Agentic RAG

Learn how to build Agentic RAG pipelines that dynamically decide when and what to retrieve, enabling more complex and adaptive information retrieval for LLMs.

intermediate2-3 hours4 steps

The play

Understand Basic RAG Limitations
Review the limitations of standard Retrieval-Augmented Generation (RAG) pipelines, such as their fixed retrieval process and inability to adapt to complex queries.
Implement a Query Planning Agent
Create an agent that analyzes the user query and breaks it down into sub-queries or determines the necessary tools for retrieval. Use LangChain or LlamaIndex agentic capabilities.
Build an Iterative Retrieval Loop
Design a loop where the LLM can decide whether to perform additional retrieval rounds based on the information gathered in previous steps. Use LangGraph to orchestrate the loop.
Integrate Tools for Augmented RAG
Extend the agent's capabilities by integrating tools beyond basic retrieval, such as calculators, web search, or database access. Use LlamaIndex Workflows to manage tool orchestration.

Starter code

Start by setting up a basic RAG pipeline using your preferred framework (LangChain, LlamaIndex). Then, identify areas where the pipeline could benefit from more dynamic retrieval.

Source

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