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A Guide to Building Agentic RAG Systems with LangGraph

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Apr 21, 2025 am 11:00 AM

This article explores Retrieval Augmented Generation (RAG) systems and how AI agents can enhance their capabilities. Traditional RAG systems, while useful for leveraging custom enterprise data, suffer from limitations such as a lack of real-time data and potential for irrelevant document retrieval. This guide proposes an Agentic Corrective RAG system to address these shortcomings.

The core improvement lies in incorporating AI agents to manage a more sophisticated workflow. This involves:

Document Grading: An LLM assesses the relevance of retrieved documents to the user's query.
Query Rewriting and Web Search: If irrelevant documents are identified, the query is rephrased, and a web search (using a tool like Tavily Search API) retrieves up-to-date information.
LangGraph Integration: The entire process is orchestrated using LangGraph, a framework for building AI agents, creating a cyclical workflow that combines static knowledge with real-time web data.

The architecture is detailed, showing how the system flows between document retrieval, relevance grading, query refinement, web search (if necessary), and final answer generation. A practical implementation using LangChain, OpenAI embeddings, and the Tavily Search API is provided. The code covers:

Dependency installation.
API key setup.
Building a vector database (using Chroma) from Wikipedia data.
Creating a query retriever, a document grader, and a QA RAG chain.
Developing query rephrasing and web search tools.
Constructing the core Agentic RAG components (retrieval, grading, query rewriting, web search, answer generation, and decision-making).
Building the agent graph with LangGraph.
Testing the system with various scenarios (relevant documents, irrelevant documents, and out-of-scope queries).

The article concludes by highlighting the advantages of the Agentic Corrective RAG system over traditional methods and encourages further exploration of building more robust and sophisticated AI agents.

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