> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-devupd-1765394015-eccef47.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Amazon Kendra

> [Amazon Kendra](https://docs.aws.amazon.com/kendra/latest/dg/what-is-kendra.html) is an intelligent search service provided by `Amazon Web Services` (`AWS`). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. `Kendra` is designed to help users find the information they need quickly and accurately, improving productivity and decision-making.

> With `Kendra`, users can search across a wide range of content types, including documents, FAQs, knowledge bases, manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and contextual meanings to provide highly relevant search results.

## Using the Amazon Kendra Index Retriever

```python theme={null}
pip install -qU  boto3
```

```python theme={null}
from langchain_community.retrievers import AmazonKendraRetriever
```

Create New Retriever

```python theme={null}
retriever = AmazonKendraRetriever(index_id="c0806df7-e76b-4bce-9b5c-d5582f6b1a03")
```

Now you can use retrieved documents from Kendra index

```python theme={null}
retriever.invoke("what is langchain")
```

***

<Callout icon="pen-to-square" iconType="regular">
  [Edit the source of this page on GitHub.](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/retrievers/amazon_kendra_retriever.mdx)
</Callout>

<Tip icon="terminal" iconType="regular">
  [Connect these docs programmatically](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
</Tip>