# pgvector-semantic-search

Configures AI agents to set up vector similarity search in PostgreSQL using pgvector. Helps store embeddings with halfvec, build HNSW indexes, and power RAG apps.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 938 installs · ★ 1874
- Author: tigerdata
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/timescale/pg-aiguide/tree/HEAD/skills/pgvector-semantic-search/SKILL.md
- Updated: 2026-10-22
- HTML: https://affpapa.org/en/nevibecode/skills/pgvector-semantic-search

## What it does

The skill establishes standard practices for vector storage and retrieval in PostgreSQL. It designates `halfvec(N)` as the primary data type alongside cosine distance (`<=>`) and HNSW indexing. Using `halfvec` cuts storage and index memory by 50% with almost no recall penalty. Vector dimensions must match everywhere.

Index parameters follow tested defaults: `m = 16` and `ef_construction = 64`. Queries rely on explicit type casts like `$1::halfvec(N)` to prevent prepared statement failures. For production stability, always run `CREATE INDEX CONCURRENTLY` after initial bulk loading rather than building indexes on every write. Retrieval recall scales up by adjusting `SET hnsw.ef_search = 100`.

Every advanced feature requires PostgreSQL 15+ and pgvector 0.8.0+, so running an outdated database image will break halfvec and quantization support right away. Avoid IVFFlat unless table writes dominate and memory cannot fit the HNSW graph.

## Requirements
- Works with: PostgreSQL

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add timescale/pg-aiguide --skill pgvector-semantic-search -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/pgvector-semantic-search/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/pgvector-semantic-search/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add timescale/pg-aiguide --skill pgvector-semantic-search -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/pgvector-semantic-search/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add timescale/pg-aiguide --skill pgvector-semantic-search -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/pgvector-semantic-search/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install timescale/pg-aiguide pgvector-semantic-search
```

2. **Manually: put the files in place**
- File: `.github/skills/pgvector-semantic-search/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add timescale/pg-aiguide --skill pgvector-semantic-search -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/timescale/pg-aiguide --path skills/pgvector-semantic-search
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/pgvector-semantic-search/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add timescale/pg-aiguide --skill pgvector-semantic-search -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/timescale/pg-aiguide/tree/HEAD/skills/pgvector-semantic-search/SKILL.md

## When to use it
- store high-dimensional embeddings in PostgreSQL
- configure HNSW indexes with `halfvec_cosine_ops`
- cut vector index memory by 50% using `halfvec`
- tune search recall with `hnsw.ef_search`

## Example prompts
- "Help me set up semantic search in PostgreSQL using pgvector"
- "Create an items table with 1536-dimension halfvec embeddings and an HNSW index"
- "How do I tune ef_search and ef_construction for better pgvector recall?"

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 28d7f20b536fa46f.

BEGIN THIRD-PARTY FILE 28d7f20b536fa46f
```text
---
name: pgvector-semantic-search
description: |
  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, 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.
license: Apache-2.0
compatibility: Requires PostgreSQL 15+ with the pgvector extension
metadata:
  author: tigerdata
---

# 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` (good starting point from published benchmarks, increase for higher recall at higher latency)
- Query pattern: `ORDER BY embedding <=> $1::halfvec(N) LIMIT k`

This setup provides a strong speed–recall tradeoff for most text-embedding workloads.

## Core Rules

- **Enable the extension** in each database: `CREATE EXTENSION IF NOT EXISTS vector;`
```
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