Hybrid Search RAG That Actually Works: BM25 + Vectors + Reranking in Python
Last Updated on January 20, 2026 by Editorial Team
Author(s): Tarun Singh
Originally published on Towards AI.
Fix “dumb RAG” using hybrid retrieval and a lightweight reranker pipeline.
If your RAG app is “kind of okay” but randomly wrong, you don’t have an LLM problem.
This article discusses the implementation of Hybrid Search RAG, focusing on the combination of BM25 for keyword retrieval, vectors for semantic search, and a reranking mechanism to improve accuracy and precision in retrieving relevant information from a dataset. It emphasizes that many existing RAG systems fail due to their reliance on merely similar-looking text instead of useful content, highlighting the importance of precise retrieval and the need to balance between recall and precision in a retrieval system.
Read the full blog for free on Medium.
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