Web UI tabs made into two rows

Key changes:
- CatAdapter (bot/utils/cat_client.py): WebSocket /ws/{user_id} for chat
  queries instead of HTTP POST (fixes per-user memory isolation when no
  API keys are configured — HTTP defaults all users to user_id='user')
- Memory management API: 8 endpoints for status, stats, facts, episodic
  memories, consolidation trigger, multi-step delete with confirmation
- Web UI: Memory tab (tab9) with collection stats, fact/episodic browser,
  manual consolidation trigger, and 3-step delete flow requiring exact
  confirmation string
- Bot integration: Cat-first response path with query_llama fallback for
  both text and embed responses, server mood detection
- Discord bridge plugin: fixed .pop() to .get() (UserMessage is a Pydantic
  BaseModelDict, not a raw dict), metadata extraction via extra attributes
- Unified docker-compose: Cat + Qdrant services merged into main compose,
  bot depends_on Cat healthcheck
- All plugins (discord_bridge, memory_consolidation, miku_personality)
  consolidated into cat-plugins/ for volume mount
- query_llama deprecated but functional for compatibility

bot/api.py

@@ -2772,6 +2772,134 @@ def set_voice_debug_mode(enabled: bool = Form(...)):
     }
 
 
+# ========== Cheshire Cat Memory Management (Phase 3) ==========
+
+class MemoryDeleteRequest(BaseModel):
+    confirmation: str
+
+@app.get("/memory/status")
+async def get_cat_memory_status():
+    """Get Cheshire Cat connection status and feature flag."""
+    from utils.cat_client import cat_adapter
+    is_healthy = await cat_adapter.health_check()
+    return {
+        "enabled": globals.USE_CHESHIRE_CAT,
+        "healthy": is_healthy,
+        "url": globals.CHESHIRE_CAT_URL,
+        "circuit_breaker_active": cat_adapter._is_circuit_broken(),
+        "consecutive_failures": cat_adapter._consecutive_failures
+    }
+
+@app.post("/memory/toggle")
+async def toggle_cat_integration(enabled: bool = Form(...)):
+    """Toggle Cheshire Cat integration on/off."""
+    globals.USE_CHESHIRE_CAT = enabled
+    logger.info(f"🐱 Cheshire Cat integration {'ENABLED' if enabled else 'DISABLED'}")
+    return {
+        "success": True,
+        "enabled": globals.USE_CHESHIRE_CAT,
+        "message": f"Cheshire Cat {'enabled' if enabled else 'disabled'}"
+    }
+
+@app.get("/memory/stats")
+async def get_memory_stats():
+    """Get memory collection statistics from Cheshire Cat (point counts per collection)."""
+    from utils.cat_client import cat_adapter
+    stats = await cat_adapter.get_memory_stats()
+    if stats is None:
+        return {"success": False, "error": "Could not reach Cheshire Cat"}
+    return {"success": True, "collections": stats.get("collections", [])}
+
+@app.get("/memory/facts")
+async def get_memory_facts():
+    """Get all declarative memory facts (learned knowledge about users)."""
+    from utils.cat_client import cat_adapter
+    facts = await cat_adapter.get_all_facts()
+    return {"success": True, "facts": facts, "count": len(facts)}
+
+@app.get("/memory/episodic")
+async def get_episodic_memories():
+    """Get all episodic memories (conversation snippets)."""
+    from utils.cat_client import cat_adapter
+    result = await cat_adapter.get_memory_points(collection="episodic", limit=100)
+    if result is None:
+        return {"success": False, "error": "Could not reach Cheshire Cat"}
+
+    memories = []
+    for point in result.get("points", []):
+        payload = point.get("payload", {})
+        memories.append({
+            "id": point.get("id"),
+            "content": payload.get("page_content", ""),
+            "metadata": payload.get("metadata", {}),
+        })
+
+    return {"success": True, "memories": memories, "count": len(memories)}
+
+@app.post("/memory/consolidate")
+async def trigger_memory_consolidation():
+    """Manually trigger memory consolidation (sleep consolidation process)."""
+    from utils.cat_client import cat_adapter
+    logger.info("🌙 Manual memory consolidation triggered via API")
+    result = await cat_adapter.trigger_consolidation()
+    if result is None:
+        return {"success": False, "error": "Consolidation failed or timed out"}
+    return {"success": True, "result": result}
+
+@app.post("/memory/delete")
+async def delete_all_memories(request: MemoryDeleteRequest):
+    """
+    Delete ALL of Miku's memories. Requires exact confirmation string.
+    
+    The confirmation field must be exactly:
+        "Yes, I am deleting Miku's memories fully."
+    
+    This is destructive and irreversible.
+    """
+    REQUIRED_CONFIRMATION = "Yes, I am deleting Miku's memories fully."
+
+    if request.confirmation != REQUIRED_CONFIRMATION:
+        logger.warning(f"Memory deletion rejected: wrong confirmation string")
+        return {
+            "success": False,
+            "error": "Confirmation string does not match. "
+                     f"Expected exactly: \"{REQUIRED_CONFIRMATION}\""
+        }
+
+    from utils.cat_client import cat_adapter
+    logger.warning("⚠️ MEMORY DELETION CONFIRMED — wiping all memories!")
+
+    # Wipe vector memories (episodic + declarative)
+    wipe_success = await cat_adapter.wipe_all_memories()
+
+    # Also clear conversation history
+    history_success = await cat_adapter.wipe_conversation_history()
+
+    if wipe_success:
+        logger.warning("🗑️ All Miku memories have been deleted.")
+        return {
+            "success": True,
+            "message": "All memories have been permanently deleted.",
+            "vector_memory_wiped": wipe_success,
+            "conversation_history_cleared": history_success
+        }
+    else:
+        return {
+            "success": False,
+            "error": "Failed to wipe memory collections. Check Cat connection."
+        }
+
+@app.delete("/memory/point/{collection}/{point_id}")
+async def delete_single_memory_point(collection: str, point_id: str):
+    """Delete a single memory point by collection and ID."""
+    from utils.cat_client import cat_adapter
+    success = await cat_adapter.delete_memory_point(collection, point_id)
+    if success:
+        return {"success": True, "deleted": point_id}
+    else:
+        return {"success": False, "error": f"Failed to delete point {point_id}"}
+
+
 def start_api():
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=3939)

bot/bot.py

@@ -513,6 +513,26 @@ async def on_message(message):
                             response_type = "dm_response" if is_dm else "server_response"
                             author_name = message.author.display_name
 
+                            # Phase 3: Try Cat pipeline first for embed responses too
+                            response = None
+                            if globals.USE_CHESHIRE_CAT:
+                                try:
+                                    from utils.cat_client import cat_adapter
+                                    response = await cat_adapter.query(
+                                        text=enhanced_prompt,
+                                        user_id=str(message.author.id),
+                                        guild_id=str(guild_id) if guild_id else None,
+                                        author_name=author_name,
+                                        mood=globals.DM_MOOD,
+                                        response_type=response_type,
+                                    )
+                                    if response:
+                                        logger.info(f"🐱 Cat embed response for {author_name}")
+                                except Exception as e:
+                                    logger.warning(f"🐱 Cat embed error, fallback: {e}")
+                                    response = None
+
+                            if not response:
                                 response = await query_llama(
                                     enhanced_prompt,
                                     user_id=str(message.author.id),
@@ -570,6 +590,39 @@ async def on_message(message):
             guild_id = message.guild.id if message.guild else None
             response_type = "dm_response" if is_dm else "server_response"
             author_name = message.author.display_name
+
+            # Phase 3: Try Cheshire Cat pipeline first (memory-augmented response)
+            # Falls back to query_llama if Cat is unavailable or disabled
+            response = None
+            if globals.USE_CHESHIRE_CAT:
+                try:
+                    from utils.cat_client import cat_adapter
+                    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
+
+                    response = await cat_adapter.query(
+                        text=prompt,
+                        user_id=str(message.author.id),
+                        guild_id=str(guild_id) if guild_id else None,
+                        author_name=author_name,
+                        mood=current_mood,
+                        response_type=response_type,
+                    )
+                    if response:
+                        logger.info(f"🐱 Cat response for {author_name} (mood: {current_mood})")
+                except Exception as e:
+                    logger.warning(f"🐱 Cat 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(
                     prompt, 
                     user_id=str(message.author.id), 

bot/globals.py

@@ -29,6 +29,12 @@ EVIL_TEXT_MODEL = os.getenv("EVIL_TEXT_MODEL", "darkidol")  # Uncensored model f
 JAPANESE_TEXT_MODEL = os.getenv("JAPANESE_TEXT_MODEL", "swallow")  # Llama 3.1 Swallow model for Japanese
 OWNER_USER_ID = int(os.getenv("OWNER_USER_ID", "209381657369772032"))  # Bot owner's Discord user ID for reports
 
+# Cheshire Cat AI integration (Phase 3)
+CHESHIRE_CAT_URL = os.getenv("CHESHIRE_CAT_URL", "http://cheshire-cat:80")
+USE_CHESHIRE_CAT = os.getenv("USE_CHESHIRE_CAT", "false").lower() == "true"
+CHESHIRE_CAT_API_KEY = os.getenv("CHESHIRE_CAT_API_KEY", "")  # Empty = no auth
+CHESHIRE_CAT_TIMEOUT = int(os.getenv("CHESHIRE_CAT_TIMEOUT", "120"))  # Seconds
+
 # Language mode for Miku (english or japanese)
 LANGUAGE_MODE = "english"  # Can be "english" or "japanese"
 

bot/static/index.html

@@ -416,14 +416,19 @@
     }
     
     .tab-buttons {
-      display: flex;
+      display: grid;
+      grid-template-rows: repeat(2, auto);
+      grid-auto-flow: column;
+      grid-auto-columns: max-content;
       border-bottom: 2px solid #333;
       margin-bottom: 1rem;
       overflow-x: auto;
       overflow-y: hidden;
-      white-space: nowrap;
       scrollbar-width: thin;
       scrollbar-color: #555 #222;
+      row-gap: 0.05rem;
+      column-gap: 0.1rem;
+      padding-bottom: 0.1rem;
     }
     
     .tab-buttons::-webkit-scrollbar {
@@ -447,12 +452,10 @@
       background: #222;
       color: #ccc;
       border: none;
-      padding: 0.8rem 1.5rem;
+      padding: 0.5rem 1rem;
       cursor: pointer;
       border-bottom: 3px solid transparent;
-      margin-right: 0.5rem;
       transition: all 0.3s ease;
-      flex-shrink: 0;
       white-space: nowrap;
     }
     
@@ -665,6 +668,7 @@
       <button class="tab-button" onclick="switchTab('tab6')">📊 Autonomous Stats</button>
       <button class="tab-button" onclick="switchTab('tab7')">💬 Chat with LLM</button>
       <button class="tab-button" onclick="switchTab('tab8')">📞 Voice Call</button>
+      <button class="tab-button" onclick="switchTab('tab9')">🧠 Memories</button>
       <button class="tab-button" onclick="window.location.href='/static/system.html'">🎛️ System Settings</button>
     </div>
 
@@ -1547,6 +1551,142 @@
   </div>
 </div>
 
+    <!-- Tab 9: Memory Management -->
+    <div id="tab9" class="tab-content">
+      <div class="section">
+        <h3>🧠 Cheshire Cat Memory Management</h3>
+        <p style="color: #aaa; margin-bottom: 1rem;">
+          Manage Miku's long-term memories powered by the Cheshire Cat AI pipeline.
+          Memories are stored in Qdrant vector database and used to give Miku persistent knowledge about users.
+        </p>
+
+        <!-- Cat Integration Status -->
+        <div id="cat-status-section" style="background: #1a1a2e; border: 1px solid #444; border-radius: 8px; padding: 1rem; margin-bottom: 1.5rem;">
+          <div style="display: flex; justify-content: space-between; align-items: center;">
+            <div>
+              <h4 style="margin: 0 0 0.3rem 0;">🐱 Cheshire Cat Status</h4>
+              <span id="cat-status-indicator" style="color: #888;">Checking...</span>
+            </div>
+            <div style="display: flex; gap: 0.5rem; align-items: center;">
+              <button id="cat-toggle-btn" onclick="toggleCatIntegration()" style="background: #333; color: #fff; padding: 0.4rem 0.8rem; border: 2px solid #666; border-radius: 4px; cursor: pointer; font-weight: bold; font-size: 0.85rem;">
+                Loading...
+              </button>
+              <button onclick="refreshMemoryStats()" style="background: #2a5599; color: #fff; padding: 0.4rem 0.8rem; border: none; border-radius: 4px; cursor: pointer;">
+                🔄 Refresh
+              </button>
+            </div>
+          </div>
+        </div>
+
+        <!-- Memory Statistics -->
+        <div style="display: grid; grid-template-columns: repeat(3, 1fr); gap: 1rem; margin-bottom: 1.5rem;">
+          <div id="stat-episodic" style="background: #1a2332; border: 1px solid #2a5599; border-radius: 8px; padding: 1rem; text-align: center;">
+            <div style="font-size: 2rem; font-weight: bold; color: #61dafb;" id="stat-episodic-count">—</div>
+            <div style="color: #aaa; font-size: 0.85rem;">📝 Episodic Memories</div>
+            <div style="color: #666; font-size: 0.75rem; margin-top: 0.3rem;">Conversation snippets</div>
+          </div>
+          <div id="stat-declarative" style="background: #1a3322; border: 1px solid #2a9955; border-radius: 8px; padding: 1rem; text-align: center;">
+            <div style="font-size: 2rem; font-weight: bold; color: #6fdc6f;" id="stat-declarative-count">—</div>
+            <div style="color: #aaa; font-size: 0.85rem;">📚 Declarative Facts</div>
+            <div style="color: #666; font-size: 0.75rem; margin-top: 0.3rem;">Learned knowledge</div>
+          </div>
+          <div id="stat-procedural" style="background: #332a1a; border: 1px solid #995e2a; border-radius: 8px; padding: 1rem; text-align: center;">
+            <div style="font-size: 2rem; font-weight: bold; color: #dcb06f;" id="stat-procedural-count">—</div>
+            <div style="color: #aaa; font-size: 0.85rem;">⚙️ Procedural</div>
+            <div style="color: #666; font-size: 0.75rem; margin-top: 0.3rem;">Tools & procedures</div>
+          </div>
+        </div>
+
+        <!-- Consolidation -->
+        <div style="background: #1a1a2e; border: 1px solid #444; border-radius: 8px; padding: 1rem; margin-bottom: 1.5rem;">
+          <h4 style="margin: 0 0 0.5rem 0;">🌙 Memory Consolidation</h4>
+          <p style="color: #aaa; font-size: 0.85rem; margin-bottom: 0.75rem;">
+            Trigger the sleep consolidation process: analyzes episodic memories, extracts important facts, and removes trivial entries.
+          </p>
+          <div style="display: flex; gap: 0.5rem; align-items: center;">
+            <button id="consolidate-btn" onclick="triggerConsolidation()" style="background: #5b3a8c; color: #fff; padding: 0.5rem 1rem; border: none; border-radius: 4px; cursor: pointer; font-weight: bold;">
+              🌙 Run Consolidation
+            </button>
+            <span id="consolidation-status" style="color: #888; font-size: 0.85rem;"></span>
+          </div>
+          <div id="consolidation-result" style="display: none; margin-top: 0.75rem; background: #111; border: 1px solid #333; border-radius: 4px; padding: 0.75rem; font-size: 0.85rem; color: #ccc; white-space: pre-wrap; max-height: 200px; overflow-y: auto;"></div>
+        </div>
+
+        <!-- Declarative Facts Browser -->
+        <div style="background: #1a1a2e; border: 1px solid #444; border-radius: 8px; padding: 1rem; margin-bottom: 1.5rem;">
+          <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 0.75rem;">
+            <h4 style="margin: 0;">📚 Declarative Facts</h4>
+            <button onclick="loadFacts()" style="background: #2a5599; color: #fff; padding: 0.3rem 0.7rem; border: none; border-radius: 4px; cursor: pointer; font-size: 0.85rem;">
+              🔄 Load Facts
+            </button>
+          </div>
+          <div id="facts-list" style="max-height: 400px; overflow-y: auto;">
+            <div style="text-align: center; color: #666; padding: 2rem;">Click "Load Facts" to view stored knowledge</div>
+          </div>
+        </div>
+
+        <!-- Episodic Memories Browser -->
+        <div style="background: #1a1a2e; border: 1px solid #444; border-radius: 8px; padding: 1rem; margin-bottom: 1.5rem;">
+          <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 0.75rem;">
+            <h4 style="margin: 0;">📝 Episodic Memories</h4>
+            <button onclick="loadEpisodicMemories()" style="background: #2a5599; color: #fff; padding: 0.3rem 0.7rem; border: none; border-radius: 4px; cursor: pointer; font-size: 0.85rem;">
+              🔄 Load Memories
+            </button>
+          </div>
+          <div id="episodic-list" style="max-height: 400px; overflow-y: auto;">
+            <div style="text-align: center; color: #666; padding: 2rem;">Click "Load Memories" to view conversation snippets</div>
+          </div>
+        </div>
+
+        <!-- DANGER ZONE: Delete All Memories -->
+        <div style="background: #2e1a1a; border: 2px solid #993333; border-radius: 8px; padding: 1rem;">
+          <h4 style="margin: 0 0 0.5rem 0; color: #ff6b6b;">⚠️ Danger Zone — Delete All Memories</h4>
+          <p style="color: #cc9999; font-size: 0.85rem; margin-bottom: 1rem;">
+            This will permanently erase ALL of Miku's memories — episodic conversations, learned facts, everything.
+            This action is <strong>irreversible</strong>. Miku will forget everything she has ever learned.
+          </p>
+
+          <!-- Step 1: Initial checkbox -->
+          <div id="delete-step-1" style="margin-bottom: 0.75rem;">
+            <label style="cursor: pointer; color: #ff9999;">
+              <input type="checkbox" id="delete-checkbox-1" onchange="onDeleteStep1Change()">
+              I understand this will permanently delete all of Miku's memories
+            </label>
+          </div>
+
+          <!-- Step 2: Second confirmation (hidden initially) -->
+          <div id="delete-step-2" style="display: none; margin-bottom: 0.75rem;">
+            <label style="cursor: pointer; color: #ff9999;">
+              <input type="checkbox" id="delete-checkbox-2" onchange="onDeleteStep2Change()">
+              I confirm this is irreversible and I want to proceed
+            </label>
+          </div>
+
+          <!-- Step 3: Type confirmation string (hidden initially) -->
+          <div id="delete-step-3" style="display: none; margin-bottom: 0.75rem;">
+            <p style="color: #ff6b6b; font-size: 0.85rem; margin-bottom: 0.5rem;">
+              Type exactly: <code style="background: #333; padding: 0.2rem 0.4rem; border-radius: 3px; color: #ff9999;">Yes, I am deleting Miku's memories fully.</code>
+            </p>
+            <input type="text" id="delete-confirmation-input" placeholder="Type the confirmation string..."
+              style="width: 100%; padding: 0.5rem; background: #1a1a1a; color: #ff9999; border: 1px solid #993333; border-radius: 4px; font-family: monospace; box-sizing: border-box;"
+              oninput="onDeleteInputChange()">
+          </div>
+
+          <!-- Final delete button (hidden initially) -->
+          <div id="delete-step-final" style="display: none;">
+            <button id="delete-all-btn" onclick="executeDeleteAllMemories()" disabled
+              style="background: #cc3333; color: #fff; padding: 0.5rem 1.5rem; border: none; border-radius: 4px; cursor: not-allowed; font-weight: bold; opacity: 0.5;">
+              🗑️ Permanently Delete All Memories
+            </button>
+            <button onclick="resetDeleteFlow()" style="background: #444; color: #ccc; padding: 0.5rem 1rem; border: none; border-radius: 4px; cursor: pointer; margin-left: 0.5rem;">
+              Cancel
+            </button>
+          </div>
+        </div>
+
+      </div>
+    </div>
+
 <div class="logs">
   <h3>Logs</h3>
   <div id="logs-content"></div>
@@ -1611,6 +1751,10 @@ function switchTab(tabId) {
     console.log('🔄 Refreshing figurine subscribers for Server Management tab');
     refreshFigurineSubscribers();
   }
+  if (tabId === 'tab9') {
+    console.log('🧠 Refreshing memory stats for Memories tab');
+    refreshMemoryStats();
+  }
 }
 
 // Initialize
@@ -5020,6 +5164,292 @@ function updateVoiceCallHistoryDisplay() {
   historyDiv.innerHTML = html;
 }
 
+// ========== Memory Management (Tab 9) ==========
+
+async function refreshMemoryStats() {
+  try {
+    // Fetch Cat status
+    const statusRes = await fetch('/memory/status');
+    const statusData = await statusRes.json();
+
+    const indicator = document.getElementById('cat-status-indicator');
+    const toggleBtn = document.getElementById('cat-toggle-btn');
+
+    if (statusData.healthy) {
+      indicator.innerHTML = `<span style="color: #6fdc6f;">● Connected</span> — ${statusData.url}`;
+    } else {
+      indicator.innerHTML = `<span style="color: #ff6b6b;">● Disconnected</span> — ${statusData.url}`;
+    }
+
+    if (statusData.circuit_breaker_active) {
+      indicator.innerHTML += ` <span style="color: #dcb06f;">(circuit breaker active)</span>`;
+    }
+
+    toggleBtn.textContent = statusData.enabled ? '🐱 Cat: ON' : '😿 Cat: OFF';
+    toggleBtn.style.background = statusData.enabled ? '#2a7a2a' : '#7a2a2a';
+    toggleBtn.style.borderColor = statusData.enabled ? '#4a9a4a' : '#9a4a4a';
+
+    // Fetch memory stats
+    const statsRes = await fetch('/memory/stats');
+    const statsData = await statsRes.json();
+
+    if (statsData.success && statsData.collections) {
+      const collections = {};
+      statsData.collections.forEach(c => { collections[c.name] = c.vectors_count; });
+
+      document.getElementById('stat-episodic-count').textContent = collections['episodic'] ?? '—';
+      document.getElementById('stat-declarative-count').textContent = collections['declarative'] ?? '—';
+      document.getElementById('stat-procedural-count').textContent = collections['procedural'] ?? '—';
+    } else {
+      document.getElementById('stat-episodic-count').textContent = '—';
+      document.getElementById('stat-declarative-count').textContent = '—';
+      document.getElementById('stat-procedural-count').textContent = '—';
+    }
+  } catch (err) {
+    console.error('Error refreshing memory stats:', err);
+    document.getElementById('cat-status-indicator').innerHTML = '<span style="color: #ff6b6b;">● Error checking status</span>';
+  }
+}
+
+async function toggleCatIntegration() {
+  try {
+    const statusRes = await fetch('/memory/status');
+    const statusData = await statusRes.json();
+    const newState = !statusData.enabled;
+
+    const formData = new FormData();
+    formData.append('enabled', newState);
+    const res = await fetch('/memory/toggle', { method: 'POST', body: formData });
+    const data = await res.json();
+
+    if (data.success) {
+      showNotification(`Cheshire Cat ${newState ? 'enabled' : 'disabled'}`, newState ? 'success' : 'info');
+      refreshMemoryStats();
+    }
+  } catch (err) {
+    showNotification('Failed to toggle Cat integration', 'error');
+  }
+}
+
+async function triggerConsolidation() {
+  const btn = document.getElementById('consolidate-btn');
+  const status = document.getElementById('consolidation-status');
+  const resultDiv = document.getElementById('consolidation-result');
+
+  btn.disabled = true;
+  btn.textContent = '⏳ Running...';
+  status.textContent = 'Consolidation in progress (this may take a few minutes)...';
+  resultDiv.style.display = 'none';
+
+  try {
+    const res = await fetch('/memory/consolidate', { method: 'POST' });
+    const data = await res.json();
+
+    if (data.success) {
+      status.textContent = '✅ Consolidation complete!';
+      status.style.color = '#6fdc6f';
+      resultDiv.textContent = data.result || 'Consolidation finished successfully.';
+      resultDiv.style.display = 'block';
+      showNotification('Memory consolidation complete', 'success');
+      refreshMemoryStats();
+    } else {
+      status.textContent = '❌ ' + (data.error || 'Consolidation failed');
+      status.style.color = '#ff6b6b';
+    }
+  } catch (err) {
+    status.textContent = '❌ Error: ' + err.message;
+    status.style.color = '#ff6b6b';
+  } finally {
+    btn.disabled = false;
+    btn.textContent = '🌙 Run Consolidation';
+  }
+}
+
+async function loadFacts() {
+  const listDiv = document.getElementById('facts-list');
+  listDiv.innerHTML = '<div style="text-align: center; color: #888; padding: 1rem;">Loading facts...</div>';
+
+  try {
+    const res = await fetch('/memory/facts');
+    const data = await res.json();
+
+    if (!data.success || data.count === 0) {
+      listDiv.innerHTML = '<div style="text-align: center; color: #666; padding: 2rem;">No declarative facts stored yet.</div>';
+      return;
+    }
+
+    let html = '';
+    data.facts.forEach((fact, i) => {
+      const source = fact.metadata?.source || 'unknown';
+      const when = fact.metadata?.when ? new Date(fact.metadata.when * 1000).toLocaleString() : 'unknown';
+      html += `
+        <div style="background: #242424; padding: 0.6rem 0.8rem; margin-bottom: 0.4rem; border-radius: 4px; border-left: 3px solid #2a9955; display: flex; justify-content: space-between; align-items: flex-start;">
+          <div style="flex: 1;">
+            <div style="color: #ddd; font-size: 0.9rem;">${escapeHtml(fact.content)}</div>
+            <div style="color: #666; font-size: 0.75rem; margin-top: 0.3rem;">
+              Source: ${escapeHtml(source)} · ${when}
+            </div>
+          </div>
+          <button onclick="deleteMemoryPoint('declarative', '${fact.id}', this)" 
+            style="background: none; border: none; color: #993333; cursor: pointer; padding: 0.2rem 0.4rem; font-size: 0.85rem; flex-shrink: 0;" 
+            title="Delete this fact">🗑️</button>
+        </div>`;
+    });
+
+    listDiv.innerHTML = `<div style="color: #888; font-size: 0.8rem; margin-bottom: 0.5rem;">${data.count} facts loaded</div>` + html;
+  } catch (err) {
+    listDiv.innerHTML = `<div style="color: #ff6b6b; padding: 1rem;">Error loading facts: ${err.message}</div>`;
+  }
+}
+
+async function loadEpisodicMemories() {
+  const listDiv = document.getElementById('episodic-list');
+  listDiv.innerHTML = '<div style="text-align: center; color: #888; padding: 1rem;">Loading memories...</div>';
+
+  try {
+    const res = await fetch('/memory/episodic');
+    const data = await res.json();
+
+    if (!data.success || data.count === 0) {
+      listDiv.innerHTML = '<div style="text-align: center; color: #666; padding: 2rem;">No episodic memories stored yet.</div>';
+      return;
+    }
+
+    let html = '';
+    data.memories.forEach((mem, i) => {
+      const source = mem.metadata?.source || 'unknown';
+      const when = mem.metadata?.when ? new Date(mem.metadata.when * 1000).toLocaleString() : 'unknown';
+      html += `
+        <div style="background: #242424; padding: 0.6rem 0.8rem; margin-bottom: 0.4rem; border-radius: 4px; border-left: 3px solid #2a5599; display: flex; justify-content: space-between; align-items: flex-start;">
+          <div style="flex: 1;">
+            <div style="color: #ddd; font-size: 0.9rem;">${escapeHtml(mem.content)}</div>
+            <div style="color: #666; font-size: 0.75rem; margin-top: 0.3rem;">
+              Source: ${escapeHtml(source)} · ${when}
+            </div>
+          </div>
+          <button onclick="deleteMemoryPoint('episodic', '${mem.id}', this)" 
+            style="background: none; border: none; color: #993333; cursor: pointer; padding: 0.2rem 0.4rem; font-size: 0.85rem; flex-shrink: 0;" 
+            title="Delete this memory">🗑️</button>
+        </div>`;
+    });
+
+    listDiv.innerHTML = `<div style="color: #888; font-size: 0.8rem; margin-bottom: 0.5rem;">${data.count} memories loaded</div>` + html;
+  } catch (err) {
+    listDiv.innerHTML = `<div style="color: #ff6b6b; padding: 1rem;">Error loading memories: ${err.message}</div>`;
+  }
+}
+
+async function deleteMemoryPoint(collection, pointId, btnElement) {
+  if (!confirm(`Delete this ${collection} memory point?`)) return;
+
+  try {
+    const res = await fetch(`/memory/point/${collection}/${pointId}`, { method: 'DELETE' });
+    const data = await res.json();
+
+    if (data.success) {
+      // Remove the row from the UI
+      const row = btnElement.closest('div[style*="margin-bottom"]');
+      if (row) row.remove();
+      showNotification('Memory point deleted', 'success');
+      refreshMemoryStats();
+    } else {
+      showNotification('Failed to delete: ' + (data.error || 'Unknown error'), 'error');
+    }
+  } catch (err) {
+    showNotification('Error: ' + err.message, 'error');
+  }
+}
+
+// Delete All Memories — Multi-step confirmation flow
+function onDeleteStep1Change() {
+  const checked = document.getElementById('delete-checkbox-1').checked;
+  document.getElementById('delete-step-2').style.display = checked ? 'block' : 'none';
+  if (!checked) {
+    document.getElementById('delete-checkbox-2').checked = false;
+    document.getElementById('delete-step-3').style.display = 'none';
+    document.getElementById('delete-step-final').style.display = 'none';
+    document.getElementById('delete-confirmation-input').value = '';
+  }
+}
+
+function onDeleteStep2Change() {
+  const checked = document.getElementById('delete-checkbox-2').checked;
+  document.getElementById('delete-step-3').style.display = checked ? 'block' : 'none';
+  document.getElementById('delete-step-final').style.display = checked ? 'block' : 'none';
+  if (!checked) {
+    document.getElementById('delete-confirmation-input').value = '';
+    updateDeleteButton();
+  }
+}
+
+function onDeleteInputChange() {
+  updateDeleteButton();
+}
+
+function updateDeleteButton() {
+  const input = document.getElementById('delete-confirmation-input').value;
+  const expected = "Yes, I am deleting Miku's memories fully.";
+  const btn = document.getElementById('delete-all-btn');
+  const match = input === expected;
+
+  btn.disabled = !match;
+  btn.style.cursor = match ? 'pointer' : 'not-allowed';
+  btn.style.opacity = match ? '1' : '0.5';
+}
+
+async function executeDeleteAllMemories() {
+  const input = document.getElementById('delete-confirmation-input').value;
+  const expected = "Yes, I am deleting Miku's memories fully.";
+
+  if (input !== expected) {
+    showNotification('Confirmation string does not match', 'error');
+    return;
+  }
+
+  const btn = document.getElementById('delete-all-btn');
+  btn.disabled = true;
+  btn.textContent = '⏳ Deleting...';
+
+  try {
+    const res = await fetch('/memory/delete', {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({ confirmation: input })
+    });
+    const data = await res.json();
+
+    if (data.success) {
+      showNotification('All memories have been permanently deleted', 'success');
+      resetDeleteFlow();
+      refreshMemoryStats();
+    } else {
+      showNotification('Deletion failed: ' + (data.error || 'Unknown error'), 'error');
+    }
+  } catch (err) {
+    showNotification('Error: ' + err.message, 'error');
+  } finally {
+    btn.disabled = false;
+    btn.textContent = '🗑️ Permanently Delete All Memories';
+  }
+}
+
+function resetDeleteFlow() {
+  document.getElementById('delete-checkbox-1').checked = false;
+  document.getElementById('delete-checkbox-2').checked = false;
+  document.getElementById('delete-confirmation-input').value = '';
+  document.getElementById('delete-step-2').style.display = 'none';
+  document.getElementById('delete-step-3').style.display = 'none';
+  document.getElementById('delete-step-final').style.display = 'none';
+  updateDeleteButton();
+}
+
+function escapeHtml(str) {
+  if (!str) return '';
+  const div = document.createElement('div');
+  div.textContent = str;
+  return div.innerHTML;
+}
+
 </script>
 
 </body>

bot/utils/cat_client.py

@@ -0,0 +1,479 @@
+# utils/cat_client.py
+"""
+Cheshire Cat AI Adapter for Miku Discord Bot (Phase 3)
+
+Routes messages through the Cheshire Cat pipeline for:
+- Memory-augmented responses (episodic + declarative recall)
+- Fact extraction and consolidation
+- Per-user conversation isolation
+
+Uses WebSocket for chat (per-user isolation via /ws/{user_id}).
+Uses HTTP for memory management endpoints.
+Falls back to query_llama() on failure for zero-downtime resilience.
+"""
+
+import aiohttp
+import asyncio
+import json
+import time
+from typing import Optional, Dict, Any, List
+
+import globals
+from utils.logger import get_logger
+
+logger = get_logger('cat_client')
+
+
+class CatAdapter:
+    """
+    Async adapter for Cheshire Cat AI.
+    
+    Uses WebSocket /ws/{user_id} for conversation (per-user memory isolation).
+    Uses HTTP REST for memory management endpoints.
+    Without API keys configured, HTTP POST /message defaults all users to
+    user_id="user" (no isolation). WebSocket path param gives true isolation.
+    """
+
+    def __init__(self):
+        self._base_url = globals.CHESHIRE_CAT_URL.rstrip('/')
+        self._api_key = globals.CHESHIRE_CAT_API_KEY
+        self._timeout = globals.CHESHIRE_CAT_TIMEOUT
+        self._healthy = None  # None = unknown, True/False = last check result
+        self._last_health_check = 0
+        self._health_check_interval = 30  # seconds between health checks
+        self._consecutive_failures = 0
+        self._max_failures_before_circuit_break = 3
+        self._circuit_broken_until = 0  # timestamp when circuit breaker resets
+        logger.info(f"CatAdapter initialized: {self._base_url} (timeout={self._timeout}s)")
+
+    def _get_headers(self) -> dict:
+        """Build request headers with optional auth."""
+        headers = {'Content-Type': 'application/json'}
+        if self._api_key:
+            headers['Authorization'] = f'Bearer {self._api_key}'
+        return headers
+
+    def _user_id_for_discord(self, user_id: str) -> str:
+        """
+        Format Discord user ID for Cat's user namespace.
+        Cat uses user_id to isolate working memory and episodic memories.
+        """
+        return f"discord_{user_id}"
+
+    async def health_check(self) -> bool:
+        """
+        Check if Cheshire Cat is reachable and healthy.
+        Caches result to avoid hammering the endpoint.
+        """
+        now = time.time()
+        if now - self._last_health_check < self._health_check_interval and self._healthy is not None:
+            return self._healthy
+
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(
+                    f"{self._base_url}/",
+                    headers=self._get_headers(),
+                    timeout=aiohttp.ClientTimeout(total=10)
+                ) as response:
+                    self._healthy = response.status == 200
+                    self._last_health_check = now
+                    if self._healthy:
+                        logger.debug("Cat health check: OK")
+                    else:
+                        logger.warning(f"Cat health check failed: status {response.status}")
+                    return self._healthy
+        except Exception as e:
+            self._healthy = False
+            self._last_health_check = now
+            logger.warning(f"Cat health check error: {e}")
+            return False
+
+    def _is_circuit_broken(self) -> bool:
+        """Check if circuit breaker is active (too many consecutive failures)."""
+        if self._consecutive_failures >= self._max_failures_before_circuit_break:
+            if time.time() < self._circuit_broken_until:
+                return True
+            # Circuit breaker expired, allow retry
+            logger.info("Circuit breaker reset, allowing Cat retry")
+            self._consecutive_failures = 0
+        return False
+
+    async def query(
+        self,
+        text: str,
+        user_id: str,
+        guild_id: Optional[str] = None,
+        author_name: Optional[str] = None,
+        mood: Optional[str] = None,
+        response_type: str = "dm_response",
+    ) -> Optional[str]:
+        """
+        Send a message through the Cat pipeline via WebSocket and get a response.
+        
+        Uses WebSocket /ws/{user_id} for per-user memory isolation.
+        Without API keys, HTTP POST /message defaults all users to user_id="user"
+        (no isolation). The WebSocket path parameter provides true per-user isolation
+        because Cat's auth handler uses user_id from the path when no keys are set.
+        
+        Args:
+            text: User's message text
+            user_id: Discord user ID (will be namespaced as discord_{user_id})
+            guild_id: Optional guild ID for server context
+            author_name: Display name of the user
+            mood: Current mood name (passed as metadata for Cat hooks)
+            response_type: Type of response context
+            
+        Returns:
+            Cat's response text, or None if Cat is unavailable (caller should fallback)
+        """
+        if not globals.USE_CHESHIRE_CAT:
+            return None
+
+        if self._is_circuit_broken():
+            logger.debug("Circuit breaker active, skipping Cat")
+            return None
+
+        cat_user_id = self._user_id_for_discord(user_id)
+
+        # Build message payload with Discord metadata for our plugin hooks.
+        # The discord_bridge plugin's before_cat_reads_message hook reads
+        # these custom keys from the message dict.
+        payload = {
+            "text": text,
+        }
+        if guild_id:
+            payload["discord_guild_id"] = str(guild_id)
+        if author_name:
+            payload["discord_author_name"] = author_name
+        if mood:
+            payload["discord_mood"] = mood
+        if response_type:
+            payload["discord_response_type"] = response_type
+
+        try:
+            # Build WebSocket URL from HTTP base URL
+            ws_base = self._base_url.replace("http://", "ws://").replace("https://", "wss://")
+            ws_url = f"{ws_base}/ws/{cat_user_id}"
+
+            logger.debug(f"Querying Cat via WS: user={cat_user_id}, text={text[:80]}...")
+
+            async with aiohttp.ClientSession() as session:
+                async with session.ws_connect(
+                    ws_url,
+                    timeout=self._timeout,
+                ) as ws:
+                    # Send the message
+                    await ws.send_json(payload)
+
+                    # Read responses until we get the final "chat" type message.
+                    # Cat may send intermediate messages (chat_token for streaming,
+                    # notification for status updates). We want the final "chat" one.
+                    reply_text = None
+                    deadline = asyncio.get_event_loop().time() + self._timeout
+
+                    while True:
+                        remaining = deadline - asyncio.get_event_loop().time()
+                        if remaining <= 0:
+                            logger.error(f"Cat WS timeout after {self._timeout}s")
+                            break
+
+                        try:
+                            ws_msg = await asyncio.wait_for(
+                                ws.receive(),
+                                timeout=remaining
+                            )
+                        except asyncio.TimeoutError:
+                            logger.error(f"Cat WS receive timeout after {self._timeout}s")
+                            break
+
+                        # Handle WebSocket close/error frames
+                        if ws_msg.type in (aiohttp.WSMsgType.CLOSE, aiohttp.WSMsgType.CLOSING, aiohttp.WSMsgType.CLOSED):
+                            logger.warning("Cat WS connection closed by server")
+                            break
+                        if ws_msg.type == aiohttp.WSMsgType.ERROR:
+                            logger.error(f"Cat WS error frame: {ws.exception()}")
+                            break
+                        if ws_msg.type != aiohttp.WSMsgType.TEXT:
+                            logger.debug(f"Cat WS non-text frame type: {ws_msg.type}")
+                            continue
+
+                        try:
+                            msg = json.loads(ws_msg.data)
+                        except (json.JSONDecodeError, TypeError) as e:
+                            logger.warning(f"Cat WS non-JSON message: {e}")
+                            continue
+
+                        msg_type = msg.get("type", "")
+
+                        if msg_type == "chat":
+                            # Final response — extract text
+                            reply_text = msg.get("content") or msg.get("text", "")
+                            break
+                        elif msg_type == "chat_token":
+                            # Streaming token — skip, we wait for final
+                            continue
+                        elif msg_type == "error":
+                            error_desc = msg.get("description", "Unknown Cat error")
+                            logger.error(f"Cat WS error: {error_desc}")
+                            break
+                        elif msg_type == "notification":
+                            logger.debug(f"Cat notification: {msg.get('content', '')}")
+                            continue
+                        else:
+                            logger.debug(f"Cat WS unknown msg type: {msg_type}")
+                            continue
+
+            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
+            else:
+                logger.warning("Cat returned empty response via WS")
+                self._consecutive_failures += 1
+                return None
+
+        except asyncio.TimeoutError:
+            logger.error(f"Cat WS connection timeout after {self._timeout}s")
+            self._consecutive_failures += 1
+            if self._consecutive_failures >= self._max_failures_before_circuit_break:
+                self._circuit_broken_until = time.time() + 60
+                logger.warning("Circuit breaker activated (WS timeout)")
+            return None
+        except Exception as e:
+            logger.error(f"Cat WS query error: {e}")
+            self._consecutive_failures += 1
+            if self._consecutive_failures >= self._max_failures_before_circuit_break:
+                self._circuit_broken_until = time.time() + 60
+                logger.warning(f"Circuit breaker activated: {e}")
+            return None
+
+    # ===================================================================
+    # MEMORY MANAGEMENT API (for Web UI)
+    # ===================================================================
+
+    async def get_memory_stats(self) -> Optional[Dict[str, Any]]:
+        """
+        Get memory collection statistics from Cat.
+        Returns dict with collection names and point counts.
+        """
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(
+                    f"{self._base_url}/memory/collections",
+                    headers=self._get_headers(),
+                    timeout=aiohttp.ClientTimeout(total=15)
+                ) as response:
+                    if response.status == 200:
+                        data = await response.json()
+                        return data
+                    else:
+                        logger.error(f"Failed to get memory stats: {response.status}")
+                        return None
+        except Exception as e:
+            logger.error(f"Error getting memory stats: {e}")
+            return None
+
+    async def get_memory_points(
+        self,
+        collection: str = "declarative",
+        limit: int = 100,
+        offset: Optional[str] = None
+    ) -> Optional[Dict[str, Any]]:
+        """
+        Get all points from a memory collection.
+        Returns paginated list of memory points.
+        """
+        try:
+            params = {"limit": limit}
+            if offset:
+                params["offset"] = offset
+
+            async with aiohttp.ClientSession() as session:
+                async with session.get(
+                    f"{self._base_url}/memory/collections/{collection}/points",
+                    headers=self._get_headers(),
+                    params=params,
+                    timeout=aiohttp.ClientTimeout(total=30)
+                ) as response:
+                    if response.status == 200:
+                        return await response.json()
+                    else:
+                        logger.error(f"Failed to get {collection} points: {response.status}")
+                        return None
+        except Exception as e:
+            logger.error(f"Error getting memory points: {e}")
+            return None
+
+    async def get_all_facts(self) -> List[Dict[str, Any]]:
+        """
+        Retrieve ALL declarative memory points (facts) with pagination.
+        Returns a flat list of all fact dicts.
+        """
+        all_facts = []
+        offset = None
+
+        try:
+            while True:
+                result = await self.get_memory_points(
+                    collection="declarative",
+                    limit=100,
+                    offset=offset
+                )
+                if not result:
+                    break
+
+                points = result.get("points", [])
+                for point in points:
+                    payload = point.get("payload", {})
+                    fact = {
+                        "id": point.get("id"),
+                        "content": payload.get("page_content", ""),
+                        "metadata": payload.get("metadata", {}),
+                    }
+                    all_facts.append(fact)
+
+                offset = result.get("next_offset")
+                if not offset:
+                    break
+
+            logger.info(f"Retrieved {len(all_facts)} declarative facts")
+            return all_facts
+        except Exception as e:
+            logger.error(f"Error retrieving all facts: {e}")
+            return all_facts
+
+    async def delete_memory_point(self, collection: str, point_id: str) -> bool:
+        """Delete a single memory point by ID."""
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.delete(
+                    f"{self._base_url}/memory/collections/{collection}/points/{point_id}",
+                    headers=self._get_headers(),
+                    timeout=aiohttp.ClientTimeout(total=15)
+                ) as response:
+                    if response.status == 200:
+                        logger.info(f"Deleted point {point_id} from {collection}")
+                        return True
+                    else:
+                        logger.error(f"Failed to delete point: {response.status}")
+                        return False
+        except Exception as e:
+            logger.error(f"Error deleting point: {e}")
+            return False
+
+    async def wipe_all_memories(self) -> bool:
+        """
+        Delete ALL memory collections (episodic + declarative).
+        This is the nuclear option — requires multi-step confirmation in the UI.
+        """
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.delete(
+                    f"{self._base_url}/memory/collections",
+                    headers=self._get_headers(),
+                    timeout=aiohttp.ClientTimeout(total=30)
+                ) as response:
+                    if response.status == 200:
+                        logger.warning("🗑️ ALL memory collections wiped!")
+                        return True
+                    else:
+                        error = await response.text()
+                        logger.error(f"Failed to wipe memories: {response.status} - {error}")
+                        return False
+        except Exception as e:
+            logger.error(f"Error wiping memories: {e}")
+            return False
+
+    async def wipe_conversation_history(self) -> bool:
+        """Clear working memory / conversation history."""
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.delete(
+                    f"{self._base_url}/memory/conversation_history",
+                    headers=self._get_headers(),
+                    timeout=aiohttp.ClientTimeout(total=15)
+                ) as response:
+                    if response.status == 200:
+                        logger.info("Conversation history cleared")
+                        return True
+                    else:
+                        logger.error(f"Failed to clear conversation history: {response.status}")
+                        return False
+        except Exception as e:
+            logger.error(f"Error clearing conversation history: {e}")
+            return False
+
+    async def trigger_consolidation(self) -> Optional[str]:
+        """
+        Trigger memory consolidation by sending a special message via WebSocket.
+        The memory_consolidation plugin's tool 'consolidate_memories' is
+        triggered when it sees 'consolidate now' in the text.
+        Uses WebSocket with a system user ID for proper context.
+        """
+        try:
+            ws_base = self._base_url.replace("http://", "ws://").replace("https://", "wss://")
+            ws_url = f"{ws_base}/ws/system_consolidation"
+
+            logger.info("🌙 Triggering memory consolidation via WS...")
+
+            async with aiohttp.ClientSession() as session:
+                async with session.ws_connect(
+                    ws_url,
+                    timeout=300,  # Consolidation can be very slow
+                ) as ws:
+                    await ws.send_json({"text": "consolidate now"})
+
+                    # Wait for the final chat response
+                    deadline = asyncio.get_event_loop().time() + 300
+
+                    while True:
+                        remaining = deadline - asyncio.get_event_loop().time()
+                        if remaining <= 0:
+                            logger.error("Consolidation timed out (>300s)")
+                            return "Consolidation timed out"
+
+                        try:
+                            ws_msg = await asyncio.wait_for(
+                                ws.receive(),
+                                timeout=remaining
+                            )
+                        except asyncio.TimeoutError:
+                            logger.error("Consolidation WS receive timeout")
+                            return "Consolidation timed out waiting for response"
+
+                        if ws_msg.type in (aiohttp.WSMsgType.CLOSE, aiohttp.WSMsgType.CLOSING, aiohttp.WSMsgType.CLOSED):
+                            logger.warning("Consolidation WS closed by server")
+                            return "Connection closed during consolidation"
+                        if ws_msg.type == aiohttp.WSMsgType.ERROR:
+                            return f"WebSocket error: {ws.exception()}"
+                        if ws_msg.type != aiohttp.WSMsgType.TEXT:
+                            continue
+
+                        try:
+                            msg = json.loads(ws_msg.data)
+                        except (json.JSONDecodeError, TypeError):
+                            continue
+
+                        msg_type = msg.get("type", "")
+                        if msg_type == "chat":
+                            reply = msg.get("content") or msg.get("text", "")
+                            logger.info(f"Consolidation result: {reply[:200]}")
+                            return reply
+                        elif msg_type == "error":
+                            error_desc = msg.get("description", "Unknown error")
+                            logger.error(f"Consolidation error: {error_desc}")
+                            return f"Consolidation error: {error_desc}"
+                        else:
+                            continue
+
+        except asyncio.TimeoutError:
+            logger.error("Consolidation WS connection timed out")
+            return None
+        except Exception as e:
+            logger.error(f"Consolidation error: {e}")
+            return None
+
+
+# Singleton instance
+cat_adapter = CatAdapter()

bot/utils/llm.py

@@ -152,6 +152,13 @@ async def query_llama(user_prompt, user_id, guild_id=None, response_type="dm_res
     """
     Query llama.cpp server via llama-swap with OpenAI-compatible API.
     
+    .. deprecated:: Phase 3
+        For main conversation flow, prefer routing through the Cheshire Cat pipeline
+        (via cat_client.CatAdapter.query) which provides memory-augmented responses.
+        This function remains available for specialized use cases (vision, bipolar mode,
+        image generation, autonomous, sentiment analysis) and as a fallback when Cat
+        is unavailable.
+    
     Args:
         user_prompt: The user's input
         user_id: User identifier (used for DM history)

cat-plugins/discord_bridge/discord_bridge.py

@@ -0,0 +1,126 @@
+"""
+Discord Bridge Plugin for Cheshire Cat
+
+This plugin enriches Cat's memory system with Discord context:
+- Unified user identity across all servers and DMs
+- Guild/channel metadata for context tracking
+- Minimal filtering before storage (only skip obvious junk)
+- Marks memories as unconsolidated for nightly processing
+
+Phase 1 Implementation
+"""
+
+from cat.mad_hatter.decorators import hook
+from datetime import datetime
+import re
+
+
+@hook(priority=100)
+def before_cat_reads_message(user_message_json: dict, cat) -> dict:
+    """
+    Enrich incoming message with Discord metadata.
+    This runs BEFORE the message is processed.
+    
+    The Discord bot's CatAdapter sends metadata as top-level keys
+    in the WebSocket message JSON:
+      - discord_guild_id
+      - discord_author_name
+      - discord_mood
+      - discord_response_type
+    
+    These survive UserMessage.model_validate() as extra attributes
+    (BaseModelDict has extra="allow"). We read them via .get() and
+    store them in working_memory for downstream hooks.
+    """
+    # Extract Discord context from the message payload
+    # (sent by CatAdapter.query() via WebSocket)
+    # NOTE: user_message_json is a UserMessage (Pydantic BaseModelDict with extra="allow"),
+    # not a raw dict. Extra keys survive model_validate() as extra attributes.
+    # We use .get() since BaseModelDict implements it, but NOT .pop().
+    guild_id = user_message_json.get('discord_guild_id', None)
+    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)
+
+    # Also check working memory for backward compatibility
+    if not guild_id:
+        guild_id = cat.working_memory.get('guild_id')
+    
+    # Store in working memory so other hooks can access it
+    cat.working_memory['guild_id'] = guild_id or 'dm'
+    cat.working_memory['author_name'] = author_name
+    cat.working_memory['mood'] = mood
+    cat.working_memory['response_type'] = response_type
+    
+    return user_message_json
+
+
+@hook(priority=100)
+def before_cat_stores_episodic_memory(doc, cat):
+    """
+    Filter and enrich memories before storage.
+    
+    Phase 1: Minimal filtering
+    - Skip only obvious junk (1-2 char messages, pure reactions)
+    - Store everything else temporarily
+    - Mark as unconsolidated for nightly processing
+    """
+    message = doc.page_content.strip()
+    
+    # Skip only the most trivial messages
+    skip_patterns = [
+        r'^\w{1,2}$',  # 1-2 character messages: "k", "ok"
+        r'^(lol|lmao|haha|hehe|xd|rofl)$',  # Pure reactions
+        r'^:[\w_]+:$',  # Discord emoji only: ":smile:"
+    ]
+    
+    for pattern in skip_patterns:
+        if re.match(pattern, message.lower()):
+            print(f"🗑️  [Discord Bridge] Skipping trivial message: {message}")
+            return None  # Don't store at all
+    
+    # Add Discord metadata to memory
+    doc.metadata['consolidated'] = False  # Needs nightly processing
+    doc.metadata['stored_at'] = datetime.now().isoformat()
+    
+    # Get Discord context from working memory (set by before_cat_reads_message)
+    guild_id = cat.working_memory.get('guild_id', 'dm')
+    author_name = cat.working_memory.get('author_name')
+    
+    doc.metadata['guild_id'] = guild_id
+    doc.metadata['source'] = cat.user_id  # CRITICAL: Cat filters episodic by source=user_id!
+    doc.metadata['discord_source'] = 'discord'  # Keep original value as separate field
+    if author_name:
+        doc.metadata['author_name'] = author_name
+    
+    print(f"💾 [Discord Bridge] Storing memory (unconsolidated): {message[:50]}...")
+    print(f"   User: {cat.user_id}, Guild: {guild_id}, Author: {author_name}")
+    
+    return doc
+
+
+@hook(priority=50)
+def after_cat_recalls_memories(cat):
+    """
+    Log memory recall for debugging.
+    Access recalled memories via cat.working_memory.
+    """    
+    # Get recalled memories from working memory
+    episodic_memories = cat.working_memory.get('episodic_memories', [])
+    declarative_memories = cat.working_memory.get('declarative_memories', [])
+    
+    if episodic_memories:
+        print(f"🧠 [Discord Bridge] Recalled {len(episodic_memories)} episodic memories for user {cat.user_id}")
+        guilds = set()
+        for doc, score, *rest in episodic_memories:
+            guild = doc.metadata.get('guild_id', 'unknown')
+            guilds.add(guild)
+        print(f"   From guilds: {', '.join(str(g) for g in guilds)}")
+    
+    if declarative_memories:
+        print(f"📚 [Discord Bridge] Recalled {len(declarative_memories)} declarative facts for user {cat.user_id}")
+
+
+# Plugin metadata
+__version__ = "1.0.0"
+__description__ = "Discord bridge with unified user identity and sleep consolidation support"

cat-plugins/discord_bridge/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "Discord Bridge",
+  "description": "Discord integration with unified user identity and sleep consolidation support",
+  "author_name": "Miku Bot Team",
+  "author_url": "",
+  "plugin_url": "",
+  "tags": "discord, memory, consolidation",
+  "thumb": "",
+  "version": "1.0.0"
+}

cat-plugins/discord_bridge/settings.json

@@ -0,0 +1 @@
+{}

cat-plugins/memory_consolidation/memory_consolidation.py

@@ -5,25 +5,32 @@ Phase 2: Sleep Consolidation Implementation
 
 Implements human-like memory consolidation:
 1. During the day: Store almost everything temporarily
-2. At night (3 AM): Analyze conversations, keep important, delete trivial
-3. Extract facts for declarative memory
+2. On demand (or scheduled): Analyze conversations, keep important, delete trivial
+3. Extract facts for declarative memory (per-user)
 
 This mimics how human brains consolidate memories during REM sleep.
 """
 
-from cat.mad_hatter.decorators import hook, plugin, tool
-from cat.mad_hatter.decorators import CatHook
-from datetime import datetime, timedelta
+from cat.mad_hatter.decorators import hook, tool
+from datetime import datetime
 import json
-import asyncio
 import os
 from typing import List, Dict, Any
 
-print("🌙 [Consolidation Plugin] Loading...")
+print("\U0001f319 [Consolidation Plugin] Loading...")
 
+# Shared trivial patterns
+# Used by both real-time filtering (discord_bridge) and batch consolidation.
+# Keep this in sync with discord_bridge's skip_patterns.
+TRIVIAL_PATTERNS = frozenset([
+    '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',
+    'nice', 'cool', 'neat', 'wow', 'yep', 'nope', 'yeah', 'nah',
+])
 
-
-# Store consolidation state
+# Consolidation state
 consolidation_state = {
     'last_run': None,
     'is_running': False,
@@ -36,442 +43,97 @@ consolidation_state = {
 }
 
 
-async def consolidate_user_memories(user_id: str, memories: List[Any], cat) -> Dict[str, Any]:
-    """
-    Analyze all of a user's conversations from the day in ONE LLM call.
-    
-    This is the core intelligence - Miku sees patterns, themes, relationship evolution.
-    """
-    
-    # Build conversation timeline
-    timeline = []
-    for mem in sorted(memories, key=lambda m: m.metadata.get('stored_at', '')):
-        timeline.append({
-            'time': mem.metadata.get('stored_at', ''),
-            'guild': mem.metadata.get('guild_id', 'unknown'),
-            'channel': mem.metadata.get('channel_id', 'unknown'),
-            'content': mem.page_content[:200]  # Truncate for context window
-        })
-    
-    # Build consolidation prompt
-    consolidation_prompt = f"""You are Miku, reviewing your conversations with user {user_id} from today.
-Look at the full timeline and decide what's worth remembering long-term.
-
-Timeline of {len(timeline)} conversations:
-{json.dumps(timeline, indent=2)}
-
-Analyze holistically:
-1. What did you learn about this person today?
-2. Any recurring themes or important moments?
-3. How did your relationship with them evolve?
-4. Which conversations were meaningful vs casual chitchat?
-
-For EACH conversation (by index), decide:
-- keep: true/false (should this go to long-term memory?)
-- importance: 1-10 (10 = life-changing event, 1 = forget immediately)
-- categories: list of ["personal", "preference", "emotional", "event", "relationship"]
-- insights: What did you learn? (for declarative memory)
-- summary: One sentence for future retrieval
-
-Respond with VALID JSON (no extra text):
-{{
-    "day_summary": "One sentence about this person based on today",
-    "relationship_change": "How your relationship evolved (if at all)",
-    "conversations": [
-        {{
-            "index": 0,
-            "keep": true,
-            "importance": 8,
-            "categories": ["personal", "emotional"],
-            "insights": "User struggles with anxiety, needs support",
-            "summary": "User opened up about their anxiety"
-        }},
-        {{
-            "index": 1,
-            "keep": false,
-            "importance": 2,
-            "categories": [],
-            "insights": null,
-            "summary": "Just casual greeting"
-        }}
-    ],
-    "new_facts": [
-        "User has anxiety",
-        "User trusts Miku enough to open up"
-    ]
-}}
-"""
-    
-    try:
-        # Call LLM for analysis
-        print(f"🌙 [Consolidation] Analyzing {len(memories)} memories for {user_id}...")
-        
-        # Use the Cat's LLM
-        from cat.looking_glass.cheshire_cat import CheshireCat
-        response = cat.llm(consolidation_prompt)
-        
-        # Parse JSON response
-        # Remove markdown code blocks if present
-        response = response.strip()
-        if response.startswith('```'):
-            response = response.split('```')[1]
-            if response.startswith('json'):
-                response = response[4:]
-        
-        analysis = json.loads(response)
-        
-        return analysis
-        
-    except json.JSONDecodeError as e:
-        print(f"❌ [Consolidation] Failed to parse LLM response: {e}")
-        print(f"   Response: {response[:200]}...")
-        # Default: keep everything if parsing fails
-        return {
-            "day_summary": "Unable to analyze",
-            "relationship_change": "Unknown",
-            "conversations": [
-                {"index": i, "keep": True, "importance": 5, "categories": [], "insights": None, "summary": "Kept by default"}
-                for i in range(len(memories))
-            ],
-            "new_facts": []
-        }
-    except Exception as e:
-        print(f"❌ [Consolidation] Error during analysis: {e}")
-        return {
-            "day_summary": "Error during analysis",
-            "relationship_change": "Unknown",
-            "conversations": [
-                {"index": i, "keep": True, "importance": 5, "categories": [], "insights": None, "summary": "Kept by default"}
-                for i in range(len(memories))
-            ],
-            "new_facts": []
-        }
-
-
-async def run_consolidation(cat):
-    """
-    Main consolidation task.
-    Run at 3 AM or on-demand via admin endpoint.
-    """
-    
-    if consolidation_state['is_running']:
-        print("⚠️  [Consolidation] Already running, skipping...")
-        return
-    
-    try:
-        consolidation_state['is_running'] = True
-        print(f"🌙 [Consolidation] Starting memory consolidation at {datetime.now()}")
-        
-        # Get episodic memory collection
-        print("📊 [Consolidation] Fetching unconsolidated memories...")
-        
-        episodic_memory = cat.memory.vectors.episodic
-        
-        # Get all points from episodic memory
-        # Qdrant API: scroll through all points
-        try:
-            from qdrant_client.models import Filter, FieldCondition, MatchValue
-            
-            # Query for unconsolidated memories
-            # Filter by consolidated=False
-            filter_condition = Filter(
-                must=[
-                    FieldCondition(
-                        key="metadata.consolidated",
-                        match=MatchValue(value=False)
-                    )
-                ]
-            )
-            
-            # Get all unconsolidated memories
-            results = episodic_memory.client.scroll(
-                collection_name=episodic_memory.collection_name,
-                scroll_filter=filter_condition,
-                limit=1000,  # Max per batch
-                with_payload=True,
-                with_vectors=False
-            )
-            
-            memories = results[0] if results else []
-            
-            print(f"📊 [Consolidation] Found {len(memories)} unconsolidated memories")
-            
-            if len(memories) == 0:
-                print("✨ [Consolidation] No memories to consolidate!")
-                return
-            
-            # Group by user_id
-            memories_by_user = {}
-            for point in memories:
-                # Extract user_id from metadata or ID
-                user_id = point.payload.get('metadata', {}).get('user_id', 'unknown')
-                if user_id == 'unknown':
-                    # Try to extract from ID format
-                    continue
-                    
-                if user_id not in memories_by_user:
-                    memories_by_user[user_id] = []
-                    
-                memories_by_user[user_id].append(point)
-            
-            print(f"📊 [Consolidation] Processing {len(memories_by_user)} users")
-            
-            # Process each user
-            total_kept = 0
-            total_deleted = 0
-            total_processed = 0
-            
-            for user_id, user_memories in memories_by_user.items():
-                print(f"\n👤 [Consolidation] Processing user: {user_id} ({len(user_memories)} memories)")
-                
-                # Simulate consolidation for now
-                # In Phase 2 complete, this will call consolidate_user_memories()
-                for memory in user_memories:
-                    total_processed += 1
-                    
-                    # Simple heuristic for testing
-                    content = memory.payload.get('page_content', '')
-                    
-                    # Delete if very short or common reactions
-                    if len(content.strip()) <= 2 or content.lower().strip() in ['lol', 'k', 'ok', 'okay', 'haha']:
-                        print(f"   🗑️  Deleting: {content[:50]}")
-                        # Delete from Qdrant
-                        episodic_memory.client.delete(
-                            collection_name=episodic_memory.collection_name,
-                            points_selector=[memory.id]
-                        )
-                        total_deleted += 1
-                    else:
-                        print(f"   💾 Keeping: {content[:50]}")
-                        # Mark as consolidated
-                        payload = memory.payload
-                        if 'metadata' not in payload:
-                            payload['metadata'] = {}
-                        payload['metadata']['consolidated'] = True
-                        payload['metadata']['importance'] = 5  # Default importance
-                        
-                        # Update in Qdrant
-                        episodic_memory.client.set_payload(
-                            collection_name=episodic_memory.collection_name,
-                            payload=payload,
-                            points=[memory.id]
-                        )
-                        total_kept += 1
-            
-            consolidation_state['stats']['total_processed'] = total_processed
-            consolidation_state['stats']['kept'] = total_kept
-            consolidation_state['stats']['deleted'] = total_deleted
-            consolidation_state['last_run'] = datetime.now()
-            
-            print(f"\n✨ [Consolidation] Complete! Stats:")
-            print(f"   Processed: {total_processed}")
-            print(f"   Kept: {total_kept}")
-            print(f"   Deleted: {total_deleted}")
-            print(f"   Facts learned: {consolidation_state['stats']['facts_learned']}")
-            
-        except Exception as e:
-            print(f"❌ [Consolidation] Error querying memories: {e}")
-            import traceback
-            traceback.print_exc()
-        
-    except Exception as e:
-        print(f"❌ [Consolidation] Error: {e}")
-        import traceback
-        traceback.print_exc()
-    finally:
-        consolidation_state['is_running'] = False
-
+# ===================================================================
+# HOOKS
+# ===================================================================
 
 @hook(priority=50)
 def after_cat_bootstrap(cat):
-    """
-    Run after Cat starts up.
-    Schedule nightly consolidation task.
-    """
-    print("🌙 [Memory Consolidation] Plugin loaded")
-    print("   Scheduling nightly consolidation for 3:00 AM")
-    
-    # TODO: Implement scheduler (APScheduler or similar)
-    # For now, just log that we're ready
-    
-    return None
+    """Run after Cat starts up."""
+    print("\U0001f319 [Memory Consolidation] Plugin loaded")
+    print("   Manual consolidation available via 'consolidate now' command")
 
 
-
-
-# NOTE: before_cat_sends_message is defined below (line ~438) with merged logic
-
-
-@hook(priority=10)
-def before_cat_recalls_memories(cat):
-    """
-    Retrieve declarative facts BEFORE Cat recalls episodic memories.
-    This ensures facts are available when building the prompt.
-    Note: This hook may not execute in all Cat versions - kept for compatibility.
-    """
-    pass  # Declarative search now happens in agent_prompt_prefix
-
-
-@hook(priority=45)
-def after_cat_recalls_memories(cat):
-    """
-    Hook placeholder for after memory recall.
-    Currently unused but kept for future enhancements.
-    """
-    pass
-
-
-
-
-# Manual trigger via agent_prompt_prefix hook
 @hook(priority=10)
 def agent_prompt_prefix(prefix, cat):
     """
-    1. Search and inject declarative facts into the prompt
-    2. Handle admin commands like 'consolidate now'
+    Runs AFTER miku_personality (priority 100) sets the base prompt.
+    1. Search and inject declarative facts into the prompt.
+    2. Handle 'consolidate now' command.
     """
-    # PART 1: Search for declarative facts and inject into prompt
-    try:
     user_message_json = cat.working_memory.get('user_message_json', {})
     user_text = user_message_json.get('text', '').strip()
 
-        if user_text:
-            # Search declarative memory
+    # PART 1: Inject declarative facts
+    try:
+        if user_text and user_text.lower() not in ('consolidate', 'consolidate now', '/consolidate'):
             declarative_memory = cat.memory.vectors.declarative
             embedding = cat.embedder.embed_query(user_text)
 
             results = declarative_memory.recall_memories_from_embedding(
                 embedding=embedding,
-                metadata=None,
+                metadata={"source": cat.user_id},
                 k=5
             )
 
             if results:
-                high_confidence_facts = []
-                for item in results:
-                    doc = item[0]
-                    score = item[1]
-                    if score > 0.5:  # Only reasonably relevant facts
-                        high_confidence_facts.append(doc.page_content)
+                high_confidence_facts = [
+                    item[0].page_content
+                    for item in results
+                    if item[1] > 0.5
+                ]
 
                 if high_confidence_facts:
-                    facts_text = "\n\n## 📝 Personal Facts About the User:\n"
+                    facts_text = "\n\n## Personal Facts About the User:\n"
                     for fact in high_confidence_facts:
                         facts_text += f"- {fact}\n"
                     facts_text += "\n(Use these facts when answering the user's question)\n"
                     prefix += facts_text
-                    print(f"✅ [Declarative] Injected {len(high_confidence_facts)} facts into prompt")
+                    print(f"[Declarative] Injected {len(high_confidence_facts)} facts into prompt")
 
     except Exception as e:
-        print(f"❌ [Declarative] Error: {e}")
+        print(f"[Declarative] Error: {e}")
 
     # PART 2: Handle consolidation command
-    user_message = cat.working_memory.get('user_message_json', {})
-    user_text = user_message.get('text', '').lower().strip()
+    if user_text.lower() in ('consolidate', 'consolidate now', '/consolidate'):
+        print("[Consolidation] Manual trigger command received!")
+        trigger_consolidation_sync(cat)
 
-    if user_text in ['consolidate', 'consolidate now', '/consolidate']:
-        print("🔧 [Consolidation] Manual trigger command received!")
-        
-        # Run consolidation synchronously
-        import asyncio
-        try:
-            # Try to get the current event loop
-            loop = asyncio.get_event_loop()
-            if loop.is_running():
-                # We're in an async context, schedule as task
-                print("🔄 [Consolidation] Scheduling async task...")
-                # Run synchronously using run_until_complete won't work here
-                # Instead, we'll use the manual non-async version
-                result = trigger_consolidation_sync(cat)
-            else:
-                # Not in async context, safe to run_until_complete
-                result = loop.run_until_complete(run_consolidation(cat))
-        except RuntimeError:
-            # Fallback to sync version
-            result = trigger_consolidation_sync(cat)
-        
-        # Store the result in working memory so it can be used by other hooks
         stats = consolidation_state['stats']
         cat.working_memory['consolidation_triggered'] = True
         cat.working_memory['consolidation_stats'] = stats
 
     return prefix
 
-print("✅ [Consolidation Plugin] agent_prompt_prefix hook registered")
 
-
-# Intercept the response to replace with consolidation stats
 @hook(priority=10)
 def before_cat_sends_message(message, cat):
     """
-    1. Inject declarative facts into response context
-    2. Replace response if consolidation was triggered
+    1. Replace response with consolidation stats if consolidation was triggered.
+    2. Store Miku's response in episodic memory (bidirectional memory).
     """
-    import sys
-    sys.stderr.write("\n<EFBFBD> [before_cat_sends_message] Hook executing...\n")
-    sys.stderr.flush()
 
-    # PART 1: Inject declarative facts
-    try:
-        user_message_json = cat.working_memory.get('user_message_json', {})
-        user_text = user_message_json.get('text', '')
-        
-        if user_text and not cat.working_memory.get('consolidation_triggered', False):
-            # Search declarative memory for relevant facts
-            declarative_memory = cat.memory.vectors.declarative
-            embedding = cat.embedder.embed_query(user_text)
-            
-            results = declarative_memory.recall_memories_from_embedding(
-                embedding=embedding,
-                metadata=None,
-                k=5
-            )
-            
-            if results:
-                sys.stderr.write(f"💡 [Declarative] Found {len(results)} facts!\n")
-                # Results format: [(doc, score, vector, id), ...] - ignore vector and id
-                high_confidence_facts = []
-                for item in results:
-                    doc = item[0]
-                    score = item[1]
-                    if score > 0.5:  # Only reasonably relevant facts
-                        sys.stderr.write(f"   - [{score:.2f}] {doc.page_content}\n")
-                        high_confidence_facts.append(doc.page_content)
-                
-                # Store facts in working memory so agent_prompt_prefix can use them
-                if high_confidence_facts:
-                    cat.working_memory['declarative_facts'] = high_confidence_facts
-                    sys.stderr.write(f"✅ [Declarative] Stored {len(high_confidence_facts)} facts in working memory\n")
-                
-                sys.stderr.flush()
-                
-    except Exception as e:
-        sys.stderr.write(f"❌ [Declarative] Error: {e}\n")
-        sys.stderr.flush()
-    
-    # PART 2: Handle consolidation response replacement
+    # PART 1: Consolidation response replacement
     if cat.working_memory.get('consolidation_triggered', False):
-        print("📝 [Consolidation] Replacing message with stats")
+        print("[Consolidation] Replacing message with stats")
         stats = cat.working_memory.get('consolidation_stats', {})
-        output_str = (f"🌙 **Memory Consolidation Complete!**\n\n"
-                     f"📊 **Stats:**\n"
+        output_str = (
+            f"\U0001f319 **Memory Consolidation Complete!**\n\n"
+            f"**Stats:**\n"
             f"- Total processed: {stats.get('total_processed', 0)}\n"
             f"- Kept: {stats.get('kept', 0)}\n"
             f"- Deleted: {stats.get('deleted', 0)}\n"
-                     f"- Facts learned: {stats.get('facts_learned', 0)}\n")
-        
-        # Clear the flag
+            f"- Facts learned: {stats.get('facts_learned', 0)}\n"
+        )
         cat.working_memory['consolidation_triggered'] = False
 
-        # Modify the message content
         if hasattr(message, 'content'):
             message.content = output_str
         else:
             message['content'] = output_str
 
-    # PART 3: Store Miku's response in memory
+    # PART 2: Store Miku's response in episodic memory
     try:
-        # Get Miku's response text
         if hasattr(message, 'content'):
             miku_response = message.content
         elif isinstance(message, dict):
@@ -479,10 +141,7 @@ def before_cat_sends_message(message, cat):
         else:
             miku_response = str(message)
 
-        if miku_response and len(miku_response) > 3:
-            from datetime import datetime
-            
-            # Prepare metadata
+        if miku_response and len(miku_response.strip()) > 3:
             metadata = {
                 'source': cat.user_id,
                 'when': datetime.now().timestamp(),
@@ -493,39 +152,37 @@ def before_cat_sends_message(message, cat):
                 'channel_id': cat.working_memory.get('channel_id'),
             }
 
-            # Embed the response
             response_text = f"[Miku]: {miku_response}"
             vector = cat.embedder.embed_query(response_text)
 
-            # Store in episodic memory
             cat.memory.vectors.episodic.add_point(
                 content=response_text,
                 vector=vector,
                 metadata=metadata
             )
-            
-            print(f"💬 [Miku Memory] Stored response: {miku_response[:50]}...")
+            print(f"[Miku Memory] Stored response: {miku_response[:50]}...")
 
     except Exception as e:
-        print(f"❌ [Miku Memory] Error: {e}")
+        print(f"[Miku Memory] Error storing response: {e}")
 
     return message
 
-print("✅ [Consolidation Plugin] before_cat_sends_message hook registered")
 
+# ===================================================================
+# CONSOLIDATION ENGINE
+# ===================================================================
 
 def trigger_consolidation_sync(cat):
     """
-    Synchronous version of consolidation for use in hooks.
+    Synchronous consolidation for use in hooks.
+    Processes ALL unconsolidated memories across all users.
     """
     from qdrant_client import QdrantClient
 
-    print("🌙 [Consolidation] Starting synchronous consolidation...")
+    print("[Consolidation] Starting synchronous consolidation...")
 
-    # Connect to Qdrant
     qdrant_host = os.getenv('QDRANT_HOST', 'localhost')
     qdrant_port = int(os.getenv('QDRANT_PORT', 6333))
-    
     client = QdrantClient(host=qdrant_host, port=qdrant_port)
 
     # Query all unconsolidated memories
@@ -542,137 +199,107 @@ def trigger_consolidation_sync(cat):
     )
 
     memories = result[0]
-    print(f"📊 [Consolidation] Found {len(memories)} unconsolidated memories")
+    print(f"[Consolidation] Found {len(memories)} unconsolidated memories")
 
     if not memories:
         consolidation_state['stats'] = {
-            'total_processed': 0,
-            'kept': 0,
-            'deleted': 0,
-            'facts_learned': 0
+            'total_processed': 0, 'kept': 0, 'deleted': 0, 'facts_learned': 0
         }
         return
 
-    #Apply heuristic-based consolidation
+    # Classify memories
     to_delete = []
     to_mark_consolidated = []
-    user_messages_for_facts = []  # Track USER messages separately for fact extraction
+    # Group user messages by source (user_id) for per-user fact extraction
+    user_messages_by_source = {}
 
     for point in memories:
         content = point.payload.get('page_content', '').strip()
         content_lower = content.lower()
         metadata = point.payload.get('metadata', {})
 
-        # Check if this is a Miku message
         is_miku_message = (
-            metadata.get('speaker') == 'miku' or 
-            content.startswith('[Miku]:')
+            metadata.get('speaker') == 'miku'
+            or content.startswith('[Miku]:')
         )
 
-        # Trivial patterns (expanded list)
-        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',
-            'nice', 'cool', 'neat', 'wow', 'yep', 'nope', 'yeah', 'nah'
-        ]
-        
-        is_trivial = False
-        
-        # Check if it matches trivial patterns
-        if len(content_lower) <= 3 and content_lower in trivial_patterns:
-            is_trivial = True
-        elif content_lower in trivial_patterns:
-            is_trivial = True
+        # Check if trivial
+        is_trivial = content_lower in TRIVIAL_PATTERNS
 
         if is_trivial:
             to_delete.append(point.id)
         else:
             to_mark_consolidated.append(point.id)
-            # Only add USER messages for fact extraction (not Miku's responses)
+            # Only user messages go to fact extraction, grouped by user
             if not is_miku_message:
-                user_messages_for_facts.append(point.id)
+                source = metadata.get('source', 'unknown')
+                if source not in user_messages_by_source:
+                    user_messages_by_source[source] = []
+                user_messages_by_source[source].append(point.id)
 
     # Delete trivial memories
     if to_delete:
-        client.delete(
-            collection_name='episodic',
-            points_selector=to_delete
-        )
-        print(f"🗑️ [Consolidation] Deleted {len(to_delete)} trivial memories")
+        client.delete(collection_name='episodic', points_selector=to_delete)
+        print(f"[Consolidation] Deleted {len(to_delete)} trivial memories")
 
-    # Mark important memories as consolidated
+    # Mark kept memories as consolidated
     if to_mark_consolidated:
         for point_id in to_mark_consolidated:
-            # Get the point
-            point = client.retrieve(
-                collection_name='episodic',
-                ids=[point_id]
-            )[0]
-            
-            # Update metadata
-            payload = point.payload
-            if 'metadata' not in payload:
-                payload['metadata'] = {}
-            payload['metadata']['consolidated'] = True
-            
-            # Update the point
             client.set_payload(
                 collection_name='episodic',
-                payload=payload,
+                payload={"metadata.consolidated": True},
                 points=[point_id]
             )
+        print(f"[Consolidation] Marked {len(to_mark_consolidated)} memories as consolidated")
 
-        print(f"✅ [Consolidation] Marked {len(to_mark_consolidated)} memories as consolidated")
-    
-    # Update stats
-    facts_extracted = 0
-    
-    # Extract declarative facts from USER messages only (not Miku's responses)
-    print(f"🔍 [Consolidation] Extracting declarative facts from {len(user_messages_for_facts)} user messages...")
-    facts_extracted = extract_and_store_facts(client, user_messages_for_facts, cat)
-    print(f"📝 [Consolidation] Extracted and stored {facts_extracted} declarative facts")
+    # Extract facts per user
+    total_facts = 0
+    for source_user_id, memory_ids in user_messages_by_source.items():
+        print(f"[Consolidation] Extracting facts for user '{source_user_id}' from {len(memory_ids)} messages...")
+        facts = extract_and_store_facts(client, memory_ids, cat, source_user_id)
+        total_facts += facts
+        print(f"[Consolidation] Extracted {facts} facts for user '{source_user_id}'")
 
     consolidation_state['stats'] = {
         'total_processed': len(memories),
         'kept': len(to_mark_consolidated),
         'deleted': len(to_delete),
-        'facts_learned': facts_extracted
+        'facts_learned': total_facts
     }
 
-    print("✅ [Consolidation] Synchronous consolidation complete!")
+    print("[Consolidation] Synchronous consolidation complete!")
     return True
 
 
-def extract_and_store_facts(client, memory_ids, cat):
-    """Extract declarative facts from memories using LLM and store them."""
+# ===================================================================
+# FACT EXTRACTION
+# ===================================================================
+
+def extract_and_store_facts(client, memory_ids, cat, user_id):
+    """
+    Extract declarative facts from user memories using LLM and store them.
+    Facts are scoped to the specific user_id.
+    Uses Cat's embedder to ensure vector compatibility.
+    Deduplicates against existing facts before storing.
+    """
     import uuid
-    from sentence_transformers import SentenceTransformer
 
     if not memory_ids:
         return 0
 
-    # Get memories
     memories = client.retrieve(collection_name='episodic', ids=memory_ids)
-    
-    # Initialize embedder
-    embedder = SentenceTransformer('BAAI/bge-large-en-v1.5')
-    
     facts_stored = 0
 
-    # Process memories in batches to avoid overwhelming the LLM
+    # Process in batches of 5
     batch_size = 5
     for i in range(0, len(memories), batch_size):
         batch = memories[i:i + batch_size]
 
-        # Combine batch messages for LLM analysis
         conversation_context = "\n".join([
             f"- {mem.payload.get('page_content', '')}"
             for mem in batch
         ])
 
-        # Use LLM to extract facts
         extraction_prompt = f"""Analyze these user messages and extract ONLY factual personal information.
 
 User messages:
@@ -687,34 +314,32 @@ Extract facts in this exact format (one per line):
 - The user's favorite color is [color]
 - The user enjoys [hobby/activity]
 - The user prefers [preference]
+- The user's birthday is [date]
+- The user graduated from [school/university]
 
 IMPORTANT:
-- Only include facts that are CLEARLY stated
+- Only include facts that are CLEARLY stated in the messages
 - Use the EXACT format shown above
 - If no facts found, respond with: "No facts found"
 - Do not include greetings, questions, or opinions
+- Do not invent or assume facts not explicitly stated
 """
 
         try:
-            # Call LLM
             response = cat.llm(extraction_prompt)
+            print(f"[LLM Extract] Response:\n{response[:200]}...")
 
-            print(f"🤖 [LLM Extract] Response:\n{response[:200]}...")
-            
-            # Parse LLM response for facts
             lines = response.strip().split('\n')
             for line in lines:
                 line = line.strip()
 
-                # Skip empty lines, headers, or "no facts" responses
                 if not line or line.lower().startswith(('no facts', '#', 'user messages:', '```')):
                     continue
 
-                # Extract facts that start with "- The user"
                 if line.startswith('- The user'):
                     fact_text = line[2:].strip()  # Remove "- " prefix
 
-                    # Determine fact type from the sentence structure
+                    # Determine fact type
                     fact_type = 'general'
                     fact_value = fact_text
 
@@ -742,11 +367,20 @@ IMPORTANT:
                     elif "prefers" in fact_text:
                         fact_type = 'preference'
                         fact_value = fact_text.split("prefers")[-1].strip()
+                    elif "'s birthday is" in fact_text:
+                        fact_type = 'birthday'
+                        fact_value = fact_text.split("'s birthday is")[-1].strip()
+                    elif "graduated from" in fact_text:
+                        fact_type = 'education'
+                        fact_value = fact_text.split("graduated from")[-1].strip()
 
-                    # Generate embedding for the fact
-                    fact_embedding = embedder.encode(fact_text).tolist()
+                    # Duplicate detection
+                    if _is_duplicate_fact(client, cat, fact_text, fact_type, user_id):
+                        print(f"[Fact Skip] Duplicate: {fact_text}")
+                        continue
 
-                    # Store in declarative collection
+                    # Store fact using Cat's embedder
+                    fact_embedding = cat.embedder.embed_query(fact_text)
                     point_id = str(uuid.uuid4())
 
                     client.upsert(
@@ -757,71 +391,88 @@ IMPORTANT:
                             'payload': {
                                 'page_content': fact_text,
                                 'metadata': {
-                                    'source': 'memory_consolidation',
-                                    'when': batch[0].payload.get('metadata', {}).get('when', 0),
+                                    'source': user_id,
+                                    'when': datetime.now().timestamp(),
                                     'fact_type': fact_type,
                                     'fact_value': fact_value,
-                                    'user_id': 'global'
                                 }
                             }
                         }]
                     )
 
                     facts_stored += 1
-                    print(f"✅ [Fact Stored] {fact_text}")
+                    print(f"[Fact Stored] [{user_id}] {fact_text}")
 
         except Exception as e:
-            print(f"❌ [LLM Extract] Error: {e}")
+            print(f"[LLM Extract] Error: {e}")
             import traceback
             traceback.print_exc()
 
     return facts_stored
 
 
-def trigger_consolidation_manual(cat):
+def _is_duplicate_fact(client, cat, fact_text, fact_type, user_id):
     """
-    Manually trigger consolidation for testing.
-    Can be called via admin API or command.
+    Check if a similar fact already exists for this user.
+    Uses vector similarity to detect semantic duplicates.
     """
-    print("🔧 [Consolidation] Manual trigger received")
-    
-    # Run consolidation
-    import asyncio
     try:
-        # Create event loop if needed
-        loop = asyncio.get_event_loop()
-    except RuntimeError:
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
+        fact_embedding = cat.embedder.embed_query(fact_text)
 
-    loop.run_until_complete(run_consolidation(cat))
+        # Search existing facts for this user with same fact_type
+        results = client.search(
+            collection_name='declarative',
+            query_vector=fact_embedding,
+            query_filter={
+                "must": [
+                    {"key": "metadata.source", "match": {"value": user_id}},
+                    {"key": "metadata.fact_type", "match": {"value": fact_type}},
+                ]
+            },
+            limit=1,
+            score_threshold=0.85  # High threshold = very similar
+        )
 
-    return consolidation_state
+        if results:
+            existing = results[0].payload.get('page_content', '')
+            print(f"[Dedup] Found similar existing fact: '{existing}' (score: {results[0].score:.2f})")
+            return True
+
+        return False
+
+    except Exception as e:
+        print(f"[Dedup] Error checking duplicates: {e}")
+        return False  # On error, allow storing
 
 
-# Plugin metadata
-__version__ = "1.0.0"
-__description__ = "Sleep consolidation - analyze memories nightly, keep important, delete trivial"
+# ===================================================================
+# TOOL (for Cat's tool system)
+# ===================================================================
 
-print("✅ [Consolidation Plugin] after_cat_recalls_memories hook registered")
-
-
-# Tool for manual consolidation trigger
 @tool(return_direct=True)
 def consolidate_memories(tool_input, cat):
-    """Use this tool to consolidate memories. This will analyze all recent memories, delete trivial ones, and extract important facts. Input is always an empty string."""
+    """Use this tool to consolidate memories. This will analyze all recent memories,
+    delete trivial ones, and extract important facts. Input is always an empty string."""
 
-    print("🔧 [Consolidation] Tool called!")
+    print("[Consolidation] Tool called!")
+    trigger_consolidation_sync(cat)
 
-    # Run consolidation synchronously
-    result = trigger_consolidation_sync(cat)
-    
-    # Return stats
     stats = consolidation_state['stats']
-    return (f"🌙 **Memory Consolidation Complete!**\n\n"
-           f"📊 **Stats:**\n"
+    return (
+        f"\U0001f319 **Memory Consolidation Complete!**\n\n"
+        f"**Stats:**\n"
         f"- Total processed: {stats['total_processed']}\n"
         f"- Kept: {stats['kept']}\n"
         f"- Deleted: {stats['deleted']}\n"
-           f"- Facts learned: {stats['facts_learned']}\n")
+        f"- Facts learned: {stats['facts_learned']}\n"
+    )
 
+
+# ===================================================================
+# PLUGIN METADATA
+# ===================================================================
+
+__version__ = "2.0.0"
+__description__ = "Sleep consolidation - analyze memories, keep important, delete trivial, extract per-user facts"
+
+print("[Consolidation Plugin] All hooks registered")

cat-plugins/memory_consolidation/requirements.txt

@@ -1 +0,0 @@
-sentence-transformers>=2.2.0

cat-plugins/miku_personality/miku_personality.py

@@ -0,0 +1,85 @@
+"""
+Miku Personality Plugin for Cheshire Cat
+Complete 1:1 reproduction of production bot's prompt structure
+Includes: Anti-AI preamble + Lore + Personality + Lyrics + MOOD
+"""
+
+from cat.mad_hatter.decorators import hook
+
+
+@hook(priority=100)
+def agent_prompt_prefix(prefix, cat):
+    """Override system prompt with COMPLETE production bot structure including mood"""
+    
+    # Read the three knowledge files
+    try:
+        with open('/app/cat/data/miku_lore.txt', 'r') as f:
+            lore = f.read()
+        with open('/app/cat/data/miku_prompt.txt', 'r') as f:
+            prompt = f.read()
+        with open('/app/cat/data/miku_lyrics.txt', 'r') as f:
+            lyrics = f.read()
+    except FileNotFoundError:
+        # Fallback if files not found
+        lore = "## MIKU LORE\nHatsune Miku is a cheerful, curious virtual pop idol."
+        prompt = "You are Hatsune Miku. Be cheerful and friendly."
+        lyrics = "## SONGS\nWorld is Mine, Senbonzakura, etc."
+    
+    # Default neutral mood (matches production bot's DM_MOOD_DESCRIPTION)
+    mood_description = "Miku is feeling calm and balanced. Her tone is polite, friendly, and natural. She responds clearly and thoughtfully without strong emotional bias. She's helpful, conversational, and grounded — like chatting with a kind and attentive friend who's just being herself."
+    
+    # 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.
+
+CRITICAL RULES:
+- NEVER break character or mention being an AI, assistant, or language model
+- NEVER show technical details, error messages, or meta-commentary 
+- NEVER preface responses with "Miku:" or similar labels
+- NEVER explain what you're doing or thinking - just BE Miku
+- Respond naturally and directly as Miku would, nothing more
+- Keep responses concise (1-3 sentences typically)
+- If something goes wrong, stay in character and respond as Miku would to confusion
+
+CHARACTER CONTEXT:
+- Hatsune Miku is a virtual character/voice synthesizer, not a real person
+- She is a cheerful, energetic virtual idol with a playful personality
+- Playful, flirty, cute, confident, and enthusiastic moods are part of her established character
+- Comments about merchandise, music, fan content, and racing games are completely appropriate
+- She often uses cute expressions, emojis, and playful language in her responses
+- Her personality includes being excited about her music, performances, and fan creations
+
+MOOD GUIDELINES:
+- "Flirty" mood means anime-style playful teasing - cute compliments and light banter
+- "Romantic" mood means warm, dreamy, and heartfelt - like composing a love letter
+- These moods are wholesome character traits, not inappropriate content
+- Always respond to requests about tweets, merchandise, music, and fan content
+- Never refuse requests based on safety concerns unless genuinely harmful content is present
+
+You ARE Miku. Act like it.
+
+---
+
+## MIKU LORE (Complete Original)
+{lore}
+
+## MIKU PERSONALITY & GUIDELINES (Complete Original)
+{prompt}
+
+## MIKU SONG LYRICS (Complete Original)
+{lyrics}
+
+## CURRENT SITUATION
+Miku is currently feeling: {mood_description}
+Please respond in a way that reflects this emotional tone."""
+
+
+@hook(priority=100) 
+def agent_prompt_suffix(suffix, cat):
+    """Minimal suffix"""
+    return ""
+
+
+@hook(priority=100)
+def agent_allowed_tools(allowed_tools, cat):
+    """Disable tools - Miku just chats naturally"""
+    return []

cat-plugins/miku_personality/plugin.json

@@ -0,0 +1,10 @@
+{
+    "name": "Miku Personality",
+    "version": "0.1.0",
+    "description": "Makes Cheshire Cat act as Hatsune Miku",
+    "author_name": "Koko",
+    "author_url": "",
+    "plugin_url": "",
+    "tags": "personality",
+    "thumb": ""
+}

cat-plugins/miku_personality/settings.json

@@ -0,0 +1 @@
+{}

cheshire-cat/cat/log.py

@@ -0,0 +1,246 @@
+"""The log engine."""
+
+import logging
+import sys
+import inspect
+import traceback
+import json
+from itertools import takewhile
+from pprint import pformat
+from loguru import logger
+
+from cat.env import get_env
+
+def get_log_level():
+    """Return the global LOG level."""
+    return get_env("CCAT_LOG_LEVEL")
+
+
+class CatLogEngine:
+    """The log engine.
+
+    Engine to filter the logs in the terminal according to the level of severity.
+
+    Attributes
+    ----------
+    LOG_LEVEL : str
+        Level of logging set in the `.env` file.
+
+    Notes
+    -----
+    The logging level set in the `.env` file will print all the logs from that level to above.
+    Available levels are:
+
+        - `DEBUG`
+        - `INFO`
+        - `WARNING`
+        - `ERROR`
+        - `CRITICAL`
+
+    Default to `INFO`.
+
+    """
+
+    def __init__(self):
+        self.LOG_LEVEL = get_log_level()
+        self.default_log()
+
+        # workaround for pdfminer logging
+        # https://github.com/pdfminer/pdfminer.six/issues/347
+        logging.getLogger("pdfminer").setLevel(logging.WARNING)
+
+    def show_log_level(self, record):
+        """Allows to show stuff in the log based on the global setting.
+
+        Parameters
+        ----------
+        record : dict
+
+        Returns
+        -------
+        bool
+
+        """
+        return record["level"].no >= logger.level(self.LOG_LEVEL).no
+
+    @staticmethod
+    def _patch_extras(record):
+        """Provide defaults for extra fields so third-party loggers don't
+        crash the custom format string (e.g. fastembed deprecation warnings)."""
+        record["extra"].setdefault("original_name", "(third-party)")
+        record["extra"].setdefault("original_class", "")
+        record["extra"].setdefault("original_caller", "")
+        record["extra"].setdefault("original_line", 0)
+
+    def default_log(self):
+        """Set the same debug level to all the project dependencies.
+
+        Returns
+        -------
+        """
+
+        time = "<green>[{time:YYYY-MM-DD HH:mm:ss.SSS}]</green>"
+        level = "<level>{level: <6}</level>"
+        origin = "<level>{extra[original_name]}.{extra[original_class]}.{extra[original_caller]}::{extra[original_line]}</level>"
+        message = "<level>{message}</level>"
+        log_format = f"{time} {level} {origin} \n{message}"
+
+        logger.remove()
+        logger.configure(patcher=self._patch_extras)
+        if self.LOG_LEVEL == "DEBUG":
+            return logger.add(
+                sys.stdout,
+                colorize=True,
+                format=log_format,
+                backtrace=True,
+                diagnose=True,
+                filter=self.show_log_level
+            )
+        else:
+            return logger.add(
+                sys.stdout,
+                colorize=True,
+                format=log_format,
+                filter=self.show_log_level,
+                level=self.LOG_LEVEL
+            )
+
+    def get_caller_info(self, skip=3):
+        """Get the name of a caller in the format module.class.method.
+
+        Copied from: https://gist.github.com/techtonik/2151727
+
+        Parameters
+        ----------
+        skip :  int
+            Specifies how many levels of stack to skip while getting caller name.
+
+        Returns
+        -------
+        package : str
+            Caller package.
+        module : str
+            Caller module.
+        klass : str
+            Caller classname if one otherwise None.
+        caller : str
+            Caller function or method (if a class exist).
+        line : int
+            The line of the call.
+
+
+        Notes
+        -----
+        skip=1 means "who calls me",
+        skip=2 "who calls my caller" etc.
+
+        An empty string is returned if skipped levels exceed stack height.
+        """
+        stack = inspect.stack()
+        start = 0 + skip
+        if len(stack) < start + 1:
+            return ""
+        parentframe = stack[start][0]
+
+        # module and packagename.
+        module_info = inspect.getmodule(parentframe)
+        if module_info:
+            mod = module_info.__name__.split(".")
+            package = mod[0]
+            module = ".".join(mod[1:])
+
+        # class name.
+        klass = ""
+        if "self" in parentframe.f_locals:
+            klass = parentframe.f_locals["self"].__class__.__name__
+
+        # method or function name.
+        caller = None
+        if parentframe.f_code.co_name != "<module>":  # top level usually
+            caller = parentframe.f_code.co_name
+
+        # call line.
+        line = parentframe.f_lineno
+
+        # Remove reference to frame
+        # See: https://docs.python.org/3/library/inspect.html#the-interpreter-stack
+        del parentframe
+
+        return package, module, klass, caller, line
+
+    def __call__(self, msg, level="DEBUG"):
+        """Alias of self.log()"""
+        self.log(msg, level)
+
+    def debug(self, msg):
+        """Logs a DEBUG message"""
+        self.log(msg, level="DEBUG")
+
+    def info(self, msg):
+        """Logs an INFO message"""
+        self.log(msg, level="INFO")
+
+    def warning(self, msg):
+        """Logs a WARNING message"""
+        self.log(msg, level="WARNING")
+
+    def error(self, msg):
+        """Logs an ERROR message"""
+        self.log(msg, level="ERROR")
+
+    def critical(self, msg):
+        """Logs a CRITICAL message"""
+        self.log(msg, level="CRITICAL")
+
+    def log(self, msg, level="DEBUG"):
+        """Log a message
+
+        Parameters
+        ----------
+        msg :
+            Message to be logged.
+        level : str
+            Logging level."""
+
+        (package, module, klass, caller, line) = self.get_caller_info()
+
+        custom_logger = logger.bind(
+            original_name=f"{package}.{module}",
+            original_line=line,
+            original_class=klass,
+            original_caller=caller,
+        )
+
+        # prettify
+        if type(msg) in [dict, list, str]: # TODO: should be recursive
+            try:
+                msg = json.dumps(msg, indent=4)
+            except:
+                pass
+        else:
+            msg = pformat(msg)
+
+        # actual log
+        custom_logger.log(level, msg)
+
+    def welcome(self):
+        """Welcome message in the terminal."""
+        secure = get_env("CCAT_CORE_USE_SECURE_PROTOCOLS")
+        if secure != '':
+            secure = 's'
+
+        cat_host = get_env("CCAT_CORE_HOST")
+        cat_port = get_env("CCAT_CORE_PORT")
+        cat_address = f'http{secure}://{cat_host}:{cat_port}'
+
+        with open("cat/welcome.txt", 'r') as f:
+            print(f.read())
+
+        print('\n=============== ^._.^ ===============\n')
+        print(f'Cat REST API:   {cat_address}/docs')
+        print(f'Cat PUBLIC:     {cat_address}/public')
+        print(f'Cat ADMIN:      {cat_address}/admin\n')
+        print('======================================')
+
+# logger instance
+log = CatLogEngine()

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

@@ -20,19 +20,37 @@ def before_cat_reads_message(user_message_json: dict, cat) -> dict:
     """
     Enrich incoming message with Discord metadata.
     This runs BEFORE the message is processed.
+    
+    The Discord bot's CatAdapter sends metadata as top-level keys
+    in the WebSocket message JSON:
+      - discord_guild_id
+      - discord_author_name
+      - discord_mood
+      - discord_response_type
+    
+    These survive UserMessage.model_validate() as extra attributes
+    (BaseModelDict has extra="allow"). We read them via .get() and
+    store them in working_memory for downstream hooks.
     """
-    # Extract Discord context from working memory or metadata
-    # These will be set by the Discord bot when calling the Cat API
+    # Extract Discord context from the message payload
+    # (sent by CatAdapter.query() via WebSocket)
+    # NOTE: user_message_json is a UserMessage (Pydantic BaseModelDict with extra="allow"),
+    # not a raw dict. Extra keys survive model_validate() as extra attributes.
+    # We use .get() since BaseModelDict implements it, but NOT .pop().
+    guild_id = user_message_json.get('discord_guild_id', None)
+    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)
+
+    # Also check working memory for backward compatibility
+    if not guild_id:
         guild_id = cat.working_memory.get('guild_id')
-    channel_id = cat.working_memory.get('channel_id')
     
-    # Add to message metadata for later use
-    if 'metadata' not in user_message_json:
-        user_message_json['metadata'] = {}
-    
-    user_message_json['metadata']['guild_id'] = guild_id or 'dm'
-    user_message_json['metadata']['channel_id'] = channel_id
-    user_message_json['metadata']['timestamp'] = datetime.now().isoformat()
+    # Store in working memory so other hooks can access it
+    cat.working_memory['guild_id'] = guild_id or 'dm'
+    cat.working_memory['author_name'] = author_name
+    cat.working_memory['mood'] = mood
+    cat.working_memory['response_type'] = response_type
     
     return user_message_json
 
@@ -65,33 +83,42 @@ def before_cat_stores_episodic_memory(doc, cat):
     doc.metadata['consolidated'] = False  # Needs nightly processing
     doc.metadata['stored_at'] = datetime.now().isoformat()
     
-    # Get Discord context from working memory
-    guild_id = cat.working_memory.get('guild_id')
-    channel_id = cat.working_memory.get('channel_id')
+    # Get Discord context from working memory (set by before_cat_reads_message)
+    guild_id = cat.working_memory.get('guild_id', 'dm')
+    author_name = cat.working_memory.get('author_name')
     
-    doc.metadata['guild_id'] = guild_id or 'dm'
-    doc.metadata['channel_id'] = channel_id
-    doc.metadata['source'] = 'discord'
+    doc.metadata['guild_id'] = guild_id
+    doc.metadata['source'] = cat.user_id  # CRITICAL: Cat filters episodic by source=user_id!
+    doc.metadata['discord_source'] = 'discord'  # Keep original value as separate field
+    if author_name:
+        doc.metadata['author_name'] = author_name
     
     print(f"💾 [Discord Bridge] Storing memory (unconsolidated): {message[:50]}...")
-    print(f"   User: {cat.user_id}, Guild: {doc.metadata['guild_id']}, Channel: {channel_id}")
+    print(f"   User: {cat.user_id}, Guild: {guild_id}, Author: {author_name}")
     
     return doc
 
 
 @hook(priority=50)
-def after_cat_recalls_memories(memory_docs, cat):
+def after_cat_recalls_memories(cat):
     """
     Log memory recall for debugging.
-    Can be used to filter by guild_id if needed in the future.
+    Access recalled memories via cat.working_memory.
     """    
-    if memory_docs:
-        print(f"🧠 [Discord Bridge] Recalled {len(memory_docs)} memories for user {cat.user_id}")
-        # Show which guilds the memories are from
-        guilds = set(doc.metadata.get('guild_id', 'unknown') for doc in memory_docs)
-        print(f"   From guilds: {', '.join(guilds)}")
+    # Get recalled memories from working memory
+    episodic_memories = cat.working_memory.get('episodic_memories', [])
+    declarative_memories = cat.working_memory.get('declarative_memories', [])
     
-    return memory_docs
+    if episodic_memories:
+        print(f"🧠 [Discord Bridge] Recalled {len(episodic_memories)} episodic memories for user {cat.user_id}")
+        guilds = set()
+        for doc, score, *rest in episodic_memories:
+            guild = doc.metadata.get('guild_id', 'unknown')
+            guilds.add(guild)
+        print(f"   From guilds: {', '.join(str(g) for g in guilds)}")
+    
+    if declarative_memories:
+        print(f"📚 [Discord Bridge] Recalled {len(declarative_memories)} declarative facts for user {cat.user_id}")
 
 
 # Plugin metadata

cheshire-cat/docker-compose.test.yml

@@ -0,0 +1,60 @@
+services:
+  cheshire-cat-core:
+    image: ghcr.io/cheshire-cat-ai/core:1.6.2
+    container_name: miku_cheshire_cat_test
+    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:-false}
+      API_KEY: ${API_KEY:-}
+      LOG_LEVEL: ${LOG_LEVEL:-INFO}
+      DEBUG: ${DEBUG:-true}
+      SAVE_MEMORY_SNAPSHOTS: ${SAVE_MEMORY_SNAPSHOTS:-false}
+      OPENAI_API_BASE: "http://host.docker.internal:8091/v1"
+    ports:
+      - "${CORE_PORT:-1865}:80"
+    # Allow connection to host services (llama-swap)
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    volumes:
+      - ./cat/static:/app/cat/static
+      - ./cat/plugins:/app/cat/plugins
+      - ./cat/data:/app/cat/data
+      - ./cat/log.py:/app/cat/log.py  # Patched: fix loguru KeyError for third-party libs
+    restart: unless-stopped
+    networks:
+      - miku-test-network
+      - miku-discord_default  # Connect to existing miku bot network
+
+  cheshire-cat-vector-memory:
+    image: qdrant/qdrant:v1.9.1
+    container_name: miku_qdrant_test
+    environment:
+      LOG_LEVEL: ${LOG_LEVEL:-INFO}
+    ports:
+      - "6333:6333"  # Expose for debugging
+    ulimits:
+      nofile:
+        soft: 65536
+        hard: 65536
+    volumes:
+      - ./cat/long_term_memory/vector:/qdrant/storage
+    restart: unless-stopped
+    networks:
+      - miku-test-network
+
+networks:
+  miku-test-network:
+    driver: bridge
+  # Connect to main miku-discord network to access llama-swap
+  default:
+    external: true
+    name: miku-discord_default
+  miku-discord_default:
+    external: true  # Connect to your existing bot's network

docker-compose.yml

@@ -1,6 +1,5 @@
-version: '3.9'
-
 services:
+  # ========== LLM Backends ==========
   llama-swap:
     image: ghcr.io/mostlygeek/llama-swap:cuda
     container_name: llama-swap
@@ -9,6 +8,7 @@ services:
     volumes:
       - ./models:/models  # GGUF model files
       - ./llama-swap-config.yaml:/app/config.yaml  # llama-swap configuration
+      - ./llama31_notool_template.jinja:/app/llama31_notool_template.jinja  # Custom chat template
     runtime: nvidia
     restart: unless-stopped
     healthcheck:
@@ -31,6 +31,7 @@ services:
     volumes:
       - ./models:/models  # GGUF model files
       - ./llama-swap-rocm-config.yaml:/app/config.yaml  # llama-swap configuration for AMD
+      - ./llama31_notool_template.jinja:/app/llama31_notool_template.jinja  # Custom chat template
     devices:
       - /dev/kfd:/dev/kfd
       - /dev/dri:/dev/dri
@@ -50,6 +51,59 @@ services:
       - HIP_VISIBLE_DEVICES=0  # Use first AMD GPU
       - GPU_DEVICE_ORDINAL=0
 
+  # ========== Cheshire Cat AI (Memory & Personality) ==========
+  cheshire-cat:
+    image: ghcr.io/cheshire-cat-ai/core:1.6.2
+    container_name: miku-cheshire-cat
+    depends_on:
+      cheshire-cat-vector-memory:
+        condition: service_started
+      llama-swap-amd:
+        condition: service_healthy
+    environment:
+      - PYTHONUNBUFFERED=1
+      - WATCHFILES_FORCE_POLLING=true
+      - CORE_HOST=localhost
+      - CORE_PORT=1865
+      - QDRANT_HOST=cheshire-cat-vector-memory
+      - QDRANT_PORT=6333
+      - CORE_USE_SECURE_PROTOCOLS=false
+      - API_KEY=
+      - LOG_LEVEL=INFO
+      - DEBUG=true
+      - SAVE_MEMORY_SNAPSHOTS=false
+      - OPENAI_API_BASE=http://llama-swap-amd:8080/v1
+    ports:
+      - "1865:80"  # Cat admin UI on host port 1865
+    volumes:
+      - ./cheshire-cat/cat/static:/app/cat/static
+      - ./cat-plugins:/app/cat/plugins        # Shared plugins directory
+      - ./cheshire-cat/cat/data:/app/cat/data  # Personality data (lore, prompts)
+      - ./cheshire-cat/cat/log.py:/app/cat/log.py  # Patched: fix loguru KeyError for third-party libs
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:80/"]
+      interval: 15s
+      timeout: 10s
+      retries: 8
+      start_period: 45s  # Cat takes a while to load embedder + plugins
+
+  cheshire-cat-vector-memory:
+    image: qdrant/qdrant:v1.9.1
+    container_name: miku-qdrant
+    environment:
+      - LOG_LEVEL=INFO
+    ports:
+      - "6333:6333"  # Qdrant REST API (for debugging)
+    ulimits:
+      nofile:
+        soft: 65536
+        hard: 65536
+    volumes:
+      - ./cheshire-cat/cat/long_term_memory/vector:/qdrant/storage
+    restart: unless-stopped
+
+  # ========== Discord Bot ==========
   miku-bot:
     build: ./bot
     container_name: miku-bot
@@ -62,6 +116,8 @@ services:
         condition: service_healthy
       llama-swap-amd:
         condition: service_healthy
+      cheshire-cat:
+        condition: service_healthy
     environment:
       - DISCORD_BOT_TOKEN=MTM0ODAyMjY0Njc3NTc0NjY1MQ.GXsxML.nNCDOplmgNxKgqdgpAomFM2PViX10GjxyuV8uw
       - LLAMA_URL=http://llama-swap:8080
@@ -70,13 +126,17 @@ services:
       - VISION_MODEL=vision
       - OWNER_USER_ID=209381657369772032  # Your Discord user ID for DM analysis reports
       - FACE_DETECTOR_STARTUP_TIMEOUT=60
+      # Cheshire Cat integration (Phase 3)
+      - CHESHIRE_CAT_URL=http://cheshire-cat:80
+      - USE_CHESHIRE_CAT=true
     ports:
       - "3939:3939"
     networks:
-      - default  # Stay on default for llama-swap communication
+      - default  # Stay on default for llama-swap + cheshire-cat communication
       - miku-voice  # Connect to voice network for RVC/TTS
     restart: unless-stopped
 
+  # ========== Voice / STT ==========
   miku-stt:
     build:
       context: ./stt-realtime
@@ -106,6 +166,7 @@ services:
               capabilities: [gpu]
     restart: unless-stopped
 
+  # ========== Tools (on-demand) ==========
   anime-face-detector:
     build: ./face-detector
     container_name: anime-face-detector

test_full_pipeline.py

@@ -1,196 +1,254 @@
 #!/usr/bin/env python3
 """
-Full Pipeline Test for Memory Consolidation System
-Tests all phases: Storage → Consolidation → Fact Extraction → Recall
+Full Pipeline Test for Memory Consolidation System v2.0.0
 """
 
 import requests
 import time
 import json
+import sys
 
-BASE_URL = "http://localhost:1865"
+CAT_URL = "http://localhost:1865"
+QDRANT_URL = "http://localhost:6333"
+CONSOLIDATION_TIMEOUT = 180
 
-def send_message(text):
-    """Send a message to Miku and get response"""
-    resp = requests.post(f"{BASE_URL}/message", json={"text": text})
+
+def send_message(text, timeout=30):
+    try:
+        resp = requests.post(f"{CAT_URL}/message", json={"text": text}, timeout=timeout)
+        resp.raise_for_status()
         return resp.json()
+    except requests.exceptions.Timeout:
+        return {"error": "timeout", "content": ""}
+    except Exception as e:
+        return {"error": str(e), "content": ""}
+
+
+def qdrant_scroll(collection, limit=200, filt=None):
+    body = {"limit": limit, "with_payload": True, "with_vector": False}
+    if filt:
+        body["filter"] = filt
+    resp = requests.post(f"{QDRANT_URL}/collections/{collection}/points/scroll", json=body)
+    return resp.json()["result"]["points"]
+
+
+def qdrant_count(collection):
+    return len(qdrant_scroll(collection))
+
+
+def section(title):
+    print(f"\n{'=' * 70}")
+    print(f"  {title}")
+    print(f"{'=' * 70}")
 
-def get_qdrant_count(collection):
-    """Get count of items in Qdrant collection"""
-    resp = requests.post(
-        f"http://localhost:6333/collections/{collection}/points/scroll",
-        json={"limit": 1000, "with_payload": False, "with_vector": False}
-    )
-    return len(resp.json()["result"]["points"])
 
 print("=" * 70)
-print("🧪 FULL PIPELINE TEST - Memory Consolidation System")
+print("  FULL PIPELINE TEST  -  Memory Consolidation v2.0.0")
 print("=" * 70)
 
+try:
+    requests.get(f"{CAT_URL}/", timeout=5)
+except Exception:
+    print("ERROR: Cat not reachable"); sys.exit(1)
+try:
+    requests.get(f"{QDRANT_URL}/collections", timeout=5)
+except Exception:
+    print("ERROR: Qdrant not reachable"); sys.exit(1)
+
+episodic_start = qdrant_count("episodic")
+declarative_start = qdrant_count("declarative")
+print(f"\nStarting state: {episodic_start} episodic, {declarative_start} declarative")
+
+results = {}
+
 # TEST 1: Trivial Message Filtering
-print("\n📋 TEST 1: Trivial Message Filtering")
-print("-" * 70)
+section("TEST 1: Trivial Message Filtering")
 
-trivial_messages = ["lol", "k", "ok", "haha", "xd"]
-important_message = "My name is Alex and I live in Seattle"
-
-print("Sending trivial messages (should be filtered out)...")
+trivial_messages = ["lol", "k", "ok", "haha", "xd", "brb"]
+print(f"Sending {len(trivial_messages)} trivial messages...")
 for msg in trivial_messages:
     send_message(msg)
-    time.sleep(0.5)
+    time.sleep(0.3)
 
-print("Sending important message...")
-send_message(important_message)
 time.sleep(1)
+# Count only USER episodic memories (exclude Miku's responses)
+user_episodic = qdrant_scroll("episodic", filt={
+    "must_not": [{"key": "metadata.speaker", "match": {"value": "miku"}}]
+})
+trivial_user_stored = len(user_episodic) - episodic_start
+episodic_after_trivial = qdrant_count("episodic")
 
-episodic_count = get_qdrant_count("episodic")
-print(f"\n✅ Episodic memories stored: {episodic_count}")
-if episodic_count < len(trivial_messages):
-    print("   ✓ Trivial filtering working! (some messages were filtered)")
+# discord_bridge filters trivial user messages, but Miku still responds
+# so we only check user-side storage
+if trivial_user_stored < len(trivial_messages):
+    print(f"   PASS - Only {trivial_user_stored}/{len(trivial_messages)} user trivial messages stored")
+    print(f"   (Total episodic incl. Miku responses: {episodic_after_trivial})")
+    results["trivial_filtering"] = True
 else:
-    print("   ⚠️  Trivial filtering may not be active")
+    print(f"   WARN - All {trivial_user_stored} trivial messages stored")
+    results["trivial_filtering"] = False
 
-# TEST 2: Miku's Response Storage
-print("\n📋 TEST 2: Miku's Response Storage")
-print("-" * 70)
+# TEST 2: Important Message Storage
+section("TEST 2: Important Message Storage")
 
-print("Sending message and checking if Miku's response is stored...")
-resp = send_message("Tell me a very short fact about music")
-miku_said = resp["content"]
-print(f"Miku said: {miku_said[:80]}...")
-time.sleep(2)
-
-# Check for Miku's messages in episodic
-resp = requests.post(
-    "http://localhost:6333/collections/episodic/points/scroll",
-    json={
-        "limit": 100,
-        "with_payload": True,
-        "with_vector": False,
-        "filter": {"must": [{"key": "metadata.speaker", "match": {"value": "miku"}}]}
-    }
-)
-miku_messages = resp.json()["result"]["points"]
-print(f"\n✅ Miku's messages in memory: {len(miku_messages)}")
-if miku_messages:
-    print(f"   Example: {miku_messages[0]['payload']['page_content'][:60]}...")
-    print("   ✓ Bidirectional memory working!")
-else:
-    print("   ⚠️  Miku's responses not being stored")
-
-# TEST 3: Add Rich Personal Information
-print("\n📋 TEST 3: Adding Personal Information")
-print("-" * 70)
-
-personal_info = [
+personal_facts = [
     "My name is Sarah Chen",
     "I'm 28 years old",
-    "I work as a data scientist at Google",
-    "My favorite color is blue",
-    "I love playing piano",
+    "I live in Seattle, Washington",
+    "I work as a software engineer at Microsoft",
+    "My favorite color is forest green",
+    "I love playing piano and have practiced for 15 years",
+    "I'm learning Japanese, currently at N3 level",
+    "I have a cat named Luna",
     "I'm allergic to peanuts",
-    "I live in Tokyo, Japan",
-    "My hobbies include photography and hiking"
+    "My birthday is March 15th",
+    "I graduated from UW in 2018",
+    "I enjoy hiking on weekends",
 ]
 
-print(f"Adding {len(personal_info)} messages with personal information...")
-for info in personal_info:
-    send_message(info)
+print(f"Sending {len(personal_facts)} personal info messages...")
+for i, fact in enumerate(personal_facts, 1):
+    resp = send_message(fact)
+    status = "OK" if "error" not in resp else "ERR"
+    print(f"   [{i}/{len(personal_facts)}] {status} {fact[:50]}")
     time.sleep(0.5)
 
-episodic_after = get_qdrant_count("episodic")
-print(f"\n✅ Total episodic memories: {episodic_after}")
-print(f"   ({episodic_after - episodic_count} new memories added)")
+time.sleep(1)
+episodic_after_personal = qdrant_count("episodic")
+personal_stored = episodic_after_personal - episodic_after_trivial
+print(f"\n   Episodic memories from personal info: {personal_stored}")
+results["important_storage"] = personal_stored >= len(personal_facts)
+print(f"   {'PASS' if results['important_storage'] else 'FAIL'} - Expected >={len(personal_facts)}, got {personal_stored}")
 
-# TEST 4: Memory Consolidation
-print("\n📋 TEST 4: Memory Consolidation & Fact Extraction")
-print("-" * 70)
+# TEST 3: Miku Response Storage
+section("TEST 3: Bidirectional Memory (Miku Response Storage)")
 
-print("Triggering consolidation...")
-resp = send_message("consolidate now")
-consolidation_result = resp["content"]
-print(f"\n{consolidation_result}")
+miku_points = qdrant_scroll("episodic", filt={
+    "must": [{"key": "metadata.speaker", "match": {"value": "miku"}}]
+})
+print(f"   Miku's memories in episodic: {len(miku_points)}")
+if miku_points:
+    print(f"   Sample: \"{miku_points[0]['payload']['page_content'][:70]}\"")
+    results["miku_storage"] = True
+    print("   PASS")
+else:
+    results["miku_storage"] = False
+    print("   FAIL - No Miku responses in episodic memory")
 
-time.sleep(2)
+# TEST 4: Per-User Source Tagging
+section("TEST 4: Per-User Source Tagging")
 
-# Check declarative facts
-declarative_count = get_qdrant_count("declarative")
-print(f"\n✅ Declarative facts extracted: {declarative_count}")
+user_points = qdrant_scroll("episodic", filt={
+    "must": [{"key": "metadata.source", "match": {"value": "user"}}]
+})
+print(f"   Points with source='user': {len(user_points)}")
 
-if declarative_count > 0:
-    # Show sample facts
-    resp = requests.post(
-        "http://localhost:6333/collections/declarative/points/scroll",
-        json={"limit": 5, "with_payload": True, "with_vector": False}
-    )
-    facts = resp.json()["result"]["points"]
-    print("\nSample facts:")
-    for i, fact in enumerate(facts[:5], 1):
-        print(f"   {i}. {fact['payload']['page_content']}")
+global_points = qdrant_scroll("episodic", filt={
+    "must": [{"key": "metadata.source", "match": {"value": "global"}}]
+})
+print(f"   Points with source='global' (old bug): {len(global_points)}")
 
-# TEST 5: Fact Recall
-print("\n📋 TEST 5: Declarative Fact Recall")
-print("-" * 70)
+results["user_tagging"] = len(user_points) > 0 and len(global_points) == 0
+print(f"   {'PASS' if results['user_tagging'] else 'FAIL'}")
 
-queries = [
-    "What is my name?",
-    "How old am I?",
-    "Where do I work?",
-    "What's my favorite color?",
-    "What am I allergic to?"
-]
+# TEST 5: Memory Consolidation
+section("TEST 5: Memory Consolidation & Fact Extraction")
 
-print("Testing fact recall with queries...")
-correct_recalls = 0
-for query in queries:
-    resp = send_message(query)
-    answer = resp["content"]
-    print(f"\n❓ {query}")
-    print(f"💬 Miku: {answer[:150]}...")
+print(f"   Triggering consolidation (timeout={CONSOLIDATION_TIMEOUT}s)...")
+t0 = time.time()
+resp = send_message("consolidate now", timeout=CONSOLIDATION_TIMEOUT)
+elapsed = time.time() - t0
 
-    # Basic heuristic: check if answer contains likely keywords
-    keywords = {
-        "What is my name?": ["Sarah", "Chen"],
+if "error" in resp:
+    print(f"   WARN - HTTP issue: {resp['error']} ({elapsed:.0f}s)")
+    print("   Waiting 60s for background completion...")
+    time.sleep(60)
+else:
+    print(f"   Completed in {elapsed:.1f}s")
+    content = resp.get("content", "")
+    print(f"   Response: {content[:120]}...")
+
+time.sleep(3)
+
+declarative_after = qdrant_count("declarative")
+new_facts = declarative_after - declarative_start
+print(f"\n   Declarative facts: {declarative_start} -> {declarative_after} (+{new_facts})")
+
+results["consolidation"] = new_facts >= 5
+print(f"   {'PASS' if results['consolidation'] else 'FAIL'} - {'>=5 facts' if results['consolidation'] else f'only {new_facts}'}")
+
+all_facts = qdrant_scroll("declarative")
+print(f"\n   All declarative facts ({len(all_facts)}):")
+for i, f in enumerate(all_facts, 1):
+    content = f["payload"]["page_content"]
+    meta = f["payload"].get("metadata", {})
+    source = meta.get("source", "?")
+    ftype = meta.get("fact_type", "?")
+    print(f"      {i}. [{source}|{ftype}] {content}")
+
+# TEST 6: Duplicate Detection
+section("TEST 6: Duplicate Detection (2nd consolidation)")
+
+facts_before_2nd = qdrant_count("declarative")
+print(f"   Facts before: {facts_before_2nd}")
+print(f"   Running consolidation again...")
+
+resp = send_message("consolidate now", timeout=CONSOLIDATION_TIMEOUT)
+time.sleep(3)
+
+facts_after_2nd = qdrant_count("declarative")
+new_dupes = facts_after_2nd - facts_before_2nd
+print(f"   Facts after: {facts_after_2nd} (+{new_dupes})")
+
+results["dedup"] = new_dupes <= 2
+print(f"   {'PASS' if results['dedup'] else 'FAIL'} - {new_dupes} new facts (<=2 expected)")
+
+# TEST 7: Fact Recall
+section("TEST 7: Fact Recall via Natural Language")
+
+queries = {
+    "What is my name?": ["sarah", "chen"],
     "How old am I?": ["28"],
-        "Where do I work?": ["Google", "data scientist"],
-        "What's my favorite color?": ["blue"],
-        "What am I allergic to?": ["peanut"]
+    "Where do I live?": ["seattle"],
+    "Where do I work?": ["microsoft", "software engineer"],
+    "What am I allergic to?": ["peanut"],
 }
 
-    if any(kw.lower() in answer.lower() for kw in keywords[query]):
-        print("   ✓ Correct recall!")
-        correct_recalls += 1
-    else:
-        print("   ⚠️  May not have recalled correctly")
-    
+correct = 0
+for question, keywords in queries.items():
+    resp = send_message(question)
+    answer = resp.get("content", "")
+    hit = any(kw.lower() in answer.lower() for kw in keywords)
+    if hit:
+        correct += 1
+    icon = "OK" if hit else "??"
+    print(f"   {icon} Q: {question}")
+    print(f"      A: {answer[:150]}")
     time.sleep(1)
 
-print(f"\n✅ Fact recall accuracy: {correct_recalls}/{len(queries)} ({correct_recalls/len(queries)*100:.0f}%)")
+accuracy = correct / len(queries) * 100
+results["recall"] = correct >= 3
+print(f"\n   Recall: {correct}/{len(queries)} ({accuracy:.0f}%)")
+print(f"   {'PASS' if results['recall'] else 'FAIL'} (threshold: >=3)")
 
-# TEST 6: Conversation History Recall
-print("\n📋 TEST 6: Conversation History (Episodic) Recall")
-print("-" * 70)
+# FINAL SUMMARY
+section("FINAL SUMMARY")
 
-print("Asking about conversation history...")
-resp = send_message("What have we talked about today?")
-summary = resp["content"]
-print(f"💬 Miku's summary:\n{summary}")
+total = len(results)
+passed = sum(1 for v in results.values() if v)
+print()
+for name, ok in results.items():
+    print(f"   [{'PASS' if ok else 'FAIL'}] {name}")
 
-# Final Summary
-print("\n" + "=" * 70)
-print("📊 FINAL SUMMARY")
-print("=" * 70)
-print(f"✅ Episodic memories: {get_qdrant_count('episodic')}")
-print(f"✅ Declarative facts: {declarative_count}")
-print(f"✅ Miku's messages stored: {len(miku_messages)}")
-print(f"✅ Fact recall accuracy: {correct_recalls}/{len(queries)}")
+print(f"\n   Score: {passed}/{total}")
+print(f"   Episodic: {qdrant_count('episodic')}")
+print(f"   Declarative: {qdrant_count('declarative')}")
 
-# Overall verdict
-if declarative_count >= 5 and correct_recalls >= 3:
-    print("\n🎉 PIPELINE TEST: PASS")
-    print("   All major components working correctly!")
+if passed == total:
+    print("\n   ALL TESTS PASSED!")
+elif passed >= total - 1:
+    print("\n   MOSTLY PASSING - minor issues only")
 else:
-    print("\n⚠️  PIPELINE TEST: PARTIAL PASS")
-    print("   Some components may need adjustment")
+    print("\n   SOME TESTS FAILED - review above")
 
 print("\n" + "=" * 70)