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https://github.com/xnth97/magic-conch
A Telegram bot powered by Azure Cognitive Services
https://github.com/xnth97/magic-conch
azure-cognitive-services chatgpt dall-e go golang telegram-bot
Last synced: about 2 months ago
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A Telegram bot powered by Azure Cognitive Services
- Host: GitHub
- URL: https://github.com/xnth97/magic-conch
- Owner: xnth97
- License: mit
- Created: 2023-03-22T01:01:54.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-08T20:17:38.000Z (9 months ago)
- Last Synced: 2024-10-15T19:21:28.537Z (about 2 months ago)
- Topics: azure-cognitive-services, chatgpt, dall-e, go, golang, telegram-bot
- Language: Go
- Homepage:
- Size: 43 KB
- Stars: 4
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome - xnth97/magic-conch - A Telegram bot powered by Azure Cognitive Services (Go)
README
# magic-conch
> Why don't you ask the magic conch?
magic-conch is a Telegram bot powered by Azure Cognitive Services.
## Features
- Integrates with ChatGPT using Azure Cognitive Services. **No OpenAI API support**, as there are too many alternatives.
- Stream conversation, similar experience to ChatGPT website.
- Support both one-to-one chat and group chat. For group chat, must use `/chat` to trigger magic conch's response.
- Use `/reset` to reset the conversation history. Each chat maintains its own conversation context.
- Use `/role` to update the system prompt (the role of assistant).
- Use `/draw` to generate an image using DALL-E and `/redraw` to regenerate using previous prompt.
## Usage
### Docker
1. Copy `EXAMPLE_config.json` as `config.json` and fill in with your own information. You must have a valid Azure model deployment. Please see __Prerequisite__ section of [Azure documentation](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/chatgpt-quickstart?tabs=command-line&pivots=rest-api). An example is like:
```js
{
// Whether to print / send debug information.
"is_debug": false,
// This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. Alternatively, you can find the value in the Azure OpenAI Studio > Playground > Code View
"base_url": "https://EXAMPLE.openai.azure.com",
// Deployment ID for the model.
"deployment_id": "gpt-35-turbo",
// This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. You can use either KEY1 or KEY2.
"api_key": "AZURE_API_KEY_HERE",
// Telegram Bot API token.
"telegram_api_key": "TELEGRAM_API_KEY_HERE",
// Telegram chat ID numbers that you want to have access to this bot. Left empty ([]) if you don't want any limitation.
"allowed_chat_ids": [],
// How many messages can be included in one conversation. The more messages included, the better ChatGPT understands the context, however also more tokens it consumes.
"past_messages_included": 10,
// Max tokens can be used.
"max_tokens": 800,
// Controls randomness. Lowering the temperature means that the model produces more repetitive and deterministic responses. Increasing the temperature results in more unexpected or creative responses.
"temperature": 0.7
// Deployment name of the image generation model.
"image_deployment_name": "dall-e-3",
// Size of image. Supported values could differ by different models. Please see `azopenai.ImageSize` for references.
"image_size": "1024x1024"
}
```
2. Run the docker image with your own `config.json`.
```sh
docker run -v ./config.json:/app/config.json -d ghcr.io/xnth97/magic-conch:latest
```
### Build
1. Clone this repo and `cd` into directory.
2. Create a `config.json` under the same directory following #2 in above Docker section.
3. Run with `go run .` or build an executable `go build .`
## License
magic-conch is available under MIT license. See the [LICENSE](LICENSE) file for more info.