pgvector-semantic-search - Configure pgvector semantic search in PostgreSQL
Set up and tune pgvector similarity search for embeddings, semantic search, RAG, and nearest-neighbor queries in PostgreSQL.
Tags
Updated: 2026-10-01Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Configure pgvector semantic search
- Create HNSW vector indexes
- Create IVFFlat vector indexes
- Tune HNSW parameters
- Tune IVFFlat parameters
- Apply binary quantization
- Optimize vector storage
- Configure filtered vector search
Inputs
- PostgreSQL database
- Embedding vectors
- Embedding dimension
- Search query embedding
- Dataset size
- Memory constraints
Outputs
- Configured PostgreSQL vector schema
- HNSW or IVFFlat indexes
- Similarity search results
- Quantized vector columns
- Configured search parameters
Requirements
- PostgreSQL 15+
- pgvector extension
- pgvector 0.8.0+ for all features
- Consistent embedding model for stored and query vectors