ChatFree (ChatAnywhere-2): Native Copilot created using the GPT API to support complementary conversations in any window.

General Introduction

ChatFree is an open source project that aims to free users' AI apps from the constraints of browsers to run locally. Created using the GPT API, Copilot aims to support a wide range of office software such as Office, Word, WPS, and more. Developed by GitHub user hmhm2022, the project provides a convenient way for users to select text and perform content completion in a variety of office software.The goal of ChatFree (ChatAnywhere-2) is to improve office efficiency and simplify the text processing process for users who need to process documents frequently.

Function List

• Localized operation: Support for AI applications to run independently in a local tool window without relying on a browser.
• Multi-software support: Compatible with Office, Word, WPS and many other office software.
• text completion: Automatically generate complementary content after selecting text to improve work efficiency.
• open source project: The code is publicly available and can be freely viewed, modified and distributed by users.
• Easy to install: A detailed installation guide is provided to help users get started quickly.
• Efficient processing: Utilize the GPT API for efficient text processing and generation.

Using Help

Installation process

1. cloning project: Run the following command in the terminal to clone the ChatFree project:

   git clone https://github.com/hmhm2022/ChatFree.git

2. Access to the catalog: Navigate to the project directory:

   cd ChatFree

3. Installation of dependencies: Install the required dependency packages according to the project's README file. Usually the following command is used:

   pip install -r requirements.txt

4. Running Projects: Execute the following command to start ChatFree:

   python main.py

Guidelines for use

1. launch an application: After starting ChatFree following the installation process, the application will run in the local environment.
2. Loading Models: Load AI models as needed, you can specify the model path in the configuration file.
3. Configuration parameters: Adjust the parameters in the configuration file to suit different usage scenarios and needs.
4. Running Tasks: Run AI tasks via command line or API calls to see the results.

Detailed Operation Procedure

1. Configuration file settings: Find the configuration file in the project directory (e.g.config.json), modify configuration parameters such as model paths, run modes, etc. as needed.
2. Load data: Place the dataset in the specified directory to ensure that the data is formatted as required.
3. operate: Execute AI tasks using command line tools or API interfaces, please refer to the project documentation for specific commands and interfaces.
4. View Results: After the task is completed, the result will be saved in the specified directory, and the details can be viewed through the log file.

common problems

• Dependency installation failed: Ensure that the Python environment is configured correctly and that all dependent package versions meet the requirements.
• Model loading error: Check that the model path is correct and that the model file is complete.
• slow-moving: Optimize configuration parameters to ensure adequate hardware resources.