264 lines
7.8 KiB
Python
264 lines
7.8 KiB
Python
"""
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AI 服务抽象层 - 支持通义千问和本地模型
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"""
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import os
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from abc import ABC, abstractmethod
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from typing import Optional, Dict, Any
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import yaml
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from pathlib import Path
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# 配置文件路径
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CONFIG_PATH = Path(__file__).parent.parent.parent / "config.yaml"
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def load_config() -> Dict[str, Any]:
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"""加载配置文件"""
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if CONFIG_PATH.exists():
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with open(CONFIG_PATH, 'r', encoding='utf-8') as f:
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return yaml.safe_load(f) or {}
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return {}
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class AIProvider(ABC):
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"""AI 服务提供者抽象基类"""
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@abstractmethod
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async def generate(self, prompt: str, context: Optional[str] = None) -> str:
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"""生成内容
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Args:
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prompt: 用户提示词
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context: 可选的上下文信息
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Returns:
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生成的文本内容
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"""
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pass
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@abstractmethod
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async def check(self, content: str, requirements: Optional[list] = None) -> Dict[str, Any]:
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"""检查内容是否包含必要信息
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Args:
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content: 要检查的内容
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requirements: 可选的检查要求列表
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Returns:
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检查结果字典，包含 passed, issues, suggestions 等字段
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"""
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pass
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class OpenAICompatibleProvider(AIProvider):
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"""OpenAI 兼容接口实现 - 支持通义千问、DeepSeek 等"""
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def __init__(
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self,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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model: str = "qwen-plus"
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):
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self.api_key = api_key or os.getenv("AI_API_KEY", "")
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self.base_url = base_url or os.getenv("AI_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")
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self.model = model
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# 初始化 OpenAI 客户端
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from openai import AsyncOpenAI
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self.client = AsyncOpenAI(
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api_key=self.api_key,
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base_url=self.base_url
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)
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async def generate(self, prompt: str, context: Optional[str] = None) -> str:
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"""使用 OpenAI 兼容接口生成内容"""
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messages = []
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if context:
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messages.append({"role": "system", "content": context})
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messages.append({"role": "user", "content": prompt})
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try:
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=messages
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)
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return response.choices[0].message.content
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except Exception as e:
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return f"调用API出错: {str(e)}"
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async def check(self, content: str, requirements: Optional[list] = None) -> Dict[str, Any]:
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"""使用 AI 检查内容"""
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check_prompt = f"""请检查以下意图编制内容是否完整，是否包含必要的信息。
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要检查的内容：
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{content}
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请检查以下方面：
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1. 测试目标是否明确
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2. 测试范围是否清晰
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3. 测试条件是否完整
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4. 预期结果是否明确
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5. 是否有遗漏的关键信息
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请以JSON格式返回检查结果：
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{{
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"passed": true/false,
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"score": 0-100,
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"issues": ["问题1", "问题2"],
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"suggestions": ["建议1", "建议2"]
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}}
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"""
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try:
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[{"role": "user", "content": check_prompt}]
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)
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result_text = response.choices[0].message.content
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# 尝试解析JSON
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import json
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try:
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# 提取JSON部分
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start = result_text.find('{')
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end = result_text.rfind('}') + 1
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if start != -1 and end > start:
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return json.loads(result_text[start:end])
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except:
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pass
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return {
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"passed": False,
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"score": 0,
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"issues": ["无法解析AI返回结果"],
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"suggestions": [],
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"raw_response": result_text
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}
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except Exception as e:
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return {
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"passed": False,
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"score": 0,
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"issues": [f"调用出错: {str(e)}"],
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"suggestions": []
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}
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class LocalModelProvider(AIProvider):
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"""本地模型实现 - 兼容 OpenAI API 格式"""
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def __init__(self, endpoint: str = "http://localhost:8000", model: str = "llama3", api_key: str = ""):
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self.endpoint = endpoint.rstrip('/')
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self.model = model
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self.api_key = api_key or os.getenv("LOCAL_MODEL_API_KEY", "not-needed")
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async def generate(self, prompt: str, context: Optional[str] = None) -> str:
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"""使用本地模型生成内容"""
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import aiohttp
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messages = []
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if context:
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messages.append({"role": "system", "content": context})
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messages.append({"role": "user", "content": prompt})
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.api_key}"
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}
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payload = {
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"model": self.model,
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"messages": messages,
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"stream": False
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}
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try:
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async with aiohttp.ClientSession() as session:
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async with session.post(
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f"{self.endpoint}/v1/chat/completions",
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headers=headers,
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json=payload
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) as response:
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if response.status == 200:
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data = await response.json()
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return data["choices"][0]["message"]["content"]
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else:
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return f"生成失败: HTTP {response.status}"
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except Exception as e:
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return f"调用本地模型出错: {str(e)}"
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async def check(self, content: str, requirements: Optional[list] = None) -> Dict[str, Any]:
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"""使用本地模型检查内容"""
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check_prompt = f"""请检查以下意图编制内容是否完整。
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内容：
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{content}
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请以JSON格式返回：{{"passed": bool, "score": int, "issues": [], "suggestions": []}}"""
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result = await self.generate(check_prompt)
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try:
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import json
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start = result.find('{')
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end = result.rfind('}') + 1
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if start != -1 and end > start:
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return json.loads(result[start:end])
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except:
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pass
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return {
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"passed": False,
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"score": 0,
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"issues": ["无法解析结果"],
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"suggestions": [],
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"raw_response": result
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}
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class AIServiceFactory:
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"""AI 服务工厂 - 根据配置创建对应的 Provider"""
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_instance: Optional[AIProvider] = None
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@classmethod
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def get_provider(cls) -> AIProvider:
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"""获取 AI Provider 单例"""
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if cls._instance is None:
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cls._instance = cls._create_provider()
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return cls._instance
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@classmethod
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def _create_provider(cls) -> AIProvider:
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"""根据配置创建 Provider"""
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config = load_config()
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ai_config = config.get("ai", {})
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# 优先从环境变量读取
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api_key = os.getenv("AI_API_KEY", "")
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base_url = os.getenv("AI_BASE_URL", "")
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model = os.getenv("AI_MODEL", "")
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# 如果环境变量未设置，从配置文件读取
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if not api_key:
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api_key = ai_config.get("api_key", "")
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if not base_url:
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base_url = ai_config.get("base_url", "https://dashscope.aliyuncs.com/compatible-mode/v1")
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if not model:
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model = ai_config.get("model", "qwen-plus")
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return OpenAICompatibleProvider(
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api_key=api_key,
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base_url=base_url,
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model=model
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)
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@classmethod
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def reset(cls):
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"""重置单例，用于切换 Provider"""
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cls._instance = None
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# 便捷函数
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def get_ai_service() -> AIProvider:
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"""获取 AI 服务实例"""
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return AIServiceFactory.get_provider()
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