Build Your Own Hybrid Search - BM25 and Vector Embeddings with Reciprocal Rank Fusion

Learn to upgrade a simple BM25 search engine into a sophisticated Hybrid Search system by combining BM25 with vector embeddings using reciprocal rank fusion. Start with an existing bm25.py file and follow a step-by-step transformation to build hybrid.py, with each modification explained in clear, accessible language. Discover what hybrid search means and how to implement embedding fields for enhanced search capabilities. Master the use of HNSW (Hierarchical Navigable Small World) for efficient vector similarity search and understand how to construct semantic ranking systems. Explore the powerful technique of combining BM25 and vector ranking through Reciprocal Rank Fusion (RRF) to create a more accurate and comprehensive search experience. The tutorial includes a complete walkthrough of the implementation process and features a user interface for testing your search system, making it practical for real-world applications.