miku-discord/readmes/TESTING_V2.md

# Testing Autonomous System V2

## Quick Start Guide

### Step 1: Enable V2 System (Optional - Test Mode)

The V2 system can run **alongside** V1 for comparison. To enable it:

**Option A: Edit `bot.py` to start V2 on bot ready**

Add this to the `on_ready()` function in `bot/bot.py`:

```python
# After existing setup code, add:
from utils.autonomous_v2_integration import start_v2_system_for_all_servers

# Start V2 autonomous system
await start_v2_system_for_all_servers(client)
```

**Option B: Manual API testing (no code changes needed)**

Just use the API endpoints to check what V2 is thinking, without actually running it.

### Step 2: Test the V2 Decision System

#### Check what V2 is "thinking" for a server:

```bash
# Get current social stats
curl http://localhost:3939/autonomous/v2/stats/<GUILD_ID>

# Example response:
{
  "status": "ok",
  "guild_id": 759889672804630530,
  "stats": {
    "loneliness": "0.42",
    "boredom": "0.65",
    "excitement": "0.15",
    "curiosity": "0.20",
    "chattiness": "0.70",
    "action_urgency": "0.48"
  }
}
```

#### Trigger a manual V2 analysis:

```bash
# See what V2 would decide right now
curl http://localhost:3939/autonomous/v2/check/<GUILD_ID>

# Example response:
{
  "status": "ok",
  "guild_id": 759889672804630530,
  "analysis": {
    "stats": { ... },
    "interest_score": "0.73",
    "triggers": [
      "KEYWORD_DETECTED (0.60): Interesting keywords: vocaloid, miku",
      "CONVERSATION_PEAK (0.60): Lots of people are chatting"
    ],
    "recent_messages": 15,
    "conversation_active": true,
    "would_call_llm": true
  }
}
```

#### Get overall V2 status:

```bash
# See V2 status for all servers
curl http://localhost:3939/autonomous/v2/status

# Example response:
{
  "status": "ok",
  "servers": {
    "759889672804630530": {
      "server_name": "Example Server",
      "loop_running": true,
      "action_urgency": "0.52",
      "loneliness": "0.30",
      "boredom": "0.45",
      "excitement": "0.20",
      "chattiness": "0.70"
    }
  }
}
```

### Step 3: Monitor Behavior

#### Watch for V2 log messages:

```bash
docker compose logs -f bot | grep -E "🧠|🎯|🤔"
```

You'll see messages like:
```
🧠 Starting autonomous decision loop for server 759889672804630530
🎯 Interest score 0.73 - Consulting LLM for server 759889672804630530
🤔 LLM decision: YES, someone mentioned you (Interest: 0.73)
```

#### Compare V1 vs V2:

**V1 logs:**
```
💬 Miku said something general in #miku-chat
```

**V2 logs:**
```
🎯 Interest score 0.82 - Consulting LLM
🤔 LLM decision: YES
💬 Miku said something general in #miku-chat
```

### Step 4: Tune the System

Edit `bot/utils/autonomous_v2.py` to adjust behavior:

```python
# How sensitive is the decision system?
self.LLM_CALL_THRESHOLD = 0.6  # Lower = more responsive (more LLM calls)
self.ACTION_THRESHOLD = 0.5     # Lower = more chatty

# How fast do stats build?
LONELINESS_BUILD_RATE = 0.01    # Higher = gets lonely faster
BOREDOM_BUILD_RATE = 0.01       # Higher = gets bored faster

# Check intervals
MIN_SLEEP = 30    # Seconds between checks during active chat
MAX_SLEEP = 180   # Seconds between checks when quiet
```

### Step 5: Understanding the Stats

#### Loneliness (0.0 - 1.0)
- **Increases**: When not mentioned for >30 minutes
- **Decreases**: When mentioned, engaged
- **Effect**: At 0.7+, seeks attention

#### Boredom (0.0 - 1.0)
- **Increases**: When quiet, hasn't spoken in >1 hour
- **Decreases**: When shares content, conversation happens
- **Effect**: At 0.7+, likely to share tweets/content

#### Excitement (0.0 - 1.0)
- **Increases**: During active conversations
- **Decreases**: Fades over time (decays fast)
- **Effect**: Higher = more likely to jump into conversation

#### Curiosity (0.0 - 1.0)
- **Increases**: Interesting keywords detected
- **Decreases**: Fades over time
- **Effect**: High curiosity = asks questions

#### Chattiness (0.0 - 1.0)
- **Set by mood**:
  - excited/bubbly: 0.85-0.9
  - neutral: 0.5
  - shy/sleepy: 0.2-0.3
  - asleep: 0.0
- **Effect**: Base multiplier for all interactions

### Step 6: Trigger Examples

Test specific triggers by creating conditions:

#### Test MENTIONED trigger:
1. Mention @Miku in the autonomous channel
2. Check stats: `curl http://localhost:3939/autonomous/v2/check/<GUILD_ID>`
3. Should show: `"triggers": ["MENTIONED (0.90): Someone mentioned me!"]`

#### Test KEYWORD trigger:
1. Say "I love Vocaloid music" in channel
2. Check stats
3. Should show: `"triggers": ["KEYWORD_DETECTED (0.60): Interesting keywords: vocaloid, music"]`

#### Test CONVERSATION_PEAK:
1. Have 3+ people chat within 5 minutes
2. Check stats
3. Should show: `"triggers": ["CONVERSATION_PEAK (0.60): Lots of people are chatting"]`

#### Test LONELINESS:
1. Don't mention Miku for 30+ minutes
2. Check stats: `curl http://localhost:3939/autonomous/v2/stats/<GUILD_ID>`
3. Watch loneliness increase over time

### Step 7: Debugging

#### V2 won't start?
```bash
# Check if import works
docker compose exec bot python -c "from utils.autonomous_v2 import autonomous_system_v2; print('OK')"
```

#### V2 never calls LLM?
```bash
# Check interest scores
curl http://localhost:3939/autonomous/v2/check/<GUILD_ID>

# If interest_score is always < 0.6:
# - Channel might be too quiet
# - Stats might not be building
# - Try mentioning Miku (instant 0.9 score)
```

#### V2 calls LLM too much?
```bash
# Increase threshold in autonomous_v2.py:
self.LLM_CALL_THRESHOLD = 0.7  # Was 0.6
```

## Performance Monitoring

### Expected LLM Call Frequency

**Quiet server (few messages):**
- V1: ~10 random calls/day
- V2: ~2-5 targeted calls/day
- **GPU usage: LOWER with V2**

**Active server (100+ messages/day):**
- V1: ~10 random calls/day (same)
- V2: ~10-20 targeted calls/day (responsive to activity)
- **GPU usage: SLIGHTLY HIGHER, but much more relevant**

### Check GPU Usage

```bash
# Monitor GPU while bot is running
nvidia-smi -l 1

# V1: GPU spikes randomly every 15 minutes
# V2: GPU spikes only when something interesting happens
```

### Monitor LLM Queue

If you notice lag:
1. Check how many LLM calls are queued
2. Increase `LLM_CALL_THRESHOLD` to reduce frequency
3. Increase check intervals for quieter periods

## Migration Path

### Phase 1: Testing (Current)
- V1 running (scheduled actions)
- V2 running (parallel, logging decisions)
- Compare behaviors
- Tune V2 parameters

### Phase 2: Gradual Replacement
```python
# In server_manager.py, comment out V1 jobs:
# scheduler.add_job(
#     self._run_autonomous_for_server,
#     IntervalTrigger(minutes=15),
#     ...
# )

# Keep V2 running
autonomous_system_v2.start_loop_for_server(guild_id, client)
```

### Phase 3: Full Migration
- Disable all V1 autonomous jobs
- Keep only V2 system
- Keep manual triggers for testing

## Troubleshooting

### "Module not found: autonomous_v2"
```bash
# Restart the bot container
docker compose restart bot
```

### "Stats always show 0.00"
- V2 decision loop might not be running
- Check: `curl http://localhost:3939/autonomous/v2/status`
- Should show: `"loop_running": true`

### "Interest score always low"
- Channel might be genuinely quiet
- Try creating activity: post messages, images, mention Miku
- Loneliness/boredom build over time (30-60 min)

### "LLM called too frequently"
- Increase thresholds in `autonomous_v2.py`
- Check which triggers are firing: use `/autonomous/v2/check`
- Adjust trigger scores if needed

## API Endpoints Reference

```
GET  /autonomous/v2/stats/{guild_id}  - Get social stats
GET  /autonomous/v2/check/{guild_id}  - Manual analysis (what would V2 do?)
GET  /autonomous/v2/status             - V2 status for all servers
```

## Next Steps

1. Run V2 for 24-48 hours
2. Compare decision quality vs V1
3. Tune thresholds based on server activity
4. Gradually phase out V1 if V2 works well
5. Add dashboard for real-time stats visualization