miku-discord/cheshire-cat/SETUP_COMPLETE.md

🎉 Cheshire Cat Test Environment Setup Complete!

📦 What Was Created

A complete standalone testing environment for evaluating Cheshire Cat AI as a memory system for Miku Bot.

Files Created:

1. docker-compose.test.yml - Docker services configuration

   • Cheshire Cat Core (connected to llama-swap)
   • Qdrant vector database
   • Connected to your existing bot network

2. .env - Environment configuration

   • Core settings
   • Qdrant settings
   • Debug mode enabled

3. test_setup.py - Automated setup script

   • Configures Cat to use llama-swap
   • Uploads Miku knowledge base
   • Runs test queries

4. benchmark_cat.py - Comprehensive performance testing

   • Tests various query types
   • Measures latency statistics
   • Voice chat simulation
   • Generates detailed reports

5. compare_systems.py - Side-by-side comparison

   • Compares Cat vs current system
   • Direct performance comparison
   • Latency analysis

6. start.sh - Quick start script

7. stop.sh - Quick stop script

8. TEST_README.md - Full documentation

🚀 Next Steps

Step 1: Start Services

./start.sh

Or manually:

docker-compose -f docker-compose.test.yml up -d

Step 2: Configure and Upload Knowledge

python3 test_setup.py

This will:

• Wait for Cat to be ready
• Configure it to use your llama-swap
• Upload miku_lore.txt, miku_prompt.txt, miku_lyrics.txt
• Run initial test queries

Step 3: Run Benchmarks

python3 benchmark_cat.py

Expected runtime: ~10-15 minutes

Look for:

• Mean latency < 1500ms = Good for voice chat
• P95 latency < 2000ms = Acceptable
• Success rate > 95% = Reliable

Step 4: Compare Systems

python3 compare_systems.py

This compares Cat directly against your current query_llama() system.

Step 5: Analyze Results

Review the output to decide:

✅ Proceed with integration if:

• Latency is acceptable (< 1500ms mean)
• RAG retrieval is accurate
• Performance is consistent

⚠️ Try optimizations if:

• Latency is borderline (1500-2000ms)
• Consider GPU embeddings
• Try hybrid approach

❌ Stick with current system if:

• Latency is too high (> 2000ms)
• RAG quality is poor
• Too many errors

🔍 Monitoring

Check Service Status

docker ps | grep miku

View Logs

docker logs miku_cheshire_cat_test -f
docker logs miku_qdrant_test -f

Access Interfaces

• Admin Panel: http://localhost:1865/admin
• API Docs: http://localhost:1865/docs
• Qdrant: http://localhost:6333/dashboard

📊 Key Metrics to Watch

From FX-6100 Analysis:

Expected Cat overhead on your CPU:

• Embedding generation: ~600ms (CPU-based)
• Vector search: ~100-200ms
• Total overhead: ~800ms

With GPU embeddings (if spare VRAM):

• Total overhead: ~250ms (much better!)

Voice Chat Viability

Your current system: ~500-1500ms Target with Cat: < 1500ms mean latency

If Cat adds ~800ms overhead:

• Simple queries: 500ms + 800ms = 1300ms ✅ OK
• Complex queries: 1500ms + 800ms = 2300ms ⚠️ Borderline

GPU embeddings would bring this to acceptable range.

🛠️ Troubleshooting

Can't connect to llama-swap?

Edit test_setup.py line 10:

# Try one of these:
LLAMA_SWAP_URL = "http://llama-swap:8080/v1"  # Docker network
LLAMA_SWAP_URL = "http://host.docker.internal:8080/v1"  # Host access
LLAMA_SWAP_URL = "http://YOUR_IP:8080/v1"  # Direct IP

Embeddings too slow?

Try GPU acceleration:

1. Edit docker-compose.test.yml to add GPU support
2. Configure embedder to use CUDA in test_setup.py

Knowledge upload fails?

Upload manually:

• Go to http://localhost:1865/admin
• Click "Rabbit Hole" tab
• Drag and drop: miku_lore.txt, miku_prompt.txt, miku_lyrics.txt

🧹 Cleanup

Stop services (keep data):

./stop.sh

Stop and remove all data:

docker-compose -f docker-compose.test.yml down -v

📈 Expected Results

Based on your FX-6100 CPU:

Pessimistic (CPU embeddings):

• Mean latency: 1600-2200ms
• Suitable for text chat: ✅
• Suitable for voice chat: ⚠️ Borderline

Optimistic (GPU embeddings):

• Mean latency: 900-1400ms
• Suitable for text chat: ✅
• Suitable for voice chat: ✅

🎯 Decision Matrix

After benchmarking:

Scenario	Action
Mean < 1500ms, RAG accurate	✅ Integrate fully
Mean 1500-2000ms	⚠️ Try GPU embeddings
Mean > 2000ms	⚠️ Hybrid approach only
Mean > 3000ms	❌ Don't use

📚 Documentation

• Full guide: TEST_README.md
• Original local-cat docs: README.md
• Cheshire Cat docs: https://cheshire-cat-ai.github.io/docs/

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✨ Summary

You now have a complete, isolated testing environment to:

1. ✅ Measure real performance on your FX-6100
2. ✅ Compare against your current system
3. ✅ Test RAG accuracy with Miku's knowledge
4. ✅ Simulate voice chat workloads
5. ✅ Make a data-driven decision

Ready to test? Run: ./start.sh

Good luck! 🚀