In this video, we upgrade our simple BM25 search engine into a Hybrid Search system using BM25 + vector embeddings with reciprocal rank fusion.
We start from the existing bm25.py file and step through every change to build hybrid.py, explaining each line in simple language.
What you will learn:
✔️ What hybrid search means
✔️ How to add embedding fields
✔️ How to use HNSW for vector similarity search
✔️ How to build semantic ranking
✔️ How to combine BM25 and vector ranking using Reciprocal Rank Fusion (RRF)
✔️ A clean step-by-step walkthrough
✔️ UI to search
Code is available here: https://github.com/abhishekkrthakur/search
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