markov-discord/cline_docs/activeContext.md

Active Context

Last Updated: 2024-12-27

Current Focus

Integrating LLM capabilities into the existing Discord bot while maintaining the unique "personality" of each server's Markov-based responses.

Active Issues

1. Response Generation

   • Need to implement hybrid Markov-LLM response system
   • Must maintain response speed within acceptable limits
   • Need to handle API rate limiting gracefully

2. Data Management

   • Implement efficient storage for embeddings
   • Design context window management
   • Handle conversation threading

3. Integration Points

   • Modify generateResponse function to support LLM
   • Add embedding generation pipeline
   • Implement context tracking

Recent Changes

• Analyzed current codebase structure
• Identified integration points for LLM
• Documented system architecture
• Created implementation plan

Active Files

Core Implementation

• src/index.ts

  • Main bot logic
  • Message handling
  • Command processing

• src/entity/

  • Database schema
  • Need to add embedding and context tables

• src/train.ts

  • Training pipeline
  • Need to add embedding generation

New Files Needed

• src/llm/

  • provider.ts (LLM service integration)
  • embedding.ts (Embedding generation)
  • context.ts (Context management)

• src/entity/

  • MessageEmbedding.ts
  • ConversationContext.ts

Next Steps

Immediate Tasks

1. Create database migrations

   • Add embedding table
   • Add context table
   • Update existing message schema

2. Implement LLM integration

   • Set up OpenAI client
   • Create response generation service
   • Add fallback mechanisms

3. Add embedding pipeline

   • Implement background processing
   • Set up batch operations
   • Add storage management

Short-term Goals

1. Test hybrid response system

   • Benchmark response times
   • Measure coherence
   • Validate context usage

2. Optimize performance

   • Implement caching
   • Add rate limiting
   • Tune batch sizes

3. Update documentation

   • Add LLM configuration guide
   • Update deployment instructions
   • Document new commands

Dependencies

• OpenAI API access
• Additional storage capacity
• Updated environment configuration

Implementation Strategy

Phase 1: Foundation

1. Database schema updates
2. Basic LLM integration
3. Simple context tracking

Phase 2: Enhancement

1. Hybrid response system
2. Advanced context management
3. Performance optimization

Phase 3: Refinement

1. User feedback integration
2. Response quality metrics
3. Fine-tuning capabilities

Notes

• Keep existing Markov system as fallback
• Monitor API usage and costs
• Consider implementing local LLM option
• Need to update help documentation
• Consider adding configuration commands