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

---
title: "WeaviateHybridRetriever"
id: weaviatehybridretriever
slug: "/weaviatehybridretriever"
description: "A Retriever that combines BM25 keyword search and vector similarity to fetch documents from the Weaviate Document Store."
---

# WeaviateHybridRetriever

A Retriever that combines BM25 keyword search and vector similarity to fetch documents from 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 a hybrid 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`: A string  <br /> <br />`query_embedding`: A list of floats |
| **Output variables** | `documents`: A list of documents (matching the query) |
| **API reference** | [Weaviate](/reference/integrations-weaviate) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/weaviate |

</div>

## Overview

The `WeaviateHybridRetriever` combines keyword-based (BM25) and vector similarity search to fetch documents from the [`WeaviateDocumentStore`](../../document-stores/weaviatedocumentstore.mdx). Weaviate executes both searches in parallel and fuses the results into a single ranked list. The Retriever requires both a text query and its corresponding embedding.

The `alpha` parameter controls how much each search method contributes to the final results:

- `alpha = 0.0`: only keyword (BM25) scoring is used,
- `alpha = 1.0`: only vector similarity scoring is used,
- Values in between blend the two; higher values favor the vector score, lower values favor BM25.

If you don't specify `alpha`, the Weaviate server default is used.

You can also use the `max_vector_distance` parameter to set a threshold for the vector component. Candidates with a distance larger than this threshold are excluded from the vector portion before blending.

See the [official Weaviate documentation](https://weaviate.io/developers/weaviate/search/hybrid#parameters) for more details on hybrid search parameters.

### Parameters

When using the `WeaviateHybridRetriever`, you need to provide both the query text and its embedding. You can do this by adding a Text Embedder to your query pipeline.

In addition to `query` and `query_embedding`, the retriever accepts optional parameters including `top_k` (the maximum number of documents to return), `filters` to narrow down the search space, and `filter_policy` to determine how filters are applied.

## 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 WeaviateHybridRetriever

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

retriever = WeaviateHybridRetriever(document_store=document_store)

## using a fake vector to keep the example simple
retriever.run(query="How many languages are there?", 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 (
    WeaviateHybridRetriever,
)

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()
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",
    WeaviateHybridRetriever(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}, "retriever": {"query": query}},
)

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

### Adjusting the Alpha Parameter

You can set the `alpha` parameter at initialization or override it at query time:

```python
from haystack_integrations.components.retrievers.weaviate import WeaviateHybridRetriever

## Favor keyword search (good for exact matches)
retriever_keyword_heavy = WeaviateHybridRetriever(
    document_store=document_store,
    alpha=0.25,
)

## Balanced hybrid search
retriever_balanced = WeaviateHybridRetriever(document_store=document_store, alpha=0.5)

## Favor vector search (good for semantic similarity)
retriever_vector_heavy = WeaviateHybridRetriever(
    document_store=document_store,
    alpha=0.75,
)

## Override alpha at query time
result = retriever_balanced.run(
    query="artificial intelligence",
    query_embedding=embedding,
    alpha=0.8,
)
```