API Reference

Extract

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Create Extract Job

extract.create(ExtractCreateParams**kwargs) -> ExtractV2Job

POST/api/v2/extract

Create an extraction job.

Extracts structured data from a document using either a saved configuration or an inline JSON Schema.

Input

Provide exactly one of:

• configuration_id — reference a saved extraction config
• configuration — inline configuration with a data_schema

Document input

Set document_input_value to a file ID (dfl-...) or a completed parse job ID (pjb-...).

The job runs asynchronously. Poll GET /extract/{job_id} or register a webhook to monitor completion.

ParametersExpand Collapse

document_input_value: str

File ID or Parse Job ID to extract from

maxLength200

organization_id: Optional[str]

project_id: Optional[str]

configuration: Optional[ExtractConfigurationParam]

Extract configuration combining parse and extract settings.

data_schema: Dict[str, Union[Dict[str, object], List[object], str, 3 more]]

JSON Schema defining the fields to extract. Validate with the /schema/validate endpoint first.

Accepts one of the following:

Dict[str, object]

List[object]

str

float

bool

cite_sources: Optional[bool]

Include citations in results

confidence_scores: Optional[bool]

Include confidence scores in results

extract_version: Optional[str]

Extract algorithm version. Use 'latest' or a date string.

extraction_target: Optional[Literal["per_doc", "per_page", "per_table_row"]]

Granularity of extraction: per_doc returns one object per document, per_page returns one object per page, per_table_row returns one object per table row

Accepts one of the following:

"per_doc"

"per_page"

"per_table_row"

lang: Optional[str]

ISO 639-1 language code for the document

max_pages: Optional[int]

Maximum number of pages to process. Omit for no limit.

minimum1

parse_config_id: Optional[str]

Saved parse configuration ID to control how the document is parsed before extraction

parse_tier: Optional[str]

Parse tier to use before extraction (fast, cost_effective, or agentic)

system_prompt: Optional[str]

Custom system prompt to guide extraction behavior

target_pages: Optional[str]

Comma-separated page numbers or ranges to process (1-based). Omit to process all pages.

tier: Optional[Literal["cost_effective", "agentic"]]

Extract tier: cost_effective (5 credits/page) or agentic (15 credits/page)

Accepts one of the following:

"cost_effective"

"agentic"

configuration_id: Optional[str]

Saved extract configuration ID (mutually exclusive with configuration)

webhook_configurations: Optional[Iterable[WebhookConfiguration]]

Outbound webhook endpoints to notify on job status changes

webhook_events: Optional[List[Literal["extract.pending", "extract.success", "extract.error", 14 more]]]

Events to subscribe to (e.g. 'parse.success', 'extract.error'). If null, all events are delivered.

Accepts one of the following:

"extract.pending"

"extract.success"

"extract.error"

"extract.partial_success"

"extract.cancelled"

"parse.pending"

"parse.running"

"parse.success"

"parse.error"

"parse.partial_success"

"parse.cancelled"

"classify.pending"

"classify.success"

"classify.error"

"classify.partial_success"

"classify.cancelled"

"unmapped_event"

webhook_headers: Optional[Dict[str, str]]

Custom HTTP headers sent with each webhook request (e.g. auth tokens)

webhook_output_format: Optional[str]

Response format sent to the webhook: 'string' (default) or 'json'

webhook_url: Optional[str]

URL to receive webhook POST notifications

ReturnsExpand Collapse

class ExtractV2Job: …

An extraction job.

id: str

Unique job identifier (job_id)

created_at: datetime

Creation timestamp

formatdate-time

document_input_value: str

File ID or parse job ID that was extracted

project_id: str

Project this job belongs to

status: str

Current job status.

• PENDING — queued, not yet started
• RUNNING — actively processing
• COMPLETED — finished successfully
• FAILED — terminated with an error
• CANCELLED — cancelled by user

updated_at: datetime

Last update timestamp

formatdate-time

configuration: Optional[ExtractConfiguration]

Extract configuration combining parse and extract settings.

data_schema: Dict[str, Union[Dict[str, object], List[object], str, 3 more]]

JSON Schema defining the fields to extract. Validate with the /schema/validate endpoint first.

Accepts one of the following:

Dict[str, object]

List[object]

str

float

bool

cite_sources: Optional[bool]

Include citations in results

confidence_scores: Optional[bool]

Include confidence scores in results

extract_version: Optional[str]

Extract algorithm version. Use 'latest' or a date string.

extraction_target: Optional[Literal["per_doc", "per_page", "per_table_row"]]

Granularity of extraction: per_doc returns one object per document, per_page returns one object per page, per_table_row returns one object per table row

Accepts one of the following:

"per_doc"

"per_page"

"per_table_row"

lang: Optional[str]

ISO 639-1 language code for the document

max_pages: Optional[int]

Maximum number of pages to process. Omit for no limit.

minimum1

parse_config_id: Optional[str]

Saved parse configuration ID to control how the document is parsed before extraction

parse_tier: Optional[str]

Parse tier to use before extraction (fast, cost_effective, or agentic)

system_prompt: Optional[str]

Custom system prompt to guide extraction behavior

target_pages: Optional[str]

Comma-separated page numbers or ranges to process (1-based). Omit to process all pages.

tier: Optional[Literal["cost_effective", "agentic"]]

Extract tier: cost_effective (5 credits/page) or agentic (15 credits/page)

Accepts one of the following:

"cost_effective"

"agentic"

configuration_id: Optional[str]

Saved extract configuration ID used for this job, if any

error_message: Optional[str]

Error details when status is FAILED

extract_metadata: Optional[ExtractJobMetadata]

Extraction metadata.

field_metadata: Optional[ExtractedFieldMetadata]

Metadata for extracted fields including document, page, and row level info.

document_metadata: Optional[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]

Document-level metadata (citations, confidence) keyed by field name

Accepts one of the following:

Dict[str, object]

List[object]

str

float

bool

page_metadata: Optional[List[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]]

Per-page metadata when extraction_target is per_page

Accepts one of the following:

Dict[str, object]

List[object]

str

float

bool

row_metadata: Optional[List[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]]

Per-row metadata when extraction_target is per_table_row

Accepts one of the following:

Dict[str, object]

List[object]

str

float

bool

parse_job_id: Optional[str]

Reference to the ParseJob ID used for parsing

parse_tier: Optional[str]

Parse tier used for parsing the document

extract_result: Optional[Union[Dict[str, Union[Dict[str, object], List[object], str, 3 more]], List[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]], null]]

Extracted data conforming to the data_schema. Returns a single object for per_doc, or an array for per_page / per_table_row.

Accepts one of the following:

Dict[str, Union[Dict[str, object], List[object], str, 3 more]]

Accepts one of the following:

Dict[str, object]

List[object]

str

float

bool

List[Dict[str, Union[Dict[str, object], List[object], str, 3 more]]]

Accepts one of the following:

Dict[str, object]

List[object]

str

float

bool

metadata: Optional[Metadata]

Job-level metadata.

usage: Optional[ExtractJobUsage]

Extraction usage metrics.

num_document_tokens: Optional[int]

Number of document tokens

num_output_tokens: Optional[int]

Number of output tokens

num_pages_extracted: Optional[int]

Number of pages extracted

Create Extract Job

Python

HTTP

TypeScript

Python

import os
from llama_cloud import LlamaCloud

client = LlamaCloud(
    api_key=os.environ.get("LLAMA_CLOUD_API_KEY"),  # This is the default and can be omitted
)
extract_v2_job = client.extract.create(
    document_input_value="dfl-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee",
)
print(extract_v2_job.id)

200 example

{
  "id": "ext-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee",
  "created_at": "2019-12-27T18:11:19.117Z",
  "document_input_value": "dfl-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee",
  "project_id": "prj-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee",
  "status": "COMPLETED",
  "updated_at": "2019-12-27T18:11:19.117Z",
  "configuration": {
    "data_schema": {
      "foo": {
        "foo": "bar"
      }
    },
    "cite_sources": true,
    "confidence_scores": true,
    "extract_version": "latest",
    "extraction_target": "per_doc",
    "lang": "en",
    "max_pages": 10,
    "parse_config_id": "cfg-11111111-2222-3333-4444-555555555555",
    "parse_tier": "fast",
    "system_prompt": "Extract all monetary values in USD. If a currency is not specified, assume USD.",
    "target_pages": "1,3,5-7",
    "tier": "cost_effective"
  },
  "configuration_id": "cfg-11111111-2222-3333-4444-555555555555",
  "error_message": "error_message",
  "extract_metadata": {
    "field_metadata": {
      "document_metadata": {
        "foo": {
          "foo": "bar"
        }
      },
      "page_metadata": [
        {
          "foo": {
            "foo": "bar"
          }
        }
      ],
      "row_metadata": [
        {
          "foo": {
            "foo": "bar"
          }
        }
      ]
    },
    "parse_job_id": "parse_job_id",
    "parse_tier": "parse_tier"
  },
  "extract_result": {
    "foo": {
      "foo": "bar"
    }
  },
  "metadata": {
    "usage": {
      "num_document_tokens": 0,
      "num_output_tokens": 0,
      "num_pages_extracted": 0
    }
  }
}

Returns Examples

200 example

{
  "id": "ext-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee",
  "created_at": "2019-12-27T18:11:19.117Z",
  "document_input_value": "dfl-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee",
  "project_id": "prj-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee",
  "status": "COMPLETED",
  "updated_at": "2019-12-27T18:11:19.117Z",
  "configuration": {
    "data_schema": {
      "foo": {
        "foo": "bar"
      }
    },
    "cite_sources": true,
    "confidence_scores": true,
    "extract_version": "latest",
    "extraction_target": "per_doc",
    "lang": "en",
    "max_pages": 10,
    "parse_config_id": "cfg-11111111-2222-3333-4444-555555555555",
    "parse_tier": "fast",
    "system_prompt": "Extract all monetary values in USD. If a currency is not specified, assume USD.",
    "target_pages": "1,3,5-7",
    "tier": "cost_effective"
  },
  "configuration_id": "cfg-11111111-2222-3333-4444-555555555555",
  "error_message": "error_message",
  "extract_metadata": {
    "field_metadata": {
      "document_metadata": {
        "foo": {
          "foo": "bar"
        }
      },
      "page_metadata": [
        {
          "foo": {
            "foo": "bar"
          }
        }
      ],
      "row_metadata": [
        {
          "foo": {
            "foo": "bar"
          }
        }
      ]
    },
    "parse_job_id": "parse_job_id",
    "parse_tier": "parse_tier"
  },
  "extract_result": {
    "foo": {
      "foo": "bar"
    }
  },
  "metadata": {
    "usage": {
      "num_document_tokens": 0,
      "num_output_tokens": 0,
      "num_pages_extracted": 0
    }
  }
}