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pgvector is an open-source PostgreSQL extension that equips PostgreSQL databases with vector data storage, indexing, and similarity search capabilities—ideal for embeddings-based applications like semantic search and recommendations. You can add an index to use approximate nearest neighbor search, which trades some recall for speed. Unlike typical indexes, you will see different results for queries after adding an approximate index. An HNSW index creates a multilayer graph. It has better query performance than IVFFlat (in terms of speed-recall tradeoff), but has slower build times and uses more memory. Also, an index can be created without any data in the table since there isn’t a training step like IVFFlat.
Features
- Native vector data type support for high-dimensional embeddings
- Exact nearest-neighbor similarity search out-of-the-box
- Supports approximate search via HNSW and IVFFlat indexes
- Compatible with multiple distance metrics (L2, cosine, inner product, L1, Hamming, Jaccard)
- Fully integrated with PostgreSQL features—ACID compliance, joins, recovery, and query planner
- No separate vector database is needed; remains within existing PostgreSQL infrastructure
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License
MIT LicenseFollow pgvector
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