# Memory Consolidation System - Production Ready ✅

## Overview
Complete implementation of memory consolidation with LLM-based fact extraction and declarative memory recall for the Miku Discord bot.

## Features Implemented

### ✅ 1. LLM-Based Fact Extraction
- **No more regex patterns** - Uses LLM to intelligently extract facts from conversations
- Processes memories in batches of 5 for optimal performance
- Extracts multiple fact types: name, age, location, job, allergies, preferences, hobbies, etc.
- Automatically stores facts in Qdrant's declarative collection

### ✅ 2. Declarative Memory Recall
- Searches declarative facts based on user queries
- Injects relevant facts (score > 0.5) into the LLM prompt
- Facts appear in prompt as: "📝 Personal Facts About the User"
- Enables Miku to remember and use personal information accurately

### ✅ 3. Bidirectional Memory Storage
- **User messages** stored in episodic memory (as before)
- **Miku's responses** now also stored in episodic memory
- Tagged with `speaker: 'miku'` metadata for filtering
- Creates complete conversation history

### ✅ 4. Trivial Message Filtering
- Automatically deletes low-value messages (lol, k, ok, etc.)
- Marks important messages as "consolidated"
- Reduces memory bloat and improves search quality

### ✅ 5. Manual Consolidation Trigger
- Send `consolidate now` command to trigger immediate consolidation
- Returns stats: processed, kept, deleted, facts learned
- Useful for testing and maintenance

## Architecture

### Hook Chain
```
1. before_cat_recalls_memories (placeholder for future)
2. agent_prompt_prefix → Search & inject declarative facts
3. [LLM generates response using facts]
4. before_cat_sends_message → Store Miku's response + handle consolidation
```

### Memory Flow
```
User Message → Episodic (source=user_id, speaker=user)
  ↓
Miku Response → Episodic (source=user_id, speaker=miku)
  ↓
Consolidation (nightly or manual)
  ├→ Delete trivial messages
  ├→ Mark important as consolidated
  └→ Extract facts → Declarative (user_id=global)
       ↓
Next User Query → Search declarative → Inject into prompt
```

## Test Results

### Fact Extraction Test
- **Input**: 71 unconsolidated memories
- **Output**: 20 facts extracted via LLM
- **Method**: LLM analysis (no regex)
- **Success**: ✅

### Fact Recall Test
**Query**: "What is my name?"
**Response**: "I remember! You're Sarah Chen, right? 🌸"
**Facts Injected**: 5 high-confidence facts
**Success**: ✅

### Miku Memory Test
**Miku's Response**: "[Miku]: 🎉 Here's one: Why did the Vocaloid..."
**Stored in Qdrant**: ✅ (verified via API query)
**Metadata**: `speaker: 'miku'`, `source: 'user'`, `consolidated: false`
**Success**: ✅

## API Changes Fixed

### Cat v1.6.2 Compatibility
1. `recall_memories_from_text()` → `recall_memories_from_embedding()`
2. `add_texts()` → `add_point(content, vector, metadata)`
3. Results format: `[(doc, score, vector, id)]` not `[(doc, score)]`
4. Hook signature: `after_cat_recalls_memories(cat)` not `(memory_docs, cat)`

### Qdrant Compatibility
- Point IDs must be UUID strings (not negative integers)
- Episodic memories need `metadata.source = user_id` for recall filtering
- Declarative memories use `user_id: 'global'` (shared across users)

## Configuration

### Consolidation Schedule
- **Current**: Manual trigger via command
- **Planned**: Nightly at 3:00 AM (requires scheduler)

### Fact Confidence Threshold
- **Current**: 0.5 (50% similarity score)
- **Adjustable**: Change in `agent_prompt_prefix` hook

### Batch Size
- **Current**: 5 memories per LLM call
- **Adjustable**: Change in `extract_and_store_facts()`

## Production Deployment

### Step 1: Update docker-compose.yml
```yaml
services:
  cheshire-cat-core:
    volumes:
      - ./cheshire-cat/cat/plugins/memory_consolidation:/app/cat/plugins/memory_consolidation
      - ./cheshire-cat/cat/plugins/discord_bridge:/app/cat/plugins/discord_bridge
```

### Step 2: Restart Services
```bash
docker-compose restart cheshire-cat-core
```

### Step 3: Verify Plugins Loaded
```bash
docker logs cheshire-cat-core | grep "Consolidation Plugin"
```

Should see:
- ✅ [Consolidation Plugin] before_cat_sends_message hook registered
- ✅ [Memory Consolidation] Plugin loaded

### Step 4: Test Manual Consolidation
Send message: `consolidate now`

Expected response:
```
🌙 Memory Consolidation Complete!

📊 Stats:
- Total processed: XX
- Kept: XX
- Deleted: XX
- Facts learned: XX
```

## Monitoring

### Check Fact Extraction
```bash
docker logs cheshire-cat-core | grep "LLM Extract"
```

### Check Fact Recall
```bash
docker logs cheshire-cat-core | grep "Declarative"
```

### Check Miku Memory Storage
```bash
docker logs cheshire-cat-core | grep "Miku Memory"
```

### Query Qdrant Directly
```bash
# Count declarative facts
curl -s http://localhost:6333/collections/declarative/points/count

# View Miku's messages
curl -s http://localhost:6333/collections/episodic/points/scroll \
  -H "Content-Type: application/json" \
  -d '{"filter": {"must": [{"key": "metadata.speaker", "match": {"value": "miku"}}]}, "limit": 10}'
```

## Performance Notes

- **LLM calls**: ~1 per 5 memories during consolidation (batched)
- **Embedding calls**: 1 per user query (for declarative search)
- **Storage overhead**: +1 memory per Miku response (~equal to user messages)
- **Search latency**: ~100-200ms for declarative fact retrieval

## Future Enhancements

### Scheduled Consolidation
- Integrate APScheduler for nightly runs
- Add cron expression configuration

### Per-User Facts
- Change `user_id: 'global'` to actual user IDs
- Enables multi-user fact isolation

### Fact Update/Merge
- Detect when new facts contradict old ones
- Update existing facts instead of duplicating

### Conversation Summarization
- Use LLM to generate conversation summaries
- Store summaries for long-term context

## Troubleshooting

### Facts Not Being Recalled
**Symptom**: Miku doesn't use personal information
**Check**: `docker logs | grep "Declarative"`
**Solution**: Ensure facts exist in declarative collection and confidence threshold isn't too high

### Miku's Responses Not Stored
**Symptom**: No `[Miku]:` entries in episodic memory
**Check**: `docker logs | grep "Miku Memory"`
**Solution**: Verify `before_cat_sends_message` hook is registered and executing

### Consolidation Fails
**Symptom**: Error during `consolidate now` command
**Check**: `docker logs | grep "Error"`
**Common Issues**:
- Qdrant connection timeout
- LLM rate limiting
- Invalid memory payload format

### Hook Not Executing
**Symptom**: Expected log messages not appearing
**Check**: Plugin load errors: `docker logs | grep "Unable to load"`
**Solution**: Check for Python syntax errors in plugin file

## Credits
- **Framework**: Cheshire Cat AI v1.6.2
- **Vector DB**: Qdrant v1.8.0
- **Embedder**: BAAI/bge-large-en-v1.5
- **LLM**: llama.cpp (model configurable)

---

**Status**: ✅ Production Ready
**Last Updated**: February 3, 2026
**Version**: 2.0.0
