---
title: "OpenSearchBM25Retriever"
id: opensearchbm25retriever
slug: "/opensearchbm25retriever"
description: "This is a keyword-based Retriever that fetches Documents matching a query from an OpenSearch Document Store."
---

# OpenSearchBM25Retriever

This is a keyword-based Retriever that fetches Documents matching a query from an OpenSearch 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 an [OpenSearchDocumentStore](../../document-stores/opensearch-document-store.mdx)                                                                                                                          |
| **Mandatory run variables**            | `query`: A query string                                                                                                                                                                                               |
| **Output variables**                   | `documents`: A list of documents matching the query                                                                                                                                                                   |
| **API reference**                      | [OpenSearch](/reference/integrations-opensearch)                                                                                                                                                                             |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/opensearch                                                                                                                          |

</div>

## Overview

`OpenSearchBM25Retriever` is a keyword-based Retriever that fetches Documents matching a query from an `OpenSearchDocumentStore`. It determines the similarity between Documents and the query based on the BM25 algorithm, which computes a weighted word overlap between the two strings.

Since the `OpenSearchBM25Retriever` matches strings based on word overlap, it’s often used to find exact matches to names of persons or products, IDs, or well-defined error messages. The BM25 algorithm is very lightweight and simple. Nevertheless, it can be hard to beat with more complex embedding-based approaches on out-of-domain data.

In addition to the `query`, the `OpenSearchBM25Retriever` accepts other optional parameters, including `top_k` (the maximum number of Documents to retrieve) and `filters` to narrow down the search space.
You can adjust how [inexact fuzzy matching](https://www.elastic.co/guide/en/elasticsearch/reference/current/common-options.html#fuzziness) is performed, using the `fuzziness` parameter.
It is also possible to specify if all terms in the query must match using the `all_terms_must_match` parameter, which defaults to `False`.

If you want more flexible matching of a query to Documents, you can use the `OpenSearchEmbeddingRetriever`, which uses vectors created by LLMs to retrieve relevant information.

### Setup and installation

[Install](https://opensearch.org/docs/latest/install-and-configure/install-opensearch/index/) and run an OpenSearch instance.

If you have Docker set up, we recommend pulling the Docker image and running it.

```shell
docker pull opensearchproject/opensearch:2.11.0
docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" -e "ES_JAVA_OPTS=-Xms1024m -Xmx1024m" opensearchproject/opensearch:2.11.0
```

As an alternative, you can go to [OpenSearch integration GitHub](https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/opensearch) and start a Docker container running OpenSearch using the provided `docker-compose.yml`:

```shell
docker compose up
```

Once you have a running OpenSearch instance, install the `opensearch-haystack` integration:

```shell
pip install opensearch-haystack
```

## Usage

### On its own

This Retriever needs the `OpensearchDocumentStore` and indexed Documents to run. You can’t use it on its own.

### 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.opensearch import (
    OpenSearchBM25Retriever,
)
from haystack_integrations.document_stores.opensearch import OpenSearchDocumentStore

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 = OpenSearchDocumentStore(
    hosts="http://localhost:9200",
    use_ssl=True,
    verify_certs=False,
    http_auth=("admin", "admin"),
)

## 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.",
    ),
]

## DuplicatePolicy.SKIP param is optional, but useful to run the script multiple times without throwing errors
document_store.write_documents(documents=documents, policy=DuplicatePolicy.SKIP)

retriever = OpenSearchBM25Retriever(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(api_key=api_key), 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.metadata", "answer_builder.metadata")
rag_pipeline.connect("retriever", "answer_builder.documents")

question = "How many languages are spoken around the world today?"
result = rag_pipeline.run(
    {
        "retriever": {"query": question},
        "prompt_builder": {"question": question},
        "answer_builder": {"query": question},
    },
)
print(result["answer_builder"]["answers"][0])
```

Here’s an example output:

```python
GeneratedAnswer(
  data='Over 7,000 languages are spoken around the world today.',
  query='How many languages are spoken around the world today?',
  documents=[
    Document(id=cfe93bc1c274908801e6670440bf2bbba54fad792770d57421f85ffa2a4fcc94, content: 'There are over 7,000 languages spoken around the world today.', score: 7.179112),
    Document(id=7f225626ad1019b273326fbaf11308edfca6d663308a4a3533ec7787367d59a2, content: 'In certain parts of the world, like the Maldives, Puerto Rico, and San Diego, you can witness the ph...', score: 1.1426818)],
  meta={'model': 'gpt-3.5-turbo-0613', 'index': 0, 'finish_reason': 'stop', 'usage': {'prompt_tokens': 86, 'completion_tokens': 13, 'total_tokens': 99}})
```

## Additional References

🧑‍🍳 Cookbook: [PDF-Based Question Answering with Amazon Bedrock and Haystack](https://haystack.deepset.ai/cookbook/amazon_bedrock_for_documentation_qa)
