Stop Choosing Between BM25 and Vector Search: Implement Hybrid Search with RRF

<p>If you've built a RAG application, you’ve probably hit this wall:</p> <ul> <li>Keyword search (Best Match 25 [BM25]) is great at exact matches.</li> <li>Vector search captures

<h1> Moving Beyond Simple Vector Search: Why Hybrid Search is Essential for RAG </h1> <p>As LLMs continue to dominate the landscape, Retrieval-Augmented Generation (RAG) has

<!-- SC_OFF --><div class="md"><p>We spent the last year building a code indexing system and tried pretty much every retrieval approach along the way. Sharing what actually worked

<p>In the rapidly evolving landscape of Generative AI, the transition from experimental Proof of Concepts (POCs) to production-grade applications is the most significant hurdle

<h1> RAG Systems in Production: Building Enterprise Knowledge Search </h1> <h2> Introduction </h2> <p>Retrieval-Augmented Generation (RAG) has revolutionized how enterprises build

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<p>Most people think hybrid search in RAG is just "run BM25 and vector search, combine the results."</p> <p>There are actually 15 distinct engineering decisions happening between

<p>When building local RAG (Retrieval-Augmented Generation) applications, edge agents, or serverless AI pipelines, developers usually hit a wall with standard vector stores: