from retab import Retab
client = Retab()
response = client.projects.create(
name="Invoices",
json_schema={
"type": "object",
"properties": {
"sender_name": {"type": "string"},
"receiver_name": {"type": "string"}
}
},
default_inference_settings=InferenceSettings(
model="gpt-4.1-mini",
temperature=0,
modality="native",
reasoning_effort="medium",
image_resolution_dpi=96,
browser_canvas="A4",
n_consensus=1,
),
)
{
"id": "<string>",
"name": "",
"json_schema": {},
"default_inference_settings": {
"model": "gpt-4.1-mini",
"temperature": 0,
"modality": "native",
"reasoning_effort": "medium",
"image_resolution_dpi": 96,
"browser_canvas": "A4",
"n_consensus": 1
},
"updated_at": "2023-11-07T05:31:56Z",
"documents": [
{
"mime_data": {
"filename": "file.pdf",
"url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADIA..."
},
"annotation": {},
"annotation_metadata": {
"extraction_id": "<string>",
"likelihoods": {},
"field_locations": {},
"agentic_field_locations": {},
"consensus_details": [
{}
],
"api_cost": {
"value": 123,
"currency": "<string>"
}
},
"id": "<string>",
"ocr_file_id": "<string>"
}
],
"iterations": [
{
"id": "<string>",
"inference_settings": {
"model": "<string>",
"temperature": 123,
"modality": "text",
"reasoning_effort": "low",
"image_resolution_dpi": 123,
"browser_canvas": "A3",
"n_consensus": 123
},
"json_schema": {},
"updated_at": "2023-11-07T05:31:56Z",
"predictions": {}
}
],
"schema_data_id": "<string>",
"schema_id": "<string>"
}
Create a new evaluation with documents, schema, and ground truth. Will store any documents that don’t already have a file_id.
from retab import Retab
client = Retab()
response = client.projects.create(
name="Invoices",
json_schema={
"type": "object",
"properties": {
"sender_name": {"type": "string"},
"receiver_name": {"type": "string"}
}
},
default_inference_settings=InferenceSettings(
model="gpt-4.1-mini",
temperature=0,
modality="native",
reasoning_effort="medium",
image_resolution_dpi=96,
browser_canvas="A4",
n_consensus=1,
),
)
{
"id": "<string>",
"name": "",
"json_schema": {},
"default_inference_settings": {
"model": "gpt-4.1-mini",
"temperature": 0,
"modality": "native",
"reasoning_effort": "medium",
"image_resolution_dpi": 96,
"browser_canvas": "A4",
"n_consensus": 1
},
"updated_at": "2023-11-07T05:31:56Z",
"documents": [
{
"mime_data": {
"filename": "file.pdf",
"url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADIA..."
},
"annotation": {},
"annotation_metadata": {
"extraction_id": "<string>",
"likelihoods": {},
"field_locations": {},
"agentic_field_locations": {},
"consensus_details": [
{}
],
"api_cost": {
"value": 123,
"currency": "<string>"
}
},
"id": "<string>",
"ocr_file_id": "<string>"
}
],
"iterations": [
{
"id": "<string>",
"inference_settings": {
"model": "<string>",
"temperature": 123,
"modality": "text",
"reasoning_effort": "low",
"image_resolution_dpi": 123,
"browser_canvas": "A3",
"n_consensus": 123
},
"json_schema": {},
"updated_at": "2023-11-07T05:31:56Z",
"predictions": {}
}
],
"schema_data_id": "<string>",
"schema_id": "<string>"
}
from retab import Retab
client = Retab()
response = client.projects.create(
name="Invoices",
json_schema={
"type": "object",
"properties": {
"sender_name": {"type": "string"},
"receiver_name": {"type": "string"}
}
},
default_inference_settings=InferenceSettings(
model="gpt-4.1-mini",
temperature=0,
modality="native",
reasoning_effort="medium",
image_resolution_dpi=96,
browser_canvas="A4",
n_consensus=1,
),
)
Successful Response
The response is of type object
.