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0a7245f721
this is a simple fix for #2219 i also added some documentation for this environment variable
34 lines
1.3 KiB
Markdown
34 lines
1.3 KiB
Markdown
# PGVector
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This page covers how to use the Postgres [PGVector](https://github.com/pgvector/pgvector) ecosystem within LangChain
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It is broken into two parts: installation and setup, and then references to specific PGVector wrappers.
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## Installation
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- Install the Python package with `pip install pgvector`
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## Setup
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1. The first step is to create a database with the `pgvector` extension installed.
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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.
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## Wrappers
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### VectorStore
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There exists a wrapper around Postgres vector databases, allowing you to use it as a vectorstore,
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whether for semantic search or example selection.
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To import this vectorstore:
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```python
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from langchain.vectorstores.pgvector import PGVector
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```
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PGVector embedding size is not autodetected. If you are using ChatGPT or any other embedding with 1536 dimensions
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default is fine. If you are going to use for example HuggingFaceEmbeddings you need to set the environment variable named `PGVECTOR_VECTOR_SIZE`
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to the needed value, In case of HuggingFaceEmbeddings is would be: `PGVECTOR_VECTOR_SIZE=768`
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### Usage
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For a more detailed walkthrough of the PGVector Wrapper, see [this notebook](../modules/indexes/vectorstores/examples/pgvector.ipynb)
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