkg_version}.zip" + +if not pkg_ext_sc.exists(): + build_zip_sc(pkg_ext_sc) +else: + logger.warning( + f"package extension was not built as path: '{pkg_ext_sc}' is not empty. Using the already existing path for deployment. " + "In case of any problems you might want to delete it and rerun the `deploy.py` script" + ) + +pkg_ext_asset_details = client.package_extensions.store( + meta_props=pkg_ext_metadata, file_path=str(pkg_ext_sc) +) +pkg_ext_asset_id = client.package_extensions.get_id(pkg_ext_asset_details) + +# Select base software specification to extend +base_sw_spec_id = client.software_specifications.get_id_by_name("runtime-24.1-py3.11") + +# Define new software specification based on base one and custom library +template_sw_spec_name = f"{pkg_name}-sw-spec" + +sw_spec_metadata = { + client.software_specifications.ConfigurationMetaNames.NAME: template_sw_spec_name, + client.software_specifications.ConfigurationMetaNames.BASE_SOFTWARE_SPECIFICATION: { + "guid": base_sw_spec_id + }, + client.software_specifications.ConfigurationMetaNames.PACKAGE_EXTENSIONS: [ + {"guid": pkg_ext_asset_id} + ], +} + +# Delete if sw_spec already exists +try: + sw_spec_id = client.software_specifications.get_id_by_name(template_sw_spec_name) + logger.warning(f"Deleting previously created sw_spec: {template_sw_spec_name}") + client.software_specifications.delete(sw_spec_id) +except ibm_watsonx_ai.wml_client_error.ResourceIdByNameNotFound: + pass + +# Store the software spec +sw_spec_asset_details = client.software_specifications.store( + meta_props=sw_spec_metadata +) + +# Get the id of the new asset +asset_id = client.software_specifications.get_id(sw_spec_asset_details) + +sw_spec_asset_details = client.software_specifications.get_details(asset_id) + + +with (root_dir / "schema" / "request.json").open("r", encoding="utf-8") as file: + request_schema = json.load(file) + +with (root_dir / "schema" / "response.json").open("r", encoding="utf-8") as file: + response_schema = json.load(file) + +meta_props = { + client.repository.AIServiceMetaNames.SOFTWARE_SPEC_ID: asset_id, + client.repository.AIServiceMetaNames.NAME: "online ai_service", + client.repository.AIServiceMetaNames.REQUEST_DOCUMENTATION: request_schema, + client.repository.AIServiceMetaNames.RESPONSE_DOCUMENTATION: response_schema, + client.repository.AIServiceMetaNames.TAGS: ["wx-agent"], +} + +stored_ai_service_details = client.repository.store_ai_service( + deployable_ai_service, meta_props +) +ai_service_id = stored_ai_service_details["metadata"].get("id") + +meta_props = { + client.deployments.ConfigurationMetaNames.NAME: f"online ai_service test", + client.deployments.ConfigurationMetaNames.ONLINE: { + "parameters": dep_config["online"]["parameters"] + }, + client.repository.AIServiceMetaNames.TAGS: ["wx-agent"], +} + +deployment_details = client.deployments.create(ai_service_id, meta_props) diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/src/langgraph_agentic_rag/__init__.py b/agents/community/langgraph-agentic-rag-multisource-retrieval/src/langgraph_agentic_rag/__init__.py new file mode 100644 index 00000000..6a397ca2 --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/src/langgraph_agentic_rag/__init__.py @@ -0,0 +1 @@ +from .tools import retriever_tool_watsonx, sql_retriever_tool_watsonx, websearch_tool_watsonx, serper_search_tool diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/src/langgraph_agentic_rag/agent.py b/agents/community/langgraph-agentic-rag-multisource-retrieval/src/langgraph_agentic_rag/agent.py new file mode 100644 index 00000000..b8c38015 --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/src/langgraph_agentic_rag/agent.py @@ -0,0 +1,268 @@ +from typing import Callable, Annotated, Sequence + +from typing_extensions import TypedDict +from langchain_core.prompts import PromptTemplate, ChatPromptTemplate + +from ibm_watsonx_ai import APIClient +from langchain_ibm import ChatWatsonx +from pydantic import BaseModel, Field +from typing import Literal + +from langgraph.graph import END, StateGraph, START +from langgraph.graph.graph import CompiledGraph +from langgraph.graph.message import add_messages + +from langgraph.prebuilt import ToolNode, tools_condition + +from langchain_core.messages import BaseMessage, SystemMessage + +from langgraph_agentic_rag import retriever_tool_watsonx, sql_retriever_tool_watsonx, websearch_tool_watsonx, serper_search_tool + + +def get_graph_closure( + client: APIClient, + model_id: str, + tool_config: dict, + base_knowledge_description: str | None = None, +) -> Callable: + """Graph generator closure.""" + + # Initialise ChatWatsonx + chat = ChatWatsonx(model_id=model_id, watsonx_client=client) + grader_model_id = "meta-llama/llama-4-maverick-17b-128e-instruct-fp8" + chat_grader = ChatWatsonx(model_id=grader_model_id, watsonx_client=client, disable_streaming = True) + + websearch_tool = serper_search_tool(api_client=client) + # websearch_tool = websearch_tool_watsonx( + # api_client=client, + # tool_config={"maxResults": 10}, + # ), + + TOOLS = [ + retriever_tool_watsonx( + api_client=client, + tool_config=tool_config, + ), + sql_retriever_tool_watsonx( + api_client=client, + ), + + websearch_tool + ] + + + # Define system prompt + default_system_prompt = ( + f"You are a helpful AI assistant, please respond to the user's query to the best of your ability!" + "\n\n" + f"Vector Store Index knowledge description: {base_knowledge_description or ''}" + ) + + class AgentState(TypedDict): + # The add_messages function defines how an update should be processed + # Default is to replace. add_messages says "append" + messages: Annotated[Sequence[BaseMessage], add_messages] + + ### Nodes + + def agent_with_instruction(instruction_prompt: str | None) -> Callable: + """System prompt will be updated by instruction prompt.""" + + def agent(state: AgentState) -> dict: + """ + Invokes the agent model to generate a response based on the current state. Given + the question, it will decide to retrieve using the retriever tool, or simply end. + + Args: + state (messages): The current state + + Returns: + dict: The updated state with the agent response appended to messages + """ + messages = state["messages"] + tools_used = [] + + for m in reversed(messages): + if hasattr(m, 'tool_call_id'): + print((f"I have tried tool: {m.name}")) + tools_used.append(m.name) + if m.type == "human": + query = m + break + + unused_tools = [t for t in TOOLS if t.name not in tools_used] + if len(unused_tools) < 1: + unused_tools = [websearch_tool] + + model = chat.bind_tools(unused_tools) + print(f"unused tools: {[u.name for u in unused_tools]}") + + # system_prompt = SystemMessage( + # default_system_prompt + "\n" + (instruction_prompt or "") + # ) + + # response = model.invoke([system_prompt] + list(messages)) + response = model.invoke([query]) + # We return a list, because this will get added to the existing list + return {"messages": [response]} + + return agent + + def generate(state: AgentState): + """ + Generate answer + + Args: + state (messages): The current state + + Returns: + dict: The updated state with re-phrased question + """ + messages = state["messages"] + + # Most recent user query + for m in reversed(messages): + if m.type == "human": + question = m.content + + # Tool content + last_message = messages[-1] + docs = last_message.content + + # Prompt + prompt = ChatPromptTemplate([ + ("user", "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If there is no context, just answer the question to be best of your knowledge. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\nQuestion: {question} \nContext: {context} \nAnswer:") + ]) + + # Chain + rag_chain = prompt | chat + + # Run + response = rag_chain.invoke({"context": docs, "question": question}) + return {"messages": [response]} + + + # Edges + def grade_documents(state) -> Literal["generate", "agent"]: + """ + Determines whether the retrieved documents are relevant to the question. + + Args: + state (messages): The current state + + Returns: + str: A decision for whether the documents are relevant or not + """ + + print("---CHECK RELEVANCE---") + + # Data model + class grade(BaseModel): + """Binary score for relevance check.""" + binary_score: str = Field(description="Relevance score 'yes' or 'no'") + + + # LLM with tool and validation + llm_with_tool = chat_grader.with_structured_output(grade) + + # Prompt + prompt = PromptTemplate( + template="""You are a grader assessing relevance of a retrieved document to a user question. + Here is the retrieved document: \n {context} \n + Here is the user question: {question} + If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. + If the document mentions it has no data, no results or no information to answer the question, it means the document is NOT relevant and grade it as 'no'. + If the data is not directly available to answer the user question, it means the document is NOT relevant and grade it as 'no'. + Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.""", + input_variables=["context", "question"], + ) + + # Chain + chain = prompt | llm_with_tool + + messages = state["messages"] + last_message = messages[-1] + + # find the last question + msg_cnt = 0 + for m in reversed(messages): + msg_cnt += 1 + if m.type == "human": + question = m.content + break + + docs = last_message.content + + scored_result = chain.invoke({"question": question, "context": docs}) + + score = scored_result.binary_score + + if msg_cnt > 12: + score = "yes" + print("number of messages since last question:",len(messages)) + + if score == "yes": + print("---DECISION: DOCS RELEVANT---") + return "generate" + + else: + print("---DECISION: DOCS NOT RELEVANT---") + return "agent" + + + def get_graph(instruction_prompt: SystemMessage | None = None) -> CompiledGraph: + """Get compiled graph with overwritten system prompt, if provided""" + + # Define a new graph + workflow = StateGraph(AgentState) + + if instruction_prompt is None: + agent = agent_with_instruction(instruction_prompt) + else: + agent = agent_with_instruction(instruction_prompt.content) + + # Define the nodes + workflow.add_node("agent", agent) # agent + retrieve = ToolNode(TOOLS) + workflow.add_node("retrieve", retrieve) # retrieval + workflow.add_node("generate", generate) # generate answer + + # Call agent node to decide to retrieve or not + workflow.add_edge(START, "agent") + + # Decide whether to retrieve + workflow.add_conditional_edges( + "agent", + # Assess agent decision + tools_condition, + { + # Translate the condition outputs to nodes in our graph + "tools": "retrieve", + END: "generate", + }, + ) + workflow.add_conditional_edges( + "retrieve", + # Assess agent decision + grade_documents, + ) + + # Edges taken after the `action` node is called. + workflow.add_edge("generate", END) + + # Compile + graph = workflow.compile() + + # Generate the graph image + # try: + # graph_image = graph.get_graph(xray=True).draw_mermaid_png() + # image_path = "multi_source_graph_output.png" + + # with open(image_path, "wb") as f: + # f.write(graph_image) + # except Exception as e: + # print(f"Error generating graph: {e}") + + return graph + + return get_graph diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/src/langgraph_agentic_rag/tools.py b/agents/community/langgraph-agentic-rag-multisource-retrieval/src/langgraph_agentic_rag/tools.py new file mode 100644 index 00000000..ef366118 --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/src/langgraph_agentic_rag/tools.py @@ -0,0 +1,139 @@ +from typing import Callable, TYPE_CHECKING +from typing import Any, List + +from ibm_cloud_sdk_core.authenticators.bearer_token_authenticator import BearerTokenAuthenticator +from ibm_secrets_manager_sdk.secrets_manager_v2 import SecretsManagerV2 + +from langchain_core.tools import tool +from langchain.chains import create_sql_query_chain +from langchain_community.utilities import SQLDatabase +from langchain_ibm import ChatWatsonx +from langchain_community.utilities import GoogleSerperAPIWrapper + + +if TYPE_CHECKING: + from ibm_watsonx_ai import APIClient + + +def get_secret(api_client, secret_name): + service_url = "https://{your_instance_ID}.au-syd.secrets-manager.appdomain.cloud" + secretsManager = SecretsManagerV2( + authenticator=BearerTokenAuthenticator(bearer_token=api_client.token), + ) + + secretsManager.set_service_url(service_url.format(instance_ID="insatnce_id")) + + response = secretsManager.get_secret_by_name_type( + secret_type='kv', + name=secret_name, + secret_group_name='default' + ) + + return response.result["data"][secret_name] + + +def sql_retriever_tool_watsonx( + api_client: "APIClient", +) -> Callable: + + db_uri = get_secret(api_client, "PG_URI") + db = SQLDatabase.from_uri(db_uri) + model_id = "meta-llama/llama-3-3-70b-instruct" + llm = ChatWatsonx(model_id=model_id, watsonx_client=api_client, disable_streaming = True) + chain = create_sql_query_chain(llm, db) + + @tool("sql_retriever", parse_docstring=True) + def sql_retriever_tool(query: str) -> str: + """ + SQL retrieval tool for sustainability statistics for suppliers such as scope 1 and scope 2 carbon emmisions and revenue. + + Args: + query: User query related to information stored in Vector Index. + + Returns: + Retrieved chunk. + """ + + return chain.invoke({"question": query}) + + return sql_retriever_tool + + + +def retriever_tool_watsonx( + api_client: "APIClient", + tool_config: dict, +) -> Callable: + + from langchain_ibm.toolkit import WatsonxToolkit + + toolkit = WatsonxToolkit(watsonx_client=api_client) + + rag_tool = toolkit.get_tool("RAGQuery") + rag_tool.set_tool_config(tool_config) + + @tool("vectordb_retriever", parse_docstring=True) + def retriever_tool(query: str) -> str: + """ + Vector Store Index retriever tool to retrieve sustainability reports. + + Args: + query: User query related to information stored in Vector Index. + + Returns: + Retrieved chunk. + """ + return rag_tool.invoke({"input": query})["output"] + + return retriever_tool + + +def websearch_tool_watsonx( + api_client: "APIClient", + tool_config: dict, +) -> Callable: + + from langchain_ibm.toolkit import WatsonxToolkit + + toolkit = WatsonxToolkit(watsonx_client=api_client) + rag_tool = toolkit.get_tool("GoogleSearch") + rag_tool.set_tool_config(tool_config) + + @tool("websearch", parse_docstring=True) + def websearch_tool(query: str) -> str: + """ + Search for online trends, news, current events, real-time information, or research topics. + + Args: + query: User query related to information on the internet. + + Returns: + Retrieved chunk. + """ + return rag_tool.invoke({"input": query})["output"] + + return websearch_tool + + +def serper_search_tool( + api_client: "APIClient", + # tool_config: dict, +) -> Callable: + apikey = get_secret(api_client, "SERPER_APIKEY") + serper_search = GoogleSerperAPIWrapper(serper_api_key=apikey) + + @tool("serpersearch", parse_docstring=True) + def serper_search_tool(query: str) -> str: + """ + Search for online trends, news, current events, real-time information, or research topics. + + Args: + query: User query related to information on the internet. + + Returns: + Retrieved chunk. + """ + return serper_search.run(query) + + return serper_search_tool + diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/tests/__init__.py b/agents/community/langgraph-agentic-rag-multisource-retrieval/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/tests/test_tools.py b/agents/community/langgraph-agentic-rag-multisource-retrieval/tests/test_tools.py new file mode 100644 index 00000000..505bc2b3 --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/tests/test_tools.py @@ -0,0 +1,27 @@ +from ibm_watsonx_ai import Credentials, APIClient + +from utils import load_config +from langgraph_agentic_rag import retriever_tool_watsonx + +config = load_config() + +dep_config = config["deployment"] + +api_client = APIClient( + credentials=Credentials( + url=dep_config["watsonx_url"], api_key=dep_config["watsonx_apikey"] + ) +) + +tool_config = { + "projectId": dep_config["custom"]["tool_config_projectId"], + "vectorIndexId": dep_config["custom"]["tool_config_vectorIndexId"], +} + + +class TestTools: + def test_dummy_vector_index_run(self): + query = "IBM" + rag_tool = retriever_tool_watsonx(api_client, tool_config) + result = rag_tool.invoke({"query": query}) + assert "output" in result # Check if the tool returns correct payload diff --git a/agents/community/langgraph-agentic-rag-multisource-retrieval/utils.py b/agents/community/langgraph-agentic-rag-multisource-retrieval/utils.py new file mode 100644 index 00000000..e211a5d2 --- /dev/null +++ b/agents/community/langgraph-agentic-rag-multisource-retrieval/utils.py @@ -0,0 +1,10 @@ +import tomllib +from pathlib import Path + + +def load_config(section: str | None = None) -> dict: + config = tomllib.loads((Path(__file__).parent / "config.toml").read_text()) + if section is not None: + return config[section] + else: + return config