Simole Domain Specific Corrective RAG with LangChain and LangGraph

In a recent LangChain community post, a member shared a fascinating example of how to create a custom RAG agent for domain-specific tasks. The agent, built using LangChain and LangGraph, demonstrates how cognitive architectures can be tailored to specific domains to achieve more effective results.

The core idea behind the agent is to use a corrective RAG approach, which involves iteratively refining the generated response based on feedback from a domain expert. This allows the agent to learn from its mistakes and improve its accuracy over time.

The post also highlights the importance of using memory and human-in-the-loop techniques to enhance the agent’s capabilities. By incorporating these elements, the agent can become more intelligent and adaptable to changing circumstances.

Overall, this example provides valuable insights into the potential of LangChain and LangGraph for building sophisticated AI agents that can be applied to a wide range of real-world problems.

Sources: Internet Archive, Langchainai, Digital Frontier, Undercode Ai & Community, Wikipedia
Image Source: OpenAI, Undercode AI DI v2