Ws-Mark-Flow AI Converter

Convert files from various sources (SharePoint, S3, Azure Blob, etc.) to Markdown and upload to destinations (Google Drive, SharePoint, etc.).

Features

• Multi-source support: SharePoint, S3, Azure Blob Storage (extensible)
• Multi-destination support: Google Drive, SharePoint, S3 (extensible)
• File conversion: PDF, DOCX, PPTX, XLSX, CSV, images, and more → Markdown
• Incremental conversion: Only converts files not already in destination
• Job persistence: MongoDB-backed job storage for resumable pipelines
• REST API: FastAPI-based API for job management
• Progress tracking: Real-time conversion progress and statistics

Architecture

┌─────────────┐     ┌──────────────┐     ┌───────────────┐
│   Source    │────▶│  Converter   │────▶│  Destination  │
│ (SharePoint)│     │ (MarkItDown) │     │(Google Drive) │
└─────────────┘     └──────────────┘     └───────────────┘
                           │
                    ┌──────▼──────┐
                    │   MongoDB   │
                    │ (Job Store) │
                    └─────────────┘

Installation

# Install dependencies
uv pip install -r requirements.txt

# Copy environment file
cp .env.example .env
# Edit .env with your MongoDB URI

# Run with auto-reload
uvicorn src.main:app --reload --port 8000

API Documentation

• API docs: http://localhost:8000/docs
• Redocly UI: http://localhost:8000/redoc
• OpenAPI spec: http://localhost:8000/openapi.json

Supported Integrations

Sources

• SharePoint (sharepoint): Microsoft Graph API
• More coming: S3, Azure Blob, Local filesystem

Destinations

• Google Drive (google_drive): Google Drive API v3
• More coming: SharePoint, S3, Azure Blob

Supported File Types

Converted using Microsoft MarkItDown, Docling or LLM-based analysis for complex PDFs & images.

• Documents: PDF, DOCX, DOC, RTF, TXT
• Presentations: PPTX, PPT
• Spreadsheets: XLSX, XLS, CSV
• Web: HTML, XML, JSON, YAML
• Images: PNG, JPG, GIF, BMP, TIFF (OCR)

Configuration

Main Environment Variables

Development

🔖 requirements

• install uv venv package management

py -m pip install --upgrade uv
# create venv
uv venv
# activate venv
#win: .venv/Scripts/activate
#linux: source .venv/bin/activate

• project requirements update

uv pip install --upgrade -r requirements.txt --prerelease

• build tools

uv pip install --upgrade setuptools build twine 

🪛 build

• clean dist and build package

if (Test-Path ./dist) {rm ./dist -r -force}; `
python -m build && twine check dist/*

• linux/mac

[ -d ./dist ] && rm -rf ./dist
python -m build && twine check dist/*

📦 test / 🧪 debugger

Install the package in editable project location

uv pip install -U -e .
uv pip show ws-mark-flow

code quality tools

# .\src\robot
uv pip install -U scanreq prospector[with_everything]
## unused requirements
scanreq -r requirements.txt -p ./src
## style/linting
prospector ./src -t pylint -t pydocstyle
## code quality/complexity
prospector ./src -t vulture -t mccabe -t mypy 
## security
prospector ./src -t dodgy -t bandit
## package
prospector ./src -t pyroma

✈️ publish

• pypi

  twine upload --verbose dist/* 
  

Docker

• Build the Docker image (override version at build time if needed)

docker build -t ws-mark-flow ./app

# Copy environment file
cp .env.example ./app/.env
# Edit .env 

docker run -p 80:80 --env-file ./app/.env ws-mark-flow
# use host.docker.internal for MongoDB connection from container to host
docker run --add-host=host.docker.internal:host-gateway -p 80:80 --env-file ./app/.env ws-mark-flow

License

MIT