Add Postgres-native vector retrieval to agent and RAG workflows with pgvector

SkillSearch

Give your AI the ability to search information by meaning rather than exact keywords, using a Postgres database with pgvector. Once added, the AI can store embeddings (numerical representations of meaning) next to your application data, create vector indexes, and query nearest neighbors. This supports semantic search, RAG, recommendations, and agent memory retrieval.

Available today. Use it from your connected AI after setup.

No other account needed.

After adding the skill, connect it to your Postgres database and start storing embeddings for the data you want to search. Then ask your AI to find information by meaning and check that it returns the closest matches.

Then ask your AI: use the Add Postgres-native vector retrieval to agent and RAG workflows with pgvector skill

What your AI can do with it

Store embeddings in the same Postgres database as your application data
Create vector indexes over stored embeddings
Query nearest neighbors to find similar items
Run semantic search over your data
Retrieve relevant context to ground RAG answers
Recall relevant memories for agent tasks or find similar items for recommendations

What this skill tells your AI

The instructions your AI receives, as published by agentskillexchange/skills in skills/add-postgres-native-vector-retrieval-to-agent-and-rag-workflows-with-pgvector/SKILL.md and read by ahel’s review.

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

Prerequisites

Postgres 13+, pgvector extension, SQL access to the target database, embeddings from the agent or RAG pipeline, and application code that can query Postgres

Installation

Use the upstream install or setup path that matches your environment:

git clone --branch v0.8.2 https://github.com/pgvector/pgvector.git
make
make install # may need sudo
Then re-run the installation instructions (run make clean before make if needed). If sudo is needed for make install, use:

Requirements and caveats from upstream:

You can also install it with Docker, Homebrew, PGXN, APT, Yum, pkg, APK, or conda-forge, and it comes preinstalled with [Postgres.app](#...
You can also install it with Docker or conda-forge.
Or load vectors in bulk using COPY (example)