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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. An example is like:
{
    // 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"
}
  1. Run the docker image with your own config.json.
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 file for more info.