The ChatGPT Telegram Bot is a powerful Telegram bot that utilizes the latest GPT models, including GPT3.5, GPT4, GPT4 Turbo, GPT4 Vision, DALLE 3, Groq Mixtral-8x7b/LLaMA2-70b and the official Claude2.1/Claude3 opus/sonnet API. It enables users to engage in efficient conversations and information searches on Telegram.

• Multiple AI Models: Integrates a variety of AI models including GPT3.5, GPT4, GPT4 Turbo, GPT4 Vision, DALLE 3, and Claude2.1 API.
• Powerful Online Search: Supports online search with DuckDuckGo and Google, providing users with a robust information retrieval tool.
• User-friendly Interface: Allows flexible model switching within the chat window and supports streaming output for a typewriter-like effect.
• Efficient Message Processing: Asynchronously processes messages, answers questions in a multi-threaded manner, supports isolated dialogues, and provides unique dialogues for different users.
• Document Interaction: Supports Q&A for PDF and TXT documents. Users can upload files directly in the chat box for use.
• Accurate Markdown Rendering: Supports precise Markdown rendering of messages, utilizing another project of mine.
• Convenient Deployment: Supports one-click Zeabur, Replit deployment with true zero cost and idiot-proof deployment process. It also supports kuma anti-sleep, as well as Docker and fly.io deployment.

Our plugin system has been successfully developed and is now fully operational. We welcome everyone to contribute their code to enrich our plugin library. All plugins can be activated or deactivated using the /info command. The following plugins are currently supported:

• Web Search: By default, DuckDuckGo search is provided. Google search is automatically activated when the GOOGLE_CSE_ID and GOOGLE_API_KEY environment variables are set.
• Time Retrieval: Retrieves the current time, date, and day of the week in the GMT+8 time zone.
• URL Summary: Automatically extracts URLs from queries and responds based on the content of the URLs.
• Version Information: Displays the current version of the bot, commit hash, update time, and developer name.

To develop plugins, please follow the steps outlined below:

• Initially, you need to add the environment variable for the plugin in the config.PLUGINS dictionary located in the config.py file. The value can be customized to be either enabled or disabled by default. It is advisable to use uppercase letters for the entire environment variable.
• Subsequently, append the function's name and description in the utils/function_call.py file.
• Then, enhance the ask_stream function in the utils/chatgpt2api.py file with the function's processing logic. You can refer to the existing examples within the ask_stream method for guidance on how to write it.
• Following that, write the function, as mentioned in the utils/function_call.py file, in the utils/plugins.py file.
• Next, in the bot.py file, augment the update_first_buttons_message function with buttons, enabling users to freely toggle plugins using the info command.
• Lastly, don't forget to add the plugin's description in the plugins section of the README.

Please note that the above steps are a general guide and may need to be adjusted based on the specific requirements of your plugin.

One-click deployment:

If you need follow-up function updates, the following deployment method is recommended:

• Fork this repository first, then register for Zeabur. Currently, Zeabur does not support free Docker container deployment. If you need to use Zeabur to deploy the bot for this project, you will need to upgrade to the Developer Plan. Fortunately, Zeabur has introduced their sponsorship program, which offers a one-month Developer Plan to all contributors of this project. If you have features you'd like to enhance, feel free to submit pull requests to this project.
• Import from your own Github repository.
• Set the required environment variables, and redeploy.
• If you need function updates in the follow-up, just synchronize this repository in your own repository and redeploy in Zeabur to get the latest functions.

After importing the Github repository, set the running command

pip install -r requirements.txt > /dev/null && python3 bot.py

Select Secrets in the Tools sidebar, add the environment variables required by the bot, where:

• WEB_HOOK: Replit will automatically assign a domain name to you, fill in https://appname.username.repl.co
• Remember to open "Always On"

Click the run button on the top of the screen to run the bot.

Official documentation: https://fly.io/docs/

Use Docker image to deploy fly.io application

flyctl launch --image yym68686/chatgpt:1.0

Enter the name of the application when prompted, and select No for initializing Postgresql or Redis.

Follow the prompts to deploy. A secondary domain name will be provided in the official control panel, which can be used to access the service.

Set environment variables

flyctl secrets set BOT_TOKEN=bottoken
flyctl secrets set API=
# optional
flyctl secrets set WEB_HOOK=https://flyio-app-name.fly.dev/
flyctl secrets set NICK=javis

View all environment variables

flyctl secrets list

Remove environment variables

flyctl secrets unset MY_SECRET DATABASE_URL

ssh to fly.io container

flyctl ssh issue --agent
# ssh connection
flyctl ssh establish

Check whether the webhook URL is correct

https://api.telegram.org/bot<token>/getWebhookInfo

Start the container

docker run -p 80:8080 --name chatbot -dit \
    -e BOT_TOKEN="telegram bot token" \
    -e API="" \
    -e API_URL= \
    yym68686/chatgpt:1.0

Or if you want to use Docker Compose, here is a docker-compose.yml example:

version: "3.5"
services:
  chatgptbot:
    container_name: chatgptbot
    image: yym68686/chatgpt:1.0
    environment:
      - BOT_TOKEN=
      - API=
      - API_URL=
    ports:
      - 80:8080

Run Docker Compose container in the background

docker-compose up -d

Package the Docker image in the repository and upload it to Docker Hub

docker build --no-cache -t chatgpt:1.0 -f Dockerfile.build --platform linux/amd64 .
docker tag chatgpt:1.0 yym68686/chatgpt:1.0
docker push yym68686/chatgpt:1.0

One-Click Restart Docker Image

set -eu
docker rm -f chatbot
docker pull yym68686/chatgpt:1.0
docker run -p 8080:8080 -dit --name chatbot \
-e BOT_TOKEN= \
-e API= \
-e API_URL= \
-e GOOGLE_API_KEY= \
-e GOOGLE_CSE_ID= \
-e claude_api_key= \
yym68686/chatgpt:1.0
docker logs -f chatbot

This script is for restarting the Docker image with a single command. It first removes the existing Docker container named "chatbot" if it exists. Then, it runs a new Docker container with the name "chatbot", exposing port 8080 and setting various environment variables. The Docker image used is "yym68686/chatgpt:1.0". Finally, it follows the logs of the "chatbot" container.

• How to get free API Key for testing?

Well, If you're facing high cost of openAI API, you can try this method for testing,

1. Join Galaxy AI Discord server.
2. Visit the Commands channel.
3. Use /generate command to generate your private API Key.
4. Then, You have to replace the API_URL variable in config.py with value https://galaxyapi.onrender.com, also put your generated API Key in the API variable.

API_URL = "https://galaxyapi.onrender.com"
API = "<your-generated-key-here>"

Alternatively, You can set these variables in Environment.

• Why can't I use Google search?

By default, DuckDuckGo search is provided. The official API for Google search needs to be applied for by the user. It can provide real-time information that GPT could not answer before, such as today's trending topics on Weibo, today's weather in a specific location, and the progress of a certain person or news event.

• How do I switch models?

You can switch between GPT3.5, GPT4, and other models using the "info" command in the chat window.

• Does it support a vector database?

No, previous versions did support it, but after installing unstructured[md,pdf] dependencies for parsing PDFs, the docker image size reached 9GB, so the support for the vector database was removed in the current version. Since the vector database is just a transitional product when the context of large language models is relatively short and there is significant information loss. With the introduction of claude2.1 200k and gpt4 Turbo 128k, vector databases have become less and less important. Although their price is lower, by comparison, I would prefer better performance.

• Can it be deployed in a group?

Yes, it supports whitelisting to prevent abuse and information leakage.

• How should I set the API_URL?

The API_URL supports all suffixes, including: https://api.openai.com/v1/chat/completions, https://api.openai.com/v1, and https://api.openai.com/. The bot will automatically allocate different endpoints based on different uses.

• Is it necessary to configure the web_hook environment variable?

The web_hook is not a mandatory environment variable. You only need to set the domain name (which must be consistent with WEB_HOOK) and other environment variables as required for your application's functionality.

• I deployed a robot with docker compose. If the documentation is placed on the server locally, which directory should it be mounted to in order to take effect? Do I need to set additional configurations and modify the code?

You can directly send the documentation to the robot through the chat box, and the robot will automatically parse the documentation. To use the documentation dialogue function, you need to enable the historical conversation feature. There is no need for additional processing of the documentation.

• I still can't get it to work... I want to use it in a group, I've set the ADMIN_LIST to myself, and the GROUP_LIST to that group, with the whitelist left empty. However, only I can use it in that group, other members in the group are prompted with no permission, what's going on?

Here's a troubleshooting guide: Please carefully check if the GROUP_LIST is correct. The ID of a Telegram group starts with a negative sign followed by a series of numbers. If it's not, please use this bot bot to reacquire the group ID.

• I've uploaded a document, but it's not responding based on the content of the document. What's going on?

To use the document question and answer feature, you must first enable the history record. You can turn on the history record through the /info command, or by setting the environment variable PASS_HISTORY to True to enable the history record by default. Please note that enabling the history record will incur additional costs, so this project does not enable the history record by default. This means that the question and answer feature cannot be used under the default settings. Before using this feature, you need to manually enable the history record.

• After setting the NICK, there's no response when I @ the bot, and it only replies when the message starts with the nick. How can I make it respond to both the nick and @botname?

In a group chat scenario, if the environment variable NICK is not set, the bot will receive all group messages and respond to all of them. Therefore, it is necessary to set NICK. After setting NICK, the bot will only respond to messages that start with NICK. So, if you want to @ the bot to get a response, you just need to set NICK to @botname. This way, when you @ the bot in the group, the bot will detect that the message starts with @botname, and it will respond to the message.

• How many messages will the history keep?

Apart from the latest gpt-4-turbo-preview model, the official context supports 128k tokens, but this project limits it to 16k tokens. All other models use the official context length settings, for example, the gpt-3.5-turbo-16k context is 16k, the gpt-4-32k context is 32k, and the Claude2 context is 200k. This limitation is implemented to save user costs, as most scenarios do not require a high context. If you have specific needs, you can modify the context limits for each model in the utils/chatgpt2api.py file.

https://core.telegram.org/bots/api

https://github.com/acheong08/ChatGPT

https://github.com/franalgaba/chatgpt-telegram-bot-serverless

https://github.com/gpchelkin/scdlbot/blob/d64d14f6c6d357ba818e80b8a0a9291c2146d6fe/scdlbot/__main__.py#L8

The markdown rendering of the message used is another project of mine.

This project is licensed under GPLv3, which means you are free to copy, distribute, and modify the software, as long as all modifications and derivative works are also released under the same license.