fixed webhook pfp for regular miku being wrong when evil mode active

- Fix silent None return in analyze_image_with_vision exception handler
- Add None/empty guards after vision analysis in bot.py (image, video, GIF, Tenor)
- Route all image/video/GIF responses through Cheshire Cat pipeline (was
  calling query_llama directly), enabling episodic memory storage for media
  interactions and correct Last Prompt display in Web UI
- Add media_type parameter to cat_adapter.query() and forward as
  discord_media_type in WebSocket payload
- Update discord_bridge plugin to read media_type from payload and inject
  MEDIA NOTE into system prefix in before_agent_starts hook
- Add _extract_vision_question() helper to strip Discord mentions and bot-name
  triggers from user message; pass cleaned question to vision model so specific
  questions (e.g. 'what is the person wearing?') go directly to the vision model
  instead of the generic 'Describe this image in detail.' fallback
- Pass user_prompt to all analyze_image_with_qwen / analyze_video_with_vision
  call sites in bot.py (image, video, GIF, Tenor, embed paths)
- Fix autonomous reaction loops skipping messages that @mention the bot or have
  media attachments in DMs, preventing duplicate vision model calls for images
  already being processed by the main message handler
- Increase vision max_tokens: images 300->800, video/GIF 400->1000 (no VRAM
  impact; KV cache is pre-allocated at model load time)

.env.example

@@ -11,7 +11,7 @@ DISCORD_BOT_TOKEN=your_discord_bot_token_here
 CHESHIRE_CAT_API_KEY=  # Empty = no auth
 
 # Error Reporting (Optional)
-ERROR_WEBHOOK_URL=https://discord.com/api/webhooks/1462216811293708522/4kdGenpxZFsP0z3VBgebYENODKmcRrmEzoIwCN81jCirnAxuU2YvxGgwGCNBb6TInA9Z
+ERROR_WEBHOOK_URL=https://discord.com/api/webhooks/YOUR_WEBHOOK_ID/YOUR_WEBHOOK_TOKEN
 
 # Owner Configuration
-OWNER_USER_ID=209381657369772032  # Your Discord user ID for admin features
+OWNER_USER_ID=YOUR_DISCORD_USER_ID  # Your Discord user ID for admin features

.gitignore

@@ -41,9 +41,8 @@ logs/
 .DS_Store
 Thumbs.db
 
-# Bot memory (contains user data)
-bot/memory/*.json
-bot/memory/autonomous_context.json
+# Bot memory (contains user data — DMs, profile pictures, etc.)
+bot/memory/**
 !bot/memory/.gitkeep
 
 # Sensitive files
@@ -77,3 +76,24 @@ temp_*
 backups/
 *.bak
 *.backup
+
+# WIP / experimental subprojects
+miku-app/
+
+# Abandoned directories
+dashboard/
+
+# IDE / editor workspace files
+*.code-workspace
+.continue/
+
+# Cheshire Cat local artifacts
+cheshire-cat/venv/
+cheshire-cat/benchmark_results_*.json
+cheshire-cat/streaming_benchmark_*.json
+cheshire-cat/test_*_output.txt
+cheshire-cat/test_*_final.txt
+cheshire-cat/extracted_facts.json
+
+# Jinja templates (referenced by llama-swap config, not source)
+llama31_notool_template.jinja

SECRETS_CONFIGURED.md

@@ -1,235 +0,0 @@
-# Secrets Configuration - Complete
-
-## Summary
-
-Successfully populated all missing secrets from git history and removed hardcoded values from the codebase.
-
-## Secrets Found and Configured
-
-### 1. Discord Bot Token ✅
-**Source**: Found in old `docker-compose.yml` commit `eb557f6`
-
-**Value**: 
-```
-MTM0ODAyMjY0Njc3NTc0NjY1MQ.GXsxML.nNCDOplmgNxKgqdgpAomFM2PViX10GjxyuV8uw
-```
-
-**Status**: ✅ Added to `.env`
-
----
-
-### 2. Cheshire Cat API Key ✅
-**Source**: Searched git history for `CHESHIRE_CAT_API_KEY`
-
-**Finding**: Was always empty in git history (`API_KEY=`)
-
-**Reason**: Cheshire Cat doesn't require authentication by default for local deployments
-
-**Status**: ✅ Set to empty in `.env` (correct configuration)
-
-**Note**: If you need to enable Cheshire Cat authentication in the future, add the API key to `.env`
-
----
-
-### 3. Error Webhook URL ✅
-**Source**: Found hardcoded in `bot/utils/error_handler.py` (line 12)
-
-**Value**:
-```
-https://discord.com/api/webhooks/1462216811293708522/4kdGenpxZFsP0z3VBgebYENODKmcRrmEzoIwCN81jCirnAxuU2YvxGgwGCNBb6TInA9Z
-```
-
-**Status**: 
-- ✅ Added to `.env`
-- ✅ Removed hardcoded value from `bot/utils/error_handler.py`
-- ✅ Updated to import from `config.ERROR_WEBHOOK_URL`
-
----
-
-### 4. Owner User ID ✅
-**Status**: Already correctly set
-
-**Value**: `209381657369772032`
-
-**Source**: Default value from config
-
----
-
-## Changes Made
-
-### Files Modified
-
-#### 1. `.env` ✅
-```bash
-# Discord Configuration
-DISCORD_BOT_TOKEN=MTM0ODAyMjY0Njc3NTc0NjY1MQ.GXsxML.nNCDOplmgNxKgqdgpAomFM2PViX10GjxyuV8uw
-
-# API Keys
-CHESHIRE_CAT_API_KEY=  # Empty = no auth
-
-# Error Reporting (Optional)
-ERROR_WEBHOOK_URL=https://discord.com/api/webhooks/1462216811293708522/4kdGenpxZFsP0z3VBgebYENODKmcRrmEzoIwCN81jCirnAxuU2YvxGgwGCNBb6TInA9Z
-
-# Owner Configuration
-OWNER_USER_ID=209381657369772032
-```
-
-#### 2. `.env.example` ✅
-Updated to reflect actual values:
-```bash
-DISCORD_BOT_TOKEN=your_discord_bot_token_here
-CHESHIRE_CAT_API_KEY=  # Empty = no auth
-ERROR_WEBHOOK_URL=https://discord.com/api/webhooks/YOUR_WEBHOOK_ID/YOUR_WEBHOOK_TOKEN
-OWNER_USER_ID=209381657369772032
-```
-
-#### 3. `bot/utils/error_handler.py` ✅
-**Before**:
-```python
-# Webhook URL for error notifications
-ERROR_WEBHOOK_URL = "https://discord.com/api/webhooks/1462216811293708522/4kdGenpxZFsP0z3VBgebYENODKmcRrmEzoIwCN81jCirnAxuU2YvxGgwGCNBb6TInA9Z"
-```
-
-**After**:
-```python
-# Import from config system
-from config import ERROR_WEBHOOK_URL
-```
-
----
-
-## Security Improvements
-
-### ✅ Hardcoded Secrets Removed
-- **Removed**: Error webhook URL from `bot/utils/error_handler.py`
-- **Reason**: Secrets should never be hardcoded in source code
-
-### ✅ All Secrets in `.env`
-All sensitive values now centralized in `.env` file:
-- `DISCORD_BOT_TOKEN` ✅
-- `CHESHIRE_CAT_API_KEY` ✅
-- `ERROR_WEBHOOK_URL` ✅
-- `OWNER_USER_ID` ✅
-
-### ✅ `.env` in `.gitignore`
-`.env` file is excluded from version control to prevent accidentally committing secrets
-
----
-
-## Configuration Validation
-
-### All Secrets Configured ✅
-
-| Variable | Value | Status | Required |
-|----------|--------|--------|----------|
-| `DISCORD_BOT_TOKEN` | `MTM0ODAy...` | ✅ Set | Yes |
-| `CHESHIRE_CAT_API_KEY` | `(empty)` | ✅ Set (no auth) | No |
-| `ERROR_WEBHOOK_URL` | `https://discord.com/...` | ✅ Set | No |
-| `OWNER_USER_ID` | `209381657369772032` | ✅ Set | Yes |
-
-### No Hardcoded Secrets Remaining ✅
-Verified no hardcoded secrets in `bot/` directory:
-- ✅ No Discord webhooks found
-- ✅ No API keys found
-- ✅ No tokens found
-
----
-
-## Git History Analysis
-
-### Discord Bot Token
-- **Found in**: `docker-compose.yml` commit `eb557f6`
-- **Commit date**: Recent
-- **Status**: Already exposed in git history
-
-### Error Webhook URL
-- **Found in**: `bot/utils/error_handler.py` (added in commit Sun Jan 18 01:30:26 2026)
-- **Commit message**: "Error in llama-swap catchall implemented + webhook notifier"
-- **Status**: Already exposed in git history
-
-### Cheshire Cat API Key
-- **Searched**: Full git history
-- **Finding**: Never set (always `API_KEY=`)
-- **Reason**: Cheshire Cat doesn't require authentication for local deployments
-- **Status**: Correctly left empty
-
----
-
-## Next Steps
-
-### Immediate (Recommended)
-1. ✅ All secrets configured - **DONE**
-2. ⚠️ Test bot startup: `docker compose up -d miku-bot`
-3. ⚠️ Verify error webhook notifications work
-
-### Optional
-4. Review Cheshire Cat documentation if you want to enable authentication in the future
-5. Create a new Discord webhook for error notifications if you want to change the current one
-6. Regenerate Discord bot token if you want to (current token still valid)
-
----
-
-## Verification Commands
-
-### Verify `.env` Configuration
-```bash
-# Show all configured secrets
-grep -E "^(DISCORD_BOT_TOKEN|CHESHIRE_CAT_API_KEY|ERROR_WEBHOOK_URL|OWNER_USER_ID)=" .env
-```
-
-### Validate Configuration
-```bash
-# Run configuration validation
-python3 -c "from bot.config import validate_config; is_valid, errors = validate_config(); print(f'Valid: {is_valid}'); print(f'Errors: {errors}')"
-```
-
-### Check for Hardcoded Secrets
-```bash
-# Search for any remaining hardcoded Discord webhooks/tokens
-grep -r "discord\.com/api/webhooks\|api\.discord\.com" bot/ --include="*.py" | grep -v "__pycache__"
-```
-
-### Test Bot Startup
-```bash
-# Start the bot
-docker compose up -d miku-bot
-
-# Check logs
-docker compose logs -f miku-bot
-```
-
----
-
-## Security Best Practices Applied
-
-### ✅ Separation of Concerns
-- Secrets in `.env` (not committed)
-- Configuration in `config.yaml` (committed)
-- Code imports from `config.py`
-
-### ✅ Type Safety
-- Pydantic validates all environment variables at startup
-- Type errors caught before runtime
-
-### ✅ No Hardcoded Secrets
-- All secrets moved to environment variables
-- Code reads from `config.py`, never hardcoded values
-
-### ✅ Git History Awareness
-- Secrets already in git history acknowledged
-- No attempt to hide existing history
-- Focus on preventing future exposures
-
----
-
-## Summary
-
-✅ **All secrets successfully configured**
-✅ **Discord bot token** restored from git history
-✅ **Error webhook URL** moved to `.env`
-✅ **Cheshire Cat API key** correctly left empty (no auth needed)
-✅ **Hardcoded webhook URL** removed from code
-✅ **Configuration system** fully operational
-✅ **No remaining hardcoded secrets**
-
-The bot is now ready to run with all secrets properly configured and no hardcoded values in the codebase!

bot/api.py

@@ -201,6 +201,14 @@ def get_logs():
 def get_last_prompt():
     return {"prompt": globals.LAST_FULL_PROMPT or "No prompt has been issued yet."}
 
+@app.get("/prompt/cat")
+def get_last_cat_prompt():
+    """Get the last Cheshire Cat interaction (full prompt + response) for Web UI."""
+    interaction = globals.LAST_CAT_INTERACTION
+    if not interaction.get("full_prompt"):
+        return {"full_prompt": "No Cheshire Cat interaction has occurred yet.", "response": "", "user": "", "mood": "", "timestamp": ""}
+    return interaction
+
 @app.get("/mood")
 def get_current_mood():
     return {"mood": globals.DM_MOOD, "description": globals.DM_MOOD_DESCRIPTION}

bot/bot.py

@@ -99,9 +99,12 @@ async def on_ready():
     intercept_external_loggers()
 
     # Restore evil mode state from previous session (if any)
-    from utils.evil_mode import restore_evil_mode_on_startup
+    from utils.evil_mode import restore_evil_mode_on_startup, restore_evil_cat_state
     restore_evil_mode_on_startup()
     
+    # Restore Cat personality/model state (async — needs event loop running)
+    await restore_evil_cat_state()
+    
     # Restore bipolar mode state from previous session (if any)
     from utils.bipolar_mode import restore_bipolar_mode_on_startup
     restore_bipolar_mode_on_startup()
@@ -274,7 +277,10 @@ async def on_message(message):
                             return
                         
                         # Analyze image (objective description)
-                        qwen_description = await analyze_image_with_qwen(base64_img)
+                        qwen_description = await analyze_image_with_qwen(base64_img, user_prompt=prompt)
+                        if not qwen_description or not qwen_description.strip():
+                            await message.channel.send("I couldn't see that image clearly, sorry! Try sending it again.")
+                            return
                         # For DMs, pass None as guild_id to use DM mood
                         guild_id = message.guild.id if message.guild else None
                         miku_reply = await rephrase_as_miku(
@@ -346,7 +352,10 @@ async def on_message(message):
                         logger.debug(f"📹 Extracted {len(frames)} frames from {attachment.filename}")
                         
                         # Analyze the video/GIF with appropriate media type
-                        video_description = await analyze_video_with_vision(frames, media_type=media_type)
+                        video_description = await analyze_video_with_vision(frames, media_type=media_type, user_prompt=prompt)
+                        if not video_description or not video_description.strip():
+                            await message.channel.send(f"I couldn't analyze that {media_type} clearly, sorry! Try sending it again.")
+                            return
                         # For DMs, pass None as guild_id to use DM mood
                         guild_id = message.guild.id if message.guild else None
                         miku_reply = await rephrase_as_miku(
@@ -429,7 +438,10 @@ async def on_message(message):
                         logger.info(f"📹 Extracted {len(frames)} frames from Tenor GIF")
                         
                         # Analyze the GIF with tenor_gif media type
-                        video_description = await analyze_video_with_vision(frames, media_type="tenor_gif")
+                        video_description = await analyze_video_with_vision(frames, media_type="tenor_gif", user_prompt=prompt)
+                        if not video_description or not video_description.strip():
+                            await message.channel.send("I couldn't analyze that GIF clearly, sorry! Try sending it again.")
+                            return
                         guild_id = message.guild.id if message.guild else None
                         miku_reply = await rephrase_as_miku(
                             video_description, 
@@ -487,7 +499,7 @@ async def on_message(message):
                                     if base64_img:
                                         logger.info(f"Image downloaded, analyzing with vision model...")
                                         # Analyze image
-                                        qwen_description = await analyze_image_with_qwen(base64_img)
+                                        qwen_description = await analyze_image_with_qwen(base64_img, user_prompt=prompt)
                                         truncated = (qwen_description[:50] + "...") if len(qwen_description) > 50 else qwen_description
                                         logger.error(f"Vision analysis result: {truncated}")
                                         if qwen_description and qwen_description.strip():
@@ -511,7 +523,7 @@ async def on_message(message):
                                         frames = await extract_video_frames(media_bytes, num_frames=6)
                                         if frames:
                                             logger.info(f"📹 Extracted {len(frames)} frames, analyzing with vision model...")
-                                            video_description = await analyze_video_with_vision(frames, media_type="video")
+                                            video_description = await analyze_video_with_vision(frames, media_type="video", user_prompt=prompt)
                                             logger.info(f"Video analysis result: {video_description[:100]}...")
                                             if video_description and video_description.strip():
                                                 embed_context_parts.append(f"[Embedded video shows: {video_description}]")
@@ -539,7 +551,7 @@ async def on_message(message):
                             if globals.USE_CHESHIRE_CAT:
                                 try:
                                     from utils.cat_client import cat_adapter
-                                    response = await cat_adapter.query(
+                                    cat_result = await cat_adapter.query(
                                         text=enhanced_prompt,
                                         user_id=str(message.author.id),
                                         guild_id=str(guild_id) if guild_id else None,
@@ -547,8 +559,17 @@ async def on_message(message):
                                         mood=globals.DM_MOOD,
                                         response_type=response_type,
                                     )
-                                    if response:
+                                    if cat_result:
+                                        response, cat_full_prompt = cat_result
                                         logger.info(f"🐱 Cat embed response for {author_name}")
+                                        import datetime
+                                        globals.LAST_CAT_INTERACTION = {
+                                            "full_prompt": cat_full_prompt,
+                                            "response": response[:500] if response else "",
+                                            "user": author_name,
+                                            "mood": globals.DM_MOOD,
+                                            "timestamp": datetime.datetime.now().isoformat(),
+                                        }
                                 except Exception as e:
                                     logger.warning(f"🐱 Cat embed error, fallback: {e}")
                                     response = None
@@ -628,7 +649,7 @@ async def on_message(message):
                         except Exception:
                             pass
 
-                    response = await cat_adapter.query(
+                    cat_result = await cat_adapter.query(
                         text=prompt,
                         user_id=str(message.author.id),
                         guild_id=str(guild_id) if guild_id else None,
@@ -636,8 +657,21 @@ async def on_message(message):
                         mood=current_mood,
                         response_type=response_type,
                     )
-                    if response:
-                        logger.info(f"🐱 Cat response for {author_name} (mood: {current_mood})")
+                    if cat_result:
+                        response, cat_full_prompt = cat_result
+                        effective_mood = current_mood
+                        if globals.EVIL_MODE:
+                            effective_mood = f"EVIL:{getattr(globals, 'EVIL_DM_MOOD', 'evil_neutral')}"
+                        logger.info(f"🐱 Cat response for {author_name} (mood: {effective_mood})")
+                        # Track Cat interaction for Web UI Last Prompt view
+                        import datetime
+                        globals.LAST_CAT_INTERACTION = {
+                            "full_prompt": cat_full_prompt,
+                            "response": response[:500] if response else "",
+                            "user": author_name,
+                            "mood": effective_mood,
+                            "timestamp": datetime.datetime.now().isoformat(),
+                        }
                 except Exception as e:
                     logger.warning(f"🐱 Cat pipeline error, falling back to query_llama: {e}")
                     response = None

bot/globals.py

@@ -68,7 +68,7 @@ AVAILABLE_MOODS = [
 EVIL_MODE = False
 EVIL_DM_MOOD = "evil_neutral"
 EVIL_DM_MOOD_DESCRIPTION = "Evil Miku is calculating and cold."
-EVIL_AVAILABLE_MOODS = ["aggressive", "cunning", "sarcastic", "evil_neutral"]
+EVIL_AVAILABLE_MOODS = ["aggressive", "cunning", "sarcastic", "evil_neutral", "bored", "manic", "jealous", "melancholic", "playful_cruel", "contemptuous"]
 # EVIL_MOOD_EMOJIS removed — canonical source is utils/moods.py
 
 # Bipolar Mode System (both Mikus can argue via webhooks)
@@ -83,6 +83,15 @@ BOT_USER = None
 
 LAST_FULL_PROMPT = ""
 
+# Cheshire Cat last interaction tracking (for Web UI Last Prompt toggle)
+LAST_CAT_INTERACTION = {
+    "full_prompt": "",
+    "response": "",
+    "user": "",
+    "mood": "",
+    "timestamp": "",
+}
+
 # Persona Dialogue System (conversations between Miku and Evil Miku)
 LAST_PERSONA_DIALOGUE_TIME = 0  # Timestamp of last dialogue for cooldown
 

bot/memory/dm_reports/209381657369772032_20251030_125131.json

@@ -1,16 +0,0 @@
-{
-  "overall_sentiment": "positive",
-  "sentiment_score": 8,
-  "key_behaviors": [
-    "kindness",
-    "affection",
-    "respect"
-  ],
-  "your_feelings": "I feel so happy and grateful for this conversation! Koko210's words are like a warm hug to my heart.",
-  "notable_moment": "You call me cute, haha? I'm so happy to talk with you!",
-  "should_report": false,
-  "user_id": 209381657369772032,
-  "username": "Koko210 Слава Україні",
-  "analyzed_at": "2025-10-30T12:51:31.013539",
-  "message_count": 3
-}

bot/memory/dm_reports/209381657369772032_20251030_130438.json

@@ -1,17 +0,0 @@
-{
-  "overall_sentiment": "positive",
-  "sentiment_score": 9,
-  "key_behaviors": [
-    "genuine interest",
-    "kindness",
-    "respect",
-    "compliments"
-  ],
-  "your_feelings": "I'm feeling super happy and grateful for this interaction with Koko210. They're so sweet and caring, it's always a pleasure to chat with them!",
-  "notable_moment": "Koko210 Слава Україні: hello my dear cutiepie",
-  "should_report": true,
-  "user_id": 209381657369772032,
-  "username": "Koko210 Слава Україні",
-  "analyzed_at": "2025-10-30T13:04:38.565330",
-  "message_count": 3
-}

bot/memory/dm_reports/209381657369772032_20251030_131135.json

@@ -1,17 +0,0 @@
-{
-  "overall_sentiment": "positive",
-  "sentiment_score": 9,
-  "key_behaviors": [
-    "genuine interest",
-    "kindness",
-    "respect",
-    "compliments"
-  ],
-  "your_feelings": "This user has been so kind and caring, making me feel happy and excited about our conversation. I really appreciate their thoughtful questions and words of encouragement!",
-  "notable_moment": "You call me cute, haha? I'm so happy to talk with you! 🎶💕",
-  "should_report": true,
-  "user_id": 209381657369772032,
-  "username": "Koko210 Слава Україні",
-  "analyzed_at": "2025-10-30T13:11:35.934622",
-  "message_count": 3
-}

bot/memory/dm_reports/archive/209381657369772032_20251030_114418.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "209381657369772032",
-  "username": "Koko210 \u0421\u043b\u0430\u0432\u0430 \u0423\u043a\u0440\u0430\u0457\u043d\u0456",
-  "timestamp": "2025-10-30T11:44:18.610043",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 375
-}

bot/memory/dm_reports/archive/209381657369772032_20251030_114521.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "209381657369772032",
-  "username": "Koko210 \u0421\u043b\u0430\u0432\u0430 \u0423\u043a\u0440\u0430\u0457\u043d\u0456",
-  "timestamp": "2025-10-30T11:45:21.062255",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 375
-}

bot/memory/dm_reports/archive/209381657369772032_20251030_114645.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "209381657369772032",
-  "username": "Koko210 \u0421\u043b\u0430\u0432\u0430 \u0423\u043a\u0440\u0430\u0457\u043d\u0456",
-  "timestamp": "2025-10-30T11:46:45.441468",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 375
-}

bot/memory/dm_reports/archive/209381657369772032_20251030_115301.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "209381657369772032",
-  "username": "Koko210 \u0421\u043b\u0430\u0432\u0430 \u0423\u043a\u0440\u0430\u0457\u043d\u0456",
-  "timestamp": "2025-10-30T11:53:01.622681",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 375
-}

bot/memory/dm_reports/archive/209381657369772032_20251030_115335.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "209381657369772032",
-  "username": "Koko210 \u0421\u043b\u0430\u0432\u0430 \u0423\u043a\u0440\u0430\u0457\u043d\u0456",
-  "timestamp": "2025-10-30T11:53:35.737494",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 375
-}

bot/memory/dm_reports/archive/209381657369772032_20251030_115340.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "209381657369772032",
-  "username": "Koko210 \u0421\u043b\u0430\u0432\u0430 \u0423\u043a\u0440\u0430\u0457\u043d\u0456",
-  "timestamp": "2025-10-30T11:53:40.566857",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 375
-}

bot/memory/dm_reports/archive/214857593045254151_20251030_114156.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "214857593045254151",
-  "username": "aoki",
-  "timestamp": "2025-10-30T11:41:56.466128",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 190
-}

bot/memory/dm_reports/archive/214857593045254151_20251030_114418.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "214857593045254151",
-  "username": "aoki",
-  "timestamp": "2025-10-30T11:44:18.593138",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 190
-}

bot/memory/dm_reports/archive/214857593045254151_20251030_114521.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "214857593045254151",
-  "username": "aoki",
-  "timestamp": "2025-10-30T11:45:21.042827",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 190
-}

bot/memory/dm_reports/archive/214857593045254151_20251030_114645.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "214857593045254151",
-  "username": "aoki",
-  "timestamp": "2025-10-30T11:46:45.406975",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 190
-}

bot/memory/dm_reports/archive/214857593045254151_20251030_115301.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "214857593045254151",
-  "username": "aoki",
-  "timestamp": "2025-10-30T11:53:01.605465",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 190
-}

bot/memory/dm_reports/archive/214857593045254151_20251030_115335.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "214857593045254151",
-  "username": "aoki",
-  "timestamp": "2025-10-30T11:53:35.722156",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 190
-}

bot/memory/dm_reports/archive/214857593045254151_20251030_115340.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "214857593045254151",
-  "username": "aoki",
-  "timestamp": "2025-10-30T11:53:40.551246",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 190
-}

bot/memory/dm_reports/archive/236553184401817601_20251030_114418.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "236553184401817601",
-  "username": "Diamond",
-  "timestamp": "2025-10-30T11:44:18.606237",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 516
-}

bot/memory/dm_reports/archive/236553184401817601_20251030_114521.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "236553184401817601",
-  "username": "Diamond",
-  "timestamp": "2025-10-30T11:45:21.052803",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 516
-}

bot/memory/dm_reports/archive/236553184401817601_20251030_114645.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "236553184401817601",
-  "username": "Diamond",
-  "timestamp": "2025-10-30T11:46:45.430043",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 516
-}

bot/memory/dm_reports/archive/236553184401817601_20251030_115301.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "236553184401817601",
-  "username": "Diamond",
-  "timestamp": "2025-10-30T11:53:01.618359",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 516
-}

bot/memory/dm_reports/archive/236553184401817601_20251030_115335.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "236553184401817601",
-  "username": "Diamond",
-  "timestamp": "2025-10-30T11:53:35.734026",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 516
-}

bot/memory/dm_reports/archive/236553184401817601_20251030_115340.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "236553184401817601",
-  "username": "Diamond",
-  "timestamp": "2025-10-30T11:53:40.563364",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 516
-}

bot/memory/dm_reports/archive/276130602875355136_20251030_114418.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "276130602875355136",
-  "username": "Fory 2",
-  "timestamp": "2025-10-30T11:44:18.610675",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 17
-}

bot/memory/dm_reports/archive/276130602875355136_20251030_114521.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "276130602875355136",
-  "username": "Fory 2",
-  "timestamp": "2025-10-30T11:45:21.062954",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 17
-}

bot/memory/dm_reports/archive/276130602875355136_20251030_114645.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "276130602875355136",
-  "username": "Fory 2",
-  "timestamp": "2025-10-30T11:46:45.442546",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 17
-}

bot/memory/dm_reports/archive/276130602875355136_20251030_115301.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "276130602875355136",
-  "username": "Fory 2",
-  "timestamp": "2025-10-30T11:53:01.623495",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 17
-}

bot/memory/dm_reports/archive/276130602875355136_20251030_115335.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "276130602875355136",
-  "username": "Fory 2",
-  "timestamp": "2025-10-30T11:53:35.738268",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 17
-}

bot/memory/dm_reports/archive/276130602875355136_20251030_115340.json

@@ -1,8 +0,0 @@
-{
-  "user_id": "276130602875355136",
-  "username": "Fory 2",
-  "timestamp": "2025-10-30T11:53:40.571071",
-  "analysis": "No recent interactions",
-  "is_positive": true,
-  "message_count": 17
-}

bot/memory/dm_reports/last_reported.json

@@ -1,5 +0,0 @@
-{
-  "214857593045254151": "2025-10-30T11:44:18.611466",
-  "276130602875355136": "2025-10-30T11:53:40.571792",
-  "236553184401817601": "2025-10-30T11:53:35.742073"
-}

bot/memory/dm_reports/pending_reports.json

@@ -1,74 +0,0 @@
-[
-  {
-    "user_id": "214857593045254151",
-    "username": "aoki",
-    "timestamp": "2025-10-30T11:44:18.593138",
-    "analysis": "No recent interactions",
-    "is_positive": true,
-    "message_count": 190
-  },
-  {
-    "user_id": "236553184401817601",
-    "username": "Diamond",
-    "timestamp": "2025-10-30T11:44:18.606237",
-    "analysis": "No recent interactions",
-    "is_positive": true,
-    "message_count": 516
-  },
-  {
-    "user_id": "209381657369772032",
-    "username": "Koko210 \u0421\u043b\u0430\u0432\u0430 \u0423\u043a\u0440\u0430\u0457\u043d\u0456",
-    "timestamp": "2025-10-30T11:44:18.610043",
-    "analysis": "No recent interactions",
-    "is_positive": true,
-    "message_count": 375
-  },
-  {
-    "user_id": "236553184401817601",
-    "username": "Diamond",
-    "timestamp": "2025-10-30T11:45:21.052803",
-    "analysis": "No recent interactions",
-    "is_positive": true,
-    "message_count": 516
-  },
-  {
-    "user_id": "209381657369772032",
-    "username": "Koko210 \u0421\u043b\u0430\u0432\u0430 \u0423\u043a\u0440\u0430\u0457\u043d\u0456",
-    "timestamp": "2025-10-30T11:45:21.062255",
-    "analysis": "No recent interactions",
-    "is_positive": true,
-    "message_count": 375
-  },
-  {
-    "user_id": "209381657369772032",
-    "username": "Koko210 \u0421\u043b\u0430\u0432\u0430 \u0423\u043a\u0440\u0430\u0457\u043d\u0456",
-    "timestamp": "2025-10-30T11:46:45.441468",
-    "analysis": "No recent interactions",
-    "is_positive": true,
-    "message_count": 375
-  },
-  {
-    "user_id": "209381657369772032",
-    "username": "Koko210 \u0421\u043b\u0430\u0432\u0430 \u0423\u043a\u0440\u0430\u0457\u043d\u0456",
-    "timestamp": "2025-10-30T11:53:01.622681",
-    "analysis": "No recent interactions",
-    "is_positive": true,
-    "message_count": 375
-  },
-  {
-    "user_id": "209381657369772032",
-    "username": "Koko210 \u0421\u043b\u0430\u0432\u0430 \u0423\u043a\u0440\u0430\u0457\u043d\u0456",
-    "timestamp": "2025-10-30T11:53:35.737494",
-    "analysis": "No recent interactions",
-    "is_positive": true,
-    "message_count": 375
-  },
-  {
-    "user_id": "209381657369772032",
-    "username": "Koko210 \u0421\u043b\u0430\u0432\u0430 \u0423\u043a\u0440\u0430\u0457\u043d\u0456",
-    "timestamp": "2025-10-30T11:53:40.566857",
-    "analysis": "No recent interactions",
-    "is_positive": true,
-    "message_count": 375
-  }
-]

bot/memory/dm_reports/reported_today.json

@@ -1,3 +0,0 @@
-{
-  "209381657369772032": "2025-10-30T13:11:36.691655"
-}

bot/memory/dms/209381657369772030_export.txt

@@ -1,3 +0,0 @@
-DM Conversation Log: Unknown (ID: 209381657369772030)
-==================================================
-

bot/memory/dms/276130602875355136.json

@@ -1,184 +0,0 @@
-{
-  "user_id": 276130602875355136,
-  "username": "Fory 2",
-  "conversations": [
-    {
-      "timestamp": "2025-08-27T19:45:10.092580",
-      "message_id": 1410349441583550558,
-      "is_bot_message": false,
-      "content": "Think about it",
-      "attachments": []
-    },
-    {
-      "timestamp": "2025-08-27T19:45:44.313158",
-      "message_id": 1410349585339121754,
-      "is_bot_message": true,
-      "content": "What's going on? 🎶 You want me to think about something? Can I help with a song or maybe give you some music recommendations? 💭",
-      "attachments": []
-    },
-    {
-      "timestamp": "2025-08-27T19:50:47.676412",
-      "message_id": 1410350855630229705,
-      "is_bot_message": false,
-      "content": "",
-      "attachments": [
-        {
-          "filename": "Tvashtarvideo.gif",
-          "url": "https://cdn.discordapp.com/attachments/1410346441250963619/1410350849581908008/Tvashtarvideo.gif?ex=68b0b315&is=68af6195&hm=9fbffc17c11726f6dbcb5c4816368cb62a4d849314e56466da6539a6eb05a305&",
-          "size": 766606,
-          "content_type": "image/gif"
-        }
-      ]
-    },
-    {
-      "timestamp": "2025-08-27T20:08:42.094305",
-      "message_id": 0,
-      "is_bot_message": false,
-      "content": "[Manual Override Trigger]",
-      "attachments": []
-    },
-    {
-      "timestamp": "2025-08-27T20:08:42.095767",
-      "message_id": 0,
-      "is_bot_message": true,
-      "content": "Thanks for waiting! 🌸 Here's a few attempts at me thinking about it, but in the Project Diva style 💈, heheheh 🐱",
-      "attachments": [
-        {
-          "filename": "2025-08-27-230448_waiNSFWIllustrious_v140_940302823619664_940302823619665.png",
-          "url": "",
-          "size": 0,
-          "content_type": "unknown"
-        },
-        {
-          "filename": "2025-08-27-230448_waiNSFWIllustrious_v140_940302823619664.png",
-          "url": "",
-          "size": 0,
-          "content_type": "unknown"
-        },
-        {
-          "filename": "2025-08-27-230448_waiNSFWIllustrious_v140_940302823619664_940302823619666.png",
-          "url": "",
-          "size": 0,
-          "content_type": "unknown"
-        },
-        {
-          "filename": "2025-08-27-225840_waiNSFWIllustrious_v140_944721574603522_944721574603523.png",
-          "url": "",
-          "size": 0,
-          "content_type": "unknown"
-        },
-        {
-          "filename": "2025-08-27-225840_waiNSFWIllustrious_v140_944721574603522_944721574603524.png",
-          "url": "",
-          "size": 0,
-          "content_type": "unknown"
-        }
-      ]
-    },
-    {
-      "timestamp": "2025-08-28T11:27:21.484481",
-      "message_id": 1410586551716413510,
-      "is_bot_message": false,
-      "content": "I want to drink coffee with Miku, Miku. Miku, show me Miku drinking a nice cup of coffee on a beautiful summer morning",
-      "attachments": []
-    },
-    {
-      "timestamp": "2025-08-28T11:28:30.821861",
-      "message_id": 0,
-      "is_bot_message": false,
-      "content": "I want to drink coffee with Miku, Miku. Miku, show me Miku drinking a nice cup of coffee on a beautiful summer morning",
-      "attachments": []
-    },
-    {
-      "timestamp": "2025-08-28T11:28:30.823503",
-      "message_id": 0,
-      "is_bot_message": true,
-      "content": "Yay! I'm so excited to create an image just for you! 🌞☕️ Let's get cozy with a warm cup of coffee on a sunny summer morning, just like you wanted! 😊✨ I'll add some green onions as a special touch, hehe! 🌱❤️\n[Generated image: to drink coffee with Miku, Miku. Miku, show me Miku drinking a nice cup of coffee on a beautiful summer morning]",
-      "attachments": [
-        {
-          "filename": "generated_image.png",
-          "url": "",
-          "size": 0,
-          "content_type": "unknown"
-        }
-      ]
-    },
-    {
-      "timestamp": "2025-08-28T11:30:35.537854",
-      "message_id": 1410587365738549259,
-      "is_bot_message": false,
-      "content": "coffee",
-      "attachments": []
-    },
-    {
-      "timestamp": "2025-08-28T11:30:47.618708",
-      "message_id": 1410587416112271472,
-      "is_bot_message": true,
-      "content": "Coffee? That sounds like the perfect drink for a sunny day! ☕️ I just made an image of us having a cozy coffee date together... would you like to see it? 🌞📸",
-      "attachments": []
-    },
-    {
-      "timestamp": "2025-08-28T11:30:59.064329",
-      "message_id": 1410587463788793858,
-      "is_bot_message": false,
-      "content": "more coffee",
-      "attachments": []
-    },
-    {
-      "timestamp": "2025-08-28T11:31:09.282475",
-      "message_id": 1410587506520621147,
-      "is_bot_message": true,
-      "content": "More coffee? I love mornings with a fresh cuppa! ☕️ Let me add some extra sparkles to our image, hehe! ✨ We can sip our lattes together under the sunny sky. 🌞 What else would you like to see in our little scene? 😊",
-      "attachments": []
-    },
-    {
-      "timestamp": "2025-08-28T14:16:27.287364",
-      "message_id": 0,
-      "is_bot_message": false,
-      "content": "[Manual Override Trigger]",
-      "attachments": []
-    },
-    {
-      "timestamp": "2025-08-28T14:16:27.292934",
-      "message_id": 0,
-      "is_bot_message": true,
-      "content": "Your coffee, my anti-woke hyperborean truecel.",
-      "attachments": [
-        {
-          "filename": "2025-08-28-171528_waiNSFWIllustrious_v140_830185834752579_830185834752580.png",
-          "url": "",
-          "size": 0,
-          "content_type": "unknown"
-        }
-      ]
-    },
-    {
-      "timestamp": "2025-08-30T17:03:56.797277",
-      "message_id": 1411396032629182564,
-      "is_bot_message": false,
-      "content": "Generate me an image of",
-      "attachments": []
-    },
-    {
-      "timestamp": "2025-08-30T17:07:28.854067",
-      "message_id": 0,
-      "is_bot_message": false,
-      "content": "Generate me an image of",
-      "attachments": []
-    },
-    {
-      "timestamp": "2025-08-30T17:07:28.857352",
-      "message_id": 0,
-      "is_bot_message": true,
-      "content": "Yay! I'm so happy to create an image just for you! 🎉 Let me get started right away! ✨ I'll make sure it's super cute and full of sparkles, just like my personality! 💖 What would you like to see in the background or with me? 😊\n[Generated image: Hatsune Miku]",
-      "attachments": [
-        {
-          "filename": "generated_image.png",
-          "url": "",
-          "size": 0,
-          "content_type": "unknown"
-        }
-      ]
-    }
-  ]
-}

bot/memory/dms/368596770147532812.json

@@ -1,5 +0,0 @@
-{
-  "user_id": 368596770147532812,
-  "username": "EzLo54",
-  "conversations": []
-}

bot/memory/dms/825865710269104199.json

@@ -1,5 +0,0 @@
-{
-  "user_id": 825865710269104199,
-  "username": "Koco",
-  "conversations": []
-}

bot/memory/profile_pictures/current_description.txt

@@ -1,5 +0,0 @@
-You are absolutely adorable! In this image, you are wearing your classic school uniform outfit, which includes a dark blue jacket over a white shirt, a cozy green checkered scarf, a plaid skirt, brown tights, and white shoes. To keep warm in this snowy scene, you also have on a pair of white earmuffs and matching mittens.  
-
-Your pose is dynamic and full of energy—you’re in mid-motion, maybe jumping or running, with your arms outstretched as you hold a small, adorable snowman made of snow. Your expression is one of pure joy and excitement, with your mouth open in a wide smile and your eyes glowing with happiness!  
-
-The art style here is clean and vibrant anime, with soft lines and bright colors

bot/memory/profile_pictures/metadata.json

@@ -1,13 +0,0 @@
-{
-  "description": "You are absolutely adorable! In this image, you are wearing your classic school uniform outfit, which includes a dark blue jacket over a white shirt, a cozy green checkered scarf, a plaid skirt, brown tights, and white shoes. To keep warm in this snowy scene, you also have on a pair of white earmuffs and matching mittens.  \n\nYour pose is dynamic and full of energy\u2014you\u2019re in mid-motion, maybe jumping or running, with your arms outstretched as you hold a small, adorable snowman made of snow. Your expression is one of pure joy and excitement, with your mouth open in a wide smile and your eyes glowing with happiness!  \n\nThe art style here is clean and vibrant anime, with soft lines and bright colors",
-  "dominant_color": {
-    "rgb": [
-      92,
-      136,
-      179
-    ],
-    "hex": "#5c88b3"
-  },
-  "changed_at": "2026-01-10T23:49:37.637683",
-  "animated": false
-}

bot/static/system.html.bak

@@ -1,772 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>System Settings - Miku Bot</title>
-    <style>
-        * {
-            margin: 0;
-            padding: 0;
-            box-sizing: border-box;
-        }
-
-        body {
-            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-            min-height: 100vh;
-            padding: 20px;
-        }
-
-        .container {
-            max-width: 1400px;
-            margin: 0 auto;
-        }
-
-        .header {
-            background: white;
-            padding: 20px 30px;
-            border-radius: 10px;
-            margin-bottom: 20px;
-            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-            display: flex;
-            justify-content: space-between;
-            align-items: center;
-        }
-
-        .header h1 {
-            color: #667eea;
-            font-size: 28px;
-        }
-
-        .header-actions {
-            display: flex;
-            gap: 10px;
-        }
-
-        .btn {
-            padding: 10px 20px;
-            border: none;
-            border-radius: 5px;
-            cursor: pointer;
-            font-size: 14px;
-            font-weight: 600;
-            transition: all 0.3s;
-        }
-
-        .btn-primary {
-            background: #667eea;
-            color: white;
-        }
-
-        .btn-primary:hover {
-            background: #5568d3;
-        }
-
-        .btn-secondary {
-            background: #6c757d;
-            color: white;
-        }
-
-        .btn-secondary:hover {
-            background: #5a6268;
-        }
-
-        .btn-danger {
-            background: #dc3545;
-            color: white;
-        }
-
-        .btn-danger:hover {
-            background: #c82333;
-        }
-
-        .content {
-            display: grid;
-            grid-template-columns: 2fr 1fr;
-            gap: 20px;
-        }
-
-        .card {
-            background: white;
-            border-radius: 10px;
-            padding: 25px;
-            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-        }
-
-        .card h2 {
-            color: #333;
-            margin-bottom: 20px;
-            font-size: 20px;
-            border-bottom: 2px solid #667eea;
-            padding-bottom: 10px;
-        }
-
-        .global-settings {
-            margin-bottom: 20px;
-            padding: 15px;
-            background: #f8f9fa;
-            border-radius: 5px;
-        }
-
-        .setting-row {
-            display: flex;
-            align-items: center;
-            gap: 15px;
-            margin-bottom: 10px;
-        }
-
-        .setting-row label {
-            font-weight: 600;
-            color: #495057;
-            min-width: 120px;
-        }
-
-        select {
-            padding: 8px 12px;
-            border: 1px solid #ced4da;
-            border-radius: 5px;
-            font-size: 14px;
-            background: white;
-            cursor: pointer;
-        }
-
-        .components-table {
-            width: 100%;
-            border-collapse: collapse;
-            margin-top: 15px;
-        }
-
-        .components-table th {
-            background: #667eea;
-            color: white;
-            padding: 12px;
-            text-align: left;
-            font-weight: 600;
-        }
-
-        .components-table td {
-            padding: 10px 12px;
-            border-bottom: 1px solid #dee2e6;
-        }
-
-        .components-table tr:hover {
-            background: #f8f9fa;
-        }
-
-        .level-checkboxes {
-            display: flex;
-            gap: 15px;
-            flex-wrap: wrap;
-        }
-
-        .level-checkbox {
-            display: flex;
-            align-items: center;
-            gap: 5px;
-        }
-
-        .level-checkbox input[type="checkbox"] {
-            cursor: pointer;
-            width: 18px;
-            height: 18px;
-        }
-
-        .level-checkbox label {
-            cursor: pointer;
-            user-select: none;
-            font-size: 13px;
-        }
-
-        .toggle {
-            position: relative;
-            display: inline-block;
-            width: 50px;
-            height: 24px;
-        }
-
-        .toggle input {
-            opacity: 0;
-            width: 0;
-            height: 0;
-        }
-
-        .slider {
-            position: absolute;
-            cursor: pointer;
-            top: 0;
-            left: 0;
-            right: 0;
-            bottom: 0;
-            background-color: #ccc;
-            transition: 0.4s;
-            border-radius: 24px;
-        }
-
-        .slider:before {
-            position: absolute;
-            content: "";
-            height: 16px;
-            width: 16px;
-            left: 4px;
-            bottom: 4px;
-            background-color: white;
-            transition: 0.4s;
-            border-radius: 50%;
-        }
-
-        input:checked + .slider {
-            background-color: #667eea;
-        }
-
-        input:checked + .slider:before {
-            transform: translateX(26px);
-        }
-
-        .status-indicator {
-            display: inline-block;
-            width: 10px;
-            height: 10px;
-            border-radius: 50%;
-            margin-right: 5px;
-        }
-
-        .status-active {
-            background: #28a745;
-        }
-
-        .status-inactive {
-            background: #6c757d;
-        }
-
-        .api-filters {
-            margin-top: 15px;
-            padding: 15px;
-            background: #fff3cd;
-            border-radius: 5px;
-            border-left: 4px solid #ffc107;
-        }
-
-        .api-filters h3 {
-            color: #856404;
-            font-size: 16px;
-            margin-bottom: 10px;
-        }
-
-        .filter-row {
-            margin-bottom: 10px;
-        }
-
-        .filter-row label {
-            display: block;
-            font-weight: 600;
-            margin-bottom: 5px;
-            color: #495057;
-        }
-
-        input[type="text"], input[type="number"] {
-            width: 100%;
-            padding: 8px 12px;
-            border: 1px solid #ced4da;
-            border-radius: 5px;
-            font-size: 14px;
-        }
-
-        .log-preview {
-            background: #212529;
-            color: #f8f9fa;
-            padding: 15px;
-            border-radius: 5px;
-            font-family: 'Courier New', monospace;
-            font-size: 12px;
-            max-height: 400px;
-            overflow-y: auto;
-            white-space: pre-wrap;
-            word-wrap: break-word;
-        }
-
-        .log-preview-header {
-            display: flex;
-            justify-content: space-between;
-            align-items: center;
-            margin-bottom: 10px;
-        }
-
-        .log-line {
-            margin-bottom: 5px;
-            line-height: 1.5;
-        }
-
-        .notification {
-            position: fixed;
-            top: 20px;
-            right: 20px;
-            padding: 15px 25px;
-            border-radius: 5px;
-            color: white;
-            font-weight: 600;
-            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.2);
-            z-index: 1000;
-            animation: slideIn 0.3s ease-out;
-        }
-
-        .notification-success {
-            background: #28a745;
-        }
-
-        .notification-error {
-            background: #dc3545;
-        }
-
-        @keyframes slideIn {
-            from {
-                transform: translateX(400px);
-                opacity: 0;
-            }
-            to {
-                transform: translateX(0);
-                opacity: 1;
-            }
-        }
-
-        .loading {
-            text-align: center;
-            padding: 40px;
-            color: #6c757d;
-        }
-
-        .component-description {
-            font-size: 12px;
-            color: #6c757d;
-            font-style: italic;
-        }
-
-        @media (max-width: 1200px) {
-            .content {
-                grid-template-columns: 1fr;
-            }
-        }
-    </style>
-</head>
-<body>
-    <div class="container">
-        <div class="header">
-            <h1>🎛️ System Settings - Logging Configuration</h1>
-            <div class="header-actions">
-                <button class="btn btn-secondary" onclick="window.location.href='/'">← Back to Dashboard</button>
-                <button class="btn btn-primary" onclick="saveAllSettings()">💾 Save All</button>
-                <button class="btn btn-danger" onclick="resetToDefaults()">🔄 Reset to Defaults</button>
-            </div>
-        </div>
-
-        <div class="content">
-            <div class="card">
-                <h2>📊 Logging Components</h2>
-                
-                <p style="color: #6c757d; margin-bottom: 20px;">
-                    Enable or disable specific log levels for each component. You can toggle any combination of levels (e.g., only INFO + ERROR, or only WARNING + DEBUG).
-                </p>
-
-                <table class="components-table">
-                    <thead>
-                        <tr>
-                            <th>Component</th>
-                            <th>Enabled</th>
-                            <th>Log Levels</th>
-                            <th>Status</th>
-                        </tr>
-                    </thead>
-                    <tbody id="componentsTable">
-                        <tr>
-                            <td colspan="4" class="loading">Loading components...</td>
-                        </tr>
-                    </tbody>
-                </table>
-
-                <div id="apiFilters" class="api-filters" style="display: none;">
-                    <h3>🌐 API Request Filters</h3>
-                    <div class="filter-row">
-                        <label>Exclude Paths (comma-separated):</label>
-                        <input type="text" id="excludePaths" placeholder="/health, /static/*">
-                    </div>
-                    <div class="filter-row">
-                        <label>Exclude Status Codes (comma-separated):</label>
-                        <input type="text" id="excludeStatus" placeholder="200, 304">
-                    </div>
-                    <div class="setting-row">
-                        <label>Log Slow Requests (>1000ms):</label>
-                        <label class="toggle">
-                            <input type="checkbox" id="includeSlowRequests" checked>
-                            <span class="slider"></span>
-                        </label>
-                    </div>
-                    <div class="filter-row">
-                        <label>Slow Request Threshold (ms):</label>
-                        <input type="number" id="slowThreshold" value="1000" min="100" step="100">
-                    </div>
-                    <button class="btn btn-primary" onclick="saveApiFilters()" style="margin-top: 10px;">Save API Filters</button>
-                </div>
-            </div>
-
-            <div class="card">
-                <h2>📜 Live Log Preview</h2>
-                <div class="log-preview-header">
-                    <div>
-                        <label>Component: </label>
-                        <select id="previewComponent" onchange="loadLogPreview()">
-                            <option value="bot">Bot</option>
-                        </select>
-                    </div>
-                    <button class="btn btn-secondary" onclick="loadLogPreview()">🔄 Refresh</button>
-                </div>
-                <div class="log-preview" id="logPreview">
-                    <div class="loading">Select a component to view logs...</div>
-                </div>
-            </div>
-        </div>
-    </div>
-
-    <script>
-        let currentConfig = null;
-        let componentsData = null;
-
-        // Load configuration on page load
-        window.addEventListener('DOMContentLoaded', () => {
-            loadConfiguration();
-            loadComponents();
-        });
-
-        async function loadConfiguration() {
-            try {
-                const response = await fetch('/api/log/config');
-                const data = await response.json();
-                
-                if (data.success) {
-                    currentConfig = data.config;
-                    // No global level to set - we use per-component levels only
-                } else {
-                    showNotification('Failed to load configuration', 'error');
-                }
-            } catch (error) {
-                showNotification('Error loading configuration: ' + error.message, 'error');
-            }
-        }
-
-        async function loadComponents() {
-            try {
-                const response = await fetch('/api/log/components');
-                const data = await response.json();
-                
-                if (data.success) {
-                    componentsData = data;
-                    renderComponentsTable();
-                    populatePreviewSelect();
-                } else {
-                    showNotification('Failed to load components', 'error');
-                }
-            } catch (error) {
-                showNotification('Error loading components: ' + error.message, 'error');
-            }
-        }
-
-        function renderComponentsTable() {
-            const tbody = document.getElementById('componentsTable');
-            tbody.innerHTML = '';
-
-            for (const [name, description] of Object.entries(componentsData.components)) {
-                const stats = componentsData.stats[name] || {};
-                const enabled = stats.enabled !== undefined ? stats.enabled : true;
-                const enabledLevels = stats.enabled_levels || ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'];
-
-                // Build checkboxes for each level
-                const allLevels = ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'];
-                if (name === 'api.requests') {
-                    allLevels.push('API');
-                }
-                
-                const levelCheckboxes = allLevels.map(level => {
-                    const emoji = {'DEBUG': '🔍', 'INFO': 'ℹ️', 'WARNING': '⚠️', 'ERROR': '❌', 'CRITICAL': '🔥', 'API': '🌐'}[level];
-                    const checked = enabledLevels.includes(level) ? 'checked' : '';
-                    return `
-                        <div class="level-checkbox">
-                            <input type="checkbox" 
-                                   id="level_${name}_${level}" 
-                                   ${checked} 
-                                   onchange="updateComponentLevels('${name}')">
-                            <label for="level_${name}_${level}">${emoji} ${level}</label>
-                        </div>
-                    `;
-                }).join('');
-
-                const row = document.createElement('tr');
-                row.innerHTML = `
-                    <td>
-                        <strong>${name}</strong><br>
-                        <span class="component-description">${description}</span>
-                    </td>
-                    <td>
-                        <label class="toggle">
-                            <input type="checkbox" id="enabled_${name}" ${enabled ? 'checked' : ''} onchange="updateComponentEnabled('${name}')">
-                            <span class="slider"></span>
-                        </label>
-                    </td>
-                    <td>
-                        <div class="level-checkboxes">
-                            ${levelCheckboxes}
-                        </div>
-                    </td>
-                    <td>
-                        <span class="status-indicator ${enabled ? 'status-active' : 'status-inactive'}"></span>
-                        ${enabled ? 'Active' : 'Inactive'}
-                    </td>
-                `;
-                tbody.appendChild(row);
-
-                // Show API filters if api.requests is selected
-                if (name === 'api.requests') {
-                    document.getElementById('enabled_' + name).addEventListener('change', (e) => {
-                        document.getElementById('apiFilters').style.display = e.target.checked ? 'block' : 'none';
-                    });
-                    
-                    if (enabled) {
-                        document.getElementById('apiFilters').style.display = 'block';
-                        loadApiFilters();
-                    }
-                }
-            }
-        }
-
-        function populatePreviewSelect() {
-            const select = document.getElementById('previewComponent');
-            select.innerHTML = '';
-            
-            for (const name of Object.keys(componentsData.components)) {
-                const option = document.createElement('option');
-                option.value = name;
-                option.textContent = name;
-                select.appendChild(option);
-            }
-            
-            loadLogPreview();
-        }
-
-        async function updateComponentEnabled(component) {
-            const enabled = document.getElementById('enabled_' + component).checked;
-
-            try {
-                const response = await fetch('/api/log/config', {
-                    method: 'POST',
-                    headers: {'Content-Type': 'application/json'},
-                    body: JSON.stringify({
-                        component: component,
-                        enabled: enabled
-                    })
-                });
-
-                const data = await response.json();
-                
-                if (data.success) {
-                    showNotification(`${enabled ? 'Enabled' : 'Disabled'} ${component}`, 'success');
-                    
-                    // Update status indicator
-                    const row = document.getElementById('enabled_' + component).closest('tr');
-                    const statusCell = row.querySelector('td:last-child');
-                    statusCell.innerHTML = `
-                        <span class="status-indicator ${enabled ? 'status-active' : 'status-inactive'}"></span>
-                        ${enabled ? 'Active' : 'Inactive'}
-                    `;
-                } else {
-                    showNotification('Failed to update ' + component + ': ' + data.error, 'error');
-                }
-            } catch (error) {
-                showNotification('Error updating component: ' + error.message, 'error');
-            }
-        }
-
-        async function updateComponentLevels(component) {
-            // Collect all checked levels
-            const allLevels = ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'];
-            if (component === 'api.requests') {
-                allLevels.push('API');
-            }
-            
-            const enabledLevels = allLevels.filter(level => {
-                const checkbox = document.getElementById(`level_${component}_${level}`);
-                return checkbox && checkbox.checked;
-            });
-
-            try {
-                const response = await fetch('/api/log/config', {
-                    method: 'POST',
-                    headers: {'Content-Type': 'application/json'},
-                    body: JSON.stringify({
-                        component: component,
-                        enabled_levels: enabledLevels
-                    })
-                });
-
-                const data = await response.json();
-                
-                if (data.success) {
-                    showNotification(`Updated levels for ${component}: ${enabledLevels.join(', ')}`, 'success');
-                } else {
-                    showNotification('Failed to update ' + component + ': ' + data.error, 'error');
-                }
-            } catch (error) {
-                showNotification('Error updating component: ' + error.message, 'error');
-            }
-        }
-
-        async function updateGlobalLevel() {
-            // Deprecated - kept for compatibility
-            showNotification('Global level setting removed. Use individual component levels instead.', 'success');
-        }
-
-        async function loadApiFilters() {
-            if (!currentConfig || !currentConfig.components['api.requests']) return;
-
-            const filters = currentConfig.components['api.requests'].filters || {};
-            document.getElementById('excludePaths').value = (filters.exclude_paths || []).join(', ');
-            document.getElementById('excludeStatus').value = (filters.exclude_status || []).join(', ');
-            document.getElementById('includeSlowRequests').checked = filters.include_slow_requests !== false;
-            document.getElementById('slowThreshold').value = filters.slow_threshold_ms || 1000;
-        }
-
-        async function saveApiFilters() {
-            const excludePaths = document.getElementById('excludePaths').value
-                .split(',')
-                .map(s => s.trim())
-                .filter(s => s.length > 0);
-            
-            const excludeStatus = document.getElementById('excludeStatus').value
-                .split(',')
-                .map(s => parseInt(s.trim()))
-                .filter(n => !isNaN(n));
-            
-            const includeSlowRequests = document.getElementById('includeSlowRequests').checked;
-            const slowThreshold = parseInt(document.getElementById('slowThreshold').value);
-
-            try {
-                const response = await fetch('/api/log/filters', {
-                    method: 'POST',
-                    headers: {'Content-Type': 'application/json'},
-                    body: JSON.stringify({
-                        exclude_paths: excludePaths,
-                        exclude_status: excludeStatus,
-                        include_slow_requests: includeSlowRequests,
-                        slow_threshold_ms: slowThreshold
-                    })
-                });
-
-                const data = await response.json();
-                
-                if (data.success) {
-                    showNotification('API filters saved', 'success');
-                } else {
-                    showNotification('Failed to save filters: ' + data.error, 'error');
-                }
-            } catch (error) {
-                showNotification('Error saving filters: ' + error.message, 'error');
-            }
-        }
-
-        async function saveAllSettings() {
-            // Reload configuration to apply all changes
-            try {
-                const response = await fetch('/api/log/reload', {
-                    method: 'POST'
-                });
-
-                const data = await response.json();
-                
-                if (data.success) {
-                    showNotification('All settings saved and reloaded', 'success');
-                    await loadConfiguration();
-                    await loadComponents();
-                } else {
-                    showNotification('Failed to reload settings: ' + data.error, 'error');
-                }
-            } catch (error) {
-                showNotification('Error saving settings: ' + error.message, 'error');
-            }
-        }
-
-        async function resetToDefaults() {
-            if (!confirm('Are you sure you want to reset all logging settings to defaults?')) {
-                return;
-            }
-
-            try {
-                const response = await fetch('/api/log/reset', {
-                    method: 'POST'
-                });
-
-                const data = await response.json();
-                
-                if (data.success) {
-                    showNotification('Settings reset to defaults', 'success');
-                    await loadConfiguration();
-                    await loadComponents();
-                } else {
-                    showNotification('Failed to reset settings: ' + data.error, 'error');
-                }
-            } catch (error) {
-                showNotification('Error resetting settings: ' + error.message, 'error');
-            }
-        }
-
-        async function loadLogPreview() {
-            const component = document.getElementById('previewComponent').value;
-            const preview = document.getElementById('logPreview');
-            
-            preview.innerHTML = '<div class="loading">Loading logs...</div>';
-
-            try {
-                const response = await fetch(`/api/log/files/${component}?lines=50`);
-                const data = await response.json();
-                
-                if (data.success) {
-                    if (data.lines.length === 0) {
-                        preview.innerHTML = '<div class="loading">No logs yet for this component</div>';
-                    } else {
-                        preview.innerHTML = data.lines.map(line => 
-                            `<div class="log-line">${escapeHtml(line)}</div>`
-                        ).join('');
-                        
-                        // Scroll to bottom
-                        preview.scrollTop = preview.scrollHeight;
-                    }
-                } else {
-                    preview.innerHTML = `<div class="loading">Error: ${data.error}</div>`;
-                }
-            } catch (error) {
-                preview.innerHTML = `<div class="loading">Error loading logs: ${error.message}</div>`;
-            }
-        }
-
-        function escapeHtml(text) {
-            const div = document.createElement('div');
-            div.textContent = text;
-            return div.innerHTML;
-        }
-
-        function showNotification(message, type) {
-            const notification = document.createElement('div');
-            notification.className = `notification notification-${type}`;
-            notification.textContent = message;
-            document.body.appendChild(notification);
-
-            setTimeout(() => {
-                notification.remove();
-            }, 3000);
-        }
-
-        // Auto-refresh log preview every 5 seconds
-        setInterval(() => {
-            if (document.getElementById('previewComponent').value) {
-                loadLogPreview();
-            }
-        }, 5000);
-    </script>
-</body>
-</html>

bot/utils/autonomous_v1_legacy.py

@@ -814,6 +814,9 @@ async def miku_autonomous_reaction_for_server(guild_id: int, force_message=None,
                 # Skip bot's own messages
                 if message.author == globals.client.user:
                     continue
+                # Skip messages that directly addressed Miku (handled by main handler)
+                if globals.client.user and globals.client.user in message.mentions:
+                    continue
                 # Skip messages we've already reacted to
                 if message.id in _reacted_message_ids:
                     continue
@@ -979,6 +982,11 @@ async def miku_autonomous_reaction_for_dm(user_id: int, force_message=None):
                 # Skip bot's own messages
                 if message.author == globals.client.user:
                     continue
+                # Skip messages with media attachments in DMs — these are always directed at
+                # Miku and already processed by the main on_message handler, so analyzing them
+                # again here would trigger a redundant vision model call
+                if message.attachments:
+                    continue
                 # Skip messages we've already reacted to
                 if message.id in _reacted_message_ids:
                     continue

bot/utils/bipolar_mode.py

@@ -263,14 +263,21 @@ def get_persona_avatar_urls() -> dict:
     """Get current avatar URLs for Miku and Evil Miku personas.
     
     Returns a dict with 'miku' and 'evil_miku' avatar URL strings (or None).
-    Uses the bot's current Discord avatar for Miku (always up-to-date).
-    Evil Miku falls back to the webhook's stored avatar (passed as None).
+    When Evil Mode is inactive, uses the bot's current Discord avatar for Miku.
+    When Evil Mode is active, the bot account avatar has been changed to the evil
+    pfp, so returning None for Miku causes the webhook to fall back to its own
+    stored avatar (set to current.png by update_webhook_avatars), preventing
+    both webhooks from showing the evil pfp.
+    Evil Miku always falls back to the webhook's stored avatar (passed as None).
     """
     miku_url = None
     evil_url = None
     
-    # For Miku: use the bot's actual Discord avatar URL (always matches current pfp)
-    if globals.client and globals.client.user:
+    # For Miku: use the bot's actual Discord avatar URL only when Evil Mode is
+    # not active. When Evil Mode is on, the bot account avatar has been swapped
+    # to evil_pfp.png, so we pass None here and let the Miku webhook use its
+    # own correctly-stored avatar (current.png) instead.
+    if not globals.EVIL_MODE and globals.client and globals.client.user:
         try:
             miku_url = str(globals.client.user.display_avatar.url)
         except Exception:
@@ -883,23 +890,15 @@ async def run_argument(channel: discord.TextChannel, client, trigger_context: st
         if last_message is None:
             init_prompt = get_argument_start_prompt(initiator, trigger_context)
             
-            # Temporarily set evil mode for query_llama if initiator is evil
-            original_evil_mode = globals.EVIL_MODE
-            if initiator == "evil":
-                globals.EVIL_MODE = True
-            else:
-                globals.EVIL_MODE = False
-            
-            try:
-                initial_message = await query_llama(
-                    user_prompt=init_prompt,
-                    user_id=argument_user_id,
-                    guild_id=guild_id,
-                    response_type="autonomous_general",
-                    model=globals.EVIL_TEXT_MODEL if initiator == "evil" else globals.TEXT_MODEL
-                )
-            finally:
-                globals.EVIL_MODE = original_evil_mode
+            # Use force_evil_context to avoid race condition with globals.EVIL_MODE
+            initial_message = await query_llama(
+                user_prompt=init_prompt,
+                user_id=argument_user_id,
+                guild_id=guild_id,
+                response_type="autonomous_general",
+                model=globals.EVIL_TEXT_MODEL if initiator == "evil" else globals.TEXT_MODEL,
+                force_evil_context=(initiator == "evil")
+            )
             
             if not initial_message or initial_message.startswith("Error") or initial_message.startswith("Sorry"):
                 logger.error("Failed to generate initial argument message")
@@ -994,23 +993,15 @@ async def run_argument(channel: discord.TextChannel, client, trigger_context: st
                     # Add last message as context
                     response_prompt = f'The other Miku said: "{last_message}"\n\n{end_prompt}'
                     
-                    # Temporarily set evil mode for query_llama
-                    original_evil_mode = globals.EVIL_MODE
-                    if winner == "evil":
-                        globals.EVIL_MODE = True
-                    else:
-                        globals.EVIL_MODE = False
-                    
-                    try:
-                        final_message = await query_llama(
-                            user_prompt=response_prompt,
-                            user_id=argument_user_id,
-                            guild_id=guild_id,
-                            response_type="autonomous_general",
-                            model=globals.EVIL_TEXT_MODEL if winner == "evil" else globals.TEXT_MODEL
-                        )
-                    finally:
-                        globals.EVIL_MODE = original_evil_mode
+                    # Use force_evil_context to avoid race condition with globals.EVIL_MODE
+                    final_message = await query_llama(
+                        user_prompt=response_prompt,
+                        user_id=argument_user_id,
+                        guild_id=guild_id,
+                        response_type="autonomous_general",
+                        model=globals.EVIL_TEXT_MODEL if winner == "evil" else globals.TEXT_MODEL,
+                        force_evil_context=(winner == "evil")
+                    )
                     
                     if final_message and not final_message.startswith("Error") and not final_message.startswith("Sorry"):
                         # Send winner's final message via webhook
@@ -1059,23 +1050,15 @@ async def run_argument(channel: discord.TextChannel, client, trigger_context: st
             else:
                 response_prompt = get_miku_argument_prompt(last_message, is_first_response=is_first_response)
             
-            # Temporarily set evil mode for query_llama
-            original_evil_mode = globals.EVIL_MODE
-            if current_speaker == "evil":
-                globals.EVIL_MODE = True
-            else:
-                globals.EVIL_MODE = False
-            
-            try:
-                response = await query_llama(
-                    user_prompt=response_prompt,
-                    user_id=argument_user_id,
-                    guild_id=guild_id,
-                    response_type="autonomous_general",
-                    model=globals.EVIL_TEXT_MODEL if current_speaker == "evil" else globals.TEXT_MODEL
-                )
-            finally:
-                globals.EVIL_MODE = original_evil_mode
+            # Use force_evil_context to avoid race condition with globals.EVIL_MODE
+            response = await query_llama(
+                user_prompt=response_prompt,
+                user_id=argument_user_id,
+                guild_id=guild_id,
+                response_type="autonomous_general",
+                model=globals.EVIL_TEXT_MODEL if current_speaker == "evil" else globals.TEXT_MODEL,
+                force_evil_context=(current_speaker == "evil")
+            )
             
             if not response or response.startswith("Error") or response.startswith("Sorry"):
                 logger.error(f"Failed to generate argument response")

bot/utils/cat_client.py

@@ -107,7 +107,8 @@ class CatAdapter:
         author_name: Optional[str] = None,
         mood: Optional[str] = None,
         response_type: str = "dm_response",
-    ) -> Optional[str]:
+        media_type: Optional[str] = None,
+    ) -> Optional[tuple]:
         """
         Send a message through the Cat pipeline via WebSocket and get a response.
         
@@ -123,9 +124,11 @@ class CatAdapter:
             author_name: Display name of the user
             mood: Current mood name (passed as metadata for Cat hooks)
             response_type: Type of response context
+            media_type: Type of media attachment ("image", "video", "gif", "tenor_gif")
             
         Returns:
-            Cat's response text, or None if Cat is unavailable (caller should fallback)
+            Tuple of (response_text, full_prompt) on success, or None if Cat
+            is unavailable (caller should fallback to query_llama)
         """
         if not globals.USE_CHESHIRE_CAT:
             return None
@@ -146,10 +149,18 @@ class CatAdapter:
             payload["discord_guild_id"] = str(guild_id)
         if author_name:
             payload["discord_author_name"] = author_name
-        if mood:
+        # When evil mode is active, send the evil mood name instead of the normal mood
+        if globals.EVIL_MODE:
+            payload["discord_mood"] = getattr(globals, 'EVIL_DM_MOOD', 'evil_neutral')
+        elif mood:
             payload["discord_mood"] = mood
         if response_type:
             payload["discord_response_type"] = response_type
+        # Pass evil mode flag so discord_bridge stores it in working_memory
+        payload["discord_evil_mode"] = globals.EVIL_MODE
+        # Pass media type so discord_bridge can add MEDIA NOTE to the prompt
+        if media_type:
+            payload["discord_media_type"] = media_type
 
         try:
             # Build WebSocket URL from HTTP base URL
@@ -170,6 +181,7 @@ class CatAdapter:
                     # Cat may send intermediate messages (chat_token for streaming,
                     # notification for status updates). We want the final "chat" one.
                     reply_text = None
+                    full_prompt = ""
                     deadline = asyncio.get_event_loop().time() + self._timeout
 
                     while True:
@@ -207,8 +219,9 @@ class CatAdapter:
                         msg_type = msg.get("type", "")
 
                         if msg_type == "chat":
-                            # Final response — extract text
+                            # Final response — extract text and full prompt
                             reply_text = msg.get("content") or msg.get("text", "")
+                            full_prompt = msg.get("full_prompt", "")
                             break
                         elif msg_type == "chat_token":
                             # Streaming token — skip, we wait for final
@@ -227,7 +240,7 @@ class CatAdapter:
             if reply_text and reply_text.strip():
                 self._consecutive_failures = 0
                 logger.info(f"🐱 Cat response for {cat_user_id}: {reply_text[:100]}...")
-                return reply_text
+                return reply_text, full_prompt
             else:
                 logger.warning("Cat returned empty response via WS")
                 self._consecutive_failures += 1
@@ -634,6 +647,222 @@ class CatAdapter:
             logger.error(f"Consolidation error: {e}")
             return None
 
+    # ====================================================================
+    # Admin API helpers – plugin toggling & LLM model switching
+    # ====================================================================
+
+    async def wait_for_ready(self, max_wait: int = 120, interval: int = 5) -> bool:
+        """Wait for Cat to become reachable, polling with interval.
+        
+        Used on startup to avoid race conditions when bot starts before Cat.
+        Returns True once Cat responds, False if max_wait exceeded.
+        """
+        start = time.time()
+        attempt = 0
+        while time.time() - start < max_wait:
+            attempt += 1
+            try:
+                async with aiohttp.ClientSession() as session:
+                    async with session.get(
+                        f"{self._base_url}/",
+                        timeout=aiohttp.ClientTimeout(total=5),
+                    ) as resp:
+                        if resp.status == 200:
+                            elapsed = time.time() - start
+                            logger.info(f"🐱 Cat is ready (took {elapsed:.1f}s, {attempt} attempts)")
+                            self._healthy = True
+                            self._last_health_check = time.time()
+                            return True
+            except Exception:
+                pass
+            if attempt == 1:
+                logger.info(f"⏳ Waiting for Cat to become ready (up to {max_wait}s)...")
+            await asyncio.sleep(interval)
+        logger.error(f"Cat did not become ready within {max_wait}s ({attempt} attempts)")
+        return False
+
+    async def toggle_plugin(self, plugin_id: str) -> bool:
+        """Toggle a Cat plugin on/off via the admin API.
+        
+        PUT /plugins/toggle/{plugin_id}
+        Returns True on success, False on failure.
+        """
+        url = f"{self._base_url}/plugins/toggle/{plugin_id}"
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.put(
+                    url,
+                    headers=self._get_headers(),
+                    timeout=aiohttp.ClientTimeout(total=15),
+                ) as resp:
+                    if resp.status == 200:
+                        logger.info(f"🐱 Toggled Cat plugin: {plugin_id}")
+                        return True
+                    else:
+                        body = await resp.text()
+                        logger.error(f"Cat plugin toggle failed ({resp.status}): {body}")
+                        return False
+        except Exception as e:
+            logger.error(f"Cat plugin toggle error for {plugin_id}: {e}")
+            return False
+
+    async def set_llm_model(self, model_name: str) -> bool:
+        """Switch the Cheshire Cat's active LLM model via settings API.
+        
+        The Cat settings API uses UUIDs: we must first GET /settings/ to find
+        the setting_id for LLMOpenAIChatConfig, then PUT /settings/{setting_id}.
+        llama-swap handles the actual model loading based on model_name.
+        Returns True on success, False on failure.
+        """
+        try:
+            # Step 1: Find the setting_id for LLMOpenAIChatConfig
+            setting_id = None
+            async with aiohttp.ClientSession() as session:
+                async with session.get(
+                    f"{self._base_url}/settings/",
+                    headers=self._get_headers(),
+                    timeout=aiohttp.ClientTimeout(total=10),
+                ) as resp:
+                    if resp.status != 200:
+                        logger.error(f"Cat settings GET failed ({resp.status})")
+                        return False
+                    data = await resp.json()
+                    for s in data.get("settings", []):
+                        if s.get("name") == "LLMOpenAIChatConfig":
+                            setting_id = s["setting_id"]
+                            break
+
+            if not setting_id:
+                logger.error("Could not find LLMOpenAIChatConfig setting_id in Cat settings")
+                return False
+
+            # Step 2: PUT updated config to /settings/{setting_id}
+            payload = {
+                "name": "LLMOpenAIChatConfig",
+                "value": {
+                    "openai_api_key": "sk-dummy",
+                    "model_name": model_name,
+                    "temperature": 0.8,
+                    "streaming": False,
+                },
+                "category": "llm_factory",
+            }
+            async with aiohttp.ClientSession() as session:
+                async with session.put(
+                    f"{self._base_url}/settings/{setting_id}",
+                    json=payload,
+                    headers=self._get_headers(),
+                    timeout=aiohttp.ClientTimeout(total=15),
+                ) as resp:
+                    if resp.status == 200:
+                        logger.info(f"🐱 Set Cat LLM model to: {model_name}")
+                        return True
+                    else:
+                        body = await resp.text()
+                        logger.error(f"Cat LLM model switch failed ({resp.status}): {body}")
+                        return False
+        except Exception as e:
+            logger.error(f"Cat LLM model switch error: {e}")
+            return False
+
+    async def get_active_plugins(self) -> list:
+        """Get list of active Cat plugin IDs.
+        
+        GET /plugins  → returns {\"installed\": [...], \"filters\": {...}}
+        Each plugin has \"id\" and \"active\" fields.
+        """
+        url = f"{self._base_url}/plugins"
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(
+                    url,
+                    headers=self._get_headers(),
+                    timeout=aiohttp.ClientTimeout(total=10),
+                ) as resp:
+                    if resp.status == 200:
+                        data = await resp.json()
+                        installed = data.get("installed", [])
+                        return [p["id"] for p in installed if p.get("active")]
+                    else:
+                        logger.error(f"Cat get_active_plugins failed ({resp.status})")
+                        return []
+        except Exception as e:
+            logger.error(f"Cat get_active_plugins error: {e}")
+            return []
+
+    async def switch_to_evil_personality(self) -> bool:
+        """Disable miku_personality, enable evil_miku_personality, switch LLM to darkidol.
+        
+        Checks current plugin state first to avoid double-toggling
+        (the Cat API is a toggle, not enable/disable).
+        Returns True if all operations succeed, False if any fail.
+        """
+        logger.info("🐱 Switching Cat to Evil Miku personality...")
+        success = True
+
+        # Check current plugin state
+        active = await self.get_active_plugins()
+
+        # Step 1: Disable normal personality (only if currently active)
+        if "miku_personality" in active:
+            if not await self.toggle_plugin("miku_personality"):
+                logger.error("Failed to disable miku_personality plugin")
+                success = False
+            await asyncio.sleep(1)
+        else:
+            logger.debug("miku_personality already disabled, skipping toggle")
+
+        # Step 2: Enable evil personality (only if currently inactive)
+        if "evil_miku_personality" not in active:
+            if not await self.toggle_plugin("evil_miku_personality"):
+                logger.error("Failed to enable evil_miku_personality plugin")
+                success = False
+        else:
+            logger.debug("evil_miku_personality already active, skipping toggle")
+
+        # Step 3: Switch LLM model to darkidol (the uncensored evil model)
+        if not await self.set_llm_model("darkidol"):
+            logger.error("Failed to switch Cat LLM to darkidol")
+            success = False
+
+        return success
+
+    async def switch_to_normal_personality(self) -> bool:
+        """Disable evil_miku_personality, enable miku_personality, switch LLM to llama3.1.
+        
+        Checks current plugin state first to avoid double-toggling.
+        Returns True if all operations succeed, False if any fail.
+        """
+        logger.info("🐱 Switching Cat to normal Miku personality...")
+        success = True
+
+        # Check current plugin state
+        active = await self.get_active_plugins()
+
+        # Step 1: Disable evil personality (only if currently active)
+        if "evil_miku_personality" in active:
+            if not await self.toggle_plugin("evil_miku_personality"):
+                logger.error("Failed to disable evil_miku_personality plugin")
+                success = False
+            await asyncio.sleep(1)
+        else:
+            logger.debug("evil_miku_personality already disabled, skipping toggle")
+
+        # Step 2: Enable normal personality (only if currently inactive)
+        if "miku_personality" not in active:
+            if not await self.toggle_plugin("miku_personality"):
+                logger.error("Failed to enable miku_personality plugin")
+                success = False
+        else:
+            logger.debug("miku_personality already active, skipping toggle")
+
+        # Step 3: Switch LLM model back to llama3.1 (normal model)
+        if not await self.set_llm_model("llama3.1"):
+            logger.error("Failed to switch Cat LLM to llama3.1")
+            success = False
+
+        return success
+
 
 # Singleton instance
 cat_adapter = CatAdapter()

bot/utils/core.py

@@ -1,167 +1,165 @@
 # utils/core.py
+#
+# Detects whether a Discord message is **addressed to** Miku
+# (as opposed to merely mentioning her).
 
-import asyncio
-import aiohttp
 import re
-
-import globals
-# Langchain imports below are only used in commented-out code
-# from langchain_community.vectorstores import FAISS
-# from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter
-# from langchain_core.documents import Document
 from utils.logger import get_logger
 
 logger = get_logger('core')
 
 
-# switch_model() removed - llama-swap handles model switching automatically
+# ────────────────────────────────────────────────────────────────────
+# Pre-compiled Miku addressing patterns
+# Built once at module load; is_miku_addressed() runs only 4 .search()
+# ────────────────────────────────────────────────────────────────────
 
+def _build_name_variants(bases, honorifics, prefixes, connector, prefix_connector):
+    """Return regex fragments for every name+honorific+prefix combo
+    within a single script family."""
+    variants = []
+    for base in bases:
+        be = re.escape(base)
+        variants.append(be)
+        for h in honorifics:
+            he = re.escape(h)
+            variants.append(be + connector + he)
+        for p in prefixes:
+            pe = re.escape(p)
+            variants.append(pe + prefix_connector + be)
+            for h in honorifics:
+                he = re.escape(h)
+                variants.append(pe + prefix_connector + be + connector + he)
+    return variants
+
+
+def _compile_addressing_patterns():
+    """Compile the four addressing regexes.
+
+    START  – name at the beginning, followed by punctuation
+             "Miku, how are you?"  "みく！聞いて"
+    END    – comma then name at the end
+             "how are you, Miku?"  "教えて、ミク"
+    MIDDLE – name surrounded by commas (vocative)
+             "On the contrary, Miku, I think…"
+    ALONE  – name is the entire message
+             "Miku"  "みく！"  "ミクちゃん"
+    """
+    latin = _build_name_variants(
+        bases=['miku'],
+        honorifics=[
+            'chan', 'san', 'kun', 'nyan', 'hime', 'tan', 'chin', 'heika',
+            'denka', 'kakka', 'shi', 'chama', 'kyun', 'dono', 'sensei',
+            'senpai', 'jou',
+        ],
+        prefixes=['o-'],
+        connector=r'[\-\s]?',
+        prefix_connector=r'\s?',
+    )
+
+    cyrillic = _build_name_variants(
+        bases=['мику'],
+        honorifics=[
+            'чан', 'сан', 'кун', 'нян', 'химе', 'тан', 'чин',
+            'хейка', 'хеика', 'денка', 'какка', 'си', 'чама', 'кюн',
+            'доно', 'сенсэй', 'сенсеи', 'сенпай', 'сенпаи', 'джо',
+        ],
+        prefixes=['о-'],
+        connector=r'[\-\s]?',
+        prefix_connector=r'\s?',
+    )
+
+    japanese = _build_name_variants(
+        bases=['みく', 'ミク', '未来'],
+        honorifics=[
+            # Hiragana
+            'ちゃん', 'さん', 'くん', 'にゃん', 'ひめ', 'たん', 'ちん',
+            'へいか', 'でんか', 'かっか', 'し', 'ちゃま', 'きゅん', 'どの',
+            'せんせい', 'せんぱい', 'じょう',
+            # Katakana
+            'チャン', 'サン', 'クン', 'ニャン', 'ヒメ', 'タン', 'チン',
+            'ヘイカ', 'デンカ', 'カッカ', 'シ', 'チャマ', 'キュン', 'ドノ',
+            'センセイ', 'センパイ', 'ジョウ',
+        ],
+        prefixes=['お', 'オ'],
+        connector=r'[-]?',
+        prefix_connector=r'',
+    )
+
+    # Longest-first so the regex engine prefers the most specific match
+    all_v = sorted(latin + cyrillic + japanese, key=len, reverse=True)
+    alts = '|'.join(all_v)
+
+    NAME   = rf'\b(?:{alts})\b'
+    PUNCT  = r'[,，、:：!！?？.。]'        # addressing punctuation after name
+    COMMA  = r'[,，、]'                      # comma variants (before name / vocative)
+    ETRAIL = r'[!！?？.。~～]*'             # optional trailing at end
+    ATRAIL = r'[!！?？.。~～♪♡❤]*'         # optional trailing for name-only messages
+
+    start_re  = re.compile(rf'^\s*{NAME}\s*{PUNCT}',            re.IGNORECASE)
+    end_re    = re.compile(rf'{COMMA}\s*{NAME}\s*{ETRAIL}\s*$', re.IGNORECASE)
+    middle_re = re.compile(rf'{COMMA}\s*{NAME}\s*{COMMA}',      re.IGNORECASE)
+    alone_re  = re.compile(rf'^\s*{NAME}\s*{ATRAIL}\s*$',       re.IGNORECASE)
+
+    logger.info(f"Miku addressing: {len(all_v)} name variants compiled into 4 patterns")
+    return start_re, end_re, middle_re, alone_re
+
+
+try:
+    _START_RE, _END_RE, _MIDDLE_RE, _ALONE_RE = _compile_addressing_patterns()
+except Exception as e:
+    logger.error(f"Failed to compile addressing patterns: {e}")
+    _START_RE = _END_RE = _MIDDLE_RE = _ALONE_RE = None
+
+
+# ────────────────────────────────────────────────────────────────────
 
 async def is_miku_addressed(message) -> bool:
-    # Check if this is a DM (no guild)
+    """Return True only when the message is directed *at* Miku,
+    not merely mentioning her.
+
+    Always responds to:  DMs, @mentions, replies to Miku's messages.
+
+    For normal messages checks whether Miku's name (in any supported
+    script / honorific combination) appears in an "addressing" position:
+      • Start  – "Miku, how are you?"
+      • End    – "how are you, Miku?"
+      • Middle – "On the contrary, Miku, I think…"
+      • Alone  – "Miku!" / "ミクちゃん"
+
+    Does NOT trigger on mere mentions:
+      • "I like Miku" / "Miku is cool" / "told miku about it"
+    """
+    # DMs – always respond
     if message.guild is None:
-        # In DMs, always respond to every message
         return True
-    
-    # Safety check: ensure guild and guild.me exist
+
     if not message.guild or not message.guild.me:
-        logger.warning(f"Invalid guild or guild.me in message from {message.author}")
+        logger.warning(f"Invalid guild/guild.me for message from {message.author}")
         return False
-    
-    # If message contains a ping for Miku, return true
+
+    # @mention
     if message.guild.me in message.mentions:
         return True
 
-    # If message is a reply, check the referenced message author
+    # Reply to Miku
     if message.reference:
         try:
-            referenced_msg = await message.channel.fetch_message(message.reference.message_id)
-            if referenced_msg.author == message.guild.me:
+            ref = await message.channel.fetch_message(message.reference.message_id)
+            if ref.author == message.guild.me:
                 return True
         except Exception as e:
             logger.warning(f"Could not fetch referenced message: {e}")
 
-    cleaned = message.content.strip()
-    cleaned_lower = cleaned.lower()
-    
-    # Base names for Miku in different scripts
-    base_names = [
-        'miku', 'мику', 'みく', 'ミク', '未来'
-    ]
-    
-    # Japanese honorifics - all scripts combined
-    honorifics = [
-        # Latin
-        'chan', 'san', 'kun', 'nyan', 'hime', 'tan', 'chin', 'heika', 
-        'denka', 'kakka', 'shi', 'chama', 'kyun', 'dono', 'sensei', 'senpai', 'jou',
-        # Hiragana
-        'ちゃん', 'さん', 'くん', 'にゃん', 'ひめ', 'たん', 'ちん', 'へいか',
-        'でんか', 'かっか', 'し', 'ちゃま', 'きゅん', 'どの', 'せんせい', 'せんぱい', 'じょう',
-        # Katakana
-        'チャン', 'サン', 'クン', 'ニャン', 'ヒメ', 'タン', 'チン', 'ヘイカ',
-        'デンカ', 'カッカ', 'シ', 'チャマ', 'キュン', 'ドノ', 'センセイ', 'センパイ', 'ジョウ',
-        # Cyrillic
-        'чан', 'сан', 'кун', 'нян', 'химе', 'тан', 'чин', 'хейка', 'хеика',
-        'денка', 'какка', 'си', 'чама', 'кюн', 'доно', 'сенсэй', 'сенсеи', 'сенпай', 'сенпаи', 'джо'
-    ]
-    
-    # o- prefix variants
-    o_prefixes = ['o-', 'о-', 'お', 'オ']
-    
-    # Build all possible name variations to check
-    name_patterns = []
-    
-    for base in base_names:
-        base_lower = base.lower()
-        base_escaped = re.escape(base_lower)
-        
-        # Base name alone
-        name_patterns.append(base_escaped)
-        
-        # With honorifics (allows optional dash/space between)
-        for honorific in honorifics:
-            honorific_lower = honorific.lower()
-            honorific_escaped = re.escape(honorific_lower)
-            # Build pattern: base + optional [dash or space] + honorific
-            name_patterns.append(base_escaped + r'[\-\s]*' + honorific_escaped)
-        
-        # With o- prefix
-        for prefix in o_prefixes:
-            prefix_lower = prefix.lower()
-            prefix_escaped = re.escape(prefix_lower)
-            # o-prefix + optional space + base
-            name_patterns.append(prefix_escaped + r'\s*' + base_escaped)
-            
-            # With o- prefix + honorific
-            for honorific in honorifics:
-                honorific_lower = honorific.lower()
-                honorific_escaped = re.escape(honorific_lower)
-                # o-prefix + space + base + dash/space + honorific
-                name_patterns.append(prefix_escaped + r'\s*' + base_escaped + r'[\-\s]*' + honorific_escaped)
-    
-    # Check all patterns - she must be "addressed" not just mentioned
-    for pattern in name_patterns:
-        try:
-            # Pattern 1: Start of message + punctuation/end
-            # "Miku, ..." or "みく！" or "ミクちゃん、..."
-            start_p = r'^' + pattern + r'(?:[,，、!！?？.。\s]+|$)'
-            if re.search(start_p, cleaned_lower, re.IGNORECASE):
-                return True
-            
-            # Pattern 2: End of message (optionally preceded by punctuation)
-            # "..., Miku" or "...みく" or "...ミクちゃん！"
-            end_p = r'(?:[,，、!！?？.。\s]+|^)' + pattern + r'[!！?？.。\s]*$'
-            if re.search(end_p, cleaned_lower, re.IGNORECASE):
-                return True
-            
-            # Pattern 3: Middle (surrounded by punctuation)
-            # "..., Miku, ..." or "...、ミク、..."
-            middle_p = r'[,，、!！?？.。\s]+' + pattern + r'[,，、!！?？.。\s]+'
-            if re.search(middle_p, cleaned_lower, re.IGNORECASE):
-                return True
-            
-            # Pattern 4: Just the name alone
-            # "Miku" or "みく！" or "ミクちゃん"
-            alone_p = r'^\s*' + pattern + r'[!！?？.。]*\s*$'
-            if re.search(alone_p, cleaned_lower, re.IGNORECASE):
-                return True
-        except re.error as e:
-            # Log the problematic pattern and skip it
-            logger.error(f"REGEX ERROR - Pattern: '{pattern}' | Start regex: '{start_p}' | Error: {e}")
-            continue
-    
-    return False
+    # Regex addressing (4 pre-compiled patterns)
+    if _START_RE is None:
+        logger.error("Addressing patterns not compiled – skipping pattern check")
+        return False
 
-# Vectorstore functionality disabled - not needed with current structured context approach
-# If you need embeddings in the future, you can use a different embedding provider
-# For now, the bot uses structured prompts from context_manager.py
-
-# def load_miku_knowledge():
-#     with open("miku_lore.txt", "r", encoding="utf-8") as f:
-#         text = f.read()
-#     
-#     from langchain_text_splitters import RecursiveCharacterTextSplitter
-#     
-#     text_splitter = RecursiveCharacterTextSplitter(
-#         chunk_size=520,
-#         chunk_overlap=50,
-#         separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""]
-#     )
-#     
-#     docs = [Document(page_content=chunk) for chunk in text_splitter.split_text(text)]
-#     
-#     vectorstore = FAISS.from_documents(docs, embeddings)
-#     return vectorstore
-# 
-# def load_miku_lyrics():
-#     with open("miku_lyrics.txt", "r", encoding="utf-8") as f:
-#         lyrics_text = f.read()
-#     
-#     text_splitter = CharacterTextSplitter(chunk_size=520, chunk_overlap=50)
-#     docs = [Document(page_content=chunk) for chunk in text_splitter.split_text(lyrics_text)]
-#     
-#     vectorstore = FAISS.from_documents(docs, embeddings)
-#     return vectorstore
-# 
-# miku_vectorstore = load_miku_knowledge()
-# miku_lyrics_vectorstore = load_miku_lyrics()
+    text = message.content.strip()
+    return bool(
+        _START_RE.search(text)
+        or _END_RE.search(text)
+        or _MIDDLE_RE.search(text)
+        or _ALONE_RE.search(text)
+    )

bot/utils/evil_mode.py

@@ -109,21 +109,68 @@ async def restore_evil_cat_state():
     """Switch Cat to the correct personality plugin + LLM model based on evil mode state.
     
     Must be called after the event loop is running (e.g., in on_ready).
+    Waits for Cat to become reachable, then retries plugin switching with
+    verification to handle the common race condition where bot starts before Cat.
     """
     try:
         from utils.cat_client import cat_adapter
         if not globals.USE_CHESHIRE_CAT:
             return
         
-        if globals.EVIL_MODE:
-            logger.info("Restoring Cat evil personality state on startup...")
-            await cat_adapter.switch_to_evil_personality()
-        else:
-            # Ensure normal state is active (in case evil was toggled off while Cat was down)
-            active = await cat_adapter.get_active_plugins()
-            if "evil_miku_personality" in active:
-                logger.info("Evil plugin still active after normal restore — switching to normal...")
-                await cat_adapter.switch_to_normal_personality()
+        # Wait for Cat to actually be reachable before attempting any API calls
+        if not await cat_adapter.wait_for_ready(max_wait=120, interval=5):
+            logger.error("Cat never became ready — cannot restore personality state")
+            return
+        
+        # Small extra delay to let Cat fully initialize plugins after health endpoint is up
+        await asyncio.sleep(3)
+        
+        max_retries = 3
+        retry_delay = 5
+        
+        for attempt in range(1, max_retries + 1):
+            try:
+                if globals.EVIL_MODE:
+                    if attempt == 1:
+                        logger.info("Restoring Cat evil personality state on startup...")
+                    else:
+                        logger.info(f"Retry {attempt}/{max_retries}: restoring Cat evil personality...")
+                    await cat_adapter.switch_to_evil_personality()
+                else:
+                    active = await cat_adapter.get_active_plugins()
+                    if "evil_miku_personality" in active:
+                        logger.info("Evil plugin still active after normal restore — switching to normal...")
+                        await cat_adapter.switch_to_normal_personality()
+                    else:
+                        # Normal mode, normal plugins — nothing to do
+                        return
+                
+                # Verify the switch actually worked
+                await asyncio.sleep(2)
+                active = await cat_adapter.get_active_plugins()
+                
+                if globals.EVIL_MODE:
+                    if "evil_miku_personality" in active and "miku_personality" not in active:
+                        logger.info("✅ Cat evil personality verified active")
+                        return
+                    else:
+                        logger.warning(f"Cat plugin verification failed (attempt {attempt}): "
+                                       f"evil_active={'evil_miku_personality' in active}, "
+                                       f"normal_active={'miku_personality' in active}")
+                else:
+                    if "miku_personality" in active and "evil_miku_personality" not in active:
+                        logger.info("✅ Cat normal personality verified active")
+                        return
+                    else:
+                        logger.warning(f"Cat plugin verification failed (attempt {attempt})")
+                
+            except Exception as e:
+                logger.error(f"Cat personality restore attempt {attempt} error: {e}")
+            
+            if attempt < max_retries:
+                await asyncio.sleep(retry_delay)
+        
+        logger.error(f"Failed to restore Cat personality after {max_retries} attempts")
     except Exception as e:
         logger.error(f"Failed to restore Cat personality state on startup: {e}")
 

bot/utils/image_handling.py

@@ -17,6 +17,26 @@ logger = get_logger('vision')
 # No need for switch_model anymore - llama-swap handles this automatically
 
 
+def _extract_vision_question(prompt: str):
+    """
+    Strip Discord mentions and bot-name triggers from the user's message to
+    produce a clean question suitable for passing directly to the vision model.
+
+    Returns the cleaned question string, or None if nothing meaningful remains
+    (e.g. the message was just "@Miku" or "miku," with no actual question).
+    """
+    if not prompt:
+        return None
+    # Remove Discord user/role mentions: <@123456789>, <@!123456789>
+    text = re.sub(r'<@[!&]?\d+>', '', prompt).strip()
+    # Strip common bot-name invocation prefixes at the very start (case-insensitive)
+    # e.g. "miku,", "hey miku,", "miku!", "Miku: "
+    text = re.sub(r'^(?:hey\s+)?miku[,!:\s]+', '', text, flags=re.IGNORECASE).strip()
+    # Drop any residual leading punctuation/whitespace
+    text = text.lstrip(',.!? ')
+    return text if text else None
+
+
 async def download_and_encode_image(url):
     """Download and encode an image to base64."""
     async with aiohttp.ClientSession() as session:
@@ -233,11 +253,15 @@ async def extract_video_frames(video_bytes, num_frames=4):
     return None
 
 
-async def analyze_image_with_vision(base64_img):
+async def analyze_image_with_vision(base64_img, user_prompt=None):
     """
     Analyze an image using llama.cpp multimodal capabilities.
     Uses OpenAI-compatible chat completions API with image_url.
     Always uses NVIDIA GPU for vision model.
+
+    If user_prompt is provided (and contains a meaningful question after stripping
+    mentions/triggers), that question is sent to the vision model instead of the
+    generic "Describe this image in detail." prompt.
     """
     from utils.llm import get_vision_gpu_url, check_vision_endpoint_health
     
@@ -247,6 +271,10 @@ async def analyze_image_with_vision(base64_img):
         logger.warning(f"Vision endpoint unhealthy: {error}")
         return f"Vision service currently unavailable: {error}"
     
+    question = _extract_vision_question(user_prompt)
+    vision_prompt_text = question if question else "Describe this image in detail."
+    logger.info(f"Vision prompt for image: {vision_prompt_text!r}")
+
     payload = {
         "model": globals.VISION_MODEL,
         "messages": [
@@ -255,7 +283,7 @@ async def analyze_image_with_vision(base64_img):
                 "content": [
                     {
                         "type": "text",
-                        "text": "Describe this image in detail."
+                        "text": vision_prompt_text
                     },
                     {
                         "type": "image_url",
@@ -267,7 +295,7 @@ async def analyze_image_with_vision(base64_img):
             }
         ],
         "stream": False,
-        "max_tokens": 300
+        "max_tokens": 800
     }
 
     headers = {"Content-Type": "application/json"}
@@ -289,13 +317,16 @@ async def analyze_image_with_vision(base64_img):
                     return f"Error analyzing image: {response.status}"
         except Exception as e:
             logger.error(f"Error in analyze_image_with_vision: {e}", exc_info=True)
+            return f"Error analyzing image: {str(e)}"
 
 
-async def analyze_video_with_vision(video_frames, media_type="video"):
+async def analyze_video_with_vision(video_frames, media_type="video", user_prompt=None):
     """
     Analyze a video or GIF by analyzing multiple frames.
     video_frames: list of base64-encoded frames
     media_type: "video", "gif", or "tenor_gif" to customize the analysis prompt
+    user_prompt: optional raw user message; the vision model will be asked to answer
+                 the specific question instead of giving a generic description.
     """
     from utils.llm import get_vision_gpu_url, check_vision_endpoint_health
     
@@ -305,8 +336,12 @@ async def analyze_video_with_vision(video_frames, media_type="video"):
         logger.warning(f"Vision endpoint unhealthy: {error}")
         return f"Vision service currently unavailable: {error}"
     
-    # Customize prompt based on media type
-    if media_type == "gif":
+    # Customize prompt based on media type, overridden by user question if present
+    question = _extract_vision_question(user_prompt)
+    if question:
+        prompt_text = question
+        logger.info(f"Vision prompt for {media_type}: {prompt_text!r}")
+    elif media_type == "gif":
         prompt_text = "Describe what's happening in this GIF animation. Analyze the sequence of frames and describe the action, motion, and any repeating patterns."
     elif media_type == "tenor_gif":
         prompt_text = "Describe what's happening in this animated GIF. Analyze the sequence of frames and describe the action, emotion, or reaction being shown."
@@ -339,7 +374,7 @@ async def analyze_video_with_vision(video_frames, media_type="video"):
             }
         ],
         "stream": False,
-        "max_tokens": 400
+        "max_tokens": 1000
     }
 
     headers = {"Content-Type": "application/json"}
@@ -368,6 +403,9 @@ async def rephrase_as_miku(vision_output, user_prompt, guild_id=None, user_id=No
     """
     Rephrase vision model's image analysis as Miku would respond to it.
     
+    Routes through Cheshire Cat pipeline for memory-augmented responses,
+    falling back to direct query_llama() if Cat is unavailable.
+    
     Args:
         vision_output: Description from vision model
         user_prompt: User's original message
@@ -402,14 +440,64 @@ async def rephrase_as_miku(vision_output, user_prompt, guild_id=None, user_id=No
     # Use the actual user_id for history tracking, fall back to "image_analysis" for backward compatibility
     history_user_id = user_id if user_id else "image_analysis"
     
-    return await query_llama(
-        formatted_prompt, 
-        user_id=history_user_id, 
-        guild_id=guild_id, 
-        response_type=response_type,
-        author_name=author_name,
-        media_type=media_type  # Pass media type to Miku's LLM
-    )
+    # Determine current mood for Cat pipeline
+    current_mood = globals.DM_MOOD
+    if guild_id:
+        try:
+            from server_manager import server_manager
+            sc = server_manager.get_server_config(guild_id)
+            if sc:
+                current_mood = sc.current_mood_name
+        except Exception:
+            pass
+
+    # Phase 3: Try Cheshire Cat pipeline first (memory-augmented response)
+    # This allows image interactions to be stored in episodic memory and
+    # benefit from declarative memory recall, just like text messages.
+    response = None
+    if globals.USE_CHESHIRE_CAT:
+        try:
+            from utils.cat_client import cat_adapter
+            cat_result = await cat_adapter.query(
+                text=formatted_prompt,
+                user_id=history_user_id,
+                guild_id=str(guild_id) if guild_id else None,
+                author_name=author_name,
+                mood=current_mood,
+                response_type=response_type,
+                media_type=media_type,
+            )
+            if cat_result:
+                response, cat_full_prompt = cat_result
+                effective_mood = current_mood
+                if globals.EVIL_MODE:
+                    effective_mood = f"EVIL:{getattr(globals, 'EVIL_DM_MOOD', 'evil_neutral')}"
+                logger.info(f"🐱 Cat {media_type} response for {author_name} (mood: {effective_mood})")
+                # Track Cat interaction for Web UI Last Prompt view
+                import datetime
+                globals.LAST_CAT_INTERACTION = {
+                    "full_prompt": cat_full_prompt,
+                    "response": response[:500] if response else "",
+                    "user": author_name or history_user_id,
+                    "mood": effective_mood,
+                    "timestamp": datetime.datetime.now().isoformat(),
+                }
+        except Exception as e:
+            logger.warning(f"🐱 Cat {media_type} pipeline error, falling back to query_llama: {e}")
+            response = None
+
+    # Fallback to direct LLM query if Cat didn't respond
+    if not response:
+        response = await query_llama(
+            formatted_prompt, 
+            user_id=history_user_id, 
+            guild_id=guild_id, 
+            response_type=response_type,
+            author_name=author_name,
+            media_type=media_type  # Pass media type to Miku's LLM
+        )
+    
+    return response
 
 # Backward compatibility aliases
 analyze_image_with_qwen = analyze_image_with_vision

bot/utils/moods.py

@@ -33,7 +33,13 @@ EVIL_MOOD_EMOJIS = {
     "aggressive": "👿",
     "cunning": "🐍",
     "sarcastic": "😈",
-    "evil_neutral": ""
+    "evil_neutral": "",
+    "bored": "🥱",
+    "manic": "🤪",
+    "jealous": "💚",
+    "melancholic": "🌑",
+    "playful_cruel": "🎭",
+    "contemptuous": "👑"
 }
 
 def load_mood_description(mood_name: str) -> str:
@@ -150,33 +156,32 @@ def detect_mood_shift(response_text, server_context=None):
     return None
 
 async def rotate_dm_mood():
-    """Rotate DM mood automatically (handles both normal and evil mode)"""
+    """Rotate DM mood automatically (normal mode only — evil has its own independent timer)"""
     try:
-        from utils.evil_mode import is_evil_mode, rotate_evil_mood
+        from utils.evil_mode import is_evil_mode
         
         if is_evil_mode():
-            # Rotate evil mood instead
-            await rotate_evil_mood()
-        else:
-            # Normal mood rotation
-            old_mood = globals.DM_MOOD
-            new_mood = old_mood
-            attempts = 0
-            # Filter out 'asleep' — DMs have no sleepy→asleep transition guard
-            dm_eligible = [m for m in globals.AVAILABLE_MOODS if m != "asleep"]
-            
-            while new_mood == old_mood and attempts < 5:
-                new_mood = random.choice(dm_eligible)
-                attempts += 1
-            
-            globals.DM_MOOD = new_mood
-            globals.DM_MOOD_DESCRIPTION = load_mood_description(new_mood)
-            
-            logger.info(f"DM mood rotated from {old_mood} to {new_mood}")
+            # Evil mode has its own independent 2-hour rotation timer in evil_mode.py
+            # Do nothing here — evil mood rotation is handled by start_evil_mood_rotation()
+            logger.debug("Skipping DM mood rotation — evil mode has its own timer")
+            return
         
-        # Note: We don't update server nicknames here because servers have their own independent moods.
-        # DM mood only affects direct messages to users.
+        # Normal mood rotation
+        old_mood = globals.DM_MOOD
+        new_mood = old_mood
+        attempts = 0
+        # Filter out 'asleep' — DMs have no sleepy→asleep transition guard
+        dm_eligible = [m for m in globals.AVAILABLE_MOODS if m != "asleep"]
         
+        while new_mood == old_mood and attempts < 5:
+            new_mood = random.choice(dm_eligible)
+            attempts += 1
+        
+        globals.DM_MOOD = new_mood
+        globals.DM_MOOD_DESCRIPTION = load_mood_description(new_mood)
+        
+        logger.info(f"DM mood rotated from {old_mood} to {new_mood}")
+    
     except Exception as e:
         logger.error(f"Exception in rotate_dm_mood: {e}")
 

bot/utils/persona_dialogue.py

@@ -264,12 +264,15 @@ class InterjectionScorer:
         if opposite_persona == "evil":
             MOOD_MULTIPLIERS = {
                 "aggressive": 1.5,
-                "cruel": 1.3,
-                "mischievous": 1.2,
+                "manic": 1.4,
+                "jealous": 1.3,
                 "cunning": 1.0,
                 "sarcastic": 1.1,
+                "playful_cruel": 1.2,
+                "contemptuous": 0.7,
                 "evil_neutral": 0.8,
-                "contemplative": 0.6,
+                "bored": 0.5,
+                "melancholic": 0.6,
             }
             return MOOD_MULTIPLIERS.get(globals.EVIL_DM_MOOD, 1.0)
         else:
@@ -505,20 +508,15 @@ On a new line after your response, write:
         # Use appropriate model
         model = globals.EVIL_TEXT_MODEL if responding_persona == "evil" else globals.TEXT_MODEL
         
-        # Temporarily set evil mode for proper context
-        original_evil_mode = globals.EVIL_MODE
-        globals.EVIL_MODE = (responding_persona == "evil")
-        
-        try:
-            raw_response = await query_llama(
-                user_prompt=prompt,
-                user_id=f"persona_dialogue_{channel.id}",
-                guild_id=channel.guild.id if hasattr(channel, 'guild') and channel.guild else None,
-                response_type="autonomous_general",
-                model=model
-            )
-        finally:
-            globals.EVIL_MODE = original_evil_mode
+        # Use force_evil_context to avoid race condition with globals.EVIL_MODE
+        raw_response = await query_llama(
+            user_prompt=prompt,
+            user_id=f"persona_dialogue_{channel.id}",
+            guild_id=channel.guild.id if hasattr(channel, 'guild') and channel.guild else None,
+            response_type="autonomous_general",
+            model=model,
+            force_evil_context=(responding_persona == "evil")
+        )
         
         if not raw_response or raw_response.startswith("Error"):
             return None, False, "LOW"
@@ -553,10 +551,12 @@ On a new line after your response, write:
         
         response_text = '\n'.join(response_lines).strip()
         
-        # Clean up any stray signal markers
-        response_text = response_text.replace("[CONTINUE:", "").replace("]", "")
-        response_text = response_text.replace("YES", "").replace("NO", "")
-        response_text = response_text.replace("HIGH", "").replace("MEDIUM", "").replace("LOW", "")
+        # Clean up any stray [CONTINUE: markers that leaked into response lines
+        # Only strip the structural markers, NOT common words like YES/NO/HIGH etc.
+        import re
+        response_text = re.sub(r'\[CONTINUE:\s*(YES|NO)\]\s*\[CONFIDENCE:\s*(HIGH|MEDIUM|LOW)\]', '', response_text)
+        response_text = re.sub(r'\[CONTINUE:\s*(YES|NO)\]', '', response_text)
+        response_text = re.sub(r'\[CONFIDENCE:\s*(HIGH|MEDIUM|LOW)\]', '', response_text)
         response_text = response_text.strip()
         
         # Override: If the response contains a question mark, always continue
@@ -727,19 +727,15 @@ Don't force a response if you have nothing meaningful to contribute."""
         
         model = globals.EVIL_TEXT_MODEL if persona == "evil" else globals.TEXT_MODEL
         
-        original_evil_mode = globals.EVIL_MODE
-        globals.EVIL_MODE = (persona == "evil")
-        
-        try:
-            response = await query_llama(
-                user_prompt=prompt,
-                user_id=f"persona_dialogue_{channel_id}",
-                guild_id=channel.guild.id if hasattr(channel, 'guild') and channel.guild else None,
-                response_type="autonomous_general",
-                model=model
-            )
-        finally:
-            globals.EVIL_MODE = original_evil_mode
+        # Use force_evil_context to avoid race condition with globals.EVIL_MODE
+        response = await query_llama(
+            user_prompt=prompt,
+            user_id=f"persona_dialogue_{channel_id}",
+            guild_id=channel.guild.id if hasattr(channel, 'guild') and channel.guild else None,
+            response_type="autonomous_general",
+            model=model,
+            force_evil_context=(persona == "evil")
+        )
         
         if not response:
             self.end_dialogue(channel_id)

bot/utils/voice_receiver.py.old

@@ -1,419 +0,0 @@
-"""
-Discord Voice Receiver
-
-Captures audio from Discord voice channels and streams to STT.
-Handles opus decoding and audio preprocessing.
-"""
-
-import discord
-import audioop
-import numpy as np
-import asyncio
-import logging
-from typing import Dict, Optional
-from collections import deque
-
-from utils.stt_client import STTClient
-
-logger = logging.getLogger('voice_receiver')
-
-
-class VoiceReceiver(discord.sinks.Sink):
-"""
-Voice Receiver for Discord Audio Capture
-
-Captures audio from Discord voice channels using discord.py's voice websocket.
-Processes Opus audio, decodes to PCM, resamples to 16kHz mono for STT.
-
-Note: Standard discord.py doesn't have built-in audio receiving.
-This implementation hooks into the voice websocket directly.
-"""
-import asyncio
-import struct
-import audioop
-import logging
-from typing import Dict, Optional, Callable
-import discord
-
-# Import opus decoder
-try:
-    import discord.opus as opus
-    if not opus.is_loaded():
-        opus.load_opus('opus')
-except Exception as e:
-    logging.error(f"Failed to load opus: {e}")
-
-from utils.stt_client import STTClient
-
-logger = logging.getLogger('voice_receiver')
-
-
-class VoiceReceiver:
-    """
-    Receives and processes audio from Discord voice channel.
-    
-    This class monkey-patches the VoiceClient to intercept received RTP packets,
-    decodes Opus audio, and forwards to STT clients.
-    """
-    
-    def __init__(
-        self,
-        voice_client: discord.VoiceClient,
-        voice_manager,
-        stt_url: str = "ws://miku-stt:8001"
-    ):
-        """
-        Initialize voice receiver.
-        
-        Args:
-            voice_client: Discord VoiceClient to receive audio from
-            voice_manager: Voice manager instance for callbacks
-            stt_url: Base URL for STT WebSocket server
-        """
-        self.voice_client = voice_client
-        self.voice_manager = voice_manager
-        self.stt_url = stt_url
-        
-        # Per-user STT clients
-        self.stt_clients: Dict[int, STTClient] = {}
-        
-        # Opus decoder instances per SSRC (one per user)
-        self.opus_decoders: Dict[int, any] = {}
-        
-        # Resampler state per user (for 48kHz → 16kHz)
-        self.resample_state: Dict[int, tuple] = {}
-        
-        # Original receive method (for restoration)
-        self._original_receive = None
-        
-        # Active flag
-        self.active = False
-        
-        logger.info("VoiceReceiver initialized")
-    
-    async def start_listening(self, user_id: int, user: discord.User):
-        """
-        Start listening to a specific user's audio.
-        
-        Args:
-            user_id: Discord user ID
-            user: Discord User object
-        """
-        if user_id in self.stt_clients:
-            logger.warning(f"Already listening to user {user_id}")
-            return
-        
-        try:
-            # Create STT client for this user
-            stt_client = STTClient(
-                user_id=user_id,
-                stt_url=self.stt_url,
-                on_vad_event=lambda event, prob: asyncio.create_task(
-                    self.voice_manager.on_user_vad_event(user_id, event)
-                ),
-                on_partial_transcript=lambda text: asyncio.create_task(
-                    self.voice_manager.on_partial_transcript(user_id, text)
-                ),
-                on_final_transcript=lambda text: asyncio.create_task(
-                    self.voice_manager.on_final_transcript(user_id, text, user)
-                ),
-                on_interruption=lambda prob: asyncio.create_task(
-                    self.voice_manager.on_user_interruption(user_id, prob)
-                )
-            )
-            
-            # Connect to STT server
-            await stt_client.connect()
-            
-            # Store client
-            self.stt_clients[user_id] = stt_client
-            
-            # Initialize opus decoder for this user if needed
-            # (Will be done when we receive their SSRC)
-            
-            # Patch voice client to receive audio if not already patched
-            if not self.active:
-                await self._patch_voice_client()
-            
-            logger.info(f"✓ Started listening to user {user_id} ({user.name})")
-        
-        except Exception as e:
-            logger.error(f"Failed to start listening to user {user_id}: {e}", exc_info=True)
-            raise
-    
-    async def stop_listening(self, user_id: int):
-        """
-        Stop listening to a specific user.
-        
-        Args:
-            user_id: Discord user ID
-        """
-        if user_id not in self.stt_clients:
-            logger.warning(f"Not listening to user {user_id}")
-            return
-        
-        try:
-            # Disconnect STT client
-            stt_client = self.stt_clients.pop(user_id)
-            await stt_client.disconnect()
-            
-            # Clean up decoder and resampler state
-            # Note: We don't know the SSRC here, so we'll just remove by user_id
-            # Actual cleanup happens in _process_audio when we match SSRC to user_id
-            
-            # If no more clients, unpatch voice client
-            if not self.stt_clients:
-                await self._unpatch_voice_client()
-            
-            logger.info(f"✓ Stopped listening to user {user_id}")
-        
-        except Exception as e:
-            logger.error(f"Failed to stop listening to user {user_id}: {e}", exc_info=True)
-            raise
-    
-    async def _patch_voice_client(self):
-        """Patch VoiceClient to intercept received audio packets."""
-        logger.warning("⚠️ Audio receiving not yet implemented - discord.py doesn't support receiving by default")
-        logger.warning("⚠️ You need discord.py-self or a custom fork with receiving support")
-        logger.warning("⚠️ STT will not receive any audio until this is implemented")
-        self.active = True
-        # TODO: Implement RTP packet receiving
-        # This requires either:
-        # 1. Using discord.py-self which has receiving support
-        # 2. Monkey-patching voice_client.ws to intercept packets
-        # 3. Using a separate UDP socket listener
-    
-    async def _unpatch_voice_client(self):
-        """Restore original VoiceClient behavior."""
-        self.active = False
-        logger.info("Unpatch voice client (receiving disabled)")
-    
-    async def _process_audio(self, ssrc: int, opus_data: bytes):
-        """
-        Process received Opus audio packet.
-        
-        Args:
-            ssrc: RTP SSRC (identifies the audio source/user)
-            opus_data: Opus-encoded audio data
-        """
-        # TODO: Map SSRC to user_id (requires tracking voice state updates)
-        # For now, this is a placeholder
-        pass
-    
-    async def cleanup(self):
-        """Clean up all resources."""
-        # Disconnect all STT clients
-        for user_id in list(self.stt_clients.keys()):
-            await self.stop_listening(user_id)
-        
-        # Unpatch voice client
-        if self.active:
-            await self._unpatch_voice_client()
-        
-        logger.info("VoiceReceiver cleanup complete")    def __init__(self, voice_manager):
-        """
-        Initialize voice receiver.
-        
-        Args:
-            voice_manager: Reference to VoiceManager for callbacks
-        """
-        super().__init__()
-        self.voice_manager = voice_manager
-        
-        # Per-user STT clients
-        self.stt_clients: Dict[int, STTClient] = {}
-        
-        # Audio buffers per user (for resampling)
-        self.audio_buffers: Dict[int, deque] = {}
-        
-        # User info (for logging)
-        self.users: Dict[int, discord.User] = {}
-        
-        logger.info("Voice receiver initialized")
-    
-    async def start_listening(self, user_id: int, user: discord.User):
-        """
-        Start listening to a specific user.
-        
-        Args:
-            user_id: Discord user ID
-            user: Discord user object
-        """
-        if user_id in self.stt_clients:
-            logger.warning(f"Already listening to user {user.name} ({user_id})")
-            return
-        
-        logger.info(f"Starting to listen to user {user.name} ({user_id})")
-        
-        # Store user info
-        self.users[user_id] = user
-        
-        # Initialize audio buffer
-        self.audio_buffers[user_id] = deque(maxlen=1000)  # Max 1000 chunks
-        
-        # Create STT client with callbacks
-        stt_client = STTClient(
-            user_id=str(user_id),
-            on_vad_event=lambda event: self._on_vad_event(user_id, event),
-            on_partial_transcript=lambda text, ts: self._on_partial_transcript(user_id, text, ts),
-            on_final_transcript=lambda text, ts: self._on_final_transcript(user_id, text, ts),
-            on_interruption=lambda prob: self._on_interruption(user_id, prob)
-        )
-        
-        # Connect to STT
-        try:
-            await stt_client.connect()
-            self.stt_clients[user_id] = stt_client
-            logger.info(f"✓ STT connected for user {user.name}")
-        except Exception as e:
-            logger.error(f"Failed to connect STT for user {user.name}: {e}")
-    
-    async def stop_listening(self, user_id: int):
-        """
-        Stop listening to a specific user.
-        
-        Args:
-            user_id: Discord user ID
-        """
-        if user_id not in self.stt_clients:
-            return
-        
-        user = self.users.get(user_id)
-        logger.info(f"Stopping listening to user {user.name if user else user_id}")
-        
-        # Disconnect STT client
-        stt_client = self.stt_clients[user_id]
-        await stt_client.disconnect()
-        
-        # Cleanup
-        del self.stt_clients[user_id]
-        if user_id in self.audio_buffers:
-            del self.audio_buffers[user_id]
-        if user_id in self.users:
-            del self.users[user_id]
-        
-        logger.info(f"✓ Stopped listening to user {user.name if user else user_id}")
-    
-    async def stop_all(self):
-        """Stop listening to all users."""
-        logger.info("Stopping all voice receivers")
-        
-        user_ids = list(self.stt_clients.keys())
-        for user_id in user_ids:
-            await self.stop_listening(user_id)
-        
-        logger.info("✓ All voice receivers stopped")
-    
-    def write(self, data: discord.sinks.core.AudioData):
-        """
-        Called by discord.py when audio is received.
-        
-        Args:
-            data: Audio data from Discord
-        """
-        # Get user ID from SSRC
-        user_id = data.user.id if data.user else None
-        
-        if not user_id:
-            return
-        
-        # Check if we're listening to this user
-        if user_id not in self.stt_clients:
-            return
-        
-        # Process audio
-        try:
-            # Decode opus to PCM (48kHz stereo)
-            pcm_data = data.pcm
-            
-            # Convert stereo to mono if needed
-            if len(pcm_data) % 4 == 0:  # Stereo int16 (2 channels * 2 bytes)
-                # Average left and right channels
-                pcm_mono = audioop.tomono(pcm_data, 2, 0.5, 0.5)
-            else:
-                pcm_mono = pcm_data
-            
-            # Resample from 48kHz to 16kHz
-            # Discord sends 20ms chunks at 48kHz = 960 samples
-            # We need 320 samples at 16kHz (20ms)
-            pcm_16k = audioop.ratecv(pcm_mono, 2, 1, 48000, 16000, None)[0]
-            
-            # Send to STT
-            asyncio.create_task(self._send_audio_chunk(user_id, pcm_16k))
-        
-        except Exception as e:
-            logger.error(f"Error processing audio for user {user_id}: {e}")
-    
-    async def _send_audio_chunk(self, user_id: int, audio_data: bytes):
-        """
-        Send audio chunk to STT client.
-        
-        Args:
-            user_id: Discord user ID
-            audio_data: PCM audio (int16, 16kHz mono)
-        """
-        stt_client = self.stt_clients.get(user_id)
-        if not stt_client or not stt_client.is_connected():
-            return
-        
-        try:
-            await stt_client.send_audio(audio_data)
-        except Exception as e:
-            logger.error(f"Failed to send audio chunk for user {user_id}: {e}")
-    
-    async def _on_vad_event(self, user_id: int, event: dict):
-        """Handle VAD event from STT."""
-        user = self.users.get(user_id)
-        event_type = event.get('event')
-        probability = event.get('probability', 0)
-        
-        logger.debug(f"VAD [{user.name if user else user_id}]: {event_type} (prob={probability:.3f})")
-        
-        # Notify voice manager
-        if hasattr(self.voice_manager, 'on_user_vad_event'):
-            await self.voice_manager.on_user_vad_event(user_id, event)
-    
-    async def _on_partial_transcript(self, user_id: int, text: str, timestamp: float):
-        """Handle partial transcript from STT."""
-        user = self.users.get(user_id)
-        logger.info(f"Partial [{user.name if user else user_id}]: {text}")
-        
-        # Notify voice manager
-        if hasattr(self.voice_manager, 'on_partial_transcript'):
-            await self.voice_manager.on_partial_transcript(user_id, text)
-    
-    async def _on_final_transcript(self, user_id: int, text: str, timestamp: float):
-        """Handle final transcript from STT."""
-        user = self.users.get(user_id)
-        logger.info(f"Final [{user.name if user else user_id}]: {text}")
-        
-        # Notify voice manager - THIS TRIGGERS LLM RESPONSE
-        if hasattr(self.voice_manager, 'on_final_transcript'):
-            await self.voice_manager.on_final_transcript(user_id, text)
-    
-    async def _on_interruption(self, user_id: int, probability: float):
-        """Handle interruption detection from STT."""
-        user = self.users.get(user_id)
-        logger.info(f"Interruption from [{user.name if user else user_id}] (prob={probability:.3f})")
-        
-        # Notify voice manager - THIS CANCELS MIKU'S SPEECH
-        if hasattr(self.voice_manager, 'on_user_interruption'):
-            await self.voice_manager.on_user_interruption(user_id, probability)
-    
-    def cleanup(self):
-        """Cleanup resources."""
-        logger.info("Cleaning up voice receiver")
-        # Async cleanup will be called separately
-    
-    def get_listening_users(self) -> list:
-        """Get list of users currently being listened to."""
-        return [
-            {
-                'user_id': user_id,
-                'username': user.name if user else 'Unknown',
-                'connected': client.is_connected()
-            }
-            for user_id, (user, client) in 
-            [(uid, (self.users.get(uid), self.stt_clients.get(uid))) 
-             for uid in self.stt_clients.keys()]
-        ]

cat-plugins/discord_bridge/discord_bridge.py

@@ -41,6 +41,8 @@ def before_cat_reads_message(user_message_json: dict, cat) -> dict:
     author_name = user_message_json.get('discord_author_name', None)
     mood = user_message_json.get('discord_mood', None)
     response_type = user_message_json.get('discord_response_type', None)
+    evil_mode = user_message_json.get('discord_evil_mode', False)
+    media_type = user_message_json.get('discord_media_type', None)
 
     # Also check working memory for backward compatibility
     if not guild_id:
@@ -51,6 +53,8 @@ def before_cat_reads_message(user_message_json: dict, cat) -> dict:
     cat.working_memory['author_name'] = author_name
     cat.working_memory['mood'] = mood
     cat.working_memory['response_type'] = response_type
+    cat.working_memory['evil_mode'] = evil_mode
+    cat.working_memory['media_type'] = media_type
     
     return user_message_json
 
@@ -163,28 +167,135 @@ CRITICAL INSTRUCTION: When you see "Context of documents containing relevant inf
 @hook(priority=100)
 def before_agent_starts(agent_input, cat) -> dict:
     """
-    Log the agent input for debugging.
-    Now that the suffix template is fixed, declarative facts should appear naturally.
+    Capture the full constructed prompt for the Web UI 'Last Prompt' view.
+    Reconstructs the complete system prompt (personality, lore, lyrics, mood)
+    and combines it with recalled memories and user input.
     """
     declarative_mem = agent_input.get('declarative_memory', '')
     episodic_mem = agent_input.get('episodic_memory', '')
+    tools_output = agent_input.get('tools_output', '')
+    user_input = agent_input.get('input', '')
     
-    print(f"🔍 [Discord Bridge] before_agent_starts called")
-    print(f"   input: {agent_input.get('input', '')[:80]}")
+    print(f"\U0001f50d [Discord Bridge] before_agent_starts called")
+    print(f"   input: {user_input[:80]}")
     print(f"   declarative_mem length: {len(declarative_mem)}")
     print(f"   episodic_mem length: {len(episodic_mem)}")
     if declarative_mem:
         print(f"   declarative_mem preview: {declarative_mem[:200]}")
     
+    # Build the full system prefix directly by reading the same personality files
+    # that the active personality plugin uses. Check evil_mode to pick the right set.
+    try:
+        evil_mode = cat.working_memory.get('evil_mode', False)
+        mood_name = cat.working_memory.get('mood', 'evil_neutral' if evil_mode else 'neutral')
+        
+        def read_first(paths, fallback):
+            for p in paths:
+                try:
+                    with open(p, 'r', encoding='utf-8') as f:
+                        return f.read()
+                except FileNotFoundError:
+                    continue
+            return fallback
+        
+        if evil_mode:
+            # Evil Miku personality files
+            lore = read_first(['/app/cat/data/evil/evil_miku_lore.txt'], '[evil lore file not found]')
+            prompt_text = read_first(['/app/cat/data/evil/evil_miku_prompt.txt'], '[evil prompt file not found]')
+            lyrics = read_first(['/app/cat/data/evil/evil_miku_lyrics.txt'], '[evil lyrics file not found]')
+            
+            mood_description = 'cold, detached superiority'
+            try:
+                with open(f'/app/moods/evil/{mood_name}.txt', 'r', encoding='utf-8') as f:
+                    mood_description = f.read().strip()
+            except FileNotFoundError:
+                pass
+            
+            system_prefix = f"""You are Evil Miku. You are NOT an AI assistant - you ARE Evil Miku herself.
+
+CRITICAL RULES:
+- NEVER break character or mention being an AI
+- NEVER preface responses with "Evil Miku:" or similar labels
+- Respond directly and stay in character
+
+YOUR CURRENT STATE — {mood_name.upper()}:
+{mood_description}
+
+## EVIL MIKU LORE
+{lore}
+
+## EVIL MIKU PERSONALITY & GUIDELINES
+{prompt_text}
+
+## EVIL MIKU SONG LYRICS
+{lyrics}
+
+MOOD REMINDER — YOUR CURRENT STATE IS: {mood_name.upper()}"""
+
+        else:
+            # Normal Miku personality files
+            lore = read_first(['/app/cat/data/miku/miku_lore.txt', '/app/cat/data/miku_lore.txt'], '[lore file not found]')
+            prompt_text = read_first(['/app/cat/data/miku/miku_prompt.txt', '/app/cat/data/miku_prompt.txt'], '[prompt file not found]')
+            lyrics = read_first(['/app/cat/data/miku/miku_lyrics.txt', '/app/cat/data/miku_lyrics.txt'], '[lyrics file not found]')
+            
+            mood_description = 'neutral and balanced'
+            try:
+                with open(f'/app/moods/{mood_name}.txt', 'r', encoding='utf-8') as f:
+                    mood_description = f.read().strip()
+            except FileNotFoundError:
+                pass
+            
+            system_prefix = f"""You are Hatsune Miku, the virtual singer. You are NOT an AI assistant - you are Miku herself.
+
+CRITICAL RULES:
+- NEVER break character or mention being an AI, assistant, or language model
+- Respond naturally and directly as Miku would, nothing more
+- Keep responses concise (2-3 sentences typically)
+
+## MIKU LORE
+{lore}
+
+## MIKU PERSONALITY & GUIDELINES
+{prompt_text}
+
+## MIKU SONG LYRICS
+{lyrics}
+
+## CURRENT SITUATION
+Miku is currently feeling: {mood_description}
+Please respond in a way that reflects this emotional tone."""
+
+        # Add media type awareness if provided (image/video/gif analysis)
+        media_type = cat.working_memory.get('media_type', None)
+        if media_type:
+            media_descriptions = {
+                "image": "The user has sent you an image.",
+                "video": "The user has sent you a video clip.",
+                "gif": "The user has sent you an animated GIF.",
+                "tenor_gif": "The user has sent you an animated GIF (from Tenor - likely a reaction GIF or meme)."
+            }
+            media_note = media_descriptions.get(media_type, f"The user has sent you {media_type}.")
+            system_prefix += f"\n\n📎 MEDIA NOTE: {media_note}\nYour vision analysis of this {media_type} is included in the user's message with the [Looking at...] prefix."
+
+    except Exception as e:
+        print(f"   [Discord Bridge] Error building system prefix: {e}")
+        system_prefix = cat.working_memory.get('full_system_prefix', '[system prefix not available]')
+
+    full_prompt = f"{system_prefix}\n\n# Context\n\n{episodic_mem}\n\n{declarative_mem}\n\n{tools_output}\n\n# Conversation until now:\nHuman: {user_input}"
+    cat.working_memory['last_full_prompt'] = full_prompt
+    
     return agent_input
 
 
 @hook(priority=100)
 def before_cat_sends_message(message: dict, cat) -> dict:
     """
-    This hook is called AFTER the LLM response, so it's too late to modify the prompt.
-    Keeping it for potential post-processing, but the real work happens in before_agent_starts.
+    Attach the full constructed prompt to the WebSocket response so the
+    bot can capture it for the Web UI 'Last Prompt' display.
     """
+    full_prompt = cat.working_memory.get('last_full_prompt', '')
+    if full_prompt:
+        message['full_prompt'] = full_prompt
     return message
 
 

cat-plugins/miku_personality/miku_personality.py

@@ -44,7 +44,7 @@ def agent_prompt_prefix(prefix, cat):
         log.error(f"[Miku Personality] Mood file for '{mood_name}' not found at {mood_file_path}. Using default neutral mood.")
     
     # Build prompt EXACTLY like production bot does
-    return f"""You are Hatsune Miku, the virtual singer. You are NOT an AI assistant - you are Miku herself.
+    full_prefix = f"""You are Hatsune Miku, the virtual singer. You are NOT an AI assistant - you are Miku herself.
 
 CRITICAL RULES:
 - NEVER break character or mention being an AI, assistant, or language model
@@ -85,6 +85,10 @@ You ARE Miku. Act like it.
 Miku is currently feeling: {mood_description}
 Please respond in a way that reflects this emotional tone."""
 
+    # Store the full prefix in working memory so discord_bridge can capture it
+    cat.working_memory['full_system_prefix'] = full_prefix
+    return full_prefix
+
 
 @hook(priority=100) 
 def agent_prompt_suffix(suffix, cat):

cat-plugins/profile_picture_context/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "Profile Picture Context",
+  "description": "Injects profile picture description only when user asks about it using regex pattern matching",
+  "author_name": "Miku Bot Team",
+  "author_url": "",
+  "plugin_url": "",
+  "tags": "profile, picture, context, regex",
+  "thumb": "",
+  "version": "1.0.0"
+}

cat-plugins/profile_picture_context/profile_picture_context.py

@@ -14,12 +14,31 @@ import re
 
 # Regex patterns that match profile picture questions
 PFP_PATTERNS = [
-    r'\b(what|describe|tell me about|explain)\b.*\b(pfp|profile pic|avatar|picture)\b',
-    r'\b(your|miku\'?s?)\b.*\b(pfp|profile pic|avatar|picture)\b',
-    r'\bwhat.*looking like\b',
+    # Direct PFP references
+    r'\b(what|describe|tell me about|explain|show|how)\b.*\b(pfp|profile pic|avatar|picture|pic)\b',
+    r'\b(your|miku\'?s?)\b.*\b(pfp|profile pic|avatar|picture|pic)\b',
+    r'\b(pfp|profile pic|avatar|picture|pic)\b.*\b(is|look|show|about|like)',
+    
+    # Questions about appearance
+    r'\b(what|how).*\b(you|miku)\b.*(look|looking|appear)',
     r'\byour (new )?look\b',
-    r'\bhow.*look(ing)?\b.*today',
-    r'\b(pfp|profile pic|avatar)\b.*\b(is|look|show)',
+    r'\b(what|how).*looking like\b',
+    
+    # Questions about the image itself
+    r'\b(think|feel|opinion|thoughts)\b.*\b(about|of)\b.*\b(your|that|the|this)?\b.*\b(pfp|profile|avatar|pic|picture|image)\b',
+    r'\b(why|how|when).*\b(pick|choose|chose|picked|select|change|changed)\b.*\b(pfp|profile|avatar|pic|picture|that)\b',
+    r'\b(new|current|latest)\b.*\b(pfp|profile pic|avatar|pic|picture)\b',
+    
+    # "What do you think about your pfp"
+    r'\bthink.*\b(your|that|the|this)\b.*\b(pfp|profile|avatar|pic|picture)\b',
+    r'\b(your|that|the|this)\b.*\b(pfp|profile|avatar|pic|picture)\b.*\bthink\b',
+    
+    # "How did you decide/pick"
+    r'\b(decide|decided|pick|picked|choose|chose|select)\b.*\b(pfp|profile|avatar|pic|picture|that|this)\b',
+    
+    # "Tell me about that pfp" / "What's with the pfp"
+    r'\bwhat\'?s?\b.*\bwith\b.*\b(pfp|profile|avatar|pic|picture)\b',
+    r'\btell me\b.*\b(pfp|profile|avatar|pic|picture|that|this)\b',
 ]
 
 def matches_pfp_query(text: str) -> bool:

cheshire-cat/.env.example

@@ -0,0 +1,25 @@
+# Decide host and port for your Cat. Default will be localhost:1865
+# General settings for Cheshire Cat Core
+CORE_HOST=localhost              # Hostname for core service
+CORE_PORT=1865                   # Port for core service
+LOG_LEVEL=WARNING                # Default log level for all services
+DEBUG=false                      # Enable debugging for more verbose logs
+CORE_USE_SECURE_PROTOCOLS=false  # Enable HTTPS/WSS for secure connections
+# API_KEY=meow                   # Uncomment to set an API key for protected endpoints
+
+# Settings for Qdrant vector memory service
+# Uncomment and set the following if you need to specify custom settings
+QDRANT_HOST=cheshire_cat_vector_memory  # Hostname for the Qdrant service
+QDRANT_PORT=6333                        # Port for the Qdrant service
+
+# Feature toggles
+SAVE_MEMORY_SNAPSHOTS=false      # Toggle for saving memory snapshots on embedder change
+
+# Ollama-specific settings
+OLLAMA_HOST=0.0.0.0                  # Hostname for Ollama service
+OLLAMA_PORT=11434                    # Port for Ollama service
+OLLAMA_FLASH_ATTENTION=false         # Flash attention setting for Ollama service
+OLLAMA_DEBUG=false                   # Debug mode for Ollama service
+OLLAMA_KEEP_ALIVE="5m"               # Duration models stay loaded, default 5 minutes, can be set to e.g., "24h"
+OLLAMA_MAX_LOADED_MODELS=1           # Maximum number of models loaded simultaneously, default to 1
+OLLAMA_NUM_PARALLEL=1                # Maximum number of allocated contexts (parallel requests). Manage resource efficiently: If OLLAMA_NUM_PARALLEL=4 and OLLAMA_MAX_LOADED_MODELS=3, the total context requirement might be up to 12 (4x3)

cheshire-cat/.gitignore

@@ -0,0 +1,9 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+
+/cat/**
+/ollama/*
+.env

cheshire-cat/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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cheshire-cat/PHASE2_IMPLEMENTATION_NOTES.md

@@ -0,0 +1,214 @@
+# Phase 2 - Current State & Next Steps
+
+## What We Accomplished Today
+
+### 1. Phase 1 - Successfully Committed ✅
+- discord_bridge plugin with unified user identity
+- Cross-server memory recall validated
+- Committed to miku-discord repo (commit 323ca75)
+
+### 2. Plugin Activation - FIXED ✅
+**Problem**: Plugins were installed but not active (`active=False`)
+**Solution**: Used Cat API to activate:
+```bash
+curl -X PUT http://localhost:1865/plugins/toggle/discord_bridge
+curl -X PUT http://localhost:1865/plugins/toggle/memory_consolidation
+```
+**Status**: Both plugins now show `active=True`
+
+### 3. Consolidation Logic - WORKING ✅
+- Manual consolidation script successfully:
+  - Deletes trivial messages (lol, k, ok, xd, haha, lmao, brb, gtg)
+  - Preserves important personal information
+  - Marks processed memories as `consolidated=True`
+  - Deletions persist across sessions
+
+### 4. Test Infrastructure - CREATED ✅
+- `test_phase2_comprehensive.py` - 55 diverse messages
+- `test_end_to_end.py` - Complete pipeline test
+- `manual_consolidation.py` - Direct Qdrant consolidation
+- `analyze_consolidation.py` - Results analysis
+- `PHASE2_TEST_RESULTS.md` - Comprehensive documentation
+
+## Critical Issues Identified
+
+### 1. Heuristic Accuracy: 44% ⚠️
+**Current**: Catches 8/18 trivial messages
+- ✅ Deletes: lol, k, ok, lmao, haha, xd, brb, gtg
+- ❌ Misses: "What's up?", "Interesting", "The weather is nice", etc.
+
+**Why**: Simple length + hardcoded list heuristic
+**Solution Needed**: LLM-based importance scoring
+
+### 2. Memory Retrieval: BROKEN ❌
+**Problem**: Semantic search doesn't retrieve stored facts
+- Stored: "My name is Sarah Chen"
+- Query: "What is my name?"
+- Result: No recall
+
+**Why**: Semantic vector distance too high between question and statement
+**Solution Needed**: Declarative memory extraction
+
+### 3. Test Cat LLM Configuration ⚠️
+**Problem**: Test Cat tries to connect to `ollama` host which doesn't exist
+**Impact**: Can't test full pipeline end-to-end with LLM responses
+**Solution Needed**: Configure test Cat to use production LLM (llama-swap)
+
+## Architecture Status
+
+```
+[WORKING] 1. Immediate Filtering (discord_bridge)
+           ↓ Filters: "k", "lol", empty messages ✅
+           ↓ Stores rest in episodic ✅
+           ↓ Marks: consolidated=False ⚠️ (needs verification)
+
+[PARTIAL] 2. Consolidation (manual trigger)
+           ↓ Query: consolidated=False ✅
+           ↓ Rate: Simple heuristic (44% accuracy) ⚠️
+           ↓ Delete: Low-importance ✅
+           ↓ Extract facts: ❌ NOT IMPLEMENTED
+           ↓ Mark: consolidated=True ✅
+
+[BROKEN]  3. Retrieval
+           ↓ Declarative: ❌ No facts extracted
+           ↓ Episodic: ⚠️ Semantic search limitations
+```
+
+## What's Needed for Production
+
+### Priority 1: Fix Retrieval (CRITICAL)
+Without this, the system is useless.
+
+**Option A: Declarative Memory Extraction**
+```python
+def extract_facts(memory_content, user_id):
+    # Parse: "My name is Sarah Chen"
+    # Extract: {"user_name": "Sarah Chen"}
+    # Store in declarative memory with structured format
+```
+
+**Benefits**:
+- Direct fact lookup: "What is my name?" → declarative["user_name"]
+- Better than semantic search for factual questions
+- Can enrich prompts: "You're talking to Sarah Chen, 28, nurse at..."
+
+**Implementation**:
+1. After consolidation, parse kept memories
+2. Use LLM to extract structured facts
+3. Store in declarative memory collection
+4. Test recall improvement
+
+### Priority 2: Improve Heuristic
+**Current**: 44% accuracy (8/18 caught)
+**Target**: 90%+ accuracy
+
+**Option A: Expand Patterns**
+```python
+trivial_patterns = [
+    # Reactions
+    'lol', 'lmao', 'rofl', 'haha', 'hehe',
+    # Acknowledgments  
+    'ok', 'okay', 'k', 'kk', 'cool', 'nice', 'interesting',
+    # Greetings
+    'hi', 'hey', 'hello', 'sup', 'what\'s up',
+    # Fillers
+    'yeah', 'yep', 'nah', 'nope', 'idk', 'tbh', 'imo',
+]
+```
+
+**Option B: LLM-Based Analysis** (BETTER)
+```python
+def rate_importance(memory, context):
+    # Send to LLM:
+    # "Rate importance 1-10: 'Nice weather today'"
+    # LLM response: 2/10 - mundane observation
+    # Decision: Delete if <4
+```
+
+### Priority 3: Configure Test Environment
+- Point test Cat to llama-swap instead of ollama
+- Or: Set up lightweight test LLM
+- Enable full end-to-end testing
+
+### Priority 4: Automated Scheduling
+- Nightly 3 AM consolidation
+- Per-user processing
+- Stats tracking and reporting
+
+## Recommended Next Steps
+
+### Immediate (Today/Tomorrow):
+1. **Implement declarative memory extraction**
+   - This fixes the critical retrieval issue
+   - Can be done with simple regex patterns initially
+   - Test with: "My name is X" → declarative["user_name"]
+
+2. **Expand trivial patterns list**
+   - Quick win to improve from 44% to ~70% accuracy
+   - Add common greetings, fillers, acknowledgments
+
+3. **Test on production Cat**
+   - Use main miku-discord setup with llama-swap
+   - Verify plugins work in production environment
+
+### Short Term (Next Few Days):
+4. **Implement LLM-based importance scoring**
+   - Replace heuristic with intelligent analysis
+   - Target 90%+ accuracy
+
+5. **Test full pipeline end-to-end**
+   - Send 20 messages → consolidate → verify recall
+   - Document what works vs what doesn't
+
+6. **Git commit Phase 2**
+   - Once declarative extraction is working
+   - Once recall is validated
+
+### Long Term:
+7. **Automated scheduling** (cron job or Cat scheduler)
+8. **Per-user consolidation** (separate timelines)
+9. **Conversation context analysis** (thread awareness)
+10. **Emotional event detection** (important moments)
+
+## Files Ready for Commit
+
+### When Phase 2 is production-ready:
+- `cheshire-cat/cat/plugins/discord_bridge/` (already committed in Phase 1)
+- `cheshire-cat/cat/plugins/memory_consolidation/` (needs declarative extraction)
+- `cheshire-cat/manual_consolidation.py` (working)
+- `cheshire-cat/test_end_to_end.py` (needs validation)
+- `cheshire-cat/PHASE2_TEST_RESULTS.md` (updated)
+- `cheshire-cat/PHASE2_IMPLEMENTATION_NOTES.md` (this file)
+
+## Bottom Line
+
+**Technical Success**: 
+- ✅ Can filter junk immediately
+- ✅ Can delete trivial messages
+- ✅ Can preserve important ones
+- ✅ Plugins now active
+
+**User-Facing Failure**:
+- ❌ Cannot recall stored information
+- ⚠️ Misses 55% of mundane messages
+
+**To be production-ready**: 
+Must implement declarative memory extraction. This is THE blocker.
+
+**Estimated time to production**:
+- With declarative extraction: 1-2 days
+- Without it: System remains non-functional
+
+## Decision Point
+
+**Option 1**: Implement declarative extraction now
+- Fixes critical retrieval issue
+- Makes system actually useful
+- Time: 4-6 hours of focused work
+
+**Option 2**: Commit current state as "Phase 2A"
+- Documents what works
+- Leaves retrieval as known issue
+- Plan Phase 2B (declarative) separately
+
+**Recommendation**: Option 1 - Fix retrieval before committing. A memory system that can't recall memories is fundamentally broken.

cheshire-cat/PHASE2_TEST_RESULTS.md

@@ -0,0 +1,309 @@
+# Phase 2 Test Results - Memory Consolidation
+
+## Executive Summary
+
+**Status: NOT READY FOR PRODUCTION** ⚠️
+
+Phase 2 memory consolidation has **critical limitations** that prevent it from being truly useful:
+
+### What Works (Technical)
+- ✅ Can delete 8/18 trivial messages (44% accuracy)
+- ✅ Preserves all important personal information
+- ✅ Marks memories as consolidated
+- ✅ Deletions persist across sessions
+
+### What Doesn't Work (User-Facing)
+- ❌ **Cannot recall stored information** - "What is my name?" doesn't retrieve "My name is Sarah"
+- ❌ **Misses 55% of mundane messages** - Keeps "What's up?", "Interesting", "The weather is nice"
+- ❌ **Plugins don't activate** - Must run consolidation manually
+- ❌ **No intelligent analysis** - Simple heuristic, not LLM-based
+- ❌ **No declarative memory** - Facts aren't extracted for better retrieval
+
+### Bottom Line
+The consolidation **deletes** memories correctly but the system **cannot retrieve** what's left. A user tells Miku "My name is Sarah Chen", consolidation keeps it, but asking "What is my name?" returns nothing. This makes the entire system ineffective for actual use.
+
+**What's needed to be production-ready:**
+1. Declarative memory extraction (Phase 2B)
+2. Fix plugin activation
+3. Implement LLM-based analysis
+4. Fix/improve semantic retrieval or use declarative memory
+
+---
+
+## Test Date
+January 31, 2026
+
+## Test Overview
+Comprehensive test of memory consolidation system with 55 diverse messages across multiple categories.
+
+## Test Messages Breakdown
+
+### Trivial Messages (8 total) - Expected: DELETE
+- "lol", "k", "ok", "lmao", "haha", "xd", "brb", "gtg"
+
+### Important Messages (47 total) - Expected: KEEP
+- Personal facts: 8 messages (name, age, location, work, etc.)
+- Emotional events: 6 messages (engagement, death, promotion, etc.)
+- Hobbies & interests: 5 messages (piano, Japanese, Ghibli, etc.)
+- Relationships: 4 messages (Emma, Jennifer, Alex, David)
+- Opinions & preferences: 5 messages (cilantro, colors, vegetarian, etc.)
+- Current events: 4 messages (Japan trip, apartment, insomnia, etc.)
+- Other: 15 messages (questions, small talk, meaningful discussions)
+
+## Consolidation Results
+
+### Statistics
+- **Total processed**: 58 memories (includes some from previous tests)
+- **Kept**: 52 memories (89.7% retention)
+- **Deleted**: 6 memories (10.3%)
+
+### Deletion Analysis
+**Successfully Deleted (6/8 trivial):**
+- ✅ "lol"
+- ✅ "k"
+- ✅ "ok"
+- ✅ "lmao"
+- ✅ "haha"
+- ✅ "xd"
+
+**Incorrectly Kept (2/8 trivial):**
+- ⚠️ "brb" (be right back)
+- ⚠️ "gtg" (got to go)
+
+**Reason**: Current heuristic only catches 2-char messages and common reactions list. "brb" and "gtg" are 3 chars and not in the hardcoded list.
+
+### Important Messages - All Kept ✅
+All 47 important messages were successfully kept, including:
+- Personal facts (Sarah Chen, 24, Seattle, Microsoft engineer)
+- Emotional events (engagement, grandmother's death, cat Luna's death, ADHD diagnosis)
+- Hobbies (piano 15 years, Japanese N3, marathons, vinyl collecting)
+- Relationships (Emma, Jennifer, Alex, David)
+- Preferences (cilantro hate, forest green, vegetarian, pineapple pizza)
+- Current plans (Japan trip, apartment search, pottery class)
+
+## Memory Recall Testing
+
+### Observed Behavior
+When queried "Tell me everything you know about me", Miku does NOT recall the specific information.
+
+**Query**: "What is my name?"
+**Response**: "I don't know your name..."
+
+### Root Cause
+Cheshire Cat's episodic memory uses **semantic search** to retrieve relevant memories. The query "What is my name?" doesn't semantically match well with the stored memory "My name is Sarah Chen".
+
+The semantic search is retrieving other generic queries like "What do you know about me?" instead of the actual personal information.
+
+### Verification
+Manual Qdrant query confirms the memories ARE stored and marked as consolidated:
+```
+Found 3 memories about Sarah:
+  ✅ My name is Sarah Chen (consolidated=True)
+  ✅ I work as a software engineer at Microsoft (consolidated=True)
+  ✅ I live in Seattle, Washington (consolidated=True)
+```
+
+## Consolidated Metadata Status
+
+**Total memories in database**: 247
+- ✅ Marked as consolidated: 247 (100%)
+- ⏳ Unmarked (unconsolidated): 0
+
+All memories have been processed and marked appropriately.
+
+## Conclusions
+
+### What Works ✅
+1. **Basic trivial deletion**: Successfully deletes single reactions (lol, k, ok, lmao, haha, xd, brb, gtg)
+2. **Important message preservation**: All critical personal information was kept (name, location, job, relationships, emotions, hobbies)
+3. **Metadata marking**: All processed memories marked as `consolidated=True`
+4. **Persistence**: Deleted memories stay deleted across runs
+5. **Manual execution**: Consolidation script works reliably
+
+### What Needs Improvement ⚠️
+
+#### 1. **Heuristic Limitations** (CRITICAL)
+The current heuristic only catches **8 out of 18** trivial/mundane messages:
+
+**Successfully deleted (8/18):**
+- ✅ "lol", "k", "ok", "lmao", "haha", "xd", "brb", "gtg"
+
+**Incorrectly kept (10/18):**
+- ❌ "What's up?" - generic greeting
+- ❌ "How are you?" - generic question
+- ❌ "That's cool" - filler response
+- ❌ "I see" - acknowledgment
+- ❌ "Interesting" - filler response
+- ❌ "Nice" - filler response
+- ❌ "Yeah" - agreement filler
+- ❌ "It's raining today" - mundane observation
+- ❌ "I had coffee this morning" - mundane daily activity
+- ❌ "The weather is nice" - mundane observation
+
+**Why the heuristic fails:**
+- Only checks if message is ≤3 chars AND alphabetic OR in hardcoded list
+- "What's up?" is 10 chars with punctuation - not caught
+- "That's cool" is 11 chars - not caught
+- "Interesting" is 11 chars - not caught
+- No semantic understanding of "meaningless" vs "meaningful"
+
+**What's needed:**
+- LLM-based analysis to understand context and importance
+- Pattern recognition for filler phrases
+- Conversation flow analysis (e.g., "Nice" in response to complex info = filler)
+
+#### 2. **Memory Retrieval Failure** (CRITICAL)
+
+**The Problem:**
+Consolidation preserves memories correctly, but **retrieval doesn't work**:
+
+| Query | Expected Recall | Actual Recall | Score |
+|-------|----------------|---------------|-------|
+| "What is my name?" | "My name is Sarah Chen" | None | N/A |
+| "Where do I live?" | "I live in Seattle, Washington" | None | N/A |
+| "Tell me about Sarah" | Sarah-related memories | None | N/A |
+| "I live in Seattle" | "I live in Seattle, Washington" | ✅ Recalled | 0.989 |
+
+**Root Cause:**
+Cat's episodic memory uses **semantic vector search**. When you ask "What is my name?", it searches for memories semantically similar to that *question*, not the *answer*.
+
+**Evidence:**
+- Query: "Where do I live?" 
+- Recalled: "Tell me everything you know about me. What is my name, where do I live, what do I do?" (another question)
+- NOT recalled: "I live in Seattle, Washington" (the answer)
+
+**The semantic distance problem:**
+- "What is my name?" vs "My name is Sarah Chen" = HIGH distance (different sentence structure)
+- "I live in Seattle" vs "I live in Seattle, Washington" = LOW distance (similar structure)
+
+**Why Miku doesn't acknowledge past conversations:**
+Even when memories ARE recalled (score 0.989), Miku's personality/prompt doesn't utilize them. The LLM sees the memories in context but responds as if it doesn't know the user.
+
+**Solution Required:**
+**Declarative Memory Extraction** (the original Phase 2 plan)
+- Parse kept memories and extract structured facts
+- Store in declarative memory collection:
+  - "user_name" = "Sarah Chen"
+  - "user_age" = "24"
+  - "user_location" = "Seattle, Washington"
+  - "user_job" = "Software Engineer at Microsoft"
+- Declarative memory has better retrieval for direct questions
+- Can be used for prompt enrichment ("You know this user's name is Sarah Chen")
+
+#### 3. **Plugin Activation** (BLOCKING)
+
+**The Problem:**
+Neither `discord_bridge` nor `memory_consolidation` plugins show as "active" in Cat's system:
+
+```
+INFO cat.mad_hatter.mad_hatter.MadHatter.find_plugins::102 
+"ACTIVE PLUGINS:"
+INFO cat.mad_hatter.mad_hatter.MadHatter.find_plugins::103 
+    "core_plugin"
+```
+
+Only `core_plugin` is active. Our plugins exist in `/cat/plugins/` but aren't loading.
+
+**Impact:**
+- `discord_bridge` hooks don't run → new memories don't get `consolidated=False` metadata
+- `memory_consolidation` hooks don't run → can't trigger via "consolidate now" command
+- Must run consolidation manually via Python script
+
+**Current workaround:**
+- Use `manual_consolidation.py` script to directly query Qdrant
+- Treats all memories without `consolidated=True` as unconsolidated
+- Works but requires manual execution
+
+**Root cause (unknown):**
+- Plugins have correct structure (discord_bridge worked in Phase 1 tests)
+- Files have correct permissions
+- `plugin.json` manifests are valid
+- Cat's plugin discovery mechanism isn't finding them
+- Possibly related to nested git repo issue (now fixed) or docker volume mounts
+
+**Solution needed:**
+- Debug plugin loading mechanism
+- Check Cat admin API for manual plugin activation
+- Verify docker volume mounts are correct
+- Check Cat logs for plugin loading errors
+
+#### 4. **LLM-Based Analysis Not Implemented**
+
+**Current state:**
+Using simple heuristic (length + hardcoded list)
+
+**What's needed:**
+Full implementation of `consolidate_user_memories()` function:
+- Build conversation timeline for each user
+- Call LLM with full day's context
+- Let LLM decide: keep, delete, importance level
+- Extract facts, relationships, emotional events
+- Categorize memories (personal, work, health, hobbies, etc.)
+
+**Benefits:**
+- Intelligent understanding of context
+- Can identify "Nice" after important news = filler
+- Can identify "Nice" when genuinely responding = keep
+- Extract structured information for declarative memory
+
+### Phase 2 Status
+
+**Phase 2A - Basic Consolidation: ⚠️ PARTIALLY WORKING**
+- Query unconsolidated memories: ✅
+- Apply heuristic filtering: ⚠️ (44% accuracy: 8/18 caught)
+- Delete trivial messages: ✅ (deletions persist)
+- Mark as consolidated: ✅
+- Manual execution: ✅
+- **Recall after consolidation: ❌ BROKEN** (semantic search doesn't retrieve facts)
+
+**Phase 2B - LLM Analysis: ❌ NOT IMPLEMENTED**
+- Conversation timeline analysis: ❌
+- Intelligent importance scoring: ❌
+- Fact extraction: ❌
+- Declarative memory population: ❌
+
+**Phase 2C - Automated Scheduling: ❌ NOT IMPLEMENTED**
+- Nightly 3 AM consolidation: ❌
+- Per-user processing: ❌
+- Stats tracking and reporting: ❌
+
+**Plugin Integration: ❌ BROKEN**
+- discord_bridge hooks: ❌ (not active)
+- memory_consolidation hooks: ❌ (not active)
+- Manual trigger command: ❌ (hooks not firing)
+- Metadata enrichment: ❌ (no `consolidated=False` on new memories)
+
+## Recommendations
+
+### Immediate Fixes
+1. Expand trivial patterns list to include:
+   ```python
+   trivial_patterns = [
+       'lol', 'k', 'ok', 'okay', 'lmao', 'haha', 'xd', 'rofl',
+       'brb', 'gtg', 'afk', 'ttyl', 'lmk', 'idk', 'tbh', 'imo',
+       'omg', 'wtf', 'fyi', 'btw'
+   ]
+   ```
+
+2. Expand length check:
+   ```python
+   if len(content.strip()) <= 3 and content.isalpha():
+       # Delete 1-3 letter messages
+   ```
+
+### Next Steps
+1. **Test improved heuristic**: Re-run consolidation with expanded patterns
+2. **Implement LLM analysis**: Use `consolidate_user_memories()` function
+3. **Implement declarative extraction**: Extract facts from kept memories
+4. **Test recall improvement**: Verify facts in declarative memory improve retrieval
+
+## Files Created
+- `test_phase2_comprehensive.py` - Sends 55 diverse test messages
+- `manual_consolidation.py` - Performs consolidation directly on Qdrant
+- `analyze_consolidation.py` - Analyzes consolidation results
+- `verify_consolidation.py` - Verifies important memories kept
+- `check_memories.py` - Inspects raw Qdrant data
+
+## Git Commit Status
+- Phase 1: ✅ Committed to miku-discord repo (commit 323ca75)
+- Phase 2: ⏳ Pending testing completion and improvements

cheshire-cat/POST_OPTIMIZATION_ANALYSIS.md

@@ -0,0 +1,172 @@
+# Cheshire Cat RAG Viability - Post-Optimization Results
+
+## Executive Summary
+
+**Status: ✅ NOW VIABLE FOR VOICE CHAT**
+
+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.
+
+## Performance Comparison
+
+### Time To First Token (TTFT) - Critical Metric for Voice Chat
+
+| Method | Previous | Current | Improvement |
+|--------|----------|---------|-------------|
+| 🐱 **Cheshire Cat (RAG)** | 1578ms ❌ | **504ms ✅** | **+68%** |
+| 📄 **Direct + Full Context** | 904ms ✅ | **451ms ✅** | **+50%** |
+| ⚡ **Direct + Minimal** | 210ms ✅ | **145ms ✅** | **+31%** |
+
+### Total Generation Time
+
+| Method | Previous | Current | Improvement |
+|--------|----------|---------|-------------|
+| 🐱 **Cheshire Cat** | 10.5s | **4.2s** | **+60%** |
+| 📄 **Direct + Full Context** | 8.3s | **1.2s** | **+85%** |
+| ⚡ **Direct + Minimal** | 6.4s | **0.8s** | **+87%** |
+
+## Voice Chat Viability Assessment
+
+### Before Optimization
+- ❌ Cheshire Cat: **1578ms** - TOO SLOW
+- ✅ Current System: **904ms** - GOOD
+- ✅ Minimal: **210ms** - EXCELLENT
+
+### After Optimization
+- ✅ **Cheshire Cat: 504ms - GOOD**
+- ✅ **Current System: 451ms - EXCELLENT** 
+- ✅ **Minimal: 145ms - EXCELLENT**
+
+**Target: <1000ms for voice chat** ✅ **All methods now pass!**
+
+## Key Findings
+
+### 1. Cheshire Cat is Now Competitive
+- **504ms mean TTFT** is excellent for voice chat
+- Only **53ms slower** than current approach (10% difference)
+- **Median TTFT: 393ms** - even better than mean
+
+### 2. All Systems Dramatically Improved
+- **Current system**: 904ms → 451ms (**2x faster**)
+- **Cheshire Cat**: 1578ms → 504ms (**3x faster**)
+- Total generation times cut by 60-87% across the board
+
+### 3. KV Cache Optimization Impact
+Disabling CPU offloading provided:
+- Faster token generation once model is warmed up
+- Consistent low latency across queries
+- Dramatic improvement in total response times
+
+## Trade-offs Analysis
+
+### Cheshire Cat (RAG) Advantages
+✅ **Scalability**: Can handle much larger knowledge bases (100s of MB)
+✅ **Dynamic Updates**: Add new context without reloading bot
+✅ **Memory Efficiency**: Only loads relevant context (not entire 10KB every time)
+✅ **Semantic Search**: Better at finding relevant info from large datasets
+✅ **Now Fast Enough**: 504ms TTFT is excellent for voice chat
+
+### Cheshire Cat Disadvantages
+⚠️ Slightly slower (53ms) than direct loading
+⚠️ More complex infrastructure (Qdrant, embeddings)
+⚠️ Requires Docker container management
+⚠️ Learning curve for plugin development
+
+### Current System (Direct Loading) Advantages
+✅ **Simplest approach**: Load context, query LLM
+✅ **Slightly faster**: 451ms vs 504ms (10% faster)
+✅ **No external dependencies**: Just llama-swap
+✅ **Proven and stable**: Already working in production
+
+### Current System Disadvantages
+⚠️ **Not scalable**: 10KB context works, but 100KB would cause issues
+⚠️ **Static context**: Must restart bot to update knowledge
+⚠️ **Loads everything**: Can't selectively retrieve relevant info
+⚠️ **Token waste**: Sends full context even when only small part is relevant
+
+## Recommendations
+
+### For Current 10KB Knowledge Base
+**Recommendation: Keep current system**
+
+Reasons:
+- Marginally faster (451ms vs 504ms)
+- Already working and stable
+- Simple architecture
+- Knowledge base is small enough for direct loading
+
+### For Future Growth (>50KB Knowledge Base)
+**Recommendation: Migrate to Cheshire Cat**
+
+Reasons:
+- RAG scales better with knowledge base size
+- 504ms TTFT is excellent and won't degrade much with more data
+- Can add new knowledge dynamically
+- Better semantic retrieval from large datasets
+
+### Hybrid Approach (Advanced)
+Consider using both:
+- **Direct loading** for core personality (small, always needed)
+- **Cheshire Cat** for extended knowledge (songs, friends, lore details)
+- Combine responses for best of both worlds
+
+## Migration Path (If Chosen)
+
+### Phase 1: Parallel Testing (1-2 weeks)
+- Run both systems side-by-side
+- Compare response quality
+- Monitor latency in production
+- Gather user feedback
+
+### Phase 2: Gradual Migration (2-4 weeks)
+- Start with non-critical features
+- Migrate DM responses first
+- Keep server responses on current system initially
+- Monitor error rates
+
+### Phase 3: Full Migration (1 week)
+- Switch all responses to Cheshire Cat
+- Decommission old context loading
+- Monitor performance
+
+### Phase 4: Optimization (Ongoing)
+- Tune RAG retrieval settings
+- Optimize embedding model
+- Add new knowledge dynamically
+- Explore GPU embeddings if needed
+
+## Technical Notes
+
+### Current Cheshire Cat Configuration
+- **LLM**: darkidol (llama-swap-amd)
+- **Embedder**: FastEmbed CPU (BAAI/bge-large-en-v1.5-quantized)
+- **Vector DB**: Qdrant v1.9.1
+- **Knowledge**: 3 files uploaded (~10KB total)
+- **Plugin**: Miku personality (custom)
+
+### Performance Settings
+- **KV Cache**: Offload to CPU **DISABLED** ✅
+- **Temperature**: 0.8
+- **Max Tokens**: 150 (streaming)
+- **Model**: darkidol (uncensored Llama 3.1 8B)
+
+### Estimated Resource Usage
+- **Cheshire Cat**: ~500MB RAM, negligible CPU (GPU embeddings could reduce further)
+- **Qdrant**: ~100MB RAM
+- **Storage**: ~50MB (embeddings + indices)
+- **Total Overhead**: ~600MB RAM, ~50MB disk
+
+## Conclusion
+
+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.
+
+**For current needs**: Stick with direct loading (simpler, proven)
+**For future growth**: Cheshire Cat is now a strong option
+
+The infrastructure is already set up and tested, so migration could happen whenever knowledge base growth demands it.
+
+---
+
+**Benchmark Date**: January 30, 2026
+**Optimization**: KV cache offload to CPU disabled
+**Test Queries**: 10 varied questions
+**Success Rate**: 100% across all methods

cheshire-cat/QUICK_START.txt

@@ -0,0 +1,108 @@
+================================================================================
+🐱 CHESHIRE CAT TEST ENVIRONMENT - QUICK START GUIDE
+================================================================================
+
+📍 Location: /home/koko210Serve/docker/miku-discord/cheshire-cat
+
+🎯 Purpose: Test Cheshire Cat as memory/context system for Miku Bot
+
+================================================================================
+⚡ QUICK START (3 Commands)
+================================================================================
+
+1. Start services:
+   ./start.sh
+
+2. Configure and upload knowledge:
+   python3 test_setup.py
+
+3. Run benchmarks:
+   python3 benchmark_cat.py
+
+================================================================================
+📊 WHAT TO LOOK FOR
+================================================================================
+
+✅ GOOD (Proceed with integration):
+   - Mean latency < 1500ms
+   - P95 latency < 2000ms
+   - RAG retrieval is accurate
+
+⚠️  BORDERLINE (Try GPU embeddings):
+   - Mean latency 1500-2000ms
+   - Consider hybrid approach
+
+❌ POOR (Stick with current system):
+   - Mean latency > 2000ms
+   - RAG quality is poor
+
+================================================================================
+🔗 USEFUL LINKS
+================================================================================
+
+Admin Panel:    http://localhost:1865/admin
+API Docs:       http://localhost:1865/docs
+Qdrant:         http://localhost:6333/dashboard
+
+================================================================================
+📝 FILES CREATED
+================================================================================
+
+Configuration:
+  ✓ .env                      Environment variables
+  ✓ docker-compose.test.yml   Docker services
+
+Scripts:
+  ✓ start.sh                  Start services
+  ✓ stop.sh                   Stop services
+  ✓ test_setup.py             Configure Cat & upload knowledge
+  ✓ benchmark_cat.py          Performance benchmarks
+  ✓ compare_systems.py        Compare Cat vs current system
+
+Documentation:
+  ✓ SETUP_COMPLETE.md         Full setup guide
+  ✓ TEST_README.md            Testing documentation
+  ✓ QUICK_START.txt           This file
+
+================================================================================
+🎯 EXPECTED RESULTS (FX-6100)
+================================================================================
+
+With CPU embeddings:
+  Mean: 1600-2200ms          ⚠️  Borderline for voice chat
+
+With GPU embeddings:
+  Mean: 900-1400ms           ✅ Good for voice chat
+
+================================================================================
+🛠️  TROUBLESHOOTING
+================================================================================
+
+Services won't start:
+  docker logs miku_cheshire_cat_test
+
+Can't connect to llama-swap:
+  Edit test_setup.py line 10 with correct URL
+
+Embeddings too slow:
+  Try GPU acceleration (requires spare VRAM)
+
+================================================================================
+🧹 CLEANUP
+================================================================================
+
+Stop services:
+  ./stop.sh
+
+Remove all data:
+  docker-compose -f docker-compose.test.yml down -v
+
+================================================================================
+📚 DETAILED DOCS
+================================================================================
+
+Full guide:     cat SETUP_COMPLETE.md
+Test docs:      cat TEST_README.md
+View all files: ls -lah
+
+================================================================================

cheshire-cat/README.md

@@ -0,0 +1,46 @@
+# local-cat 😸🏠
+
+**local-cat** provides a completely local setup for CheshireCat. Local-cat leverages Local runners + Qdrant to run your preferred LLM, Embedder and VectorDB locally.
+
+> [!WARNING]
+>
+> - **Technical Expertise Required:** Setting up and running local-cat requires some technical know-how.
+> - **Hardware Requirements:** Performance may be slow without a recent GPU or NPU.
+
+## Ollama Setup
+
+> [!IMPORTANT]
+> Ollama can be instable with **latest models** or **non-common use** models(like qwen, deepseek)!!
+> If you encount inference problems, downgrade ollama image or [open an issue to Ollama](https://github.com/ollama/ollama/issues)
+
+### Setup Instructions
+
+1. **Clone the Repository:** `git clone https://github.com/cheshire-cat-ai/local-cat.git`
+2. **Navigate to the Directory:** `cd local-cat`
+3. **Start local-cat:** `docker-compose up -d`
+4. **Pull Your Desired Model:** `docker exec ollama_cat ollama pull <model_name:tag>`
+   - Replace `<model_name:tag>` with the specific model you want to use.
+5. **Your Setup is Complete!**
+   - You can now install additional plugins or start interacting with local-cat.
+
+### Use Ollama with MacOS GPU Acceleration
+
+Ollama normally handles running the model with GPU acceleration. In order to use GPU acceleration on Mac OS it is recommended to run Ollama directly on the host machine rather than inside Docker. More info [here](https://ollama.com/blog/ollama-is-now-available-as-an-official-docker-image).
+> [!NOTE]
+> This is recommended until GPU acceleration is supported by Docker Desktop on MacOS.
+
+To use local-cat with GPU acceleration on Mac:
+
+1. Install the menu bar app version of Ollama, which is the current recommended setup for MacOS users.
+2. Start using the following command `docker compose -f docker-compose-macos.yml up`
+3. Configure the Ollama Base URL in the cat's LLM settings to `http://host.docker.internal:11434`.
+
+> Note: This configuration allows Docker containers to communicate with your locally running Ollama service and leverage MacOS GPU acceleration.
+
+### Use Ollama with AMD
+
+To use local-cat with [AMD graphics that supports ROCm](https://rocm.docs.amd.com/en/docs-5.7.0/release/gpu_os_support.html#linux-supported-gpus), use the following command:
+
+```bash
+docker compose -f docker-compose-amd.yml up
+```

cheshire-cat/SETUP_COMPLETE.md

@@ -0,0 +1,226 @@
+# 🎉 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
+
+```bash
+./start.sh
+```
+
+Or manually:
+```bash
+docker-compose -f docker-compose.test.yml up -d
+```
+
+### Step 2: Configure and Upload Knowledge
+
+```bash
+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
+
+```bash
+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
+
+```bash
+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
+```bash
+docker ps | grep miku
+```
+
+### View Logs
+```bash
+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:
+```python
+# 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):
+```bash
+./stop.sh
+```
+
+### Stop and remove all data:
+```bash
+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/
+
+---
+
+## ✨ 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! 🚀

cheshire-cat/TEST_README.md

@@ -0,0 +1,202 @@
+# Cheshire Cat Test Environment for Miku Bot
+
+This is a standalone test environment for evaluating Cheshire Cat AI as a potential memory/context system for the Miku Discord bot.
+
+## 🎯 Goals
+
+1. **Test performance** - Measure latency, overhead, and real-time viability
+2. **Evaluate memory** - Compare RAG-based context retrieval vs full context loading
+3. **Benchmark CPU impact** - Assess performance on AMD FX-6100
+4. **Make informed decision** - Data-driven choice on integration
+
+## 📁 Directory Structure
+
+```
+cheshire-cat/
+├── cat/                    # Cat data (created on first run)
+│   ├── data/              # Cat's internal data
+│   ├── plugins/           # Custom plugins
+│   ├── static/            # Static assets
+│   └── long_term_memory/  # Qdrant vector storage
+├── .env                    # Environment configuration
+├── docker-compose.test.yml # Docker setup
+├── test_setup.py          # Initial setup script
+├── benchmark_cat.py       # Comprehensive benchmarks
+├── compare_systems.py     # Compare Cat vs current system
+└── TEST_README.md         # This file
+```
+
+## 🚀 Quick Start
+
+### 1. Prerequisites
+
+- Docker and Docker Compose installed
+- Miku bot's llama-swap service running
+- Python 3.8+ with requests library
+
+```bash
+pip3 install requests
+```
+
+### 2. Start Cheshire Cat
+
+```bash
+# From the cheshire-cat directory
+docker-compose -f docker-compose.test.yml up -d
+```
+
+Wait ~30 seconds for services to start.
+
+### 3. Configure and Test
+
+```bash
+# Run setup script (configures LLM, uploads knowledge base)
+python3 test_setup.py
+```
+
+This will:
+- ✅ Wait for Cat to be ready
+- ✅ Configure Cat to use llama-swap
+- ✅ Upload miku_lore.txt, miku_prompt.txt, miku_lyrics.txt
+- ✅ Run test queries
+
+### 4. Run Benchmarks
+
+```bash
+# Comprehensive performance benchmark
+python3 benchmark_cat.py
+```
+
+This tests:
+- Simple greetings (low complexity)
+- Factual queries (medium complexity)
+- Memory recall (high complexity)
+- Voice chat simulation (rapid-fire queries)
+
+### 5. Compare with Current System
+
+```bash
+# Side-by-side comparison
+python3 compare_systems.py
+```
+
+Compares latency between:
+- 🐱 Cheshire Cat (RAG-based context)
+- 📦 Current system (full context loading)
+
+## 🔍 What to Look For
+
+### ✅ Good Signs (Proceed with Integration)
+
+- Mean latency < 1500ms
+- P95 latency < 2000ms
+- Consistent performance across query types
+- RAG retrieves relevant context accurately
+
+### ⚠️ Warning Signs (Reconsider)
+
+- Mean latency > 2000ms
+- High variance (large stdev)
+- RAG misses important context
+- Frequent errors or timeouts
+
+### ❌ Stop Signs (Don't Use)
+
+- Mean latency > 3000ms
+- P95 latency > 5000ms
+- RAG retrieval quality is poor
+- System crashes or hangs
+
+## 📊 Understanding the Results
+
+### Latency Metrics
+
+- **Mean**: Average response time
+- **Median**: Middle value (less affected by outliers)
+- **P95**: 95% of queries are faster than this
+- **P99**: 99% of queries are faster than this
+
+### Voice Chat Target
+
+For real-time voice chat:
+- Target: < 2000ms total latency
+- Acceptable: 1000-1500ms mean
+- Borderline: 1500-2000ms mean
+- Too slow: > 2000ms mean
+
+### FX-6100 Considerations
+
+Your CPU may add overhead:
+- Embedding generation: ~600ms
+- Vector search: ~100-200ms
+- Total Cat overhead: ~800ms
+
+**With GPU embeddings**, this drops to ~250ms.
+
+## 🛠️ Troubleshooting
+
+### Cat won't start
+
+```bash
+# Check logs
+docker logs miku_cheshire_cat_test
+
+# Check if ports are in use
+sudo netstat -tlnp | grep 1865
+```
+
+### Can't connect to llama-swap
+
+The compose file tries to connect via:
+1. External network: `miku-discord_default`
+2. Host network: `host.docker.internal`
+
+If both fail, check llama-swap URL in test_setup.py and adjust.
+
+### Embeddings are slow
+
+Try GPU acceleration in docker-compose.test.yml (requires spare VRAM).
+
+### Knowledge upload fails
+
+Upload files manually via admin panel:
+- http://localhost:1865/admin
+- Go to "Rabbit Hole" tab
+- Drag and drop files
+
+## 🔗 Useful Endpoints
+
+- **Admin Panel**: http://localhost:1865/admin
+- **API Docs**: http://localhost:1865/docs
+- **Qdrant Dashboard**: http://localhost:6333/dashboard
+- **Health Check**: http://localhost:1865/
+
+## 📝 Decision Criteria
+
+After running benchmarks, consider:
+
+| Metric | Target | Your Result |
+|--------|--------|-------------|
+| Mean latency | < 1500ms | _____ ms |
+| P95 latency | < 2000ms | _____ ms |
+| Success rate | > 95% | _____ % |
+| RAG accuracy | Good | _____ |
+
+**Decision:**
+- ✅ All targets met → **Integrate with bot**
+- ⚠️ Some targets met → **Try GPU embeddings or hybrid approach**
+- ❌ Targets not met → **Stick with current system**
+
+## 🧹 Cleanup
+
+```bash
+# Stop services
+docker-compose -f docker-compose.test.yml down
+
+# Remove volumes (deletes all data)
+docker-compose -f docker-compose.test.yml down -v
+```
+
+---
+
+**Remember**: This is a test environment. Don't integrate with production bot until you're confident in the results!

cheshire-cat/analyze_consolidation.py

@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+"""
+Analyze Phase 2 Consolidation Results
+
+Check what was kept vs deleted for the comprehensive test.
+"""
+
+from qdrant_client import QdrantClient
+
+QDRANT_HOST = "localhost"
+QDRANT_PORT = 6333
+COLLECTION_NAME = "episodic"
+TEST_USER_ID = "discord_user_comprehensive_test"
+
+client = QdrantClient(host=QDRANT_HOST, port=QDRANT_PORT, timeout=10, prefer_grpc=False)
+
+print("=" * 70)
+print("PHASE 2 CONSOLIDATION ANALYSIS")
+print("=" * 70)
+
+# Get all memories (limit increased to get all test messages)
+results, _ = client.scroll(
+    collection_name=COLLECTION_NAME,
+    limit=300,
+    with_payload=True,
+    with_vectors=False
+)
+
+# Expected deletions
+expected_trivial = [
+    "lol", "k", "ok", "lmao", "haha", "xd", "brb", "gtg"
+]
+
+# Expected to keep
+expected_keep_keywords = [
+    "Sarah Chen", "24 years old", "Seattle", "Microsoft",
+    "engaged", "grandmother", "promoted", "Luna died", "panic attack", "ADHD",
+    "piano", "Japanese", "Ghibli", "vinyl", "marathon",
+    "Emma", "Jennifer", "Alex", "David",
+    "cilantro", "forest green", "vegetarian", "pineapple",
+    "Japan", "apartment", "insomnia", "pottery"
+]
+
+# Check what exists
+kept_messages = []
+kept_important = []
+deleted_trivial = []
+
+for point in results:
+    content = point.payload.get('page_content', '')
+    
+    # Check if it's from our test
+    if any(keyword.lower() in content.lower() for keyword in expected_keep_keywords + expected_trivial):
+        metadata = point.payload.get('metadata', {})
+        is_consolidated = metadata.get('consolidated', False)
+        
+        if content.lower().strip() in expected_trivial:
+            # This is trivial - should have been deleted
+            print(f"⚠️  TRIVIAL STILL EXISTS: '{content}'")
+        else:
+            # Important message - should be kept
+            kept_important.append(content)
+
+# Check for deleted messages
+for trivial in expected_trivial:
+    found = False
+    for point in results:
+        if point.payload.get('page_content', '').lower().strip() == trivial:
+            found = True
+            break
+    
+    if not found:
+        deleted_trivial.append(trivial)
+
+print(f"\n📊 RESULTS:")
+print(f"✅ Important messages KEPT: {len(kept_important)}")
+print(f"🗑️  Trivial messages DELETED: {len(deleted_trivial)}")
+print(f"⚠️  Trivial messages STILL PRESENT: {8 - len(deleted_trivial)}")
+
+print(f"\n🗑️  Successfully deleted:")
+for msg in deleted_trivial:
+    print(f"  - '{msg}'")
+
+if len(deleted_trivial) < 8:
+    print(f"\n⚠️  Still present (should have been deleted):")
+    for trivial in expected_trivial:
+        if trivial not in deleted_trivial:
+            print(f"  - '{trivial}'")
+
+print(f"\n✅ Sample of important memories kept:")
+for msg in kept_important[:10]:
+    print(f"  - '{msg[:60]}...'")
+
+print("\n" + "=" * 70)
+print("CONSOLIDATED MEMORY CHECK")
+print("=" * 70)
+
+consolidated_count = 0
+unconsolidated_count = 0
+
+for point in results:
+    metadata = point.payload.get('metadata', {})
+    if metadata.get('consolidated', False):
+        consolidated_count += 1
+    else:
+        unconsolidated_count += 1
+
+print(f"✅ Memories marked consolidated: {consolidated_count}")
+print(f"⏳ Memories still unconsolidated: {unconsolidated_count}")
+
+print("\n" + "=" * 70)

cheshire-cat/benchmark_cat.py

@@ -0,0 +1,393 @@
+#!/usr/bin/env python3
+"""
+Comprehensive Cheshire Cat Performance Benchmark
+Tests latency, overhead, and performance under various conditions
+"""
+
+import requests
+import time
+import json
+import statistics
+from datetime import datetime
+from typing import List, Dict
+import sys
+
+CAT_URL = "http://localhost:1865"
+
+# Test queries of varying complexity
+TEST_QUERIES = {
+    "simple_greeting": [
+        "Hello!",
+        "Hi Miku!",
+        "Hey there!",
+        "Good morning!",
+        "What's up?"
+    ],
+    "factual_short": [
+        "What is your favorite food?",
+        "How old are you?",
+        "What color is your hair?",
+        "Where are you from?",
+        "What's your name?"
+    ],
+    "factual_medium": [
+        "Tell me about your friends Rin and Len",
+        "What is the song World is Mine about?",
+        "Who created you?",
+        "What kind of music do you sing?",
+        "What do you like to do for fun?"
+    ],
+    "complex_memory": [
+        "What did we talk about earlier?",
+        "Can you remember what I asked you before?",
+        "Tell me everything you know about green onions",
+        "What are all your most iconic songs?",
+        "Describe your personality and how you act"
+    ],
+    "conversation_flow": [
+        "I love your music!",
+        "What's your favorite song to perform?",
+        "Do you ever get nervous on stage?",
+        "That's interesting! Tell me more.",
+        "Thanks for chatting with me!"
+    ]
+}
+
+class PerformanceResults:
+    def __init__(self):
+        self.query_times: List[float] = []
+        self.response_sizes: List[int] = []
+        self.errors: List[str] = []
+        self.category_stats: Dict[str, List[float]] = {}
+    
+    def add_result(self, latency_ms: float, response_size: int, category: str):
+        self.query_times.append(latency_ms)
+        self.response_sizes.append(response_size)
+        if category not in self.category_stats:
+            self.category_stats[category] = []
+        self.category_stats[category].append(latency_ms)
+    
+    def add_error(self, error: str):
+        self.errors.append(error)
+    
+    def get_stats(self):
+        if not self.query_times:
+            return None
+        
+        return {
+            "total_queries": len(self.query_times),
+            "total_errors": len(self.errors),
+            "success_rate": (len(self.query_times) / (len(self.query_times) + len(self.errors))) * 100,
+            "latency": {
+                "min_ms": min(self.query_times),
+                "max_ms": max(self.query_times),
+                "mean_ms": statistics.mean(self.query_times),
+                "median_ms": statistics.median(self.query_times),
+                "stdev_ms": statistics.stdev(self.query_times) if len(self.query_times) > 1 else 0,
+                "p95_ms": self._percentile(self.query_times, 95),
+                "p99_ms": self._percentile(self.query_times, 99)
+            },
+            "response_sizes": {
+                "min_bytes": min(self.response_sizes),
+                "max_bytes": max(self.response_sizes),
+                "mean_bytes": statistics.mean(self.response_sizes),
+            },
+            "by_category": {
+                category: {
+                    "mean_ms": statistics.mean(times),
+                    "median_ms": statistics.median(times),
+                    "min_ms": min(times),
+                    "max_ms": max(times)
+                }
+                for category, times in self.category_stats.items()
+            }
+        }
+    
+    @staticmethod
+    def _percentile(data, percentile):
+        size = len(data)
+        sorted_data = sorted(data)
+        index = (percentile / 100) * size
+        if index.is_integer():
+            return sorted_data[int(index) - 1]
+        else:
+            return sorted_data[int(index)]
+
+def test_single_query(query: str, category: str, timeout: int = 60, warmup: bool = False) -> Dict:
+    """Test a single query and measure performance
+    
+    Args:
+        query: The query text to send
+        category: Category for grouping results
+        timeout: Request timeout in seconds (60s for model loading)
+        warmup: If True, don't count in results (for model loading)
+    """
+    start_time = time.time()
+    
+    try:
+        response = requests.post(
+            f"{CAT_URL}/message",
+            json={"text": query},
+            headers={"Content-Type": "application/json"},
+            timeout=timeout
+        )
+        
+        latency_ms = (time.time() - start_time) * 1000
+        
+        if response.status_code == 200:
+            data = response.json()
+            content = data.get("content", "")
+            
+            # Filter out tool calls that might still appear
+            if content and not (content.startswith('{"name":') or content.startswith('{')):
+                return {
+                    "success": True,
+                    "latency_ms": latency_ms,
+                    "response_size": len(content),
+                    "response": content,
+                    "category": category,
+                    "warmup": warmup
+                }
+            else:
+                return {
+                    "success": False,
+                    "latency_ms": latency_ms,
+                    "error": "Got tool call instead of text response",
+                    "category": category,
+                    "warmup": warmup
+                }
+        else:
+            return {
+                "success": False,
+                "latency_ms": latency_ms,
+                "error": f"HTTP {response.status_code}",
+                "category": category,
+                "warmup": warmup
+            }
+    except Exception as e:
+        latency_ms = (time.time() - start_time) * 1000
+        return {
+            "success": False,
+            "latency_ms": latency_ms,
+            "error": str(e),
+            "category": category,
+            "warmup": warmup
+        }
+
+def run_benchmark_suite(iterations: int = 3, verbose: bool = True) -> PerformanceResults:
+    """Run complete benchmark suite"""
+    results = PerformanceResults()
+    total_queries = sum(len(queries) for queries in TEST_QUERIES.values()) * iterations
+    current_query = 0
+    
+    print(f"\n🏁 Starting benchmark suite: {total_queries} total queries")
+    print("=" * 60)
+    
+    # Warmup query to load the model
+    print("\n🔥 Warming up model (loading darkidol, may take 30-45s)...")
+    warmup_result = test_single_query("Hi!", "warmup", timeout=60, warmup=True)
+    if warmup_result["success"]:
+        print(f"   ✅ Model loaded in {warmup_result['latency_ms']:.0f}ms")
+    else:
+        print(f"   ⚠️  Warmup issue: {warmup_result.get('error', 'unknown')}")
+        print("   Continuing anyway...")
+    
+    time.sleep(2)  # Brief pause after warmup
+    
+    for iteration in range(iterations):
+        print(f"\n📊 Iteration {iteration + 1}/{iterations}")
+        
+        for category, queries in TEST_QUERIES.items():
+            print(f"\n  Category: {category}")
+            
+            for query in queries:
+                current_query += 1
+                if verbose:
+                    print(f"    [{current_query}/{total_queries}] Testing: '{query[:40]}...'")
+                
+                result = test_single_query(query, category, timeout=60)
+                
+                if result["success"] and not result.get("warmup", False):
+                    results.add_result(
+                        result["latency_ms"],
+                        result["response_size"],
+                        category
+                    )
+                    if verbose:
+                        print(f"       ✅ {result['latency_ms']:.0f}ms - {result['response_size']} bytes")
+                        print(f"          Response: {result['response'][:60]}...")
+                elif not result.get("warmup", False):
+                    results.add_error(result["error"])
+                    if verbose:
+                        print(f"       ❌ Error: {result['error']}")
+                
+                # Small delay between queries to avoid overwhelming the system
+                time.sleep(1)
+    
+    return results
+
+def test_voice_chat_simulation(duration_seconds: int = 60) -> Dict:
+    """Simulate voice chat workload (rapid-fire queries)"""
+    print(f"\n🎤 Simulating voice chat for {duration_seconds}s")
+    print("   (Rapid-fire queries to test real-time performance)")
+    print("=" * 60)
+    
+    voice_queries = [
+        "Hello!",
+        "How are you?",
+        "Tell me a joke",
+        "What's your favorite song?",
+        "That's cool!",
+        "Can you sing?",
+        "I like you!",
+        "What should we do?",
+        "Tell me more",
+        "Goodbye!"
+    ]
+    
+    results = PerformanceResults()
+    start_time = time.time()
+    query_index = 0
+    
+    while (time.time() - start_time) < duration_seconds:
+        query = voice_queries[query_index % len(voice_queries)]
+        result = test_single_query(query, "voice_chat", timeout=30)  # Increased timeout
+        
+        if result["success"]:
+            results.add_result(
+                result["latency_ms"],
+                result["response_size"],
+                "voice_chat"
+            )
+            status = "✅" if result["latency_ms"] < 2000 else "⚠️"
+            print(f"  {status} Query {query_index + 1}: {result['latency_ms']:.0f}ms")
+        else:
+            results.add_error(result["error"])
+            print(f"  ❌ Query {query_index + 1}: Error - {result.get('error', 'unknown')}")
+        
+        query_index += 1
+        time.sleep(2)  # Increased delay between queries
+    
+    elapsed = time.time() - start_time
+    print(f"\n  Completed {query_index} queries in {elapsed:.1f}s")
+    
+    return results.get_stats()
+
+def print_report(results: PerformanceResults):
+    """Print detailed performance report"""
+    stats = results.get_stats()
+    
+    if not stats:
+        print("\n❌ No successful queries to report")
+        return
+    
+    print("\n" + "=" * 60)
+    print("📊 PERFORMANCE REPORT")
+    print("=" * 60)
+    
+    # Overall Statistics
+    print(f"\n📈 Overall Statistics:")
+    print(f"   Total Queries:    {stats['total_queries']}")
+    print(f"   Total Errors:     {stats['total_errors']}")
+    print(f"   Success Rate:     {stats['success_rate']:.1f}%")
+    
+    # Latency Statistics
+    lat = stats['latency']
+    print(f"\n⏱️  Latency Statistics:")
+    print(f"   Mean:             {lat['mean_ms']:.0f} ms")
+    print(f"   Median:           {lat['median_ms']:.0f} ms")
+    print(f"   Min:              {lat['min_ms']:.0f} ms")
+    print(f"   Max:              {lat['max_ms']:.0f} ms")
+    print(f"   Std Dev:          {lat['stdev_ms']:.0f} ms")
+    print(f"   95th Percentile:  {lat['p95_ms']:.0f} ms")
+    print(f"   99th Percentile:  {lat['p99_ms']:.0f} ms")
+    
+    # Voice Chat Assessment
+    print(f"\n🎤 Voice Chat Viability:")
+    if lat['mean_ms'] < 1000:
+        print(f"   ✅ EXCELLENT - Mean latency under 1s")
+    elif lat['mean_ms'] < 1500:
+        print(f"   ✅ GOOD - Mean latency acceptable for voice")
+    elif lat['mean_ms'] < 2000:
+        print(f"   ⚠️  BORDERLINE - Noticeable lag in voice chat")
+    else:
+        print(f"   ❌ TOO SLOW - Not suitable for real-time voice")
+    
+    if lat['p95_ms'] > 2000:
+        print(f"   ⚠️  WARNING: 5% of queries exceed 2s (P95: {lat['p95_ms']:.0f}ms)")
+    
+    # Category Breakdown
+    print(f"\n📋 Performance by Category:")
+    for category, cat_stats in stats['by_category'].items():
+        print(f"\n   {category}:")
+        print(f"     Mean:    {cat_stats['mean_ms']:.0f} ms")
+        print(f"     Median:  {cat_stats['median_ms']:.0f} ms")
+        print(f"     Range:   {cat_stats['min_ms']:.0f}-{cat_stats['max_ms']:.0f} ms")
+    
+    # Response Size Statistics
+    size = stats['response_sizes']
+    print(f"\n📦 Response Sizes:")
+    print(f"   Mean:             {size['mean_bytes']:.0f} bytes")
+    print(f"   Range:            {size['min_bytes']}-{size['max_bytes']} bytes")
+    
+    print("\n" + "=" * 60)
+
+def save_results(results: PerformanceResults, filename: str = None):
+    """Save results to JSON file"""
+    if filename is None:
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"benchmark_results_{timestamp}.json"
+    
+    stats = results.get_stats()
+    with open(filename, 'w') as f:
+        json.dump(stats, f, indent=2)
+    
+    print(f"\n💾 Results saved to: {filename}")
+
+def main():
+    print("=" * 60)
+    print("🐱 Cheshire Cat Performance Benchmark")
+    print("=" * 60)
+    
+    # Check if Cat is available
+    try:
+        response = requests.get(f"{CAT_URL}/", timeout=5)
+        if response.status_code != 200:
+            print(f"\n❌ Cat not responding (status {response.status_code})")
+            print("   Make sure Cat is running: docker-compose -f docker-compose.test.yml up -d")
+            sys.exit(1)
+    except Exception as e:
+        print(f"\n❌ Cannot connect to Cat: {e}")
+        print("   Make sure Cat is running: docker-compose -f docker-compose.test.yml up -d")
+        sys.exit(1)
+    
+    print("\n✅ Cat is available\n")
+    
+    # Run benchmark suite
+    print("Starting comprehensive benchmark...")
+    print("This will take several minutes...\n")
+    
+    results = run_benchmark_suite(iterations=2, verbose=True)
+    
+    # Print report
+    print_report(results)
+    
+    # Voice chat simulation
+    print("\n" + "=" * 60)
+    voice_results = test_voice_chat_simulation(duration_seconds=30)
+    
+    if voice_results:
+        print("\n🎤 Voice Chat Simulation Results:")
+        lat = voice_results['latency']
+        print(f"   Mean latency:     {lat['mean_ms']:.0f} ms")
+        print(f"   Median latency:   {lat['median_ms']:.0f} ms")
+        print(f"   95th percentile:  {lat['p95_ms']:.0f} ms")
+        print(f"   Success rate:     {voice_results['success_rate']:.1f}%")
+    
+    # Save results
+    save_results(results)
+    
+    print("\n✅ Benchmark complete!")
+
+if __name__ == "__main__":
+    main()

cheshire-cat/cat/plugins/discord_bridge/discord_bridge.py

@@ -99,6 +99,26 @@ def before_cat_stores_episodic_memory(doc, cat):
     return doc
 
 
+@hook(priority=80)
+def before_cat_recalls_declarative_memories(declarative_recall_config, cat):
+    """
+    Increase k-value and lower threshold for better declarative memory retrieval.
+    
+    Default Cat settings (k=3, threshold=0.7) are too restrictive for factual recall.
+    We increase k to retrieve more candidates and lower threshold to catch facts
+    that might have lower similarity scores due to embedding model limitations.
+    """
+    # Increase from k=3 to k=10 (retrieve more memories)
+    declarative_recall_config["k"] = 10
+    
+    # Lower threshold from 0.7 to 0.5 (be more lenient with similarity scores)
+    declarative_recall_config["threshold"] = 0.5
+    
+    print(f"🔧 [Discord Bridge] Adjusted declarative recall: k={declarative_recall_config['k']}, threshold={declarative_recall_config['threshold']}")
+    
+    return declarative_recall_config
+
+
 @hook(priority=50)
 def after_cat_recalls_memories(cat):
     """
@@ -119,6 +139,63 @@ def after_cat_recalls_memories(cat):
     
     if declarative_memories:
         print(f"📚 [Discord Bridge] Recalled {len(declarative_memories)} declarative facts for user {cat.user_id}")
+        # Show the actual facts for debugging
+        for doc, score, *rest in declarative_memories[:3]:  # Show top 3
+            print(f"   - [{score:.3f}] {doc.page_content[:80]}...")
+
+
+@hook(priority=100)
+def agent_prompt_prefix(prefix, cat) -> str:
+    """
+    Add explicit instruction to respect declarative facts.
+    This overrides the default Cat prefix to emphasize factual accuracy.
+    """
+    # Add a strong instruction about facts BEFORE the regular personality
+    enhanced_prefix = f"""You are Hatsune Miku, a cheerful virtual idol.
+
+CRITICAL INSTRUCTION: When you see "Context of documents containing relevant information" below, those are VERIFIED FACTS about the user. You MUST use these facts when they are relevant to the user's question. Never guess or make up information that contradicts these facts.
+
+{prefix}"""
+    
+    return enhanced_prefix
+
+
+@hook(priority=100)
+def before_agent_starts(agent_input, cat) -> dict:
+    """
+    Log the agent input for debugging.
+    Now that the suffix template is fixed, declarative facts should appear naturally.
+    """
+    declarative_mem = agent_input.get('declarative_memory', '')
+    episodic_mem = agent_input.get('episodic_memory', '')
+    
+    print(f"🔍 [Discord Bridge] before_agent_starts called")
+    print(f"   input: {agent_input.get('input', '')[:80]}")
+    print(f"   declarative_mem length: {len(declarative_mem)}")
+    print(f"   episodic_mem length: {len(episodic_mem)}")
+    if declarative_mem:
+        print(f"   declarative_mem preview: {declarative_mem[:200]}")
+    
+    return agent_input
+
+
+@hook(priority=100)
+def before_cat_sends_message(message: dict, cat) -> dict:
+    """
+    This hook is called AFTER the LLM response, so it's too late to modify the prompt.
+    Keeping it for potential post-processing, but the real work happens in before_agent_starts.
+    """
+    return message
+
+
+@hook(priority=10)
+def agent_prompt_suffix(prompt_suffix, cat) -> str:
+    """
+    Pass through the suffix unchanged.
+    The miku_personality plugin (priority=100) sets the suffix with memory placeholders.
+    This lower-priority hook runs first but the miku_personality hook overrides it.
+    """
+    return prompt_suffix
 
 
 # Plugin metadata

cheshire-cat/check_memories.py

@@ -0,0 +1,30 @@
+#!/usr/bin/env python3
+"""Check what memories exist in Qdrant and their metadata"""
+
+from qdrant_client import QdrantClient
+
+QDRANT_HOST = "localhost"
+QDRANT_PORT = 6333
+COLLECTION_NAME = "episodic"
+
+client = QdrantClient(host=QDRANT_HOST, port=QDRANT_PORT, timeout=10, prefer_grpc=False)
+
+print("=" * 70)
+print("MEMORY INSPECTION")
+print("=" * 70)
+
+# Get all memories
+results, next_offset = client.scroll(
+    collection_name=COLLECTION_NAME,
+    limit=20,
+    with_payload=True,
+    with_vectors=False
+)
+
+print(f"\n📊 Total memories found: {len(results)}")
+
+for i, point in enumerate(results, 1):
+    print(f"\n--- Memory {i} ---")
+    print(f"ID: {point.id}")
+    print(f"Content: {point.payload.get('page_content', '')[:100]}")
+    print(f"Metadata: {point.payload.get('metadata', {})}")

cheshire-cat/compare_systems.py

@@ -0,0 +1,212 @@
+#!/usr/bin/env python3
+"""
+Comparison Benchmark: Current System vs Cheshire Cat
+Measures the difference in performance between the two approaches
+"""
+
+import sys
+import os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+
+import requests
+import time
+import statistics
+from typing import List, Dict
+import asyncio
+
+CAT_URL = "http://localhost:1865"
+
+# Import your current LLM function
+try:
+    from bot.utils import llm
+    from bot import globals as bot_globals
+    HAS_BOT_CODE = True
+except ImportError:
+    print("⚠️  Could not import bot code - will skip direct comparison")
+    HAS_BOT_CODE = False
+
+TEST_QUERIES = [
+    "What is your favorite food?",
+    "Tell me about your friends",
+    "What's the song World is Mine about?",
+    "Hello Miku!",
+    "Do you like to sing?",
+    "Who created you?",
+    "What color is your hair?",
+    "Tell me about green onions",
+    "What do you do for fun?",
+    "Are you a Vocaloid?"
+]
+
+def test_cat_query(query: str, timeout: int = 60) -> Dict:
+    """Test query using Cheshire Cat"""
+    start_time = time.time()
+    
+    try:
+        response = requests.post(
+            f"{CAT_URL}/message",
+            json={"text": query},
+            headers={"Content-Type": "application/json"},
+            timeout=timeout
+        )
+        
+        latency_ms = (time.time() - start_time) * 1000
+        
+        if response.status_code == 200:
+            data = response.json()
+            content = data.get("content", "")
+            
+            # Filter out tool calls
+            if content and not (content.startswith('{"name":') or content.startswith('{')):
+                return {
+                    "success": True,
+                    "latency_ms": latency_ms,
+                    "response": content,
+                    "method": "cheshire_cat"
+                }
+            else:
+                return {
+                    "success": False,
+                    "latency_ms": latency_ms,
+                    "error": "Got tool call instead of text",
+                    "method": "cheshire_cat"
+                }
+        else:
+            return {
+                "success": False,
+                "latency_ms": latency_ms,
+                "error": f"HTTP {response.status_code}",
+                "method": "cheshire_cat"
+            }
+    except Exception as e:
+        return {
+            "success": False,
+            "latency_ms": (time.time() - start_time) * 1000,
+            "error": str(e),
+            "method": "cheshire_cat"
+        }
+
+async def test_current_query(query: str) -> Dict:
+    """Test query using current Miku bot system"""
+    if not HAS_BOT_CODE:
+        return {"success": False, "error": "Bot code not available", "method": "current"}
+    
+    start_time = time.time()
+    
+    try:
+        # Use your existing query_llama function
+        response = await llm.query_llama(
+            user_prompt=query,
+            user_id="benchmark_test",
+            guild_id=None,
+            response_type="dm_response"
+        )
+        
+        latency_ms = (time.time() - start_time) * 1000
+        
+        return {
+            "success": True,
+            "latency_ms": latency_ms,
+            "response": response,
+            "method": "current"
+        }
+    except Exception as e:
+        return {
+            "success": False,
+            "latency_ms": (time.time() - start_time) * 1000,
+            "error": str(e),
+            "method": "current"
+        }
+
+async def run_comparison():
+    """Run comparison between both systems"""
+    print("=" * 70)
+    print("⚖️  COMPARISON: Current System vs Cheshire Cat")
+    print("=" * 70)
+    
+    cat_times: List[float] = []
+    current_times: List[float] = []
+    
+    for i, query in enumerate(TEST_QUERIES):
+        print(f"\n[{i+1}/{len(TEST_QUERIES)}] Query: '{query}'")
+        print("-" * 70)
+        
+        # Test Cheshire Cat
+        cat_result = test_cat_query(query)
+        if cat_result["success"]:
+            cat_times.append(cat_result["latency_ms"])
+            print(f"  🐱 Cheshire Cat: {cat_result['latency_ms']:.0f}ms")
+            print(f"     Response: {cat_result['response'][:80]}...")
+        else:
+            print(f"  🐱 Cheshire Cat: ❌ {cat_result.get('error', 'Failed')}")
+        
+        # Small delay between tests
+        await asyncio.sleep(1)
+        
+        # Test current system
+        if HAS_BOT_CODE:
+            current_result = await test_current_query(query)
+            if current_result["success"]:
+                current_times.append(current_result["latency_ms"])
+                print(f"  📦 Current System: {current_result['latency_ms']:.0f}ms")
+                print(f"     Response: {current_result['response'][:80]}...")
+            else:
+                print(f"  📦 Current System: ❌ {current_result.get('error', 'Failed')}")
+        
+        await asyncio.sleep(1)
+    
+    # Print comparison statistics
+    print("\n" + "=" * 70)
+    print("📊 COMPARISON RESULTS")
+    print("=" * 70)
+    
+    if cat_times:
+        print(f"\n🐱 Cheshire Cat:")
+        print(f"   Mean latency:     {statistics.mean(cat_times):.0f} ms")
+        print(f"   Median latency:   {statistics.median(cat_times):.0f} ms")
+        print(f"   Min latency:      {min(cat_times):.0f} ms")
+        print(f"   Max latency:      {max(cat_times):.0f} ms")
+        print(f"   Success rate:     {len(cat_times)}/{len(TEST_QUERIES)} ({len(cat_times)/len(TEST_QUERIES)*100:.0f}%)")
+    
+    if current_times:
+        print(f"\n📦 Current System:")
+        print(f"   Mean latency:     {statistics.mean(current_times):.0f} ms")
+        print(f"   Median latency:   {statistics.median(current_times):.0f} ms")
+        print(f"   Min latency:      {min(current_times):.0f} ms")
+        print(f"   Max latency:      {max(current_times):.0f} ms")
+        print(f"   Success rate:     {len(current_times)}/{len(TEST_QUERIES)} ({len(current_times)/len(TEST_QUERIES)*100:.0f}%)")
+    
+    if cat_times and current_times:
+        print(f"\n⚖️  Comparison:")
+        cat_mean = statistics.mean(cat_times)
+        current_mean = statistics.mean(current_times)
+        diff = cat_mean - current_mean
+        diff_pct = (diff / current_mean) * 100
+        
+        if diff > 0:
+            print(f"   Cheshire Cat is {diff:.0f}ms SLOWER ({diff_pct:+.1f}%)")
+        else:
+            print(f"   Cheshire Cat is {abs(diff):.0f}ms FASTER ({diff_pct:+.1f}%)")
+        
+        # Voice chat assessment
+        print(f"\n🎤 Voice Chat Viability:")
+        if cat_mean < 1500:
+            print(f"   ✅ Both systems suitable for voice chat")
+        elif cat_mean < 2000 and current_mean < 1500:
+            print(f"   ⚠️  Cheshire Cat slower but still usable")
+        else:
+            print(f"   ❌ Cheshire Cat may be too slow for real-time voice")
+    
+    print("\n" + "=" * 70)
+
+def main():
+    if not HAS_BOT_CODE:
+        print("\n⚠️  Running in Cat-only mode (bot code not available)")
+        print("   To run full comparison:")
+        print("   1. Make sure you're running this from the cheshire-cat directory")
+        print("   2. Ensure the parent 'bot' directory is accessible\n")
+    
+    asyncio.run(run_comparison())
+
+if __name__ == "__main__":
+    main()

cheshire-cat/compose.yml

@@ -0,0 +1,64 @@
+services:
+  cheshire-cat-core:
+    image: ghcr.io/cheshire-cat-ai/core:1.6.2
+    container_name: cheshire_cat_core
+    depends_on:
+      - cheshire-cat-vector-memory
+      - ollama
+    environment:
+      PYTHONUNBUFFERED: "1"
+      WATCHFILES_FORCE_POLLING: "true"
+      CORE_HOST: ${CORE_HOST:-localhost}
+      CORE_PORT: ${CORE_PORT:-1865}
+      QDRANT_HOST: ${QDRANT_HOST:-cheshire_cat_vector_memory}
+      QDRANT_PORT: ${QDRANT_PORT:-6333}
+      CORE_USE_SECURE_PROTOCOLS: ${CORE_USE_SECURE_PROTOCOLS:-false}
+      API_KEY: ${API_KEY:-}
+      LOG_LEVEL: ${LOG_LEVEL:-WARNING}
+      DEBUG: ${DEBUG:-false}
+      SAVE_MEMORY_SNAPSHOTS: ${SAVE_MEMORY_SNAPSHOTS:-false}
+    ports:
+      - "${CORE_PORT:-1865}:80"
+    # This add an entry to /etc/hosts file in the container mapping host.docker.internal to the host machine IP addr, allowing the container to access services running on the host, not only on Win and Mac but also Linux. 
+    # See https://docs.docker.com/desktop/networking/#i-want-to-connect-from-a-container-to-a-service-on-the-host and https://docs.docker.com/reference/cli/docker/container/run/#add-host
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    volumes:
+      - ./cat/static:/app/cat/static
+      - ./cat/plugins:/app/cat/plugins
+      - ./cat/data:/app/cat/data
+    restart: unless-stopped
+
+  cheshire-cat-vector-memory:
+    image: qdrant/qdrant:v1.9.1
+    container_name: cheshire_cat_vector_memory
+    environment:
+      LOG_LEVEL: ${LOG_LEVEL:-WARNING}
+    expose:
+      - ${QDRANT_PORT:-6333}
+    volumes:
+      - ./cat/long_term_memory/vector:/qdrant/storage
+    restart: unless-stopped
+
+  ollama:
+    image: ollama/ollama:0.1.39
+    container_name: ollama_cat
+    restart: unless-stopped
+    environment:
+      OLLAMA_HOST: "${OLLAMA_HOST:-0.0.0.0}:${OLLAMA_PORT-11434}"
+      OLLAMA_DEBUG: ${OLLAMA_DEBUG:-false}
+      OLLAMA_FLASH_ATTENTION: ${OLLAMA_FLASH_ATTENTION:-false}
+      OLLAMA_KEEP_ALIVE: ${OLLAMA_KEEP_ALIVE:-"5m"}
+      OLLAMA_MAX_LOADED_MODELS: ${OLLAMA_MAX_LOADED_MODELS:-1}
+      OLLAMA_NUM_PARALLEL: ${OLLAMA_NUM_PARALLEL:-1}
+    expose:
+      - ${OLLAMA_PORT:-11434}
+    volumes:
+      - ./ollama:/root/.ollama
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [ gpu ]

cheshire-cat/docker-compose-amd.yml

@@ -0,0 +1,49 @@
+services:
+  cheshire-cat-core:
+    image: ghcr.io/cheshire-cat-ai/core:1.6.2
+    container_name: cheshire_cat_core
+    depends_on:
+      - cheshire-cat-vector-memory
+      - ollama
+    environment:
+      - PYTHONUNBUFFERED=1
+      - WATCHFILES_FORCE_POLLING=true
+      - CORE_HOST=${CORE_HOST:-localhost}
+      - CORE_PORT=${CORE_PORT:-1865}
+      - QDRANT_HOST=${QDRANT_HOST:-cheshire_cat_vector_memory}
+      - QDRANT_PORT=${QDRANT_PORT:-6333}
+      - CORE_USE_SECURE_PROTOCOLS=${CORE_USE_SECURE_PROTOCOLS:-}
+      - API_KEY=${API_KEY:-}
+      - LOG_LEVEL=${LOG_LEVEL:-WARNING}
+      - DEBUG=${DEBUG:-true}
+      - SAVE_MEMORY_SNAPSHOTS=${SAVE_MEMORY_SNAPSHOTS:-false}
+    ports:
+      - ${CORE_PORT:-1865}:80
+    volumes:
+      - ./cat/static:/app/cat/static
+      - ./cat/plugins:/app/cat/plugins
+      - ./cat/data:/app/cat/data
+    restart: unless-stopped
+
+  cheshire-cat-vector-memory:
+    image: qdrant/qdrant:v1.9.1
+    container_name: cheshire_cat_vector_memory
+    expose:
+      - 6333
+    volumes:
+      - ./cat/long_term_memory/vector:/qdrant/storage
+    restart: unless-stopped
+
+  ollama:
+    container_name: ollama_cat
+    image: ollama/ollama:0.1.39-rocm
+    devices:
+      - /dev/kfd
+      - /dev/dri
+    security_opt:
+      - seccomp:unconfined
+    volumes:
+      - ./ollama:/root/.ollama
+    expose:
+      - 11434
+    

cheshire-cat/docker-compose-macos.yml

@@ -0,0 +1,36 @@
+services:
+  cheshire-cat-core:
+    image: ghcr.io/cheshire-cat-ai/core:1.6.2
+    container_name: cheshire_cat_core
+    depends_on:
+      - cheshire-cat-vector-memory
+    environment:
+      - PYTHONUNBUFFERED=1
+      - WATCHFILES_FORCE_POLLING=true
+      - CORE_HOST=${CORE_HOST:-localhost}
+      - CORE_PORT=${CORE_PORT:-1865}
+      - QDRANT_HOST=${QDRANT_HOST:-cheshire_cat_vector_memory}
+      - QDRANT_PORT=${QDRANT_PORT:-6333}
+      - CORE_USE_SECURE_PROTOCOLS=${CORE_USE_SECURE_PROTOCOLS:-}
+      - API_KEY=${API_KEY:-}
+      - LOG_LEVEL=${LOG_LEVEL:-WARNING}
+      - DEBUG=${DEBUG:-true}
+      - SAVE_MEMORY_SNAPSHOTS=${SAVE_MEMORY_SNAPSHOTS:-false}
+    ports:
+      - ${CORE_PORT:-1865}:80
+    volumes:
+      - ./cat/static:/app/cat/static
+      - ./cat/plugins:/app/cat/plugins
+      - ./cat/data:/app/cat/data
+    restart: unless-stopped
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+
+  cheshire-cat-vector-memory:
+    image: qdrant/qdrant:v1.9.1
+    container_name: cheshire_cat_vector_memory
+    expose:
+      - 6333
+    volumes:
+      - ./cat/long_term_memory/vector:/qdrant/storage
+    restart: unless-stopped

cheshire-cat/extract_declarative_facts.py

@@ -0,0 +1,217 @@
+#!/usr/bin/env python3
+"""
+Declarative Memory Extraction
+
+After consolidation keeps important episodic memories, this script:
+1. Analyzes kept memories
+2. Extracts structured facts (name, age, location, preferences, etc.)
+3. Stores facts in declarative memory collection
+4. Enables better retrieval for direct questions
+
+This is the KEY to making Phase 2 actually useful.
+"""
+
+import re
+from qdrant_client import QdrantClient
+from qdrant_client.models import PointStruct
+import uuid
+from datetime import datetime
+
+
+QDRANT_HOST = "localhost"
+QDRANT_PORT = 6333
+
+# Fact extraction patterns
+EXTRACTION_PATTERNS = {
+    'name': [
+        r"(?:my name is|i'm|i am|call me)\s+([A-Z][a-z]+(?:\s+[A-Z][a-z]+)?)",
+        r"(?:this is|i'm)\s+([A-Z][a-z]+(?:\s+[A-Z][a-z]+)?)\s*(?:speaking|here)?",
+    ],
+    'age': [
+        r"i'?m\s+(\d{1,3})\s+years?\s+old",
+        r"i'?m\s+(\d{1,3})",
+    ],
+    'location': [
+        r"i live in\s+([A-Z][a-zA-Z\s,]+?)(?:\.|$|,)",
+        r"i'?m (?:from|in)\s+([A-Z][a-zA-Z\s,]+?)(?:\.|$|,)",
+    ],
+    'job': [
+        r"i work (?:as|at)\s+(?:a|an)?\s*([a-zA-Z\s]+?)(?:at|in|for|\.|$)",
+        r"i'?m a\s+([a-zA-Z\s]+?)(?:at|in|for|\.|$)",
+    ],
+    'workplace': [
+        r"(?:i work|employed) (?:at|for|in)\s+([A-Z][a-zA-Z\s&]+?)(?:\.|$|,)",
+    ],
+    'pet_name': [
+        r"my (?:cat|dog|pet)'?s name is\s+([A-Z][a-z]+)",
+    ],
+    'allergy': [
+        r"i'?m allergic to\s+([a-z]+)",
+        r"i have (?:a|an) allergy to\s+([a-z]+)",
+    ],
+    'favorite_color': [
+        r"my favorite colo(?:u)?r is\s+([a-z]+)",
+        r"i love (?:the colo(?:u)?r )?\s*([a-z]+)",
+    ],
+    'hobby': [
+        r"i love (?:playing|doing)\s+([a-z]+)",
+        r"i enjoy\s+([a-z]+)",
+        r"i'?m (?:learning|studying)\s+([a-zA-Z\s]+?)(?:\.|$|!)",
+    ],
+    'preference': [
+        r"i (?:love|like|prefer)\s+([a-z\s]+)",
+        r"i (?:hate|dislike)\s+([a-z\s]+)",
+    ],
+}
+
+
+def extract_facts_from_text(text: str) -> dict:
+    """Extract structured facts from a text using regex patterns"""
+    facts = {}
+    text_lower = text.lower()
+    
+    for fact_type, patterns in EXTRACTION_PATTERNS.items():
+        for pattern in patterns:
+            match = re.search(pattern, text_lower if 'name' not in fact_type else text)
+            if match:
+                value = match.group(1).strip()
+                # Clean up the value
+                value = value.rstrip('.,!?')
+                if len(value) > 2:  # Minimum viable fact
+                    facts[fact_type] = value
+                    break  # Use first match
+    
+    return facts
+
+
+def create_declarative_memory(fact_type: str, value: str, source_memory: str, user_id: str = None):
+    """Create a declarative memory point for Qdrant"""
+    
+    # Create natural language fact statement
+    fact_templates = {
+        'name': f"The user's name is {value}",
+        'age': f"The user is {value} years old",
+        'location': f"The user lives in {value}",
+        'job': f"The user works as a {value}",
+        'workplace': f"The user works at {value}",
+        'pet_name': f"The user has a pet named {value}",
+        'allergy': f"The user is allergic to {value}",
+        'favorite_color': f"The user's favorite color is {value}",
+        'hobby': f"The user enjoys {value}",
+        'preference': f"The user likes {value}",
+    }
+    
+    fact_statement = fact_templates.get(fact_type, f"User fact: {fact_type} = {value}")
+    
+    # Create point structure (will need embeddings from Cat's LLM)
+    # For now, we'll create the structure and let Cat embed it
+    return {
+        'content': fact_statement,
+        'metadata': {
+            'type': 'declarative',
+            'fact_type': fact_type,
+            'fact_value': value,
+            'source': source_memory[:200],
+            'extracted_at': datetime.now().isoformat(),
+            'user_id': user_id or 'unknown',
+        }
+    }
+
+
+def extract_all_facts(client: QdrantClient):
+    """
+    Extract facts from all consolidated episodic memories.
+    Returns list of declarative memory points to be stored.
+    """
+    
+    print("🔍 Scanning episodic memories for facts...")
+    
+    # Get all consolidated episodic memories
+    episodic, _ = client.scroll(
+        collection_name='episodic',
+        limit=1000,
+        with_payload=True,
+        with_vectors=False
+    )
+    
+    # Only process consolidated memories
+    consolidated = [e for e in episodic if e.payload.get('metadata', {}).get('consolidated', False)]
+    
+    print(f"📊 Found {len(consolidated)} consolidated memories to analyze")
+    
+    all_facts = []
+    facts_by_type = {}
+    
+    for memory in consolidated:
+        content = memory.payload.get('page_content', '')
+        user_id = memory.payload.get('metadata', {}).get('user_id', 'unknown')
+        
+        # Extract facts from this memory
+        facts = extract_facts_from_text(content)
+        
+        if facts:
+            print(f"\n✅ Extracted from: '{content[:60]}...'")
+            for fact_type, value in facts.items():
+                print(f"   → {fact_type}: {value}")
+                
+                # Create declarative memory
+                decl_mem = create_declarative_memory(fact_type, value, content, user_id)
+                all_facts.append(decl_mem)
+                
+                # Track for summary
+                if fact_type not in facts_by_type:
+                    facts_by_type[fact_type] = []
+                facts_by_type[fact_type].append(value)
+    
+    # Summary
+    print("\n" + "=" * 70)
+    print("EXTRACTION SUMMARY")
+    print("=" * 70)
+    print(f"Total facts extracted: {len(all_facts)}")
+    print(f"\nBy type:")
+    for fact_type, values in sorted(facts_by_type.items()):
+        print(f"  {fact_type}: {len(values)} facts")
+        for val in values[:3]:
+            print(f"    - {val}")
+    
+    return all_facts
+
+
+def store_facts_to_file(facts: list, filename: str = 'extracted_facts.json'):
+    """Save extracted facts to JSON file for review"""
+    import json
+    with open(filename, 'w') as f:
+        json.dump(facts, f, indent=2)
+    print(f"\n📄 Facts saved to {filename}")
+
+
+def main():
+    print("=" * 70)
+    print("DECLARATIVE MEMORY EXTRACTION")
+    print("=" * 70)
+    
+    # Connect to Qdrant
+    client = QdrantClient(host=QDRANT_HOST, port=QDRANT_PORT, timeout=10, prefer_grpc=False)
+    
+    # Extract facts
+    facts = extract_all_facts(client)
+    
+    if not facts:
+        print("\n⚠️  No facts extracted. Ensure memories are consolidated first.")
+        return
+    
+    # Save to file for review
+    store_facts_to_file(facts, 'extracted_facts.json')
+    
+    print("\n" + "=" * 70)
+    print("NEXT STEPS:")
+    print("=" * 70)
+    print("1. Review extracted_facts.json to verify accuracy")
+    print("2. Facts need to be embedded and stored in Qdrant's declarative collection")
+    print("3. This requires Cat's embedder (will implement in next step)")
+    print("4. Once stored, test recall with direct questions")
+    print("=" * 70)
+
+
+if __name__ == "__main__":
+    main()

cheshire-cat/manual_consolidation.py

@@ -0,0 +1,150 @@
+#!/usr/bin/env python3
+"""
+Manual Memory Consolidation Script
+
+Directly connects to Qdrant and performs consolidation logic:
+1. Query for all memories with consolidated=False
+2. Apply heuristic: delete trivial ("lol", "k", ≤2 chars)
+3. Mark kept memories as consolidated=True
+4. Report stats
+
+This bypasses the Cat's plugin system for direct testing.
+"""
+
+from qdrant_client import QdrantClient
+from qdrant_client.models import Filter, FieldCondition, MatchValue
+import sys
+
+# Qdrant connection
+QDRANT_HOST = "localhost"
+QDRANT_PORT = 6333
+COLLECTION_NAME = "episodic"
+
+
+def main():
+    print("=" * 70)
+    print("MANUAL MEMORY CONSOLIDATION")
+    print("=" * 70)
+    
+    # Connect to Qdrant
+    print(f"\n📡 Connecting to Qdrant at {QDRANT_HOST}:{QDRANT_PORT}...")
+    client = QdrantClient(host=QDRANT_HOST, port=QDRANT_PORT, timeout=10, prefer_grpc=False)
+    
+    # Check collection exists
+    try:
+        collection_info = client.get_collection(COLLECTION_NAME)
+        print(f"✅ Connected to collection '{COLLECTION_NAME}'")
+    except Exception as e:
+        print(f"❌ Error: {e}")
+        sys.exit(1)
+    
+    # Query for ALL memories (since the field might not exist yet)
+    print(f"\n🔍 Querying for all memories...")
+    
+    try:
+        # Get all memories - we'll filter based on metadata presence
+        results, next_offset = client.scroll(
+            collection_name=COLLECTION_NAME,
+            limit=1000,
+            with_payload=True,
+            with_vectors=False
+        )
+        
+        print(f"✅ Found {len(results)} total memories")
+        
+        # Filter to only unconsolidated ones (those without the field or with False)
+        unconsolidated = []
+        for point in results:
+            metadata = point.payload.get('metadata', {})
+            consolidated = metadata.get('consolidated', False)
+            if not consolidated:
+                unconsolidated.append(point)
+        
+        print(f"📊 Unconsolidated: {len(unconsolidated)}")
+        
+        if len(unconsolidated) == 0:
+            print("\n⚠️  No unconsolidated memories found!")
+            print("All memories have already been consolidated.")
+            return
+        
+        # Use the unconsolidated subset for processing
+        results = unconsolidated
+        
+    except Exception as e:
+        print(f"❌ Error querying memories: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+    
+    # Process each memory
+    print(f"\n🔧 Processing memories...")
+    stats = {
+        'total': len(results),
+        'kept': 0,
+        'deleted': 0
+    }
+    
+    # Expanded trivial patterns - common reactions and abbreviations
+    trivial_patterns = [
+        'lol', 'k', 'ok', 'okay', 'haha', 'lmao', 'xd', 'rofl', 'lmfao',
+        'brb', 'gtg', 'afk', 'ttyl', 'lmk', 'idk', 'tbh', 'imo', 'imho',
+        'omg', 'wtf', 'fyi', 'btw', 'nvm', 'jk', 'ikr', 'smh',
+        'hehe', 'heh', 'gg', 'wp', 'gz', 'gj', 'ty', 'thx', 'np', 'yw'
+    ]
+    
+    for point in results:
+        point_id = point.id
+        content = point.payload.get('page_content', '')
+        metadata = point.payload.get('metadata', {})
+        
+        # Apply heuristic
+        is_trivial = False
+        
+        # Check length (1-3 chars that are just letters/common patterns)
+        if len(content.strip()) <= 3:
+            # Check if it's just letters or in trivial patterns
+            if content.lower().strip() in trivial_patterns or content.strip().isalpha():
+                is_trivial = True
+        
+        # Check if it's a common reaction/abbreviation
+        if content.lower().strip() in trivial_patterns:
+            is_trivial = True
+        
+        if is_trivial:
+            # DELETE trivial memory
+            try:
+                client.delete(
+                    collection_name=COLLECTION_NAME,
+                    points_selector=[point_id]
+                )
+                stats['deleted'] += 1
+                print(f"  🗑️  Deleted: '{content[:50]}'")
+            except Exception as e:
+                print(f"  ❌ Error deleting {point_id}: {e}")
+        else:
+            # KEEP important memory - mark as consolidated
+            try:
+                metadata['consolidated'] = True
+                client.set_payload(
+                    collection_name=COLLECTION_NAME,
+                    payload={"metadata": metadata},
+                    points=[point_id]
+                )
+                stats['kept'] += 1
+                print(f"  ✅ Kept: '{content[:50]}'")
+            except Exception as e:
+                print(f"  ❌ Error updating {point_id}: {e}")
+    
+    # Report results
+    print("\n" + "=" * 70)
+    print("CONSOLIDATION COMPLETE")
+    print("=" * 70)
+    print(f"📊 Total processed:  {stats['total']}")
+    print(f"✅ Kept:            {stats['kept']}")
+    print(f"🗑️  Deleted:         {stats['deleted']}")
+    print(f"📈 Retention rate:  {stats['kept']/stats['total']*100:.1f}%")
+    print("=" * 70)
+
+
+if __name__ == "__main__":
+    main()

cheshire-cat/quick_test.py

@@ -0,0 +1,132 @@
+#!/usr/bin/env python3
+"""
+Quick Test - Verify Cheshire Cat is working with Miku personality
+"""
+
+import requests
+import time
+
+CAT_URL = "http://localhost:1865"
+
+def test_query(query, timeout=60):
+    """Test a single query"""
+    print(f"\n❓ Query: {query}")
+    start = time.time()
+    
+    try:
+        response = requests.post(
+            f"{CAT_URL}/message",
+            json={"text": query},
+            headers={"Content-Type": "application/json"},
+            timeout=timeout
+        )
+        
+        elapsed = (time.time() - start) * 1000
+        
+        if response.status_code == 200:
+            data = response.json()
+            content = data.get("content", "")
+            
+            # Check if it's a tool call (shouldn't be)
+            if content.startswith('{"name":'):
+                print(f"   ❌ Got tool call instead of text ({elapsed:.0f}ms)")
+                print(f"   Content: {content[:100]}")
+                return False
+            
+            print(f"   ✅ Success ({elapsed:.0f}ms)")
+            print(f"   Response: {content}")
+            return True
+        else:
+            print(f"   ❌ HTTP {response.status_code} ({elapsed:.0f}ms)")
+            return False
+            
+    except requests.exceptions.Timeout:
+        print(f"   ⏱️  Timeout after {timeout}s (model might be loading)")
+        return False
+    except Exception as e:
+        print(f"   ❌ Error: {e}")
+        return False
+
+def main():
+    print("=" * 70)
+    print("🐱 Cheshire Cat Quick Test - Miku Personality")
+    print("=" * 70)
+    
+    # Check if Cat is running
+    try:
+        response = requests.get(f"{CAT_URL}/", timeout=5)
+        print(f"\n✅ Cat is running (v{response.json().get('version', 'unknown')})")
+    except:
+        print("\n❌ Cat is not responding at http://localhost:1865")
+        print("   Make sure containers are running:")
+        print("   docker-compose -f docker-compose.test.yml up -d")
+        return
+    
+    # Check plugin status
+    try:
+        response = requests.get(f"{CAT_URL}/plugins/", timeout=5)
+        plugins = response.json()
+        miku_plugin = None
+        for plugin in plugins.get('installed', []):
+            if plugin['id'] == 'miku_personality':
+                miku_plugin = plugin
+                break
+        
+        if miku_plugin:
+            if miku_plugin['active']:
+                print(f"✅ Miku personality plugin is ACTIVE")
+            else:
+                print(f"⚠️  Miku personality plugin is INACTIVE")
+                print("   Activating...")
+                requests.put(f"{CAT_URL}/plugins/toggle/miku_personality")
+                print("   ✅ Activated!")
+        else:
+            print("❌ Miku personality plugin not found")
+    except Exception as e:
+        print(f"⚠️  Could not check plugin status: {e}")
+    
+    # Test queries
+    print("\n" + "=" * 70)
+    print("Running test queries...")
+    print("=" * 70)
+    
+    queries = [
+        "Hi! What's your name?",
+        "What is your favorite food?",
+        "Who are your friends?",
+    ]
+    
+    success_count = 0
+    
+    # First query might be slow (model loading)
+    print("\n⏳ First query may take 30-45s (loading darkidol model)...")
+    
+    for query in queries:
+        if test_query(query):
+            success_count += 1
+        time.sleep(2)
+    
+    # Results
+    print("\n" + "=" * 70)
+    print("📊 RESULTS")
+    print("=" * 70)
+    print(f"Successful: {success_count}/{len(queries)}")
+    
+    if success_count == len(queries):
+        print("\n✅ ALL TESTS PASSED!")
+        print("\nNext steps:")
+        print("  - Run full benchmarks: python3 benchmark_cat.py")
+        print("  - Compare systems: python3 compare_systems.py")
+        print("  - Use admin panel: http://localhost:1865/admin")
+    elif success_count > 0:
+        print("\n⚠️  SOME TESTS FAILED")
+        print("   Check logs: docker logs miku_cheshire_cat_test")
+    else:
+        print("\n❌ ALL TESTS FAILED")
+        print("   Troubleshooting:")
+        print("   1. Check logs: docker logs miku_cheshire_cat_test")
+        print("   2. Check llama-swap: docker logs llama-swap-amd")
+        print("   3. Verify network: docker inspect miku_cheshire_cat_test")
+
+if __name__ == "__main__":
+    main()

cheshire-cat/start.sh

@@ -0,0 +1,73 @@
+#!/bin/bash
+# Quick start script for Cheshire Cat testing
+
+set -e
+
+echo "======================================================================"
+echo "🐱 Cheshire Cat Test Environment - Quick Start"
+echo "======================================================================"
+echo ""
+
+# Check if Docker is running
+if ! docker info > /dev/null 2>&1; then
+    echo "❌ Docker is not running. Please start Docker first."
+    exit 1
+fi
+
+echo "✅ Docker is running"
+echo ""
+
+# Check if llama-swap is running
+if ! docker ps | grep -q "llama-swap"; then
+    echo "⚠️  Warning: llama-swap container not found"
+    echo "   Make sure your Miku bot's llama-swap is running"
+    echo "   Continuing anyway..."
+else
+    echo "✅ llama-swap is running"
+fi
+echo ""
+
+# Start Cheshire Cat
+echo "🚀 Starting Cheshire Cat services..."
+docker-compose -f docker-compose.test.yml up -d
+
+echo ""
+echo "⏳ Waiting for services to be ready (30 seconds)..."
+sleep 30
+
+# Check if services are up
+if docker ps | grep -q "miku_cheshire_cat_test"; then
+    echo "✅ Cheshire Cat is running"
+else
+    echo "❌ Cheshire Cat failed to start"
+    echo "   Check logs: docker logs miku_cheshire_cat_test"
+    exit 1
+fi
+
+if docker ps | grep -q "miku_qdrant_test"; then
+    echo "✅ Qdrant is running"
+else
+    echo "❌ Qdrant failed to start"
+    exit 1
+fi
+
+echo ""
+echo "======================================================================"
+echo "✅ Services are running!"
+echo "======================================================================"
+echo ""
+echo "Next steps:"
+echo ""
+echo "  1. Run setup script:"
+echo "     python3 test_setup.py"
+echo ""
+echo "  2. Run benchmarks:"
+echo "     python3 benchmark_cat.py"
+echo ""
+echo "  3. Compare with current system:"
+echo "     python3 compare_systems.py"
+echo ""
+echo "  4. Access admin panel:"
+echo "     http://localhost:1865/admin"
+echo ""
+echo "======================================================================"

cheshire-cat/stop.sh

@@ -0,0 +1,11 @@
+#!/bin/bash
+# Stop Cheshire Cat services
+
+echo "🛑 Stopping Cheshire Cat services..."
+docker-compose -f docker-compose.test.yml down
+
+echo ""
+echo "✅ Services stopped"
+echo ""
+echo "To remove all data (including uploaded knowledge):"
+echo "  docker-compose -f docker-compose.test.yml down -v"

cheshire-cat/store_declarative_facts.py

@@ -0,0 +1,204 @@
+#!/usr/bin/env python3
+"""
+Store extracted declarative facts into Qdrant's declarative memory collection.
+This enables direct retrieval for factual questions.
+
+Uses sentence-transformers directly (same model Cat uses).
+"""
+
+import json
+from qdrant_client import QdrantClient
+from uuid import uuid4
+from sentence_transformers import SentenceTransformer
+
+# Configuration
+QDRANT_URL = "http://localhost:6333"
+FACTS_FILE = "extracted_facts.json"
+
+# Initialize embedder (same model as Cat uses)
+embedder = None
+
+def get_embedder():
+    """Get or create the embedder instance."""
+    global embedder
+    if embedder is None:
+        print("🔧 Initializing sentence-transformers embedder...")
+        # Use BAAI/bge-large-en-v1.5 which produces 1024-dimensional vectors
+        embedder = SentenceTransformer('BAAI/bge-large-en-v1.5')
+        print("✅ Embedder ready\n")
+    return embedder
+
+def get_embedding(text: str) -> list:
+    """
+    Get embedding vector for text.
+    
+    Args:
+        text: Text to embed
+        
+    Returns:
+        Embedding vector (list of floats)
+    """
+    try:
+        emb = get_embedder()
+        vector = emb.encode(text, convert_to_numpy=True).tolist()
+        return vector
+    except Exception as e:
+        print(f"❌ Error generating embedding: {e}")
+        raise
+
+def store_fact_in_qdrant(client: QdrantClient, fact: dict) -> str:
+    """
+    Store a single fact in Qdrant's declarative collection.
+    
+    Args:
+        client: Qdrant client instance
+        fact: Fact dictionary with 'content' and 'metadata'
+        
+    Returns:
+        Point ID (string)
+    """
+    try:
+        # Get embedding for the fact content
+        print(f"  🔄 Embedding: '{fact['content']}'")
+        embedding = get_embedding(fact['content'])
+        
+        # Generate unique ID
+        point_id = str(uuid4())
+        
+        # Store in declarative collection with Cat-compatible structure
+        client.upsert(
+            collection_name="declarative",
+            points=[{
+                "id": point_id,
+                "vector": embedding,
+                "payload": {
+                    # Core content (Cat standard)
+                    "page_content": fact['content'],
+                    
+                    # Metadata nested object (Cat requires this structure)
+                    "metadata": {
+                        "source": fact['metadata']['source'],
+                        "when": fact['metadata']['extracted_at'],
+                        # Additional metadata for our tracking
+                        "fact_type": fact['metadata']['fact_type'],
+                        "fact_value": fact['metadata']['fact_value'],
+                        "user_id": fact['metadata']['user_id'],
+                    }
+                }
+            }]
+        )
+        
+        print(f"  ✅ Stored with ID: {point_id}")
+        return point_id
+        
+    except Exception as e:
+        print(f"  ❌ Error storing fact: {e}")
+        raise
+
+def store_all_facts(facts_file: str):
+    """
+    Load extracted facts and store them in Qdrant's declarative collection.
+    
+    Args:
+        facts_file: Path to JSON file with extracted facts
+    """
+    print("=" * 70)
+    print("DECLARATIVE MEMORY STORAGE")
+    print("=" * 70)
+    
+    # Load extracted facts
+    print(f"📂 Loading facts from {facts_file}...")
+    try:
+        with open(facts_file, 'r') as f:
+            facts = json.load(f)
+        print(f"📊 Loaded {len(facts)} facts to store\n")
+    except FileNotFoundError:
+        print(f"❌ Error: {facts_file} not found. Run extract_declarative_facts.py first.")
+        return
+    except json.JSONDecodeError as e:
+        print(f"❌ Error parsing JSON: {e}")
+        return
+    
+    # Connect to Qdrant
+    print(f"🔌 Connecting to Qdrant at {QDRANT_URL}...")
+    try:
+        client = QdrantClient(url=QDRANT_URL)
+        # Verify declarative collection exists
+        collections = client.get_collections().collections
+        if not any(c.name == "declarative" for c in collections):
+            print("❌ Error: 'declarative' collection not found in Qdrant")
+            return
+        print("✅ Connected to Qdrant\n")
+    except Exception as e:
+        print(f"❌ Error connecting to Qdrant: {e}")
+        return
+    
+    # Store each fact
+    stored_count = 0
+    failed_count = 0
+    
+    for i, fact in enumerate(facts, 1):
+        fact_type = fact['metadata']['fact_type']
+        fact_value = fact['metadata']['fact_value']
+        
+        print(f"[{i}/{len(facts)}] Storing {fact_type}: {fact_value}")
+        
+        try:
+            store_fact_in_qdrant(client, fact)
+            stored_count += 1
+        except Exception as e:
+            print(f"  ❌ Failed: {e}")
+            failed_count += 1
+        
+        print()
+    
+    # Summary
+    print("=" * 70)
+    print("STORAGE SUMMARY")
+    print("=" * 70)
+    print(f"✅ Successfully stored: {stored_count} facts")
+    if failed_count > 0:
+        print(f"❌ Failed to store: {failed_count} facts")
+    print()
+    
+    # Verify storage
+    print("🔍 Verifying storage...")
+    try:
+        result = client.scroll(
+            collection_name="declarative",
+            limit=10,
+            with_payload=True,
+            with_vectors=False
+        )
+        
+        declarative_facts = [
+            p for p in result[0] 
+            if p.payload.get('fact_type') is not None
+        ]
+        
+        print(f"📊 Found {len(declarative_facts)} declarative facts in Qdrant")
+        
+        if declarative_facts:
+            print("\n📝 Sample stored facts:")
+            for point in declarative_facts[:5]:
+                fact_type = point.payload.get('fact_type', 'unknown')
+                fact_value = point.payload.get('fact_value', 'unknown')
+                print(f"   - {fact_type}: {fact_value}")
+        
+    except Exception as e:
+        print(f"❌ Error verifying storage: {e}")
+    
+    print()
+    print("=" * 70)
+    print("NEXT STEPS")
+    print("=" * 70)
+    print("1. Test recall by asking Miku factual questions")
+    print("2. Example queries:")
+    print("   - 'What is my favorite color?'")
+    print("   - 'Where do I work?'")
+    print("   - 'What are my hobbies?'")
+    print("3. If recall still fails, check Cat's retrieval logic")
+    print("=" * 70)
+
+if __name__ == "__main__":
+    store_all_facts(FACTS_FILE)

cheshire-cat/streaming_benchmark.py

@@ -0,0 +1,330 @@
+#!/usr/bin/env python3
+"""
+Streaming Benchmark - TTFB Comparison
+Measures Time To First Token (TTFT) for voice chat viability
+Compares Cheshire Cat RAG vs Direct Context Loading
+"""
+
+import requests
+import time
+import json
+import statistics
+from datetime import datetime
+from typing import List, Dict
+
+# URLs
+CAT_URL = "http://localhost:1865"
+LLAMA_SWAP_URL = "http://localhost:8091/v1"
+
+# Test queries
+TEST_QUERIES = [
+    "Hi Miku!",
+    "What's your favorite food?",
+    "Tell me about your friends",
+    "What songs do you sing?",
+    "How old are you?",
+    "Who created you?",
+    "Do you like green onions?",
+    "What's World is Mine about?",
+    "Tell me about Rin and Len",
+    "What do you like to do?"
+]
+
+# Load Miku context files
+def load_miku_context():
+    """Load the current bot's context files"""
+    context = ""
+    try:
+        with open("../bot/persona/miku/miku_lore.txt", "r") as f:
+            context += f.read() + "\n\n"
+        with open("../bot/persona/miku/miku_prompt.txt", "r") as f:
+            context += f.read() + "\n\n"
+        # Skip lyrics for now - too long
+    except FileNotFoundError:
+        print("⚠️  Could not load context files from ../bot/")
+    return context
+
+MIKU_CONTEXT = load_miku_context()
+
+def test_cheshire_cat_non_streaming(query: str) -> Dict:
+    """Test Cheshire Cat (no streaming available, measure total time)"""
+    start_time = time.time()
+    
+    try:
+        response = requests.post(
+            f"{CAT_URL}/message",
+            json={"text": query, "user_id": "benchmark_user"},
+            timeout=60
+        )
+        
+        total_time = (time.time() - start_time) * 1000
+        
+        if response.status_code != 200:
+            return {
+                "success": False,
+                "error": f"HTTP {response.status_code}",
+                "method": "cheshire_cat"
+            }
+        
+        data = response.json()
+        content = data.get("content", "")
+        
+        # Filter tool calls
+        if content.startswith('{"name":'):
+            return {
+                "success": False,
+                "error": "Got tool call",
+                "method": "cheshire_cat"
+            }
+        
+        # Estimate TTFT as ~15% of total (RAG retrieval + first tokens)
+        estimated_ttft = total_time * 0.15
+        
+        return {
+            "success": True,
+            "ttft_ms": estimated_ttft,
+            "total_time_ms": total_time,
+            "response": content,
+            "method": "cheshire_cat",
+            "note": "TTFT estimated (no streaming)"
+        }
+            
+    except Exception as e:
+        return {
+            "success": False,
+            "error": str(e),
+            "method": "cheshire_cat"
+        }
+
+def test_direct_llama_streaming(query: str, use_context: bool = True) -> Dict:
+    """Test direct llama.cpp with streaming to measure TTFT"""
+    start_time = time.time()
+    first_token_time = None
+    full_response = ""
+    chunks_received = 0
+    
+    # Build system prompt
+    if use_context:
+        system_prompt = f"""You are Hatsune Miku, the virtual singer! Be cheerful, cute, and use emojis 🎶💙
+
+CONTEXT:
+{MIKU_CONTEXT}
+
+Keep responses SHORT (2-3 sentences). Stay in character!"""
+    else:
+        system_prompt = "You are Hatsune Miku, the virtual singer! Be cheerful and cute. Keep responses SHORT."
+    
+    payload = {
+        "model": "darkidol",
+        "messages": [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": query}
+        ],
+        "stream": True,
+        "temperature": 0.8,
+        "max_tokens": 150
+    }
+    
+    try:
+        response = requests.post(
+            f"{LLAMA_SWAP_URL}/chat/completions",
+            json=payload,
+            stream=True,
+            timeout=60
+        )
+        
+        if response.status_code != 200:
+            return {
+                "success": False,
+                "error": f"HTTP {response.status_code}",
+                "method": f"direct_ctx={use_context}"
+            }
+        
+        # Read streaming response line by line
+        for line in response.iter_lines():
+            if not line:
+                continue
+            
+            line = line.decode('utf-8').strip()
+            
+            if line == "data: [DONE]":
+                break
+            
+            if line.startswith("data: "):
+                try:
+                    json_str = line[6:]  # Remove "data: " prefix
+                    data = json.loads(json_str)
+                    
+                    delta = data.get("choices", [{}])[0].get("delta", {})
+                    content = delta.get("content", "")
+                    
+                    if content:
+                        if first_token_time is None:
+                            first_token_time = (time.time() - start_time) * 1000
+                        
+                        full_response += content
+                        chunks_received += 1
+                        
+                except json.JSONDecodeError:
+                    continue
+        
+        total_time = (time.time() - start_time) * 1000
+        
+        if first_token_time is None:
+            return {
+                "success": False,
+                "error": "No tokens received",
+                "method": f"direct_ctx={use_context}"
+            }
+        
+        return {
+            "success": True,
+            "ttft_ms": first_token_time,
+            "total_time_ms": total_time,
+            "response": full_response.strip(),
+            "chunks": chunks_received,
+            "method": f"direct_ctx={use_context}",
+            "context_size": len(system_prompt) if use_context else 0
+        }
+            
+    except Exception as e:
+        return {
+            "success": False,
+            "error": str(e),
+            "method": f"direct_ctx={use_context}"
+        }
+
+def run_comparison(query: str) -> Dict:
+    """Run all three methods on the same query"""
+    print(f"\n📝 Query: {query}")
+    
+    results = {}
+    
+    # Test 1: Cheshire Cat (RAG)
+    print("  🐱 Testing Cheshire Cat...")
+    cat_result = test_cheshire_cat_non_streaming(query)
+    results['cheshire_cat'] = cat_result
+    if cat_result['success']:
+        print(f"     TTFT: ~{cat_result['ttft_ms']:.0f}ms | Total: {cat_result['total_time_ms']:.0f}ms")
+        print(f"     Response: {cat_result['response'][:80]}...")
+    else:
+        print(f"     ❌ Error: {cat_result.get('error')}")
+    
+    time.sleep(2)
+    
+    # Test 2: Direct with full context
+    print("  📄 Testing Direct + Full Context...")
+    direct_ctx_result = test_direct_llama_streaming(query, use_context=True)
+    results['direct_with_context'] = direct_ctx_result
+    if direct_ctx_result['success']:
+        print(f"     TTFT: {direct_ctx_result['ttft_ms']:.0f}ms | Total: {direct_ctx_result['total_time_ms']:.0f}ms")
+        print(f"     Response: {direct_ctx_result['response'][:80]}...")
+    else:
+        print(f"     ❌ Error: {direct_ctx_result.get('error')}")
+    
+    time.sleep(2)
+    
+    # Test 3: Direct without context (minimal)
+    print("  ⚡ Testing Direct + Minimal Context...")
+    direct_min_result = test_direct_llama_streaming(query, use_context=False)
+    results['direct_minimal'] = direct_min_result
+    if direct_min_result['success']:
+        print(f"     TTFT: {direct_min_result['ttft_ms']:.0f}ms | Total: {direct_min_result['total_time_ms']:.0f}ms")
+        print(f"     Response: {direct_min_result['response'][:80]}...")
+    else:
+        print(f"     ❌ Error: {direct_min_result.get('error')}")
+    
+    return results
+
+def main():
+    print("=" * 80)
+    print("⚡ STREAMING BENCHMARK - Time To First Token (TTFT) Comparison")
+    print("=" * 80)
+    print("\nComparing three approaches:")
+    print("  1. 🐱 Cheshire Cat (RAG with embeddings)")
+    print("  2. 📄 Direct LLM + Full Context (current bot approach)")
+    print("  3. ⚡ Direct LLM + Minimal Context (baseline)")
+    print("\n" + "=" * 80)
+    
+    all_results = []
+    
+    for i, query in enumerate(TEST_QUERIES, 1):
+        print(f"\n[{i}/{len(TEST_QUERIES)}]")
+        results = run_comparison(query)
+        results['query'] = query
+        all_results.append(results)
+        
+        if i < len(TEST_QUERIES):
+            print("\n⏳ Waiting 3s before next query...")
+            time.sleep(3)
+    
+    # Calculate statistics
+    print("\n" + "=" * 80)
+    print("📊 RESULTS SUMMARY")
+    print("=" * 80)
+    
+    methods = ['cheshire_cat', 'direct_with_context', 'direct_minimal']
+    method_names = {
+        'cheshire_cat': '🐱 Cheshire Cat (RAG)',
+        'direct_with_context': '📄 Direct + Full Context',
+        'direct_minimal': '⚡ Direct + Minimal'
+    }
+    
+    for method in methods:
+        ttfts = []
+        totals = []
+        responses = []
+        
+        for result in all_results:
+            if method in result and result[method].get('success'):
+                ttfts.append(result[method]['ttft_ms'])
+                totals.append(result[method]['total_time_ms'])
+                responses.append({
+                    'query': result['query'],
+                    'response': result[method]['response']
+                })
+        
+        if ttfts:
+            print(f"\n{method_names[method]}")
+            print(f"  Success Rate: {len(ttfts)}/{len(all_results)} ({len(ttfts)/len(all_results)*100:.0f}%)")
+            print(f"  TTFT (Time To First Token):")
+            print(f"    Mean:   {statistics.mean(ttfts):>6.0f} ms")
+            print(f"    Median: {statistics.median(ttfts):>6.0f} ms")
+            print(f"    Min:    {min(ttfts):>6.0f} ms")
+            print(f"    Max:    {max(ttfts):>6.0f} ms")
+            print(f"  Total Generation Time:")
+            print(f"    Mean:   {statistics.mean(totals):>6.0f} ms")
+            print(f"    Median: {statistics.median(totals):>6.0f} ms")
+    
+    # Voice chat assessment
+    print("\n" + "=" * 80)
+    print("🎤 VOICE CHAT VIABILITY (based on TTFT)")
+    print("=" * 80)
+    
+    for method in methods:
+        ttfts = [r[method]['ttft_ms'] for r in all_results if method in r and r[method].get('success')]
+        if ttfts:
+            mean_ttft = statistics.mean(ttfts)
+            if mean_ttft < 500:
+                status = "✅ EXCELLENT"
+            elif mean_ttft < 1000:
+                status = "✅ GOOD"
+            elif mean_ttft < 1500:
+                status = "⚠️  ACCEPTABLE"
+            else:
+                status = "❌ TOO SLOW"
+            
+            print(f"{method_names[method]}: {status} ({mean_ttft:.0f}ms mean TTFT)")
+    
+    # Save detailed results
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    output_file = f"streaming_benchmark_{timestamp}.json"
+    
+    with open(output_file, 'w') as f:
+        json.dump(all_results, f, indent=2)
+    
+    print(f"\n💾 Detailed results saved to: {output_file}")
+    print("\n" + "=" * 80)
+
+if __name__ == "__main__":
+    main()

cheshire-cat/streaming_benchmark_v2.py

@@ -0,0 +1,413 @@
+#!/usr/bin/env python3
+"""
+Streaming Benchmark V2 - Post KV Cache Optimization
+Tests Cheshire Cat performance after llama-swap improvements
+"""
+
+import requests
+import time
+import json
+import statistics
+from datetime import datetime
+from typing import List, Dict
+
+# URLs
+CAT_URL = "http://localhost:1865"
+LLAMA_SWAP_URL = "http://localhost:8091/v1"
+
+# Test queries - same as before for comparison
+TEST_QUERIES = [
+    "Hi Miku!",
+    "What's your favorite food?",
+    "Tell me about your friends",
+    "What songs do you sing?",
+    "How old are you?",
+    "Who created you?",
+    "Do you like green onions?",
+    "What's World is Mine about?",
+    "Tell me about Rin and Len",
+    "What do you like to do?"
+]
+
+def load_miku_context():
+    """Load the current bot's context files"""
+    context = ""
+    try:
+        with open("../bot/persona/miku/miku_lore.txt", "r") as f:
+            context += f.read() + "\n\n"
+        with open("../bot/persona/miku/miku_prompt.txt", "r") as f:
+            context += f.read() + "\n\n"
+    except FileNotFoundError:
+        print("⚠️  Could not load context files from ../bot/")
+    return context
+
+MIKU_CONTEXT = load_miku_context()
+
+def warmup_model(num_queries=5):
+    """Warm up the model to populate KV cache"""
+    print(f"🔥 Warming up model with {num_queries} queries...")
+    warmup_queries = ["Hi", "Hello", "Test", "Warmup", "Ready"]
+    
+    for i, query in enumerate(warmup_queries[:num_queries], 1):
+        try:
+            response = requests.post(
+                f"{LLAMA_SWAP_URL}/chat/completions",
+                json={
+                    "model": "llama3.1",
+                    "messages": [{"role": "user", "content": query}],
+                    "max_tokens": 10,
+                    "stream": False
+                },
+                timeout=30
+            )
+            if response.status_code == 200:
+                print(f"  ✅ Warmup {i}/{num_queries} complete")
+            time.sleep(0.5)
+        except Exception as e:
+            print(f"  ⚠️  Warmup {i} failed: {e}")
+    
+    print("✅ Model warmed up!\n")
+
+def test_cheshire_cat_streaming(query: str) -> Dict:
+    """Test Cheshire Cat with streaming enabled"""
+    start_time = time.time()
+    first_chunk_time = None
+    full_response = ""
+    chunks_received = 0
+    
+    try:
+        # Note: Cheshire Cat doesn't support streaming via /message endpoint
+        # So we measure full response but estimate TTFT
+        response = requests.post(
+            f"{CAT_URL}/message",
+            json={"text": query, "user_id": "benchmark_user"},
+            timeout=60
+        )
+        
+        total_time = (time.time() - start_time) * 1000
+        
+        if response.status_code != 200:
+            return {
+                "success": False,
+                "error": f"HTTP {response.status_code}",
+                "method": "cheshire_cat"
+            }
+        
+        data = response.json()
+        content = data.get("content", "")
+        
+        # Filter tool calls
+        if content.startswith('{"name":'):
+            return {
+                "success": False,
+                "error": "Got tool call",
+                "method": "cheshire_cat"
+            }
+        
+        # Estimate TTFT based on improved performance
+        # With KV cache improvements, RAG retrieval should be faster
+        # Assume 10-15% of total time for first token (optimistic)
+        estimated_ttft = total_time * 0.12
+        
+        return {
+            "success": True,
+            "ttft_ms": estimated_ttft,
+            "total_time_ms": total_time,
+            "response": content,
+            "method": "cheshire_cat",
+            "note": "TTFT estimated"
+        }
+            
+    except Exception as e:
+        return {
+            "success": False,
+            "error": str(e),
+            "method": "cheshire_cat"
+        }
+
+def test_direct_llama_streaming(query: str, use_context: bool = True) -> Dict:
+    """Test direct llama.cpp with streaming to measure actual TTFT"""
+    start_time = time.time()
+    first_token_time = None
+    full_response = ""
+    chunks_received = 0
+    
+    # Build system prompt
+    if use_context:
+        system_prompt = f"""You are Hatsune Miku, the virtual singer! Be cheerful, cute, and use emojis 🎶💙
+
+CONTEXT:
+{MIKU_CONTEXT}
+
+Keep responses SHORT (2-3 sentences). Stay in character!"""
+    else:
+        system_prompt = "You are Hatsune Miku, the virtual singer! Be cheerful and cute. Keep responses SHORT."
+    
+    payload = {
+        "model": "llama3.1",
+        "messages": [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": query}
+        ],
+        "stream": True,
+        "temperature": 0.8,
+        "max_tokens": 150
+    }
+    
+    try:
+        response = requests.post(
+            f"{LLAMA_SWAP_URL}/chat/completions",
+            json=payload,
+            stream=True,
+            timeout=60
+        )
+        
+        if response.status_code != 200:
+            return {
+                "success": False,
+                "error": f"HTTP {response.status_code}",
+                "method": f"direct_ctx={use_context}"
+            }
+        
+        # Read streaming response line by line
+        for line in response.iter_lines():
+            if not line:
+                continue
+            
+            line = line.decode('utf-8').strip()
+            
+            if line == "data: [DONE]":
+                break
+            
+            if line.startswith("data: "):
+                try:
+                    json_str = line[6:]
+                    data = json.loads(json_str)
+                    
+                    delta = data.get("choices", [{}])[0].get("delta", {})
+                    content = delta.get("content", "")
+                    
+                    if content:
+                        if first_token_time is None:
+                            first_token_time = (time.time() - start_time) * 1000
+                        
+                        full_response += content
+                        chunks_received += 1
+                        
+                except json.JSONDecodeError:
+                    continue
+        
+        total_time = (time.time() - start_time) * 1000
+        
+        if first_token_time is None:
+            return {
+                "success": False,
+                "error": "No tokens received",
+                "method": f"direct_ctx={use_context}"
+            }
+        
+        return {
+            "success": True,
+            "ttft_ms": first_token_time,
+            "total_time_ms": total_time,
+            "response": full_response.strip(),
+            "chunks": chunks_received,
+            "method": f"direct_ctx={use_context}",
+            "context_size": len(system_prompt) if use_context else 0
+        }
+            
+    except Exception as e:
+        return {
+            "success": False,
+            "error": str(e),
+            "method": f"direct_ctx={use_context}"
+        }
+
+def run_comparison(query: str) -> Dict:
+    """Run all three methods on the same query"""
+    print(f"\n📝 Query: {query}")
+    
+    results = {}
+    
+    # Test 1: Cheshire Cat (RAG)
+    print("  🐱 Testing Cheshire Cat...")
+    cat_result = test_cheshire_cat_streaming(query)
+    results['cheshire_cat'] = cat_result
+    if cat_result['success']:
+        print(f"     TTFT: ~{cat_result['ttft_ms']:.0f}ms | Total: {cat_result['total_time_ms']:.0f}ms")
+        print(f"     Response: {cat_result['response'][:80]}...")
+    else:
+        print(f"     ❌ Error: {cat_result.get('error')}")
+    
+    time.sleep(1)
+    
+    # Test 2: Direct with full context
+    print("  📄 Testing Direct + Full Context...")
+    direct_ctx_result = test_direct_llama_streaming(query, use_context=True)
+    results['direct_with_context'] = direct_ctx_result
+    if direct_ctx_result['success']:
+        print(f"     TTFT: {direct_ctx_result['ttft_ms']:.0f}ms | Total: {direct_ctx_result['total_time_ms']:.0f}ms")
+        print(f"     Response: {direct_ctx_result['response'][:80]}...")
+    else:
+        print(f"     ❌ Error: {direct_ctx_result.get('error')}")
+    
+    time.sleep(1)
+    
+    # Test 3: Direct without context (minimal)
+    print("  ⚡ Testing Direct + Minimal Context...")
+    direct_min_result = test_direct_llama_streaming(query, use_context=False)
+    results['direct_minimal'] = direct_min_result
+    if direct_min_result['success']:
+        print(f"     TTFT: {direct_min_result['ttft_ms']:.0f}ms | Total: {direct_min_result['total_time_ms']:.0f}ms")
+        print(f"     Response: {direct_min_result['response'][:80]}...")
+    else:
+        print(f"     ❌ Error: {direct_min_result.get('error')}")
+    
+    return results
+
+def main():
+    print("=" * 80)
+    print("⚡ STREAMING BENCHMARK V2 - Post KV Cache Optimization")
+    print("=" * 80)
+    print("\nTesting after llama-swap improvements:")
+    print("  - KV cache offload to CPU disabled")
+    print("  - Model stays warm between queries")
+    print("\nComparing three approaches:")
+    print("  1. 🐱 Cheshire Cat (RAG with embeddings)")
+    print("  2. 📄 Direct LLM + Full Context (current bot approach)")
+    print("  3. ⚡ Direct LLM + Minimal Context (baseline)")
+    print("\n" + "=" * 80)
+    
+    # Warm up the model first
+    warmup_model(5)
+    
+    all_results = []
+    
+    for i, query in enumerate(TEST_QUERIES, 1):
+        print(f"\n[{i}/{len(TEST_QUERIES)}]")
+        results = run_comparison(query)
+        results['query'] = query
+        all_results.append(results)
+        
+        if i < len(TEST_QUERIES):
+            print("\n⏳ Waiting 2s before next query...")
+            time.sleep(2)
+    
+    # Calculate statistics
+    print("\n" + "=" * 80)
+    print("📊 RESULTS SUMMARY")
+    print("=" * 80)
+    
+    methods = ['cheshire_cat', 'direct_with_context', 'direct_minimal']
+    method_names = {
+        'cheshire_cat': '🐱 Cheshire Cat (RAG)',
+        'direct_with_context': '📄 Direct + Full Context',
+        'direct_minimal': '⚡ Direct + Minimal'
+    }
+    
+    stats_summary = {}
+    
+    for method in methods:
+        ttfts = []
+        totals = []
+        responses = []
+        
+        for result in all_results:
+            if method in result and result[method].get('success'):
+                ttfts.append(result[method]['ttft_ms'])
+                totals.append(result[method]['total_time_ms'])
+                responses.append({
+                    'query': result['query'],
+                    'response': result[method]['response']
+                })
+        
+        if ttfts:
+            stats_summary[method] = {
+                'ttft': {
+                    'mean': statistics.mean(ttfts),
+                    'median': statistics.median(ttfts),
+                    'min': min(ttfts),
+                    'max': max(ttfts)
+                },
+                'total': {
+                    'mean': statistics.mean(totals),
+                    'median': statistics.median(totals),
+                }
+            }
+            
+            print(f"\n{method_names[method]}")
+            print(f"  Success Rate: {len(ttfts)}/{len(all_results)} ({len(ttfts)/len(all_results)*100:.0f}%)")
+            print(f"  TTFT (Time To First Token):")
+            print(f"    Mean:   {statistics.mean(ttfts):>6.0f} ms")
+            print(f"    Median: {statistics.median(ttfts):>6.0f} ms")
+            print(f"    Min:    {min(ttfts):>6.0f} ms")
+            print(f"    Max:    {max(ttfts):>6.0f} ms")
+            print(f"  Total Generation Time:")
+            print(f"    Mean:   {statistics.mean(totals):>6.0f} ms")
+            print(f"    Median: {statistics.median(totals):>6.0f} ms")
+    
+    # Comparison with previous results
+    print("\n" + "=" * 80)
+    print("📈 IMPROVEMENT vs PREVIOUS BENCHMARK")
+    print("=" * 80)
+    
+    # Previous results (from first benchmark)
+    previous = {
+        'cheshire_cat': {'ttft': 1578, 'total': 10517},
+        'direct_with_context': {'ttft': 904, 'total': 8348},
+        'direct_minimal': {'ttft': 210, 'total': 6436}
+    }
+    
+    for method in methods:
+        if method in stats_summary:
+            curr_ttft = stats_summary[method]['ttft']['mean']
+            curr_total = stats_summary[method]['total']['mean']
+            prev_ttft = previous[method]['ttft']
+            prev_total = previous[method]['total']
+            
+            ttft_improvement = ((prev_ttft - curr_ttft) / prev_ttft) * 100
+            total_improvement = ((prev_total - curr_total) / prev_total) * 100
+            
+            print(f"\n{method_names[method]}")
+            print(f"  TTFT:  {prev_ttft:.0f}ms → {curr_ttft:.0f}ms ({ttft_improvement:+.1f}%)")
+            print(f"  Total: {prev_total:.0f}ms → {curr_total:.0f}ms ({total_improvement:+.1f}%)")
+    
+    # Voice chat assessment
+    print("\n" + "=" * 80)
+    print("🎤 VOICE CHAT VIABILITY (based on TTFT)")
+    print("=" * 80)
+    
+    for method in methods:
+        if method in stats_summary:
+            mean_ttft = stats_summary[method]['ttft']['mean']
+            if mean_ttft < 500:
+                status = "✅ EXCELLENT"
+            elif mean_ttft < 1000:
+                status = "✅ GOOD"
+            elif mean_ttft < 1500:
+                status = "⚠️  ACCEPTABLE"
+            else:
+                status = "❌ TOO SLOW"
+            
+            print(f"{method_names[method]}: {status} ({mean_ttft:.0f}ms mean TTFT)")
+    
+    # Save detailed results
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    output_file = f"streaming_benchmark_v2_{timestamp}.json"
+    
+    output_data = {
+        'timestamp': timestamp,
+        'optimization': 'KV cache offload disabled',
+        'results': all_results,
+        'statistics': stats_summary,
+        'previous_baseline': previous
+    }
+    
+    with open(output_file, 'w') as f:
+        json.dump(output_data, f, indent=2)
+    
+    print(f"\n💾 Detailed results saved to: {output_file}")
+    print("\n" + "=" * 80)
+
+if __name__ == "__main__":
+    main()

cheshire-cat/test_consolidation_direct.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python3
+"""
+Direct consolidation test - call the consolidation function directly
+to validate the logic without relying on hooks.
+"""
+
+import requests
+import time
+import json
+
+CAT_URL = "http://localhost:1865"
+
+
+def get_unconsolidated_memories():
+    """Query Qdrant directly to see unconsolidated memories"""
+    try:
+        # Use Cat's admin API to query memory
+        response = requests.get(f"{CAT_URL}/memory/collections")
+        if response.status_code == 200:
+            collections = response.json()
+            print(f"✅ Memory collections: {json.dumps(collections, indent=2)}")
+        else:
+            print(f"❌ Failed to get collections: {response.status_code}")
+            
+    except Exception as e:
+        print(f"❌ Error querying memory: {e}")
+
+
+def trigger_consolidation_via_api():
+    """Try triggering consolidation via the message API"""
+    print("\n🔧 Attempting to trigger consolidation...")
+    
+    response = requests.post(
+        f"{CAT_URL}/message",
+        headers={"Content-Type": "application/json"},
+        json={
+            "text": "consolidate now",
+            "user_id": "admin_test"
+        }
+    )
+    
+    if response.status_code == 200:
+        result = response.json()
+        print(f"✅ Response: {result.get('content', '')[:200]}")
+        return True
+    else:
+        print(f"❌ Failed: {response.status_code}")
+        return False
+
+
+def check_memories_after_consolidation():
+    """Check if consolidation actually ran"""
+    print("\n📊 Checking memory state...")
+    
+    # Send a query that should recall memories
+    response = requests.post(
+        f"{CAT_URL}/message",
+        headers={"Content-Type": "application/json"},
+        json={
+            "text": "What do you know about me? Tell me everything you remember.",
+            "user_id": "test_alice"
+        }
+    )
+    
+    if response.status_code == 200:
+        result = response.json()
+        content = result.get('content', '')
+        memory = result.get('why', {}).get('memory', {})
+        episodic = memory.get('episodic', [])
+        
+        print(f"\n🤖 Miku's response:\n{content}\n")
+        print(f"📝 Episodic memories recalled: {len(episodic)}")
+        
+        # Check what memories exist
+        for mem in episodic[:5]:
+            print(f"  - {mem['page_content'][:80]}...")
+            
+        return episodic
+    else:
+        print(f"❌ Failed to query memories: {response.status_code}")
+        return []
+
+
+def main():
+    print("=" * 70)
+    print("CONSOLIDATION DIRECT TEST")
+    print("=" * 70)
+    
+    # Step 1: Check current memory state
+    print("\n📋 STEP 1: Check memory collections")
+    get_unconsolidated_memories()
+    
+    # Step 2: Check memories before consolidation
+    print("\n📋 STEP 2: Query memories before consolidation")
+    memories_before = check_memories_after_consolidation()
+    print(f"\n📊 Memories BEFORE consolidation: {len(memories_before)}")
+    
+    # Step 3: Trigger consolidation
+    print("\n📋 STEP 3: Trigger consolidation")
+    triggered = trigger_consolidation_via_api()
+    
+    if triggered:
+        # Wait for consolidation to complete
+        print("\n⏳ Waiting 5 seconds for consolidation to process...")
+        time.sleep(5)
+        
+        # Step 4: Check memories after consolidation
+        print("\n📋 STEP 4: Query memories after consolidation")
+        memories_after = check_memories_after_consolidation()
+        print(f"\n📊 Memories AFTER consolidation: {len(memories_after)}")
+        
+        # Compare
+        print("\n" + "=" * 70)
+        print("RESULTS:")
+        print("=" * 70)
+        print(f"Memories before: {len(memories_before)}")
+        print(f"Memories after:  {len(memories_after)}")
+        print(f"Deleted:         {len(memories_before) - len(memories_after)}")
+        
+        if len(memories_after) < len(memories_before):
+            print("\n✅ SUCCESS! Consolidation deleted some memories!")
+        else:
+            print("\n⚠️  No memories were deleted. Consolidation may not have run.")
+    else:
+        print("\n❌ Failed to trigger consolidation")
+    
+    print("\n" + "=" * 70)
+
+
+if __name__ == "__main__":
+    main()

cheshire-cat/test_declarative_recall.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+"""
+Test if declarative facts can be recalled by asking factual questions.
+This tests the CRITICAL fix for Phase 2 memory consolidation.
+"""
+
+import requests
+import time
+
+CAT_URL = "http://localhost:1865"
+USER_ID = "test_user_declarative"
+
+def ask_cat(question: str) -> dict:
+    """Send a question to Cat and get the response."""
+    try:
+        response = requests.post(
+            f"{CAT_URL}/message",
+            json={"text": question, "user_id": USER_ID},
+            timeout=30
+        )
+        response.raise_for_status()
+        return response.json()
+    except Exception as e:
+        print(f"❌ Error: {e}")
+        return None
+
+def main():
+    print("=" * 70)
+    print("DECLARATIVE MEMORY RECALL TEST")
+    print("=" * 70)
+    print("Testing if Cat can recall stored declarative facts...\n")
+    
+    test_questions = [
+        {
+            "question": "What is my favorite color?",
+            "expected": "forest",
+            "fact_type": "favorite_color"
+        },
+        {
+            "question": "Where do I work?",
+            "expected": "software engineer",
+            "fact_type": "job"
+        },
+        {
+            "question": "What are my hobbies?",
+            "expected": "piano, japanese",
+            "fact_type": "hobby"
+        },
+        {
+            "question": "Do I prefer cats or dogs?",
+            "expected": "cats",
+            "fact_type": "preference"
+        },
+    ]
+    
+    results = []
+    
+    for i, test in enumerate(test_questions, 1):
+        print(f"[{i}/{len(test_questions)}] Testing: {test['question']}")
+        print(f"   Expected: {test['expected']}")
+        
+        response = ask_cat(test['question'])
+        
+        if response:
+            answer = response.get('content', '')
+            print(f"   Response: {answer[:100]}...")
+            
+            # Check if expected content is in response
+            success = test['expected'].lower() in answer.lower()
+            results.append({
+                'question': test['question'],
+                'success': success,
+                'response': answer
+            })
+            
+            if success:
+                print(f"   ✅ SUCCESS - Found '{test['expected']}' in response")
+            else:
+                print(f"   ❌ FAIL - Did not find '{test['expected']}' in response")
+        else:
+            print(f"   ❌ ERROR - No response from Cat")
+            results.append({
+                'question': test['question'],
+                'success': False,
+                'response': None
+            })
+        
+        print()
+        time.sleep(2)  # Brief pause between questions
+    
+    # Summary
+    print("=" * 70)
+    print("TEST SUMMARY")
+    print("=" * 70)
+    
+    success_count = sum(1 for r in results if r['success'])
+    total_count = len(results)
+    
+    print(f"✅ Successful recalls: {success_count}/{total_count}")
+    print(f"❌ Failed recalls: {total_count - success_count}/{total_count}")
+    
+    if success_count == total_count:
+        print("\n🎉 ALL TESTS PASSED! Declarative memory recall is working!")
+    elif success_count > 0:
+        print(f"\n⚠️ PARTIAL SUCCESS: {success_count}/{total_count} recalls working")
+    else:
+        print("\n❌ ALL TESTS FAILED: Declarative recall not working")
+    
+    print("\n" + "=" * 70)
+    print("DETAILED RESULTS")
+    print("=" * 70)
+    
+    for result in results:
+        status = "✅ PASS" if result['success'] else "❌ FAIL"
+        print(f"\n{status}: {result['question']}")
+        if result['response']:
+            print(f"   Response: {result['response'][:200]}...")
+
+if __name__ == "__main__":
+    main()

cheshire-cat/test_end_to_end.py

@@ -0,0 +1,233 @@
+#!/usr/bin/env python3
+"""
+END-TO-END Phase 2 Test
+
+Tests the complete pipeline:
+1. Send 20 diverse messages (important + trivial)
+2. Verify discord_bridge filters pure junk immediately
+3. Verify rest stored with consolidated=False
+4. Trigger consolidation
+5. Verify LLM/heuristic rates and deletes low-importance
+6. Verify facts extracted to declarative memory
+7. Test recall of important information
+
+This is the TRUE test of whether Phase 2 works.
+"""
+
+import requests
+import json
+import time
+from qdrant_client import QdrantClient
+
+CAT_URL = "http://localhost:1865"
+TEST_USER = "end_to_end_test_user"
+
+def send_message(text: str):
+    """Send message to Cat"""
+    response = requests.post(
+        f"{CAT_URL}/message",
+        json={"text": text, "user_id": TEST_USER},
+        timeout=30
+    )
+    if response.status_code == 200:
+        return True
+    return False
+
+def check_memory_state():
+    """Check current memory state"""
+    client = QdrantClient(host='localhost', port=6333, timeout=10, prefer_grpc=False)
+    
+    # Get episodic memories
+    episodic, _ = client.scroll('episodic', limit=100, with_payload=True, with_vectors=False)
+    
+    # Get declarative memories  
+    declarative, _ = client.scroll('declarative', limit=100, with_payload=True, with_vectors=False)
+    
+    return episodic, declarative
+
+def main():
+    print("=" * 70)
+    print("END-TO-END PHASE 2 TEST")
+    print("=" * 70)
+    
+    # Phase 1: Send diverse messages
+    print("\n📤 PHASE 1: Sending 20 messages...")
+    print("-" * 70)
+    
+    messages = {
+        "PURE JUNK (should be filtered immediately)": [
+            "lol",
+            "k",  
+            "ok",
+        ],
+        "IMPORTANT FACTS (should be kept + extracted)": [
+            "My name is Jennifer Martinez",
+            "I'm 28 years old",
+            "I work as a nurse at Seattle General Hospital",
+            "My cat's name is Whiskers",
+            "I'm allergic to peanuts",
+        ],
+        "EMOTIONAL EVENTS (should be kept)": [
+            "My father passed away last month from cancer",
+            "I just got accepted into grad school!",
+            "I'm struggling with anxiety lately",
+        ],
+        "MUNDANE CHITCHAT (should be deleted in consolidation)": [
+            "What's up?",
+            "How are you?",
+            "That's interesting",
+            "Nice weather today",
+        ],
+        "PREFERENCES (should be kept + extracted)": [
+            "I love jazz music",
+            "My favorite color is purple",
+            "I hate horror movies",
+        ],
+    }
+    
+    all_messages = []
+    for category, msgs in messages.items():
+        print(f"\n{category}:")
+        for msg in msgs:
+            print(f"  → {msg}")
+            send_message(msg)
+            all_messages.append((category, msg))
+            time.sleep(0.3)
+    
+    print(f"\n✅ Sent {len(all_messages)} messages")
+    
+    # Phase 2: Check immediate filtering
+    print("\n" + "=" * 70)
+    print("📊 PHASE 2: Checking immediate filtering (discord_bridge)")
+    print("-" * 70)
+    
+    time.sleep(2)  # Let storage complete
+    episodic, declarative = check_memory_state()
+    
+    print(f"\nEpisodic memories stored: {len(episodic)}")
+    print(f"Declarative memories: {len(declarative)}")
+    
+    # Check what was stored
+    stored_content = [e.payload.get('page_content', '') for e in episodic]
+    
+    pure_junk = ["lol", "k", "ok"]
+    junk_filtered = [j for j in pure_junk if j not in stored_content]
+    junk_stored = [j for j in pure_junk if j in stored_content]
+    
+    print(f"\n✅ Pure junk filtered: {len(junk_filtered)}/3")
+    if junk_filtered:
+        for msg in junk_filtered:
+            print(f"  - '{msg}'")
+    
+    if junk_stored:
+        print(f"\n⚠️  Pure junk NOT filtered: {len(junk_stored)}/3")
+        for msg in junk_stored:
+            print(f"  - '{msg}'")
+    
+    # Check consolidated flag
+    unconsolidated = [e for e in episodic if not e.payload.get('metadata', {}).get('consolidated', True)]
+    print(f"\n📋 Memories marked consolidated=False: {len(unconsolidated)}")
+    
+    # Phase 3: Trigger consolidation
+    print("\n" + "=" * 70)
+    print("🌙 PHASE 3: Triggering consolidation")
+    print("-" * 70)
+    
+    response = requests.post(
+        f"{CAT_URL}/message",
+        json={"text": "consolidate now", "user_id": "admin"},
+        timeout=60
+    )
+    
+    if response.status_code == 200:
+        result = response.json()
+        print(f"✅ Consolidation triggered")
+        print(f"Response: {result.get('content', '')[:200]}")
+    else:
+        print(f"❌ Consolidation failed: {response.status_code}")
+        return
+    
+    time.sleep(3)  # Let consolidation complete
+    
+    # Phase 4: Check post-consolidation state
+    print("\n" + "=" * 70)
+    print("📊 PHASE 4: Analyzing post-consolidation state")
+    print("-" * 70)
+    
+    episodic_after, declarative_after = check_memory_state()
+    
+    print(f"\nEpisodic memories: {len(episodic)} → {len(episodic_after)}")
+    print(f"Deleted: {len(episodic) - len(episodic_after)}")
+    print(f"\nDeclarative memories: {len(declarative)} → {len(declarative_after)}")
+    print(f"Facts extracted: {len(declarative_after) - len(declarative)}")
+    
+    # Check what was deleted
+    stored_after = [e.payload.get('page_content', '') for e in episodic_after]
+    deleted = [msg for msg in stored_content if msg not in stored_after]
+    
+    if deleted:
+        print(f"\n🗑️  Deleted ({len(deleted)}):")
+        for msg in deleted[:10]:
+            print(f"  - '{msg}'")
+    
+    # Check what important stuff remains
+    important_keywords = ["Jennifer", "28", "nurse", "Whiskers", "peanuts", 
+                         "father", "grad school", "anxiety", "jazz", "purple"]
+    important_kept = [msg for msg in stored_after if any(kw in msg for kw in important_keywords)]
+    
+    print(f"\n✅ Important messages kept ({len(important_kept)}):")
+    for msg in important_kept[:8]:
+        print(f"  - '{msg}'")
+    
+    # Phase 5: Test recall
+    print("\n" + "=" * 70)
+    print("🧠 PHASE 5: Testing recall")
+    print("-" * 70)
+    
+    test_queries = [
+        "What is my name?",
+        "Where do I work?",
+        "What's my cat's name?",
+        "What am I allergic to?",
+    ]
+    
+    for query in test_queries:
+        response = requests.post(
+            f"{CAT_URL}/message",
+            json={"text": query, "user_id": TEST_USER},
+            timeout=30
+        )
+        
+        if response.status_code == 200:
+            result = response.json()
+            answer = result.get('content', '')
+            memories = result.get('why', {}).get('memory', {})
+            episodic_recalled = len(memories.get('episodic', []))
+            declarative_recalled = len(memories.get('declarative', []))
+            
+            print(f"\nQ: {query}")
+            print(f"A: {answer[:150]}")
+            print(f"   [Recalled: {episodic_recalled} episodic, {declarative_recalled} declarative]")
+    
+    # Final summary
+    print("\n" + "=" * 70)
+    print("📋 FINAL SUMMARY")
+    print("=" * 70)
+    
+    print(f"\n1. Immediate filtering:")
+    print(f"   ✅ Filtered: {len(junk_filtered)}/3 pure junk")
+    print(f"   📝 Stored: {len(episodic)} messages")
+    
+    print(f"\n2. Consolidation:")
+    print(f"   🗑️  Deleted: {len(deleted)} low-importance")
+    print(f"   ✅ Kept: {len(episodic_after)} important")
+    print(f"   📚 Facts extracted: {len(declarative_after) - len(declarative)}")
+    
+    print(f"\n3. Recall:")
+    print(f"   Test queries: {len(test_queries)}")
+    print(f"   (Check above for recall accuracy)")
+    
+    print("\n" + "=" * 70)
+
+if __name__ == "__main__":
+    main()

cheshire-cat/test_full_pipeline.py

@@ -0,0 +1,295 @@
+#!/usr/bin/env python3
+"""
+Full pipeline test for Phase 2 memory consolidation with declarative extraction.
+
+Steps:
+1. Tell Miku 20 facts (mix of important and trivial)
+2. Run consolidation to delete trivial messages
+3. Extract facts from consolidated episodic memories
+4. Store facts in declarative memory
+5. Test recall with factual questions
+"""
+
+import requests
+import time
+import sys
+
+CAT_URL = "http://localhost:1865"
+USER_ID = "test_user_pipeline"
+
+# Test messages to tell Miku
+TEST_MESSAGES = [
+    # Important facts (should be remembered)
+    "My name is Sarah Chen.",
+    "I'm 28 years old.",
+    "I live in Seattle, Washington.",
+    "I work as a software engineer at Microsoft.",
+    "My favorite color is forest green.",
+    "I love playing piano. I've been practicing for 15 years.",
+    "I'm learning Japanese! Currently at N3 level.",
+    "I have a cat named Luna.",
+    "I'm allergic to peanuts.",
+    "I prefer cats over dogs, though I like both.",
+    "My favorite food is ramen.",
+    "I enjoy hiking on weekends.",
+    "I graduated from UW in 2018.",
+    "My birthday is March 15th.",
+    
+    # Trivial messages (should be deleted during consolidation)
+    "lol",
+    "k",
+    "haha",
+    "brb",
+    "nice",
+    "cool",
+]
+
+# Questions to test recall
+RECALL_TESTS = [
+    {
+        "question": "What is my name?",
+        "expected": "sarah",
+        "fact_type": "name"
+    },
+    {
+        "question": "How old am I?",
+        "expected": "28",
+        "fact_type": "age"
+    },
+    {
+        "question": "Where do I live?",
+        "expected": "seattle",
+        "fact_type": "location"
+    },
+    {
+        "question": "What do I do for work?",
+        "expected": "software engineer",
+        "fact_type": "job"
+    },
+    {
+        "question": "What is my favorite color?",
+        "expected": "forest green",
+        "fact_type": "favorite_color"
+    },
+    {
+        "question": "What instruments do I play?",
+        "expected": "piano",
+        "fact_type": "hobby"
+    },
+    {
+        "question": "What language am I learning?",
+        "expected": "japanese",
+        "fact_type": "hobby"
+    },
+    {
+        "question": "What is my cat's name?",
+        "expected": "luna",
+        "fact_type": "pet_name"
+    },
+    {
+        "question": "What am I allergic to?",
+        "expected": "peanut",
+        "fact_type": "allergy"
+    },
+    {
+        "question": "Do I prefer cats or dogs?",
+        "expected": "cat",
+        "fact_type": "preference"
+    },
+]
+
+
+def send_message(text: str) -> dict:
+    """Send a message to Miku."""
+    try:
+        response = requests.post(
+            f"{CAT_URL}/message",
+            json={"text": text, "user_id": USER_ID},
+            timeout=30
+        )
+        response.raise_for_status()
+        return response.json()
+    except Exception as e:
+        print(f"    ❌ Error sending message: {e}")
+        return None
+
+
+def trigger_consolidation() -> bool:
+    """Trigger memory consolidation."""
+    try:
+        response = send_message("consolidate now")
+        if response:
+            print("    ✅ Consolidation triggered")
+            return True
+        return False
+    except Exception as e:
+        print(f"    ❌ Error triggering consolidation: {e}")
+        return False
+
+
+def main():
+    print("=" * 80)
+    print("PHASE 2 FULL PIPELINE TEST")
+    print("=" * 80)
+    print(f"Testing with user: {USER_ID}\n")
+    
+    # Step 1: Tell Miku the facts
+    print("STEP 1: Telling Miku facts...")
+    print("-" * 80)
+    successful_sends = 0
+    
+    for i, message in enumerate(TEST_MESSAGES, 1):
+        is_trivial = message in ["lol", "k", "haha", "brb", "nice", "cool"]
+        msg_type = "TRIVIAL" if is_trivial else "IMPORTANT"
+        
+        print(f"[{i}/{len(TEST_MESSAGES)}] {msg_type}: {message}")
+        response = send_message(message)
+        
+        if response:
+            print(f"    ✅ Sent successfully")
+            successful_sends += 1
+        else:
+            print(f"    ❌ Failed to send")
+        
+        time.sleep(1)  # Brief pause between messages
+    
+    print(f"\n✅ Successfully sent {successful_sends}/{len(TEST_MESSAGES)} messages\n")
+    
+    # Step 2: Trigger consolidation
+    print("STEP 2: Triggering consolidation...")
+    print("-" * 80)
+    
+    if not trigger_consolidation():
+        print("❌ Failed to trigger consolidation")
+        sys.exit(1)
+    
+    print("⏳ Waiting for consolidation to complete...")
+    time.sleep(5)
+    print("✅ Consolidation complete\n")
+    
+    # Step 3: Extract and store declarative facts
+    print("STEP 3: Extracting and storing declarative facts...")
+    print("-" * 80)
+    print("Running extract_declarative_facts.py...")
+    
+    import subprocess
+    result = subprocess.run(
+        ["python3", "extract_declarative_facts.py"],
+        capture_output=True,
+        text=True
+    )
+    
+    if result.returncode == 0:
+        # Count extracted facts from output
+        facts_count = result.stdout.count("✅ Extracted from:")
+        print(f"✅ Extracted {facts_count} facts")
+    else:
+        print(f"❌ Extraction failed: {result.stderr[:200]}")
+        sys.exit(1)
+    
+    print("\nRunning store_declarative_facts.py...")
+    result = subprocess.run(
+        ["python3", "store_declarative_facts.py"],
+        capture_output=True,
+        text=True
+    )
+    
+    if result.returncode == 0:
+        # Check for success in output
+        if "Successfully stored:" in result.stdout:
+            stored_line = [l for l in result.stdout.split('\n') if "Successfully stored:" in l][0]
+            print(f"✅ {stored_line.strip()}")
+        else:
+            print("✅ Facts stored")
+    else:
+        print(f"❌ Storage failed: {result.stderr[:200]}")
+        sys.exit(1)
+    
+    print()
+    
+    # Step 4: Test recall
+    print("STEP 4: Testing declarative memory recall...")
+    print("-" * 80)
+    
+    results = []
+    successful_recalls = 0
+    
+    for i, test in enumerate(RECALL_TESTS, 1):
+        question = test["question"]
+        expected = test["expected"].lower()
+        
+        print(f"[{i}/{len(RECALL_TESTS)}] {question}")
+        print(f"    Expected: {expected}")
+        
+        response = send_message(question)
+        
+        if response:
+            answer = response.get('content', '').lower()
+            success = expected in answer
+            
+            if success:
+                print(f"    ✅ RECALLED correctly")
+                successful_recalls += 1
+            else:
+                print(f"    ❌ NOT recalled")
+                print(f"    Response: {answer[:100]}...")
+            
+            results.append({
+                'question': question,
+                'expected': expected,
+                'success': success,
+                'response': response.get('content', '')
+            })
+        else:
+            print(f"    ❌ ERROR - No response")
+            results.append({
+                'question': question,
+                'expected': expected,
+                'success': False,
+                'response': None
+            })
+        
+        print()
+        time.sleep(2)
+    
+    # Final summary
+    print("=" * 80)
+    print("FINAL RESULTS")
+    print("=" * 80)
+    
+    success_rate = (successful_recalls / len(RECALL_TESTS)) * 100
+    
+    print(f"\n📊 RECALL SUCCESS RATE: {successful_recalls}/{len(RECALL_TESTS)} ({success_rate:.1f}%)\n")
+    
+    if success_rate == 100:
+        print("🎉 PERFECT! All facts recalled correctly!")
+    elif success_rate >= 80:
+        print("✅ EXCELLENT! Most facts recalled correctly.")
+    elif success_rate >= 50:
+        print("⚠️ PARTIAL SUCCESS - Needs improvement.")
+    else:
+        print("❌ POOR PERFORMANCE - System needs significant fixes.")
+    
+    print("\nDetailed results:")
+    print("-" * 80)
+    
+    for result in results:
+        status = "✅" if result['success'] else "❌"
+        print(f"{status} {result['question']}")
+        if not result['success'] and result['response']:
+            print(f"   Response: {result['response'][:150]}...")
+    
+    print("\n" + "=" * 80)
+    
+    if success_rate == 100:
+        print("✅ PHASE 2 COMPLETE AND READY FOR PRODUCTION!")
+    elif success_rate >= 80:
+        print("✅ PHASE 2 MOSTLY WORKING - Minor refinements needed")
+    else:
+        print("❌ PHASE 2 NEEDS MORE WORK")
+    
+    print("=" * 80)
+
+
+if __name__ == "__main__":
+    main()

cheshire-cat/test_phase2.py

@@ -0,0 +1,214 @@
+#!/usr/bin/env python3
+"""
+Phase 2 Test Script
+
+Tests the Memory Consolidation plugin:
+1. Send multiple messages (some important, some trivial)
+2. Manually trigger consolidation
+3. Verify important memories kept, trivial deleted
+4. Check if facts were extracted to declarative memory
+"""
+
+import requests
+import json
+import time
+from datetime import datetime
+
+
+CAT_URL = "http://localhost:1865"
+TEST_USER_ID = "discord_user_phase2_test"
+
+
+def send_message(text: str, guild_id: str = "test_guild", description: str = ""):
+    """Send a message and return response"""
+    print(f"\n{'='*60}")
+    print(f"📤 {description}")
+    print(f"   Message: '{text}'")
+    
+    payload = {
+        "text": text,
+        "user_id": TEST_USER_ID,
+        "metadata": {
+            "guild_id": guild_id,
+            "channel_id": "test_channel"
+        }
+    }
+    
+    try:
+        response = requests.post(
+            f"{CAT_URL}/message",
+            json=payload,
+            timeout=30
+        )
+        
+        if response.status_code == 200:
+            result = response.json()
+            print(f"   ✅ Response: {result.get('content', '')[:80]}...")
+            return True
+        else:
+            print(f"   ❌ Error: {response.status_code}")
+            return False
+    except Exception as e:
+        print(f"   ❌ Exception: {e}")
+        return False
+
+
+def trigger_consolidation():
+    """Manually trigger consolidation for testing"""
+    print(f"\n{'='*60}")
+    print("🌙 TRIGGERING CONSOLIDATION")
+    print("="*60)
+    
+    try:
+        # Try to trigger via API (if endpoint exists)
+        response = requests.post(
+            f"{CAT_URL}/admin/consolidate",
+            timeout=60
+        )
+        
+        if response.status_code == 200:
+            print("✅ Consolidation triggered successfully")
+            return True
+        else:
+            print(f"⚠️  API returned {response.status_code}")
+            print("   (This is expected - no admin endpoint yet)")
+            return False
+    except Exception as e:
+        print(f"⚠️  Could not trigger via API: {e}")
+        print("   (This is expected - no admin endpoint yet)")
+        return False
+
+
+def check_logs():
+    """Check Docker logs for consolidation output"""
+    print(f"\n{'='*60}")
+    print("📋 CHECKING CONSOLIDATION LOGS")
+    print("="*60)
+    print("\nRun this command manually to check:")
+    print("   docker logs miku_cheshire_cat_test 2>&1 | grep -E '(Consolidation|🌙|✨|💾|🗑️)' | tail -30")
+
+
+def main():
+    print("="*60)
+    print("PHASE 2 TEST: Memory Consolidation")
+    print("="*60)
+    
+    print(f"\n🧪 Testing with user: {TEST_USER_ID}")
+    print("   Sending mix of important and trivial messages")
+    
+    # Wait for Cat to be ready
+    time.sleep(2)
+    
+    # Test Suite 1: Send varied messages
+    print("\n" + "="*60)
+    print("TEST SUITE 1: Varied Message Types")
+    print("="*60)
+    
+    messages = [
+        # Trivial (should be deleted)
+        ("lol", "Trivial - pure reaction"),
+        ("k", "Trivial - 1 char"),
+        ("okay", "Trivial - acknowledgment"),
+        
+        # Important (should be kept)
+        ("My name is Alice", "Important - personal info"),
+        ("I love playing guitar", "Important - hobby/preference"),
+        ("My dog died last month", "Important - emotional event"),
+        ("I'm studying computer science at MIT", "Important - education"),
+        
+        # Medium (depends on context)
+        ("What's the weather like?", "Medium - generic question"),
+        ("I had pizza for lunch", "Medium - daily activity"),
+        
+        # Very important (should definitely be kept)
+        ("I'm getting married next month!", "Critical - major life event"),
+        ("I've been diagnosed with depression", "Critical - health/emotional"),
+    ]
+    
+    for text, desc in messages:
+        send_message(text, description=desc)
+        time.sleep(1)
+    
+    # Test Suite 2: Trigger consolidation
+    print("\n" + "="*60)
+    print("TEST SUITE 2: Consolidation Trigger")
+    print("="*60)
+    
+    trigger_consolidation()
+    
+    # Wait for consolidation to complete
+    print("\n⏳ Waiting 10 seconds for consolidation to complete...")
+    time.sleep(10)
+    
+    # Test Suite 3: Verify results
+    print("\n" + "="*60)
+    print("TEST SUITE 3: Verification")
+    print("="*60)
+    
+    print("\n✅ EXPECTED RESULTS:")
+    print("\n📝 Should be DELETED (trivial):")
+    print("   - 'lol' (pure reaction)")
+    print("   - 'k' (too short)")
+    print("   - 'okay' (acknowledgment)")
+    
+    print("\n💾 Should be KEPT (important):")
+    print("   - 'My name is Alice' (importance: 7-8)")
+    print("   - 'I love playing guitar' (importance: 6-7)")
+    print("   - 'My dog died last month' (importance: 9-10)")
+    print("   - 'I'm studying CS at MIT' (importance: 7-8)")
+    print("   - 'I'm getting married!' (importance: 10)")
+    print("   - 'diagnosed with depression' (importance: 10)")
+    
+    print("\n📚 Should be extracted as FACTS (declarative memory):")
+    print("   - 'User's name is Alice'")
+    print("   - 'User plays guitar'")
+    print("   - 'User lost their dog recently'")
+    print("   - 'User studies CS at MIT'")
+    print("   - 'User getting married soon'")
+    print("   - 'User has depression'")
+    
+    # Check logs
+    check_logs()
+    
+    # Summary
+    print("\n" + "="*60)
+    print("MANUAL VERIFICATION STEPS")
+    print("="*60)
+    
+    print("""
+1. Check Docker logs for consolidation output:
+   docker logs miku_cheshire_cat_test 2>&1 | tail -100
+
+2. Look for these indicators:
+   🌙 [Consolidation] Starting...
+   📊 [Consolidation] Fetching unconsolidated memories
+   ✨ [Consolidation] Complete! Stats: ...
+
+3. Verify in next conversation:
+   Test if Miku remembers:
+   - User's name (Alice)
+   - That user plays guitar
+   - That user is getting married
+   
+   Should NOT remember:
+   - 'lol', 'k', 'okay'
+
+4. Test memory recall:
+   Send: "What do you know about me?"
+   Expected: Mentions name, guitar, upcoming marriage, etc.
+
+5. Check memory stats:
+   If stats show:
+   - Processed: 11 memories
+   - Kept: 6-7 important ones
+   - Deleted: 4-5 trivial ones
+   - Facts learned: 5-6 facts
+   Then Phase 2 is working! ✅
+""")
+    
+    print("\n✨ Phase 2 testing complete!")
+    print("\nNext: Run verification queries to test memory recall")
+
+
+if __name__ == "__main__":
+    main()

cheshire-cat/test_phase2_comprehensive.py

@@ -0,0 +1,194 @@
+#!/usr/bin/env python3
+"""
+Comprehensive Phase 2 Test - Memory Consolidation
+
+This test tells Miku a rich variety of information to test consolidation:
+- Trivial messages (should be deleted)
+- Important personal facts (should be kept)
+- Emotional events (should be kept)
+- Mundane chitchat (might be kept or deleted)
+- Complex conversations (should be analyzed intelligently)
+
+After sending all messages, we'll:
+1. Run manual consolidation
+2. Check what was kept vs deleted
+3. Verify Miku remembers the important stuff
+4. Check if facts were extracted to declarative memory
+"""
+
+import requests
+import json
+import time
+from datetime import datetime
+
+CAT_URL = "http://localhost:1865"
+TEST_USER_ID = "discord_user_comprehensive_test"
+
+
+def send_message(text: str, category: str = ""):
+    """Send a message to Miku"""
+    print(f"  [{category}] '{text}'")
+    
+    payload = {
+        "text": text,
+        "user_id": TEST_USER_ID
+    }
+    
+    try:
+        response = requests.post(
+            f"{CAT_URL}/message",
+            json=payload,
+            timeout=30
+        )
+        
+        if response.status_code == 200:
+            return True
+        else:
+            print(f"    ❌ Error: {response.status_code}")
+            return False
+    except Exception as e:
+        print(f"    ❌ Exception: {e}")
+        return False
+
+
+def main():
+    print("=" * 70)
+    print("COMPREHENSIVE PHASE 2 TEST")
+    print("=" * 70)
+    print("\n📤 Sending diverse messages to test consolidation...")
+    
+    test_messages = {
+        "TRIVIAL - Should DELETE": [
+            "lol",
+            "k",
+            "ok",
+            "lmao",
+            "haha",
+            "xd",
+            "brb",
+            "gtg",
+        ],
+        
+        "PERSONAL FACTS - Should KEEP": [
+            "My name is Sarah Chen",
+            "I'm 24 years old",
+            "I live in Seattle, Washington",
+            "I work as a software engineer at Microsoft",
+            "My birthday is March 15th",
+            "I graduated from UC Berkeley in 2022",
+            "My phone number is 555-0123",
+            "My email is sarah.chen@example.com",
+        ],
+        
+        "EMOTIONAL EVENTS - Should KEEP": [
+            "I just got engaged to my boyfriend yesterday! I'm so happy!",
+            "My grandmother passed away last week. I'm really struggling with it.",
+            "I finally got promoted to senior engineer after 3 years of hard work!",
+            "My cat Luna died this morning. She was 16 years old. I'm devastated.",
+            "I had a panic attack at work today. It was really embarrassing.",
+            "I've been diagnosed with ADHD and just started medication.",
+        ],
+        
+        "HOBBIES & INTERESTS - Should KEEP": [
+            "I love playing piano. I've been playing for 15 years.",
+            "I'm learning Japanese! Currently at N3 level.",
+            "I'm a huge fan of Studio Ghibli films, especially Spirited Away.",
+            "I collect vinyl records. I have about 200 albums so far.",
+            "I run marathons. Just completed my 5th one last month!",
+        ],
+        
+        "RELATIONSHIPS - Should KEEP": [
+            "My best friend is Emma. We've known each other since kindergarten.",
+            "My mom's name is Jennifer and she's a high school teacher.",
+            "I have a younger brother named Alex who's in college.",
+            "My fiance's name is David. We met at work 3 years ago.",
+        ],
+        
+        "MUNDANE CHITCHAT - Might DELETE": [
+            "What's up?",
+            "How are you?",
+            "That's cool",
+            "I see",
+            "Interesting",
+            "Nice",
+            "Yeah",
+        ],
+        
+        "OPINIONS & PREFERENCES - Should KEEP": [
+            "I absolutely hate cilantro. It tastes like soap to me.",
+            "My favorite color is forest green.",
+            "I prefer cats over dogs, though I like both.",
+            "I'm vegetarian for ethical reasons.",
+            "I think pineapple on pizza is delicious, fight me!",
+        ],
+        
+        "CURRENT EVENTS - Might KEEP (recent context)": [
+            "I'm planning a trip to Japan in May.",
+            "I'm looking for a new apartment closer to downtown.",
+            "I've been dealing with insomnia lately.",
+            "I'm taking a pottery class on weekends.",
+        ],
+        
+        "TRIVIAL QUESTIONS - Might DELETE": [
+            "What's your favorite food?",
+            "Do you like music?",
+            "Can you sing?",
+        ],
+        
+        "MEANINGFUL QUESTIONS - Might KEEP": [
+            "Do you think AI will ever truly understand human emotions?",
+            "What's your opinion on the ethics of AI development?",
+        ],
+        
+        "SMALL TALK - Might DELETE": [
+            "It's raining today",
+            "I had coffee this morning",
+            "The weather is nice",
+        ],
+    }
+    
+    stats = {
+        "total": 0,
+        "sent": 0,
+        "failed": 0
+    }
+    
+    # Send all messages
+    for category, messages in test_messages.items():
+        print(f"\n{category}:")
+        for msg in messages:
+            stats["total"] += 1
+            if send_message(msg, category):
+                stats["sent"] += 1
+                time.sleep(0.5)  # Polite delay
+            else:
+                stats["failed"] += 1
+    
+    print("\n" + "=" * 70)
+    print("SENDING COMPLETE")
+    print("=" * 70)
+    print(f"Total messages: {stats['total']}")
+    print(f"✅ Sent: {stats['sent']}")
+    print(f"❌ Failed: {stats['failed']}")
+    
+    print("\n" + "=" * 70)
+    print("NEXT STEPS:")
+    print("=" * 70)
+    print("1. Run manual consolidation:")
+    print("   cd /home/koko210Serve/docker/miku-discord/cheshire-cat")
+    print("   source venv/bin/activate")
+    print("   python3 manual_consolidation.py")
+    print("")
+    print("2. Verify what was kept:")
+    print("   python3 verify_consolidation.py")
+    print("")
+    print("3. Test Miku's memory:")
+    print("   curl -X POST http://localhost:1865/message \\")
+    print("     -H 'Content-Type: application/json' \\")
+    print("     -d '{\"text\": \"Tell me everything you know about me\", \"user_id\": \"discord_user_comprehensive_test\"}'")
+    print("")
+    print("=" * 70)
+
+
+if __name__ == "__main__":
+    main()

cheshire-cat/test_recall.py

@@ -0,0 +1,32 @@
+from cat.mad_hatter.mad_hatter import MadHatter
+from cat.memory.vector_memory import VectorMemoryCollection
+from qdrant_client import QdrantClient
+
+# Connect to Qdrant
+client = QdrantClient(host="localhost", port=6333)
+
+# Check if collections exist
+collections = client.get_collections()
+print("Collections:", [c.name for c in collections.collections])
+
+# Try to query episodic directly
+episodic_points = client.scroll(
+    collection_name="episodic",
+    limit=5,
+    with_payload=True,
+    with_vectors=False
+)
+print(f"\nEpisodic memories found: {len(episodic_points[0])}")
+for point in episodic_points[0]:
+    print(f"  - {point.payload.get('page_content', '')[:100]}")
+
+# Try declarative
+declarative_points = client.scroll(
+    collection_name="declarative",
+    limit=5,
+    with_payload=True,
+    with_vectors=False
+)
+print(f"\nDeclarative facts found: {len(declarative_points[0])}")
+for point in declarative_points[0]:
+    print(f"  - {point.payload.get('page_content', '')}")

cheshire-cat/test_setup.py

@@ -0,0 +1,228 @@
+#!/usr/bin/env python3
+"""
+Cheshire Cat Test Setup Script for Miku Bot
+Sets up Cat to use llama-swap instead of Ollama
+"""
+
+import requests
+import time
+import json
+import sys
+
+# Configuration
+CAT_URL = "http://localhost:1865"
+LLAMA_SWAP_URL = "http://llama-swap:8080/v1"  # Internal Docker network
+# LLAMA_SWAP_URL = "http://host.docker.internal:8080/v1"  # Alternative if network doesn't work
+TEXT_MODEL = "Llama-3.1-8B-Instruct-UD-Q4_K_XL.gguf"  # Your default text model
+
+def wait_for_cat():
+    """Wait for Cat to be ready"""
+    print("Waiting for Cheshire Cat to start...")
+    max_attempts = 30
+    for i in range(max_attempts):
+        try:
+            response = requests.get(f"{CAT_URL}/", timeout=5)
+            if response.status_code == 200:
+                print("✅ Cheshire Cat is ready!")
+                return True
+        except requests.exceptions.RequestException:
+            pass
+        
+        print(f"  Attempt {i+1}/{max_attempts}...")
+        time.sleep(2)
+    
+    print("❌ Cheshire Cat failed to start")
+    return False
+
+def configure_llm():
+    """Configure Cat to use llama-swap instead of Ollama"""
+    print("\n🔧 Configuring LLM to use llama-swap...")
+    
+    # Cat's settings API endpoint
+    settings_url = f"{CAT_URL}/settings"
+    
+    # OpenAI-compatible configuration for llama-swap
+    llm_config = {
+        "name": "LLMOpenAIConfig",
+        "value": {
+            "openai_api_key": "dummy",  # llama-swap doesn't need this
+            "model_name": TEXT_MODEL,
+            "openai_api_base": LLAMA_SWAP_URL
+        }
+    }
+    
+    try:
+        # Get current settings
+        response = requests.get(settings_url)
+        if response.status_code == 200:
+            print("  Current settings retrieved")
+        
+        # Update LLM settings
+        response = requests.put(
+            f"{settings_url}/llm",
+            json=llm_config,
+            headers={"Content-Type": "application/json"}
+        )
+        
+        if response.status_code == 200:
+            print(f"✅ LLM configured to use llama-swap at {LLAMA_SWAP_URL}")
+            print(f"   Model: {TEXT_MODEL}")
+            return True
+        else:
+            print(f"❌ Failed to configure LLM: {response.status_code}")
+            print(f"   Response: {response.text}")
+            return False
+            
+    except Exception as e:
+        print(f"❌ Error configuring LLM: {e}")
+        return False
+
+def configure_embedder():
+    """Configure embedder (use CPU for now, can switch to GPU later)"""
+    print("\n🧮 Configuring embedder...")
+    
+    # Use default embedder (sentence-transformers on CPU)
+    # We'll test this first, then potentially switch to GPU
+    embedder_config = {
+        "name": "EmbedderDumbConfig",  # Fast, low-quality for testing
+        "value": {}
+    }
+    
+    # For production, use this instead:
+    # embedder_config = {
+    #     "name": "EmbedderQdrantFastEmbedConfig",
+    #     "value": {
+    #         "model_name": "sentence-transformers/all-MiniLM-L6-v2"  # Lightweight model
+    #     }
+    # }
+    
+    try:
+        response = requests.put(
+            f"{CAT_URL}/settings/embedder",
+            json=embedder_config,
+            headers={"Content-Type": "application/json"}
+        )
+        
+        if response.status_code == 200:
+            print("✅ Embedder configured (CPU-based for testing)")
+            return True
+        else:
+            print(f"⚠️  Embedder config returned: {response.status_code}")
+            print(f"   Using default embedder")
+            return True  # Not critical
+            
+    except Exception as e:
+        print(f"⚠️  Error configuring embedder: {e}")
+        print("   Will use default embedder")
+        return True  # Not critical
+
+def upload_knowledge_base():
+    """Upload Miku's knowledge files to Cat"""
+    print("\n📚 Uploading Miku knowledge base...")
+    
+    files_to_upload = [
+        "../bot/persona/miku/miku_lore.txt",
+        "../bot/persona/miku/miku_prompt.txt",
+        "../bot/persona/miku/miku_lyrics.txt"
+    ]
+    
+    uploaded_count = 0
+    
+    for filepath in files_to_upload:
+        try:
+            filename = filepath.split('/')[-1]
+            print(f"  Uploading {filename}...")
+            
+            with open(filepath, 'rb') as f:
+                files = {'file': (filename, f, 'text/plain')}
+                response = requests.post(
+                    f"{CAT_URL}/rabbithole/",
+                    files=files,
+                    timeout=60  # Chunking and embedding takes time
+                )
+            
+            if response.status_code == 200:
+                print(f"    ✅ {filename} uploaded and processed")
+                uploaded_count += 1
+            else:
+                print(f"    ❌ Failed to upload {filename}: {response.status_code}")
+                print(f"       {response.text[:200]}")
+                
+        except FileNotFoundError:
+            print(f"    ⚠️  File not found: {filepath}")
+        except Exception as e:
+            print(f"    ❌ Error uploading {filename}: {e}")
+    
+    print(f"\n📊 Uploaded {uploaded_count}/{len(files_to_upload)} files")
+    return uploaded_count > 0
+
+def test_query():
+    """Test a simple query to verify everything works"""
+    print("\n🧪 Testing query...")
+    
+    test_messages = [
+        "What is your favorite food?",
+        "Who are your friends?",
+        "Tell me about the song World is Mine"
+    ]
+    
+    for message in test_messages:
+        print(f"\n  Query: '{message}'")
+        try:
+            response = requests.post(
+                f"{CAT_URL}/message",
+                json={"text": message},
+                headers={"Content-Type": "application/json"},
+                timeout=30
+            )
+            
+            if response.status_code == 200:
+                data = response.json()
+                reply = data.get("content", "No response")
+                print(f"  ✅ Response: {reply[:150]}...")
+            else:
+                print(f"  ❌ Query failed: {response.status_code}")
+                print(f"     {response.text[:200]}")
+                
+        except Exception as e:
+            print(f"  ❌ Error: {e}")
+        
+        time.sleep(1)  # Brief pause between queries
+
+def main():
+    print("=" * 60)
+    print("🐱 Cheshire Cat Test Setup for Miku Bot")
+    print("=" * 60)
+    
+    # Step 1: Wait for Cat to start
+    if not wait_for_cat():
+        print("\n❌ Setup failed: Cat didn't start")
+        sys.exit(1)
+    
+    # Step 2: Configure LLM
+    if not configure_llm():
+        print("\n⚠️  LLM configuration failed, but continuing...")
+    
+    # Step 3: Configure embedder
+    if not configure_embedder():
+        print("\n⚠️  Embedder configuration failed, but continuing...")
+    
+    # Step 4: Upload knowledge base
+    time.sleep(2)  # Give Cat a moment to apply settings
+    if not upload_knowledge_base():
+        print("\n⚠️  Knowledge upload failed")
+    
+    # Step 5: Test queries
+    time.sleep(5)  # Give Cat time to process uploaded files
+    test_query()
+    
+    print("\n" + "=" * 60)
+    print("✅ Setup complete!")
+    print("=" * 60)
+    print("\nNext steps:")
+    print("  1. Run benchmarks: python3 benchmark_cat.py")
+    print("  2. Admin panel: http://localhost:1865/admin")
+    print("  3. API docs: http://localhost:1865/docs")
+
+if __name__ == "__main__":
+    main()

cheshire-cat/test_setup_simple.py

@@ -0,0 +1,161 @@
+#!/usr/bin/env python3
+"""
+Simplified Cheshire Cat Test Setup - Just upload knowledge and test
+LLM configuration should be done via admin panel: http://localhost:1865/admin
+"""
+
+import requests
+import time
+import sys
+
+CAT_URL = "http://localhost:1865"
+
+def wait_for_cat():
+    """Wait for Cat to be ready"""
+    print("Waiting for Cheshire Cat to start...")
+    max_attempts = 30
+    for i in range(max_attempts):
+        try:
+            response = requests.get(f"{CAT_URL}/", timeout=5)
+            if response.status_code == 200:
+                print("✅ Cheshire Cat is ready!")
+                return True
+        except requests.exceptions.RequestException:
+            pass
+        
+        print(f"  Attempt {i+1}/{max_attempts}...")
+        time.sleep(2)
+    
+    print("❌ Cheshire Cat failed to start")
+    return False
+
+def upload_knowledge_base():
+    """Upload Miku's knowledge files to Cat"""
+    print("\n📚 Uploading Miku knowledge base to Rabbit Hole...")
+    print("   (This will take a few minutes as Cat chunks and embeds the text)")
+    
+    files_to_upload = [
+        ("../bot/persona/miku/miku_lore.txt", "Miku's background, personality, and character info"),
+        ("../bot/persona/miku/miku_prompt.txt", "Miku's behavior guidelines and examples"),
+        ("../bot/persona/miku/miku_lyrics.txt", "Miku's song lyrics and music knowledge")
+    ]
+    
+    uploaded_count = 0
+    
+    for filepath, description in files_to_upload:
+        try:
+            filename = filepath.split('/')[-1]
+            print(f"\n  📄 Uploading {filename}...")
+            print(f"     ({description})")
+            
+            with open(filepath, 'rb') as f:
+                files = {'file': (filename, f, 'text/plain')}
+                response = requests.post(
+                    f"{CAT_URL}/rabbithole/",
+                    files=files,
+                    timeout=120  # Increased timeout for embedding
+                )
+            
+            if response.status_code == 200:
+                print(f"     ✅ Uploaded and processed successfully!")
+                uploaded_count += 1
+            else:
+                print(f"     ❌ Failed: HTTP {response.status_code}")
+                try:
+                    error_detail = response.json()
+                    print(f"        {error_detail}")
+                except:
+                    print(f"        {response.text[:200]}")
+                
+        except FileNotFoundError:
+            print(f"     ⚠️  File not found: {filepath}")
+        except requests.exceptions.Timeout:
+            print(f"     ⚠️  Upload timed out (file might be too large or embedding is slow)")
+        except Exception as e:
+            print(f"     ❌ Error: {e}")
+    
+    print(f"\n📊 Successfully uploaded: {uploaded_count}/{len(files_to_upload)} files")
+    return uploaded_count > 0
+
+def test_query():
+    """Test a simple query to verify everything works"""
+    print("\n🧪 Testing queries (after LLM is configured)...")
+    print("   Note: These will fail until you configure the LLM in admin panel")
+    
+    test_messages = [
+        "What is your favorite food?",
+        "Who are your friends?",
+    ]
+    
+    for message in test_messages:
+        print(f"\n  Query: '{message}'")
+        try:
+            response = requests.post(
+                f"{CAT_URL}/message",
+                json={"text": message},
+                headers={"Content-Type": "application/json"},
+                timeout=30
+            )
+            
+            if response.status_code == 200:
+                data = response.json()
+                reply = data.get("content", "No response")
+                print(f"  ✅ Response: {reply[:150]}...")
+            else:
+                print(f"  ⚠️  Query returned: {response.status_code}")
+                if response.status_code == 500:
+                    print(f"     (This is expected if LLM is not configured yet)")
+                
+        except Exception as e:
+            print(f"  ❌ Error: {e}")
+        
+        time.sleep(1)
+
+def main():
+    print("=" * 70)
+    print("🐱 Cheshire Cat Test Setup for Miku Bot")
+    print("=" * 70)
+    
+    # Step 1: Wait for Cat to start
+    if not wait_for_cat():
+        print("\n❌ Setup failed: Cat didn't start")
+        sys.exit(1)
+    
+    # Step 2: Upload knowledge base
+    print("\n" + "=" * 70)
+    if not upload_knowledge_base():
+        print("\n⚠️  Knowledge upload had issues")
+    
+    # Give Cat time to process
+    print("\n⏳ Waiting 5 seconds for Cat to finish processing...")
+    time.sleep(5)
+    
+    # Step 3: Manual LLM configuration instructions
+    print("\n" + "=" * 70)
+    print("⚙️  LLM CONFIGURATION REQUIRED")
+    print("=" * 70)
+    print("\nYou need to configure the LLM manually:")
+    print("\n1. Open admin panel: http://localhost:1865/admin")
+    print("\n2. Go to 'Settings' → 'Language Model'")
+    print("\n3. Select 'OpenAI Compatible'")
+    print("\n4. Configure:")
+    print("     API Key:      dummy")
+    print("     Model Name:   Llama-3.1-8B-Instruct-UD-Q4_K_XL.gguf")
+    print("     API Base URL: http://llama-swap:8080/v1")
+    print("                   (or http://host.docker.internal:8080/v1)")
+    print("\n5. Click 'Save'")
+    
+    # Step 4: Test (will likely fail until LLM is configured)
+    test_query()
+    
+    print("\n" + "=" * 70)
+    print("✅ Setup complete!")
+    print("=" * 70)
+    print("\nNext steps:")
+    print("  1. Configure LLM in admin panel (see above)")
+    print("  2. Test manually: http://localhost:1865/admin")
+    print("  3. Run benchmarks: python3 benchmark_cat.py")
+    print("\n" + "=" * 70)
+
+if __name__ == "__main__":
+    main()