deepset-ai--haystack/docs-website/docs/pipeline-components/retrievers/alloydbembeddingretriever.mdx

---
title: "AlloyDBEmbeddingRetriever"
id: alloydbembeddingretriever
slug: "/alloydbembeddingretriever"
description: "An embedding-based Retriever compatible with the AlloyDB Document Store."
---

# AlloyDBEmbeddingRetriever

An embedding-based Retriever compatible with the AlloyDB 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 a [`TransformersExtractiveReader`](../readers/transformersextractivereader.mdx)   in an extractive QA pipeline |
| **Mandatory init variables**           | `document_store`: An instance of an [AlloyDBDocumentStore](../../document-stores/alloydbdocumentstore.mdx)                                                                                                                                                                                       |
| **Mandatory run variables**            | `query_embedding`: A vector representing the query (a list of floats)                                                                                                                                                                                                       |
| **Output variables**                   | `documents`: A list of documents                                                                                                                                                                                                                                            |
| **API reference**                      | [AlloyDB](/reference/integrations-alloydb)                                                                                                                                                                                                                                  |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/alloydb                                                                                                                                                                                     |
| **Package name** | `alloydb-haystack` |

</div>

## Overview

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

When using the `AlloyDBEmbeddingRetriever` 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 `AlloyDBEmbeddingRetriever` accepts other optional parameters, including `top_k` (the maximum number of Documents to retrieve), `filters` to narrow down the search space, and `vector_function` to override the similarity function set on the Document Store.

Some relevant parameters that impact embedding retrieval must be defined when the corresponding `AlloyDBDocumentStore` is initialized: these include `embedding_dimension`, `vector_function`, and the search strategy (`"exact_nearest_neighbor"` or `"hnsw"`).

## Installation

Install the `alloydb-haystack` integration:

```shell
pip install alloydb-haystack
```

To set up an AlloyDB cluster and instance, follow the [AlloyDB quickstart](https://cloud.google.com/alloydb/docs/quickstart).

The examples on this page use Sentence Transformers embedders that have moved to the `sentence-transformers-haystack` package. Install it to run the examples:

```shell
pip install sentence-transformers-haystack
```

## Usage

### On its own

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

Set the `ALLOYDB_INSTANCE_URI`, `ALLOYDB_USER`, and `ALLOYDB_PASSWORD` environment variables to connect to your AlloyDB instance.

```python
from haystack_integrations.document_stores.alloydb import AlloyDBDocumentStore
from haystack_integrations.components.retrievers.alloydb import (
    AlloyDBEmbeddingRetriever,
)

document_store = AlloyDBDocumentStore()
retriever = AlloyDBEmbeddingRetriever(document_store=document_store)

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

### In a Pipeline

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

from haystack_integrations.document_stores.alloydb import AlloyDBDocumentStore
from haystack_integrations.components.retrievers.alloydb import (
    AlloyDBEmbeddingRetriever,
)

document_store = AlloyDBDocumentStore(
    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()
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",
    AlloyDBEmbeddingRetriever(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])
```