> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-lsapi-1779323176-451247e.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# AWS s3 file integration

> Integrate with the AWS s3 file document loader using LangChain Python.

> [Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) is an object storage service.

> [AWS S3 Buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingBucket.html)

This covers how to load document objects from an `AWS S3 File` object.

```python theme={null}
from langchain_community.document_loaders import S3FileLoader
```

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

```python theme={null}
loader = S3FileLoader("testing-hwc", "fake.docx")
```

```python theme={null}
loader.load()
```

```python theme={null}
[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': 's3://testing-hwc/fake.docx'}, lookup_index=0)]
```

## Configuring the AWS Boto3 client

You can configure the AWS [Boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) client by passing
named arguments when creating the S3DirectoryLoader.
This is useful for instance when AWS credentials can't be set as environment variables.
See the [list of parameters](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session) that can be configured.

```python theme={null}
loader = S3FileLoader(
    "testing-hwc", "fake.docx", aws_access_key_id="xxxx", aws_secret_access_key="yyyy"
)
```

```python theme={null}
loader.load()
```

***

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