add: cheshire-cat configuration, tooling, tests, and documentation
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
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cheshire-cat/PHASE2_IMPLEMENTATION_NOTES.md
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cheshire-cat/PHASE2_IMPLEMENTATION_NOTES.md
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# Phase 2 - Current State & Next Steps
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## What We Accomplished Today
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### 1. Phase 1 - Successfully Committed ✅
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- discord_bridge plugin with unified user identity
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- Cross-server memory recall validated
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- Committed to miku-discord repo (commit 323ca75)
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### 2. Plugin Activation - FIXED ✅
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**Problem**: Plugins were installed but not active (`active=False`)
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**Solution**: Used Cat API to activate:
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```bash
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curl -X PUT http://localhost:1865/plugins/toggle/discord_bridge
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curl -X PUT http://localhost:1865/plugins/toggle/memory_consolidation
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```
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**Status**: Both plugins now show `active=True`
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### 3. Consolidation Logic - WORKING ✅
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- Manual consolidation script successfully:
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- Deletes trivial messages (lol, k, ok, xd, haha, lmao, brb, gtg)
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- Preserves important personal information
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- Marks processed memories as `consolidated=True`
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- Deletions persist across sessions
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### 4. Test Infrastructure - CREATED ✅
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- `test_phase2_comprehensive.py` - 55 diverse messages
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- `test_end_to_end.py` - Complete pipeline test
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- `manual_consolidation.py` - Direct Qdrant consolidation
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- `analyze_consolidation.py` - Results analysis
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- `PHASE2_TEST_RESULTS.md` - Comprehensive documentation
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## Critical Issues Identified
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### 1. Heuristic Accuracy: 44% ⚠️
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**Current**: Catches 8/18 trivial messages
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- ✅ Deletes: lol, k, ok, lmao, haha, xd, brb, gtg
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- ❌ Misses: "What's up?", "Interesting", "The weather is nice", etc.
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**Why**: Simple length + hardcoded list heuristic
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**Solution Needed**: LLM-based importance scoring
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### 2. Memory Retrieval: BROKEN ❌
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**Problem**: Semantic search doesn't retrieve stored facts
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- Stored: "My name is Sarah Chen"
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- Query: "What is my name?"
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- Result: No recall
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**Why**: Semantic vector distance too high between question and statement
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**Solution Needed**: Declarative memory extraction
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### 3. Test Cat LLM Configuration ⚠️
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**Problem**: Test Cat tries to connect to `ollama` host which doesn't exist
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**Impact**: Can't test full pipeline end-to-end with LLM responses
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**Solution Needed**: Configure test Cat to use production LLM (llama-swap)
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## Architecture Status
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```
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[WORKING] 1. Immediate Filtering (discord_bridge)
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↓ Filters: "k", "lol", empty messages ✅
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↓ Stores rest in episodic ✅
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↓ Marks: consolidated=False ⚠️ (needs verification)
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[PARTIAL] 2. Consolidation (manual trigger)
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↓ Query: consolidated=False ✅
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↓ Rate: Simple heuristic (44% accuracy) ⚠️
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↓ Delete: Low-importance ✅
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↓ Extract facts: ❌ NOT IMPLEMENTED
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↓ Mark: consolidated=True ✅
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[BROKEN] 3. Retrieval
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↓ Declarative: ❌ No facts extracted
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↓ Episodic: ⚠️ Semantic search limitations
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```
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## What's Needed for Production
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### Priority 1: Fix Retrieval (CRITICAL)
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Without this, the system is useless.
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**Option A: Declarative Memory Extraction**
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```python
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def extract_facts(memory_content, user_id):
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# Parse: "My name is Sarah Chen"
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# Extract: {"user_name": "Sarah Chen"}
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# Store in declarative memory with structured format
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```
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**Benefits**:
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- Direct fact lookup: "What is my name?" → declarative["user_name"]
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- Better than semantic search for factual questions
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- Can enrich prompts: "You're talking to Sarah Chen, 28, nurse at..."
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**Implementation**:
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1. After consolidation, parse kept memories
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2. Use LLM to extract structured facts
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3. Store in declarative memory collection
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4. Test recall improvement
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### Priority 2: Improve Heuristic
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**Current**: 44% accuracy (8/18 caught)
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**Target**: 90%+ accuracy
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**Option A: Expand Patterns**
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```python
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trivial_patterns = [
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# Reactions
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'lol', 'lmao', 'rofl', 'haha', 'hehe',
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# Acknowledgments
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'ok', 'okay', 'k', 'kk', 'cool', 'nice', 'interesting',
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# Greetings
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'hi', 'hey', 'hello', 'sup', 'what\'s up',
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# Fillers
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'yeah', 'yep', 'nah', 'nope', 'idk', 'tbh', 'imo',
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]
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```
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**Option B: LLM-Based Analysis** (BETTER)
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```python
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def rate_importance(memory, context):
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# Send to LLM:
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# "Rate importance 1-10: 'Nice weather today'"
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# LLM response: 2/10 - mundane observation
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# Decision: Delete if <4
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```
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### Priority 3: Configure Test Environment
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- Point test Cat to llama-swap instead of ollama
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- Or: Set up lightweight test LLM
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- Enable full end-to-end testing
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### Priority 4: Automated Scheduling
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- Nightly 3 AM consolidation
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- Per-user processing
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- Stats tracking and reporting
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## Recommended Next Steps
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### Immediate (Today/Tomorrow):
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1. **Implement declarative memory extraction**
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- This fixes the critical retrieval issue
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- Can be done with simple regex patterns initially
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- Test with: "My name is X" → declarative["user_name"]
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2. **Expand trivial patterns list**
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- Quick win to improve from 44% to ~70% accuracy
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- Add common greetings, fillers, acknowledgments
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3. **Test on production Cat**
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- Use main miku-discord setup with llama-swap
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- Verify plugins work in production environment
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### Short Term (Next Few Days):
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4. **Implement LLM-based importance scoring**
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- Replace heuristic with intelligent analysis
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- Target 90%+ accuracy
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5. **Test full pipeline end-to-end**
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- Send 20 messages → consolidate → verify recall
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- Document what works vs what doesn't
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6. **Git commit Phase 2**
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- Once declarative extraction is working
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- Once recall is validated
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### Long Term:
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7. **Automated scheduling** (cron job or Cat scheduler)
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8. **Per-user consolidation** (separate timelines)
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9. **Conversation context analysis** (thread awareness)
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10. **Emotional event detection** (important moments)
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## Files Ready for Commit
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### When Phase 2 is production-ready:
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- `cheshire-cat/cat/plugins/discord_bridge/` (already committed in Phase 1)
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- `cheshire-cat/cat/plugins/memory_consolidation/` (needs declarative extraction)
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- `cheshire-cat/manual_consolidation.py` (working)
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- `cheshire-cat/test_end_to_end.py` (needs validation)
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- `cheshire-cat/PHASE2_TEST_RESULTS.md` (updated)
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- `cheshire-cat/PHASE2_IMPLEMENTATION_NOTES.md` (this file)
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## Bottom Line
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**Technical Success**:
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- ✅ Can filter junk immediately
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- ✅ Can delete trivial messages
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- ✅ Can preserve important ones
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- ✅ Plugins now active
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**User-Facing Failure**:
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- ❌ Cannot recall stored information
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- ⚠️ Misses 55% of mundane messages
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**To be production-ready**:
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Must implement declarative memory extraction. This is THE blocker.
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**Estimated time to production**:
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- With declarative extraction: 1-2 days
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- Without it: System remains non-functional
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## Decision Point
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**Option 1**: Implement declarative extraction now
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- Fixes critical retrieval issue
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- Makes system actually useful
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- Time: 4-6 hours of focused work
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**Option 2**: Commit current state as "Phase 2A"
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- Documents what works
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- Leaves retrieval as known issue
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- Plan Phase 2B (declarative) separately
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**Recommendation**: Option 1 - Fix retrieval before committing. A memory system that can't recall memories is fundamentally broken.
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