deepset-ai--haystack/docs-website/versioned_docs/version-2.21/pipeline-components/retrievers/pgvectorembeddingretriever.mdx

---
title: "PgvectorEmbeddingRetriever"
id: pgvectorembeddingretriever
slug: "/pgvectorembeddingretriever"
description: "An embedding-based Retriever compatible with the Pgvector Document Store."
---

# PgvectorEmbeddingRetriever

An embedding-based Retriever compatible with the Pgvector Document Store.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | 1. After a Text Embedder and before a [`PromptBuilder`](../builders/promptbuilder.mdx)   in a RAG pipeline 2. The last component in the semantic search pipeline 3. After a Text Embedder and before an [`ExtractiveReader`](../readers/extractivereader.mdx)   in an extractive QA pipeline |
| **Mandatory init variables**           | `document_store`: An instance of a [PgvectorDocumentStore](../../document-stores/pgvectordocumentstore.mdx)                                                                                                                                                                                     |
| **Mandatory run variables**            | `query_embedding`: A vector representing the query (a list of floats)                                                                                                                                                                                                     |
| **Output variables**                   | `documents`: A list of documents                                                                                                                                                                                                                                          |
| **API reference**                      | [Pgvector](/reference/integrations-pgvector)                                                                                                                                                                                                                                     |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/pgvector                                                                                                                                                                                |

</div>

## Overview

The `PgvectorEmbeddingRetriever` is an embedding-based Retriever compatible with the `PgvectorDocumentStore`. It compares the query and Document embeddings and fetches the Documents most relevant to the query from the `PgvectorDocumentStore` based on the outcome.

When using the `PgvectorEmbeddingRetriever` in your Pipeline, make sure it has the query and Document embeddings available. You can do so by adding a Document Embedder to your indexing Pipeline and a Text Embedder to your query Pipeline.

In addition to the `query_embedding`, the `PgvectorEmbeddingRetriever` accepts other optional parameters, including `top_k` (the maximum number of Documents to retrieve) and `filters` to narrow down the search space.

Some relevant parameters that impact the embedding retrieval must be defined when the corresponding `PgvectorDocumentStore` is initialized: these include embedding dimension, vector function, and some others related to the search strategy (exact nearest neighbor or HNSW).

## 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

### On its own

This Retriever needs the `PgvectorDocumentStore` and indexed Documents to run.

```python
import os
from haystack_integrations.document_stores.pgvector import PgvectorDocumentStore
from haystack_integrations.components.retrievers.pgvector import (
    PgvectorEmbeddingRetriever,
)

os.environ["PG_CONN_STR"] = "postgresql://postgres:postgres@localhost:5432/postgres"

document_store = PgvectorDocumentStore()
retriever = PgvectorEmbeddingRetriever(document_store=document_store)

## using a fake vector to keep the example simple
retriever.run(query_embedding=[0.1] * 768)
```

### In a Pipeline

```python
import os
from haystack.document_stores import DuplicatePolicy
from haystack import Document, Pipeline
from haystack.components.embedders import (
    SentenceTransformersTextEmbedder,
    SentenceTransformersDocumentEmbedder,
)

from haystack_integrations.document_stores.pgvector import PgvectorDocumentStore
from haystack_integrations.components.retrievers.pgvector import (
    PgvectorEmbeddingRetriever,
)

os.environ["PG_CONN_STR"] = "postgresql://postgres:postgres@localhost:5432/postgres"

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

documents = [
    Document(content="There are over 7,000 languages spoken around the world today."),
    Document(
        content="Elephants have been observed to behave in a way that indicates a high level of self-awareness, such as recognizing themselves in mirrors.",
    ),
    Document(
        content="In certain parts of the world, like the Maldives, Puerto Rico, and San Diego, you can witness the phenomenon of bioluminescent waves.",
    ),
]

document_embedder = SentenceTransformersDocumentEmbedder()
document_embedder.warm_up()
documents_with_embeddings = document_embedder.run(documents)

document_store.write_documents(
    documents_with_embeddings.get("documents"),
    policy=DuplicatePolicy.OVERWRITE,
)

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
query_pipeline.add_component(
    "retriever",
    PgvectorEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "How many languages are there?"

result = query_pipeline.run({"text_embedder": {"text": query}})

print(result["retriever"]["documents"][0])
```