# 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