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https://github.com/zhinianboke/xianyu-auto-reply.git
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Update ai_reply_engine.py
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zhinianboke
parent
e49f45ba6e
commit
dd319014e7
@ -7,6 +7,7 @@ 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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@ -60,17 +61,108 @@ class AIReplyEngine:
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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")
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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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@ -78,45 +170,55 @@ class AIReplyEngine:
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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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client = self.get_client(cookie_id)
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if not client:
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return 'default'
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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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response = client.chat.completions.create(
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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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max_tokens=10,
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temperature=0.1
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)
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intent = response.choices[0].message.content.strip().lower()
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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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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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client = self.get_client(cookie_id)
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if not client:
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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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@ -177,17 +279,21 @@ class AIReplyEngine:
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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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response = client.chat.completions.create(
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model=settings['model_name'],
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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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max_tokens=100,
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temperature=0.7
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)
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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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reply = response.choices[0].message.content.strip()
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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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@ -202,6 +308,11 @@ class AIReplyEngine:
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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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