timescale/pg-aiguideUse 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.
