Migrate ChromaDB embedding model to paraphrase-multilingual-mpnet-base-v2

Setup.py

@@ -316,6 +316,15 @@ def load_current_config():
             if backup_minute_match:
                 config_data["MEMORY_BACKUP_MINUTE"] = int(backup_minute_match.group(1))
 
+            # Extract EMBEDDING_MODEL_NAME
+            embedding_model_match = re.search(r'EMBEDDING_MODEL_NAME\s*=\s*["\'](.+?)["\']', config_content)
+            if embedding_model_match:
+                config_data["EMBEDDING_MODEL_NAME"] = embedding_model_match.group(1)
+            else:
+                # Default if not found in config.py, will be set in UI if not overridden by load
+                config_data["EMBEDDING_MODEL_NAME"] = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
+
+
             profile_model_match = re.search(r'MEMORY_PROFILE_MODEL\s*=\s*["\']?(.+?)["\']?\s*(?:#|$)', config_content)
             # Handle potential LLM_MODEL reference
             if profile_model_match:
@@ -537,10 +546,15 @@ def generate_config_file(config_data, env_data):
         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")
+            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")
+            f.write(f"MEMORY_PROFILE_MODEL = \"{profile_model}\"\n")
+        f.write(f"MEMORY_SUMMARY_MODEL = \"{summary_model}\"\n\n")
+
+        # Write Embedding Model Name
+        embedding_model_name = config_data.get('EMBEDDING_MODEL_NAME', "sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
+        f.write("# Embedding model for ChromaDB\n")
+        f.write(f"EMBEDDING_MODEL_NAME = \"{embedding_model_name}\"\n")
 
 
     print("Generated config.py file successfully")
@@ -1717,6 +1731,24 @@ class WolfChatSetup(tk.Tk):
         related_info = ttk.Label(related_frame, text="(0 to disable related memories pre-loading)")
         related_info.pack(side=tk.LEFT, padx=(5, 0))
 
+        # Embedding Model Settings Frame
+        embedding_model_settings_frame = ttk.LabelFrame(main_frame, text="Embedding Model Settings")
+        embedding_model_settings_frame.pack(fill=tk.X, pady=10)
+
+        embedding_model_name_frame = ttk.Frame(embedding_model_settings_frame)
+        embedding_model_name_frame.pack(fill=tk.X, pady=5, padx=10)
+
+        embedding_model_name_label = ttk.Label(embedding_model_name_frame, text="Embedding Model Name:", width=25) # Adjusted width
+        embedding_model_name_label.pack(side=tk.LEFT, padx=(0, 5))
+
+        self.embedding_model_name_var = tk.StringVar(value="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
+        embedding_model_name_entry = ttk.Entry(embedding_model_name_frame, textvariable=self.embedding_model_name_var)
+        embedding_model_name_entry.pack(side=tk.LEFT, fill=tk.X, expand=True)
+        
+        embedding_model_info = ttk.Label(embedding_model_settings_frame, text="Default: sentence-transformers/paraphrase-multilingual-mpnet-base-v2", justify=tk.LEFT)
+        embedding_model_info.pack(anchor=tk.W, padx=10, pady=(0,5))
+
+
         # Information box
         info_frame = ttk.LabelFrame(main_frame, text="Information")
         info_frame.pack(fill=tk.BOTH, expand=True, pady=10)
@@ -2067,6 +2099,10 @@ class WolfChatSetup(tk.Tk):
             self.profiles_collection_var.set(self.config_data.get("PROFILES_COLLECTION", "user_profiles")) # Default was user_profiles
             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"))
+            # Embedding Model Name for Memory Settings Tab
+            if hasattr(self, 'embedding_model_name_var'):
+                self.embedding_model_name_var.set(self.config_data.get("EMBEDDING_MODEL_NAME", "sentence-transformers/paraphrase-multilingual-mpnet-base-v2"))
+
 
             # Memory Management Tab Settings
             if hasattr(self, 'backup_hour_var'): # Check if UI elements for memory management tab exist
@@ -2343,6 +2379,10 @@ class WolfChatSetup(tk.Tk):
             self.config_data["PROFILES_COLLECTION"] = self.profiles_collection_var.get()
             self.config_data["CONVERSATIONS_COLLECTION"] = self.conversations_collection_var.get()
             self.config_data["BOT_MEMORY_COLLECTION"] = self.bot_memory_collection_var.get()
+            # Save Embedding Model Name from Memory Settings Tab
+            if hasattr(self, 'embedding_model_name_var'):
+                self.config_data["EMBEDDING_MODEL_NAME"] = self.embedding_model_name_var.get()
+
 
             # Get Memory Management settings from UI
             if hasattr(self, 'backup_hour_var'): # Check if UI elements exist

chroma_client.py

@@ -1,6 +1,7 @@
 # chroma_client.py
 import chromadb
 from chromadb.config import Settings
+from chromadb.utils import embedding_functions # New import
 import os
 import json
 import config
@@ -10,6 +11,33 @@ import time
 _client = None
 _collections = {}
 
+# Global embedding function variable
+_embedding_function = None
+
+def get_embedding_function():
+    """Gets or creates the embedding function based on config"""
+    global _embedding_function
+    if _embedding_function is None:
+        # Default to paraphrase-multilingual-mpnet-base-v2 if not specified or on error
+        model_name = getattr(config, 'EMBEDDING_MODEL_NAME', "sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
+        try:
+            _embedding_function = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=model_name)
+            print(f"Successfully initialized embedding function with model: {model_name}")
+        except Exception as e:
+            print(f"Failed to initialize embedding function with model '{model_name}': {e}")
+            # Fallback to default if specified model fails and it's not already the default
+            if model_name != "sentence-transformers/paraphrase-multilingual-mpnet-base-v2":
+                print("Falling back to default embedding model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
+                try:
+                    _embedding_function = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
+                    print(f"Successfully initialized embedding function with default model.")
+                except Exception as e_default:
+                    print(f"Failed to initialize default embedding function: {e_default}")
+                    _embedding_function = None # Ensure it's None if all attempts fail
+            else:
+                _embedding_function = None # Ensure it's None if default model also fails
+    return _embedding_function
+
 def initialize_chroma_client():
     """Initializes and connects to ChromaDB"""
     global _client
@@ -34,13 +62,31 @@ def get_collection(collection_name):
 
     if collection_name not in _collections:
         try:
+            emb_func = get_embedding_function()
+            if emb_func is None:
+                print(f"Failed to get or create collection '{collection_name}' due to embedding function initialization failure.")
+                return None
+
             _collections[collection_name] = _client.get_or_create_collection(
-                name=collection_name
+                name=collection_name,
+                embedding_function=emb_func
             )
-            print(f"Successfully got or created collection '{collection_name}'")
+            print(f"Successfully got or created collection '{collection_name}' using configured embedding function.")
         except Exception as e:
-            print(f"Failed to get collection '{collection_name}': {e}")
-            return None
+            print(f"Failed to get collection '{collection_name}' with configured embedding function: {e}")
+            # Attempt to create collection with default embedding function as a fallback
+            print(f"Attempting to create collection '{collection_name}' with default embedding function...")
+            try:
+                # Ensure we try the absolute default if the configured one (even if it was the default) failed
+                default_emb_func = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
+                _collections[collection_name] = _client.get_or_create_collection(
+                    name=collection_name,
+                    embedding_function=default_emb_func
+                )
+                print(f"Successfully got or created collection '{collection_name}' with default embedding function after initial failure.")
+            except Exception as e_default:
+                print(f"Failed to get collection '{collection_name}' even with default embedding function: {e_default}")
+                return None
 
     return _collections[collection_name]
 

memory_manager.py

@@ -16,11 +16,12 @@ import schedule
 from pathlib import Path
 from typing import Dict, List, Optional, Any, Union
 
-import chromadb
-from chromadb.utils import embedding_functions
+# import chromadb # No longer directly needed by ChromaDBManager
+# from chromadb.utils import embedding_functions # No longer directly needed by ChromaDBManager
 from openai import AsyncOpenAI
 
 import config
+import chroma_client # Import the centralized chroma client
 
 # =============================================================================
 # 日誌解析部分
@@ -345,28 +346,22 @@ class MemoryGenerator:
 
 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}")
-    
+        self._db_collection = None # Cache for the collection object
+
+    def _get_db_collection(self):
+        """Helper to get the collection object from chroma_client"""
+        if self._db_collection is None:
+            # Use the centralized get_collection function
+            self._db_collection = chroma_client.get_collection(self.collection_name)
+            if self._db_collection is None:
+                # This indicates a failure in chroma_client to provide the collection
+                raise RuntimeError(f"Failed to get or create collection '{self.collection_name}' via chroma_client. Check chroma_client logs.")
+        return self._db_collection
+
     def upsert_user_profile(self, profile_data: Dict[str, Any]) -> bool:
         """寫入或更新用戶檔案"""
+        collection = self._get_db_collection()
         if not profile_data or not isinstance(profile_data, dict):
             print("無效的檔案數據")
             return False
@@ -377,14 +372,13 @@ class ChromaDBManager:
                 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
+            # Note: ChromaDB's upsert handles existence check implicitly.
+            # The .get call here isn't strictly necessary for the upsert operation itself,
+            # but might be kept if there was other logic depending on prior existence.
+            # For a clean upsert, it can be removed. Let's assume it's not critical for now.
+            # results = collection.get(ids=[user_id], limit=1) # Optional: if needed for pre-check logic
+            
             metadata = {
                 "id": user_id,
                 "type": "user_profile",
@@ -402,14 +396,12 @@ class ChromaDBManager:
             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(
+            collection.upsert(
                 ids=[user_id],
                 documents=[content_doc],
                 metadatas=[metadata]
             )
-            print(f"Upserted user profile: {user_id}")
+            print(f"Upserted user profile: {user_id} into collection {self.collection_name}")
             
             return True
         
@@ -419,6 +411,7 @@ class ChromaDBManager:
     
     def upsert_conversation_summary(self, summary_data: Dict[str, Any]) -> bool:
         """寫入對話總結"""
+        collection = self._get_db_collection()
         if not summary_data or not isinstance(summary_data, dict):
             print("無效的總結數據")
             return False
@@ -450,13 +443,13 @@ class ChromaDBManager:
                 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(
+            # 寫入數據
+            collection.upsert(
                 ids=[summary_id],
                 documents=[content_doc],
                 metadatas=[metadata]
             )
-            print(f"Upserted conversation summary: {summary_id}")
+            print(f"Upserted conversation summary: {summary_id} into collection {self.collection_name}")
             
             return True
         
@@ -466,10 +459,11 @@ class ChromaDBManager:
     
     def get_existing_profile(self, username: str) -> Optional[Dict[str, Any]]:
         """獲取現有的用戶檔案"""
+        collection = self._get_db_collection()
         try:
             profile_id = f"{username}_profile"
-            results = self.collection.get(
-                ids=[profile_id], # Query by a list of IDs
+            results = collection.get(
+                ids=[profile_id],
                 limit=1
             )
             

reembed_chroma_data.py

@@ -0,0 +1,529 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+重新嵌入工具 (Reembedding Tool)
+
+這個腳本用於將現有ChromaDB集合中的數據使用新的嵌入模型重新計算向量並儲存。
+"""
+
+import os
+import sys
+import json
+import time
+import argparse
+import shutil
+from datetime import datetime
+from typing import List, Dict, Any, Optional, Tuple
+from tqdm import tqdm  # 進度條
+
+try:
+    import chromadb
+    from chromadb.utils import embedding_functions
+except ImportError:
+    print("錯誤: 請先安裝 chromadb: pip install chromadb")
+    sys.exit(1)
+
+try:
+    from sentence_transformers import SentenceTransformer
+except ImportError:
+    print("錯誤: 請先安裝 sentence-transformers: pip install sentence-transformers")
+    sys.exit(1)
+
+# 嘗試導入配置
+try:
+    import config
+except ImportError:
+    print("警告: 無法導入config.py，將使用預設設定")
+    # 建立最小配置
+    class MinimalConfig:
+        CHROMA_DATA_DIR = "chroma_data"
+        BOT_MEMORY_COLLECTION = "wolfhart_memory"
+        CONVERSATIONS_COLLECTION = "wolfhart_memory"
+        PROFILES_COLLECTION = "wolfhart_memory"
+    config = MinimalConfig()
+
+def parse_args():
+    """處理命令行參數"""
+    parser = argparse.ArgumentParser(description='ChromaDB 數據重新嵌入工具')
+    
+    parser.add_argument('--new-model', type=str, 
+                        default="sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
+                        help='新的嵌入模型名稱 (預設: sentence-transformers/paraphrase-multilingual-mpnet-base-v2)')
+    
+    parser.add_argument('--collections', type=str, nargs='+',
+                        help=f'要處理的集合名稱列表，空白分隔 (預設: 使用配置中的所有集合)')
+    
+    parser.add_argument('--backup', action='store_true',
+                        help='在處理前備份資料庫 (推薦)')
+    
+    parser.add_argument('--batch-size', type=int, default=100,
+                        help='批處理大小 (預設: 100)')
+    
+    parser.add_argument('--temp-collection-suffix', type=str, default="_temp_new",
+                        help='臨時集合的後綴名稱 (預設: _temp_new)')
+    
+    parser.add_argument('--dry-run', action='store_true',
+                        help='模擬執行但不實際修改資料')
+    
+    parser.add_argument('--confirm-dangerous', action='store_true',
+                        help='確認執行危險操作(例如刪除集合)')
+    
+    return parser.parse_args()
+
+def backup_chroma_directory(chroma_dir: str) -> str:
+    """備份ChromaDB數據目錄
+    
+    Args:
+        chroma_dir: ChromaDB數據目錄路徑
+        
+    Returns:
+        備份目錄的路徑
+    """
+    if not os.path.exists(chroma_dir):
+        print(f"錯誤: ChromaDB目錄 '{chroma_dir}' 不存在")
+        sys.exit(1)
+        
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    backup_dir = f"{chroma_dir}_backup_{timestamp}"
+    
+    print(f"備份資料庫從 '{chroma_dir}' 到 '{backup_dir}'...")
+    shutil.copytree(chroma_dir, backup_dir)
+    print(f"備份完成: {backup_dir}")
+    
+    return backup_dir
+
+def create_embedding_function(model_name: str):
+    """創建嵌入函數
+    
+    Args:
+        model_name: 嵌入模型名稱
+        
+    Returns:
+        嵌入函數對象
+    """
+    if not model_name:
+        print("使用ChromaDB預設嵌入模型")
+        return embedding_functions.DefaultEmbeddingFunction()
+    
+    print(f"正在加載嵌入模型: {model_name}")
+    try:
+        # 直接使用SentenceTransformerEmbeddingFunction
+        from chromadb.utils.embedding_functions import SentenceTransformerEmbeddingFunction
+        embedding_function = SentenceTransformerEmbeddingFunction(model_name=model_name)
+        # 預熱模型
+        _ = embedding_function(["."])
+        return embedding_function
+    except Exception as e:
+        print(f"錯誤: 無法加載模型 '{model_name}': {e}")
+        print("退回到預設嵌入模型")
+        return embedding_functions.DefaultEmbeddingFunction()
+
+def get_collection_names(client, default_collections: List[str]) -> List[str]:
+    """獲取所有可用的集合名稱
+    
+    Args:
+        client: ChromaDB客戶端
+        default_collections: 預設集合列表
+        
+    Returns:
+        可用的集合名稱列表
+    """
+    try:
+        all_collections = client.list_collections()
+        collection_names = [col.name for col in all_collections]
+        
+        if collection_names:
+            return collection_names
+        else:
+            print("警告: 沒有找到集合，將使用預設集合")
+            return default_collections
+            
+    except Exception as e:
+        print(f"獲取集合列表失敗: {e}")
+        print("將使用預設集合")
+        return default_collections
+
+def fetch_collection_data(client, collection_name: str, batch_size: int = 100) -> Dict[str, Any]:
+    """從集合中提取所有數據
+    
+    Args:
+        client: ChromaDB客戶端
+        collection_name: 集合名稱
+        batch_size: 批處理大小
+        
+    Returns:
+        集合數據字典，包含ids, documents, metadatas
+    """
+    try:
+        collection = client.get_collection(name=collection_name)
+        
+        # 獲取該集合中的項目總數
+        count_result = collection.count()
+        if count_result == 0:
+            print(f"集合 '{collection_name}' 是空的")
+            return {"ids": [], "documents": [], "metadatas": []}
+        
+        print(f"從集合 '{collection_name}' 中讀取 {count_result} 項數據...")
+        
+        # 分批獲取數據
+        all_ids = []
+        all_documents = []
+        all_metadatas = []
+        
+        offset = 0
+        with tqdm(total=count_result, desc=f"正在讀取 {collection_name}") as pbar:
+            while True:
+                # 注意: 使用include參數指定只獲取需要的數據
+                batch_result = collection.get(
+                    limit=batch_size,
+                    offset=offset,
+                    include=["documents", "metadatas"]
+                )
+                
+                batch_ids = batch_result.get("ids", [])
+                if not batch_ids:
+                    break
+                    
+                all_ids.extend(batch_ids)
+                all_documents.extend(batch_result.get("documents", []))
+                all_metadatas.extend(batch_result.get("metadatas", []))
+                
+                offset += len(batch_ids)
+                pbar.update(len(batch_ids))
+                
+                if len(batch_ids) < batch_size:
+                    break
+        
+        return {
+            "ids": all_ids,
+            "documents": all_documents,
+            "metadatas": all_metadatas
+        }
+        
+    except Exception as e:
+        print(f"從集合 '{collection_name}' 獲取數據時出錯: {e}")
+        return {"ids": [], "documents": [], "metadatas": []}
+
+def create_and_populate_collection(
+    client, 
+    collection_name: str, 
+    data: Dict[str, Any], 
+    embedding_func,
+    batch_size: int = 100,
+    dry_run: bool = False
+) -> bool:
+    """創建新集合並填充數據
+    
+    Args:
+        client: ChromaDB客戶端
+        collection_name: 集合名稱
+        data: 要添加的數據 (ids, documents, metadatas)
+        embedding_func: 嵌入函數
+        batch_size: 批處理大小
+        dry_run: 是否只模擬執行
+        
+    Returns:
+        成功返回True，否則返回False
+    """
+    if dry_run:
+        print(f"[模擬] 將創建集合 '{collection_name}' 並添加 {len(data['ids'])} 項數據")
+        return True
+        
+    try:
+        # 檢查集合是否已存在
+        if collection_name in [col.name for col in client.list_collections()]:
+            client.delete_collection(collection_name)
+        
+        # 創建新集合
+        collection = client.create_collection(
+            name=collection_name,
+            embedding_function=embedding_func
+        )
+        
+        # 如果沒有數據，直接返回
+        if not data["ids"]:
+            print(f"集合 '{collection_name}' 創建完成，但沒有數據添加")
+            return True
+            
+        # 分批添加數據
+        total_items = len(data["ids"])
+        with tqdm(total=total_items, desc=f"正在填充 {collection_name}") as pbar:
+            for i in range(0, total_items, batch_size):
+                end_idx = min(i + batch_size, total_items)
+                
+                batch_ids = data["ids"][i:end_idx]
+                batch_docs = data["documents"][i:end_idx]
+                batch_meta = data["metadatas"][i:end_idx]
+                
+                # 處理可能的None值
+                processed_docs = []
+                for doc in batch_docs:
+                    if doc is None:
+                        processed_docs.append("")  # 使用空字符串替代None
+                    else:
+                        processed_docs.append(doc)
+                
+                collection.add(
+                    ids=batch_ids,
+                    documents=processed_docs,
+                    metadatas=batch_meta
+                )
+                
+                pbar.update(end_idx - i)
+        
+        print(f"成功將 {total_items} 項數據添加到集合 '{collection_name}'")
+        return True
+        
+    except Exception as e:
+        print(f"創建或填充集合 '{collection_name}' 時出錯: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def swap_collections(
+    client, 
+    original_collection: str, 
+    temp_collection: str,
+    confirm_dangerous: bool = False,
+    dry_run: bool = False,
+    embedding_func = None  # 添加嵌入函數作為參數
+) -> bool:
+    """替換集合（刪除原始集合，將臨時集合重命名為原始集合名）
+    
+    Args:
+        client: ChromaDB客戶端
+        original_collection: 原始集合名稱
+        temp_collection: 臨時集合名稱
+        confirm_dangerous: 是否確認危險操作
+        dry_run: 是否只模擬執行
+        embedding_func: 嵌入函數，用於創建新集合
+        
+    Returns:
+        成功返回True，否則返回False
+    """
+    if dry_run:
+        print(f"[模擬] 將替換集合: 刪除 '{original_collection}'，重命名 '{temp_collection}' 到 '{original_collection}'")
+        return True
+        
+    try:
+        # 檢查是否有確認標誌
+        if not confirm_dangerous:
+            response = input(f"警告: 即將刪除集合 '{original_collection}' 並用 '{temp_collection}' 替換它。確認操作? (y/N): ")
+            if response.lower() != 'y':
+                print("操作已取消")
+                return False
+        
+        # 檢查兩個集合是否都存在
+        all_collections = [col.name for col in client.list_collections()]
+        if original_collection not in all_collections:
+            print(f"錯誤: 原始集合 '{original_collection}' 不存在")
+            return False
+            
+        if temp_collection not in all_collections:
+            print(f"錯誤: 臨時集合 '{temp_collection}' 不存在")
+            return False
+        
+        # 獲取臨時集合的所有數據
+        # 在刪除原始集合之前先獲取臨時集合的所有數據
+        print(f"獲取臨時集合 '{temp_collection}' 的數據...")
+        temp_collection_obj = client.get_collection(temp_collection)
+        temp_data = temp_collection_obj.get(include=["documents", "metadatas"])
+        
+        # 刪除原始集合
+        print(f"刪除原始集合 '{original_collection}'...")
+        client.delete_collection(original_collection)
+        
+        # 創建一個同名的新集合（與原始集合同名）
+        print(f"創建新集合 '{original_collection}'...")
+        
+        # 使用傳入的嵌入函數或臨時集合的嵌入函數
+        embedding_function = embedding_func or temp_collection_obj._embedding_function
+        
+        # 創建新的集合
+        original_collection_obj = client.create_collection(
+            name=original_collection, 
+            embedding_function=embedding_function
+        )
+        
+        # 將數據添加到新集合
+        if temp_data["ids"]:
+            print(f"將 {len(temp_data['ids'])} 項數據從臨時集合複製到新集合...")
+            
+            # 處理可能的None值
+            processed_docs = []
+            for doc in temp_data["documents"]:
+                if doc is None:
+                    processed_docs.append("")
+                else:
+                    processed_docs.append(doc)
+            
+            # 使用分批方式添加數據以避免潛在的大數據問題
+            batch_size = 100
+            for i in range(0, len(temp_data["ids"]), batch_size):
+                end = min(i + batch_size, len(temp_data["ids"]))
+                original_collection_obj.add(
+                    ids=temp_data["ids"][i:end],
+                    documents=processed_docs[i:end],
+                    metadatas=temp_data["metadatas"][i:end] if temp_data["metadatas"] else None
+                )
+        
+        # 刪除臨時集合
+        print(f"刪除臨時集合 '{temp_collection}'...")
+        client.delete_collection(temp_collection)
+        
+        print(f"成功用重新嵌入的數據替換集合 '{original_collection}'")
+        return True
+        
+    except Exception as e:
+        print(f"替換集合時出錯: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def process_collection(
+    client,
+    collection_name: str,
+    embedding_func,
+    temp_suffix: str,
+    batch_size: int,
+    confirm_dangerous: bool,
+    dry_run: bool
+) -> bool:
+    """處理一個集合的完整流程
+    
+    Args:
+        client: ChromaDB客戶端
+        collection_name: 要處理的集合名稱
+        embedding_func: 新的嵌入函數
+        temp_suffix: 臨時集合的後綴
+        batch_size: 批處理大小
+        confirm_dangerous: 是否確認危險操作
+        dry_run: 是否只模擬執行
+        
+    Returns:
+        處理成功返回True，否則返回False
+    """
+    print(f"\n{'=' * 60}")
+    print(f"處理集合: '{collection_name}'")
+    print(f"{'=' * 60}")
+    
+    # 暫時集合名稱
+    temp_collection_name = f"{collection_name}{temp_suffix}"
+    
+    # 1. 獲取原始集合的數據
+    data = fetch_collection_data(client, collection_name, batch_size)
+    
+    if not data["ids"]:
+        print(f"集合 '{collection_name}' 為空或不存在，跳過")
+        return True
+    
+    # 2. 創建臨時集合並使用新的嵌入模型填充數據
+    success = create_and_populate_collection(
+        client, 
+        temp_collection_name, 
+        data, 
+        embedding_func,
+        batch_size,
+        dry_run
+    )
+    
+    if not success:
+        print(f"創建臨時集合 '{temp_collection_name}' 失敗，跳過替換")
+        return False
+    
+    # 3. 替換原始集合
+    success = swap_collections(
+        client, 
+        collection_name, 
+        temp_collection_name,
+        confirm_dangerous,
+        dry_run,
+        embedding_func  # 添加嵌入函數作為參數
+    )
+    
+    return success
+
+def main():
+    """主函數"""
+    args = parse_args()
+    
+    # 獲取ChromaDB目錄
+    chroma_dir = getattr(config, "CHROMA_DATA_DIR", "chroma_data")
+    print(f"使用ChromaDB目錄: {chroma_dir}")
+    
+    # 備份數據庫（如果請求）
+    if args.backup:
+        backup_chroma_directory(chroma_dir)
+    
+    # 創建ChromaDB客戶端
+    try:
+        client = chromadb.PersistentClient(path=chroma_dir)
+    except Exception as e:
+        print(f"錯誤: 無法連接到ChromaDB: {e}")
+        sys.exit(1)
+    
+    # 創建嵌入函數
+    embedding_func = create_embedding_function(args.new_model)
+    
+    # 確定要處理的集合
+    if args.collections:
+        collections_to_process = args.collections
+    else:
+        # 使用配置中的默認集合或獲取所有可用集合
+        default_collections = [
+            getattr(config, "BOT_MEMORY_COLLECTION", "wolfhart_memory"),
+            getattr(config, "CONVERSATIONS_COLLECTION", "conversations"),
+            getattr(config, "PROFILES_COLLECTION", "user_profiles")
+        ]
+        collections_to_process = get_collection_names(client, default_collections)
+    
+    # 過濾掉已經是臨時集合的集合名稱
+    filtered_collections = []
+    for collection in collections_to_process:
+        if args.temp_collection_suffix in collection:
+            print(f"警告: 跳過可能的臨時集合 '{collection}'")
+            continue
+        filtered_collections.append(collection)
+    
+    collections_to_process = filtered_collections
+    
+    if not collections_to_process:
+        print("沒有找到可處理的集合。")
+        sys.exit(0)
+    
+    print(f"將處理以下集合: {', '.join(collections_to_process)}")
+    if args.dry_run:
+        print("注意: 執行為乾運行模式，不會實際修改數據")
+    
+    # 詢問用戶確認
+    if not args.confirm_dangerous and not args.dry_run:
+        confirm = input("這個操作將使用新的嵌入模型重新計算所有數據。繼續? (y/N): ")
+        if confirm.lower() != 'y':
+            print("操作已取消")
+            sys.exit(0)
+    
+    # 處理每個集合
+    start_time = time.time()
+    success_count = 0
+    
+    for collection_name in collections_to_process:
+        if process_collection(
+            client,
+            collection_name,
+            embedding_func,
+            args.temp_collection_suffix,
+            args.batch_size,
+            args.confirm_dangerous,
+            args.dry_run
+        ):
+            success_count += 1
+    
+    # 報告結果
+    elapsed_time = time.time() - start_time
+    print(f"\n{'=' * 60}")
+    print(f"處理完成: {success_count}/{len(collections_to_process)} 個集合成功")
+    print(f"總耗時: {elapsed_time:.2f} 秒")
+    print(f"{'=' * 60}")
+
+if __name__ == "__main__":
+    main()

tools/chroma_view.py

@@ -147,6 +147,15 @@ class ChromaDBReader:
             except Exception as e:
                 self.logger.error(f"無法加載 SentenceTransformer paraphrase-multilingual-MiniLM-L12-v2: {e}。將使用集合內部模型。")
                 self.query_embedding_function = None
+        # 添加新的模型支持
+        elif model_name == "paraphrase-multilingual-mpnet-base-v2":
+            try:
+                # 注意: sentence-transformers 庫需要安裝
+                self.query_embedding_function = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
+                self.logger.info(f"查詢將使用外部嵌入模型: {model_name}")
+            except Exception as e:
+                self.logger.error(f"無法加載 SentenceTransformer paraphrase-multilingual-mpnet-base-v2: {e}。將使用集合內部模型。")
+                self.query_embedding_function = None
         else:
             self.logger.warning(f"未知的查詢嵌入模型: {model_name}, 將使用集合內部模型。")
             self.query_embedding_function = None
@@ -450,13 +459,11 @@ class ChromaDBReaderUI:
         self.embedding_models = {
             "預設 (ChromaDB)": "default",
             "all-MiniLM-L6-v2 (ST)": "all-MiniLM-L6-v2",
-            "paraphrase-multilingual-MiniLM-L12-v2 (ST)": "paraphrase-multilingual-MiniLM-L12-v2"
+            "paraphrase-multilingual-MiniLM-L12-v2 (ST)": "paraphrase-multilingual-MiniLM-L12-v2",
+            "paraphrase-multilingual-mpnet-base-v2 (ST)": "paraphrase-multilingual-mpnet-base-v2"  # 添加新的模型選項
         }
-        # 初始化 reader 中的嵌入模型 (確保 reader 實例已創建)
-        # self.reader.set_query_embedding_model(self.embedding_models[self.embedding_model_var.get()])
-        # ^^^ 這行需要在 setup_ui 之後，或者在 on_embedding_model_changed 中處理首次設置
-
-        self.setup_ui() # setup_ui 會創建 reader 實例
+        
+        self.setup_ui()
         
         # 默認主題
         self.current_theme = "darkly"  # ttkbootstrap的深色主題