---
title: "WeaviateEmbeddingRetriever"
id: weaviateembeddingretriever
slug: "/weaviateembeddingretriever"
description: "This is an embedding Retriever compatible with the Weaviate Document Store."
---

# WeaviateEmbeddingRetriever

This is an embedding Retriever compatible with the Weaviate 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 [WeaviateDocumentStore](../../document-stores/weaviatedocumentstore.mdx)                                                                                                                                                                                     |
| **Mandatory run variables**            | `query_embedding`: A list of floats                                                                                                                                                                                                                                       |
| **Output variables**                   | `documents`: A list of documents                                                                                                                                                                                                                                          |
| **API reference**                      | [Weaviate](/reference/integrations-weaviate)                                                                                                                                                                                                                                     |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/weaviate                                                                                                                                                                                |
| **Package name** | `weaviate-haystack` |

</div>

## Overview

The `WeaviateEmbeddingRetriever` is an embedding-based Retriever compatible with the [`WeaviateDocumentStore`](../../document-stores/weaviatedocumentstore.mdx). It compares the query and Document embeddings and fetches the Documents most relevant to the query from the `WeaviateDocumentStore` based on the outcome.

### Parameters

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

You can also specify `distance`, the maximum allowed distance between embeddings, and `certainty`, the normalized distance between the result items and the search embedding. The behavior of `distance` depends on the Collection’s distance metric used. See the [official Weaviate documentation](https://weaviate.io/developers/weaviate/api/graphql/search-operators#variables) for more information.

The embedding similarity function depends on the vectorizer used in the `WeaviateDocumentStore` collection. Check out the [official Weaviate documentation](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules) to see all the supported vectorizers.

## Usage

### Installation

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

```shell
pip install weaviate-haystack
```

### On its own

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

```python
from haystack_integrations.document_stores.weaviate.document_store import (
    WeaviateDocumentStore,
)
from haystack_integrations.components.retrievers.weaviate import (
    WeaviateEmbeddingRetriever,
)

document_store = WeaviateDocumentStore(url="http://localhost:8080")

retriever = WeaviateEmbeddingRetriever(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.document_stores.types import DuplicatePolicy
from haystack import Document
from haystack import Pipeline
from haystack.components.embedders import (
    SentenceTransformersTextEmbedder,
    SentenceTransformersDocumentEmbedder,
)

from haystack_integrations.document_stores.weaviate.document_store import (
    WeaviateDocumentStore,
)
from haystack_integrations.components.retrievers.weaviate import (
    WeaviateEmbeddingRetriever,
)

document_store = WeaviateDocumentStore(url="http://localhost:8080")

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",
    WeaviateEmbeddingRetriever(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])
```
