allen-li1231/treehop-rag
Highly Efficient Query Rewriter for Passage Retrieval in the realm of Retrieval-Augmented Generation (RAG)
This project helps anyone working with AI chatbots or information retrieval systems quickly answer complex questions that require stitching together information from multiple sources. It takes a complex query and an existing database of documents, then efficiently finds and returns the most relevant passages needed to answer the question, even if it requires several 'hops' between related pieces of information. This is ideal for developers building faster and more cost-effective RAG (Retrieval-Augmented Generation) applications.
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Use this if you need to make your multi-hop question-answering systems significantly faster and more resource-efficient without sacrificing accuracy.
Not ideal if your queries are always simple and can be answered from a single document, or if you don't mind slower response times and higher computational costs from traditional LLM-based query rewriting.
Stars
30
Forks
4
Language
Python
License
MIT
Category
Last pushed
May 06, 2025
Commits (30d)
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