Pgvector Semantic Search
timescale/pg-aiguide/skills/pgvector-semantic-searchby timescale187be00d317a51210c132010d947f4546ffb5eefApache-2.0Listed Oct 9, 2026Updated Oct 9, 2026
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.
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npx skills add https://github.com/timescale/pg-aiguide/tree/187be00d317a51210c132010d947f4546ffb5eef/skills/pgvector-semantic-searchSource and attribution
Source:timescale/pg-aiguideinskills/pgvector-semantic-searchat commit187be00
License: Apache-2.0
Content belongs to its original authors. SourceWeft indexes it from a public repository.
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