ChatGPT on WeChat

🤖️ Turn your WeChat into ChatGPT within only 2 steps! 🤖️

Acknowledgement & Features

This project is implemented based on this amazing project that I contibuted before, with Wechaty SDK and OpenAI API, we achieve:

fast and robust connection to a set of AI models with different features, typically gpt-3.5-turbo which powers ChatGPT
stable and persistent deployment on cloud servers Railway

0. Table of Content

1. How to Deploy this Bot?
- 1.1 Deploy in Local
- 1.2 Deploy on Cloud
  - 1.2.1 Configure on Railway
  - 1.2.2 Deploy & Login on Railway
2. Any Fancy Advanced Settings?
3. How to Contribute to this Project?
Thanks for your support!

1. How to Deploy this Bot?

You can deploy in local or on cloud, whatever you want.

The deploy on cloud method is recommended.

1.1 Deploy in Local

1.1.1 Get your OpenAI API Keys

openaiApiKey can be generated in the API Keys Page in your OpenAI account
openaiOrganizationID is optional, which can be found in the Settings Page in your Open AI account

1.1.2 Configure Environment Variables

You can copy the template config.yaml.example into a new file config.yaml, and paste the configurations:

openaiApiKey: "<your_openai_api_key>"
openaiOrganizationID: "<your_organization_id>"
chatgptTriggerKeyword: "<your_keyword>"

Or you can export the environment variables listed in .env.sample to your system, which is a more encouraged method to keep your OpenAI API Key safe:

export OPENAI_API_KEY="sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
export OPENAI_ORGANIZATION_KEY="org-XXXXXXXXXXXXXXX"
export CHATGPT_TRIGGER_KEYWORD="机器人你好："

Please note:

chatgptTriggerKeyword is the keyword which can trigger auto-reply:
- In private chat, the message starts with it will trigger auto-reply
- In group chat, the message starts with @Name <keyword> will trigger auto-reply
chatgptTriggerKeyword can be empty string, which means:
- In private chat, every messages will trigger auto-reply
- In group chat, only "@ the bot" will trigger auto-reply

1.1.3 Setup the Docker

Setup Docker Image

docker build -t openai-on-wechat .

Setup Docker Container

docker run -v $(pwd)/config.yaml:/app/config.yaml openai-on-wechat

1.1.4 Login your WeChat

Once you deploy the bot successfully, just follow the terminal or Logs in Docker container prompt carefully:

Scan the QR Code with mobile WeChat
Click "Log in" to allow desktop login (where our bot stays)
Wait a few seconds and start chatting!

🤖 Enjoy your powerful chatbot! 🤖

1.2 Deploy on Cloud

Click the button below to fork this repo and deploy with Railway!

1.2.1 Configure on `Railway`

Fill in the following blanks:

Your forked repo name (can be any name you like)
Choose make it private or not (also up to you)
Environment variables (for how to get OpenAI API keys, please refer to 1.1.1 Get your OpenAI API Keys

Please note:

Make sure the environment variables are set in RailWay instead of writing directly in config.yaml. It's really NOT recommended to implicitly write out your OpenAI API Key in public repo. Anyone with your key can get access to the OpenAI API services, and it's possbile for you to lose money if you pay for that.

1.2.2 Deploy & Login on `Railway`

The deploy process is automatic. It may take a few minutes for the first time. As you see the Success, click the tab to see the details. (which is your secret WeChat console!)

Click Deply Logs and you will see everything is setting up, wait for a QR Code to pop up. Scan it as if you are login to your desktop WeChat, and click "Log in" on your mobile WeChat.

Finally, everything is good to go! You will see the logs when people sending you messagem, and whenever the chatbot is auto-triggered to reply.

🤖 Enjoy your powerful chatbot! 🤖

2. Any Fancy Advanced Settings?

2.1 Config Reply in Error

When the OpenAI API encounters some errors (e.g. over-crowded traffic, no authorization, ...), the chatbot will auto-reply the pre-configured message.

You can change it in src/chatgpt.js:

const chatgptErrorMessage = "🤖️：ChatGPT摆烂了，请稍后再试～";

2.2 Config `OpenAI` Models

You can change whatever OpenAI Models you like to handle task at different capability & time-consumption trade-off. (e.g. model with better capability costs more time to respond)

Currently, we use the latest gpt-3.5-turbo model. According to OpenAI doc,

ChatGPT is powered by gpt-3.5-turbo, OpenAI’s most advanced language model.

Also, for the same model, we can configure dozens of parameter (e.g. answer randomness, maximum word limit...). For example, for the temperature field:

Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

You can configure all of them in src/chatgpt.js:

const ChatGPTModelConfig = {
  // this model field is required
  model: "gpt-3.5-turbo",
  // add your OpenAI model parameters below
  temperature: 0.8,
  // max_tokens: 2000,
};

For more details, please refer to OpenAI Models Doc.

2.3 Config Model Features

You can change whatever features you like to handle different types of tasks. (e.g. complete text, edit text, generate code...)

Currently, we use createChatCompletion() powered by gpt-3.5-turbo model, which:

take a series of messages as input, and return a model-generated message as output.

You can configure in src/chatgpt.js:

const response = await this.OpenAI.createChatCompletion({
  ...ChatGPTModelConfig,
  messages: inputMessages,
});

For more details, please refer to OpenAI API Doc.

2.4 Add Customized Task Handler

You can add your own task handlers to expand the ability of this chatbot!

In src/chatgpt.ts ChatGPTBot.onCustimzedTask(), write your own task handler:

// e.g. if a message starts with "Hello", the bot sends "World!"
if (message.text().startsWith("Hello")) {
  await message.say("World!");
  return;
}

3. How to Contribute to this Project?

You can raise some issues, fork this repo, commit your code, submit pull request, and after code review, we can merge your patch. I'm really looking forward to develop more interesting features!

waholulu/ChatGPT-On-WeChat