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Pgvector Semantic Search

Checked
timescale·vlatest·database·Apache-2.0

Find relevant data quickly using Timescale's pgvector semantic search powered by vector embeddings.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add timescale/pg-aiguide --skill pgvector-semantic-search -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

timescale

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned timescale/pg-aiguide at b236d3583f.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
b236d3583f
Manifest
skills/pgvector-semantic-search/SKILL.md
Scanned
1 file, 321 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

pgvector-semantic-search

What it does

This skill enables AI agents to perform semantic search directly within PostgreSQL databases using the pgvector extension. It allows agents to retrieve relevant records based on meaning and context rather than relying solely on exact keyword matching.

When to use it

  • Retrieving customer support tickets that are semantically similar to a user's vague query.
  • Finding specific documents in a legal database based on the concept of "liability" rather than the exact word.
  • Locating relevant product recommendations by analyzing the semantic relationship between user preferences and item descriptions.

Key capabilities

  • Semantic Similarity Search: Queries data based on vector embeddings to find conceptually related records.
  • PostgreSQL Integration: Executes search operations directly inside the database engine without external retrieval pipelines.
  • Contextual Retrieval: Understands natural language nuances to return accurate results even with imprecise input.

Example prompts

  • "Find all support tickets from last month that discuss issues similar to 'slow loading times on mobile devices'."
  • "Retrieve customer records where the feedback sentiment matches the concept of 'frustration with billing processes'."
  • "Search for articles in our knowledge base that explain how to reset passwords, focusing on semantic relevance rather than exact keywords."

Tips & gotchas

Ensure your database tables have vector columns populated with embeddings before attempting semantic searches. Performance may vary depending on the size of the dataset and the complexity of the vector index configuration.