deepset-ai--haystack/docs-website/versioned_docs/version-2.30/pipeline-components/retrievers/vespaembeddingretriever.mdx

---
title: "VespaEmbeddingRetriever"
id: vespaembeddingretriever
slug: "/vespaembeddingretriever"
description: "An embedding-based Retriever compatible with the Vespa Document Store."
---

# VespaEmbeddingRetriever

An embedding-based Retriever compatible with the Vespa 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 [VespaDocumentStore](../../document-stores/vespadocumentstore.mdx)                                                                                                                                       |
| **Mandatory run variables**            | `query_embedding`: A vector representing the query (a list of floats)                                                                                                                                                                       |
| **Output variables**                   | `documents`: A list of documents                                                                                                                                                                                                            |
| **API reference**                      | [Vespa](/reference/integrations-vespa)                                                                                                                                                                                                       |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/vespa                                                                                                                                                        |
| **Package name** | `vespa-haystack` |

</div>

## Overview

The `VespaEmbeddingRetriever` is a dense embedding-based Retriever compatible with the `VespaDocumentStore`. It uses Vespa's [nearest-neighbor search](https://docs.vespa.ai/en/nearest-neighbor-search.html) to find Documents whose embedding is closest to the query embedding and applies a configurable rank profile to score them.

When using the `VespaEmbeddingRetriever` 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 `VespaEmbeddingRetriever` accepts other optional parameters, including `top_k` (the maximum number of Documents to retrieve) and `filters` to narrow down the search space.

The retriever expects the underlying Vespa application to expose:

- A tensor field for embeddings (named `embedding` by default, configurable on the Document Store via `embedding_field`).
- A rank profile that scores nearest-neighbor candidates (named `semantic` by default, configurable via the `ranking` parameter). The profile typically uses `closeness(field, embedding)` and takes a query input tensor (named `query_embedding` by default, configurable via `query_tensor_name`).

You can additionally tune retrieval with `target_hits`, which sets how many neighbors each Vespa content node considers per query before first-phase ranking.

## Installation

Install the `vespa-haystack` integration:

```shell
pip install vespa-haystack
```

To run Vespa locally, see the [Vespa quick start](https://docs.vespa.ai/en/vespa-quick-start.html).

## Usage

### On its own

This Retriever needs the `VespaDocumentStore` and indexed Documents to run. Set the `VESPA_URL` environment variable (or pass `url=...` to the Document Store) to connect to your Vespa application.

```python
from haystack_integrations.document_stores.vespa import VespaDocumentStore
from haystack_integrations.components.retrievers.vespa import (
    VespaEmbeddingRetriever,
)

document_store = VespaDocumentStore(schema="doc", namespace="doc")
retriever = VespaEmbeddingRetriever(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.components.embedders import (
    SentenceTransformersDocumentEmbedder,
    SentenceTransformersTextEmbedder,
)
from haystack.components.writers import DocumentWriter

from haystack_integrations.document_stores.vespa import VespaDocumentStore
from haystack_integrations.components.retrievers.vespa import (
    VespaEmbeddingRetriever,
)

document_store = VespaDocumentStore(
    schema="doc",
    namespace="doc",
    content_field="content",
    embedding_field="embedding",
    metadata_fields=["category"],
)

documents = [
    Document(
        content="Haystack integrates with Vespa for search.",
        meta={"category": "docs"},
    ),
    Document(
        content="Vespa supports lexical and vector retrieval.",
        meta={"category": "docs"},
    ),
    Document(content="Cats sleep most of the day.", meta={"category": "animals"}),
]

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

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
query_pipeline.add_component(
    "retriever",
    VespaEmbeddingRetriever(
        document_store=document_store,
        top_k=2,
        query_tensor_name="query_embedding",
    ),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "semantic vector search"

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

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