GMDevDept / TendouAris

💬 Telegram AI chatbot supporting multiple popular language models

“ようこそ先生。アリスは先生を待っていました。”

English | 简体中文

TendouAris

Introduction

Aris is a telegram chatbot which supports popular LLMs and customized character presets. In the future will add support for more language models.

Model Name	Support	Custom Prompt	Image Output
Gemini Pro	✅	✅	🚫
gpt-3.5-turbo	✅	✅	🚫
gpt-4	✅	✅	🚫
New Bing	✅	🚫	✅
Google Bard	✅	🚫	✅
Claude	✅	🚫	🚫

Note: Bard cannot generate picture by prompt, it can only send pictures fetched from Google search.

Contributing

Aris supports importing add-on presets from template, which makes it pretty easy to contribute your own character preset to her, even for those who have no coding basis.

To add your own preset to Aris, follow the steps below:

1. Create a new text file (.txt, .py, etc.) on your local machine.
2. Copy texts from the template and paste it into the file you just created.
3. Edit your prompts following the instructions in the template, just like how you create your own custom preset using the bot's command menu in Telegram. Refer to other currently available add-on presets if there's any confusion.
4. Submit your preset by creating an issue, either attach the text file (recommended) or paste the content of your preset into the issue description.

Alternatively, if you have experience in git, you can also fork this repo and submit your preset by creating a pull request. Just put your preset.py file under /presets directory and make sure to the file name same as the id of the preset.

Deployment

1. Download source code and go to project root directory.

   git clone https://github.com/HanaokaYuzu/TendouAris.git
   cd TendouAris

2. Rename .env.sample to .env and fill the values.

3. (Optional) To enable Bing model, export cookies from https://bing.com/chat and save it as bing_cookies.json under \srv directory. Sample file is provided for format reference.

4. (Optional) To enable Claude model, export cookies from https://claude.ai and save it as claude_cookies.json under \srv directory. Sample file is provided for format reference.

5. Build docker container.

   docker-compose up -d --build
   docker image prune -f   # Remove unused dangling images (optional)