mirror of
https://github.com/zhinianboke/xianyu-auto-reply.git
synced 2025-08-30 01:27:35 +08:00
385 lines
16 KiB
Python
385 lines
16 KiB
Python
"""
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AI回复引擎模块
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集成XianyuAutoAgent的AI回复功能到现有项目中
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"""
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import os
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import json
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import time
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import sqlite3
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import requests
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from typing import List, Dict, Optional
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from loguru import logger
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from openai import OpenAI
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from db_manager import db_manager
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class AIReplyEngine:
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"""AI回复引擎"""
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def __init__(self):
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self.clients = {} # 存储不同账号的OpenAI客户端
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self.agents = {} # 存储不同账号的Agent实例
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self._init_default_prompts()
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def _init_default_prompts(self):
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"""初始化默认提示词"""
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self.default_prompts = {
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'classify': '''你是一个意图分类专家，需要判断用户消息的意图类型。
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请根据用户消息内容，返回以下意图之一：
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- price: 价格相关（议价、优惠、降价等）
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- tech: 技术相关（产品参数、使用方法、故障等）
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- default: 其他一般咨询
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只返回意图类型，不要其他内容。''',
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'price': '''你是一位经验丰富的销售专家，擅长议价。
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语言要求：简短直接，每句≤10字，总字数≤40字。
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议价策略：
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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
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2. 接近最大议价轮数时要坚持底线，强调商品价值
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3. 优惠不能超过设定的最大百分比和金额
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4. 语气要友好但坚定，突出商品优势
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注意：结合商品信息、对话历史和议价设置，给出合适的回复。''',
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'tech': '''你是一位技术专家，专业解答产品相关问题。
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语言要求：简短专业，每句≤10字，总字数≤40字。
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回答重点：产品功能、使用方法、注意事项。
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注意：基于商品信息回答，避免过度承诺。''',
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'default': '''你是一位资深电商卖家，提供优质客服。
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语言要求：简短友好，每句≤10字，总字数≤40字。
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回答重点：商品介绍、物流、售后等常见问题。
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注意：结合商品信息，给出实用建议。'''
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}
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def get_client(self, cookie_id: str) -> Optional[OpenAI]:
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"""获取指定账号的OpenAI客户端"""
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if cookie_id not in self.clients:
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settings = db_manager.get_ai_reply_settings(cookie_id)
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if not settings['ai_enabled'] or not settings['api_key']:
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return None
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try:
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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'}")
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self.clients[cookie_id] = OpenAI(
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api_key=settings['api_key'],
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base_url=settings['base_url']
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)
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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}")
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except Exception as e:
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logger.error(f"创建OpenAI客户端失败 {cookie_id}: {e}")
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return None
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return self.clients[cookie_id]
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def _is_dashscope_api(self, settings: dict) -> bool:
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"""判断是否为DashScope API - 只有选择自定义模型时才使用"""
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model_name = settings.get('model_name', '')
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base_url = settings.get('base_url', '')
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# 只有当模型名称为"custom"或"自定义"时，才使用DashScope API格式
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# 其他情况都使用OpenAI兼容格式
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is_custom_model = model_name.lower() in ['custom', '自定义', 'dashscope', 'qwen-custom']
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is_dashscope_url = 'dashscope.aliyuncs.com' in base_url
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logger.info(f"API类型判断: model_name={model_name}, is_custom_model={is_custom_model}, is_dashscope_url={is_dashscope_url}")
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return is_custom_model and is_dashscope_url
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def _call_dashscope_api(self, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str:
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"""调用DashScope API"""
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# 提取app_id从base_url
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base_url = settings['base_url']
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if '/apps/' in base_url:
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app_id = base_url.split('/apps/')[-1].split('/')[0]
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else:
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raise ValueError("DashScope API URL中未找到app_id")
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# 构建请求URL
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url = f"https://dashscope.aliyuncs.com/api/v1/apps/{app_id}/completion"
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# 构建提示词（将messages合并为单个prompt）
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system_content = ""
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user_content = ""
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for msg in messages:
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if msg['role'] == 'system':
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system_content = msg['content']
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elif msg['role'] == 'user':
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user_content = msg['content']
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# 构建更清晰的prompt格式
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if system_content and user_content:
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prompt = f"{system_content}\n\n用户问题：{user_content}\n\n请直接回答用户的问题："
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elif user_content:
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prompt = user_content
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else:
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prompt = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
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# 构建请求数据
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data = {
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"input": {
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"prompt": prompt
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},
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"parameters": {
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"max_tokens": max_tokens,
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"temperature": temperature
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},
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"debug": {}
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}
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headers = {
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"Authorization": f"Bearer {settings['api_key']}",
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"Content-Type": "application/json"
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}
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logger.info(f"DashScope API请求: {url}")
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logger.info(f"发送的prompt: {prompt}")
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logger.debug(f"请求数据: {json.dumps(data, ensure_ascii=False)}")
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response = requests.post(url, headers=headers, json=data, timeout=30)
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if response.status_code != 200:
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logger.error(f"DashScope API请求失败: {response.status_code} - {response.text}")
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raise Exception(f"DashScope API请求失败: {response.status_code} - {response.text}")
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result = response.json()
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logger.debug(f"DashScope API响应: {json.dumps(result, ensure_ascii=False)}")
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# 提取回复内容
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if 'output' in result and 'text' in result['output']:
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return result['output']['text'].strip()
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else:
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raise Exception(f"DashScope API响应格式错误: {result}")
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def _call_openai_api(self, client: OpenAI, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str:
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"""调用OpenAI兼容API"""
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response = client.chat.completions.create(
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model=settings['model_name'],
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messages=messages,
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max_tokens=max_tokens,
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temperature=temperature
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)
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return response.choices[0].message.content.strip()
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def is_ai_enabled(self, cookie_id: str) -> bool:
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"""检查指定账号是否启用AI回复"""
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settings = db_manager.get_ai_reply_settings(cookie_id)
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return settings['ai_enabled']
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def detect_intent(self, message: str, cookie_id: str) -> str:
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"""检测用户消息意图"""
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try:
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settings = db_manager.get_ai_reply_settings(cookie_id)
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if not settings['ai_enabled'] or not settings['api_key']:
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return 'default'
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custom_prompts = json.loads(settings['custom_prompts']) if settings['custom_prompts'] else {}
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classify_prompt = custom_prompts.get('classify', self.default_prompts['classify'])
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# 打印调试信息
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logger.info(f"AI设置调试 {cookie_id}: base_url={settings['base_url']}, model={settings['model_name']}")
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messages = [
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{"role": "system", "content": classify_prompt},
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{"role": "user", "content": message}
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]
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# 根据API类型选择调用方式
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if self._is_dashscope_api(settings):
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logger.info(f"使用DashScope API进行意图检测")
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response_text = self._call_dashscope_api(settings, messages, max_tokens=10, temperature=0.1)
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else:
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logger.info(f"使用OpenAI兼容API进行意图检测")
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client = self.get_client(cookie_id)
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if not client:
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return 'default'
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logger.info(f"OpenAI客户端base_url: {client.base_url}")
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response_text = self._call_openai_api(client, settings, messages, max_tokens=10, temperature=0.1)
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intent = response_text.lower()
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if intent in ['price', 'tech', 'default']:
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return intent
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else:
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return 'default'
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except Exception as e:
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logger.error(f"意图检测失败 {cookie_id}: {e}")
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# 打印更详细的错误信息
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if hasattr(e, 'response') and hasattr(e.response, 'url'):
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logger.error(f"请求URL: {e.response.url}")
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if hasattr(e, 'request') and hasattr(e.request, 'url'):
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logger.error(f"请求URL: {e.request.url}")
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return 'default'
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def generate_reply(self, message: str, item_info: dict, chat_id: str,
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cookie_id: str, user_id: str, item_id: str) -> Optional[str]:
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"""生成AI回复"""
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if not self.is_ai_enabled(cookie_id):
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return None
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try:
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# 1. 获取AI回复设置
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settings = db_manager.get_ai_reply_settings(cookie_id)
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# 2. 检测意图
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intent = self.detect_intent(message, cookie_id)
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logger.info(f"检测到意图: {intent} (账号: {cookie_id})")
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# 3. 获取对话历史
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context = self.get_conversation_context(chat_id, cookie_id)
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# 4. 获取议价次数
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bargain_count = self.get_bargain_count(chat_id, cookie_id)
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# 5. 检查议价轮数限制
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if intent == "price":
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max_bargain_rounds = settings.get('max_bargain_rounds', 3)
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if bargain_count >= max_bargain_rounds:
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logger.info(f"议价次数已达上限 ({bargain_count}/{max_bargain_rounds})，拒绝继续议价")
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# 返回拒绝议价的回复
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refuse_reply = f"抱歉，这个价格已经是最优惠的了，不能再便宜了哦！"
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# 保存对话记录
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self.save_conversation(chat_id, cookie_id, user_id, item_id, "user", message, intent)
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self.save_conversation(chat_id, cookie_id, user_id, item_id, "assistant", refuse_reply, intent)
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return refuse_reply
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# 6. 构建提示词
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custom_prompts = json.loads(settings['custom_prompts']) if settings['custom_prompts'] else {}
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system_prompt = custom_prompts.get(intent, self.default_prompts[intent])
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# 7. 构建商品信息
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item_desc = f"商品标题: {item_info.get('title', '未知')}\n"
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item_desc += f"商品价格: {item_info.get('price', '未知')}元\n"
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item_desc += f"商品描述: {item_info.get('desc', '无')}"
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# 8. 构建对话历史
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context_str = "\n".join([f"{msg['role']}: {msg['content']}" for msg in context[-10:]]) # 最近10条
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# 9. 构建用户消息
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max_bargain_rounds = settings.get('max_bargain_rounds', 3)
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max_discount_percent = settings.get('max_discount_percent', 10)
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max_discount_amount = settings.get('max_discount_amount', 100)
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user_prompt = f"""商品信息：
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{item_desc}
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对话历史：
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{context_str}
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议价设置：
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- 当前议价次数：{bargain_count}
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- 最大议价轮数：{max_bargain_rounds}
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- 最大优惠百分比：{max_discount_percent}%
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- 最大优惠金额：{max_discount_amount}元
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用户消息：{message}
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请根据以上信息生成回复："""
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# 10. 调用AI生成回复
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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]
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# 根据API类型选择调用方式
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if self._is_dashscope_api(settings):
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logger.info(f"使用DashScope API生成回复")
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reply = self._call_dashscope_api(settings, messages, max_tokens=100, temperature=0.7)
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else:
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logger.info(f"使用OpenAI兼容API生成回复")
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client = self.get_client(cookie_id)
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if not client:
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return None
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reply = self._call_openai_api(client, settings, messages, max_tokens=100, temperature=0.7)
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# 11. 保存对话记录
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self.save_conversation(chat_id, cookie_id, user_id, item_id, "user", message, intent)
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self.save_conversation(chat_id, cookie_id, user_id, item_id, "assistant", reply, intent)
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# 12. 更新议价次数
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if intent == "price":
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self.increment_bargain_count(chat_id, cookie_id)
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logger.info(f"AI回复生成成功 (账号: {cookie_id}): {reply}")
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return reply
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except Exception as e:
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logger.error(f"AI回复生成失败 {cookie_id}: {e}")
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# 打印更详细的错误信息
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if hasattr(e, 'response') and hasattr(e.response, 'url'):
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logger.error(f"请求URL: {e.response.url}")
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if hasattr(e, 'request') and hasattr(e.request, 'url'):
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logger.error(f"请求URL: {e.request.url}")
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return None
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def get_conversation_context(self, chat_id: str, cookie_id: str, limit: int = 20) -> List[Dict]:
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"""获取对话上下文"""
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try:
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with db_manager.lock:
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cursor = db_manager.conn.cursor()
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cursor.execute('''
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SELECT role, content FROM ai_conversations
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WHERE chat_id = ? AND cookie_id = ?
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ORDER BY created_at DESC LIMIT ?
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''', (chat_id, cookie_id, limit))
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results = cursor.fetchall()
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# 反转顺序，使其按时间正序
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context = [{"role": row[0], "content": row[1]} for row in reversed(results)]
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return context
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except Exception as e:
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logger.error(f"获取对话上下文失败: {e}")
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return []
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def save_conversation(self, chat_id: str, cookie_id: str, user_id: str,
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item_id: str, role: str, content: str, intent: str = None):
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"""保存对话记录"""
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try:
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with db_manager.lock:
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cursor = db_manager.conn.cursor()
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cursor.execute('''
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INSERT INTO ai_conversations
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(cookie_id, chat_id, user_id, item_id, role, content, intent)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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''', (cookie_id, chat_id, user_id, item_id, role, content, intent))
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db_manager.conn.commit()
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except Exception as e:
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logger.error(f"保存对话记录失败: {e}")
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def get_bargain_count(self, chat_id: str, cookie_id: str) -> int:
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"""获取议价次数"""
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try:
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with db_manager.lock:
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cursor = db_manager.conn.cursor()
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cursor.execute('''
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SELECT COUNT(*) FROM ai_conversations
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WHERE chat_id = ? AND cookie_id = ? AND intent = 'price' AND role = 'user'
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''', (chat_id, cookie_id))
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result = cursor.fetchone()
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return result[0] if result else 0
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except Exception as e:
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logger.error(f"获取议价次数失败: {e}")
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return 0
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def increment_bargain_count(self, chat_id: str, cookie_id: str):
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"""增加议价次数（通过保存记录自动增加）"""
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# 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
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pass
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def clear_client_cache(self, cookie_id: str = None):
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"""清理客户端缓存"""
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if cookie_id:
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self.clients.pop(cookie_id, None)
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logger.info(f"清理账号 {cookie_id} 的客户端缓存")
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else:
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self.clients.clear()
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logger.info("清理所有客户端缓存")
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# 全局AI回复引擎实例
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ai_reply_engine = AIReplyEngine()
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