""" AI回复引擎模块 集成XianyuAutoAgent的AI回复功能到现有项目中 """ import os import json import time import sqlite3 import requests 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): self.clients = {} # 存储不同账号的OpenAI客户端 self.agents = {} # 存储不同账号的Agent实例 self._init_default_prompts() 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，需要判断用户消息的意图类型。 请根据用户消息内容，返回以下意图之一： - price: 价格相关（议价、优惠、降价等） - tech: 技术相关（产品参数、使用方法、故障等） - default: 其他一般咨询 只返回意图类型，不要其他内容。''', '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 get_client(self, cookie_id: str) -> Optional[OpenAI]: """获取指定账号的OpenAI客户端""" if cookie_id not in self.clients: 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OpenAI客户端 {cookie_id}: base_url={settings['base_url']}, api_key={'***' + settings['api_key'][-4:] if settings['api_key'] else 'None'}") self.clients[cookie_id] = 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: {self.clients[cookie_id].base_url}") except Exception as e: logger.error(f"创建OpenAI客户端失败 {cookie_id}: {e}") return None return self.clients[cookie_id] 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', '') # 只有当模型名称为"custom"或"自定义"时，才使用DashScope API格式 # 其他情况都使用OpenAI兼容格式 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 _call_dashscope_api(self, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str: """调用DashScope API""" # 提取app_id从base_url 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") # 构建请求URL url = f"https://dashscope.aliyuncs.com/api/v1/apps/{app_id}/completion" # 构建提示词（将messages合并为单个prompt） system_content = "" user_content = "" for msg in messages: if msg['role'] == 'system': system_content = msg['content'] elif msg['role'] == 'user': user_content = msg['content'] # 构建更清晰的prompt格式 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]) # 构建请求数据 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}") 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)}") # 提取回复内容 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_openai_api(self, client: OpenAI, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str: """调用OpenAI兼容API""" response = client.chat.completions.create( model=settings['model_name'], messages=messages, max_tokens=max_tokens, temperature=temperature ) return response.choices[0].message.content.strip() 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""" try: settings = db_manager.get_ai_reply_settings(cookie_id) if not settings['ai_enabled'] or not settings['api_key']: return 'default' custom_prompts = json.loads(settings['custom_prompts']) if settings['custom_prompts'] else {} classify_prompt = custom_prompts.get('classify', self.default_prompts['classify']) # 打印调试信息 logger.info(f"AI设置调试 {cookie_id}: base_url={settings['base_url']}, model={settings['model_name']}") messages = [ {"role": "system", "content": classify_prompt}, {"role": "user", "content": message} ] # 根据API类型选择调用方式 if self._is_dashscope_api(settings): logger.info(f"使用DashScope API进行意图检测") response_text = self._call_dashscope_api(settings, messages, max_tokens=10, temperature=0.1) else: logger.info(f"使用OpenAI兼容API进行意图检测") client = self.get_client(cookie_id) if not client: return 'default' logger.info(f"OpenAI客户端base_url: {client.base_url}") response_text = self._call_openai_api(client, settings, messages, max_tokens=10, temperature=0.1) intent = response_text.lower() if intent in ['price', 'tech', 'default']: return intent else: return 'default' except Exception as e: logger.error(f"意图检测失败 {cookie_id}: {e}") # 打印更详细的错误信息 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 'default' def generate_reply(self, message: str, item_info: dict, chat_id: str, cookie_id: str, user_id: str, item_id: str) -> Optional[str]: """生成AI回复""" if not self.is_ai_enabled(cookie_id): return None try: # 1. 获取AI回复设置 settings = db_manager.get_ai_reply_settings(cookie_id) # 2. 检测意图 intent = self.detect_intent(message, cookie_id) logger.info(f"检测到意图: {intent} (账号: {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 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") # 返回拒绝议价的回复 refuse_reply = f"抱歉，这个价格已经是最优惠的了，不能再便宜了哦！" # 保存对话记录 self.save_conversation(chat_id, cookie_id, user_id, item_id, "user", message, intent) 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} ] # 根据API类型选择调用方式 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) else: logger.info(f"使用OpenAI兼容API生成回复") client = self.get_client(cookie_id) if not client: return None reply = self._call_openai_api(client, settings, messages, max_tokens=100, temperature=0.7) # 11. 保存对话记录 self.save_conversation(chat_id, cookie_id, user_id, item_id, "user", message, intent) self.save_conversation(chat_id, cookie_id, user_id, item_id, "assistant", reply, intent) # 12. 更新议价次数 if intent == "price": self.increment_bargain_count(chat_id, cookie_id) 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}") # 打印更详细的错误信息 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 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() # 反转顺序，使其按时间正序 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): """保存对话记录""" 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() except Exception as e: logger.error(f"保存对话记录失败: {e}") 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 increment_bargain_count(self, chat_id: str, cookie_id: str): """增加议价次数（通过保存记录自动增加）""" # 议价次数通过查询price意图的用户消息数量来计算，无需单独操作 pass def clear_client_cache(self, cookie_id: str = None): """清理客户端缓存""" if cookie_id: self.clients.pop(cookie_id, None) logger.info(f"清理账号 {cookie_id} 的客户端缓存") else: self.clients.clear() logger.info("清理所有客户端缓存") # 全局AI回复引擎实例 ai_reply_engine = AIReplyEngine()