From a43c6a10aced0639266160fcc18a8e84d9a99cd2 Mon Sep 17 00:00:00 2001 From: gameloader Date: Thu, 27 Nov 2025 19:14:26 +0800 Subject: [PATCH] feat(memory): replace Mem0 with local RAG using SQLite, ChromaDB, and bge-small --- api/chat_service.py | 4 +- memory_module/__init__.py | 4 +- memory_module/memory_integration.py | 369 +++-- pyproject.toml | 2 + uv.lock | 1951 ++++++++++++++++++++++++++- 5 files changed, 2220 insertions(+), 110 deletions(-) diff --git a/api/chat_service.py b/api/chat_service.py index 4340d10..7afd909 100644 --- a/api/chat_service.py +++ b/api/chat_service.py @@ -13,7 +13,7 @@ from config import ( MEM0_CONFIG, ) from api.doubao_tts import text_to_speech -from memory_module.memory_integration import Mem0Integration +from memory_module import LocalMemoryIntegration logger = logging.getLogger(__name__) @@ -21,7 +21,7 @@ logger = logging.getLogger(__name__) class ChatService: def __init__(self, user_id: str = None): self.user_id = user_id or DEFAULT_USER_ID - self.mem0_integration = Mem0Integration(MEM0_CONFIG) + self.mem0_integration = LocalMemoryIntegration(MEM0_CONFIG) # 使用新类 self._initialized = False def initialize(self): diff --git a/memory_module/__init__.py b/memory_module/__init__.py index 18efcb9..c91eb1c 100644 --- a/memory_module/__init__.py +++ b/memory_module/__init__.py @@ -1,3 +1,3 @@ -from .memory_integration import Mem0Integration +from .memory_integration import LocalMemoryIntegration -__all__ = ["Mem0Integration"] \ No newline at end of file +__all__ = ["LocalMemoryIntegration"] diff --git a/memory_module/memory_integration.py b/memory_module/memory_integration.py index bc4d41b..5ada8c7 100644 --- a/memory_module/memory_integration.py +++ b/memory_module/memory_integration.py @@ -1,32 +1,47 @@ import os +import sqlite3 +import chromadb +from sentence_transformers import SentenceTransformer from typing import List, Dict, Any, Optional from datetime import datetime import json -import openai +import openai # 我们仍然需要它来调用 豆包LLM import threading -from mem0 import Memory import logging import textwrap +# 使用你项目中的日志配置 logger = logging.getLogger(__name__) -class Mem0Integration: - """Mem0 integration for memory retrieval and storage in RAG pipeline.""" +class LocalMemoryIntegration: + """ + 使用本地轻量级 RAG (SQLite + ChromaDB + bge-small) 替换 Mem0。 + + 它保留了原始的 LLM 调用和复杂的 JSON Prompt 逻辑， + 只将 RAG 检索和存储部分本地化。 + """ def __init__(self, config: Dict[str, Any]): - """Initialize Mem0 with configuration.""" + """ + 初始化本地记忆模块和 OpenAI (豆包) 客户端。 + """ self.config = config - self.memory = Memory.from_config(config) - # Initialize OpenAI client for chat completion - self.openai_client = openai.OpenAI( - api_key=config["llm"]["config"]["api_key"], - base_url=config["llm"]["config"].get("openai_base_url") - ) - self.llm_model = config["llm"]["config"]["model"] - - # System prompt template aligned with virtual persona requirements + # 1. 初始化 LLM 客户端 (豆包) - 这部分逻辑保留 + # config["llm"] 对应你 README 中的 LLM 配置 + try: + self.openai_client = openai.OpenAI( + api_key=config["llm"]["config"]["api_key"], + base_url=config["llm"]["config"].get("openai_base_url") + ) + self.llm_model = config["llm"]["config"]["model"] + logger.info(f"OpenAI (Doubao) LLM client initialized for model: {self.llm_model}") + except KeyError as e: + logger.error(f"LLM config missing key: {e}. Check your MEM0_CONFIG['llm'].") + raise + + # 2. 保留核心的 System Prompt (从你发的文件中完整复制) self.system_prompt_template = textwrap.dedent(""" 你是虚拟人对话引擎。 @@ -106,7 +121,7 @@ class Mem0Integration: * **生气**（惹得虚拟人生气）：回复时情绪带有生气/气死/离谱/无语/滚/别烦，动作触发"不满" - * **否定**：不/并不/没那么 + 情绪词 → 降级或改判 动作触发"摇头" + * **否定**：不/并不/没那么 + 情绪词 → 降级或改判    动作触发"摇头" 肯定：是/没错/肯定的+判断实时情绪，动作触发"点头" @@ -172,51 +187,165 @@ class Mem0Integration: ``` """).strip() - + + # 3. 初始化本地 SQLite (存储原始文本) + db_path = os.getenv('SQLITE_DB_PATH', 'local_memory.db') + self.db_conn = sqlite3.connect(db_path, check_same_thread=False) + self.db_conn.execute(""" + CREATE TABLE IF NOT EXISTS memories ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + user_id TEXT NOT NULL, + role TEXT NOT NULL, + content TEXT NOT NULL, + chroma_id TEXT NOT NULL UNIQUE, + timestamp TEXT NOT NULL + ); + """) + self.db_conn.execute("CREATE INDEX IF NOT EXISTS idx_user_id ON memories (user_id);") + logger.info(f"SQLite database initialized at {db_path}") + + # 4. 初始化本地 ChromaDB (存储向量) + chroma_path = os.getenv('CHROMA_DB_PATH', './chroma_db_store') + self.chroma_client = chromadb.PersistentClient(path=chroma_path) + logger.info(f"ChromaDB persistent client initialized at {chroma_path}") + + # 5. 初始化本地 Embedding 模型 (低内存占用) + model_name = 'bge-small-zh-v1.5' # 约 100MB 内存 + try: + logger.info(f"Loading local embedding model: {model_name}...") + self.embedding_model = SentenceTransformer(model_name, device='cpu') + logger.info("Local embedding model loaded successfully.") + except Exception as e: + logger.error(f"Failed to load embedding model '{model_name}': {e}") + raise + + def _get_user_collection(self, user_id: str): + """获取或创建该用户的 ChromaDB 集合""" + collection_name = f"user_memory_{user_id.replace('-', '_')}" # 确保 user_id + return self.chroma_client.get_or_create_collection(name=collection_name) + + def _get_max_id(self, user_id: str): + """获取该用户当前最大的 memory ID""" + try: + cursor = self.db_conn.execute("SELECT MAX(id) FROM memories WHERE user_id = ?", (user_id,)) + max_id = cursor.fetchone()[0] + return max_id if max_id is not None else 0 + except Exception as e: + logger.error(f"Failed to get max id from SQLite for user {user_id}: {e}") + return 0 + + # --- 实现 Mem0Integration 的公共接口 --- + def search_memories(self, query: str, user_id: str, limit: int = 5) -> List[Any]: - """Search for relevant memories about the user.""" + """ + (本地实现) 搜索相关记忆。 + 返回一个字典列表，模仿 mem0 的输出格式。 + """ + logger.debug(f"Searching local memories for user {user_id} with query: {query}") try: - results = self.memory.search( - query=query, - user_id=user_id, - limit=limit - ) - # Handle dictionary response format - check for both 'memories' and 'results' keys - if isinstance(results, dict): - memories = results.get("memories", results.get("results", [])) - return memories - else: - logger.error(f"Unexpected search results format: {type(results)}") + collection = self._get_user_collection(user_id) + if collection.count() == 0: + logger.debug("No memories found for this user.") return [] - except Exception as e: - logger.error(f"Failed to search memories: {e}") - return [] - - def add_memory(self, messages: List[Dict[str, str]], user_id: str, metadata: Optional[Dict] = None) -> Dict[str, Any]: - """Add a memory for the user.""" - try: - # Debug: Log what we're trying to add - logger.debug(f"Adding memory for user {user_id}") - logger.debug(f"Messages: {messages}") - logger.debug(f"Metadata: {metadata}") + + # 1. 本地向量化查询 + query_embedding = self.embedding_model.encode(query).tolist() - result = self.memory.add( - messages=messages, - user_id=user_id, - metadata=metadata or {}, - infer= False + # 2. 本地 ChromaDB (KNN) 搜索 + results = collection.query( + query_embeddings=[query_embedding], + n_results=min(limit, collection.count()) # 确保 n_results 不超过集合大小 ) - # Debug: Log the result - logger.debug(f"Add memory result: {result}") - return result + # 3. 格式化为原函数期望的格式 + formatted_results = [] + if results and 'metadatas' in results and 'documents' in results: + for doc, meta in zip(results['documents'][0], results['metadatas'][0]): + formatted_results.append({ + "memory": doc, # format_memories_for_prompt 会用这个 key + "content": meta.get("content", doc), # 备用 + "role": meta.get("role", "unknown") + }) + + logger.debug(f"Found {len(formatted_results)} local memories.") + return formatted_results + except Exception as e: - logger.error(f"Failed to add memory: {e}") - logger.exception("Exception details:") - return {} + logger.error(f"Failed to search local memories: {e}") + return [] + + def add_memory(self, messages: List[Dict[str, str]], user_id: str, metadata: Optional[Dict] = None) -> Dict[str, Any]: + """(本地实现) 添加新的记忆。""" + logger.debug(f"Adding local memory for user {user_id}") + if metadata is None: + metadata = {} + + try: + collection = self._get_user_collection(user_id) + + texts_to_embed = [] + docs_to_store = [] + metadatas_to_store = [] + ids_to_store = [] + + current_max_id = self._get_max_id(user_id) + + for i, msg in enumerate(messages): + role = msg.get("role") + content = msg.get("content") + if not role or not content: + continue + + current_max_id += 1 + chroma_id = f"mem_{user_id}_{current_max_id}" + timestamp = metadata.get("timestamp", datetime.now().isoformat()) + + # 1. 存入 SQLite + self.db_conn.execute( + "INSERT INTO memories (user_id, role, content, chroma_id, timestamp) VALUES (?, ?, ?, ?, ?)", + (user_id, role, content, chroma_id, timestamp) + ) + + # 2. 准备存入 ChromaDB + full_text = f"{role}: {content}" # 向量化的文本 + texts_to_embed.append(full_text) + docs_to_store.append(full_text) # 存在 chroma 的 document 字段 + metadatas_to_store.append({ # 存在 chroma 的 metadata 字段 + "role": role, + "content": content, # 存一份原始内容 + "timestamp": timestamp + }) + ids_to_store.append(chroma_id) + + if not texts_to_embed: + return {"success": True, "message": "No valid messages to add."} + + embeddings = self.embedding_model.encode( + texts_to_embed, + batch_size=8 + ).tolist() + + # 3. 批量存入 ChromaDB + collection.add( + embeddings=embeddings, + documents=docs_to_store, + metadatas=metadatas_to_store, + ids=ids_to_store + ) + + self.db_conn.commit() + logger.debug(f"Successfully added {len(texts_to_embed)} local memories.") + return {"success": True, "message": f"Added {len(texts_to_embed)} memories."} + + except Exception as e: + logger.error(f"Error in add_memory (local): {e}") + self.db_conn.rollback() + return {"success": False, "error": str(e)} + # --- 保留你原有的辅助函数和核心逻辑 --- + def _extract_reply_for_memory(self, assistant_response: Any) -> str: - """Extract the assistant reply text from structured responses for memory storage.""" + """(原样保留) 从结构化响应中提取用于记忆的助手回复文本。""" if assistant_response is None: return "" @@ -228,21 +357,32 @@ class Mem0Integration: return "" try: + # 尝试解析 JSON, 兼容你 `chat_service` 可能遇到的 "```json\n{...}\n```" + if raw_text.startswith("```json"): + raw_text = raw_text.split("```json\n", 1)[-1].split("\n```")[0] + data = json.loads(raw_text) reply = data.get("reply", "") reply_str = str(reply).strip() - return reply_str if reply_str else raw_text + # 如果 'reply' 为空，也返回原始文本，以防 LLM 返回了非 JSON 格式的有效回复 + return reply_str if reply_str else raw_text except Exception: + # 如果解析失败，返回原始文本 return raw_text def format_memories_for_prompt(self, memories: List[Any]) -> str: - """Format memories into bullet points for injection into the system prompt.""" + """ + (轻微修改) 将记忆格式化为项目符号点，以便注入系统提示。 + 严格遵守 Prompt 中的 "MEM_INJECT_TOPK: 1–2 条" 规则。 + """ if not memories: return "" formatted = [] - for memory in memories: + # 只取最相关的 2 条 + for memory in memories[:2]: if isinstance(memory, dict): + # 我们的 search_memories 返回 'memory' 键 memory_text = memory.get("memory") or memory.get("content") or "" elif isinstance(memory, str): memory_text = memory @@ -256,17 +396,20 @@ class Mem0Integration: return "\n".join(formatted) def generate_response_with_memory(self, user_input: str, user_id: str) -> Dict[str, Any]: - """Generate a response using memories and store the interaction.""" - # Step 1: Search for relevant memories - memories = self.search_memories(user_input, user_id, limit=5) + """ + (逻辑保留，调用本地) 使用记忆生成响应并存储交互。 + """ + # 步骤 1: 搜索相关记忆 (调用我们的本地搜索) + # Prompt 建议 1-2 条，我们搜 5 条，format_memories_for_prompt 会帮我们筛选 + memories = self.search_memories(user_input, user_id, limit=5) - # Step 2: Prepare system prompt with memory injection + # 步骤 2: 准备系统提示 (逻辑保留) memory_block = self.format_memories_for_prompt(memories) system_prompt = self.system_prompt_template.replace( "{memory_block}", memory_block if memory_block else "" ).strip() - # Step 3: Generate response using OpenAI + # 步骤 3: 生成响应 (逻辑保留 - 仍然调用豆包 LLM) try: response = self.openai_client.chat.completions.create( model=self.llm_model, @@ -274,92 +417,112 @@ class Mem0Integration: {"role": "system", "content": system_prompt}, {"role": "user", "content": user_input} ], - reasoning_effort="minimal", + # reasoning_effort="minimal", # 你原始代码里的，如果豆包不支持就删掉 + # 推荐开启 JSON 模式，让豆包强制输出 JSON + response_format={"type": "json_object"} ) assistant_response = response.choices[0].message.content reply_for_memory = self._extract_reply_for_memory(assistant_response) - if not reply_for_memory: + if not reply_for_memory: # 确保不存空记忆 reply_for_memory = assistant_response - # Step 5: Store the interaction as new memories (异步执行) + # 步骤 5: 异步存储交互 (逻辑保留 - 调用我们的本地 add_memory) messages = [ {"role": "user", "content": user_input}, - {"role": "assistant", "content": reply_for_memory} + {"role": "assistant", "content": reply_for_memory} # 只存回复文本 ] - - # Store with metadata including timestamp metadata = { "timestamp": datetime.now().isoformat(), "type": "chat_interaction" } - # 异步存储记忆，不阻塞主流程 + # (原样保留) 启动异步线程存储记忆 def store_memory_async(): try: self.add_memory(messages, user_id, metadata) except Exception as e: - print(f"[WARNING] Async memory storage failed: {e}") + # 原代码是 print, 统一用 logger + logger.warning(f"[WARNING] Async local memory storage failed: {e}") - # 启动异步线程存储记忆 memory_thread = threading.Thread(target=store_memory_async, daemon=True) memory_thread.start() return { "success": True, - "response": assistant_response, + "response": assistant_response, # 返回完整的 JSON 字符串给 chat_service "user_id": user_id } except Exception as e: - print(f"[ERROR] Failed to generate response: {e}") + logger.error(f"[ERROR] Failed to generate response: {e}") return { "success": False, "error": str(e), "user_id": user_id } - def get_all_memories(self, user_id: str) -> List[Any]: - """Get all memories for a user.""" - try: - memories = self.memory.get_all(user_id=user_id) - # Handle dictionary response format - if isinstance(memories, dict): - # Check for different possible key names - if "memories" in memories: - memories_list = memories.get("memories", []) - elif "results" in memories: - memories_list = memories.get("results", []) - else: - # Try to find any list in the dict - for key, value in memories.items(): - if isinstance(value, list): - memories_list = value - break - else: - memories_list = [] - return memories_list - else: - print(f"[ERROR] Unexpected memories format: {type(memories)}") - return [] - except Exception as e: - print(f"[ERROR] Failed to get all memories: {e}") - return [] + # --- 其他辅助 API 的本地实现 --- - def delete_memory(self, memory_id: str) -> bool: - """Delete a specific memory.""" + def get_all_memories(self, user_id: str) -> List[Any]: + """(本地实现) 从 SQLite 获取所有原始文本历史""" + logger.debug(f"Getting all local memories for user {user_id}") try: - self.memory.delete(memory_id) + cursor = self.db_conn.execute( + "SELECT id, role, content, timestamp, chroma_id FROM memories WHERE user_id = ? ORDER BY id ASC", + (user_id,) + ) + return [{ + "id": row[4], # 返回 chroma_id 作为唯一标识符 + "sqlite_id": row[0], + "memory": f"{row[1]}: {row[2]}", + "role": row[1], + "content": row[2], + "timestamp": row[3] + } for row in cursor.fetchall()] + except Exception as e: + logger.error(f"Error in get_all_memories (local): {e}") + return [] + + def delete_memory(self, memory_id: str) -> bool: + """(本地实现) 删除特定记忆 (memory_id 应该是 chroma_id)""" + logger.debug(f"Deleting local memory: {memory_id}") + try: + # 1. 从 SQLite 删除 + cursor = self.db_conn.execute("SELECT user_id FROM memories WHERE chroma_id = ?", (memory_id,)) + row = cursor.fetchone() + if not row: + logger.warning(f"Memory ID {memory_id} not found in SQLite.") + return False + + user_id = row[0] + self.db_conn.execute("DELETE FROM memories WHERE chroma_id = ?", (memory_id,)) + + # 2. 从 ChromaDB 删除 + collection = self._get_user_collection(user_id) + collection.delete(ids=[memory_id]) + + self.db_conn.commit() return True except Exception as e: - print(f"[ERROR] Failed to delete memory: {e}") + logger.error(f"Failed to delete memory (local): {e}") + self.db_conn.rollback() return False def delete_all_memories(self, user_id: str) -> bool: - """Delete all memories for a user.""" + """(本地实现) 删除用户的所有记忆""" + logger.debug(f"Deleting all local memories for user {user_id}") try: - self.memory.delete_all(user_id=user_id) + # 1. 从 SQLite 删除 + self.db_conn.execute("DELETE FROM memories WHERE user_id = ?", (user_id,)) + self.db_conn.commit() + + # 2. 从 ChromaDB 删除 (删除整个 collection) + collection_name = f"user_memory_{user_id.replace('-', '_')}" + self.chroma_client.delete_collection(name=collection_name) + return True except Exception as e: - print(f"[ERROR] Failed to delete all memories: {e}") + logger.error(f"Failed to delete all memories (local): {e}") + self.db_conn.rollback() return False diff --git a/pyproject.toml b/pyproject.toml index 2b392e8..c4b62cc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,6 +5,7 @@ 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