Build Amazon's Recommendation Engine with Qdrant (Python Tutorial)

Описание: Ever wondered how Amazon knows exactly what you want to buy? It's not magic—it's vector search with Qdrant!

In this tutorial, I'll show you how to build a production-ready product recommendation engine from scratch using Python and Qdrant, the blazing-fast vector database. We'll generate 10,000 realistic products, convert them into vectors using sentence transformers, store them in Qdrant, and build real-time recommendations that work in milliseconds.

What You'll Learn:
What vector databases are and why they matter
How to convert products into 384-dimensional vectors
How to store and search vectors in Qdrant
How to build "Customers Also Bought" recommendations
How to implement semantic search (search by meaning, not keywords)
How to add filters (price, rating, category) to vector search
How to visualize 10,000 products in vector space
Real-world applications at Netflix, Spotify, TikTok, and Amazon

Tech Stack:
Qdrant (vector database)
Sentence Transformers (embeddings)
Python + Google Colab
t-SNE for visualization

Resources:
Qdrant Documentation: https://qdrant.tech/documentation/
Sentence Transformers: https://www.sbert.net/
Colab Notebook: [LINK IN COMMENTS]

Key Takeaway:
Vector databases like Qdrant transform how we build recommendation systems. Instead of rigid rules or slow collaborative filtering, we use the mathematics of meaning to find similar items in milliseconds.

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