Python SDK

from pulse import Pulse
from pulse.types import (
    ExtractRequestFigureProcessing,
    ExtractRequestExtensions,
    ExtractRequestExtensionsChunking,
    ExtractRequestExtensionsAltOutputs,
)

client = Pulse(api_key="YOUR_API_KEY")

# Basic extraction from URL
response = client.extract(
    file_url="https://example.com/document.pdf"
)
print(response.markdown)
print(response.extraction_id)

# With figure processing and extensions
response = client.extract(
    file_url="https://example.com/document.pdf",
    figure_processing=ExtractRequestFigureProcessing(
        description=True,
    ),
    extensions=ExtractRequestExtensions(
        chunking=ExtractRequestExtensionsChunking(
            chunk_types=["semantic"],
            chunk_size=1000,
        ),
        alt_outputs=ExtractRequestExtensionsAltOutputs(
            return_html=True,
        ),
    ),
)
print(response.extensions.chunking)
print(response.extensions.alt_outputs.html)

# Async extraction
response = client.extract(
    file_url="https://example.com/document.pdf",
    async_=True
)
print(response.job_id)  # poll via client.jobs.get_job(job_id=...)

{
  "markdown": "<string>",
  "extensions": {
    "chunking": {
      "semantic": [
        "<string>"
      ],
      "header": [
        "<string>"
      ],
      "page": [
        "<string>"
      ],
      "recursive": [
        "<string>"
      ]
    },
    "merge_tables": {},
    "footnote_references": [
      {
        "symbol": "<string>",
        "footnoteTextId": "<string>",
        "referenceTextIds": [
          "<string>"
        ]
      }
    ],
    "alt_outputs": {
      "wlbb": {
        "words": [
          {
            "id": "<string>",
            "text": "<string>",
            "page_number": 2,
            "bounding_box": [
              123
            ],
            "average_word_confidence": 123
          }
        ],
        "error": "<string>"
      },
      "html": "<string>",
      "xml": "<string>"
    }
  },
  "bounding_boxes": {
    "Images": [
      {
        "id": "<string>",
        "content": "<string>",
        "page_number": 2,
        "bounding_box": [
          123
        ],
        "image_url": "<string>",
        "description": "<string>",
        "classification": {
          "confidence": 0.5,
          "model": "<string>",
          "error": "<string>"
        },
        "sheet_name": "<string>",
        "sheet_index": 123,
        "workbook_sheet_index": 123,
        "excel_range": "<string>",
        "chart_type": "<string>",
        "chart_title": "<string>",
        "source_ranges": [
          "<string>"
        ],
        "render_error": "<string>",
        "description_error": "<string>"
      }
    ],
    "Tables": [
      {
        "table_info": {
          "id": "<string>",
          "dimensions": [
            123
          ],
          "excel_range": "<string>",
          "sheet_name": "<string>",
          "sheet_index": 123,
          "workbook_sheet_index": 123,
          "section_index": 123,
          "section_type": "<string>",
          "section_name": "<string>",
          "table_name": "<string>",
          "layout_type": "<string>",
          "is_chart": true,
          "chart_type": "<string>",
          "chart_title": "<string>",
          "source_ranges": [
            "<string>"
          ],
          "location": {}
        },
        "cell_data": [
          {}
        ]
      }
    ],
    "Text": [
      {
        "id": "<string>",
        "content": "<string>",
        "page_number": 2,
        "bounding_box": [
          123
        ],
        "excel_range": "<string>",
        "sheet_name": "<string>",
        "sheet_index": 123,
        "workbook_sheet_index": 123
      }
    ],
    "Title": [
      {
        "id": "<string>",
        "content": "<string>",
        "page_number": 2,
        "bounding_box": [
          123
        ],
        "excel_range": "<string>",
        "sheet_name": "<string>",
        "sheet_index": 123,
        "workbook_sheet_index": 123
      }
    ],
    "Footer": [
      {
        "id": "<string>",
        "content": "<string>",
        "page_number": 2,
        "bounding_box": [
          123
        ],
        "excel_range": "<string>",
        "sheet_name": "<string>",
        "sheet_index": 123,
        "workbook_sheet_index": 123
      }
    ],
    "markdown_with_ids": "<string>"
  },
  "extraction_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "extraction_url": "<string>",
  "page_count": 2,
  "plan_info": {
    "tier": "<string>",
    "total_credits_used": 123,
    "pages_used": 1,
    "note": "<string>"
  },
  "credits_used": 123,
  "warnings": [
    "<string>"
  ],
  "html": "<string>",
  "chunks": {
    "semantic": [
      "<string>"
    ],
    "header": [
      "<string>"
    ],
    "page": [
      "<string>"
    ],
    "recursive": [
      "<string>"
    ]
  },
  "plan-info": {
    "tier": "<string>",
    "pages_used": 123,
    "note": "<string>"
  },
  "structured_output": {
    "values": {},
    "citations": {}
  },
  "input_schema": {},
  "schema_error": "<string>"
}

Pipeline Steps

Extract File

The primary endpoint for the Pulse API. Parses uploaded documents or remote file URLs and returns rich markdown content with optional structured data extraction based on user-provided schemas and extraction options.

Set async: true to return immediately with a job ID for polling via GET /job/{jobId}. Otherwise processes synchronously.

Both sync and async modes return HTTP 200. When async is true the response body contains { job_id, status, message } instead of the full extraction result.

POST

extract

Python SDK

from pulse import Pulse
from pulse.types import (
    ExtractRequestFigureProcessing,
    ExtractRequestExtensions,
    ExtractRequestExtensionsChunking,
    ExtractRequestExtensionsAltOutputs,
)

client = Pulse(api_key="YOUR_API_KEY")

# Basic extraction from URL
response = client.extract(
    file_url="https://example.com/document.pdf"
)
print(response.markdown)
print(response.extraction_id)

# With figure processing and extensions
response = client.extract(
    file_url="https://example.com/document.pdf",
    figure_processing=ExtractRequestFigureProcessing(
        description=True,
    ),
    extensions=ExtractRequestExtensions(
        chunking=ExtractRequestExtensionsChunking(
            chunk_types=["semantic"],
            chunk_size=1000,
        ),
        alt_outputs=ExtractRequestExtensionsAltOutputs(
            return_html=True,
        ),
    ),
)
print(response.extensions.chunking)
print(response.extensions.alt_outputs.html)

# Async extraction
response = client.extract(
    file_url="https://example.com/document.pdf",
    async_=True
)
print(response.job_id)  # poll via client.jobs.get_job(job_id=...)

{
  "markdown": "<string>",
  "extensions": {
    "chunking": {
      "semantic": [
        "<string>"
      ],
      "header": [
        "<string>"
      ],
      "page": [
        "<string>"
      ],
      "recursive": [
        "<string>"
      ]
    },
    "merge_tables": {},
    "footnote_references": [
      {
        "symbol": "<string>",
        "footnoteTextId": "<string>",
        "referenceTextIds": [
          "<string>"
        ]
      }
    ],
    "alt_outputs": {
      "wlbb": {
        "words": [
          {
            "id": "<string>",
            "text": "<string>",
            "page_number": 2,
            "bounding_box": [
              123
            ],
            "average_word_confidence": 123
          }
        ],
        "error": "<string>"
      },
      "html": "<string>",
      "xml": "<string>"
    }
  },
  "bounding_boxes": {
    "Images": [
      {
        "id": "<string>",
        "content": "<string>",
        "page_number": 2,
        "bounding_box": [
          123
        ],
        "image_url": "<string>",
        "description": "<string>",
        "classification": {
          "confidence": 0.5,
          "model": "<string>",
          "error": "<string>"
        },
        "sheet_name": "<string>",
        "sheet_index": 123,
        "workbook_sheet_index": 123,
        "excel_range": "<string>",
        "chart_type": "<string>",
        "chart_title": "<string>",
        "source_ranges": [
          "<string>"
        ],
        "render_error": "<string>",
        "description_error": "<string>"
      }
    ],
    "Tables": [
      {
        "table_info": {
          "id": "<string>",
          "dimensions": [
            123
          ],
          "excel_range": "<string>",
          "sheet_name": "<string>",
          "sheet_index": 123,
          "workbook_sheet_index": 123,
          "section_index": 123,
          "section_type": "<string>",
          "section_name": "<string>",
          "table_name": "<string>",
          "layout_type": "<string>",
          "is_chart": true,
          "chart_type": "<string>",
          "chart_title": "<string>",
          "source_ranges": [
            "<string>"
          ],
          "location": {}
        },
        "cell_data": [
          {}
        ]
      }
    ],
    "Text": [
      {
        "id": "<string>",
        "content": "<string>",
        "page_number": 2,
        "bounding_box": [
          123
        ],
        "excel_range": "<string>",
        "sheet_name": "<string>",
        "sheet_index": 123,
        "workbook_sheet_index": 123
      }
    ],
    "Title": [
      {
        "id": "<string>",
        "content": "<string>",
        "page_number": 2,
        "bounding_box": [
          123
        ],
        "excel_range": "<string>",
        "sheet_name": "<string>",
        "sheet_index": 123,
        "workbook_sheet_index": 123
      }
    ],
    "Footer": [
      {
        "id": "<string>",
        "content": "<string>",
        "page_number": 2,
        "bounding_box": [
          123
        ],
        "excel_range": "<string>",
        "sheet_name": "<string>",
        "sheet_index": 123,
        "workbook_sheet_index": 123
      }
    ],
    "markdown_with_ids": "<string>"
  },
  "extraction_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "extraction_url": "<string>",
  "page_count": 2,
  "plan_info": {
    "tier": "<string>",
    "total_credits_used": 123,
    "pages_used": 1,
    "note": "<string>"
  },
  "credits_used": 123,
  "warnings": [
    "<string>"
  ],
  "html": "<string>",
  "chunks": {
    "semantic": [
      "<string>"
    ],
    "header": [
      "<string>"
    ],
    "page": [
      "<string>"
    ],
    "recursive": [
      "<string>"
    ]
  },
  "plan-info": {
    "tier": "<string>",
    "pages_used": 123,
    "note": "<string>"
  },
  "structured_output": {
    "values": {},
    "citations": {}
  },
  "input_schema": {},
  "schema_error": "<string>"
}

Overview

Pipeline Step 1 — Extract is always the first step. After extraction, you can optionally split the document into topics, apply schema extraction to get structured data, or use tables for span-aware table extraction.

Extract content from documents. Returns markdown or HTML formatted content with optional structured data extraction. For large results (typically documents over 70 pages, or any response above 5 MB), the API returns a one-time download link at https://api.runpulse.com/large_results/{job_id} instead of inlining the payload. See Large Document Response below.

For large documents or batch processing workflows, set async: true to process asynchronously and poll for results via GET /job/jobId.

To process many files at once, use Batch Extract. It accepts an S3 prefix, local directory, or list of URLs and runs /extract on each file in parallel.

Async Mode

Set async: true to return immediately with a job ID for polling:

{
  "file_url": "https://example.com/document.pdf",
  "async": true
}

Async Response (200):

{
  "job_id": "abc123-def456",
  "status": "pending",
  "message": "Document processing started"
}

Use GET /job/{job_id} to poll for completion.

Request

Document Source

Provide the document using one of these methods:

Field	Type	Description
`file`	binary	Document file to upload directly (multipart/form-data).
`file_url`	string	Public or pre-signed URL that Pulse will download and extract.

Extraction Options

Field	Type	Default	Description
`model`	string (enum)	`default`	Extraction model to use. One of `default` or `pulse-ultra-2`. `pulse-ultra-2` uses Pulse’s vision-language model with built-in refinement, figure/chart extraction, and word-level bounding boxes.
`pages`	string	-	Page range filter (1-indexed). Supports segments like `1-2` or mixed ranges like `1-2,5`. Page 1 is the first page.
`figure_processing`	object	-	Settings that control how figures in the document are processed. These affect the markdown output directly and do not produce additional output fields. See Figure Processing.
`extensions`	object	-	Settings that enable additional processing or alternate output formats. Each enabled extension produces a corresponding result under `response.extensions.*`. See Extensions.
`spreadsheet`	object	-	Settings for Excel/spreadsheet extraction. Controls handling of hidden rows, columns, sheets, and the automatic trimming of empty trailing rows/columns past the last data-bearing cell. Applies to `.xlsx`, `.xlsm`, and `.xls` files. See Spreadsheet Options.
`storage`	object	-	Options for persisting extraction artifacts. See Storage Options.
`async`	boolean	`false`	If `true`, returns immediately with a `job_id` for polling via `GET /job/{jobId}`.
`structured_output`	object	-	⚠️ Deprecated — Use the `/schema` endpoint after extraction instead. Still works for backward compatibility.

Figure Processing

Settings under figure_processing control how figures (images, charts, diagrams) and embedded visuals are processed. Applies to both PDFs/images (figures detected from layout) and spreadsheets (charts and embedded images read directly from the workbook). Affects the markdown output and the bounding_boxes.Images[] array.

Field	Type	Default	Description
`figure_processing.description`	boolean	`false`	Generate descriptive captions for extracted visuals. Captions appear under `bounding_boxes.Images[].description` and inline in the markdown output. Applies to both detected charts and non-chart images.
`figure_processing.show_images`	boolean	`false`	Return image URLs for extracted visuals. URLs appear under `bounding_boxes.Images[].image_url` and resolve to a Pulse-hosted PNG/JPEG served from `GET /results/{jobId}/images/{filename}`. Applies to both detected charts and non-chart images.

For spreadsheets specifically, show_images: true collects every embedded chart and image in the workbook and emits one entry per visual under bounding_boxes.Images, with chart-specific fields like chart_type, chart_title, and source_ranges populated. See Bounding Boxes for the full field list.

Spreadsheet Options

Settings under spreadsheet control how Excel workbooks (.xlsx, .xlsm, .xls) are processed. By default, hidden rows, columns, and sheets are excluded from extraction output, and cell values are rendered the way Excel displays them. Phantom-cell trimming is opt-in.

Field	Type	Default	Description
`spreadsheet.include_hidden_rows`	boolean	`false`	Include rows that are hidden in the Excel workbook.
`spreadsheet.include_hidden_cols`	boolean	`false`	Include columns that are hidden in the Excel workbook.
`spreadsheet.include_hidden_sheets`	boolean	`false`	Include sheets that are hidden in the Excel workbook.
`spreadsheet.use_raw_values`	boolean	`false`	Emit the underlying numeric value for number cells instead of the Excel display-formatted text — e.g. `1201.67` rather than `$1,202` when the cell uses a rounded currency format. Useful when downstream processing needs exact amounts (cent-level precision) rather than what the workbook shows visually. Percent-formatted cells and dates keep their display rendering. Does not apply to legacy `.xls` files.
`spreadsheet.only_data_rows`	boolean	`false`	When `true`, trim trailing empty rows past the last cell carrying a value or formula. See Phantom-cell trimming below.
`spreadsheet.only_data_cols`	boolean	`false`	When `true`, trim trailing empty columns past the last cell carrying a value or formula. Same rationale as `only_data_rows`.

These settings accept both camelCase (includeHiddenRows, onlyDataRows) and snake_case (include_hidden_rows, only_data_rows) formats.

Phantom-cell trimming (`only_data_rows` / `only_data_cols`)

Excel files exported from claims systems, ERPs, and other automated pipelines routinely declare a “used range” that extends hundreds of thousands of rows past where the data actually ends. A typical case: a 57 MB workbook with only ~500 rows of real data, where the other ~1,000,000 rows are empty cells that exist only because they were once selected and styled. These phantom cells inflate file size by orders of magnitude and can exhaust parser memory on the extraction pipeline. Set only_data_rows: true and only_data_cols: true to have Pulse scan each sheet once before parsing, find the largest row and column containing a value or formula, and ignore everything beyond that extent. Surviving cells keep their original A1 coordinates (e.g., a value at B7 in the source is still B7 in the output), so any citation or bounding box that references a specific cell remains stable. The trim only kicks in on large sheets (≥5 MB of XML per sheet), so small, well-formed workbooks pay no overhead either way. Both flags default to false.

Pulse Ultra 2 Options

These options are available only when model: pulse-ultra-2 is set. Passing any of them with the default model returns a 400 error listing the offending fields.

Field	Type	Default	Description
`refine`	boolean	`false`	Run a full-page OCR and formatting correction pass after extraction. Improves accuracy on dense layouts, numerical values, and table structure. Adds ~1–2s per page. Overridden by `refine_options` if both are provided.
`refine_options`	object	-	Granular refinement targets. Takes precedence over the boolean `refine` flag. See below.
`refine_options.tables`	boolean	`false`	Fix table cell values, structure, and headers against the source image.
`refine_options.text`	boolean	`false`	Fix OCR errors, missing or extra content, and numerical accuracy (tables untouched).
`refine_options.formatting`	boolean	`false`	Add strikethrough, italic, bold, super/subscript, and LaTeX formatting (tables untouched).
`extract_figure`	boolean	`false`	Convert charts and data visualizations into HTML `<table>` blocks, wrapped in `<figure-table>` tags. Useful for financial decks, dashboards, and scientific charts.
`figure_description`	boolean	`false`	Generate a 1–2 paragraph natural-language description of each picture, wrapped in `<figure-description>` tags. Combines well with `extract_figure`.
`additional_prompt`	string	`""`	Extra context injected into the extraction prompt. Use to steer extraction toward a specific domain or attention focus. Max 4000 characters.
`custom_image_prompt`	string	`""`	Extra context appended to the prompt used by `figure_description` and `extract_figure`. Tunes image and chart interpretation. Max 2000 characters.
`custom_refine_prompt`	string	`""`	Extra context appended to the refinement prompt. Only applies when `refine: true` or `refine_options` is set. Max 2000 characters.

Markdown output additions

When extract_figure or figure_description is enabled, figures in response.markdown include additional tags:

<figure data-page="1">
  <figure-table>...HTML table for the chart...</figure-table>
  <figure-description>...1–2 paragraph description...</figure-description>
</figure>

When refine (or refine_options) is set, markdown content is post-processed page-by-page; output is cleaner but typically grows ~1.5–3x in size for dense documents. No new tags are introduced.

Extensions

Settings under extensions enable additional processing passes or alternate output formats. Each enabled extension produces a corresponding output field under response.extensions.*. For example, enabling extensions.chunking produces response.extensions.chunking, and enabling extensions.alt_outputs.return_html produces response.extensions.alt_outputs.html.

Field	Type	Default	Description
`extensions.footnote_references`	boolean	`false`	Link footnote markers to their corresponding footnote text.
`extensions.chunking`	object	-	Chunking configuration. See below.
`extensions.chunking.chunk_types`	string[]	-	List of chunking strategies: `semantic`, `header`, `page`, `recursive`.
`extensions.chunking.chunk_size`	integer	-	Maximum characters per chunk.
`extensions.alt_outputs`	object	-	Alternate output formats. See below.
`extensions.alt_outputs.wlbb`	boolean	`false`	Enable word-level bounding boxes (PDF only). Results in `response.extensions.alt_outputs.wlbb`.
`extensions.alt_outputs.return_html`	boolean	`false`	Include HTML representation. `response.markdown` is still present; HTML is at `response.extensions.alt_outputs.html`.
`extensions.alt_outputs.return_xml`	boolean	`false`	Include XML representation (work in progress).

`pulse-ultra-2` Rate Limits

Requests made with model: pulse-ultra-2 are subject to dedicated rate limits, separate from standard extraction:

Limit	Value
Per minute	5 extractions
Per hour	20 extractions
File size	50 MB
Concurrent	2 per API key

The concurrent limit is the one that most commonly applies in practice — long-running extractions held open while new requests arrive will trip it first.

Storage Options

Control whether extractions are saved to your extraction library:

Field	Type	Default	Description
`storage.enabled`	boolean	`true`	Whether to persist extraction artifacts. Set to `false` for temporary extractions.
`storage.folder_name`	string	-	Target folder name to save the extraction to. Creates the folder if it doesn’t exist.
`storage.folder_id`	string (uuid)	-	Target folder ID to save the extraction to. Takes precedence over `folder_name`.

Deprecated Fields

The following input fields are deprecated and will be removed in a future version. They are still accepted for backward compatibility.

Field	Replacement
`show_images`	Use `figure_processing.show_images`
`chunking`	Use `extensions.chunking.chunk_types` (array instead of comma-separated string)
`chunk_size`	Use `extensions.chunking.chunk_size`
`return_html`	Use `extensions.alt_outputs.return_html`
`structured_output`	Use `/schema` endpoint after extraction. Pass `extraction_id` + `schema_config`. Accepts `schema`, `schema_prompt`, and `effort`.
`schema`	Use `/schema` endpoint after extraction
`schema_prompt`	Use `/schema` endpoint with `schema_config.schema_prompt`
`custom_prompt`	No replacement
`thinking`	No replacement

When legacy input fields are used, the API returns a deprecation warning in the warnings array directing you to the updated field names. See the latest documentation for details.

Response

The response structure varies based on document size to optimize for different use cases.

Standard Response (Under 70 Pages)

For documents under 70 pages, results are returned directly in the response body:

{
  "markdown": "# Document Title\n\nExtracted content...",
  "page_count": 15,
  "extraction_id": "abc123-def456-ghi789",
  "extraction_url": "https://platform.runpulse.com/dashboard/extractions/abc123",
  "credits_used": 1.0,
  "plan_info": {
    "tier": "growth",
    "pages_used": 15,
    "total_credits_used": 49.5,
    "note": "Pulse Ultra"
  },
  "bounding_boxes": {
    "Title": [],
    "Text": [],
    "Tables": [],
    "Images": [
      {
        "id": "excel_image_1_1",
        "visual_type": "chart",
        "image_url": "https://api.runpulse.com/results/abc123-def456-ghi789/images/excel_image_1_1.png",
        "chart_type": "BarChart",
        "chart_title": "Revenue",
        "excel_range": "D2",
        "sheet_name": "Charts"
      }
    ],
    "markdown_with_ids": "<p data-bb-text-id=\"txt-1\">..."
  },
  "extensions": {
    "chunking": {
      "semantic": ["chunk 1...", "chunk 2..."],
      "header": ["section 1...", "section 2..."]
    },
    "altOutputs": {
      "html": "<html>...</html>"
    }
  },
  "warnings": []
}

Response Fields

Field	Type	Description
`markdown`	string	Clean markdown content extracted from the document. Always present.
`page_count`	integer	Total number of pages processed.
`extraction_id`	string (uuid)	Persisted extraction ID. Present when storage is enabled (default). Use with `/split` and `/schema`.
`extraction_url`	string	URL to view the extraction in the Pulse Platform. Present when storage is enabled.
`credits_used`	number	Credits consumed by this request. Only present when the org has the credit billing system enabled.
`plan_info`	object	Billing tier and cumulative usage information for the calling org, including this request. Includes `tier`, `total_credits_used` (primary billing metric), `pages_used` (legacy), and an optional `note`.
`bounding_boxes`	object	Typed bounding-box data — `Images`, `Tables`, `Text`, `Title`, `Footer`, plus `markdown_with_ids`. See Bounding Boxes for the full field list including the chart/image fields under `Images`.
`extensions`	object	Output from enabled extensions. Only keys for enabled extensions are present. See below.
`extensions.chunking`	object	Chunk results by strategy (when `extensions.chunking` is enabled).
`extensions.footnoteReferences`	array	List of detected footnotes with their in-text references (when `extensions.footnote_references` is enabled). See Footnote References below.
`extensions.altOutputs.wlbb`	object	Word-level bounding boxes (when `extensions.alt_outputs.wlbb` is enabled).
`extensions.altOutputs.html`	string	HTML representation (when `extensions.alt_outputs.return_html` is enabled).
`extensions.altOutputs.xml`	string	XML representation (when `extensions.alt_outputs.return_xml` is enabled, WIP).
`warnings`	array	Non-fatal warnings generated during extraction, including deprecation notices for legacy input usage.

Deprecated Response Fields

Field	Replacement	Description
`html`	`extensions.altOutputs.html`	Present when legacy `return_html` input is used.
`chunks`	`extensions.chunking`	Present when legacy `chunking` input is used.
`plan-info`	`plan_info`	Present when only legacy inputs are used.
`structured_output`	Use `/schema`	Present when deprecated `structured_output` input was used.
`input_schema`	Use `/schema`	Echo of the applied schema (deprecated path only).
`schema_error`	Use `/schema`	Error message if schema processing failed (deprecated path only).

Large Document Response (70+ Pages)

For documents with 70 or more pages — or any response payload above the 5 MB inline threshold — the API returns a one-time download link to /large_results/{job_id} instead of inlining the payload. This prevents timeout issues and keeps the immediate response small.

{
  "is_url": true,
  "url": "https://api.runpulse.com/large_results/abc123-def456-ghi789",
  "plan_info": {
    "tier": "growth",
    "pages_used": 150,
    "total_credits_used": 312.0
  }
}

Large Document Response Fields

Field	Type	Description
`is_url`	boolean	Always `true` for large document responses. Use this to detect URL-based responses.
`url`	string	One-time download link of the form `https://api.runpulse.com/large_results/{job_id}`. The link streams the complete extraction result the first time it is fetched and is then invalidated (subsequent reads return `410 Gone`). It also expires 1 hour after the job completes. Authenticate the request with your `x-api-key` header.
`plan_info`	object	Billing information including pages used and plan tier.

/large_results/{job_id} links are single-use and expire 1 hour after the job completes. Download and persist the payload immediately — do not pass the URL through queues or share it across workers.

Handling Large Document Responses

import requests
from pulse import Pulse

API_KEY = "YOUR_API_KEY"
client = Pulse(api_key=API_KEY)

response = client.extract(
    file_url="https://www.impact-bank.com/user/file/dummy_statement.pdf"
)

if hasattr(response, "is_url") and response.is_url:
    full_result = requests.get(
        response.url,
        headers={"x-api-key": API_KEY},
    ).json()
    print(full_result["markdown"])
else:
    print(response.markdown)

Because /large_results/{job_id} is one-time use, persist the result to your own storage on first download. If you need to access the result later, enable storage.enabled and retrieve it from your extraction library on the Pulse Platform.

Example Usage

Basic Extraction

from pulse import Pulse
from pulse.types import (
    ExtractRequestFigureProcessing,
    ExtractRequestExtensions,
    ExtractRequestExtensionsAltOutputs,
)

client = Pulse(api_key="YOUR_API_KEY")

# Extract from URL with figure processing and HTML output
response = client.extract(
    file_url="https://www.impact-bank.com/user/file/dummy_statement.pdf",
    figure_processing=ExtractRequestFigureProcessing(
        description=True,
    ),
    extensions=ExtractRequestExtensions(
        alt_outputs=ExtractRequestExtensionsAltOutputs(
            return_html=True,
        ),
    ),
)

print(f"Markdown: {response.markdown}")
print(f"HTML: {response.extensions.alt_outputs.html}")
print(f"Extraction ID: {response.extraction_id}")

File Upload

from pulse.types import ExtractRequestFigureProcessing

# Upload and extract a local file
with open("document.pdf", "rb") as f:
    response = client.extract(
        file=f,
    figure_processing=ExtractRequestFigureProcessing(
        description=True,
    ),
    )

Structured Data (Extract → Schema)

The structured_output parameter on /extract is deprecated. Use the /schema endpoint after extraction instead. This gives you better control, re-runnability, and support for split-mode schemas.

Recommended two-step approach:

# Step 1: Extract the document
response = client.extract(
    file_url="https://www.impact-bank.com/user/file/dummy_statement.pdf"
)

extraction_id = response.extraction_id

# Step 2: Apply schema separately
schema_result = client.schema(
    extraction_id=extraction_id,
    schema_config={
        "input_schema": {
            "type": "object",
            "properties": {
                "total": {"type": "number"},
                "vendor": {"type": "string"}
            }
        },
        "schema_prompt": "Extract invoice total and vendor"
    }
)

print(schema_result.schema_output)

Page Range and Chunking

from pulse.types import (
    ExtractRequestExtensions,
    ExtractRequestExtensionsChunking,
)

response = client.extract(
    file_url="https://www.impact-bank.com/user/file/dummy_statement.pdf",
    pages="1-5,10",  # 1-indexed
    extensions=ExtractRequestExtensions(
        chunking=ExtractRequestExtensionsChunking(
            chunk_types=["semantic", "page"],
            chunk_size=1000,
        ),
    ),
)

# Chunk data is in extensions.chunking
print(response.extensions.chunking.semantic)
print(response.extensions.chunking.page)

Footnote References

Enable extensions.footnote_references to detect footnote markers (e.g. *, †, 1) in body text and link them to the footnote explanation paragraphs at the bottom of the page. Each result item includes the marker symbol, the bounding-box text ID of the footnote, and the bounding-box text IDs of all body-text paragraphs that reference it.

from pulse.types import ExtractRequestExtensions

response = client.extract(
    file_url="https://example.com/research-paper.pdf",
    extensions=ExtractRequestExtensions(
        footnote_references=True,
    ),
)

# Footnote links are in extensions.footnote_references
for ref in response.extensions.footnote_references:
    print(f"Marker: {ref.symbol}")
    print(f"  Footnote: {ref.footnote_text_id}")
    print(f"  Referenced by: {ref.reference_text_ids}")

Example Response

{
  "markdown": "...",
  "bounding_boxes": { ... },
  "extensions": {
    "footnoteReferences": [
      {
        "symbol": "*",
        "footnoteTextId": "txt-11",
        "referenceTextIds": ["txt-4", "txt-5", "txt-6", "txt-7", "txt-8"]
      },
      {
        "symbol": "†",
        "footnoteTextId": "txt-12",
        "referenceTextIds": ["txt-8"]
      },
      {
        "symbol": "4",
        "footnoteTextId": "txt-48",
        "referenceTextIds": ["txt-45"]
      }
    ]
  }
}

Footnote Reference Fields

Field	Type	Description
`symbol`	string	The footnote marker symbol as detected in the document (e.g. `*`, `†`, `‡`, `1`, `#`).
`footnoteTextId`	string	The bounding-box text ID (e.g. `txt-11`) of the footnote explanation paragraph. Cross-reference with `bounding_boxes.Footer` to get the footnote’s content and position.
`referenceTextIds`	string[]	Bounding-box text IDs of body-text paragraphs that contain a reference to this footnote. Cross-reference with `bounding_boxes.Text` to get each paragraph’s content and position.

Footnote reference detection uses Azure Document Intelligence for paragraph classification, supplemented by PyMuPDF native text extraction for accurate symbol identification. This handles common OCR confusion between visually similar symbols like †/+ and ‡/#. Supported marker types include numbered (1, 2, 3), symbolic (*, †, ‡, §, #), and lettered (a, b, c) footnotes.

Excel Spreadsheet Options

from pulse import Pulse
from pulse.types import ExtractRequestSpreadsheet

client = Pulse(api_key="YOUR_API_KEY")

# Extract from Excel with hidden content included
response = client.extract(
    file=open("financials.xlsx", "rb"),
    spreadsheet=ExtractRequestSpreadsheet(
        include_hidden_rows=True,
        include_hidden_cols=True,
        include_hidden_sheets=False,
    ),
)

print(response.markdown)

Workbooks exported from claims systems, ERPs, and other automated pipelines often declare a “used range” that extends hundreds of thousands of rows past where the data actually ends. Set spreadsheet.only_data_rows: true and spreadsheet.only_data_cols: true to have Pulse trim those trailing empty “phantom” rows and columns before parsing. Surviving cells keep their original A1 coordinates, so any citation or bounding box that references a specific cell remains stable. Both flags default to false. See Spreadsheet Options for the full reference.

Excel Charts and Embedded Images

When you set figure_processing.show_images: true on an Excel workbook, every embedded chart and image is collected from the workbook directly and returned under bounding_boxes.Images[]. Each entry carries a Pulse-hosted image_url you can fetch via results.getImage (or any HTTP client with your API key) to get the raw PNG/JPEG bytes.

import re
from pulse import Pulse
from pulse.types import ExtractRequestFigureProcessing

client = Pulse(api_key="YOUR_API_KEY")

# 1) Extract the workbook with show_images enabled.
response = client.extract(
    file=open("financials.xlsx", "rb"),
    figure_processing=ExtractRequestFigureProcessing(
        show_images=True,
        description=False,
    ),
)

# 2) Walk the typed Images array.
for img in response.bounding_boxes.images or []:
    print(f"{img.id}: {img.visual_type} '{img.chart_title}' @ {img.excel_range}")
    print(f"    url: {img.image_url}")

# 3) Fetch the bytes for one chart.
img = response.bounding_boxes.images[0]
m = re.search(r"/results/([^/]+)/images/([^/?#]+)", img.image_url)
job_id, filename = m.group(1), m.group(2)

chunks = list(client.results.get_image(job_id=job_id, filename=filename))
with open("chart.png", "wb") as f:
    f.write(b"".join(chunks))

Example `bounding_boxes.Images` Entry

{
  "id": "excel_image_1_1",
  "visual_type": "chart",
  "page_number": 1,
  "bounding_box": [],
  "image_url": "https://api.runpulse.com/results/13e3e75f-.../images/excel_image_1_1.png",
  "sheet_name": "Charts",
  "excel_range": "D2",
  "chart_type": "BarChart",
  "chart_title": "Revenue",
  "source_ranges": ["'Charts'!$A$2:$A$5", "'Charts'!$B$2:$B$5"],
  "description": "Bar chart showing revenue by quarter."
}

See Bounding Boxes — Images Array for the full field reference and Get Result Image for the auth requirement on image_url.

Disable Storage

response = client.extract(
    file_url="https://www.impact-bank.com/user/file/dummy_statement.pdf",
    storage={"enabled": False}
)

Authorizations

x-api-key

string

header

required

API key for authentication

Body

Input schema for multipart/form-data requests (file upload or file_url).

file

required

Document to upload directly. Required unless file_url is specified.

file_url

string<uri>

Public or pre-signed URL that Pulse will download and extract.

model

enum<string>

Extraction model to use. pulse-ultra-2 uses Pulse's vision-language model with built-in refinement, figure/chart extraction, and word-level bounding boxes. Omit or pass default for standard extraction.

Available options:

default,

pulse-ultra-2

pages

string

Page range filter (1-indexed, where page 1 is the first page). Supports segments such as 1-2 or mixed ranges like 1-2,5.

Pattern: ^[0-9]+(-[0-9]+)?(,[0-9]+(-[0-9]+)?)*$

figure_processing

object

Settings that control how figures and embedded visuals are processed. Applies to both PDFs/images (figures detected from layout) and spreadsheets (charts and embedded images read directly from the workbook). Affects the markdown output and the bounding_boxes.Images[] array.

Show child attributes

spreadsheet

object

Settings for Excel/spreadsheet extraction. Controls handling of hidden rows, columns, and sheets, whether numeric cells are rendered using their display format or underlying raw value, and optional trimming of empty phantom rows/columns past the last data-bearing cell. Applies to .xlsx, .xlsm, and .xls files. Accepts both camelCase and snake_case field names.

Show child attributes

extensions

object

Settings that enable additional processing passes or alternate output formats. Each enabled extension produces a corresponding output field under response.extensions.*.

Show child attributes

storage

object

Options for persisting extraction artifacts. When enabled (default), artifacts are saved to storage and a database record is created.

Show child attributes

async

boolean

default:false

If true, returns immediately with a job_id for polling via GET /job/{jobId}. Otherwise processes synchronously.

refine

boolean

default:false

Run a full-page OCR and formatting correction pass after extraction. Improves accuracy on dense layouts, numerical values, and table structure. Adds ~1-2s per page. Overridden by refine_options if both are provided. Requires model: pulse-ultra-2.

refine_options

object

Granular refinement targets. Takes precedence over the boolean refine flag. Requires model: pulse-ultra-2.

Show child attributes

extract_figure

boolean

default:false

Convert charts and data visualizations into HTML <table> blocks, wrapped in <figure-table> tags inside response.markdown. Useful for financial decks, dashboards, and scientific charts. Requires model: pulse-ultra-2.

figure_description

boolean

default:false

Generate a 1-2 paragraph natural-language description of each picture, wrapped in <figure-description> tags inside response.markdown. Combines well with extract_figure. Requires model: pulse-ultra-2.

additional_prompt

string

default:""

Extra context injected into the extraction prompt. Use to steer extraction toward a specific domain or attention focus. Requires model: pulse-ultra-2.

Maximum string length: 4000

custom_image_prompt

string

default:""

Extra context appended to the prompt used by figure_description and extract_figure. Tunes image and chart interpretation for your domain. Requires model: pulse-ultra-2.

Maximum string length: 2000

custom_refine_prompt

string

default:""

Extra context appended to the refinement prompt. Only applies when refine: true or refine_options is set. Requires model: pulse-ultra-2.

Maximum string length: 2000

chunking

string

deprecated

Deprecated -- Use extensions.chunking.chunk_types instead. Comma-separated list of chunking strategies.

chunk_size

integer

deprecated

Deprecated -- Use extensions.chunking.chunk_size instead.

Required range: x >= 1

show_images

boolean

default:false

deprecated

Deprecated -- Use figure_processing.show_images instead.

return_html

boolean

default:false

deprecated

Deprecated -- Use extensions.alt_outputs.return_html instead.

Response

When async=false (default): full extraction result with markdown, bounding boxes, chunks, etc. When async=true: job submission acknowledgement with job_id.

Option 1
Option 2

Full extraction result returned by the synchronous /extract endpoint. Contains the extracted markdown, optional extensions output, bounding boxes, and storage metadata.

markdown

string

Primary markdown content extracted from the document. Always present in the new format.

extensions

object

Output from enabled extensions. Each key corresponds to an extension that was enabled in the request under extensions.*. Only keys for enabled extensions are present.

Show child attributes

bounding_boxes

object

Positional bounding-box data for text, titles, headers, footers, images, and tables. Images carries chart/image visuals (with image_url when figure_processing.show_images is enabled), Tables the detected tables, and Text/Title/Footer the paragraph/title/footer regions. Additional keys (e.g. markdown_with_ids, defined_names) round-trip without being typed.

Show child attributes

extraction_id

string<uuid>

Persisted extraction ID. Present when storage is enabled (default). Use with /split and /schema endpoints.

extraction_url

string

URL to view the extraction on the Pulse platform. Present when storage is enabled.

page_count

integer

Number of pages processed.

Required range: x >= 1

plan_info

object

Billing tier and cumulative usage information. Includes total_credits_used (primary billing metric) and pages_used (legacy compatibility).

Show child attributes

credits_used

number<float> | null

Credits consumed by this request. Only present when the organization has the credit billing system enabled.

warnings

string[]

Non-fatal warnings generated during extraction. Includes deprecation notices when legacy input parameters are used, as well as processing warnings (e.g. word-level bounding box limitations).

html

string

deprecated

Deprecated -- Use extensions.alt_outputs.html instead. Present when the legacy return_html input was used.

chunks

object

deprecated

Deprecated -- Use extensions.chunking instead. Present when the legacy chunking input was used.

Show child attributes

plan-info

object

deprecated

Deprecated -- Use plan_info (underscore) instead. Present when only legacy input parameters are used.

Show child attributes

structured_output

object

deprecated

Deprecated -- Only present when the deprecated structured_output input parameter was used. Use the /schema endpoint instead.

Show child attributes

input_schema

object

deprecated

Deprecated -- Echo of the schema that was applied.

schema_error

string

deprecated

Deprecated -- Error message if schema processing failed.

Pipeline Overview Split Document

⌘I

​Overview

​Async Mode

​Request

​Document Source

​Extraction Options

​Figure Processing

​Spreadsheet Options

​Phantom-cell trimming (only_data_rows / only_data_cols)

​Pulse Ultra 2 Options

​Markdown output additions

​Extensions

​pulse-ultra-2 Rate Limits

​Storage Options

​Deprecated Fields

​Response

​Standard Response (Under 70 Pages)

​Response Fields

​Deprecated Response Fields

​Large Document Response (70+ Pages)

​Large Document Response Fields

​Handling Large Document Responses

​Example Usage

​Basic Extraction

​File Upload

​Structured Data (Extract → Schema)

​Page Range and Chunking

​Footnote References

​Example Response

​Footnote Reference Fields

​Excel Spreadsheet Options

​Excel Charts and Embedded Images

​Example bounding_boxes.Images Entry

​Disable Storage

Authorizations

Body

Response

Overview

Async Mode

Request

Document Source

Extraction Options

Figure Processing

Spreadsheet Options

Phantom-cell trimming (`only_data_rows` / `only_data_cols`)

Pulse Ultra 2 Options

Markdown output additions

Extensions

`pulse-ultra-2` Rate Limits

Storage Options

Deprecated Fields

Response

Standard Response (Under 70 Pages)

Response Fields

Deprecated Response Fields

Large Document Response (70+ Pages)

Large Document Response Fields

Handling Large Document Responses

Example Usage

Basic Extraction

File Upload

Structured Data (Extract → Schema)

Page Range and Chunking

Footnote References

Example Response

Footnote Reference Fields

Excel Spreadsheet Options

Excel Charts and Embedded Images

Example `bounding_boxes.Images` Entry

Disable Storage