Build your own Hybrid Search

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Abhishek Thakur Open on YouTube ↗
Build an App Use Case Model Routing Technique Developers Audience Intermediate Level Vector Search Technique Semantic Search Topic Vespa.ai Company Reciprocal Rank Fusion Technique
From the creator

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 Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

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Makers residency 4th edition

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Makers residency 4th edition
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