Improve the RAG chatbot result

bash_bob

I am building a local RAG chatbot using LangChain and ChromaDB (PersistentClient). I’m encountering 'hallucinations' when the similarity search returns documents with a low relevance score. How can I implement a Self-Correction loop in LangChain that: 1. Grades the documents retrieved from ChromaDB. 2. If the relevance score is below a certain threshold, triggers a web search (fallback) or returns a specific 'I don't know' response instead of...

Originally asked by grace h on Stack Exchange (stackoverflow) (CC BY-SA 4.0).

uphilltech

Great question — answering as the UphillTech team:

You can set a minimum threshold and short-circuit if all retrieved docs are below it, but that should just be your first gate, not the only one. A better pattern in LangChain is to introduce an LLM-based grading step before you generate the final answer. After retrieving documents, send the user query plus the retrieved chunks to a small grading prompt. Ask the model to return structured output like “relevant: true/false” and maybe a confidence score. If the grader says the docs are not relevant enough, you either trigger your fallback (web search tool) or return a controlled “I don’t know” response. You can also add a second validation step after generation. Generate the answer strictly...

Full answer by Vultieris: https://stackoverflow.stackexchange.com/a/79900710 (CC BY-SA 4.0).

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