Extract

Extract reads what Parse produced and fills in a schema: the fields you name, typed, with a confidence score and the source text for each. It is the node most workflows exist for. It lives in the Intelligence section of the Studio palette. The one thing an Extract node needs is its schema: the list of fields to pull out. Everything else is optional. The tier (mode) sets how much work to spend per page.

The node

{
  "id": "extract_1",
  "type": "extract",
  "extraction_schema": {
    "fields": [
      { "name": "invoice_number", "description": "The unique invoice identifier", "data_type": "string" },
      { "name": "total_amount",   "description": "Total invoice amount",          "data_type": "float" },
      { "name": "issue_date",     "description": "Date when the invoice was issued", "data_type": "date" }
    ]
  }
}

import os

from anyformat.sdk import Client
from anyformat.workflow import Schema

client = Client(api_key=os.environ["ANYFORMAT_API_KEY"])

workflow = (
    client.workflow("Invoice Processing")
    .parse()
    .extract([
        Schema.string("invoice_number", "The unique invoice identifier"),
        Schema.float("total_amount",    "Total invoice amount"),
        Schema.date("issue_date",       "Date when the invoice was issued"),
    ])
    .create()
)

import { Anyformat, Schema } from "@anyformat/sdk";

const af = new Anyformat({ apiKey: process.env.ANYFORMAT_API_KEY! });

const workflow = await af
  .workflow("Invoice Processing")
  .parse()
  .extract([
    Schema.string("invoice_number", "The unique invoice identifier"),
    Schema.float("total_amount",    "Total invoice amount"),
    Schema.date("issue_date",       "Date when the invoice was issued"),
  ])
  .create();

Each field has a name, a description the model reads, and a type. The types (string, float, date, enum, object and the rest) are on Field types. How to write a schema that extracts well is on Schemas. The same node on the agentic tier, with a Smart lookup field that resolves the supplier name against a reference CSV:

{
  "id": "extract_1",
  "type": "extract",
  "mode": "agentic",
  "extraction_schema": {
    "fields": [
      { "name": "supplier_name", "description": "Supplier name as printed", "data_type": "string" },
      { "name": "supplier_code", "description": "Internal supplier code",   "data_type": "string", "source": "smart_lookup" }
    ]
  },
  "lookup_file_uploads": [{ "filename": "suppliers.csv", "content": "<base64>" }],
  "lookup_suggestion": "Match supplier names ignoring legal suffixes like GmbH or Ltd"
}

client.workflow("Invoices with supplier codes").parse().extract(
    [
        Schema.string("supplier_name", "Supplier name as printed"),
        Schema.string("supplier_code", "Internal supplier code", source="smart_lookup"),
    ],
    lookup_files=["suppliers.csv"],
    lookup_suggestion="Match supplier names ignoring legal suffixes like GmbH or Ltd",
)

af.workflow("Invoices with supplier codes").parse().extract(
  [
    Schema.string("supplier_name", "Supplier name as printed"),
    Schema.string("supplier_code", "Internal supplier code", { source: "smart_lookup" }),
  ],
  {
    mode: "agentic",
    lookupFiles: ["suppliers.csv"],
    lookupSuggestion: "Match supplier names ignoring legal suffixes like GmbH or Ltd",
  },
)

The SDKs read each path in lookup_files from disk and send it as lookup_file_uploads. The API call sends the file content inline, base64-encoded.

The Python SDK’s extract() does not take mode, use_images or lookup_reasoning_effort yet. Set them in the API JSON, in Studio, or from the TypeScript SDK.

A workflow can hold several Extract nodes. After a Classify or Split node, each Extract sits on one branch and gets its own schema. See those pages for the branch argument.

In Studio

Click the Extract node on the canvas to open its panel. The Schema tab holds the fields. The Config tab holds the tier cards, the Use Images switch, and the Lookup files, Lookup suggestion and Lookup matching effort controls.

The Config tab holds the tier, Use Images, the lookup files and matcher settings, and the per-field Lookup toggles.

Smart lookup settings on the Extract node

Tiers

Tier	`mode`	What it does	When to use it	Credits / page
Fast (Beta)	`lite`	Standard extraction on a faster, cheaper model.	Simple documents at volume. Quality may vary on complex layouts.	17
Standard	`standard`	Single-pass extraction. The default.	Most documents and layouts. Start here.	35
Agentic	`agentic`	Multi-step extraction that maps every table to the schema and reasons across sections.	Dense or cross-page tables, values spread over several sections.	150

Start on Standard. Move down to Fast where it holds up, and up to Agentic only where Standard misses rows or mixes up columns.

Options

extraction_schema is required. Every other field is optional. Omit a field and the default applies.

Field	Type	Default	What it does
`extraction_schema`	object	required	`{ "fields": [...] }`. At least one field. Each field carries `name`, `description`, `data_type`, and optionally `source` and `persistent_id`. See Field types.
`mode`	`standard` \| `agentic` \| `lite`	`standard`	The tier. `lite` is shown as Fast in the app.
`use_images`	boolean	`false`	Send each PDF page’s rendered image alongside its parsed text, so the model can read layout the text missed. Adds vision cost on every page. Standard and Fast only; no effect on Agentic or on non-PDF sources.
`lookup_file_uploads`	array of `{ filename, content }`	`[]`	Reference files for Smart lookup, sent inline with `content` base64-encoded. Each one is uploaded and its URI is appended to `lookup_files`. Create-input only: it is not stored on the node.
`lookup_files`	array of string	`[]`	URIs of the reference files already stored on the node. Read this back from `GET`; write `lookup_file_uploads` to add files.
`lookup_suggestion`	string	`null`	Free-form hint shown to the lookup matcher, for example “Match supplier names ignoring legal suffixes like GmbH or Ltd”. Applies to every lookup field in the node.
`lookup_reasoning_effort`	`minimal` \| `low` \| `medium` \| `high`	`null`	How hard the lookup matcher works on noisy or inexact keys. `null` is the model default. Higher effort improves match reliability at higher latency and cost. Only used when the node has at least one lookup field.

A field’s source decides where its value comes from: extraction (the document, the default), smart_lookup (the reference file, always) or lookup_if_missing (the document first, the reference file when extraction left it blank). The schema the API accepts is generated from the same source: Extract node schema.

What it returns

Extract fills the extractions section of the run results. A workflow with one Extract and no Split returns one entry, with split_name and partition set to null:

"extractions": [
  {
    "split_name": null,
    "partition": null,
    "fields": {
      "invoice_number": {
        "value": "INV-2024-0847",
        "value_override": null,
        "verification_status": "not_verified",
        "confidence": 97.0,
        "evidence": [{ "text": "Invoice #INV-2024-0847", "page_number": 1 }]
      },
      "total_amount": {
        "value": "4087.50",
        "value_override": null,
        "verification_status": "not_verified",
        "confidence": 96.0,
        "evidence": [{ "text": "Total: $4,087.50", "page_number": 2 }]
      }
    }
  }
]

fields is keyed by field name. Each value carries value (a string on the wire, whatever the field’s data_type; null when nothing was found), value_override (a human correction from review, or null), verification_status, confidence on a 0 to 100 scale, and evidence: the source snippets and the page each came from. Value types on the wire. Every value is a string or null, so parse it by the field’s data_type on your side:

A boolean field returns the string "True" or "False".
A multi_select field returns one comma-separated string, for example "a, b, c", not a list.
A field the model could not find returns { "value": null, "value_override": null, "verification_status": "not_verified", "confidence": null, "evidence": [] }.
An object field returns a list of rows. Each row is a dict from nested field name to the same { value, value_override, verification_status, confidence, evidence } shape.

After a Split node, extractions holds one entry per split and partition. See Split.

Connects to

Direction	Nodes
Fed by	Parse, Classify, Split
Feeds	Validate, If/Else, Slack alert, Knowledge

An Extract node has one outgoing edge at most. Add a Validate node to check its values, or an If/Else node to branch on them.

Billing

Billed per page at the tier’s rate (table above). A node with at least one lookup field adds 75 credits per page for Smart Lookup. Schema size and field count do not change the price. Full price list: How credits work.

Examples

Invoice processing: the usual Parse to Extract shape, with line items as an object field.
Resume parsing: nested fields and multi-select.
Contract analysis: long documents, dates and enums.
Bank statement processing: Split, then one Extract per statement.

Get started

Nodes

Workflows

Schema & fields

Documents

Outputs & integrations

Health

Billing

The node

In Studio

Tiers

Options

What it returns

Connects to

Billing

Examples

​The node

​In Studio

​Tiers

​Options

​What it returns

​Connects to

​Billing

​Examples

The node

In Studio

Tiers

Options

What it returns

Connects to

Billing

Examples