Parsy — Document Intelligence Engine
Parsy is a high-performance, privacy-preserving document intelligence engine.
Parse PDFs, DOCX, HTML, TXT, CSV, JSON, and XML into clean Markdown, JSON, HTML, or CSV — fully locally with no data leaving your machine.
✨ Features
- ⚡ Blazing fast — Zero-copy PyMuPDF extraction, parallel page processing
- 🔒 Private — Runs 100% locally, no internet required after install
- 📄 Multi-format — PDF, DOCX, TXT, HTML, CSV, JSON, XML, XLSX
- 🧠 ML-powered (optional) — Layout regions, semantic heading clusters, table structure
- 🌊 SSE streaming — Real-time progressive output for large documents
- 🖥️ CLI + Python API + REST API — Use however you want
📦 Installation
pip install parsy-doc
With optional ML structural analysis:
pip install parsy-doc[ml]
With OCR support (requires Tesseract installed on your system):
pip install parsy-doc[ocr]
Full installation:
pip install parsy-doc[all]
🚀 Quick Start
Python API
import parsy
# Parse a PDF to Markdown
result = parsy.parse("contract.pdf")
print(result.markdown)
# Parse to JSON
result = parsy.parse("invoice.pdf", format="json")
print(result.json)
# Access tables directly
for table in result.tables:
print(table)
# View parsing metrics
print(result.metrics)
# → {'wordCount': 4231, 'pageCount': 18, 'charCount': 24100, ...}
Async API (FastAPI, asyncio)
import parsy
import asyncio
async def main():
result = await parsy.parse_async("report.pdf", format="markdown")
print(result.markdown)
asyncio.run(main())
💻 Command-Line Interface
After installing, a parsy command is available globally:
# Parse a PDF and print to terminal
parsy parse report.pdf
# Save to file in JSON format
parsy parse invoice.pdf --format json --output invoice.json
# Parse all PDFs in a folder (batch mode)
parsy parse docs/*.pdf --format markdown --output ./results/
# Enable ML analysis
parsy parse research_paper.pdf --ml
# Launch the local REST API server + web UI
parsy serve
# Custom host and port
parsy serve --host 0.0.0.0 --port 8080
# Check version
parsy --version
🌐 REST API Server
Start the streaming REST API server:
parsy serve
# → http://localhost:8000
Then use the web UI at http://localhost:8000 or POST to the API:
curl -X POST http://localhost:8000/parse \
-F "file=@report.pdf" \
-F "format=markdown"
The API uses Server-Sent Events (SSE) for real-time streaming progress.
⚙️ Options
| Option | Values | Default | Description |
|---|---|---|---|
format |
markdown, json, html, csv, plaintext |
markdown |
Output format |
tables |
bool |
True |
Extract tables |
clean |
bool |
True |
Whitespace cleanup |
use_ml |
bool |
False |
Enable ML structural analysis |
🔬 Supported File Types
| Format | Extension | Notes |
|---|---|---|
| PDF (digital) | .pdf |
Full text + table extraction |
| PDF (scanned) | .pdf |
OCR via Tesseract (pip install parsy-doc[ocr]) |
| Word | .docx |
Headings, paragraphs, tables |
| HTML | .html, .htm |
Semantic extraction |
| Plain text | .txt, .rtf |
Encoding auto-detected |
| Markdown | .md |
Passthrough + re-format |
| Spreadsheet | .xlsx, .csv |
Table-centric output |
| Data | .json, .xml |
Structured extraction |
🏛️ Architecture
parsy parse() / parse_async()
│
▼
DocumentRouter ← heuristic inspection
│
┌─────┴──────────┐
│ │
Fast Parser Vision OCR ← PyMuPDF / Tesseract
│
▼
Normalizer ← Markdown, JSON, HTML, CSV
│
▼
ParseResult
📄 License
MIT — see LICENSE
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