This article details the construction of a hybrid Retrieval-Augmented Generation (RAG) system that combines the strengths of both semantic and keyword search. It addresses the limitations of single-mode retrieval, where dense vector search excels at understanding meaning but struggles with exact matches, while keyword search like BM25 is precise but lacks semantic understanding. The tutorial demonstrates how to build this system using FAISS for dense search, BM25 for keyword search, Reciprocal Rank Fusion for merging results, and LangGraph for orchestration, ultimately aiming to improve document Q&A applications. AI
IMPACT Enhances document Q&A systems by combining semantic and keyword search for more accurate results.
RANK_REASON Tutorial on building a specific RAG system implementation.
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