OpenClaw

pgvector

PostgreSQL vector database skill with pgvector extension. Enables vector similarity search, embeddings storage, RAG (Retrieval-Augmented Generation) pipelines, and hybrid search combining vector and keyword search. Use when: storing/retrieving embeddings, building AI applications with vector search, implementing RAG, similarity matching, semantic search, or any use case requiring vector database functionality.

damiencronw0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install damiencronw/pgvector

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

pgvector Skill

PostgreSQL + pgvector extension for vector similarity search.

Quick Connect

# Connect to pgvector database (default port 5433)
psql -h localhost -p 5433 -U damien -d postgres

# Or use environment variables
export PGHOST=localhost
export PGPORT=5433
export PGUSER=damien
export PGPASSWORD=''
export PGDATABASE=postgres

Environment

Host: localhost
Port: 5433
User: damien
Password: (empty)
Database: postgres

Core Capabilities

1. Create Vector Table

-- Basic vector table (1536 dimensions for OpenAI embeddings)
CREATE TABLE IF NOT EXISTS documents (
    id BIGSERIAL PRIMARY KEY,
    content TEXT NOT NULL,
    embedding vector(1536) NOT NULL,
    metadata JSONB,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Create HNSW index for fast similarity search
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64);

-- Or use IVFFlat index (faster build, slower search)
CREATE INDEX ON documents USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);

2. Insert Embeddings

-- Manual insert (replace with actual embedding)
INSERT INTO documents (content, embedding)
VALUES ('Your text here', '[0.1, 0.2, ..., 0.1536]');

-- With metadata
INSERT INTO documents (content, embedding, metadata)
VALUES (
    'AI is transforming technology',
    '[0.1, 0.3, ..., 0.5]',
    '{"source": "article", "author": "John"}'::jsonb
);

3. Vector Similarity Search

-- Cosine similarity (most common)
SELECT id, content, (1 - (embedding <=> '[query_embedding]')) AS similarity
FROM documents
ORDER BY embedding <=> '[query_embedding]'
LIMIT 5;

-- Euclidean distance
SELECT id, content, (embedding <-> '[query_embedding]') AS distance
FROM documents
ORDER BY embedding <-> '[query_embedding]'
LIMIT 5;

-- Inner product (for normalized vectors)
SELECT id, content, (embedding <#> '[query_embedding]') AS similarity
FROM documents
ORDER BY embe...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/damiencronw/pgvector
Version: —
Commit: 24bc0ea6eeb8d59a424aed9d706526b9ce373996
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# pgvector Skill

PostgreSQL + pgvector extension for vector similarity search.

## Quick Connect

```bash
# Connect to pgvector database (default port 5433)
psql -h localhost -p 5433 -U damien -d postgres

# Or use environment variables
export PGHOST=localhost
export PGPORT=5433
export PGUSER=damien
export PGPASSWORD=''
export PGDATABASE=postgres
```

## Environment

- **Host**: localhost
- **Port**: 5433
- **User**: damien
- **Password**: (empty)
- **Database**: postgres

## Core Capabilities

### 1. Create Vector Table

```sql
-- Basic vector table (1536 dimensions for OpenAI embeddings)
CREATE TABLE IF NOT EXISTS documents (
    id BIGSERIAL PRIMARY KEY,
    content TEXT NOT NULL,
    embedding vector(1536) NOT NULL,
    metadata JSONB,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Create HNSW index for fast similarity search
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64);

-- Or use IVFFlat index (faster build, slower search)
CREATE INDEX ON documents USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);
```

### 2. Insert Embeddings

```sql
-- Manual insert (replace with actual embedding)
INSERT INTO documents (content, embedding)
VALUES ('Your text here', '[0.1, 0.2, ..., 0.1536]');

-- With metadata
INSERT INTO documents (content, embedding, metadata)
VALUES (
    'AI is transforming technology',
    '[0.1, 0.3, ..., 0.5]',
    '{"source": "article", "author": "John"}'::jsonb
);
```

### 3. Vector Similarity Search

```sql
-- Cosine similarity (most common)
SELECT id, content, (1 - (embedding <=> '[query_embedding]')) AS similarity
FROM documents
ORDER BY embedding <=> '[query_embedding]'
LIMIT 5;

-- Euclidean distance
SELECT id, content, (embedding <-> '[query_embedding]') AS distance
FROM documents
ORDER BY embedding <-> '[query_embedding]'
LIMIT 5;

-- Inner product (for normalized vectors)
SELECT id, content, (embedding <#> '[query_embedding]') AS similarity
FROM documents
ORDER BY embe...

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