FANA LLM v0.3.3 - Store Data to Database | Optimized Entry Point Response and Discord New Triggers

Data Storage to Database

• Implemented a new function store_incoming_message that stores all incoming messages to a Supabase database.

• Messages are stored with metadata including user ID, session ID, and timestamp.

• Embeddings are generated for each message to facilitate future retrieval and analysis.

• This feature can be toggled on/off, allowing the bot to function either as a data collection tool or a full-fledged conversational AI.

• New: Content is now converted to MarkdownV2 format before storage, enhancing text formatting capabilities.

Task Determination Optimizations

• Improved the process_user_input function to handle different modes of operation efficiently.

• When in data storage mode, the bot immediately stores the message and returns without further processing.

• In full processing mode, the bot performs language detection, translation, and generates appropriate responses.

Discord Integration New Triggers and Storing Data for FAQ

• Fana can now be triggered with any keyword on Discord.

• Fana can now listen to Discord channels, particularly focusing on announcement channels.

• All messages from monitored channels are automatically stored in the database, creating a comprehensive record of announcements and discussions.

• Stored messages now preserve rich text formatting thanks to MarkdownV2 conversion.

Enhanced Greetings and Short FAQ Handling

• Implemented a more efficient system for handling greetings and short, frequently asked questions.

• The bot now checks for exact matches in a predefined list of greetings before proceeding with more complex processing.

• For short FAQs, the bot can provide quick responses without needing to access the full database or go through the entire RAG (Retrieval-Augmented Generation) flow.

Flexible Operation Modes

• The bot can now operate in two distinct modes: a. Data Collection Mode: Focuses on storing incoming messages to the database without generating responses. b. Full Conversational Mode: Processes inputs, generates responses, and interacts with users as a conversational AI.

MarkdownV2 Integration

• Implemented MarkdownV2 conversion in the store_to_database function.

• Uses the markdown2 library to convert content to MarkdownV2 format before storage.

• Supports advanced Markdown features like fenced code blocks and tables.

• Ensures that stored messages retain rich text formatting, improving readability and data quality.

Use Case Scenarios

Discord Announcement Data Storing

• The bot can be deployed to listen to specific Discord announcement channels.

• All announcements are automatically stored in the database, creating a searchable archive of important information.

• This data can be used for future reference, analysis, or to train the bot on organization-specific information.

• MarkdownV2 support ensures that announcements with complex formatting are accurately preserved.

Quick Response System

• For greetings and common short questions, the bot can provide immediate responses without accessing the full database.

• This optimization improves response time for simple interactions and reduces computational load.

Comprehensive Data Collection

• When set to data collection mode, the bot can silently collect and store all messages from specified channels or conversations.

• This feature is valuable for building custom datasets, monitoring discussions, or creating a knowledge base for future AI training.

• The MarkdownV2 conversion ensures that collected data retains its original formatting, enhancing the quality of the dataset.

Conclusion

This version of FANA LLM represents a significant step forward in flexibility, functionality, and data handling quality. The addition of MarkdownV2 support and improved code modularity enhances both the bot's data collection capabilities and the quality of stored information. It can adapt to various use cases, from simple data collection to complex conversational interactions, making it a versatile tool for different scenarios in community management, information archiving, and AI-assisted communication.