Get Extract Job

extract.get(, ) -> ExtractV2Job

GET/api/v2/extract/{job_id}

Get a single extraction job by ID.

Returns the job status and results when complete. Use expand=configuration to include the full configuration used, and expand=extract_metadata for per-field metadata.

ParametersExpand Collapse

job_id: str

expand: Optional[SequenceNotStr[str]]

Additional fields to include: configuration, extract_metadata

organization_id: Optional[str]

project_id: Optional[str]

ReturnsExpand Collapse

class ExtractV2Job: …

An extraction job.

id: str

Unique job identifier (job_id)

created_at: datetime

Creation timestamp

formatdate-time

file_input: 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.

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’ for the default pipeline, a date string (e.g. ‘2026-01-08’), or ‘continuous’ to opt into schema-guided splitting for documents with repeating entities.

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

One of the following:

"per_doc"

"per_page"

"per_table_row"

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. Defaults to the extract tier if not specified.

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)

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]]]

Per-field metadata keyed by field name from your schema. Scalar fields (e.g. vendor) map to a FieldMetadataEntry with citation and confidence. Array fields (e.g. items) map to a list where each element contains per-sub-field FieldMetadataEntry objects, indexed by array position. Nested objects contain sub-field entries recursively.

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

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

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.

One of the following:

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

One of the following:

Dict[str, object]

List[object]

str

float

bool

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

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

Get Extract Job

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.get(
    job_id="job_id",
)
print(extract_v2_job.id)

{
  "id": "ext-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee",
  "created_at": "2019-12-27T18:11:19.117Z",
  "file_input": "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",
    "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": {
        "items": [
          {
            "amount": {
              "citation": [
                {
                  "matching_text": "$10.00",
                  "page": 1
                }
              ],
              "confidence": 1
            },
            "description": {
              "citation": [
                {
                  "matching_text": "$10/month",
                  "page": 1
                }
              ],
              "confidence": 0.998
            }
          }
        ],
        "total": {
          "citation": "bar",
          "confidence": "bar"
        },
        "vendor": {
          "citation": "bar",
          "confidence": "bar",
          "extraction_confidence": "bar",
          "parsing_confidence": "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

{
  "id": "ext-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee",
  "created_at": "2019-12-27T18:11:19.117Z",
  "file_input": "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",
    "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": {
        "items": [
          {
            "amount": {
              "citation": [
                {
                  "matching_text": "$10.00",
                  "page": 1
                }
              ],
              "confidence": 1
            },
            "description": {
              "citation": [
                {
                  "matching_text": "$10/month",
                  "page": 1
                }
              ],
              "confidence": 0.998
            }
          }
        ],
        "total": {
          "citation": "bar",
          "confidence": "bar"
        },
        "vendor": {
          "citation": "bar",
          "confidence": "bar",
          "extraction_confidence": "bar",
          "parsing_confidence": "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
    }
  }
}