---
title: "AzureAISearchBM25Retriever"
id: azureaisearchbm25retriever
slug: "/azureaisearchbm25retriever"
description: "A keyword-based Retriever that fetches Documents matching a query from the Azure AI Search Document Store."
---

# AzureAISearchBM25Retriever

A keyword-based Retriever that fetches Documents matching a query from the Azure AI Search Document Store.

A keyword-based Retriever that fetches documents matching a query from the Azure AI Search Document Store.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | 1. Before a [`PromptBuilder`](../builders/promptbuilder.mdx) in a RAG pipeline 2. The last component in the semantic search pipeline 3. Before an [`ExtractiveReader`](../readers/extractivereader.mdx) in an extractive QA pipeline |
| **Mandatory init variables**           | `document_store`: An instance of [`AzureAISearchDocumentStore`](../../document-stores/azureaisearchdocumentstore.mdx)                                                                                                                   |
| **Mandatory run variables**            | `query`: A string                                                                                                                                                                                                 |
| **Output variables**                   | `documents`: A list of documents (matching the query)                                                                                                                                                             |
| **API reference**                      | [Azure AI Search](/reference/integrations-azure_ai_search)                                                                                                                                                               |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/azure_ai_search                                                                                                                 |

</div>

## Overview

The `AzureAISearchBM25Retriever` is a keyword-based Retriever designed to fetch documents that match a query from an `AzureAISearchDocumentStore`. It uses the BM25 algorithm which calculates a weighted word overlap between the query and the documents to determine their similarity. The Retriever accepts textual query but you can also provide a combination of terms with boolean operators. Some examples of valid queries could be `"pool"`, `"pool spa"`, and `"pool spa +airport"`.

In addition to the `query`, the `AzureAISearchBM25Retriever` accepts other optional parameters, including `top_k` (the maximum number of documents to retrieve) and `filters` to narrow down the search space.

If your search index includes a [semantic configuration](https://learn.microsoft.com/en-us/azure/search/semantic-how-to-query-request), you can enable semantic ranking to apply it to the Retriever's results. For more details, refer to the [Azure AI documentation](https://learn.microsoft.com/en-us/azure/search/hybrid-search-how-to-query#semantic-hybrid-search).

If you want a combination of BM25 and vector retrieval, use the `AzureAISearchHybridRetriever`, which uses both vector search and BM25 search to match documents and query.

## Usage

### Installation

This integration requires you to have an active Azure subscription with a deployed [Azure AI Search](https://azure.microsoft.com/en-us/products/ai-services/ai-search) service.

To start using Azure AI search with Haystack, install the package with:

```shell
pip install azure-ai-search-haystack
```

### On its own

This Retriever needs `AzureAISearchDocumentStore` and indexed documents to run.

```python
from haystack import Document
from haystack_integrations.components.retrievers.azure_ai_search import (
    AzureAISearchBM25Retriever,
)
from haystack_integrations.document_stores.azure_ai_search import (
    AzureAISearchDocumentStore,
)

document_store = AzureAISearchDocumentStore(index_name="haystack_docs")
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_store.write_documents(documents=documents)

retriever = AzureAISearchBM25Retriever(document_store=document_store)
retriever.run(query="How many languages are spoken around the world today?")
```

### In a RAG pipeline

The below example shows how to use the `AzureAISearchBM25Retriever` in a RAG pipeline. Set your `OPENAI_API_KEY` as an environment variable and then run the following code:

```python

from haystack_integrations.components.retrievers.azure_ai_search import (
    AzureAISearchBM25Retriever,
)
from haystack_integrations.document_stores.azure_ai_search import (
    AzureAISearchDocumentStore,
)

from haystack import Document
from haystack import Pipeline
from haystack.components.builders.answer_builder import AnswerBuilder
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack.components.generators import OpenAIGenerator
from haystack.document_stores.types import DuplicatePolicy

import os

api_key = os.environ["OPENAI_API_KEY"]

## Create a RAG query pipeline
prompt_template = """
    Given these documents, answer the question.\nDocuments:
    {% for doc in documents %}
        {{ doc.content }}
    {% endfor %}

    \nQuestion: {{question}}
    \nAnswer:
    """

document_store = AzureAISearchDocumentStore(index_name="haystack-docs")

## Add Documents
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.",
    ),
]

## policy param is optional, as AzureAISearchDocumentStore has a default policy of DuplicatePolicy.OVERWRITE
document_store.write_documents(documents=documents, policy=DuplicatePolicy.OVERWRITE)

retriever = AzureAISearchBM25Retriever(document_store=document_store)
rag_pipeline = Pipeline()
rag_pipeline.add_component(name="retriever", instance=retriever)
rag_pipeline.add_component(
    instance=PromptBuilder(template=prompt_template),
    name="prompt_builder",
)
rag_pipeline.add_component(instance=OpenAIGenerator(), name="llm")
rag_pipeline.add_component(instance=AnswerBuilder(), name="answer_builder")
rag_pipeline.connect("retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")
rag_pipeline.connect("llm.replies", "answer_builder.replies")
rag_pipeline.connect("llm.meta", "answer_builder.meta")
rag_pipeline.connect("retriever", "answer_builder.documents")

question = "Tell me something about languages?"
result = rag_pipeline.run(
    {
        "retriever": {"query": question},
        "prompt_builder": {"question": question},
        "answer_builder": {"query": question},
    },
)
print(result["answer_builder"]["answers"][0])
```
