553 lines
25 KiB
Python
553 lines
25 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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【P0/P1 最小化修改版】
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- 修复 P1-1 (高成本): detect_intent 改为本地关键词
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- 修复 P0-2 (部署陷阱): 移除客户端缓存，实现无状态
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- 修复 P1-3 (健壮性): 增强 Gemini 消息格式化
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- 遵照指示，未修复 P0-1 (议价竞争条件)
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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 # 确保已导入
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import threading
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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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# 修复 P0-2: 移除有状态的缓存，以支持多进程部署
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# self.clients = {} # 已移除
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# self.agents = {} # 已移除
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# self.client_last_used = {} # 已移除
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self._init_default_prompts()
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# 用于控制同一chat_id消息的串行处理
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self._chat_locks = {}
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self._chat_locks_lock = threading.Lock()
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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...（此提示词已不再被 detect_intent 使用）''',
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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 _create_openai_client(self, cookie_id: str) -> Optional[OpenAI]:
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"""
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(原 get_client) 创建指定账号的OpenAI客户端
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修复 P0-2: 移除了缓存逻辑，以支持多进程无状态部署
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"""
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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'}")
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client = 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: {client.base_url}")
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return client
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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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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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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 _is_gemini_api(self, settings: dict) -> bool:
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"""判断是否为Gemini API (通过模型名称)"""
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model_name = settings.get('model_name', '').lower()
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return 'gemini' in model_name
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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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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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url = f"https://dashscope.aliyuncs.com/api/v1/apps/{app_id}/completion"
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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'] # 假设 user prompt 已在 generate_reply 中构建好
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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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data = {
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"input": {"prompt": prompt},
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"parameters": {"max_tokens": max_tokens, "temperature": temperature},
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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[:100]}...") # 避免 prompt 过长
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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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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_gemini_api(self, settings: dict, messages: list, max_tokens: int = 100, temperature: float = 0.7) -> str:
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"""
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调用Google Gemini REST API (v1beta)
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"""
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api_key = settings['api_key']
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model_name = settings['model_name']
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url = f"https://generativelanguage.googleapis.com/v1beta/models/{model_name}:generateContent?key={api_key}"
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headers = {"Content-Type": "application/json"}
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# --- 转换消息格式 (修复 P1-3: 增强健壮性) ---
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system_instruction = ""
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user_content_parts = []
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# 遍历消息，找到 system 和所有的 user parts
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for msg in messages:
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if msg['role'] == 'system':
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system_instruction = msg['content']
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elif msg['role'] == 'user':
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# 我们只关心 user content
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user_content_parts.append(msg['content'])
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# 将所有 user parts 合并为最后的 user_content
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# 在我们的使用场景中 (generate_reply)，只会有一个 user part，但这样更安全
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user_content = "\n".join(user_content_parts)
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if not user_content:
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logger.warning(f"Gemini API 调用: 未在消息中找到 'user' 角色内容。Messages: {messages}")
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raise ValueError("未在消息中找到用户内容 (user content)")
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# --- 消息格式转换结束 ---
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payload = {
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"contents": [
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{
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"role": "user",
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"parts": [{"text": user_content}]
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}
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],
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"generationConfig": {
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"temperature": temperature,
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"maxOutputTokens": max_tokens
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}
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}
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if system_instruction:
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payload["systemInstruction"] = {
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"parts": [{"text": system_instruction}]
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}
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logger.info(f"Calling Gemini REST API: {url.split('?')[0]}")
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logger.debug(f"Gemini Payload: {json.dumps(payload, ensure_ascii=False)}")
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response = requests.post(url, headers=headers, json=payload, timeout=30)
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if response.status_code != 200:
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logger.error(f"Gemini API 请求失败: {response.status_code} - {response.text}")
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raise Exception(f"Gemini API 请求失败: {response.status_code} - {response.text}")
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result = response.json()
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logger.debug(f"Gemini API 响应: {json.dumps(result, ensure_ascii=False)}")
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try:
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reply_text = result['candidates'][0]['content']['parts'][0]['text']
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return reply_text.strip()
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except (KeyError, IndexError, TypeError) as e:
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logger.error(f"Gemini API 响应格式错误: {result} - {e}")
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raise Exception(f"Gemini 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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try:
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logger.info(f"调用OpenAI API: model={settings['model_name']}, base_url={settings.get('base_url', 'default')}")
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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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except Exception as e:
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logger.error(f"OpenAI API调用失败: {e}")
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# 如果有详细的错误信息，打印出来
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if hasattr(e, 'response'):
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logger.error(f"响应状态码: {getattr(e.response, 'status_code', 'unknown')}")
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logger.error(f"响应内容: {getattr(e.response, 'text', 'unknown')}")
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raise
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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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"""
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检测用户消息意图 (基于关键词的本地检测)
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修复 P1-1: 移除了AI调用，以降低成本和延迟。
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"""
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try:
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# 检查AI是否启用，如果未启用，不应执行任何AI相关逻辑
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# 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
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settings = db_manager.get_ai_reply_settings(cookie_id)
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if not settings['ai_enabled']:
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return 'default'
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msg_lower = message.lower()
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# 价格相关关键词
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price_keywords = [
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'便宜', '优惠', '刀', '降价', '包邮', '价格', '多少钱', '能少', '还能', '最低', '底价',
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'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
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]
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# 同样，你也可以通过正则表达式来匹配纯数字，比如 "100" "80"
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# 但那可能有点复杂，先加关键词是最小改动
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if any(kw in msg_lower for kw in price_keywords):
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logger.debug(f"本地意图检测: price ({message})")
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return 'price'
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# 技术相关关键词
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tech_keywords = ['怎么用', '参数', '坏了', '故障', '设置', '说明书', '功能', '用法', '教程', '驱动']
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if any(kw in msg_lower for kw in tech_keywords):
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logger.debug(f"本地意图检测: tech ({message})")
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return 'tech'
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logger.debug(f"本地意图检测: default ({message})")
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return 'default'
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except Exception as e:
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logger.error(f"本地意图检测失败 {cookie_id}: {e}")
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return 'default'
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def _get_chat_lock(self, chat_id: str) -> threading.Lock:
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"""获取指定chat_id的锁，如果不存在则创建"""
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with self._chat_locks_lock:
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if chat_id not in self._chat_locks:
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self._chat_locks[chat_id] = threading.Lock()
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return self._chat_locks[chat_id]
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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,
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skip_wait: bool = False) -> 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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# 先检测意图（用于后续保存）
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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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# 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
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message_created_at = self.save_conversation(chat_id, cookie_id, user_id, item_id, "user", message, intent)
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# 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
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if not skip_wait:
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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})")
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# 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）
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time.sleep(10)
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else:
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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})")
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# 获取该chat_id的锁，确保同一对话的消息串行处理
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chat_lock = self._get_chat_lock(chat_id)
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# 使用锁确保同一chat_id的消息串行处理
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with chat_lock:
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# 获取最近时间窗口内的所有用户消息
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# 如果 skip_wait=True（外部防抖），查询窗口为6秒（1秒防抖 + 5秒缓冲）
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# 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）
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query_seconds = 6 if skip_wait else 25
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recent_messages = self._get_recent_user_messages(chat_id, cookie_id, seconds=query_seconds)
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logger.info(f"【{cookie_id}】最近{query_seconds}秒内的消息: {[msg['content'][:20] for msg in recent_messages]}")
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if recent_messages and len(recent_messages) > 0:
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# 只处理最后一条消息（时间戳最新的）
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latest_message = recent_messages[-1]
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if message_created_at != latest_message['created_at']:
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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']})")
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return None
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else:
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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})")
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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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# 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 (P0-1 竞争条件风险点 - 遵照指示未修改)
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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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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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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：
|
||
{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()
|