Pgvector Embeddings

🌐Community
by constructive-io · vlatest · Repository

This skill generates vector embeddings for your PostgreSQL data using PgVector, enabling powerful semantic search and similarity analysis.

Install on your platform

We auto-selected Claude Code based on this skill’s supported platforms.

1

Run in terminal (recommended)

terminal
claude mcp add pgvector-embeddings npx -- -y @trustedskills/pgvector-embeddings
2

Or manually add to ~/.claude/settings.json

~/.claude/settings.json
{
  "mcpServers": {
    "pgvector-embeddings": {
      "command": "npx",
      "args": [
        "-y",
        "@trustedskills/pgvector-embeddings"
      ]
    }
  }
}

Requires Claude Code (claude CLI). Run claude --version to verify your install.

About This Skill

What it does

This skill enables AI agents to work with vector embeddings stored in a PostgreSQL database using the pgvector extension. It allows for efficient similarity searches and retrieval of data based on semantic meaning, rather than exact matches. The agent can query the database for similar vectors, enabling tasks like finding related documents or recommending products.

When to use it

  • Semantic Search: Retrieve documents or content that are conceptually similar to a given query, even if they don't share keywords.
  • Recommendation Systems: Find items (products, articles, etc.) with embeddings close to a user’s preferences.
  • Anomaly Detection: Identify data points that have significantly different vector representations compared to the norm.
  • Knowledge Base Retrieval: Quickly access relevant information from a large knowledge base by searching for similar embedding vectors.

Key capabilities

  • Integration with PostgreSQL and pgvector extension.
  • Vector similarity search functionality.
  • Retrieval of data based on semantic meaning.

Example prompts

  • "Find documents in the database that are semantically similar to 'machine learning applications'."
  • "Recommend products to a user whose preferences have an embedding vector of [insert vector here]."
  • "Retrieve all entries with embeddings closest to this one: [insert vector here]."

Tips & gotchas

  • Requires a PostgreSQL database with the pgvector extension installed and configured.
  • The quality of results depends heavily on the quality of the underlying embeddings.

Tags

🛡️

TrustedSkills Verification

Unlike other registries that point to live repositories, TrustedSkills pins every skill to a verified commit hash. This protects you from malicious updates — what you install today is exactly what was reviewed and verified.

Security Audits

Gen Agent Trust HubPass
SocketPass
SnykPass

Details

Version
vlatest
License
Author
constructive-io
Installs
7

🌐 Community

Passed automated security scans.