LLM-based PDF Reading Assistant

中文版文档

The PDF Reading Assistant is a reading assistant based on large language models (LLM), specifically designed to convert complex foreign literature into easy-to-read versions. Compared with traditional translation software, the PDF Reading Assistant has clear advantages.

Product Features

• Deep Understanding Based on LLM: The PDF Reading Assistant uses the latest Large Language Models (LLM) technology for document translation and content generation, allowing for deeper semantic understanding and accurate translation.

• Optimized Reading Experience: The LLM can generate easy-to-read content, making complex foreign literature easier to understand, thereby optimizing the user's reading experience.

  • Adjustable Generation Length: Users can adjust parameters to customize the length of the generated content to satisfy different reading needs.
  • Handling High-Density Information: The PDF Reading Assistant can efficiently process the high-density information in the literature, appropriately extrapolate, and help readers better understand and absorb.

Features

• Use Large Language Models (LLMs) to translate and extrapolate high-density information papers and articles, support multiple languages.
• Support for preserving the original layout and format of the source PDF.
• Support for OpenAI models, and local models that have implemented the OpenAI interface, such as: ChatGLM2.
• Modular and object-oriented design, easy to customize and extend.
• A graphical user interface (GUI) based on streamlit in the browser, supports batch operations, can be deployed on the network.
• Package standalone programs that do not depend on the command line to start for windwos and macOS respectively.
• Improve translation quality by using custom trained translation models.
• Support customization for different reading levels or preferences to meet content generation needs at different levels.

Effects

Product iteration in progress, the effects section will be updated irregularly

Interactive Interface

Comparison of Generated Results

Environment

Any Python3 running environment

Getting Started

1. Clone the repository and switch to the repository root directory.
2. Use pip install -r requirements.txt to install dependencies.
3. Run streamlit run server.py.

Enjoy it! 😊

License

This project is licensed under GPL-3.0. For more details, please see the LICENSE file.