Extract fields with anchors

Structured extraction whose every value carries a verified anchor.

Structured extraction whose every value carries a verified anchor.

When to use it

Extract declared fields and rows with source anchors. Choose static fields or the input-supplied field mode, then define the row grain where relevant. Review missing anchors, unresolved rows, confidence, and coverage rather than accepting an extracted value solely because it is present.

Add and configure

1. Add Extract fields with anchors from the workflow step picker.
2. Give the step a recognisable name and connect its required inputs.
3. Set the options below. Nested names identify fields within an object or a list item.
4. Validate the recipe, inspect the test result, then run it on the intended data.

The step identifier used in the editor’s code view is extract_anchored.

Settings

Inputs and outputs

Effects, testing, and recovery

Calls a model and can consume model budget.

The declared preview behaviour is to execute this step. Reads, processing, or model usage can therefore be real. Read dry runs before using a preview with live connections.

No duplicate-prevention key is declared for this step. Do not assume that manually repeating the step is free of repeated effects.

Example: One row of invoice fields per document

Step settings:

{
  "fields": [
    {
      "description": "The invoice identifier as printed",
      "name": "invoice_number"
    },
    {
      "description": "Amount due incl. tax",
      "name": "total",
      "type": "number"
    },
    {
      "name": "issued_on",
      "type": "date"
    }
  ],
  "model": "openai:gpt-4.1-mini"
}

Input records:

[
  {
    "index": 0,
    "object_key": "invoices/inv-0042.pdf",
    "text": "Invoice INV-0042"
  },
  {
    "index": 1,
    "object_key": "invoices/inv-0042.pdf",
    "text": "Issued 2026-09-01. Total due: 1,250.00 EUR"
  }
]

Example: One row per investor (entity grain) on a reasoning model

Step settings:

{
  "fields": [
    {
      "name": "investor",
      "scope": "per_entity"
    },
    {
      "name": "shares",
      "scope": "per_entity",
      "type": "number"
    },
    {
      "name": "closing_date",
      "scope": "per_document",
      "type": "date"
    }
  ],
  "max_tokens": 8000,
  "model": "openai:gpt-5-mini",
  "reasoning_effort": "low",
  "row_entity": {
    "field_key": "investor",
    "label": "investor"
  },
  "row_grain": "entity"
}

Example: Fields supplied per run, keeping typed unverified values

Step settings:

{
  "fields_from_input": true,
  "keep_unverified_values": true
}

Input records:

[
  {
    "fields": [
      {
        "description": "Renewal period",
        "name": "renewal_term"
      }
    ]
  },
  {
    "index": 0,
    "object_key": "invoices/inv-0042.pdf",
    "text": "Invoice INV-0042"
  },
  {
    "index": 1,
    "object_key": "invoices/inv-0042.pdf",
    "text": "Issued 2026-09-01. Total due: 1,250.00 EUR"
  }
]

Check the result

Inspect the selected run’s status, counts, errors, and output records. An empty collection can be a valid result, and a completed step can contain per-record failures where the step defines that behaviour. Check the next step’s expected record shape before publishing or delivering the result.