pdf-anonymizer-core 0.3.0

A core library to anonymize PDF, Markdown, and plain text files using LLMs.

PDF Anonymizer Core

This package provides the core functionality for the PDF/Text anonymizer, including text extraction, LLM-driven anonymization, and deanonymization logic. It is used by pdf-anonymizer-cli.

Installation for Development

This project uses uv and is structured as a monorepo. To install the necessary dependencies for development, run the following command from the root of the repository:

# From the repository root
uv sync

This will install the pdf-anonymizer-core package in editable mode.

Environment Variables

The core library itself does not load .env files. Environment variables must be loaded by the application that uses this library (e.g., pdf-anonymizer-cli) or set in your shell.

• GOOGLE_API_KEY: Required when using Google's Gemini models.
• OLLAMA_HOST: Optional, defaults to http://localhost:11434 when using local Ollama models.

API Usage

anonymize_file()

Anonymizes a single file and returns the anonymized text and a mapping of original entities to their placeholders.

from pdf_anonymizer_core.core import anonymize_file
from pdf_anonymizer_core.prompts import detailed

# Example of programmatic usage
text, mapping = anonymize_file(
    file_path="/path/to/file.pdf",
    prompt_template=detailed.prompt_template,
    model_name="gemini-2.5-flash"
)

if text and mapping:
    print("Anonymized Text:", text)
    print("Mapping:", mapping)

deanonymize_file()

Reverts anonymization using a mapping file.

from pdf_anonymizer_core.utils import deanonymize_file

# Assumes you have an anonymized file and a mapping file
deanonymized_text, stats = deanonymize_file(
    anonymized_file="path/to/anonymized.md",
    mapping_file="path/to/mapping.json"
)

if deanonymized_text:
    print("Deanonymized Text:", deanonymized_text)

Configuration

You can import default configurations and available models from the conf module.

from pdf_anonymizer_core.conf import (
    DEFAULT_MODEL_NAME,
    ModelName,
    PromptEnum,
)

print(f"Default model: {DEFAULT_MODEL_NAME}")
print(f"Available Google models: {[m.value for m in ModelName if 'gemini' in m.value]}")