> ## Documentation Index
> Fetch the complete documentation index at: https://docs.neum.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# S3 Connector

> Retrieve data from S3

Connect to an existing Azure Blob Storage and pull any data stored. To connect Azure Blob Storage you will need your credentials to access the resource including the connection string and container name you want to access.

## Properties

Required properties:

* `aws_key_id`: AWS key ID with permission to S3 instance
* `aws_access_key`: AWS access key with permission to S3 instance
* `bucket_name`: Name of bucket for data extraction

Optional properties:

* `prefix`: File prefix to filter on

Available metadata

* `key`: Key / name of the file in S3
* `last_modified`: Time of last modification
* `metadata`: Attached metadata to the file.

Compatible loaders:

* AutoLoader
* HTMLLoader
* MarkdownLoader
* NeumCSVLoader
* NeumJSONLoader
* PDFLoader

## Usage

<CodeGroup>
  ```python Local Development
  from neumai.DataConnectors import S3Connector
  from neumai.Shared import Selector

  s3_connector =  S3Connector(
      aws_key_id = "<INSER AWS ACCESS KEY ID>",
      aws_access_key = "<INSER AWS ACCESS KEY>",
      bucket_name = "<INSER BUCKER NAME IN S3>",
      selector = Selector(
          to_metadata=['key']
      )
  )
  ```

  ```json Cloud
  {
      "sources":[
          {
              "data_connector": {
                  "connector_name":"S3Connector",
                  "connector_information":{
                      "aws_key_id":"<INSER AWS ACCESS KEY ID>",
                      "aws_access_key":"<INSER AWS ACCESS KEY>",
                      "bucket_name":"<INSER BUCKER NAME IN S3>",
                      "selector": {
                          to_metadata=['key']
                      }
                  },
              }
          }
      ]
  }
  ```
</CodeGroup>