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Project Documentation

Welcome to the technical developer documentation for PDF Anonymizer.

This section covers everything you need to install, run, configure, and extend the codebase. PDF Anonymizer is built as a modular Python monorepo, designed to keep developer tooling lightweight and separate from core business logic.

Monorepo Project Structure

The project separates the command-line application from the underlying core library using a monorepo setup:

anonymizer/
├── .github/workflows/          # CI/CD & Deploy workflows
├── data/                       # Local data directory for sample files
├── docs/                       # MkDocs documentation source files
├── packages/
│   ├── pdf-anonymizer-core/    # Core SDK package (logic, providers, prompts)
│   └── pdf-anonymizer-cli/     # CLI executable wrapper using Typer
├── tests/                      # Global pytest test suite
├── Makefile                    # Developer shortcut commands
├── pyproject.toml              # Workspace and dev dependencies config
└── mkdocs.yml                  # MkDocs site configuration

The Packages

The project contains two decoupled Python packages inside packages/:

pdf-anonymizer-core

Contains all the core engines, including:

  • Text extraction from PDF, Markdown, and plain text formats.
  • LLM router and adapters for various providers (Ollama, Gemini, OpenAI, etc.).
  • System prompt templates (simple and detailed layouts).
  • Streaming chunk utility to process large text files.
  • Mapping and restoration engine for deanonymization.

pdf-anonymizer-cli

A CLI tool built on top of pdf-anonymizer-core that:

  • Exposes a command-line interface using Typer (supporting autocompletion, clean logs, and command help).
  • Handles loading local .env files automatically.
  • Manages output file paths for anonymized logs and mapping tables.

Getting Started

To dive deeper into the technical details, navigate through the following guides:

  • Installation & Setup: Learn how to set up the development environment using uv, manage packages, and define environment variables.
  • CLI Reference: Explore the command-line arguments, options (including --config-profile), custom model strings, and usage examples.
  • SDK & API Usage: Learn how to import PDF Anonymizer as a Python library in your own applications.
  • API Reference (auto): Living signature reference generated from source docstrings.
  • Recipes & Common Workflows: Practical end-to-end examples — fully local Ollama usage, safe external LLM workflows, batching, entity filtering, profiles, caching, debugging, and more.
  • Troubleshooting: Common errors (auth, rate limits, LLM parsing, empty results, large files) and solutions.
  • Architecture Design: Understand the data flow, prompt styling, LLM adapters, and file splitting mechanisms.