title	tags
Chainlit Cookbook: codeinterpreter	chainlit cookbook codeinterpreter

Chainlit Codeinterpreter

Chainlit is an AI-driven project designed to carry out various tasks. It is built on a plugin architecture that can easily extend new features.

Project Features

• Plugin Architecture: Chainlit uses a flexible and extensible plugin architecture. Each plugin is a folder containing functions.py and config.json files, defining the functionality and configuration of the plugin.

• Function Manager: The function manager is responsible for parsing and calling the functions defined in the functions.py file of each plugin.

• AI Driven: Chainlit leverages the power of AI to understand and generate human language, enabling it to handle a variety of tasks.

Current Plugins

1. General Plugin: This plugin provides the functionality of displaying and uploading images.

2. Python Interpreter Plugin: This plugin includes a Python executor for running Python code, which is very useful for tasks such as data analysis and table processing.

3. Vue Plugin: Currently under development, this plugin is designed to work with Vue projects, automating the entire Vue project modification through the chat interface.

Plugin Structure and Usage

Each plugin is a directory in the 'plugins' folder. The directory name is the name of the plugin. Each plugin directory contains at least two files:

1. functions.py: This file contains the functions provided by the plugin. Each function should be a top-level function (i.e., not a method of a class or an inner function), and should be named in a way that reflects its functionality. Functions can be synchronous or asynchronous.

2. config.json: This file contains the configuration of the plugin. It is a JSON file with a required field: enabled. If enabled is set to true, the functions of the plugin will be imported and available for use. If enabled is set to false, the functions of the plugin will not be imported.

To use a plugin, make sure it is enabled in its config.json file. Once enabled, the functions provided by the plugin will be automatically imported when the script runs. The AI assistant will be able to call these functions in the conversation.

Creating Plugins

To create a plugin, follow these steps:

1. Create a new directory in the 'plugins' folder. The directory name will be the name of the plugin.

2. In the new directory, create a file named functions.py. In this file, define the functions you want the plugin to provide. Each function should be a top-level function and should be named in a way that reflects its functionality.

3. In the same directory, create a file named config.json. In this file, add the following JSON:

{
    "enabled": true
}

This will enable the plugin by default. If you want to disable the plugin, you can change true to false.

Running the Project

To run this project, you need to follow these steps:

1. First, you need to create a .env file in the root directory of the project. You can do this by copying the .env.example file.

2. In the .env file, you need to provide your OpenAI API key. This should be a string, like OPENAI_API_KEY=your_api_key_here. Please replace your_api_key_here with your actual OpenAI API key.

3. Still in the .env file, you need to set the base URL for the OpenAI API. This should be a string, like OPENAI_API_BASE=https://api.openai.com/v1.

4. After saving the .env file, you need to install Chainlit. You can do this with pip: pip install chainlit.

5. Once Chainlit is installed, you can run the project with the following command: chainlit run app.py -w.

Contributing

Contributions are welcome! Please read the contribution guide to learn how to contribute to this project.

License

This project is licensed under the terms of the MIT license.

title : OpenAI Functions CodeInterpreter tags : [openai, codeinterpreter]