API Reference

Extraction

Runs

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Get Run By Job Id

extraction.runs.get_by_job(strjob_id, RunGetByJobParams**kwargs) -> ExtractRun

GET/api/v1/extraction/runs/by-job/{job_id}

Get Run By Job Id

ParametersExpand Collapse

job_id: str

organization_id: Optional[str]

project_id: Optional[str]

ReturnsExpand Collapse

class ExtractRun: …

Schema for an extraction run.

id: str

The id of the extraction run

formatuuid

config: ExtractConfig

The config used for extraction

chunk_mode: Optional[Literal["PAGE", "SECTION"]]

The mode to use for chunking the document.

Accepts one of the following:

"PAGE"

"SECTION"

Deprecatedcitation_bbox: Optional[bool]

Whether to fetch citation bounding boxes for the extraction. Only available in PREMIUM mode. Deprecated: this is now synonymous with cite_sources.

cite_sources: Optional[bool]

Whether to cite sources for the extraction.

confidence_scores: Optional[bool]

Whether to fetch confidence scores for the extraction.

extract_model: Optional[Union[Literal["openai-gpt-4-1", "openai-gpt-4-1-mini", "openai-gpt-4-1-nano", 8 more], str, null]]

The extract model to use for data extraction. If not provided, uses the default for the extraction mode.

Accepts one of the following:

Literal["openai-gpt-4-1", "openai-gpt-4-1-mini", "openai-gpt-4-1-nano", 8 more]

Extract model options.

Accepts one of the following:

"openai-gpt-4-1"

"openai-gpt-4-1-mini"

"openai-gpt-4-1-nano"

"openai-gpt-5"

"openai-gpt-5-mini"

"gemini-2.0-flash"

"gemini-2.5-flash"

"gemini-2.5-flash-lite"

"gemini-2.5-pro"

"openai-gpt-4o"

"openai-gpt-4o-mini"

str

extraction_mode: Optional[Literal["FAST", "BALANCED", "PREMIUM", "MULTIMODAL"]]

The extraction mode specified (FAST, BALANCED, MULTIMODAL, PREMIUM).

Accepts one of the following:

"FAST"

"BALANCED"

"PREMIUM"

"MULTIMODAL"

extraction_target: Optional[Literal["PER_DOC", "PER_PAGE", "PER_TABLE_ROW"]]

The extraction target specified.

Accepts one of the following:

"PER_DOC"

"PER_PAGE"

"PER_TABLE_ROW"

high_resolution_mode: Optional[bool]

Whether to use high resolution mode for the extraction.

invalidate_cache: Optional[bool]

Whether to invalidate the cache for the extraction.

multimodal_fast_mode: Optional[bool]

DEPRECATED: Whether to use fast mode for multimodal extraction.

num_pages_context: Optional[int]

Number of pages to pass as context on long document extraction.

minimum1

page_range: Optional[str]

Comma-separated list of page numbers or ranges to extract from (1-based, e.g., '1,3,5-7,9' or '1-3,8-10').

parse_model: Optional[Literal["openai-gpt-4o", "openai-gpt-4o-mini", "openai-gpt-4-1", 23 more]]

Public model names.

Accepts one of the following:

"openai-gpt-4o"

"openai-gpt-4o-mini"

"openai-gpt-4-1"

"openai-gpt-4-1-mini"

"openai-gpt-4-1-nano"

"openai-gpt-5"

"openai-gpt-5-mini"

"openai-gpt-5-nano"

"openai-text-embedding-3-large"

"openai-text-embedding-3-small"

"openai-whisper-1"

"anthropic-sonnet-3.5"

"anthropic-sonnet-3.5-v2"

"anthropic-sonnet-3.7"

"anthropic-sonnet-4.0"

"anthropic-sonnet-4.5"

"anthropic-haiku-3.5"

"anthropic-haiku-4.5"

"gemini-2.5-flash"

"gemini-3.0-pro"

"gemini-2.5-pro"

"gemini-2.0-flash"

"gemini-2.0-flash-lite"

"gemini-2.5-flash-lite"

"gemini-1.5-flash"

"gemini-1.5-pro"

priority: Optional[Literal["low", "medium", "high", "critical"]]

The priority for the request. This field may be ignored or overwritten depending on the organization tier.

Accepts one of the following:

"low"

"medium"

"high"

"critical"

system_prompt: Optional[str]

The system prompt to use for the extraction.

use_reasoning: Optional[bool]

Whether to use reasoning for the extraction.

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

The schema used for extraction

Accepts one of the following:

Dict[str, object]

List[object]

str

float

bool

extraction_agent_id: str

The id of the extraction agent

formatuuid

from_ui: bool

Whether this extraction run was triggered from the UI

project_id: str

The id of the project that the extraction run belongs to

formatuuid

status: Literal["CREATED", "PENDING", "SUCCESS", "ERROR"]

The status of the extraction run

Accepts one of the following:

"CREATED"

"PENDING"

"SUCCESS"

"ERROR"

created_at: Optional[datetime]

Creation datetime

formatdate-time

data: 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]]

The data extracted from the file

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

error: Optional[str]

The error that occurred during extraction

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

The metadata extracted from the file

Accepts one of the following:

Dict[str, object]

List[object]

str

float

bool

Deprecatedfile: Optional[File]

Schema for a file.

id: str

Unique identifier

formatuuid

name: str

project_id: str

The ID of the project that the file belongs to

formatuuid

created_at: Optional[datetime]

Creation datetime

formatdate-time

data_source_id: Optional[str]

The ID of the data source that the file belongs to

formatuuid

expires_at: Optional[datetime]

The expiration date for the file. Files past this date can be deleted.

formatdate-time

external_file_id: Optional[str]

The ID of the file in the external system

file_size: Optional[int]

Size of the file in bytes

minimum0

file_type: Optional[str]

File type (e.g. pdf, docx, etc.)

maxLength3000

minLength1

last_modified_at: Optional[datetime]

The last modified time of the file

formatdate-time

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

Permission information for the file

Accepts one of the following:

Dict[str, object]

List[object]

str

float

bool

purpose: Optional[str]

The intended purpose of the file (e.g., 'user_data', 'parse', 'extract', 'split', 'classify')

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

Resource information for the file

Accepts one of the following:

Dict[str, object]

List[object]

str

float

bool

updated_at: Optional[datetime]

Update datetime

formatdate-time

file_id: Optional[str]

The id of the file that the extract was extracted from

formatuuid

job_id: Optional[str]

The id of the job that the extraction run belongs to

formatuuid

updated_at: Optional[datetime]

Update datetime

formatdate-time

Get Run By Job Id

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_run = client.extraction.runs.get_by_job(
    job_id="182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
)
print(extract_run.id)

200 example

{
  "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "config": {
    "chunk_mode": "PAGE",
    "citation_bbox": true,
    "cite_sources": true,
    "confidence_scores": true,
    "extract_model": "openai-gpt-4-1",
    "extraction_mode": "FAST",
    "extraction_target": "PER_DOC",
    "high_resolution_mode": true,
    "invalidate_cache": true,
    "multimodal_fast_mode": true,
    "num_pages_context": 1,
    "page_range": "page_range",
    "parse_model": "openai-gpt-4o",
    "priority": "low",
    "system_prompt": "system_prompt",
    "use_reasoning": true
  },
  "data_schema": {
    "foo": {
      "foo": "bar"
    }
  },
  "extraction_agent_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "from_ui": true,
  "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "status": "CREATED",
  "created_at": "2019-12-27T18:11:19.117Z",
  "data": {
    "foo": {
      "foo": "bar"
    }
  },
  "error": "error",
  "extraction_metadata": {
    "foo": {
      "foo": "bar"
    }
  },
  "file": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "name": "x",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "created_at": "2019-12-27T18:11:19.117Z",
    "data_source_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "expires_at": "2019-12-27T18:11:19.117Z",
    "external_file_id": "external_file_id",
    "file_size": 0,
    "file_type": "x",
    "last_modified_at": "2019-12-27T18:11:19.117Z",
    "permission_info": {
      "foo": {
        "foo": "bar"
      }
    },
    "purpose": "purpose",
    "resource_info": {
      "foo": {
        "foo": "bar"
      }
    },
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "job_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "updated_at": "2019-12-27T18:11:19.117Z"
}

Returns Examples

200 example

{
  "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "config": {
    "chunk_mode": "PAGE",
    "citation_bbox": true,
    "cite_sources": true,
    "confidence_scores": true,
    "extract_model": "openai-gpt-4-1",
    "extraction_mode": "FAST",
    "extraction_target": "PER_DOC",
    "high_resolution_mode": true,
    "invalidate_cache": true,
    "multimodal_fast_mode": true,
    "num_pages_context": 1,
    "page_range": "page_range",
    "parse_model": "openai-gpt-4o",
    "priority": "low",
    "system_prompt": "system_prompt",
    "use_reasoning": true
  },
  "data_schema": {
    "foo": {
      "foo": "bar"
    }
  },
  "extraction_agent_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "from_ui": true,
  "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "status": "CREATED",
  "created_at": "2019-12-27T18:11:19.117Z",
  "data": {
    "foo": {
      "foo": "bar"
    }
  },
  "error": "error",
  "extraction_metadata": {
    "foo": {
      "foo": "bar"
    }
  },
  "file": {
    "id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "name": "x",
    "project_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "created_at": "2019-12-27T18:11:19.117Z",
    "data_source_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "expires_at": "2019-12-27T18:11:19.117Z",
    "external_file_id": "external_file_id",
    "file_size": 0,
    "file_type": "x",
    "last_modified_at": "2019-12-27T18:11:19.117Z",
    "permission_info": {
      "foo": {
        "foo": "bar"
      }
    },
    "purpose": "purpose",
    "resource_info": {
      "foo": {
        "foo": "bar"
      }
    },
    "updated_at": "2019-12-27T18:11:19.117Z"
  },
  "file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "job_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
  "updated_at": "2019-12-27T18:11:19.117Z"
}