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* add missing headers * external integrations header row * implement headerless tables * more tables with key-value pairs
79 lines
3.2 KiB
Plaintext
79 lines
3.2 KiB
Plaintext
---
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title: "PgvectorDocumentStore"
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id: pgvectordocumentstore
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slug: "/pgvectordocumentstore"
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description: ""
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---
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# PgvectorDocumentStore
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<div className="key-value-table">
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| :------------ | :------------------------------------------------------------------------------------------ |
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| API reference | [Pgvector](/reference/integrations-pgvector) |
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| GitHub link | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/pgvector/ |
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</div>
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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.
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For more information, see the [pgvector repository](https://github.com/pgvector/pgvector).
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Pgvector Document Store supports embedding retrieval and metadata filtering.
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## Installation
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To quickly set up a PostgreSQL database with pgvector, you can use Docker:
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```shell
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docker run -d -p 5432:5432 -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=postgres -e POSTGRES_DB=postgres ankane/pgvector
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```
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For more information on installing pgvector, visit the [pgvector GitHub repository](https://github.com/pgvector/pgvector).
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To use pgvector with Haystack, install the `pgvector-haystack` integration:
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```shell
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pip install pgvector-haystack
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```
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## Usage
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Define the connection string to your PostgreSQL database in the `PG_CONN_STR` environment variable. For example:
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```shell Shell
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export PG_CONN_STR="postgresql://postgres:postgres@localhost:5432/postgres"
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```
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## Initialization
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Initialize a `PgvectorDocumentStore` object that’s connected to the PostgreSQL database and writes documents to it:
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```python
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from haystack_integrations.document_stores.pgvector import PgvectorDocumentStore
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from haystack import Document
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document_store = PgvectorDocumentStore(
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embedding_dimension=768,
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vector_function="cosine_similarity",
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recreate_table=True,
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search_strategy="hnsw",
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)
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document_store.write_documents([
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Document(content="This is first", embedding=[0.1]*768),
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Document(content="This is second", embedding=[0.3]*768)
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])
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print(document_store.count_documents())
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```
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To learn more about the initialization parameters, see our [API docs](/reference/integrations-pgvector#pgvectordocumentstore).
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To properly compute embeddings for your documents, you can use a Document Embedder (for instance, the [`SentenceTransformersDocumentEmbedder`](../pipeline-components/embedders/sentencetransformersdocumentembedder.mdx)).
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### Supported Retrievers
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- [`PgvectorEmbeddingRetriever`](../pipeline-components/retrievers/pgvectorembeddingretriever.mdx): An embedding-based Retriever that fetches documents from the Document Store based on a query embedding provided to the Retriever.
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- [`PgvectorKeywordRetriever`](../pipeline-components/retrievers/pgvectorembeddingretriever.mdx): A keyword-based Retriever that fetches documents matching a query from the Pgvector Document Store.
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