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Agentic RAG with LangGraph: Dynamic Retrieval and Summarization

  • Agentic RAG Architecture: The system employs an agent, implemented using LangGraph, to dynamically determine if retrieval is necessary for answering a user query, contrasting with static retrieval setups. The agent selects the appropriate tool and refines the query.
  • Dynamic Flow: Depending on the query, the system either directly uses Groq's Llama 3 70B for a quick response or retrieves context from MongoDB, summarizes it, and injects it back into the conversation node before generating the final answer.
  • LangGraph Implementation Details: The LangGraph implementation involves defining nodes (conversation, retrieval, summarization, connector) and edges, including conditional edges based on `line graph prebuild` classes, to control the flow between nodes. A loop is created between the conversation, retrieval, and summarization nodes to iteratively refine the context.
  • Conditional Summarization: A conditional edge triggers a summarization node after 15 interactions (30 total messages) to compress the conversation history.
  • Tool Selection: According to reactions, it's unclear whether LangGraph natively manages tool selection or if it's abstracted in the agent layer, raising questions about the pluggability of retrieval backends like MongoDB.
Source:

https://www.linkedin.com/posts/migueloteropedrido_%F0%9D%90%80%F0%9D%90%A0%F0%9D%90%9E%F0%9D%90%A7%F0%9D%90%AD%F0%9D%90%A2%F0%9D%90%9C-%F0%9D%90%91%F0%9D%90%80%F0%9D%90%86-with-langgraph-a-3-activity-7325799064627724288-suS3

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