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

---
title: "ValkeyEmbeddingRetriever"
id: valkeyembeddingretriever
slug: "/valkeyembeddingretriever"
description: "This is an embedding Retriever compatible with the Valkey Document Store."
---

# ValkeyEmbeddingRetriever

This is an embedding Retriever compatible with the Valkey Document Store.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | 1. After a Text Embedder and before a [`ChatPromptBuilder`](../builders/chatpromptbuilder.mdx) or [`PromptBuilder`](../builders/promptbuilder.mdx) in a RAG pipeline 2. The last component in a 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 a [ValkeyDocumentStore](../../document-stores/valkeydocumentstore.mdx)                                                                                                                                                                                     |
| **Mandatory run variables**            | `query_embedding`: A list of floats                                                                                                                                                                                                                                       |
| **Output variables**                   | `documents`: A list of documents                                                                                                                                                                                                                                          |
| **API reference**                      | [Valkey](/reference/integrations-valkey)                                                                                                                                                                                                                                     |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/valkey                                                                                                                                                                                |
| **Package name** | `valkey-haystack` |

</div>

## Overview

The `ValkeyEmbeddingRetriever` is an embedding-based Retriever compatible with the [`ValkeyDocumentStore`](../../document-stores/valkeydocumentstore.mdx). It compares the query and Document embeddings and fetches the Documents most relevant to the query from the `ValkeyDocumentStore` based on vector similarity.

### Parameters

When using the `ValkeyEmbeddingRetriever` in your system, ensure the query and Document [embeddings](../embedders.mdx) are 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 `ValkeyEmbeddingRetriever` accepts other optional parameters, including `top_k` (the maximum number of Documents to retrieve) and `filters` to narrow down the search space.

## Usage

### Installation

To start using Valkey with Haystack, install the package with:

```shell
pip install valkey-haystack
```

### On its own

This Retriever needs an instance of `ValkeyDocumentStore` and indexed Documents to run.

```python
from haystack_integrations.document_stores.valkey import ValkeyDocumentStore
from haystack_integrations.components.retrievers.valkey import ValkeyEmbeddingRetriever

document_store = ValkeyDocumentStore(
    nodes_list=[("localhost", 6379)],
    index_name="my_documents",
    embedding_dim=768,
    distance_metric="cosine",
)

retriever = ValkeyEmbeddingRetriever(document_store=document_store)

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

### In a Pipeline

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
```

```python
from haystack import Document, Pipeline
from haystack_integrations.components.embedders.sentence_transformers import (
    SentenceTransformersDocumentEmbedder,
    SentenceTransformersTextEmbedder,
)
from haystack.components.writers import DocumentWriter
from haystack_integrations.document_stores.valkey import ValkeyDocumentStore
from haystack_integrations.components.retrievers.valkey import ValkeyEmbeddingRetriever

document_store = ValkeyDocumentStore(
    nodes_list=[("localhost", 6379)],
    index_name="my_documents",
    embedding_dim=768,
    distance_metric="cosine",
)

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.",
    ),
]

indexing = Pipeline()
indexing.add_component("embedder", SentenceTransformersDocumentEmbedder())
indexing.add_component("writer", DocumentWriter(document_store))
indexing.connect("embedder.documents", "writer.documents")
indexing.run({"embedder": {"documents": documents}})

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
query_pipeline.add_component(
    "retriever",
    ValkeyEmbeddingRetriever(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])
```

For a full RAG example with `ValkeyEmbeddingRetriever`, see the [ValkeyDocumentStore](../../document-stores/valkeydocumentstore.mdx#using-valkey-in-a-rag-pipeline) documentation.