Upstage Document Parse

Extract structured content from documents using Upstage's Document Parse API.

Supported Formats

PDF (up to 1000 pages with async), PNG, JPG, JPEG, TIFF, BMP, GIF, WEBP, DOCX, PPTX, XLSX, HWP

Installation

clawhub install upstage-document-parse

API Key Setup

1. Get your API key from Upstage Console
2. Configure the API key:

openclaw config set skills.entries.upstage-document-parse.apiKey "your-api-key"

Or add to ~/.openclaw/openclaw.json:

{
  "skills": {
    "entries": {
      "upstage-document-parse": {
        "apiKey": "your-api-key"
      }
    }
  }
}

Usage Examples

Just ask the agent to parse your document:

"Parse this PDF: ~/Documents/report.pdf"
"Parse: ~/Documents/report.jpg"

Sync API (Small Documents)

For small documents (recommended < 20 pages).

Parameters

Basic Parsing

curl -X POST "https://api.upstage.ai/v1/document-digitization" \
  -H "Authorization: Bearer $UPSTAGE_API_KEY" \
  -F "document=@/path/to/file.pdf" \
  -F "model=document-parse"

Extract Markdown

curl -X POST "https://api.upstage.ai/v1/document-digitization" \
  -H "Authorization: Bearer $UPSTAGE_API_KEY" \
  -F "document=@report.pdf" \
  -F "model=document-parse" \
  -F "output_formats=['markdown']"

Enhanced Mode for Complex Documents

curl -X POST "https://api.upstage.ai/v1/document-digitization" \
  -H "Authorization: Bearer $UPSTAGE_API_KEY" \
  -F "document=@complex.pdf" \
  -F "model=document-parse" \
  -F "mode=enhanced" \
  -F "output_formats=['html', 'markdown']"

Force OCR for Scanned Documents

curl -X POST "https://api.upstage.ai/v1/document-digitization" \
  -H "Authorization: Bearer $UPSTAGE_API_KEY" \
  -F "document=@scan.pdf" \
  -F "model=document-parse" \
  -F "ocr=force"

Extract Table Images as Base64

curl -X POST "https://api.upstage.ai/v1/document-digitization" \
  -H "Authorization: Bearer $UPSTAGE_API_KEY" \
  -F "document=@invoice.pdf" \
  -F "model=document-parse" \
  -F "base64_encoding=['table']"

Response Structure

{
  "api": "2.0",
  "model": "document-parse-251217",
  "content": {
    "html": "<h1>...</h1>",
    "markdown": "# ...",
    "text": "..."
  },
  "elements": [
    {
      "id": 0,
      "category": "heading1",
      "content": { "html": "...", "markdown": "...", "text": "..." },
      "page": 1,
      "coordinates": [{"x": 0.06, "y": 0.05}, ...]
    }
  ],
  "usage": { "pages": 1 }
}

Element Categories

paragraph, heading1, heading2, heading3, list, table, figure, chart, equation, caption, header, footer, index, footnote

Async API (Large Documents)

For documents up to 1000 pages. Documents are processed in batches of 10 pages.

Submit Request

curl -X POST "https://api.upstage.ai/v1/document-digitization/async" \
  -H "Authorization: Bearer $UPSTAGE_API_KEY" \
  -F "document=@large.pdf" \
  -F "model=document-parse" \
  -F "output_formats=['markdown']"

Response:

{"request_id": "uuid-here"}

Check Status & Get Results

curl "https://api.upstage.ai/v1/document-digitization/requests/{request_id}" \
  -H "Authorization: Bearer $UPSTAGE_API_KEY"

Response includes download_url for each batch (available for 30 days).

List All Requests

curl "https://api.upstage.ai/v1/document-digitization/requests" \
  -H "Authorization: Bearer $UPSTAGE_API_KEY"

Status Values

• submitted: Request received
• started: Processing in progress
• completed: Ready for download
• failed: Error occurred (check failure_message)

Notes

• Results stored for 30 days
• Download URLs expire after 15 minutes (re-fetch status to get new URLs)
• Documents split into batches of up to 10 pages

Python Usage

import requests

api_key = "up_xxx"

# Sync
with open("doc.pdf", "rb") as f:
    response = requests.post(
        "https://api.upstage.ai/v1/document-digitization",
        headers={"Authorization": f"Bearer {api_key}"},
        files={"document": f},
        data={"model": "document-parse", "output_formats": "['markdown']"}
    )
print(response.json()["content"]["markdown"])

# Async for large docs
with open("large.pdf", "rb") as f:
    r = requests.post(
        "https://api.upstage.ai/v1/document-digitization/async",
        headers={"Authorization": f"Bearer {api_key}"},
        files={"document": f},
        data={"model": "document-parse"}
    )
request_id = r.json()["request_id"]

# Poll for results
import time
while True:
    status = requests.get(
        f"https://api.upstage.ai/v1/document-digitization/requests/{request_id}",
        headers={"Authorization": f"Bearer {api_key}"}
    ).json()
    if status["status"] == "completed":
        break
    time.sleep(5)

LangChain Integration

from langchain_upstage import UpstageDocumentParseLoader

loader = UpstageDocumentParseLoader(
    file_path="document.pdf",
    output_format="markdown",
    ocr="auto"
)
docs = loader.load()

Environment Variable (Alternative)

You can also set the API key as an environment variable:

export UPSTAGE_API_KEY="your-api-key"

Tips

• Use mode=enhanced for complex tables, charts, images
• Use mode=auto to let API decide per page
• Use async API for documents > 20 pages
• Use ocr=force for scanned PDFs or images
• merge_multipage_tables=true combines split tables (max 20 pages with enhanced mode)
• Results from async API available for 30 days
• Server-side timeout: 5 minutes per request (sync API)
• Standard documents process in ~3 seconds