Implementing Agentic Retrieval-Augmented Generation (RAG) with LangGraph
Retrieval-Augmented Generation (RAG) has become a pivotal technique in enhancing the capabilities of language models by providing them with access to external knowledge bases.
Let's explore how to implement an Agentic RAG system using LangChain and LangGraph. We’ll walk through setting up a retrieval agent that intelligently decides when to fetch information from an index and when to generate responses directly.
Introduction to Agentic RAG
Agentic RAG systems empower language models to make decisions about when to retrieve information from an external source and when to generate responses directly. By integrating retrieval capabilities into the decision-making process of the language model (LLM), we can enhance the relevance and accuracy of generated responses, especially when dealing with specific domains or up-to-date information.
Prerequisites
- Basic understanding of Python programming.
- Familiarity with language models and prompt engineering.
- Knowledge of LangChain and LangGraph libraries.
