Qdrant Hybrid Search Tutorial

In this video I walk through hybrid search using Qdrant (vector database). You'll learn what semantic (dense) and keyword (sparse) search are, why hybrid search gives you the best of both worlds, how embeddings are created at ingest time, how retrieval + re-ranking (using ColBERT) works, and a live demo showing collection schema, stored vectors and result behaviour.

Dense Embeddings (semantic search) using all-MiniLM-L6-v2
Sparse Embeddings (keyword search) using BM25 (Qdrant/bm25)
Late Interaction Embeddings (reranking) using ColBERT (colbertv2.0)

What you'll learn
What is hybrid search and why use it
Differences between semantic (dense) vs keyword (sparse/BM25) search
re-ranking with ColBERT
How to create collections and store dense, sparse and late-interaction embeddings in Qdrant
How retrieval and reranking flow works, and how to inspect vectors in the Qdrant UI

Tools & models used
Qdrant (vector DB)
fastembed / sentence-transformers (dense embeddings)
BM25 (sparse embeddings)
ColBERT (re-ranker)
Python Qdrant client (demo code)

Code & resources
Qdrant Hybrid Search Documentation: https://qdrant.tech/documentation/adv...
FastEmbed Library: https://github.com/qdrant/fastembed
Qdrant Python Client: https://github.com/qdrant/qdrant-client

If you want the demo code or have questions about reproducing this setup, drop a comment below. I’ll share the repo/snippets.

-----------------------------------
00:00 — Intro & what this video covers
01:25 — Why hybrid search (e-commerce example)
02:10 — Components of hybrid search: dense, sparse & the re-ranker
04:08 — Coding Demo
09:00 — Interpreting results: semantic hits vs keyword hits
11:00 — Inspecting collection schema and stored vectors in Qdrant
12:30 — Deep dive: ColBERT token vectors and storage impact
14:00 — Visualizing vectors in Qdrant Cloud UI
15:00 — Wrap-up, tips & next steps

Doc: https://qdrant.tech/documentation/adv...
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