koko210Serve
ae1e0aa144
Configuration: - .env.example, .gitignore, compose.yml (main docker compose) - docker-compose-amd.yml (ROCm), docker-compose-macos.yml - start.sh, stop.sh convenience scripts - LICENSE (Apache 2.0, from upstream Cheshire Cat) Memory management utilities: - analyze_consolidation.py, manual_consolidation.py, verify_consolidation.py - check_memories.py, extract_declarative_facts.py, store_declarative_facts.py - compare_systems.py (system comparison tool) - benchmark_cat.py, streaming_benchmark.py, streaming_benchmark_v2.py Test suite: - quick_test.py, test_setup.py, test_setup_simple.py - test_consolidation_direct.py, test_declarative_recall.py, test_recall.py - test_end_to_end.py, test_full_pipeline.py - test_phase2.py, test_phase2_comprehensive.py Documentation: - README.md, QUICK_START.txt, TEST_README.md, SETUP_COMPLETE.md - PHASE2_IMPLEMENTATION_NOTES.md, PHASE2_TEST_RESULTS.md - POST_OPTIMIZATION_ANALYSIS.md
173 lines
5.9 KiB
Markdown
173 lines
5.9 KiB
Markdown
# Cheshire Cat RAG Viability - Post-Optimization Results
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## Executive Summary
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**Status: ✅ NOW VIABLE FOR VOICE CHAT**
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After disabling KV cache offloading to CPU in llama-swap, Cheshire Cat's RAG approach is now competitive with direct context loading for real-time voice chat applications.
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## Performance Comparison
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### Time To First Token (TTFT) - Critical Metric for Voice Chat
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| Method | Previous | Current | Improvement |
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|--------|----------|---------|-------------|
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| 🐱 **Cheshire Cat (RAG)** | 1578ms ❌ | **504ms ✅** | **+68%** |
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| 📄 **Direct + Full Context** | 904ms ✅ | **451ms ✅** | **+50%** |
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| ⚡ **Direct + Minimal** | 210ms ✅ | **145ms ✅** | **+31%** |
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### Total Generation Time
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| Method | Previous | Current | Improvement |
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|--------|----------|---------|-------------|
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| 🐱 **Cheshire Cat** | 10.5s | **4.2s** | **+60%** |
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| 📄 **Direct + Full Context** | 8.3s | **1.2s** | **+85%** |
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| ⚡ **Direct + Minimal** | 6.4s | **0.8s** | **+87%** |
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## Voice Chat Viability Assessment
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### Before Optimization
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- ❌ Cheshire Cat: **1578ms** - TOO SLOW
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- ✅ Current System: **904ms** - GOOD
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- ✅ Minimal: **210ms** - EXCELLENT
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### After Optimization
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- ✅ **Cheshire Cat: 504ms - GOOD**
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- ✅ **Current System: 451ms - EXCELLENT**
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- ✅ **Minimal: 145ms - EXCELLENT**
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**Target: <1000ms for voice chat** ✅ **All methods now pass!**
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## Key Findings
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### 1. Cheshire Cat is Now Competitive
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- **504ms mean TTFT** is excellent for voice chat
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- Only **53ms slower** than current approach (10% difference)
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- **Median TTFT: 393ms** - even better than mean
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### 2. All Systems Dramatically Improved
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- **Current system**: 904ms → 451ms (**2x faster**)
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- **Cheshire Cat**: 1578ms → 504ms (**3x faster**)
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- Total generation times cut by 60-87% across the board
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### 3. KV Cache Optimization Impact
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Disabling CPU offloading provided:
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- Faster token generation once model is warmed up
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- Consistent low latency across queries
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- Dramatic improvement in total response times
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## Trade-offs Analysis
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### Cheshire Cat (RAG) Advantages
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✅ **Scalability**: Can handle much larger knowledge bases (100s of MB)
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✅ **Dynamic Updates**: Add new context without reloading bot
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✅ **Memory Efficiency**: Only loads relevant context (not entire 10KB every time)
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✅ **Semantic Search**: Better at finding relevant info from large datasets
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✅ **Now Fast Enough**: 504ms TTFT is excellent for voice chat
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### Cheshire Cat Disadvantages
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⚠️ Slightly slower (53ms) than direct loading
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⚠️ More complex infrastructure (Qdrant, embeddings)
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⚠️ Requires Docker container management
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⚠️ Learning curve for plugin development
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### Current System (Direct Loading) Advantages
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✅ **Simplest approach**: Load context, query LLM
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✅ **Slightly faster**: 451ms vs 504ms (10% faster)
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✅ **No external dependencies**: Just llama-swap
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✅ **Proven and stable**: Already working in production
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### Current System Disadvantages
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⚠️ **Not scalable**: 10KB context works, but 100KB would cause issues
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⚠️ **Static context**: Must restart bot to update knowledge
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⚠️ **Loads everything**: Can't selectively retrieve relevant info
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⚠️ **Token waste**: Sends full context even when only small part is relevant
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## Recommendations
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### For Current 10KB Knowledge Base
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**Recommendation: Keep current system**
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Reasons:
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- Marginally faster (451ms vs 504ms)
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- Already working and stable
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- Simple architecture
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- Knowledge base is small enough for direct loading
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### For Future Growth (>50KB Knowledge Base)
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**Recommendation: Migrate to Cheshire Cat**
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Reasons:
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- RAG scales better with knowledge base size
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- 504ms TTFT is excellent and won't degrade much with more data
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- Can add new knowledge dynamically
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- Better semantic retrieval from large datasets
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### Hybrid Approach (Advanced)
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Consider using both:
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- **Direct loading** for core personality (small, always needed)
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- **Cheshire Cat** for extended knowledge (songs, friends, lore details)
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- Combine responses for best of both worlds
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## Migration Path (If Chosen)
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### Phase 1: Parallel Testing (1-2 weeks)
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- Run both systems side-by-side
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- Compare response quality
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- Monitor latency in production
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- Gather user feedback
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### Phase 2: Gradual Migration (2-4 weeks)
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- Start with non-critical features
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- Migrate DM responses first
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- Keep server responses on current system initially
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- Monitor error rates
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### Phase 3: Full Migration (1 week)
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- Switch all responses to Cheshire Cat
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- Decommission old context loading
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- Monitor performance
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### Phase 4: Optimization (Ongoing)
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- Tune RAG retrieval settings
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- Optimize embedding model
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- Add new knowledge dynamically
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- Explore GPU embeddings if needed
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## Technical Notes
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### Current Cheshire Cat Configuration
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- **LLM**: darkidol (llama-swap-amd)
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- **Embedder**: FastEmbed CPU (BAAI/bge-large-en-v1.5-quantized)
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- **Vector DB**: Qdrant v1.9.1
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- **Knowledge**: 3 files uploaded (~10KB total)
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- **Plugin**: Miku personality (custom)
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### Performance Settings
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- **KV Cache**: Offload to CPU **DISABLED** ✅
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- **Temperature**: 0.8
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- **Max Tokens**: 150 (streaming)
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- **Model**: darkidol (uncensored Llama 3.1 8B)
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### Estimated Resource Usage
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- **Cheshire Cat**: ~500MB RAM, negligible CPU (GPU embeddings could reduce further)
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- **Qdrant**: ~100MB RAM
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- **Storage**: ~50MB (embeddings + indices)
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- **Total Overhead**: ~600MB RAM, ~50MB disk
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## Conclusion
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The KV cache optimization has transformed Cheshire Cat from **unviable (1578ms) to viable (504ms)** for voice chat. Both systems now perform excellently, with Cheshire Cat offering better scalability at a marginal 53ms latency cost.
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**For current needs**: Stick with direct loading (simpler, proven)
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**For future growth**: Cheshire Cat is now a strong option
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The infrastructure is already set up and tested, so migration could happen whenever knowledge base growth demands it.
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---
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**Benchmark Date**: January 30, 2026
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**Optimization**: KV cache offload to CPU disabled
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**Test Queries**: 10 varied questions
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**Success Rate**: 100% across all methods
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