Get Extraction Result

mindee.get_inference
ActionAI & modelsconnection required

Calls the Mindee API with the connection's credentials.

Fetch a finished extraction inference by id and return its fields as plain values.

Connection

Required
Yes — the node will not save without one
Auth type
API key
Providers
Mindee
Secret shape
the V2 API key, or {"api_key": "…"}
Config it needs
base_url, model_id
Resolved by
config.connection_id, else node.connection_id

Configuration

1 field, 1 required. A dotted path is a nested object and name[] an array of objects — the editor renders the same tree.

FieldTypeRequiredDefaultConstraintsExample
inference_id
UUID of the finished inference — the tail of the job's result_url.
stringYes———

Output

One shape.

result

FieldTypeExample
product
Which product ran: extraction, ocr, classification, split or crop.
string—
inference_id
UUID of the inference.
string—
page_count
Number of pages Mindee found in the file.
number—
mime_type
MIME type Mindee detected for the file.
string—
inference
The whole inference object exactly as Mindee returned it.
object—
completed
Always true — a fetched inference has finished.
boolean—
model_id
UUID of the model that produced the inference.
string—
filename
File name as Mindee recorded it.
string—
values
Every extracted field reduced to a plain value, keyed by the field name in the model's data schema — object fields become nested objects and list fields become arrays.
object—
fields
The same fields with Mindee's full envelope per field: value, confidence and locations.
object—
raw_text
The document's full text with pages joined by newlines; empty unless raw_text was requested.
string—
{
  "completed": false,
  "fields": {},
  "filename": "",
  "inference": {},
  "inference_id": "",
  "mime_type": "",
  "model_id": "",
  "page_count": 0,
  "product": "",
  "raw_text": "",
  "values": {}
}

Notes

  • A model set to delete extracted data when fetched returns 404 on any later read of the same inference.

See Get Extraction Result run in a workflow.

In a technical demo we add the Mindee connection, drop the node on the canvas and let Workflow Health pre-flight it before the first real run.