Implementing Hybrid Semantic Search in SQLite with Binary Embeddings and Hamming Distance
By
enz
Warm and crisp on the edges. A bagel with a bit of bite.
Summary
This technical article demonstrates how to implement semantic search in SQLite using binary embeddings and Hamming distance, enabling hybrid search that combines traditional keyword matching with meaning-based retrieval. The author explains how to overcome SQLite's FTS5 extension limitations by using binary embeddings (compact vector representations) and Hamming distance calculations for similarity search, allowing developers to build hybrid search capabilities without relying on external vector databases.
Key quotes
· 4 pulledSQLite's FTS5 extension provides excellent text search with BM25 ranking. However, it doesn't support semantic search, which means you can't combine keyword matching with meaning-based retrieval, a technique known as hybrid search.
This article shows how I implemented semantic search in SQLite using binary embeddings and Hamming distance, enabling hybrid search without external vector databases.
Binary embeddings and Hamming distance provide a practical approach to semantic search in SQLite, allowing developers to build hybrid search capabilities without complex infrastructure.
The combination of traditional keyword search with semantic understanding creates more powerful and flexible search applications directly within SQLite.
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