> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1774360645-bf2eec8.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# PGVector integrations

> Integrate with PGVector using LangChain Python.

This page covers how to use the Postgres [PGVector](https://github.com/pgvector/pgvector) ecosystem within LangChain
It is broken into two parts: installation and setup, and then references to specific PGVector wrappers.

## Installation

* Install the Python package with `pip install pgvector`

## Setup

1. The first step is to create a database with the `pgvector` extension installed.

   Follow the steps at [PGVector Installation Steps](https://github.com/pgvector/pgvector#installation) to install the database and the extension. The docker image is the easiest way to get started.

## Wrappers

### VectorStore

There exists a wrapper around Postgres vector databases, allowing you to use it as a vectorstore,
whether for semantic search or example selection.

To import this vectorstore:

```python theme={null}
from langchain_community.vectorstores.pgvector import PGVector
```

### Usage

For a more detailed walkthrough of the PGVector Wrapper, see [this notebook](/oss/python/integrations/vectorstores/pgvector)

***

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