Cancel Extract Job

client.extract.cancel(, ?, ?): ExtractV2Job { id, created_at, file_input, 10 more }

POST/api/v2/extract/{job_id}/cancel

Cancel a running extraction job.

Stops processing and marks the job as CANCELLED. Returns the updated job. Jobs already in a terminal state (COMPLETED, FAILED, CANCELLED) cannot be cancelled.

ParametersExpand Collapse

jobID: string

params: ExtractCancelParams { organization_id, project_id }

organization_id?: string | null

project_id?: string | null

ReturnsExpand Collapse

ExtractV2Job { id, created_at, file_input, 10 more }

An extraction job.

id: string

Unique job identifier (job_id)

created_at: string

Creation timestamp

formatdate-time

file_input: string

File ID or parse job ID that was extracted

project_id: string

Project this job belongs to

status: string

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: string

Last update timestamp

formatdate-time

configuration?: ExtractConfiguration { data_schema, cite_sources, confidence_scores, 11 more } | null

Extract configuration combining parse and extract settings.

data_schema: Record<string, Record<string, unknown> | Array<unknown> | string | 2 more | null>

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

One of the following:

Record<string, unknown>

Array<unknown>

string

number

boolean

cite_sources?: boolean

Include citations in results. Returned under extract_metadata (auto-included when set). Text-level on turbo (no bounding boxes).

confidence_scores?: boolean

Include confidence scores in results. Returned under extract_metadata (auto-included when set).

disable_cache?: boolean

Disable reuse and storage of Extract results

extraction_target?: "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?: number | null

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

minimum1

parse_config_id?: string | null

Saved parse configuration ID to control how the document is parsed before extraction. Turbo extract does not support parse configuration or produce a parse output; use another tier if your workflow requires parsed text.

parse_tier?: "agentic" | "agentic_plus" | "cost_effective" | "fast" | null

Parse tier to use before extraction. Defaults to the extract tier if not specified. Turbo extract does not support parse configuration or produce a parse output; use another tier if your workflow requires parsed text.

One of the following:

"agentic"

"agentic_plus"

"cost_effective"

"fast"

sheet_names?: Array<string> | null

Optional worksheet names to extract when spreadsheet_mode is on. Overrides target_pages for spreadsheets; omit to extract every sheet. Names are matched exactly (case-sensitive) — pass them as a list, e.g. [“Sheet 1”, “My Sheet”].

spreadsheet_mode?: boolean

Beta. When true, extract structured data directly from a spreadsheet workbook (.xlsx/.xls/.csv) — the agent reads cells straight from the workbook instead of the standard document path. Off by default (spreadsheets keep the standard path). Requires the agentic_plus tier. Billed on the standard per-page extract rate, against a page count derived from workbook size. Citations and confidence scores are not available in this mode.

system_prompt?: string | null

Custom system prompt to guide extraction behavior

target_pages?: string | null

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

tier?: "agentic" | "agentic_plus" | "cost_effective" | "turbo"

Extract tier: cost_effective (5 credits/page), agentic (15 credits/page), agentic_plus (50 credits/page), or turbo (35 credits/page)

One of the following:

"agentic"

"agentic_plus"

"cost_effective"

"turbo"

version?: string

Use ‘latest’ for the latest release for the selected tier or a date string (YYYY-MM-DD format) to pin to the nearest release at or before that date. Job responses always report the concrete resolved version the job runs, fixed at job creation; saved configurations keep the value as provided.

configuration_id?: string | null

Saved extract configuration ID used for this job, if any

error_message?: string | null

Error details when status is FAILED

extract_metadata?: ExtractJobMetadata { field_metadata, parse_job_id, parse_tier } | null

Extraction metadata.

field_metadata?: ExtractedFieldMetadata { document_metadata, page_metadata, row_metadata } | null

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

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:

Record<string, unknown>

Array<unknown>

string

number

boolean

Per-page metadata when extraction_target is per_page

One of the following:

Record<string, unknown>

Array<unknown>

string

number

boolean

Per-row metadata when extraction_target is per_table_row

One of the following:

Record<string, unknown>

Array<unknown>

string

number

boolean

parse_job_id?: string | null

Reference to the ParseJob ID used for parsing

parse_tier?: string | null

Parse tier used for parsing the document

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:

Record<string, Record<string, unknown> | Array<unknown> | string | 2 more | null>

Record<string, unknown>

Array<unknown>

string

number

boolean

Array<Record<string, Record<string, unknown> | Array<unknown> | string | 2 more | null>>

Record<string, unknown>

Array<unknown>

string

number

boolean

metadata?: Metadata | null

Job-level metadata.

usage?: ExtractJobUsage { num_pages_billed, num_pages_extracted } | null

Extraction usage metrics.

num_pages_billed?: number | null

Number of effective pages billed

num_pages_extracted?: number | null

Number of pages extracted

usage?: Usage | null

Usage recorded against an extract job.

A parse job can back several extract jobs, so each of them reports that same parse cost in its total.

credits?: number | null

Total credits billed against this job. Null until billing has recorded it.

extract_credits?: number | null

Credits billed for the extraction itself

parse_credits?: number | null

Credits billed against the parse job backing this extract job

Cancel Extract Job

import LlamaCloud from '@llamaindex/llama-cloud';

const client = new LlamaCloud({
  apiKey: process.env['LLAMA_CLOUD_API_KEY'], // This is the default and can be omitted
});

const extractV2Job = await client.extract.cancel('job_id');

console.log(extractV2Job.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,
    "disable_cache": true,
    "extraction_target": "per_doc",
    "max_pages": 10,
    "parse_config_id": "cfg-11111111-2222-3333-4444-555555555555",
    "parse_tier": "fast",
    "sheet_names": [
      "Sheet 1",
      "Q4 Summary"
    ],
    "spreadsheet_mode": true,
    "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",
    "version": "latest"
  },
  "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_pages_billed": 0,
      "num_pages_extracted": 0
    }
  },
  "usage": {
    "credits": 30,
    "extract_credits": 45,
    "parse_credits": 30
  }
}

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,
    "disable_cache": true,
    "extraction_target": "per_doc",
    "max_pages": 10,
    "parse_config_id": "cfg-11111111-2222-3333-4444-555555555555",
    "parse_tier": "fast",
    "sheet_names": [
      "Sheet 1",
      "Q4 Summary"
    ],
    "spreadsheet_mode": true,
    "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",
    "version": "latest"
  },
  "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_pages_billed": 0,
      "num_pages_extracted": 0
    }
  },
  "usage": {
    "credits": 30,
    "extract_credits": 45,
    "parse_credits": 30
  }
}

Note for AI agents: this documentation is built for programmatic access. - Overview of all docs: https://developers.llamaindex.ai/llms.txt - Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters. - A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/for-agents/mcp/ - Other LlamaIndex tooling for agents — the LlamaParse Platform MCP server, agent skills and plugins, and the n8n node — is mapped at https://developers.llamaindex.ai/for-agents/