DLM Performance Optimization Plan

1. Profile code to identify performance bottlenecks 2. Optimize embedding generation and caching 3. Improve training pipeline efficiency 4. Add intelligent caching mechanisms 5. Reduce memory footprint for large operations | Component | Issue | Impact | Priority | |-----------|-------|--------|----------| | **Embedding Generation** | Repeated API calls | High latency | 🔴 HIGH | | **Training Pipeline** | No batch processing | Slow training | 🟡 MEDIUM | | **File I/O** | No caching | Repeated reads | 🟡 MEDIUM | | **Conversation Search** | Linear search | Slow with many convos | 🔴 HIGH | | **Model Loading** | Loaded each time | Startup delay | 🟢 LOW | **Current Issues:** - Multiple API calls for similar content - No caching mechanism - No batch processing support **Current Issues:** - Sequential epoch processing - No data prefetching - Checkpoint saving blocks training **Optimization Opportunities:** - Batch data loading - Parallel data processing - Async checkpoint saving - GPU utilization monitoring