chore: import upstream snapshot with attribution

docs-website/docs/document-stores/pgvectordocumentstore.mdx

@@ -0,0 +1,109 @@
+---
+title: "PgvectorDocumentStore"
+id: pgvectordocumentstore
+slug: "/pgvectordocumentstore"
+---
+
+# PgvectorDocumentStore
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| API reference | [Pgvector](/reference/integrations-pgvector)                                                       |
+| GitHub link   | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/pgvector/ |
+
+</div>
+
+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
+
+### Connection String
+
+Define the connection string to your PostgreSQL database in the `PG_CONN_STR` environment variable. Two formats are supported:
+
+**URI format:**
+
+```shell
+export PG_CONN_STR="postgresql://USER:PASSWORD@HOST:PORT/DB_NAME"
+```
+
+**Keyword/value format:**
+
+```shell
+export PG_CONN_STR="host=HOST port=PORT dbname=DB_NAME user=USER password=PASSWORD"
+```
+
+:::caution[Special Characters in Connection URIs]
+
+When using the URI format, special characters in the password must be [percent-encoded](https://en.wikipedia.org/wiki/Percent-encoding). Otherwise, connection errors may occur. A password like `p=ssword` would cause the error `psycopg.OperationalError: [Errno -2] Name or service not known`.
+
+For example, if your password is `p=ssword`, the connection string should be:
+
+```shell
+export PG_CONN_STR="postgresql://postgres:p%3Dssword@localhost:5432/postgres"
+```
+
+Alternatively, use the keyword/value format, which does not require percent-encoding:
+
+```shell
+export PG_CONN_STR="host=localhost port=5432 dbname=postgres user=postgres password=p=ssword"
+```
+
+:::
+
+For more details, see the [PostgreSQL connection string documentation](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING).
+
+## 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`](../pipeline-components/embedders/sentencetransformersdocumentembedder.mdx)).
+
+### Supported Retrievers
+
+- [`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.
+- [`PgvectorKeywordRetriever`](../pipeline-components/retrievers/pgvectorembeddingretriever.mdx): A keyword-based Retriever that fetches documents matching a query from the Pgvector Document Store.