sarabesh/HybridRAG
A hybrid retrieval system for RAG that combines vector search and graph search, integrating unstructured and structured data. It retrieves context using embeddings and a knowledge graph, then passes it to an LLM for generating accurate responses.
This project helps you build more accurate and comprehensive AI chatbots or question-answering systems. It takes your unstructured documents and structured data (like a knowledge graph) as input, intelligently combines information from both, and then uses a large language model to generate precise answers. Data scientists and AI solution architects would use this to power their RAG applications.
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Use this if you need to build a robust RAG system that leverages both the semantic understanding of unstructured text and the factual precision of structured knowledge graphs.
Not ideal if your application solely relies on unstructured text for retrieval or if you don't have structured data in a knowledge graph.
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38
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5
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Last pushed
Apr 15, 2025
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