deepset-ai--haystack/docs-website/versioned_docs/version-2.21/pipeline-components/rankers/amazonbedrockranker.mdx

---
title: "AmazonBedrockRanker"
id: amazonbedrockranker
slug: "/amazonbedrockranker"
description: "Use this component to rank documents based on their similarity to the query using Amazon Bedrock models."
---

# AmazonBedrockRanker

Use this component to rank documents based on their similarity to the query using Amazon Bedrock models.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | In a query pipeline, after a component that returns a list of documents such as a [Retriever](../retrievers.mdx) |
| **Mandatory init variables** | `aws_access_key_id`: AWS access key ID. Can be set with AWS_ACCESS_KEY_ID env var.  <br /> <br />`aws_secret_access_key`: AWS secret access key. Can be set with AWS_SECRET_ACCESS_KEY env var.  <br /> <br />`aws_region_name`: AWS region name. Can be set with AWS_DEFAULT_REGION env var. |
| **Mandatory run variables** | `documents`: A list of document objects  <br /> <br />`query`: A query string |
| **Output variables** | `documents`: A list of document objects |
| **API reference** | [Amazon Bedrock](/reference/integrations-amazon-bedrock) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/amazon_bedrock/ |

</div>

## Overview

`AmazonBedrockRanker` ranks documents based on semantic relevance to a specified query. It uses Amazon Bedrock Rerank API. This list of all supported models can be found in Amazon’s [documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/rerank-supported.html). The default model for this Ranker is `cohere.rerank-v3-5:0`.

You can also specify the `top_k` parameter to set the maximum number of documents to return.

### Installation

To start using Amazon Bedrock with Haystack, install the `amazon-bedrock-haystack` package:

```shell
pip install amazon-bedrock-haystack
```

### Authentication

This component uses AWS for authentication. You can use the AWS CLI to authenticate through your IAM. For more information on setting up an IAM identity-based policy, see the [official documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/security_iam_id-based-policy-examples.html).

:::info[Using AWS CLI]

Consider using AWS CLI as a more straightforward tool to manage your AWS services. With AWS CLI, you can quickly configure your [boto3 credentials](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html). This way, you won't need to provide detailed authentication parameters when initializing Amazon Bedrock in Haystack.
:::

To use this component, initialize it with the model name. The AWS credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_DEFAULT_REGION`) should be set as environment variables, configured as described above, or passed as [Secret](../../concepts/secret-management.mdx) arguments. Make sure the region you set supports Amazon Bedrock.

## Usage

### On its own

This example uses `AmazonBedrockRanker` to rank two simple documents. To run the Ranker, pass a `query` and provide the `documents`.

```python
from haystack import Document
from haystack_integrations.components.rankers.amazon_bedrock import AmazonBedrockRanker

docs = [Document(content="Paris"), Document(content="Berlin")]

ranker = AmazonBedrockRanker()

ranker.run(query="City in France", documents=docs, top_k=1)
```

### In a pipeline

Below is an example of a pipeline that retrieves documents from an `InMemoryDocumentStore` based on keyword search (using `InMemoryBM25Retriever`). It then uses the `AmazonBedrockRanker` to rank the retrieved documents according to their similarity to the query. The pipeline uses the default settings of the Ranker.

```python
from haystack import Document, Pipeline
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.rankers.amazon_bedrock import AmazonBedrockRanker

docs = [
    Document(content="Paris is in France"),
    Document(content="Berlin is in Germany"),
    Document(content="Lyon is in France"),
]
document_store = InMemoryDocumentStore()
document_store.write_documents(docs)

retriever = InMemoryBM25Retriever(document_store=document_store)
ranker = AmazonBedrockRanker()

document_ranker_pipeline = Pipeline()
document_ranker_pipeline.add_component(instance=retriever, name="retriever")
document_ranker_pipeline.add_component(instance=ranker, name="ranker")

document_ranker_pipeline.connect("retriever.documents", "ranker.documents")

query = "Cities in France"
res = document_ranker_pipeline.run(
    data={
        "retriever": {"query": query, "top_k": 3},
        "ranker": {"query": query, "top_k": 2},
    },
)
```