pdf-vision-processor 0.1.1

FastAPI service for LLM-powered PDF analysis

PDF Vision Processor

A FastAPI service that converts PDFs into page images, sends them to an OpenAI-compatible Vision LLM, and exposes the parsed layout/markdown through both REST APIs and a built-in UI. The project now ships as an installable package named pdf-vision-processor so it can be deployed with a single CLI command.

Installation (PyPI)

python -m venv venv && source venv/bin/activate
pip install pdf-vision-processor
pdf-vision-processor --host 0.0.0.0 --port 8080

The CLI loads a .env file from your current working directory (if present), applies overrides from environment variables prefixed with PDF_VISION_PROCESSOR_, and falls back to sensible defaults. Static assets and Jinja templates are bundled inside the wheel.

Note: PyMuPDF (fitz) is expected to be available in the runtime environment (e.g., preinstalled in the platform image). The PyPI package does not vendor it; install pymupdf manually if your deployment does not already include it.

Configuration

Setting	CLI Flag	Environment Variable	Default
Host interface	--host	PDF_VISION_PROCESSOR_HOST	127.0.0.1
Port	--port	PDF_VISION_PROCESSOR_PORT	8000
Reload	--reload/--no-reload	PDF_VISION_PROCESSOR_RELOAD	False
Log level	--log-level	PDF_VISION_PROCESSOR_LOG_LEVEL	info
Data directory	--data-dir	PDF_VISION_PROCESSOR_DATA_DIR	~/.pdf-vision-processor/data
Logs directory	--logs-dir	PDF_VISION_PROCESSOR_LOG_DIR	~/.pdf-vision-processor/logs
Database URL	--database-url	PDF_VISION_PROCESSOR_DATABASE_URL	sqlite:///<home>/db.sqlite3

Additional LLM specific variables (e.g., LLM_API_KEY, LLM_BASE_URL, LLM_MODEL, LLM_MAX_TOKENS) continue to be read from the environment; see docs/env_config.md for the full catalog. When no overrides are provided, uploads, derived images, and logs are stored under ~/.pdf-vision-processor/ so the package works even when installed system-wide.

Optional project-level configuration can also be provided through a config.toml file with sections named [server], [paths], and [database]. The CLI resolves settings in the following order: CLI flags → environment variables → config.toml → defaults.

Running from Source

Developers contributing to the repository can still use run.py, which now delegates directly to the packaged CLI to ensure consistent behavior:

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python run.py --host 127.0.0.1 --port 8000 --reload

Ensure your virtual environment already has pymupdf installed (or install it manually) before running the processor locally.

Assets, templates, and compatibility shims remain under the legacy app/ namespace for existing imports, but all active code now lives inside the pdf_vision_processor/ package (matching what ships to PyPI).

Usage

1. Upload – Visit / and upload a PDF.
2. Process – Click Process next to the document to kick off background ingestion.
3. Inspect – Open the document view to see rendered pages, bounding box overlays, and extracted Markdown side-by-side.

API Examples

# Upload a PDF
curl -X POST -F "file=@/path/to/document.pdf" http://localhost:8000/upload

# Start processing (replace {id} with the returned document id)
curl -X POST http://localhost:8000/process/{id}

# Retrieve page analysis
curl http://localhost:8000/pages/{page_id}/analysis

Packaging & Release Workflow

Use the helper script to build and validate distribution artifacts:

python scripts/build_dist.py --bump patch
# optional: publish to TestPyPI / PyPI with twine upload dist/*

The script bumps the version (optional), cleans previous artifacts, runs setup.py sdist bdist_wheel, and executes twine check so we catch metadata issues before publication. See docs/plans/publishing_checklist.md for the full release checklist.