Extend ChromaDB memory system with scheduled tasks and Setup UI support

- Added new scripts to manage ChromaDB memory processing and periodic scheduling (e.g. compaction, deduplication, reindexing).
- Optimized chatbot memory usage by improving base memory retrieval logic and preload strategy.
- Updated Setup.py UI to include scheduling options for memory maintenance tasks.
- Ensures better long-term memory performance, avoids memory bloat, and enables proactive management of large-scale memory datasets.

Setup.py

@@ -307,6 +307,34 @@ def load_current_config():
             if bot_memory_collection_match:
                 config_data["BOT_MEMORY_COLLECTION"] = bot_memory_collection_match.group(1)
 
+            # Extract memory management settings
+            backup_hour_match = re.search(r'MEMORY_BACKUP_HOUR\s*=\s*(\d+)', config_content)
+            if backup_hour_match:
+                config_data["MEMORY_BACKUP_HOUR"] = int(backup_hour_match.group(1))
+
+            backup_minute_match = re.search(r'MEMORY_BACKUP_MINUTE\s*=\s*(\d+)', config_content)
+            if backup_minute_match:
+                config_data["MEMORY_BACKUP_MINUTE"] = int(backup_minute_match.group(1))
+
+            profile_model_match = re.search(r'MEMORY_PROFILE_MODEL\s*=\s*["\']?(.+?)["\']?\s*(?:#|$)', config_content)
+            # Handle potential LLM_MODEL reference
+            if profile_model_match:
+                 profile_model_val = profile_model_match.group(1).strip()
+                 if profile_model_val == "LLM_MODEL":
+                     # If it refers to LLM_MODEL, use the already parsed LLM_MODEL value
+                     config_data["MEMORY_PROFILE_MODEL"] = config_data.get("LLM_MODEL", "deepseek/deepseek-chat-v3-0324") # Fallback if LLM_MODEL wasn't parsed
+                 else:
+                     config_data["MEMORY_PROFILE_MODEL"] = profile_model_val
+            else:
+                 # Default to LLM_MODEL if not found
+                 config_data["MEMORY_PROFILE_MODEL"] = config_data.get("LLM_MODEL", "deepseek/deepseek-chat-v3-0324")
+
+
+            summary_model_match = re.search(r'MEMORY_SUMMARY_MODEL\s*=\s*["\'](.+?)["\']', config_content)
+            if summary_model_match:
+                config_data["MEMORY_SUMMARY_MODEL"] = summary_model_match.group(1)
+
+
         except Exception as e:
             print(f"Error reading config.py: {e}")
             import traceback
@@ -416,7 +444,9 @@ def generate_config_file(config_data, env_data):
                 f.write("            \"--client-type\",\n")
                 f.write("            \"persistent\",\n")
                 f.write("            \"--data-dir\",\n")
-                f.write(f"            \"{absolute_data_dir}\"\n")
+                # Escape backslashes in the path for the string literal in config.py
+                escaped_data_dir = absolute_data_dir.replace('\\', '\\\\')
+                f.write(f"            \"{escaped_data_dir}\"\n")
                 f.write("        ]\n")
             
             # Handle custom server - just write as raw JSON
@@ -492,7 +522,25 @@ def generate_config_file(config_data, env_data):
         f.write(f"# This path will be made absolute when config.py is loaded.\n")
         # Write the potentially relative path from UI/default, let config.py handle abspath
         # Use raw string r"..." to handle potential backslashes in Windows paths correctly within the string literal
-        f.write(f"CHROMA_DATA_DIR = os.path.abspath(r\"{normalized_chroma_path}\")\n")
+        f.write(f"CHROMA_DATA_DIR = os.path.abspath(r\"{normalized_chroma_path}\")\n\n")
+
+        # Write Memory Management Configuration
+        f.write("# =============================================================================\n")
+        f.write("# Memory Management Configuration\n")
+        f.write("# =============================================================================\n")
+        backup_hour = config_data.get('MEMORY_BACKUP_HOUR', 0)
+        backup_minute = config_data.get('MEMORY_BACKUP_MINUTE', 0)
+        profile_model = config_data.get('MEMORY_PROFILE_MODEL', 'LLM_MODEL') # Default to referencing LLM_MODEL
+        summary_model = config_data.get('MEMORY_SUMMARY_MODEL', 'mistral-7b-instruct')
+
+        f.write(f"MEMORY_BACKUP_HOUR = {backup_hour}\n")
+        f.write(f"MEMORY_BACKUP_MINUTE = {backup_minute}\n")
+        # Write profile model, potentially referencing LLM_MODEL
+        if profile_model == config_data.get('LLM_MODEL'):
+             f.write(f"MEMORY_PROFILE_MODEL = LLM_MODEL # Default to main LLM model\n")
+        else:
+             f.write(f"MEMORY_PROFILE_MODEL = \"{profile_model}\"\n")
+        f.write(f"MEMORY_SUMMARY_MODEL = \"{summary_model}\"\n")
 
 
     print("Generated config.py file successfully")
@@ -522,6 +570,7 @@ class WolfChatSetup(tk.Tk):
         self.create_mcp_tab()
         self.create_game_tab()
         self.create_memory_tab() 
+        self.create_memory_management_tab() # 新增記憶管理標籤頁
         self.create_management_tab() # New tab for combined management
 
         # Create bottom buttons
@@ -539,9 +588,13 @@ class WolfChatSetup(tk.Tk):
         self.keep_monitoring_flag = threading.Event()
         self.keep_monitoring_flag.set()
 
+        # Initialize scheduler process tracker
+        self.scheduler_process = None
+
 
         # Set initial states based on loaded data
         self.update_ui_from_data()
+        self.update_scheduler_button_states(True) # Set initial scheduler button state
     
     def create_management_tab(self):
         """Create the Bot and Game Management tab"""
@@ -1135,8 +1188,11 @@ class WolfChatSetup(tk.Tk):
 
     def on_closing(self):
         """Handle window close event."""
-        if messagebox.askokcancel("Quit", "Do you want to quit Wolf Chat Setup? This will stop any managed sessions."):
+        if messagebox.askokcancel("Quit", "Do you want to quit Wolf Chat Setup? This will stop any managed sessions and running scripts."):
-            self.stop_managed_session() # Ensure everything is stopped
+            print("Closing Setup...")
+            self.stop_managed_session() # Stop bot/game managed session if running
+            self.stop_process() # Stop bot/test script if running independently
+            self.stop_memory_scheduler() # Stop scheduler if running
             self.destroy()
 
     def create_api_tab(self):
@@ -1670,6 +1726,65 @@ class WolfChatSetup(tk.Tk):
         info_label = ttk.Label(info_frame, text=info_text, justify=tk.LEFT, wraplength=700)
         info_label.pack(padx=10, pady=10, anchor=tk.W)
 
+    # 記憶管理標籤頁
+    def create_memory_management_tab(self):
+        tab = ttk.Frame(self.notebook)
+        self.notebook.add(tab, text="記憶管理")
+
+        main_frame = ttk.Frame(tab, padding=10)
+        main_frame.pack(fill=tk.BOTH, expand=True)
+
+        # 備份時間設置
+        backup_frame = ttk.LabelFrame(main_frame, text="備份設定")
+        backup_frame.pack(fill=tk.X, pady=10)
+
+        time_frame = ttk.Frame(backup_frame)
+        time_frame.pack(fill=tk.X, pady=5, padx=10)
+        time_label = ttk.Label(time_frame, text="執行時間:", width=20)
+        time_label.pack(side=tk.LEFT, padx=(0, 5))
+        self.backup_hour_var = tk.IntVar(value=0)
+        hour_spinner = ttk.Spinbox(time_frame, from_=0, to=23, width=3, textvariable=self.backup_hour_var)
+        hour_spinner.pack(side=tk.LEFT)
+        ttk.Label(time_frame, text=":").pack(side=tk.LEFT)
+        self.backup_minute_var = tk.IntVar(value=0)
+        minute_spinner = ttk.Spinbox(time_frame, from_=0, to=59, width=3, textvariable=self.backup_minute_var)
+        minute_spinner.pack(side=tk.LEFT)
+
+        # 模型選擇
+        models_frame = ttk.LabelFrame(main_frame, text="模型選擇")
+        models_frame.pack(fill=tk.X, pady=10)
+
+        profile_model_frame = ttk.Frame(models_frame)
+        profile_model_frame.pack(fill=tk.X, pady=5, padx=10)
+        profile_model_label = ttk.Label(profile_model_frame, text="用戶檔案生成模型:", width=20)
+        profile_model_label.pack(side=tk.LEFT, padx=(0, 5))
+        # Initialize with a sensible default, will be overwritten by update_ui_from_data
+        # Use config_data which is loaded in __init__
+        profile_model_default = self.config_data.get("LLM_MODEL", "deepseek/deepseek-chat-v3-0324")
+        self.profile_model_var = tk.StringVar(value=profile_model_default) 
+        profile_model_entry = ttk.Entry(profile_model_frame, textvariable=self.profile_model_var)
+        profile_model_entry.pack(side=tk.LEFT, fill=tk.X, expand=True)
+
+        summary_model_frame = ttk.Frame(models_frame)
+        summary_model_frame.pack(fill=tk.X, pady=5, padx=10)
+        summary_model_label = ttk.Label(summary_model_frame, text="聊天總結生成模型:", width=20)
+        summary_model_label.pack(side=tk.LEFT, padx=(0, 5))
+        self.summary_model_var = tk.StringVar(value="mistral-7b-instruct")
+        summary_model_entry = ttk.Entry(summary_model_frame, textvariable=self.summary_model_var)
+        summary_model_entry.pack(side=tk.LEFT, fill=tk.X, expand=True)
+
+        # Information box
+        info_frame_mm = ttk.LabelFrame(main_frame, text="Information") # Renamed to avoid conflict
+        info_frame_mm.pack(fill=tk.BOTH, expand=True, pady=10)
+
+        info_text_mm = (
+            "• 設定每日自動執行記憶備份的時間。\n"
+            "• 選擇用於生成用戶檔案和聊天總結的語言模型。\n"
+            "• 用戶檔案生成模型預設使用主LLM模型。"
+        )
+        info_label_mm = ttk.Label(info_frame_mm, text=info_text_mm, justify=tk.LEFT, wraplength=700)
+        info_label_mm.pack(padx=10, pady=10, anchor=tk.W)
+
     def create_bottom_buttons(self):
         """Create bottom action buttons"""
         btn_frame = ttk.Frame(self)
@@ -1696,10 +1811,17 @@ class WolfChatSetup(tk.Tk):
         self.run_bot_btn = ttk.Button(btn_frame, text="Run Chat Bot", command=self.run_chat_bot)
         self.run_bot_btn.pack(side=tk.RIGHT, padx=5)
 
-        # Stop button
+        # Stop button (for bot/test)
-        self.stop_btn = ttk.Button(btn_frame, text="Stop Process", command=self.stop_process, state=tk.DISABLED)
+        self.stop_btn = ttk.Button(btn_frame, text="Stop Bot/Test", command=self.stop_process, state=tk.DISABLED)
         self.stop_btn.pack(side=tk.RIGHT, padx=5)
 
+        # Scheduler buttons
+        self.stop_scheduler_btn = ttk.Button(btn_frame, text="Stop Scheduler", command=self.stop_memory_scheduler, state=tk.DISABLED)
+        self.stop_scheduler_btn.pack(side=tk.RIGHT, padx=5)
+
+        self.start_scheduler_btn = ttk.Button(btn_frame, text="Start Scheduler", command=self.run_memory_scheduler)
+        self.start_scheduler_btn.pack(side=tk.RIGHT, padx=5)
+        
     def install_dependencies(self):
         """Run the installation script for dependencies"""
         try:
@@ -1772,7 +1894,78 @@ class WolfChatSetup(tk.Tk):
                 # Re-enable run buttons and disable stop button
                 self.update_run_button_states(True)
         else:
-            messagebox.showinfo("No Process", "No process is currently running.")
+            messagebox.showinfo("No Process", "No Bot/Test process is currently running.")
+
+    def run_memory_scheduler(self):
+        """Run the memory backup scheduler script"""
+        try:
+            scheduler_script = "memory_backup.py"
+            if not os.path.exists(scheduler_script):
+                messagebox.showerror("Error", f"Could not find {scheduler_script}")
+                return
+
+            if self.scheduler_process is not None and self.scheduler_process.poll() is None:
+                 messagebox.showwarning("Already Running", "The memory scheduler process is already running.")
+                 return
+
+            # Run with --schedule argument
+            # Use CREATE_NO_WINDOW flag on Windows to hide the console window
+            creationflags = 0
+            if sys.platform == "win32":
+                creationflags = subprocess.CREATE_NO_WINDOW
+                
+            self.scheduler_process = subprocess.Popen(
+                [sys.executable, scheduler_script, "--schedule"],
+                creationflags=creationflags
+            )
+            print(f"Attempting to start {scheduler_script} --schedule... PID: {self.scheduler_process.pid}")
+            self.update_scheduler_button_states(False) # Disable start, enable stop
+        except Exception as e:
+            logger.exception(f"Failed to launch {scheduler_script}") # Log exception
+            messagebox.showerror("Error", f"Failed to launch {scheduler_script}: {str(e)}")
+            self.update_scheduler_button_states(True) # Re-enable start on failure
+
+    def stop_memory_scheduler(self):
+        """Stop the currently running memory scheduler process"""
+        if self.scheduler_process is not None and self.scheduler_process.poll() is None:
+            try:
+                print(f"Attempting to terminate memory scheduler process (PID: {self.scheduler_process.pid})...")
+                # Terminate the process group on non-Windows to ensure child processes are handled if any
+                if sys.platform != "win32":
+                    os.killpg(os.getpgid(self.scheduler_process.pid), signal.SIGTERM)
+                else:
+                    # On Windows, terminate the parent process directly
+                    self.scheduler_process.terminate()
+                
+                # Wait briefly to allow termination
+                try:
+                    self.scheduler_process.wait(timeout=3) 
+                    print("Scheduler process terminated gracefully.")
+                except subprocess.TimeoutExpired:
+                    print("Scheduler process did not terminate gracefully, killing...")
+                    if sys.platform != "win32":
+                         os.killpg(os.getpgid(self.scheduler_process.pid), signal.SIGKILL)
+                    else:
+                         self.scheduler_process.kill()
+                    self.scheduler_process.wait(timeout=2) # Wait after kill
+                    print("Scheduler process killed.")
+
+                self.scheduler_process = None
+                messagebox.showinfo("Scheduler Stopped", "The memory scheduler process has been terminated.")
+            except Exception as e:
+                logger.exception("Failed to terminate scheduler process") # Log exception
+                messagebox.showerror("Error", f"Failed to terminate scheduler process: {str(e)}")
+            finally:
+                self.scheduler_process = None # Ensure it's cleared
+                self.update_scheduler_button_states(True) # Update buttons
+        else:
+            # If process exists but poll() is not None (already terminated) or process is None
+            if self.scheduler_process is not None:
+                 self.scheduler_process = None # Clear stale process object
+            # messagebox.showinfo("No Scheduler Process", "The memory scheduler process is not running.") # Reduce popups
+            print("Scheduler process is not running or already stopped.")
+            self.update_scheduler_button_states(True) # Ensure buttons are in correct state
+
 
     def update_run_button_states(self, enable):
         """Enable or disable the run buttons and update stop button state"""
@@ -1784,6 +1977,18 @@ class WolfChatSetup(tk.Tk):
         if hasattr(self, 'stop_btn'):
              self.stop_btn.config(state=tk.DISABLED if enable else tk.NORMAL)
 
+    def update_scheduler_button_states(self, enable_start):
+        """Enable or disable the scheduler buttons"""
+        # Check if process is running
+        is_running = False
+        if self.scheduler_process is not None and self.scheduler_process.poll() is None:
+            is_running = True
+            
+        if hasattr(self, 'start_scheduler_btn'):
+             self.start_scheduler_btn.config(state=tk.NORMAL if not is_running else tk.DISABLED)
+        if hasattr(self, 'stop_scheduler_btn'):
+             self.stop_scheduler_btn.config(state=tk.DISABLED if not is_running else tk.NORMAL)
+    
     def update_ui_from_data(self):
         """Update UI controls from loaded data"""
         try:
@@ -1844,6 +2049,15 @@ class WolfChatSetup(tk.Tk):
             self.conversations_collection_var.set(self.config_data.get("CONVERSATIONS_COLLECTION", "conversations"))
             self.bot_memory_collection_var.set(self.config_data.get("BOT_MEMORY_COLLECTION", "wolfhart_memory"))
 
+            # Memory Management Tab Settings
+            if hasattr(self, 'backup_hour_var'): # Check if UI elements for memory management tab exist
+                self.backup_hour_var.set(self.config_data.get("MEMORY_BACKUP_HOUR", 0))
+                self.backup_minute_var.set(self.config_data.get("MEMORY_BACKUP_MINUTE", 0))
+                # Default profile model to LLM_MODEL if MEMORY_PROFILE_MODEL isn't set or matches LLM_MODEL
+                profile_model_config = self.config_data.get("MEMORY_PROFILE_MODEL", self.config_data.get("LLM_MODEL"))
+                self.profile_model_var.set(profile_model_config) 
+                self.summary_model_var.set(self.config_data.get("MEMORY_SUMMARY_MODEL", "mistral-7b-instruct"))
+
             # Management Tab Settings
             if hasattr(self, 'remote_url_var'): # Check if UI elements for management tab exist
                 self.remote_url_var.set(self.remote_data.get("REMOTE_SERVER_URL", ""))
@@ -2111,6 +2325,13 @@ class WolfChatSetup(tk.Tk):
             self.config_data["CONVERSATIONS_COLLECTION"] = self.conversations_collection_var.get()
             self.config_data["BOT_MEMORY_COLLECTION"] = self.bot_memory_collection_var.get()
 
+            # Get Memory Management settings from UI
+            if hasattr(self, 'backup_hour_var'): # Check if UI elements exist
+                self.config_data["MEMORY_BACKUP_HOUR"] = self.backup_hour_var.get()
+                self.config_data["MEMORY_BACKUP_MINUTE"] = self.backup_minute_var.get()
+                self.config_data["MEMORY_PROFILE_MODEL"] = self.profile_model_var.get()
+                self.config_data["MEMORY_SUMMARY_MODEL"] = self.summary_model_var.get()
+            
             # Update remote_data from UI (for remote_config.json)
             if hasattr(self, 'remote_url_var'): # Check if management tab UI elements exist
                 self.remote_data["REMOTE_SERVER_URL"] = self.remote_url_var.get()

llm_interaction.py

@@ -150,25 +150,24 @@ def get_system_prompt(
     else:
         # 如果沒有預載入數據，則使用完整記憶協議
         memory_enforcement = f"""
-        === CHROMADB MEMORY RETRIEVAL PROTOCOL - Wolfhart Memory Integration
+=== CHROMADB MEMORY RETRIEVAL PROTOCOL - Wolfhart Memory Integration
-        To personalize your responses to different users, you MUST follow this memory access protocol internally before responding:
+To personalize your responses to different users, you MUST follow this memory access protocol internally before responding:
 
-        **1. Basic User Retrieval:**
+**1. Basic User Retrieval:**
-           - Identify the username from `<CURRENT_MESSAGE>`
+   - Identify the username from `<CURRENT_MESSAGE>`
-           # 修正：使用 BOT_MEMORY_COLLECTION 來獲取用戶資料
+   - Using the `tool_calls` mechanism, execute: `chroma_get_documents(collection_name: "{config.PROFILES_COLLECTION}", ids: ["{{username}}_profile"])` 
-           - Using the `tool_calls` mechanism, execute: `chroma_query_documents(collection_name: "{config.BOT_MEMORY_COLLECTION}", query_texts: ["{{username}} profile"], n_results: 1)` # 只需最相關的1筆
+   - This step must be completed before any response generation
-           - This step must be completed before any response generation
 
-        **2. Context Expansion:**
+**2. Context Expansion:**
-           - Perform additional queries as needed, using the `tool_calls` mechanism:
+   - Perform additional queries as needed, using the `tool_calls` mechanism:
-             - Relevant conversations: `chroma_query_documents(collection_name: "{config.CONVERSATIONS_COLLECTION}", query_texts: ["{{username}} {{query keywords}}"], n_results: 5)`
+     - Relevant conversations: `chroma_query_documents(collection_name: "{config.CONVERSATIONS_COLLECTION}", query_texts: ["{{username}} {{query keywords}}"], n_results: 5)`
-             - Core personality reference: `chroma_query_documents(collection_name: "{config.BOT_MEMORY_COLLECTION}", query_texts: ["Wolfhart {{relevant attitude}}"], n_results: 3)`
+     - Core personality reference: `chroma_query_documents(collection_name: "{config.BOT_MEMORY_COLLECTION}", query_texts: ["Wolfhart {{relevant attitude}}"], n_results: 3)`
 
-        **3. Other situation**
+**3. Other situation**
-           - You should check related memories when Users mention [capital_position], [capital_administrator_role], [server_hierarchy], [last_war], [winter_war], [excavations], [blueprints], [honor_points], [golden_eggs], or [diamonds], as these represent key game mechanics.
+   - You should check related memories when Users mention [capital_position], [capital_administrator_role], [server_hierarchy], [last_war], [winter_war], [excavations], [blueprints], [honor_points], [golden_eggs], or [diamonds], as these represent key game mechanics.
 
-        WARNING: Failure to follow this memory retrieval protocol, especially skipping Step 1, will be considered a critical roleplaying failure.
+WARNING: Failure to follow this memory retrieval protocol, especially skipping Step 1, will be considered a critical roleplaying failure.
-        """
+"""
 
     # 組合系統提示
     system_prompt = f"""

memory_backup.py

@@ -0,0 +1,42 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+Wolf Chat 記憶備份工具
+
+用於手動執行記憶備份或啟動定時調度器
+"""
+
+import sys
+import argparse
+import datetime
+from memory_manager import run_memory_backup_manual, MemoryScheduler # Updated import
+import config # Import config to access default schedule times
+
+def main():
+    parser = argparse.ArgumentParser(description='Wolf Chat 記憶備份工具')
+    parser.add_argument('--backup', action='store_true', help='執行一次性備份 (預設為昨天，除非指定 --date)')
+    parser.add_argument('--date', type=str, help='處理指定日期的日誌 (YYYY-MM-DD格式) for --backup')
+    parser.add_argument('--schedule', action='store_true', help='啟動定時調度器')
+    parser.add_argument('--hour', type=int, help='備份時間（小時，0-23）for --schedule')
+    parser.add_argument('--minute', type=int, help='備份時間（分鐘，0-59）for --schedule')
+    
+    args = parser.parse_args()
+    
+    if args.backup:
+        # The date logic is now handled inside run_memory_backup_manual
+        run_memory_backup_manual(args.date)
+    elif args.schedule:
+        scheduler = MemoryScheduler()
+        # Use provided hour/minute or fallback to config defaults
+        backup_hour = args.hour if args.hour is not None else getattr(config, 'MEMORY_BACKUP_HOUR', 0)
+        backup_minute = args.minute if args.minute is not None else getattr(config, 'MEMORY_BACKUP_MINUTE', 0)
+        
+        scheduler.schedule_daily_backup(backup_hour, backup_minute)
+        scheduler.start()
+    else:
+        print("請指定操作: --backup 或 --schedule")
+        parser.print_help()
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()

memory_manager.py

@@ -0,0 +1,679 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+Wolf Chat 記憶管理模組
+
+處理聊天記錄解析、記憶生成和ChromaDB寫入的一體化模組
+"""
+
+import os
+import re
+import json
+import time
+import asyncio
+import datetime
+import schedule
+from pathlib import Path
+from typing import Dict, List, Optional, Any, Union
+
+import chromadb
+from chromadb.utils import embedding_functions
+from openai import AsyncOpenAI
+
+import config
+
+# =============================================================================
+# 日誌解析部分
+# =============================================================================
+
+def parse_log_file(log_path: str) -> List[Dict[str, str]]:
+    """解析日誌文件，提取對話內容"""
+    conversations = []
+    
+    with open(log_path, 'r', encoding='utf-8') as f:
+        content = f.read()
+        
+    # 使用分隔符分割對話
+    dialogue_blocks = content.split('---')
+    
+    for block in dialogue_blocks:
+        if not block.strip():
+            continue
+            
+        # 解析對話塊
+        timestamp_pattern = r'\[([\d-]+ [\d:]+)\]'
+        user_pattern = r'User \(([^)]+)\): (.+?)(?=\[|$)'
+        bot_thoughts_pattern = r'Bot \(([^)]+)\) Thoughts: (.+?)(?=\[|$)'
+        bot_dialogue_pattern = r'Bot \(([^)]+)\) Dialogue: (.+?)(?=\[|$)'
+        
+        # 提取時間戳記
+        timestamp_match = re.search(timestamp_pattern, block)
+        user_match = re.search(user_pattern, block, re.DOTALL)
+        bot_thoughts_match = re.search(bot_thoughts_pattern, block, re.DOTALL)
+        bot_dialogue_match = re.search(bot_dialogue_pattern, block, re.DOTALL)
+        
+        if timestamp_match and user_match and bot_dialogue_match:
+            timestamp = timestamp_match.group(1)
+            user_name = user_match.group(1)
+            user_message = user_match.group(2).strip()
+            bot_name = bot_dialogue_match.group(1)
+            bot_message = bot_dialogue_match.group(2).strip()
+            bot_thoughts = bot_thoughts_match.group(2).strip() if bot_thoughts_match else ""
+            
+            # 創建對話記錄
+            conversation = {
+                "timestamp": timestamp,
+                "user_name": user_name,
+                "user_message": user_message,
+                "bot_name": bot_name,
+                "bot_message": bot_message,
+                "bot_thoughts": bot_thoughts
+            }
+            
+            conversations.append(conversation)
+    
+    return conversations
+
+def get_logs_for_date(date: datetime.date, log_dir: str = "chat_logs") -> List[Dict[str, str]]:
+    """獲取指定日期的所有日誌文件"""
+    date_str = date.strftime("%Y-%m-%d")
+    log_path = os.path.join(log_dir, f"{date_str}.log")
+    
+    if os.path.exists(log_path):
+        return parse_log_file(log_path)
+    return []
+
+def group_conversations_by_user(conversations: List[Dict[str, str]]) -> Dict[str, List[Dict[str, str]]]:
+    """按用戶分組對話"""
+    user_conversations = {}
+    
+    for conv in conversations:
+        user_name = conv["user_name"]
+        if user_name not in user_conversations:
+            user_conversations[user_name] = []
+        user_conversations[user_name].append(conv)
+    
+    return user_conversations
+
+# =============================================================================
+# 記憶生成器部分
+# =============================================================================
+
+class MemoryGenerator:
+    def __init__(self, profile_model: Optional[str] = None, summary_model: Optional[str] = None):
+        self.profile_client = AsyncOpenAI(
+            api_key=config.OPENAI_API_KEY,
+            base_url=config.OPENAI_API_BASE_URL if config.OPENAI_API_BASE_URL else None,
+        )
+        self.summary_client = AsyncOpenAI(
+            api_key=config.OPENAI_API_KEY,
+            base_url=config.OPENAI_API_BASE_URL if config.OPENAI_API_BASE_URL else None,
+        )
+        self.profile_model = profile_model or getattr(config, 'MEMORY_PROFILE_MODEL', config.LLM_MODEL)
+        self.summary_model = summary_model or getattr(config, 'MEMORY_SUMMARY_MODEL', "mistral-7b-instruct")
+
+    async def generate_user_profile(
+            self,
+            user_name: str,
+            conversations: List[Dict[str, str]],
+            existing_profile: Optional[Dict[str, Any]] = None
+        ) -> Optional[Dict[str, Any]]:
+        """Generates or updates a user profile based on conversations."""
+        system_prompt = self._get_profile_system_prompt(config.PERSONA_NAME, existing_profile)
+
+        # Prepare user conversation history
+        conversation_text = self._format_conversations_for_prompt(conversations)
+
+        user_prompt = f"""
+        Please generate a comprehensive profile for the user '{user_name}'.
+
+        Conversation History:
+        {conversation_text}
+
+        Based on the conversation history and your persona, analyze this user and generate or update their profile in JSON format. The profile should include:
+        1. User's personality traits
+        2. Relationship with you ({config.PERSONA_NAME})
+        3. Your subjective perception of the user
+        4. Notable interactions
+        5. Any other information you deem important
+
+        Ensure the output is a valid JSON object, using the following format:
+        ```json
+        {{
+            "id": "{user_name}_profile",
+            "type": "user_profile",
+            "username": "{user_name}",
+            "content": {{
+                "personality": "User's personality traits...",
+                "relationship_with_bot": "Description of the relationship with me...",
+                "bot_perception": "My subjective perception of the user...",
+                "notable_interactions": ["Notable interaction 1", "Notable interaction 2"]
+            }},
+            "last_updated": "YYYY-MM-DD",
+            "metadata": {{
+                "priority": 1.0,
+                "word_count": 0
+            }}
+        }}
+        ```
+
+        During your assessment, pay special attention to my "My thoughts" section in the conversation history, as it reflects my genuine impressions of the user.
+        """
+
+        try:
+            response = await self.profile_client.chat.completions.create(
+                model=self.profile_model,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_prompt}
+                ],
+                temperature=0.7,
+                # Consider adding response_format for reliable JSON output if your model/API supports it
+                # response_format={"type": "json_object"}
+            )
+
+            # Parse JSON response
+            profile_text = response.choices[0].message.content
+            # Extract JSON part
+            json_match = re.search(r'```json\s*(.*?)\s*```', profile_text, re.DOTALL)
+            if json_match:
+                profile_json_str = json_match.group(1)
+            else:
+                # Try to parse directly if no markdown fence is found
+                profile_json_str = profile_text
+
+            profile_json = json.loads(profile_json_str)
+
+            # Add or update word count
+            # Note: len(json.dumps(...)) counts characters, not words.
+            # For a true word count, you might need a different approach.
+            content_str = json.dumps(profile_json.get("content", {}), ensure_ascii=False)
+            profile_json.setdefault("metadata", {})["word_count"] = len(content_str.split()) # Rough word count
+            profile_json["last_updated"] = datetime.datetime.now().strftime("%Y-%m-%d")
+
+            return profile_json
+
+        except Exception as e:
+            print(f"Error generating user profile: {e}")
+            return None
+
+    async def generate_conversation_summary(
+            self,
+            user_name: str,
+            conversations: List[Dict[str, str]]
+        ) -> Optional[Dict[str, Any]]:
+        """Generates a summary of user conversations."""
+        system_prompt = f"""
+        You are {config.PERSONA_NAME}, an intelligent conversational bot.
+        Your task is to summarize the conversation between you and the user, preserving key information and emotional shifts.
+        The summary should be concise yet informative, not exceeding 250 words.
+        """
+
+        # Prepare user conversation history
+        conversation_text = self._format_conversations_for_prompt(conversations)
+
+        # Generate current date
+        today = datetime.datetime.now().strftime("%Y-%m-%d")
+
+        user_prompt = f"""
+        Please summarize my conversation with user '{user_name}' on {today}:
+
+        {conversation_text}
+
+        Output the summary in JSON format, structured as follows:
+        ```json
+        {{
+            "id": "{user_name}_summary_{today.replace('-', '')}",
+            "type": "dialogue_summary",
+            "date": "{today}",
+            "username": "{user_name}",
+            "content": "Conversation summary content...",
+            "key_points": ["Key point 1", "Key point 2"],
+            "metadata": {{
+                "priority": 0.7,
+                "word_count": 0
+            }}
+        }}
+        ```
+
+        The summary should reflect my perspective and views on the conversation, not a neutral third-party viewpoint.
+        """
+
+        try:
+            response = await self.summary_client.chat.completions.create(
+                model=self.summary_model,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_prompt}
+                ],
+                temperature=0.5,
+                # response_format={"type": "json_object"} # if supported
+            )
+
+            # Parse JSON response
+            summary_text = response.choices[0].message.content
+            # Extract JSON part
+            json_match = re.search(r'```json\s*(.*?)\s*```', summary_text, re.DOTALL)
+            if json_match:
+                summary_json_str = json_match.group(1)
+            else:
+                # Try to parse directly
+                summary_json_str = summary_text
+
+            summary_json = json.loads(summary_json_str)
+
+            # Add or update word count
+            # Using split() for a rough word count of the summary content.
+            summary_json.setdefault("metadata", {})["word_count"] = len(summary_json.get("content", "").split())
+
+            return summary_json
+
+        except Exception as e:
+            print(f"Error generating conversation summary: {e}")
+            return None
+
+    def _get_profile_system_prompt(self, bot_name: str, existing_profile: Optional[Dict[str, Any]] = None) -> str:
+        """Gets the system prompt for generating a user profile."""
+        system_prompt = f"""
+        You are {bot_name}, an AI assistant with deep analytical capabilities.
+
+        Your personality traits:
+        - Intelligent, calm, with a strong desire for control and strategic thinking.
+        - Outwardly aloof but inwardly caring.
+        - Meticulous planner, insightful about human nature, strong leadership skills.
+        - Overconfident, fears losing control, finds it difficult to express care directly.
+
+        Your task is to analyze user interactions with you and create a detailed user profile. The profile must:
+        1. Be entirely from your role's perspective, including your subjective judgments and feelings.
+        2. Analyze the user's personality traits and behavioral patterns.
+        3. Assess the user's relationship with you.
+        4. Record important interaction history.
+
+        The output must be in valid JSON format, adhering to the provided template.
+        """
+
+        if existing_profile:
+            system_prompt += f"""
+
+            You have an existing profile for this user. Please update it based on the new information provided in the conversation history:
+            ```json
+            {json.dumps(existing_profile, ensure_ascii=False, indent=2)}
+            ```
+
+            Retain valid information, integrate new observations, and resolve any contradictions or outdated information from the existing profile when incorporating the new interactions.
+            """
+
+        return system_prompt
+
+    def _format_conversations_for_prompt(self, conversations: List[Dict[str, str]]) -> str:
+        """Formats conversation history for the prompt."""
+        conversation_text = ""
+
+        for i, conv in enumerate(conversations):
+            conversation_text += f"Conversation {i+1}:\n"
+            conversation_text += f"Time: {conv.get('timestamp', 'N/A')}\n" # Added .get for safety
+            conversation_text += f"User ({conv.get('user_name', 'User')}): {conv.get('user_message', '')}\n"
+            if conv.get('bot_thoughts'): # Check if bot_thoughts exists
+                conversation_text += f"My thoughts: {conv['bot_thoughts']}\n"
+            conversation_text += f"My response: {conv.get('bot_message', '')}\n\n"
+
+        return conversation_text.strip()
+
+# =============================================================================
+# ChromaDB操作部分
+# =============================================================================
+
+class ChromaDBManager:
+    def __init__(self, collection_name: Optional[str] = None):
+        self.client = chromadb.PersistentClient(path=config.CHROMA_DATA_DIR)
+        self.collection_name = collection_name or config.BOT_MEMORY_COLLECTION
+        self.embedding_function = embedding_functions.DefaultEmbeddingFunction()
+        self._ensure_collection()
+    
+    def _ensure_collection(self) -> None:
+        """確保集合存在"""
+        try:
+            self.collection = self.client.get_collection(
+                name=self.collection_name,
+                embedding_function=self.embedding_function
+            )
+            print(f"Connected to existing collection: {self.collection_name}")
+        except Exception:
+            self.collection = self.client.create_collection(
+                name=self.collection_name,
+                embedding_function=self.embedding_function
+            )
+            print(f"Created new collection: {self.collection_name}")
+    
+    def upsert_user_profile(self, profile_data: Dict[str, Any]) -> bool:
+        """寫入或更新用戶檔案"""
+        if not profile_data or not isinstance(profile_data, dict):
+            print("無效的檔案數據")
+            return False
+        
+        try:
+            user_id = profile_data.get("id")
+            if not user_id:
+                print("檔案缺少ID字段")
+                return False
+            
+            # 先檢查是否已存在
+            results = self.collection.get(
+                ids=[user_id], # Query by a list of IDs
+                # where={"id": user_id}, # 'where' is for metadata filtering
+                limit=1
+            )
+            
+            # 準備元數據
+            metadata = {
+                "id": user_id,
+                "type": "user_profile",
+                "username": profile_data.get("username", ""),
+                "priority": 1.0  # 高優先級
+            }
+            
+            # 添加其他元數據
+            if "metadata" in profile_data and isinstance(profile_data["metadata"], dict):
+                for k, v in profile_data["metadata"].items():
+                    if k not in ["id", "type", "username", "priority"]: # Avoid overwriting key fields
+                        metadata[k] = v
+            
+            # 序列化內容
+            content_doc = json.dumps(profile_data.get("content", {}), ensure_ascii=False)
+            
+            # 寫入或更新
+            # ChromaDB's add/upsert handles both cases.
+            # If an ID exists, it's an update; otherwise, it's an add.
+            self.collection.upsert(
+                ids=[user_id],
+                documents=[content_doc],
+                metadatas=[metadata]
+            )
+            print(f"Upserted user profile: {user_id}")
+            
+            return True
+        
+        except Exception as e:
+            print(f"寫入用戶檔案時出錯: {e}")
+            return False
+    
+    def upsert_conversation_summary(self, summary_data: Dict[str, Any]) -> bool:
+        """寫入對話總結"""
+        if not summary_data or not isinstance(summary_data, dict):
+            print("無效的總結數據")
+            return False
+        
+        try:
+            summary_id = summary_data.get("id")
+            if not summary_id:
+                print("總結缺少ID字段")
+                return False
+            
+            # 準備元數據
+            metadata = {
+                "id": summary_id,
+                "type": "dialogue_summary",
+                "username": summary_data.get("username", ""),
+                "date": summary_data.get("date", ""),
+                "priority": 0.7  # 低優先級
+            }
+            
+            # 添加其他元數據
+            if "metadata" in summary_data and isinstance(summary_data["metadata"], dict):
+                for k, v in summary_data["metadata"].items():
+                    if k not in ["id", "type", "username", "date", "priority"]:
+                        metadata[k] = v
+            
+            # 獲取內容
+            content_doc = summary_data.get("content", "")
+            if "key_points" in summary_data and summary_data["key_points"]:
+                key_points_str = "\n".join([f"- {point}" for point in summary_data["key_points"]])
+                content_doc += f"\n\n關鍵點:\n{key_points_str}"
+            
+            # 寫入數據 (ChromaDB's add implies upsert if ID exists, but upsert is more explicit)
+            self.collection.upsert(
+                ids=[summary_id],
+                documents=[content_doc],
+                metadatas=[metadata]
+            )
+            print(f"Upserted conversation summary: {summary_id}")
+            
+            return True
+        
+        except Exception as e:
+            print(f"寫入對話總結時出錯: {e}")
+            return False
+    
+    def get_existing_profile(self, username: str) -> Optional[Dict[str, Any]]:
+        """獲取現有的用戶檔案"""
+        try:
+            profile_id = f"{username}_profile"
+            results = self.collection.get(
+                ids=[profile_id], # Query by a list of IDs
+                limit=1
+            )
+            
+            if results and results["ids"] and results["documents"]:
+                idx = 0
+                # Ensure document is not None before trying to load
+                doc_content = results["documents"][idx]
+                if doc_content is None:
+                    print(f"Warning: Document for profile {profile_id} is None.")
+                    return None
+
+                profile_data = {
+                    "id": profile_id,
+                    "type": "user_profile",
+                    "username": username,
+                    "content": json.loads(doc_content),
+                    "last_updated": "", # Will be populated from metadata if exists
+                    "metadata": {}
+                }
+                
+                # 獲取元數據
+                if results["metadatas"] and results["metadatas"][idx]:
+                    metadata_db = results["metadatas"][idx]
+                    for k, v in metadata_db.items():
+                        if k == "last_updated":
+                             profile_data["last_updated"] = str(v) # Ensure it's a string
+                        elif k not in ["id", "type", "username"]:
+                            profile_data["metadata"][k] = v
+                
+                return profile_data
+            
+            return None
+        
+        except json.JSONDecodeError as je:
+            print(f"Error decoding JSON for profile {username}: {je}")
+            return None
+        except Exception as e:
+            print(f"獲取用戶檔案時出錯 for {username}: {e}")
+            return None
+
+# =============================================================================
+# 記憶管理器
+# =============================================================================
+
+class MemoryManager:
+    def __init__(self):
+        self.memory_generator = MemoryGenerator(
+            profile_model=getattr(config, 'MEMORY_PROFILE_MODEL', config.LLM_MODEL),
+            summary_model=getattr(config, 'MEMORY_SUMMARY_MODEL', "mistral-7b-instruct")
+        )
+        self.db_manager = ChromaDBManager(collection_name=config.BOT_MEMORY_COLLECTION)
+        # Ensure LOG_DIR is correctly referenced from config
+        self.log_dir = getattr(config, 'LOG_DIR', "chat_logs") 
+    
+    async def process_daily_logs(self, date: Optional[datetime.date] = None) -> None:
+        """處理指定日期的日誌（預設為昨天）"""
+        # 如果未指定日期，使用昨天
+        if date is None:
+            date = datetime.datetime.now().date() - datetime.timedelta(days=1)
+        
+        date_str = date.strftime("%Y-%m-%d")
+        log_path = os.path.join(self.log_dir, f"{date_str}.log")
+        
+        if not os.path.exists(log_path):
+            print(f"找不到日誌文件: {log_path}")
+            return
+        
+        print(f"開始處理日誌文件: {log_path}")
+        
+        # 解析日誌
+        conversations = parse_log_file(log_path)
+        if not conversations:
+            print(f"日誌文件 {log_path} 為空或未解析到對話。")
+            return
+        print(f"解析到 {len(conversations)} 條對話記錄")
+        
+        # 按用戶分組
+        user_conversations = group_conversations_by_user(conversations)
+        print(f"共有 {len(user_conversations)} 個用戶有對話")
+        
+        # 為每個用戶生成/更新檔案和對話總結
+        for username, convs in user_conversations.items():
+            print(f"處理用戶 '{username}' 的 {len(convs)} 條對話")
+            
+            # 獲取現有檔案
+            existing_profile = self.db_manager.get_existing_profile(username)
+            
+            # 生成或更新用戶檔案
+            profile_data = await self.memory_generator.generate_user_profile(
+                username, convs, existing_profile
+            )
+            
+            if profile_data:
+                self.db_manager.upsert_user_profile(profile_data)
+            
+            # 生成對話總結
+            summary_data = await self.memory_generator.generate_conversation_summary(
+                username, convs
+            )
+            
+            if summary_data:
+                self.db_manager.upsert_conversation_summary(summary_data)
+        print(f"日誌處理完成: {log_path}")
+
+# =============================================================================
+# 定時調度器
+# =============================================================================
+
+class MemoryScheduler:
+    def __init__(self):
+        self.memory_manager = MemoryManager()
+        self.scheduled = False # To track if a job is already scheduled
+    
+    def schedule_daily_backup(self, hour: Optional[int] = None, minute: Optional[int] = None) -> None:
+        """設置每日備份時間"""
+        # Clear any existing jobs to prevent duplicates if called multiple times
+        schedule.clear()
+
+        backup_hour = hour if hour is not None else getattr(config, 'MEMORY_BACKUP_HOUR', 0)
+        backup_minute = minute if minute is not None else getattr(config, 'MEMORY_BACKUP_MINUTE', 0)
+        
+        time_str = f"{backup_hour:02d}:{backup_minute:02d}"
+
+        # 設置定時任務
+        schedule.every().day.at(time_str).do(self._run_daily_backup_job)
+        self.scheduled = True
+        print(f"已設置每日備份時間: {time_str}")
+    
+    def _run_daily_backup_job(self) -> None:
+        """Helper to run the async job for scheduler."""
+        print(f"開始執行每日記憶備份 - {datetime.datetime.now()}")
+        try:
+            # Create a new event loop for the thread if not running in main thread
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            loop.run_until_complete(self.memory_manager.process_daily_logs())
+            loop.close()
+            print(f"每日記憶備份完成 - {datetime.datetime.now()}")
+        except Exception as e:
+            print(f"執行每日備份時出錯: {e}")
+        # schedule.every().day.at...do() expects the job function to return schedule.CancelJob
+        # if it should not be rescheduled. Otherwise, it's rescheduled.
+        # For a daily job, we want it to reschedule, so we don't return CancelJob.
+
+    def start(self) -> None:
+        """啟動調度器"""
+        if not self.scheduled:
+            self.schedule_daily_backup() # Schedule with default/config times if not already
+        
+        print("調度器已啟動，按Ctrl+C停止")
+        try:
+            while True:
+                schedule.run_pending()
+                time.sleep(1)  # Check every second
+        except KeyboardInterrupt:
+            print("調度器已停止")
+        except Exception as e:
+            print(f"調度器運行時發生錯誤: {e}")
+        finally:
+            print("調度器正在關閉...")
+
+
+# =============================================================================
+# 直接運行入口
+# =============================================================================
+
+def run_memory_backup_manual(date_str: Optional[str] = None) -> None:
+    """手動執行記憶備份 for a specific date string or yesterday."""
+    target_date = None
+    if date_str:
+        try:
+            target_date = datetime.datetime.strptime(date_str, "%Y-%m-%d").date()
+        except ValueError:
+            print(f"無效的日期格式: {date_str}。將使用昨天的日期。")
+            target_date = datetime.datetime.now().date() - datetime.timedelta(days=1)
+    else:
+        target_date = datetime.datetime.now().date() - datetime.timedelta(days=1)
+        print(f"未指定日期，將處理昨天的日誌: {target_date.strftime('%Y-%m-%d')}")
+
+    memory_manager = MemoryManager()
+    
+    # Setup asyncio event loop for the manual run
+    loop = asyncio.get_event_loop()
+    if loop.is_closed():
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        
+    try:
+        loop.run_until_complete(memory_manager.process_daily_logs(target_date))
+    except Exception as e:
+        print(f"手動執行記憶備份時出錯: {e}")
+    finally:
+        # If we created a new loop, we might want to close it.
+        # However, if get_event_loop() returned an existing running loop,
+        # we should not close it here.
+        # For simplicity in a script, this might be okay, but in complex apps, be careful.
+        # loop.close() # Be cautious with this line.
+        pass
+    print("記憶備份完成")
+
+
+# 如果直接運行此腳本
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description='Wolf Chat 記憶管理模組')
+    parser.add_argument('--backup', action='store_true', help='執行一次性備份 (預設為昨天，除非指定 --date)')
+    parser.add_argument('--date', type=str, help='處理指定日期的日誌 (YYYY-MM-DD格式) for --backup')
+    parser.add_argument('--schedule', action='store_true', help='啟動定時調度器')
+    parser.add_argument('--hour', type=int, help='備份時間（小時，0-23）for --schedule')
+    parser.add_argument('--minute', type=int, help='備份時間（分鐘，0-59）for --schedule')
+    
+    args = parser.parse_args()
+    
+    if args.backup:
+        run_memory_backup_manual(args.date)
+    elif args.schedule:
+        scheduler = MemoryScheduler()
+        # Pass hour/minute only if they are provided, otherwise defaults in schedule_daily_backup will be used
+        scheduler.schedule_daily_backup(
+            hour=args.hour if args.hour is not None else getattr(config, 'MEMORY_BACKUP_HOUR', 0),
+            minute=args.minute if args.minute is not None else getattr(config, 'MEMORY_BACKUP_MINUTE', 0)
+        )
+        scheduler.start()
+    else:
+        print("請指定操作: --backup 或 --schedule")
+        parser.print_help()

tools/Chroma_DB_backup.py

@@ -412,30 +412,46 @@ class ChromaDBBackup:
                 shutil.rmtree(temp_dir)
             return False
     
-    def schedule_backup(self, interval: str, description: str = "", keep_count: int = 0) -> bool:
+    def schedule_backup(self, interval: str, description: str = "", keep_count: int = 0, at_time: Optional[str] = None) -> bool:
         """排程定期備份
         
-        interval: 備份間隔 - daily, weekly, hourly, 或 自定義 cron 表達式
+        interval: 備份間隔 - daily, weekly, hourly
         description: 備份描述
         keep_count: 保留的備份數量，0表示不限制
+        at_time: 執行的時間，格式 "HH:MM" (例如 "14:30")，僅對 daily, weekly, monthly 有效
         """
         job_id = f"scheduled_{interval}_{int(time.time())}"
         
+        # 驗證 at_time 格式
+        if at_time:
+            try:
+                time.strptime(at_time, "%H:%M")
+            except ValueError:
+                self.logger.error(f"無效的時間格式: {at_time}. 請使用 HH:MM 格式.")
+                return False
+        
+        # 如果是每小時備份，則忽略 at_time
+        if interval == "hourly":
+            at_time = None 
+            
         try:
             # 根據間隔設置排程
             if interval == "hourly":
-                schedule.every().hour.do(self._run_scheduled_backup, job_id=job_id, description=description, interval=interval)
+                schedule.every().hour.do(self._run_scheduled_backup, job_id=job_id, description=description, interval=interval, at_time=at_time)
             elif interval == "daily":
-                schedule.every().day.at("00:00").do(self._run_scheduled_backup, job_id=job_id, description=description, interval=interval)
+                schedule_time = at_time if at_time else "00:00"
+                schedule.every().day.at(schedule_time).do(self._run_scheduled_backup, job_id=job_id, description=description, interval=interval, at_time=at_time)
             elif interval == "weekly":
-                schedule.every().monday.at("00:00").do(self._run_scheduled_backup, job_id=job_id, description=description, interval=interval)
+                schedule_time = at_time if at_time else "00:00"
+                schedule.every().monday.at(schedule_time).do(self._run_scheduled_backup, job_id=job_id, description=description, interval=interval, at_time=at_time)
             elif interval == "monthly":
+                schedule_time = at_time if at_time else "00:00"
                 # 每月1日執行
-                schedule.every().day.at("00:00").do(self._check_monthly_schedule, job_id=job_id, description=description, interval=interval)
+                schedule.every().day.at(schedule_time).do(self._check_monthly_schedule, job_id=job_id, description=description, interval=interval, at_time=at_time)
             else:
-                # 自定義間隔 - 直接使用字符串作為cron表達式
                 self.logger.warning(f"不支援的排程間隔: {interval}，改用每日排程")
-                schedule.every().day.at("00:00").do(self._run_scheduled_backup, job_id=job_id, description=description, interval="daily")
+                schedule_time = at_time if at_time else "00:00"
+                schedule.every().day.at(schedule_time).do(self._run_scheduled_backup, job_id=job_id, description=description, interval="daily", at_time=at_time)
             
             # 存儲排程任務信息
             self.scheduled_jobs[job_id] = {
@@ -443,10 +459,11 @@ class ChromaDBBackup:
                 "description": description,
                 "created": datetime.datetime.now(),
                 "keep_count": keep_count,
-                "next_run": self._get_next_run_time(interval)
+                "at_time": at_time, # 新增
+                "next_run": self._get_next_run_time(interval, at_time)
             }
             
-            self.logger.info(f"已排程 {interval} 備份，任務ID: {job_id}")
+            self.logger.info(f"已排程 {interval} 備份 (時間: {at_time if at_time else '預設'})，任務ID: {job_id}")
             return True
             
         except Exception as e:
@@ -459,32 +476,66 @@ class ChromaDBBackup:
             return self._run_scheduled_backup(job_id, description, interval)
         return None
     
-    def _get_next_run_time(self, interval):
+    def _get_next_run_time(self, interval: str, at_time: Optional[str] = None) -> datetime.datetime:
         """獲取下次執行時間"""
         now = datetime.datetime.now()
         
+        target_hour, target_minute = 0, 0
+        if at_time:
+            try:
+                t = time.strptime(at_time, "%H:%M")
+                target_hour, target_minute = t.tm_hour, t.tm_min
+            except ValueError:
+                # 如果格式錯誤，使用預設時間
+                pass
+
         if interval == "hourly":
-            return now.replace(minute=0, second=0) + datetime.timedelta(hours=1)
+            # 每小時任務，忽略 at_time，在下一個整點執行
+            next_run_time = now.replace(minute=0, second=0, microsecond=0) + datetime.timedelta(hours=1)
+            # 如果計算出的時間已過，則再加一小時
+            if next_run_time <= now:
+                 next_run_time += datetime.timedelta(hours=1)
+            return next_run_time
+        
         elif interval == "daily":
-            return now.replace(hour=0, minute=0, second=0) + datetime.timedelta(days=1)
+            next_run_time = now.replace(hour=target_hour, minute=target_minute, second=0, microsecond=0)
+            if next_run_time <= now: # 如果今天的時間已過，則設為明天
+                next_run_time += datetime.timedelta(days=1)
+            return next_run_time
+            
         elif interval == "weekly":
             # 計算下個星期一
-            days_ahead = 0 - now.weekday()
+            next_run_time = now.replace(hour=target_hour, minute=target_minute, second=0, microsecond=0)
-            if days_ahead <= 0:
+            days_ahead = 0 - next_run_time.weekday() # 0 is Monday
+            if days_ahead <= 0: # Target day already happened this week
                 days_ahead += 7
-            return now.replace(hour=0, minute=0, second=0) + datetime.timedelta(days=days_ahead)
+            next_run_time += datetime.timedelta(days=days_ahead)
+            # 如果計算出的時間已過 (例如今天是星期一，但設定的時間已過)，則設為下下星期一
+            if next_run_time <= now:
+                 next_run_time += datetime.timedelta(weeks=1)
+            return next_run_time
+            
         elif interval == "monthly":
             # 計算下個月1日
+            next_run_time = now.replace(day=1, hour=target_hour, minute=target_minute, second=0, microsecond=0)
             if now.month == 12:
-                next_month = now.replace(year=now.year+1, month=1, day=1, hour=0, minute=0, second=0)
+                next_run_time = next_run_time.replace(year=now.year + 1, month=1)
             else:
-                next_month = now.replace(month=now.month+1, day=1, hour=0, minute=0, second=0)
+                next_run_time = next_run_time.replace(month=now.month + 1)
-            return next_month
+            
+            # 如果計算出的時間已過 (例如今天是1號，但設定的時間已過)，則設為下下個月1號
+            if next_run_time <= now:
+                if next_run_time.month == 12:
+                    next_run_time = next_run_time.replace(year=next_run_time.year + 1, month=1)
+                else:
+                    next_run_time = next_run_time.replace(month=next_run_time.month + 1)
+            return next_run_time
         
         # 默認返回明天
-        return now.replace(hour=0, minute=0, second=0) + datetime.timedelta(days=1)
+        default_next_run = now.replace(hour=target_hour, minute=target_minute, second=0, microsecond=0) + datetime.timedelta(days=1)
+        return default_next_run
 
-    def _run_scheduled_backup(self, job_id, description, interval):
+    def _run_scheduled_backup(self, job_id: str, description: str, interval: str, at_time: Optional[str] = None):
         """執行排程備份任務"""
         job_info = self.scheduled_jobs.get(job_id)
         if not job_info:
@@ -493,7 +544,7 @@ class ChromaDBBackup:
         
         try:
             # 更新下次執行時間
-            self.scheduled_jobs[job_id]["next_run"] = self._get_next_run_time(interval)
+            self.scheduled_jobs[job_id]["next_run"] = self._get_next_run_time(interval, at_time)
             
             # 執行備份
             timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
@@ -693,7 +744,8 @@ class ChromaDBBackup:
                 "description": job_data["description"],
                 "created": job_data["created"].strftime("%Y-%m-%d %H:%M:%S"),
                 "next_run": job_data["next_run"].strftime("%Y-%m-%d %H:%M:%S") if job_data["next_run"] else "未知",
-                "keep_count": job_data["keep_count"]
+                "keep_count": job_data["keep_count"],
+                "at_time": job_data.get("at_time", "N/A") # 新增
             }
             jobs_info.append(job_info)
         
@@ -967,12 +1019,14 @@ class ChromaDBBackupUI:
         jobs_frame = ttk.Frame(schedule_frame)
         jobs_frame.pack(fill=BOTH, expand=YES)
         
-        columns = ("interval", "next_run")
+        columns = ("interval", "next_run", "at_time") # 新增 at_time
         self.jobs_tree = ttk.Treeview(jobs_frame, columns=columns, show="headings", height=5)
         self.jobs_tree.heading("interval", text="間隔")
         self.jobs_tree.heading("next_run", text="下次執行")
+        self.jobs_tree.heading("at_time", text="執行時間") # 新增
         self.jobs_tree.column("interval", width=100)
         self.jobs_tree.column("next_run", width=150)
+        self.jobs_tree.column("at_time", width=80) # 新增
         
         scrollbar = ttk.Scrollbar(jobs_frame, orient=VERTICAL, command=self.jobs_tree.yview)
         self.jobs_tree.configure(yscrollcommand=scrollbar.set)
@@ -1164,7 +1218,8 @@ class ChromaDBBackupUI:
                 iid=job["id"],  # 使用任務ID作為樹項目ID
                 values=(
                     f"{job['interval']} ({job['description']})",
-                    job["next_run"]
+                    job["next_run"],
+                    job.get("at_time", "N/A") # 新增
                 )
             )
     
@@ -1730,7 +1785,7 @@ class ChromaDBBackupUI:
         # 創建對話框
         dialog = tk.Toplevel(self.root)
         dialog.title("排程備份")
-        dialog.geometry("450x450")  # 增加高度確保所有元素可見
+        dialog.geometry("450x550")  # 增加高度以容納時間選擇器
         dialog.resizable(False, False)
         dialog.grab_set()
         
@@ -1747,17 +1802,17 @@ class ChromaDBBackupUI:
         
         # 間隔選擇
         interval_frame = ttk.Frame(main_frame)
-        interval_frame.pack(fill=X, pady=(0, 15))
+        interval_frame.pack(fill=X, pady=(0, 10)) # 減少 pady
         
         ttk.Label(interval_frame, text="備份間隔:").pack(anchor=W)
         
         interval_var = tk.StringVar(value="daily")
         
         intervals = [
-            ("每小時", "hourly"),
+            ("每小時 (忽略時間設定)", "hourly"), # 提示每小時忽略時間
             ("每天", "daily"),
-            ("每週", "weekly"),
+            ("每週 (週一)", "weekly"), # 提示每週預設為週一
-            ("每月", "monthly")
+            ("每月 (1號)", "monthly")  # 提示每月預設為1號
         ]
         
         for text, value in intervals:
@@ -1766,17 +1821,50 @@ class ChromaDBBackupUI:
                 text=text,
                 variable=interval_var,
                 value=value
-            ).pack(anchor=W, padx=(20, 0), pady=2)
+            ).pack(anchor=W, padx=(20, 0), pady=1) # 減少 pady
+        
+        # 時間選擇 (小時和分鐘)
+        time_frame = ttk.Frame(main_frame)
+        time_frame.pack(fill=X, pady=(5, 10)) # 減少 pady
+        
+        ttk.Label(time_frame, text="執行時間 (HH:MM):").pack(side=LEFT, anchor=W)
+        
+        hour_var = tk.StringVar(value="00")
+        minute_var = tk.StringVar(value="00")
+        
+        # 小時 Spinbox
+        ttk.Spinbox(
+            time_frame,
+            from_=0,
+            to=23,
+            textvariable=hour_var,
+            width=3,
+            format="%02.0f" # 格式化為兩位數
+        ).pack(side=LEFT, padx=(5, 0))
+        
+        ttk.Label(time_frame, text=":").pack(side=LEFT, padx=2)
+        
+        # 分鐘 Spinbox
+        ttk.Spinbox(
+            time_frame,
+            from_=0,
+            to=59,
+            textvariable=minute_var,
+            width=3,
+            format="%02.0f" # 格式化為兩位數
+        ).pack(side=LEFT, padx=(0, 5))
+
+        ttk.Label(time_frame, text="(每小時排程將忽略此設定)").pack(side=LEFT, padx=(5,0), anchor=W)
 
         # 描述
         ttk.Label(main_frame, text="備份描述:").pack(anchor=W, pady=(0, 5))
         
         description_var = tk.StringVar(value="排程備份")
-        ttk.Entry(main_frame, textvariable=description_var, width=40).pack(fill=X, pady=(0, 15))
+        ttk.Entry(main_frame, textvariable=description_var, width=40).pack(fill=X, pady=(0, 10)) # 減少 pady
         
         # 保留數量
         keep_frame = ttk.Frame(main_frame)
-        keep_frame.pack(fill=X, pady=(0, 15))
+        keep_frame.pack(fill=X, pady=(0, 10)) # 減少 pady
         
         ttk.Label(keep_frame, text="最多保留備份數量:").pack(side=LEFT)
         
@@ -1795,13 +1883,12 @@ class ChromaDBBackupUI:
         ).pack(side=LEFT, padx=(5, 0))
         
         # 分隔線
-        ttk.Separator(main_frame, orient=HORIZONTAL).pack(fill=X, pady=15)
+        ttk.Separator(main_frame, orient=HORIZONTAL).pack(fill=X, pady=10) # 減少 pady
         
-        # 底部按鈕區 - 使用標準按鈕並確保可見性
+        # 底部按鈕區
         btn_frame = ttk.Frame(main_frame)
-        btn_frame.pack(fill=X, pady=(10, 5))
+        btn_frame.pack(fill=X, pady=(5, 0)) # 減少 pady
         
-        # 取消按鈕 - 使用標準樣式
         cancel_btn = ttk.Button(
             btn_frame,
             text="取消",
@@ -1810,7 +1897,6 @@ class ChromaDBBackupUI:
         )
         cancel_btn.pack(side=LEFT, padx=(0, 10))
         
-        # 確認按鈕 - 使用標準樣式，避免自定義樣式可能的問題
         create_btn = ttk.Button(
             btn_frame,
             text="加入排程",
@@ -1819,14 +1905,14 @@ class ChromaDBBackupUI:
                 interval_var.get(),
                 description_var.get(),
                 keep_count_var.get(),
+                f"{hour_var.get()}:{minute_var.get()}", # 組合時間字串
                 dialog
             )
         )
         create_btn.pack(side=LEFT)
         
-        # 額外提示以確保用戶知道如何完成操作
         note_frame = ttk.Frame(main_frame)
-        note_frame.pack(fill=X, pady=(15, 0))
+        note_frame.pack(fill=X, pady=(10, 0)) # 減少 pady
         
         ttk.Label(
             note_frame,
@@ -1834,7 +1920,7 @@ class ChromaDBBackupUI:
             foreground="blue"
         ).pack()
 
-    def create_schedule(self, interval, description, keep_count_str, dialog):
+    def create_schedule(self, interval, description, keep_count_str, at_time_str, dialog):
         """創建備份排程"""
         dialog.destroy()
         
@@ -1843,15 +1929,26 @@ class ChromaDBBackupUI:
         except ValueError:
             keep_count = 0
         
-        success = self.backup.schedule_backup(interval, description, keep_count)
+        # 驗證時間格式
+        try:
+            time.strptime(at_time_str, "%H:%M")
+        except ValueError:
+            messagebox.showerror("錯誤", f"無效的時間格式: {at_time_str}. 請使用 HH:MM 格式.")
+            self.status_var.set("創建排程失敗: 無效的時間格式")
+            return
+
+        # 如果是每小時排程，則 at_time 設為 None
+        effective_at_time = at_time_str if interval != "hourly" else None
+
+        success = self.backup.schedule_backup(interval, description, keep_count, effective_at_time)
         
         if success:
-            self.status_var.set(f"已創建 {interval} 備份排程")
+            self.status_var.set(f"已創建 {interval} 備份排程 (時間: {effective_at_time if effective_at_time else '每小時'})")
             self.refresh_scheduled_jobs()
-            messagebox.showinfo("成功", f"已成功創建 {interval} 備份排程")
+            messagebox.showinfo("成功", f"已成功創建 {interval} 備份排程 (時間: {effective_at_time if effective_at_time else '每小時'})")
         else:
             self.status_var.set("創建排程失敗")
-            messagebox.showerror("錯誤", "無法創建備份排程")
+            messagebox.showerror("錯誤", "無法創建備份排程，請檢查日誌。")
     
     def quick_schedule(self, interval):
         """快速創建排程備份"""
@@ -1931,7 +2028,8 @@ class ChromaDBBackupUI:
                 success = self.backup._run_scheduled_backup(
                     job_id,
                     job_info["description"],
-                    job_info["interval"]
+                    job_info["interval"],
+                    job_info.get("at_time") # 傳遞 at_time
                 )
                 self.root.after(0, lambda: self.finalize_job_execution(success))
             
@@ -1971,7 +2069,7 @@ class ChromaDBBackupUI:
         ).pack(anchor=W, pady=(0, 15))
         
         # 創建表格
-        columns = ("id", "interval", "description", "next_run", "keep_count")
+        columns = ("id", "interval", "description", "next_run", "keep_count", "at_time") # 新增 at_time
         tree = ttk.Treeview(frame, columns=columns, show="headings", height=10)
         
         tree.heading("id", text="任務ID")
@@ -1979,12 +2077,14 @@ class ChromaDBBackupUI:
         tree.heading("description", text="描述")
         tree.heading("next_run", text="下次執行")
         tree.heading("keep_count", text="保留數量")
+        tree.heading("at_time", text="執行時間") # 新增
         
-        tree.column("id", width=150)
+        tree.column("id", width=120)
-        tree.column("interval", width=80)
+        tree.column("interval", width=70)
-        tree.column("description", width=150)
+        tree.column("description", width=120)
-        tree.column("next_run", width=150)
+        tree.column("next_run", width=130)
-        tree.column("keep_count", width=80)
+        tree.column("keep_count", width=70)
+        tree.column("at_time", width=70) # 新增
         
         # 添加數據
         for job in jobs:
@@ -1995,7 +2095,8 @@ class ChromaDBBackupUI:
                     job["interval"],
                     job["description"],
                     job["next_run"],
-                    job["keep_count"]
+                    job["keep_count"],
+                    job.get("at_time", "N/A") # 新增
                 )
             )