---
title: "PgvectorDocumentStore"
id: pgvectordocumentstore
slug: "/pgvectordocumentstore"
description: ""
---

# PgvectorDocumentStore

|               |                                                                                             |
| :------------ | :------------------------------------------------------------------------------------------ |
| API reference | [Pgvector](/reference/integrations-pgvector)                                                       |
| GitHub link   | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/pgvector/ |

Pgvector is an extension for PostgreSQL that enhances its capabilities with vector similarity search. It builds upon the classic features of PostgreSQL, such as ACID compliance and point-in-time recovery, and introduces the ability to perform exact and approximate nearest neighbor search using vectors.

For more information, see the [pgvector repository](https://github.com/pgvector/pgvector).

Pgvector Document Store supports embedding retrieval and metadata filtering.

## Installation

To quickly set up a PostgreSQL database with pgvector, you can use Docker:

```shell
docker run -d -p 5432:5432 -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=postgres -e POSTGRES_DB=postgres ankane/pgvector
```

For more information on installing pgvector, visit the [pgvector GitHub repository](https://github.com/pgvector/pgvector).

To use pgvector with Haystack, install the `pgvector-haystack` integration:

```shell
pip install pgvector-haystack
```

## Usage

Define the connection string to your PostgreSQL database in the `PG_CONN_STR` environment variable. For example:

```shell Shell
export PG_CONN_STR="postgresql://postgres:postgres@localhost:5432/postgres"
```

## Initialization

Initialize a `PgvectorDocumentStore` object that’s connected to the PostgreSQL database and writes documents to it:

```python
from haystack_integrations.document_stores.pgvector import PgvectorDocumentStore
from haystack import Document

document_store = PgvectorDocumentStore(
    embedding_dimension=768,
    vector_function="cosine_similarity",
    recreate_table=True,
    search_strategy="hnsw",
)

document_store.write_documents([
    Document(content="This is first", embedding=[0.1]*768),
    Document(content="This is second", embedding=[0.3]*768)
    ])
print(document_store.count_documents())
```

To learn more about the initialization parameters, see our [API docs](/reference/integrations-pgvector#pgvectordocumentstore).

To properly compute embeddings for your documents, you can use a Document Embedder (for instance, the [`SentenceTransformersDocumentEmbedder`](/docs/sentencetransformersdocumentembedder)).

### Supported Retrievers

- [`PgvectorEmbeddingRetriever`](/docs/pgvectorembeddingretriever): An embedding-based Retriever that fetches documents from the Document Store based on a query embedding provided to the Retriever.
- [`PgvectorKeywordRetriever`](/docs/pgvectorembeddingretriever): A keyword-based Retriever that fetches documents matching a query from the Pgvector Document Store.