Extract

Overview

The Extract pipeline is the simplest and most common way to use Pulse. Upload a document and get back clean, layout-aware markdown along with extracted tables, figures, bounding boxes, and optional chunks. This is the starting point for every other pipeline — Extract → Schema and Extract → Split → Schema both build on top of this step.

When to Use

RAG ingestion — feed clean markdown into a vector database
Search indexing — convert documents to searchable text
Content migration — pull content out of PDFs into your CMS
Table extraction — grab structured tables from financial reports, invoices, or spreadsheets
General-purpose parsing — convert any supported file type to machine-readable text

Supported File Types

Pulse handles a wide range of document formats out of the box:

Category	Extensions
PDF	`.pdf` — text-based, scanned/image-based, mixed, multi-page
Images	`.jpg`, `.jpeg`, `.png` — scans, photos, screenshots
Office	`.docx`, `.pptx`, `.xlsx` — Word, PowerPoint, Excel
Web	`.html`, `.htm` — saved web pages, HTML emails

For the full breakdown including processing tips per format, see Supported File Types.

How to Use in the Playground

What You Get Back

Field	Description
`markdown`	Full document text with layout-aware markdown formatting
`extensions.alt_outputs.html`	HTML output (if `extensions.alt_outputs.return_html` was enabled)
`extensions.chunking`	Chunk data (if `extensions.chunking` was configured)
`bounding_boxes`	Coordinates for every text block, table, and figure
`extraction_id`	Saved extraction ID — use this for subsequent `/split` or `/schema` calls
`extraction_url`	Presigned URL to the stored extraction result
`page_count`	Number of pages processed

The extraction_id is the key to the rest of the Pulse pipeline. Once you have it, you can run Schema or Split on the same extraction without re-processing the document.

API Usage

Python
TypeScript
curl

from pulse import Pulse

client = Pulse(api_key="YOUR_API_KEY")

# Synchronous extraction
result = client.extract(
    file=open("invoice.pdf", "rb"),
    storage={"enabled": True}
)

print(result.markdown)
print(f"Extraction ID: {result.extraction_id}")

import { PulseClient } from "pulse-ts-sdk";
import fs from "fs";

const client = new PulseClient({
    apiKey: "YOUR_API_KEY"
});

const result = await client.extract({
    file: fs.createReadStream("invoice.pdf"),
    storage: { enabled: true }
});

console.log(result.markdown);
console.log("Extraction ID:", result.extraction_id);

curl -X POST https://api.runpulse.com/extract \
  -H "x-api-key: YOUR_API_KEY" \
  -F "file=@invoice.pdf" \
  -F 'storage={"enabled": true}'

For large documents, use async mode and poll for results:

# Async extraction
result = client.extract(
    file=open("large_report.pdf", "rb"),
    async_=True,
    storage={"enabled": True}
)

job_id = result.job_id
# Poll GET /job/{job_id} until status is "completed"

See Async Processing for the full polling flow.

After Extraction

Once you have your extraction_id, you can:

Add Schema

Extract structured data fields with a JSON Schema

Split & Schema

Divide into sections and extract per-section structured data

Tables

Extract structured tables with span detection and cross-page merging

Extract API Reference

Full API documentation for the /extract endpoint

Supported File Types

Detailed breakdown of every supported format

Platform Reference

Pipelines

Forms

Production Workflow

Library & Exports

Overview

When to Use

Supported File Types

How to Use in the Playground

What You Get Back

API Usage

After Extraction

Add Schema

Split & Schema

Tables

Extract API Reference

Supported File Types

​Overview

​When to Use

​Supported File Types

​How to Use in the Playground

​What You Get Back

​API Usage

​After Extraction

Add Schema

Split & Schema

Tables

​Related

Extract API Reference

Supported File Types

Overview

When to Use

Supported File Types

How to Use in the Playground

What You Get Back

API Usage

After Extraction

Related