Software Development
Pgvector Semantic Search
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
- Publisher / source
- timescale/pg-aiguide (found on GitHub top repositories)
- Upstream record
- timescale/pg-aiguide
- Version
- Version 1
- Published
- Publication review
- Approved for publication · recorded passed scan 2026-11-08
- Content fingerprint
0c23028b9bc8f9a8bc4f45042d5996877533f60d7798a074ce73373ebc3e24f6