""" AI回复引擎模块 集成XianyuAutoAgent的AI回复功能到现有项目中 【P0/P1 最小化修改版】 - 修复 P1-1 (高成本): detect_intent 改为本地关键词 - 修复 P0-2 (部署陷阱): 移除客户端缓存，实现无状态 - 修复 P1-3 (健壮性): 增强 Gemini 消息格式化 - 遵照指示，未修复 P0-1 (议价竞争条件) """ import os import json import time import sqlite3 import requests # 确保已导入 import threading from typing import List, Dict, Optional from loguru import logger from openai import OpenAI from db_manager import db_manager class AIReplyEngine: """AI回复引擎""" def __init__(self): # 修复 P0-2: 移除有状态的缓存，以支持多进程部署 # self.clients = {} # 已移除 # self.agents = {} # 已移除 # self.client_last_used = {} # 已移除 self._init_default_prompts() # 用于控制同一chat_id消息的串行处理 self._chat_locks = {} self._chat_locks_lock = threading.Lock() def _init_default_prompts(self): """初始化默认提示词""" self.default_prompts = { 'classify': '''你是一个意图分类专家...（此提示词已不再被 detect_intent 使用）''', 'price': '''你是一位经验丰富的销售专家，擅长议价。 语言要求：简短直接，每句≤10字，总字数≤40字。 议价策略： 1. 根据议价次数递减优惠：第1次小幅优惠，第2次中等优惠，第3次最大优惠 2. 接近最大议价轮数时要坚持底线，强调商品价值 3. 优惠不能超过设定的最大百分比和金额 4. 语气要友好但坚定，突出商品优势 注意：结合商品信息、对话历史和议价设置，给出合适的回复。''', 'tech': '''你是一位技术专家，专业解答产品相关问题。 语言要求：简短专业，每句≤10字，总字数≤40字。 回答重点：产品功能、使用方法、注意事项。 注意：基于商品信息回答，避免过度承诺。''', 'default': '''你是一位资深电商卖家，提供优质客服。 语言要求：简短友好，每句≤10字，总字数≤40字。 回答重点：商品介绍、物流、售后等常见问题。 注意：结合商品信息，给出实用建议。''' } def _create_openai_client(self, cookie_id: str) -> Optional[OpenAI]: """ (原 get_client) 创建指定账号的OpenAI客户端 修复 P0-2: 移除了缓存逻辑，以支持多进程无状态部署 """ settings = db_manager.get_ai_reply_settings(cookie_id) if not settings['ai_enabled'] or not settings['api_key']: return None try: logger.info(f"创建新的OpenAI客户端实例 {cookie_id}: base_url={settings['base_url']}, api_key={'***' + settings['api_key'][-4:] if settings['api_key'] else 'None'}") client = OpenAI( api_key=settings['api_key'], base_url=settings['base_url'] ) logger.info(f"为账号 {cookie_id} 创建OpenAI客户端成功，实际base_url: {client.base_url}") return client except Exception as e: logger.error(f"创建OpenAI客户端失败 {cookie_id}: {e}") return None def _is_dashscope_api(self, settings: dict) -> bool: """判断是否为DashScope API - 只有选择自定义模型时才使用""" model_name = settings.get('model_name', '') base_url = settings.get('base_url', '') is_custom_model = model_name.lower() in ['custom', '自定义', 'dashscope', 'qwen-custom'] is_dashscope_url = 'dashscope.aliyuncs.com' in base_url logger.info(f"API类型判断: model_name={model_name}, is_custom_model={is_custom_model}, is_dashscope_url={is_dashscope_url}") return is_custom_model and is_dashscope_url def _is_gemini_api(self, settings: dict) -> bool: """判断是否为Gemini API (通过模型名称)""" model_name = settings.get('model_name', '').lower() return 'gemini' in model_name def _call_dashscope_api(self, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str: """调用DashScope API""" base_url = settings['base_url'] if '/apps/' in base_url: app_id = base_url.split('/apps/')[-1].split('/')[0] else: raise ValueError("DashScope API URL中未找到app_id") url = f"https://dashscope.aliyuncs.com/api/v1/apps/{app_id}/completion" system_content = "" user_content = "" for msg in messages: if msg['role'] == 'system': system_content = msg['content'] elif msg['role'] == 'user': user_content = msg['content'] # 假设 user prompt 已在 generate_reply 中构建好 if system_content and user_content: prompt = f"{system_content}\n\n用户问题：{user_content}\n\n请直接回答用户的问题：" elif user_content: prompt = user_content else: prompt = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages]) data = { "input": {"prompt": prompt}, "parameters": {"max_tokens": max_tokens, "temperature": temperature}, "debug": {} } headers = { "Authorization": f"Bearer {settings['api_key']}", "Content-Type": "application/json" } logger.info(f"DashScope API请求: {url}") logger.info(f"发送的prompt: {prompt[:100]}...") # 避免 prompt 过长 logger.debug(f"请求数据: {json.dumps(data, ensure_ascii=False)}") response = requests.post(url, headers=headers, json=data, timeout=30) if response.status_code != 200: logger.error(f"DashScope API请求失败: {response.status_code} - {response.text}") raise Exception(f"DashScope API请求失败: {response.status_code} - {response.text}") result = response.json() logger.debug(f"DashScope API响应: {json.dumps(result, ensure_ascii=False)}") if 'output' in result and 'text' in result['output']: return result['output']['text'].strip() else: raise Exception(f"DashScope API响应格式错误: {result}") def _call_gemini_api(self, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str: """ 调用Google Gemini REST API (v1beta) """ api_key = settings['api_key'] model_name = settings['model_name'] url = f"https://generativelanguage.googleapis.com/v1beta/models/{model_name}:generateContent?key={api_key}" headers = {"Content-Type": "application/json"} # --- 转换消息格式 (修复 P1-3: 增强健壮性) --- system_instruction = "" user_content_parts = [] # 遍历消息，找到 system 和所有的 user parts for msg in messages: if msg['role'] == 'system': system_instruction = msg['content'] elif msg['role'] == 'user': # 我们只关心 user content user_content_parts.append(msg['content']) # 将所有 user parts 合并为最后的 user_content # 在我们的使用场景中 (generate_reply)，只会有一个 user part，但这样更安全 user_content = "\n".join(user_content_parts) if not user_content: logger.warning(f"Gemini API 调用: 未在消息中找到 'user' 角色内容。Messages: {messages}") raise ValueError("未在消息中找到用户内容 (user content)") # --- 消息格式转换结束 --- payload = { "contents": [ { "role": "user", "parts": [{"text": user_content}] } ], "generationConfig": { "temperature": temperature, "maxOutputTokens": max_tokens } } if system_instruction: payload["systemInstruction"] = { "parts": [{"text": system_instruction}] } logger.info(f"Calling Gemini REST API: {url.split('?')[0]}") logger.debug(f"Gemini Payload: {json.dumps(payload, ensure_ascii=False)}") response = requests.post(url, headers=headers, json=payload, timeout=30) if response.status_code != 200: logger.error(f"Gemini API 请求失败: {response.status_code} - {response.text}") raise Exception(f"Gemini API 请求失败: {response.status_code} - {response.text}") result = response.json() logger.debug(f"Gemini API 响应: {json.dumps(result, ensure_ascii=False)}") try: reply_text = result['candidates'][0]['content']['parts'][0]['text'] return reply_text.strip() except (KeyError, IndexError, TypeError) as e: logger.error(f"Gemini API 响应格式错误: {result} - {e}") raise Exception(f"Gemini API 响应格式错误: {result}") def _call_openai_api(self, client: OpenAI, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str: """调用OpenAI兼容API""" try: logger.info(f"调用OpenAI API: model={settings['model_name']}, base_url={settings.get('base_url', 'default')}") response = client.chat.completions.create( model=settings['model_name'], messages=messages, max_tokens=max_tokens, temperature=temperature ) return response.choices[0].message.content.strip() except Exception as e: logger.error(f"OpenAI API调用失败: {e}") # 如果有详细的错误信息，打印出来 if hasattr(e, 'response'): logger.error(f"响应状态码: {getattr(e.response, 'status_code', 'unknown')}") logger.error(f"响应内容: {getattr(e.response, 'text', 'unknown')}") raise def is_ai_enabled(self, cookie_id: str) -> bool: """检查指定账号是否启用AI回复""" settings = db_manager.get_ai_reply_settings(cookie_id) return settings['ai_enabled'] def detect_intent(self, message: str, cookie_id: str) -> str: """ 检测用户消息意图 (基于关键词的本地检测) 修复 P1-1: 移除了AI调用，以降低成本和延迟。 """ try: # 检查AI是否启用，如果未启用，不应执行任何AI相关逻辑 # 注意：此检查在 generate_reply 的开头已经做过，但保留此处作为第二道防线 settings = db_manager.get_ai_reply_settings(cookie_id) if not settings['ai_enabled']: return 'default' msg_lower = message.lower() # 价格相关关键词 price_keywords = [ '便宜', '优惠', '刀', '降价', '包邮', '价格', '多少钱', '能少', '还能', '最低', '底价', '实诚价', '到100', '能到', '包个邮', '给个价', '什么价' # <-- 增加这些“口语化”的词 ] # 同样，你也可以通过正则表达式来匹配纯数字，比如 "100" "80" # 但那可能有点复杂，先加关键词是最小改动 if any(kw in msg_lower for kw in price_keywords): logger.debug(f"本地意图检测: price ({message})") return 'price' # 技术相关关键词 tech_keywords = ['怎么用', '参数', '坏了', '故障', '设置', '说明书', '功能', '用法', '教程', '驱动'] if any(kw in msg_lower for kw in tech_keywords): logger.debug(f"本地意图检测: tech ({message})") return 'tech' logger.debug(f"本地意图检测: default ({message})") return 'default' except Exception as e: logger.error(f"本地意图检测失败 {cookie_id}: {e}") return 'default' def _get_chat_lock(self, chat_id: str) -> threading.Lock: """获取指定chat_id的锁，如果不存在则创建""" with self._chat_locks_lock: if chat_id not in self._chat_locks: self._chat_locks[chat_id] = threading.Lock() return self._chat_locks[chat_id] def generate_reply(self, message: str, item_info: dict, chat_id: str, cookie_id: str, user_id: str, item_id: str, skip_wait: bool = False) -> Optional[str]: """生成AI回复""" if not self.is_ai_enabled(cookie_id): return None try: # 先检测意图（用于后续保存） intent = self.detect_intent(message, cookie_id) logger.info(f"检测到意图: {intent} (账号: {cookie_id})") # 在锁外先保存用户消息到数据库，让所有消息都能立即保存 message_created_at = self.save_conversation(chat_id, cookie_id, user_id, item_id, "user", message, intent) # 如果调用方已经实现了去抖（debounce），可以通过 skip_wait=True 跳过内部等待 if not skip_wait: logger.info(f"【{cookie_id}】消息已保存，等待10秒收集后续消息: {message[:20]}... (时间:{message_created_at})") # 固定等待10秒，等待可能的后续消息（在锁外延迟，避免阻塞其他消息保存） time.sleep(10) else: logger.info(f"【{cookie_id}】消息已保存（外部防抖已启用，跳过内部等待）: {message[:20]}... (时间:{message_created_at})") # 获取该chat_id的锁，确保同一对话的消息串行处理 chat_lock = self._get_chat_lock(chat_id) # 使用锁确保同一chat_id的消息串行处理 with chat_lock: # 获取最近时间窗口内的所有用户消息 # 如果 skip_wait=True（外部防抖），查询窗口为6秒（1秒防抖 + 5秒缓冲） # 如果 skip_wait=False（内部等待），查询窗口为25秒（10秒等待 + 10秒消息间隔 + 5秒缓冲） query_seconds = 6 if skip_wait else 25 recent_messages = self._get_recent_user_messages(chat_id, cookie_id, seconds=query_seconds) logger.info(f"【{cookie_id}】最近{query_seconds}秒内的消息: {[msg['content'][:20] for msg in recent_messages]}") if recent_messages and len(recent_messages) > 0: # 只处理最后一条消息（时间戳最新的） latest_message = recent_messages[-1] if message_created_at != latest_message['created_at']: logger.info(f"【{cookie_id}】检测到有更新的消息，跳过当前消息: {message[:20]}... (时间:{message_created_at})，最新消息: {latest_message['content'][:20]}... (时间:{latest_message['created_at']})") return None else: logger.info(f"【{cookie_id}】当前消息是最新消息，开始处理: {message[:20]}... (时间:{message_created_at})") # 1. 获取AI回复设置 settings = db_manager.get_ai_reply_settings(cookie_id) # 3. 获取对话历史 context = self.get_conversation_context(chat_id, cookie_id) # 4. 获取议价次数 bargain_count = self.get_bargain_count(chat_id, cookie_id) # 5. 检查议价轮数限制 (P0-1 竞争条件风险点 - 遵照指示未修改) if intent == "price": max_bargain_rounds = settings.get('max_bargain_rounds', 3) if bargain_count >= max_bargain_rounds: logger.info(f"议价次数已达上限 ({bargain_count}/{max_bargain_rounds})，拒绝继续议价") refuse_reply = f"抱歉，这个价格已经是最优惠的了，不能再便宜了哦！" self.save_conversation(chat_id, cookie_id, user_id, item_id, "assistant", refuse_reply, intent) return refuse_reply # 6. 构建提示词 custom_prompts = json.loads(settings['custom_prompts']) if settings['custom_prompts'] else {} system_prompt = custom_prompts.get(intent, self.default_prompts[intent]) # 7. 构建商品信息 item_desc = f"商品标题: {item_info.get('title', '未知')}\n" item_desc += f"商品价格: {item_info.get('price', '未知')}元\n" item_desc += f"商品描述: {item_info.get('desc', '无')}" # 8. 构建对话历史 context_str = "\n".join([f"{msg['role']}: {msg['content']}" for msg in context[-10:]]) # 最近10条 # 9. 构建用户消息 max_bargain_rounds = settings.get('max_bargain_rounds', 3) max_discount_percent = settings.get('max_discount_percent', 10) max_discount_amount = settings.get('max_discount_amount', 100) user_prompt = f"""商品信息： {item_desc} 对话历史： {context_str} 议价设置： - 当前议价次数：{bargain_count} - 最大议价轮数：{max_bargain_rounds} - 最大优惠百分比：{max_discount_percent}% - 最大优惠金额：{max_discount_amount}元 用户消息：{message} 请根据以上信息生成回复：""" # 10. 调用AI生成回复 messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt} ] reply = None # 初始化 reply 变量 if self._is_dashscope_api(settings): logger.info(f"使用DashScope API生成回复") reply = self._call_dashscope_api(settings, messages, max_tokens=100, temperature=0.7) elif self._is_gemini_api(settings): logger.info(f"使用Gemini API生成回复") reply = self._call_gemini_api(settings, messages, max_tokens=100, temperature=0.7) else: logger.info(f"使用OpenAI兼容API生成回复") # 修复 P0-2: 调用已修改的无状态客户端创建方法 client = self._create_openai_client(cookie_id) if not client: return None logger.info(f"messages:{messages}") reply = self._call_openai_api(client, settings, messages, max_tokens=100, temperature=0.7) # 11. 保存AI回复到对话记录 self.save_conversation(chat_id, cookie_id, user_id, item_id, "assistant", reply, intent) # 12. 更新议价次数 (此方法已在 get_bargain_count 中通过 SQL COUNT(*) 隐式实现) if intent == "price": # self.increment_bargain_count(chat_id, cookie_id) # 此行原先就没有，保持不变 pass logger.info(f"AI回复生成成功 (账号: {cookie_id}): {reply}") return reply except Exception as e: logger.error(f"AI回复生成失败 {cookie_id}: {e}") if hasattr(e, 'response') and hasattr(e.response, 'url'): logger.error(f"请求URL: {e.response.url}") if hasattr(e, 'request') and hasattr(e.request, 'url'): logger.error(f"请求URL: {e.request.url}") return None async def generate_reply_async(self, message: str, item_info: dict, chat_id: str, cookie_id: str, user_id: str, item_id: str, skip_wait: bool = False) -> Optional[str]: """ 异步包装器：在独立线程池中执行同步的 `generate_reply`，并返回结果。 这样可以在异步代码中直接 await，而不阻塞事件循环。 """ try: import asyncio as _asyncio return await _asyncio.to_thread(self.generate_reply, message, item_info, chat_id, cookie_id, user_id, item_id, skip_wait) except Exception as e: logger.error(f"异步生成回复失败: {e}") return None def get_conversation_context(self, chat_id: str, cookie_id: str, limit: int = 20) -> List[Dict]: """获取对话上下文""" try: with db_manager.lock: cursor = db_manager.conn.cursor() cursor.execute(''' SELECT role, content FROM ai_conversations WHERE chat_id = ? AND cookie_id = ? ORDER BY created_at DESC LIMIT ? ''', (chat_id, cookie_id, limit)) results = cursor.fetchall() context = [{"role": row[0], "content": row[1]} for row in reversed(results)] return context except Exception as e: logger.error(f"获取对话上下文失败: {e}") return [] def save_conversation(self, chat_id: str, cookie_id: str, user_id: str, item_id: str, role: str, content: str, intent: str = None) -> Optional[str]: """保存对话记录，返回创建时间""" try: with db_manager.lock: cursor = db_manager.conn.cursor() cursor.execute(''' INSERT INTO ai_conversations (cookie_id, chat_id, user_id, item_id, role, content, intent) VALUES (?, ?, ?, ?, ?, ?, ?) ''', (cookie_id, chat_id, user_id, item_id, role, content, intent)) db_manager.conn.commit() # 获取刚插入记录的created_at cursor.execute(''' SELECT created_at FROM ai_conversations WHERE rowid = last_insert_rowid() ''') result = cursor.fetchone() return result[0] if result else None except Exception as e: logger.error(f"保存对话记录失败: {e}") return None def get_bargain_count(self, chat_id: str, cookie_id: str) -> int: """获取议价次数""" try: with db_manager.lock: cursor = db_manager.conn.cursor() cursor.execute(''' SELECT COUNT(*) FROM ai_conversations WHERE chat_id = ? AND cookie_id = ? AND intent = 'price' AND role = 'user' ''', (chat_id, cookie_id)) result = cursor.fetchone() return result[0] if result else 0 except Exception as e: logger.error(f"获取议价次数失败: {e}") return 0 def _get_recent_user_messages(self, chat_id: str, cookie_id: str, seconds: int = 2) -> List[Dict]: """获取最近seconds秒内的所有用户消息（包含内容和时间戳）""" try: with db_manager.lock: cursor = db_manager.conn.cursor() # 先查询所有该chat的user消息，用于调试 cursor.execute(''' SELECT content, created_at, julianday('now') - julianday(created_at) as time_diff_days, (julianday('now') - julianday(created_at)) * 86400.0 as time_diff_seconds FROM ai_conversations WHERE chat_id = ? AND cookie_id = ? AND role = 'user' ORDER BY created_at DESC LIMIT 10 ''', (chat_id, cookie_id)) all_messages = cursor.fetchall() logger.info(f"【调试】chat_id={chat_id} 最近10条user消息: {[(msg[0][:10], msg[1], f'{msg[3]:.2f}秒前') for msg in all_messages]}") # 正式查询 cursor.execute(''' SELECT content, created_at FROM ai_conversations WHERE chat_id = ? AND cookie_id = ? AND role = 'user' AND julianday('now') - julianday(created_at) < (? / 86400.0) ORDER BY created_at ASC ''', (chat_id, cookie_id, seconds)) results = cursor.fetchall() return [{"content": row[0], "created_at": row[1]} for row in results] except Exception as e: logger.error(f"获取最近用户消息列表失败: {e}") return [] def increment_bargain_count(self, chat_id: str, cookie_id: str): """(此方法已废弃，通过 get_bargain_count 的 SQL 查询实现)""" pass # # --- 修复 P0-2: 移除所有有状态的缓存管理方法 --- # # def clear_client_cache(self, cookie_id: str = None): # """(已移除) 清理客户端缓存""" # pass # def cleanup_unused_clients(self, max_idle_hours: int = 24): # """(已移除) 清理长时间未使用的客户端""" # pass # 全局AI回复引擎实例 ai_reply_engine = AIReplyEngine()