> ## 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.

# CSVLoader

> The CSVLoader is dedicated to parsing CSV files and converting them into NeumDocument objects, with support for custom encoding and selective data extraction.

The `CSVLoader` is a component of the Neum AI framework that processes CSV files, transforming them into `NeumDocument` objects. It allows for the specification of encoding, column selection, and other CSV reading parameters.

## Properties

Required properties:

* None

Optional properties:

* `id_key`: Specifies the column to use as the document ID.
* `source_column`: Designates a specific column from which to extract the main content.
* `encoding`: Sets the character encoding for reading the CSV file.
* `csv_args`: Allows for additional arguments to be passed to the CSV reader.

Available metadata:

* `custom`: Metadata fields can be customized based on the contents of the CSV file. Simply pass a list of columns. (i.e. \["column1" , "column2"])

Available content:

* `custom`: Content is dynamically derived from the CSV file based on specified keys or entire row data. Simply pass a list of columns. (i.e. \["column1" , "column2"])

<CodeGroup>
  ```python Local Development
  from neumai.Shared import Selector
  from neumai.Loaders import CSVLoader

  csv_loader = CSVLoader(
      loader_information={
          "id_key": "id",
          "encoding": "utf-8-sig",
      },
      selector=Selector(
          to_embed=["text"],
          to_metadata=["id", "category"]
      )
  )

  ```

  ```json Cloud
  {
      "sources":[
          {   
              # Add a data connector
              "loader": {
                  "loader_name":"CSVLoader",
                  "loader_information":{
                      "id_key": "id",
                      "encoding": "utf-8-sig",
                  },
                  "selector": {
                      "to_embed": [
                          "text"
                      ],
                      "to_metadata": [
                          "id",
                          "category"
                      ]
                  }
              }
          }
      ]
  }
  ```
</CodeGroup>