git:20260710.2925510 to git:20260710.62afd0f
20 added, 15 removed. Audit B to A.
---
- title: "Add Postgres-native vector retrieval to agent and RAG workflows with pgvector"
+ name: "Add Postgres-native vector retrieval to agent and RAG workflows with pgvector"
+ slug: "add-postgres-native-vector-retrieval-to-agent-and-rag-workflows-with-pgvector"
description: "Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval."
+ github_stars: 21709
verification: "security_reviewed"
source: "https://github.com/pgvector/pgvector"
author: "pgvector"
publisher_type: "organization"
- category:
- - "Data Extraction & Transformation"
- framework:
- - "Multi-Framework"
+ category: "Data Extraction & Transformation"
+ framework: "Multi-Framework"
tool_ecosystem:
github_repo: "pgvector/pgvector"
github_stars: 21709
---
# Add Postgres-native vector retrieval to agent and RAG workflows with pgvector
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
- Choose whichever fits your setup:
+ 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:
- 1. Copy this skill folder into your local skills directory.
- 2. Clone the repo and symlink or copy the skill into your agent workspace.
- 3. Add the repo as a git submodule if you manage shared skills centrally.
- 4. Install it through your internal provisioning or packaging workflow.
- 5. Download the folder directly from GitHub and place it in your skills collection.
+ Requirements and caveats from upstream:
+ - You can also install it with [Docker](#docker), [Homebrew](#homebrew), [PGXN](#pgxn), [APT](#apt), [Yum](#yum), [pkg](#pkg), [APK](#apk), or [conda-forge](#conda-forge), and it comes preinstalled with [Postgres.app](#...
+ - You can also install it with [Docker](#docker) or [conda-forge](#conda-forge).
+ - Or load vectors in bulk using COPY ([example](https://github.com/pgvector/pgvector-python/blob/master/examples/loading/example.py))
- Install command or upstream instructions:
+ Basic usage or getting-started notes:
+ - Compile and install the extension (supports Postgres 13+)
+ - cd /tmp
+ - cd pgvector
- ```
- Install pgvector using the upstream package, source, Docker, Homebrew, PGXN, APT, Yum, or provider-specific path, run `CREATE EXTENSION vector;`, add vector columns and indexes, then query embeddings with the documented distance operators from the agent or RAG application.
- ```
+ - Source: https://github.com/pgvector/pgvector
+ - Extracted from upstream docs: https://raw.githubusercontent.com/pgvector/pgvector/HEAD/README.md
## Documentation
- https://github.com/pgvector/pgvector#readme
## Source
- [Agent Skill Exchange](https://agentskillexchange.com/skills/add-postgres-native-vector-retrieval-to-agent-and-rag-workflows-with-pgvector/)