chensyCN/LogicRAG
Source code of LogicRAG at AAAI'26.
This project helps AI developers enhance their Retrieval Augmented Generation (RAG) systems by allowing them to handle complex, multi-step queries without needing to pre-build knowledge graphs. It takes a complex question and a corpus of documents as input, then outputs a more accurate answer with an interpretable reasoning path. Developers building advanced RAG applications for question-answering, data analysis, or content generation would use this.
180 stars.
Use this if you are a developer building RAG systems and need to process complex questions requiring multi-step reasoning from large or frequently updated knowledge bases without the overhead of maintaining pre-built knowledge graphs.
Not ideal if you are looking for a plug-and-play solution for simple factual retrieval or if you do not have the technical expertise to integrate a Python library into an existing RAG pipeline.
Stars
180
Forks
31
Language
Python
License
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Category
Last pushed
Dec 13, 2025
Commits (30d)
0
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