Add pgvector

integrations/pgvector-documentstore.md

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+---
+layout: integration
+name: pgvector
+description: A Document Store for storing and retrieval from pgvector
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: deepset-ai
+pypi: https://pypi.org/project/pgvector-haystack/
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/pgvector
+type: Document Store
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+version: Haystack 2.0
+toc: true
+---
+
+[![PyPI - Version](https://img.shields.io/pypi/v/pgvector-haystack.svg)](https://pypi.org/project/pgvector-haystack/)
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pgvector-haystack.svg)](https://pypi.org/project/pgvector-haystack/)
+[![test](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/pgvector.yml/badge.svg)](https://github.com/deepset-ai/haystack-core-integrations/actions/workflows/pgvector.yml)
+
+-----
+
+**Table of Contents**
+
+- Pgvector Document Store for Haystack
+  - [Installation](#installation)
+  - [Usage](#usage)
+  - [Examples](#examples)
+  - [License](#license)
+
+## Installation
+pgvector is an extension for PostgreSQL that adds support for vector similarity search.
+
+To quickly setup a PostgreSQL database with pgvector, you can use Docker:
+```bash
+docker run -d -p 5432:5432 -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=postgres -e POSTGRES_DB=postgres ankane/pgvector
+```
+
+For more information on how to install pgvector, visit the [pgvector GitHub repository](https://github.com/pgvector/pgvector).
+
+Use `pip` to install `pgvector-haystack`:
+```bash
+pip install pgvector-haystack
+```
+## Usage
+Once installed, initialize PgvectorDocumentStore:
+
+```python
+from haystack_integrations.document_stores.pgvector import PgvectorDocumentStore
+
+document_store = PgvectorDocumentStore(
+    connection_string="postgresql://postgres:postgres@localhost:5432/postgres",
+    table_name="haystack_docs",
+    embedding_dimension=768,
+    vector_function="cosine_similarity",
+    recreate_table=True,
+    search_strategy="hnsw",
+)
+```
+
+### Writing Documents to PgvectorDocumentStore
+To write documents to `PgvectorDocumentStore`, create an indexing Pipeline.
+
+```python
+from haystack.components.converters import TextFileToDocument
+from haystack.components.writers import DocumentWriter
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+
+indexing = Pipeline()
+indexing.add_component("converter", TextFileToDocument())
+indexing.add_component("embedder", SentenceTransformersDocumentEmbedder())
+indexing.add_component("writer", DocumentWriter(document_store))
+indexing.connect("converter", "embedder")
+indexing.connect("embedder", "writer")
+indexing.run({"converter": {"sources": file_paths}})
+```
+
+### Retrieval from PgvectorDocumentStore
+You can retrieve Documents similar to a given query using a simple Pipeline.
+
+```python
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack_integrations.components.retrievers.pgvector import PgvectorEmbeddingRetriever
+
+querying = Pipeline()
+querying.add_component("embedder", SentenceTransformersTextEmbedder())
+querying.add_component("retriever", PgvectorEmbeddingRetriever(document_store=document_store, top_k=3))
+querying.connect("embedder", "retriever")
+
+results = querying.run({"embedder": {"text": "my query"}})
+```
+
+## Examples
+You can find a code example showing how to use the Document Store and the Retriever under the `examples/` folder of [this repo](https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/pgvector).
+
+## License
+
+`pgvector-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.