pdf-anonymizer-cli 0.3.2

CLI for a tool to anonymize PDF, Markdown, and plain text files using LLMs.

🦉🫥 PDF Anonymizer CLI

A command-line interface for anonymizing PDF, Markdown, and plain text files using LLMs.

• High-Quality Anonymization: Leverages LLMs to identify and replace Personally Identifiable Information (PII) with high accuracy.
• Large File Support: Consistently anonymizes large files (tested up to 1GB).
• Multi-Provider & Cost-Effective: Free to use with local Ollama models. It also supports major providers like OpenAI, Anthropic, Google, Hugging Face, and OpenRouter.
• Reversible: Supports deanonymization to recover original data when needed.
• Multi-Format: Works with PDF, Markdown, and plain text files.

Installation

Install the CLI with your favorite package manager. To use a specific LLM provider, you must install the corresponding extra.

• Google: pip install "pdf-anonymizer-cli[google]"
• Ollama: pip install "pdf-anonymizer-cli[ollama]"
• Hugging Face: pip install "pdf-anonymizer-cli[huggingface]"
• OpenRouter: pip install "pdf-anonymizer-cli[openrouter]"
• OpenAI: pip install "pdf-anonymizer-cli[openai]"
• Anthropic: pip install "pdf-anonymizer-cli[anthropic]"

You can also install multiple extras at once:

pip install "pdf-anonymizer-cli[google,openrouter]"

This installs the pdf-anonymizer executable.

Environment Variables

The CLI will automatically load a .env file from the current directory or any parent directory. For consistency, it's recommended to place a single .env file at the root of the repository.

• GOOGLE_API_KEY: Required when using Google models.
• HUGGING_FACE_TOKEN: Required when using Hugging Face models. You can get a token from here.
• OPENROUTER_API_KEY: Required when using OpenRouter models.
• OPENAI_API_KEY: Required when using OpenAI models.
• ANTHROPIC_API_KEY: Required when using Anthropic models.
• OLLAMA_HOST: Optional, defaults to http://localhost:11434 when using Ollama models.

Example .env file:

GOOGLE_API_KEY="YOUR_API_KEY_HERE"
HUGGING_FACE_TOKEN="YOUR_HF_TOKEN_HERE"
OPENROUTER_API_KEY="YOUR_OPENROUTER_KEY"

Usage

Anonymize

The run command anonymizes one or more files.

pdf-anonymizer run FILE_PATH [FILE_PATH ...] \
  [--characters-to-anonymize INTEGER] \
  [--prompt-name {simple|detailed}] \
  [--model-name TEXT] \
  [--anonymized-entities PATH]

Arguments:

• FILE_PATH: Path to one or several PDF, Markdown, or text files for anonymization.

Options:

• --characters-to-anonymize INTEGER: Number of characters to process in each chunk (default: 100000).
• --prompt-name [simple|detailed]: The prompt template to use (default: detailed).
• --model-name TEXT: The language model to use.
• --anonymized-entities PATH: Path to a file with a list of entities to anonymize.

Models: You can use any of the predefined models below, or specify a new model using the format "provider/model-name". For example: --model-name "google/gemini-flash-latest".

• Google: gemini-2.5-pro, gemini-2.5-flash (default), gemini-2.5-flash-lite.
• Ollama: gemma:7b, phi4-mini.
• Hugging Face: openai/gpt-oss-20b, mistralai/Mistral-7B-Instruct-v0.1, HuggingFaceH4/zephyr-7b-beta.
• OpenRouter: openai/gpt-4o, google/gemini-pro.
• OpenAI: gpt-4o, gpt-5.
• Anthropic: claude-4-sonet, claude-4.5-sonet.

Examples

Basic anonymization with the default model (Google):

pdf-anonymizer run document.pdf

A new model (Google) and a simple prompt:

pdf-anonymizer run notes.md --model-name "google/gemini-flash-latest" --prompt-name simple

Using an OpenRouter model:

pdf-anonymizer run report.pdf --model-name "openai/gpt-4o"

Deanonymize

The deanonymize command reverts anonymization using a mapping file.

pdf-anonymizer deanonymize ANONYMIZED_FILE MAPPING_FILE

Arguments:

• ANONYMIZED_FILE: Path to the anonymized text file.
• MAPPING_FILE: Path to the JSON mapping file.

Example:

pdf-anonymizer deanonymize \
    data/anonymized/document.anonymized.md \
    data/mappings/document.mapping.json

This will create a deanonymized version of the file at data/deanonymized/document.deanonymized.md.