pgvector

name	pgvector
description	pgvector - PostgreSQL extension for vector similarity search. Use for embedding storage, cosine similarity, IVFFlat indexes, and HNSW indexes. For this project prefer pgvector-setup (Supabase + OpenAI embeddings) and pgvector-semantic-search (tuning); use this skill for low-level API reference only.

Pgvector Skill

Comprehensive assistance with pgvector development, generated from official documentation.

This project: We use OpenAI text-embedding-3-small (1536) in Supabase. For setup and RAG flows use pgvector-setup; for tuning use pgvector-semantic-search. This skill is for pgvector API/reference details (operators, index options) when the other two don't cover it.

When to Use This Skill

This skill should be triggered when:

• Working with pgvector
• Asking about pgvector features or APIs
• Implementing pgvector solutions
• Debugging pgvector code
• Learning pgvector best practices

Quick Reference

Common Patterns

SQL — Enable extension (Supabase):

CREATE EXTENSION IF NOT EXISTS vector WITH SCHEMA extensions;
-- Set search_path in functions: SET search_path = public, extensions

SQL — Create table with embeddings:

CREATE TABLE items (
  id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
  content TEXT NOT NULL,
  embedding vector(1536) NOT NULL
);
CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops);

SQL — Cosine similarity search:

SELECT id, content, 1 - (embedding <=> $1) AS similarity
FROM items
ORDER BY embedding <=> $1
LIMIT 10;

TypeScript (Supabase RPC):

const { data } = await supabase.rpc('match_items', {
  query_embedding: embedding,
  match_threshold: 0.78,
  match_count: 10,
});

Distance Operators

Operator	Distance	Index Ops
<=>	Cosine	vector_cosine_ops
<->	Euclidean (L2)	vector_l2_ops
<#>	Inner product	vector_ip_ops

Reference Files

This skill includes comprehensive documentation in references/:

• pgvector.md - Pgvector documentation

Use view to read specific reference files when detailed information is needed.

Working with This Skill

For Beginners

Start with the getting_started or tutorials reference files for foundational concepts.

For Specific Features

Use the appropriate category reference file (api, guides, etc.) for detailed information.

For Code Examples

The quick reference section above contains common patterns extracted from the official docs.

Resources

references/

Organized documentation extracted from official sources. These files contain:

• Detailed explanations
• Code examples with language annotations
• Links to original documentation
• Table of contents for quick navigation

scripts/

Add helper scripts here for common automation tasks.

assets/

Add templates, boilerplate, or example projects here.

Notes

• This skill was automatically generated from official documentation
• Reference files preserve the structure and examples from source docs
• Code examples include language detection for better syntax highlighting
• Quick reference patterns are extracted from common usage examples in the docs

Updating

To refresh this skill with updated documentation:

1. Re-run the scraper with the same configuration
2. The skill will be rebuilt with the latest information