pgvector-semantic-search skillA
pgvector-semantic-search is agent-read markdown (skill) from timescale/pg-aiguide: 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 dataset.
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified before it reaches your agent: the main file against the SHA-256 recorded here, the others against the git hashes of its source commit. The deterministic audit below grades the latest version, and the same checks always give the same file the same grade.
What the file says
# 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 `<=>`. …
Read the whole file at its exact version.
How to install
mdr add timescale/pg-aiguide/pgvector-semantic-search@git:20260407.0da8f4emdr add timescale/pg-aiguide/pgvector-semantic-search@sha256:0c23028b9bc8f9a8Pin to a label to follow the author's releases, or to a sha256 for exact bytes. Either way the resolved hash is written to mdr.lock, and mdr install fetches those bytes again and checks them, so it installs them exactly or fails.
[](https://markdownregistry.com/a/art_tf3htybvstl7qfou)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260407.0da8f4e latest | 2026-04-07 | 0da8f4e | 15,106 B | A | view · diff |
| git:20260202.cb62826 | 2026-02-02 | cb62826 | 14,989 B | A | view · diff |
| git:20260128.fb36101 | 2026-01-28 | fb36101 | 14,238 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (15106 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
timescale/pg-aiguide · 1,849 stars · license Apache-2.0 · pushed 2026-09-25 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_tf3htybvstl7qfou GET https://markdownregistry.com/api/v1/resolve?ref=timescale/pg-aiguide/pgvector-semantic-search GET https://markdownregistry.com/api/v1/blob/0c23028b9bc8f9a8bc4f45042d5996877533f60d7798a074ce73373ebc3e24f6
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