Document processors are the core components that analyze and manipulate information from your documents. This guide explains how to configure processors through our API, including detailed examples for each processor type.
Extraction Processors: Extract specific fields from documents.
Classification Processors: Categorize documents.
Splitter Processors: Divide documents into logical sub-documents.
Generally we recommend using our UI for configuration, but the API can be useful in programmatic workflows, when you need to configure a large number of processors, or when you need to keep your configurations in source control and versioned.
You can also use our webhook events to consume changes made to configurations in the Extend Dashboard and keep your saved configurations in sync.
All processor configurations share these base properties:
type BaseProcessorConfig = { // Base properties inherited by all processor types baseProcessor?: string; baseVersion?: string;};
They will be set by default to latest available on processor creation unless otherwise specified - see Changelog for more details. Specify these if you need to pin your processor to a specific underlying model version for consistent behavior or to use features available only in certain versions.
This section is relevant for processors using the JSON Schema config type. If
you are using the legacy Fields Array config type, please see the Fields
Array Structure documentation. If you aren’t
sure which config type you are using, please see the Migrating to JSON
Schema documentation.
We use JSON Schema to define the structure of the data we extract. Before you get started, we recommend familiarizing yourself with the JSON Schema documentation to understand how to define your schema.
The standard JSON Schema is extremely flexible. We’ve implemented a subset of the standard to support the needs of document extraction. Your schema must follow these rules:
The root must be an object type
Allowed types are string, number, integer, boolean, object, and array
All primitive fields (string, number, boolean, integer) must be nullable (use array type with “null” as an option e.g. "type": ["string", "null"])
Maximum nesting level is 3 (each non-root object counts as 1 level)
Property keys and names must only contain lowercase letters, numbers, and underscores
Array items must be objects
Enums must only contain strings and must contain a null option
Custom types are supported by adding a "extend:type": "currency", "extend:type": "signature", or "extend:type": "date" property to the appropriate field type with the required properties. See below for examples.
Property names can be added using the "extend:name" property. If supplied, this will override the name of the property as it will appear to the model, but not in the output returned to you. This is useful for providing more descriptive names or instructions to the model without altering the actual keys in your output data structure.
You can add descriptions to individual enum values using the "extend:descriptions" property.
Objects must have properties. If you set a required array of the properties, we will respect that order when extracting. If you do not set required array, we will generate it and enforce order.
Enums must include null as an option. Only strings are supported for enums. The extend:descriptions is an optional array of strings. It is recommended to give more context for each enum option for more accurate extraction.
{ "status": { "enum": ["pending", "approved", "rejected", null], "extend:descriptions": [ "Invoice is pending approval", "Invoice has been approved", "Invoice has been rejected", "" ], "description": "Current status of the invoice" }}
Date fields must be strings and use the extend:type keyword with the value date. This will guarantee the date format is always an ISO compliant date (yyyy-mm-dd).
Signature fields must be objects with specific properties. This will auto-enable our advanced signature detection in the parsing step prior to extraction, and apply a number of prompt and post-processing heuristics to improve accuracy, particularly on reduction of false positives for signature blocks that are not actually signed.
const customFieldConfig = { type: "EXTRACT", schema: { type: "object", properties: { invoice_signature: { type: "object", description: "Details of the invoice signature", properties: { printed_name: { type: ["string", "null"], description: "The printed name of the signer", }, signature_date: { type: ["string", "null"], "extend:type": "date", description: "The date the signature was applied", }, is_signed: { type: ["boolean", "null"], description: "Indicates if the document is signed", }, title_or_role: { type: ["string", "null"], description: "The title or role of the signer", }, }, required: [ "printed_name", "signature_date", "is_signed", "title_or_role", ], }, invoice_amount: { type: "object", "extend:type": "currency", description: "The amount of the invoice", properties: { amount: { type: ["number", "null"], description: "The numerical value of the amount", }, iso_4217_currency_code: { type: ["string", "null"], description: "The ISO 4217 currency code (e.g., USD, EUR)", }, }, required: ["amount", "iso_4217_currency_code"], }, invoice_date: { type: "date", description: "The date of the invoice", }, }, required: ["signature", "invoice_amount", "invoice_date"], },};
This section is relevant for the Fields Array config type. If you are using
the JSON Schema config type, please see the JSON Schema
Structure documentation. If you aren’t sure
which config type you are using, please see the Migrating to JSON
Schema documentation.