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, cos
Profile →npx skills add timescale/pg-aiguide --skill pgvector-semantic-search --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# pgvector for Semantic Search Semantic search finds content by meaning rather than exact keywords. An embedding model converts text into high-dimensional vectors, where similar meanings map to nearby points. pgvector stores these vectors in PostgreSQL and uses approximate nearest neighbor (ANN) indexes to find the closest matches quickly—scaling to millions of rows without leaving the database. Store your text alongside its embedding, then query by converting your search text to a vector and returning the rows with the smallest distance. This guide covers pgvector setup and tuning—not embedding model selection or text chunking, which significantly affect search quality. Requires pgvector 0.8.0+ for all features (`halfvec`, `binary_quantize`, iterative scan). ## Golden Path (Default Setup) Use this configuration unless you have a specific reason not to. - Embedding column data type: `halfvec(N)` where `N` is your embedding dimension (must match everywhere). Examples use 1536; replace with your dimension `N`. - Distance: cosine (`<=>`) - Index: HNSW (`m = 16`, `ef_construction = 64`). Use `halfvec_cosine_ops` and query with `<=>`. - Query-time recall: `SET hnsw.ef_search = 100` (goo
- Golden Path (Default Setup)
- Core Rules
- Type Rules
- Standard Pattern
- HNSW Index
- HNSW Parameters
- IVFFlat Index (Generally Not Recommended)
- Quantization Strategies
- Guidelines for 1536-dim vectors
- Binary Quantization (For Very Large Datasets)
- Performance by Dataset Size
- Filtering Best Practices
- Iterative scan (recommended when filters are selective)
- Choose the right filtering strategy
What does the pgvector-semantic-search skill do?
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, cos
How do I install it?
Run `npx skills add timescale/pg-aiguide --skill pgvector-semantic-search --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From timescale/pg-aiguide, a repository with 1,806 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.