markov-discord/cline_docs/productContext.md

Product Context

Last Updated: 2024-12-27

Why we're building this

• To create an engaging Discord bot that learns from and interacts with server conversations
• To provide natural, contextually relevant responses using both Markov chains and LLM capabilities
• To maintain conversation history and generate responses that feel authentic to each server's culture

Core user problems/solutions

Problems:

• Current Markov responses can be incoherent or lack context
• No semantic understanding of conversation context
• Limited ability to generate coherent long-form responses

Solutions:

• Integrate LLM to enhance response quality while maintaining server-specific voice
• Use existing message database for both Markov and LLM training
• Combine Markov's randomness with LLM's coherence

Key workflows

1. Message Collection

   • Listen to channels
   • Store messages in SQLite
   • Track message context and metadata

2. Response Generation

   • Current: Markov chain generation
   • Proposed: Hybrid Markov-LLM generation
   • Context-aware responses

3. Training

   • Batch processing of channel history
   • JSON import support
   • Continuous learning from new messages

Product direction and priorities

1. Short term

   • Implement LLM integration for response generation
   • Maintain existing Markov functionality as fallback
   • Add context window for more relevant responses

2. Medium term

   • Fine-tune LLM on server-specific data
   • Implement response quality metrics
   • Add conversation memory

3. Long term

   • Advanced context understanding
   • Personality adaptation per server
   • Multi-modal response capabilities