Extract IBANs, Currencies, and Addresses from Financial Documents - Validated, Not Just Strings

The provided code snippet showcases how to use the /no/ endpoint (likely a placeholder for an actual API endpoint) to extract structured data from financial documents, such as bank transfers. The process involves defining a schema that specifies which fields should be extracted and their types, then uploading the document(s) to be processed.

Here's a breakdown of what each part does:

1. Schema Definition: A list of objects is defined where each object represents a field in the document you want to extract data from:

   • name: The name of the field.
   • type: Specifies the type of value expected for this field (e.g., DATE, TEXT, CURRENCY_CODE).
   • description: A brief description of what the field represents.

2. Document Upload: Documents are uploaded as part of the request, and their paths or references are included in the schema definition under each field's source if needed.

3. Response Structure:

   • The response contains a success status indicating whether the extraction was successful.
   • data: Contains extracted values for each specified field with additional details like confidence scores and citations.

Example Response Breakdown

For the "transaction_date" field, the response

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