GaianetBot

I am developing a Telegram bot using grammy to interface with Gaianet’s LLM (Large Language Model). The bot's primary function is to answer user queries related to the Gaianet protocol and network by leveraging the power of decentralized AI.

Project Goals:

1. Provide User Support & Information: The bot serves as a question-and-answer platform for users seeking information on the Gaianet protocol or network.
2. Integrate Gaianet's LLM Model: Utilizing Gaianet’s public LLM API, the bot can intelligently respond to user queries in real time, offering accurate and contextually relevant answers.
3. Enhance Retrieval-Augmented Generation (RAG): By employing RAG techniques, the bot will enhance its performance in answering queries by accessing and parsing additional external resources like links. This will involve building a vector database to store parsed data and optimize responses.

Technical Overview:

• Telegram Bot with grammy: The Telegram bot is built using grammy, a flexible and efficient framework for bot development. The bot handles user interactions and relays their queries to the server.
• Express Server for Gaianet API Integration: The architecture features an Express server that processes incoming requests from the Telegram bot, makes calls to Gaianet’s LLM API, and sends back responses. A separate server is dedicated to handling external resources for the RAG process.
• Markdown Formatting in Telegram: The bot utilizes Markdown formatting to structure responses, allowing for better readability and a richer user experience. Currently, there is a challenge with handling Markdown syntax errors like 'Bad Request: can't parse entities,' which is being resolved.
• Gemma Node for Enhanced Decentralization: You are setting up a Gemma node, allowing the bot to operate more efficiently within Gaianet's decentralized framework.

Future Developments:

• Building Vector Database: As part of the RAG approach, you'll build a vector database that allows the bot to retrieve and parse additional external data sources to improve response accuracy.
• Enhanced AI Integration: With the integration of the Gaianet LLM model and future RAG enhancements, your bot will become a robust decentralized AI system that can handle complex, data-driven queries.