1.0.0初始化源代码
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yuqianqian10204095yu
301
backend/app/services/ai_service.py
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301
backend/app/services/ai_service.py
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"""
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AI 大模型对接服务
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支持 OpenAI、智谱、百度文心、阿里通义等主流大模型
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"""
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import logging
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from typing import Optional, Dict, Any, List
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from datetime import datetime
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import json
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logger = logging.getLogger(__name__)
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class AIService:
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"""AI 服务类"""
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def __init__(self):
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self._client_cache: Dict[str, Any] = {}
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async def generate_comment(
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self,
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news_content: str,
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writing_style: str,
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persona_description: Optional[str] = None,
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model_config: Optional[Dict[str, Any]] = None
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) -> Optional[Dict[str, Any]]:
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"""
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AI 生成评论
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:param news_content: 新闻内容
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:param writing_style: 写作风格
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:param persona_description: 人格描述
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:param model_config: 模型配置
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:return: 生成结果（包含 content, tokens_used 等）
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"""
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prompt = self._build_comment_prompt(
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news_content,
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writing_style,
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persona_description
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)
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return await self._call_ai_api(prompt, model_config)
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async def generate_reply(
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self,
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original_comment: str,
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news_content: str,
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writing_style: str,
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persona_description: Optional[str] = None,
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model_config: Optional[Dict[str, Any]] = None
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) -> Optional[Dict[str, Any]]:
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"""
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AI 生成回复
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:param original_comment: 原评论
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:param news_content: 新闻内容
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:param writing_style: 写作风格
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:param persona_description: 人格描述
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:param model_config: 模型配置
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:return: 生成结果
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"""
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prompt = self._build_reply_prompt(
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original_comment,
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news_content,
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writing_style,
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persona_description
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)
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return await self._call_ai_api(prompt, model_config)
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def _build_comment_prompt(
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self,
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news_content: str,
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writing_style: str,
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persona_description: Optional[str] = None
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) -> str:
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"""构建评论提示词"""
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base_prompt = f"""你是一位虚拟用户，请根据以下要求写一条简短的评论：
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写作风格：{writing_style}
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"""
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if persona_description:
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base_prompt += f"\n人格特征：{persona_description}\n"
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base_prompt += f"""
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新闻内容：
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{news_content[:1000]} # 限制长度
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请写一条 50-100 字的评论，要符合你的写作风格和人格特征。直接输出评论内容，不要有其他说明。"""
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return base_prompt
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def _build_reply_prompt(
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self,
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original_comment: str,
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news_content: str,
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writing_style: str,
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persona_description: Optional[str] = None
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) -> str:
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"""构建回复提示词"""
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base_prompt = f"""你是一位虚拟用户，请根据以下要求回复另一条评论：
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写作风格：{writing_style}
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"""
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if persona_description:
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base_prompt += f"\n人格特征：{persona_description}\n"
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base_prompt += f"""
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新闻内容：
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{news_content[:500]}
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原评论：
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{original_comment}
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请写一条 30-80 字的回复，要符合你的写作风格和人格特征。直接输出回复内容，不要有其他说明。"""
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return base_prompt
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async def _call_ai_api(
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self,
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prompt: str,
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model_config: Optional[Dict[str, Any]] = None
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) -> Optional[Dict[str, Any]]:
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"""
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调用 AI API（根据 model_config 中的 provider 选择对应模型）
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:param prompt: 提示词
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:param model_config: 模型配置
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:return: 生成结果
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"""
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if not model_config:
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# 使用默认配置（需要从数据库加载）
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from app.models.ai_model import AIModelConfig
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from app.models.base import get_db
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with get_db() as db:
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default_model = db.query(AIModelConfig).filter(
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AIModelConfig.is_default == True,
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AIModelConfig.is_active == True
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).first()
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if not default_model:
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logger.error("No default AI model configured")
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return None
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model_config = {
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"provider": default_model.provider,
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"model_name": default_model.model_name,
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"api_key": default_model.api_key,
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"api_url": default_model.api_url,
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"temperature": default_model.temperature,
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"max_tokens": default_model.max_tokens,
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}
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provider = model_config.get("provider", "").lower()
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try:
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if provider == "openai":
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return await self._call_openai(prompt, model_config)
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elif provider == "zhipu":
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return await self._call_zhipu(prompt, model_config)
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elif provider in ["baidu", "wenxin"]:
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return await self._call_baidu_wenxin(prompt, model_config)
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elif provider in ["aliyun", "dashscope"]:
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return await self._call_aliyun_dashscope(prompt, model_config)
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else:
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logger.error(f"Unsupported AI provider: {provider}")
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return None
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except Exception as e:
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logger.error(f"AI API call error: {e}")
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return None
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async def _call_openai(
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self,
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prompt: str,
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config: Dict[str, Any]
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) -> Optional[Dict[str, Any]]:
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"""调用 OpenAI API"""
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try:
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from openai import AsyncOpenAI
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client = AsyncOpenAI(
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api_key=config["api_key"],
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base_url=config.get("api_url")
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)
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response = await client.chat.completions.create(
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model=config.get("model_name", "gpt-3.5-turbo"),
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messages=[
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{"role": "user", "content": prompt}
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],
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temperature=config.get("temperature", 0.7),
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max_tokens=config.get("max_tokens", 1000)
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)
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content = response.choices[0].message.content
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tokens_used = response.usage.total_tokens if response.usage else 0
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logger.info(f"OpenAI generated content, tokens: {tokens_used}")
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return {
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"content": content,
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"tokens_used": tokens_used,
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"provider": "openai",
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"model": config.get("model_name", "gpt-3.5-turbo")
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}
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except Exception as e:
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logger.error(f"OpenAI API error: {e}")
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return None
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async def _call_zhipu(
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self,
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prompt: str,
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config: Dict[str, Any]
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) -> Optional[Dict[str, Any]]:
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"""调用智谱 AI API"""
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try:
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from zhipuai import ZhipuAI
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client = ZhipuAI(api_key=config["api_key"])
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response = client.chat.completions.create(
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model=config.get("model_name", "glm-4"),
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messages=[
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{"role": "user", "content": prompt}
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],
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temperature=config.get("temperature", 0.7),
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max_tokens=config.get("max_tokens", 1000)
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)
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content = response.choices[0].message.content
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tokens_used = response.usage.total_tokens if response.usage else 0
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logger.info(f"Zhipu AI generated content, tokens: {tokens_used}")
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return {
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"content": content,
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"tokens_used": tokens_used,
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"provider": "zhipu",
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"model": config.get("model_name", "glm-4")
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}
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except Exception as e:
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logger.error(f"Zhipu AI API error: {e}")
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return None
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async def _call_baidu_wenxin(
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self,
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prompt: str,
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config: Dict[str, Any]
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) -> Optional[Dict[str, Any]]:
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"""调用百度文心一言 API"""
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# TODO: 实现百度文心一言 API 调用
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logger.warning("Baidu Wenxin API not implemented yet")
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return None
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async def _call_aliyun_dashscope(
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self,
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prompt: str,
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config: Dict[str, Any]
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) -> Optional[Dict[str, Any]]:
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"""调用阿里云通义千问 API"""
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# TODO: 实现阿里云 DashScope API 调用
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logger.warning("Aliyun DashScope API not implemented yet")
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return None
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async def test_model(
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self,
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model_config: Dict[str, Any],
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test_prompt: str = "测试评论"
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) -> Dict[str, Any]:
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"""
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测试模型配置
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:param model_config: 模型配置
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:param test_prompt: 测试提示词
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:return: 测试结果
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"""
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import time
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start_time = time.time()
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result = await self._call_ai_api(test_prompt, model_config)
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cost_time = time.time() - start_time
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if result:
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return {
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"success": True,
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"content": result.get("content"),
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"tokens_used": result.get("tokens_used", 0),
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"cost_time": round(cost_time, 2),
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"error_message": None
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}
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else:
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return {
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"success": False,
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"content": None,
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"tokens_used": 0,
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"cost_time": round(cost_time, 2),
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"error_message": "Failed to generate content"
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}
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# 创建全局服务实例
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ai_service = AIService()
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