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Literal AI

Use the Literal AI API

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Overview

This node integrates with the Literal AI API to manage datasets and their items. Specifically, for the "Dataset" resource and the "Delete Item" operation, it deletes a single item from a dataset by its unique identifier. This is useful when you need to remove outdated, incorrect, or unwanted data entries from your dataset to maintain data quality.

Practical examples include:

  • Cleaning up training data by removing irrelevant or erroneous dataset items.
  • Managing dataset lifecycle by deleting specific items after review or processing.
  • Automating dataset maintenance workflows where items are programmatically removed based on certain criteria.

Properties

Name Meaning
Dataset Item ID The unique identifier of the dataset item to delete.

Output

The node outputs a JSON object under the json field containing the response from the Literal AI API after attempting to delete the dataset item. This typically includes confirmation of deletion or relevant status information returned by the API.

No binary data output is produced by this operation.

Example output structure:

{
  "content": {
    // API response confirming deletion or related info
  }
}

Dependencies

  • Requires an active connection to the Literal AI API.
  • Needs an API key credential configured in n8n for authentication with the Literal AI service.
  • The node uses the official Literal AI client library (@literalai/client) bundled within the node.

Troubleshooting

  • Common issues:

    • Invalid or missing Dataset Item ID: Ensure the provided ID is correct and exists in the target dataset.
    • Authentication errors: Verify that the API key credential is correctly set up and has sufficient permissions.
    • Network or API downtime: Check connectivity and Literal AI service status.

  • Error messages:

    • "error": "Item not found" — The specified dataset item ID does not exist; verify the ID.
    • "error": "Unauthorized" — API key is invalid or lacks permission; update credentials.
    • Other API errors will be passed through; enabling "Continue On Fail" allows workflow continuation despite errors.

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