{ "fields": [ { "name": "Invoice Number", "key": "invoice_number", "data_type": "string", "description": "The unique identifier for the invoice" }, { "name": "Invoice Date", "key": "invoice_date", "data_type": "date", "description": "The date the invoice was issued" }, { "name": "Total Amount", "key": "total_amount", "data_type": "number", "description": "The total amount due on the invoice" }, { "name": "Is Paid", "key": "is_paid", "data_type": "boolean", "description": "Whether the invoice has been paid" } ], "tables": [ { "name": "Line Items", "key": "line_items", "description": "The list of products or services on the invoice", "columns": [ { "name": "Description", "key": "description", "data_type": "string" }, { "name": "Quantity", "key": "quantity", "data_type": "number" }, { "name": "Unit Price", "key": "unit_price", "data_type": "number" }, { "name": "Amount", "key": "amount", "data_type": "number" } ] } ]}
Features
Data Types
Define the expected format of extracted data
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{ "fields": [ { "name": "Invoice Number", "key": "invoice_number", "data_type": "string", "description": "The unique identifier for the invoice" }, { "name": "Invoice Date", "key": "invoice_date", "data_type": "date", "description": "The date the invoice was issued" }, { "name": "Total Amount", "key": "total_amount", "data_type": "number", "description": "The total amount due on the invoice" }, { "name": "Is Paid", "key": "is_paid", "data_type": "boolean", "description": "Whether the invoice has been paid" } ], "tables": [ { "name": "Line Items", "key": "line_items", "description": "The list of products or services on the invoice", "columns": [ { "name": "Description", "key": "description", "data_type": "string" }, { "name": "Quantity", "key": "quantity", "data_type": "number" }, { "name": "Unit Price", "key": "unit_price", "data_type": "number" }, { "name": "Amount", "key": "amount", "data_type": "number" } ] } ]}
Data types define the expected format of data extracted from your documents. TableFlow supports multiple data types to ensure data is correctly formatted and validated during extraction.Data types can be set on:
Template fields
Table columns
If no data type is specified, the field or column will default to the string type.
For numeric values, such as amounts, quantities, and measurements.Accepts: Integers and decimal numbers of any size Output: The extracted value as a number, or null if blank Validation: Non-numeric values are flagged for review
For date and datetime values, supporting various formats.Accepts: Date strings in various formats (e.g., “2023-04-15”, “Apr 15, 2023”, “15/04/2023”) Output: The extracted value as an RFC 3339 datetime (e.g., “2023-04-15T00:00:00Z”), or null if blank Validation: Invalid date formats are flagged for review
For true/false values.Accepts: “true”, “t”, “1”, “yes”, “y” for true; “false”, “f”, “0”, “no”, “n” for false (case-insensitive) Output: The extracted value as a boolean (true/false), or null if blank Validation: Non-boolean values are flagged for review
For defining a nested table structure within a template. This is particularly useful for complex documents that have multiple tables or nested data.Output: A structured array of rows with column values