259 lines
8.3 KiB
Python
259 lines
8.3 KiB
Python
# src/services/llm/llm_factory.py
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"""LLM工厂 - 统一创建和管理LLM客户端"""
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from typing import Optional, Dict, Any
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from loguru import logger
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from functools import lru_cache
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from .base_client import BaseLLMClient, LLMConfig, LLMProvider, LLMResponse
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from .deepseek_client import DeepSeekClient
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from .qwen_client import QwenClient, QwenVLClient
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# 默认模型映射
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DEFAULT_MODELS = {
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LLMProvider.DEEPSEEK: "deepseek-v4-flash",
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LLMProvider.QWEN: "qwen3.5-flash",
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LLMProvider.QWEN_VL: "qwen3-vl-plus"
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}
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# API基础URL（使用统一代理服务）
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DEFAULT_BASE_URLS = {
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LLMProvider.DEEPSEEK: "http://6.86.80.4:30080/v1",
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LLMProvider.QWEN: "http://6.86.80.4:30080/v1",
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LLMProvider.QWEN_VL: "http://6.86.80.4:30080/v1"
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}
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class LLMFactory:
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"""
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LLM客户端工厂（支持全局缓存）
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支持的提供商和模型：
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- DeepSeek: deepseek-chat (DeepSeek-V3), deepseek-coder
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- Qwen: qwen-turbo, qwen-plus, qwen-max, qwen-long
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- QwenVL: qwen-vl-plus, qwen-vl-max (多模态)
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使用示例：
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factory = LLMFactory()
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# 使用默认配置
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client = factory.create("deepseek")
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# 自定义配置
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client = factory.create("qwen", model="qwen-max", temperature=0.5)
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# 调用LLM
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response = client.complete("你好，介绍一下自己")
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"""
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# 全局客户端缓存（类级别，跨实例共享）
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_global_instances: Dict[str, BaseLLMClient] = {}
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def __init__(self):
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self._config_cache: Dict[str, Any] = {}
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def create(
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self,
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provider: str,
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api_key: Optional[str] = None,
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model: Optional[str] = None,
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base_url: Optional[str] = None,
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max_tokens: int = 4096,
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temperature: float = 0.7,
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**kwargs
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) -> BaseLLMClient:
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"""
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创建LLM客户端
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Args:
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provider: 提供商名称 ("deepseek", "qwen", "qwen_vl")
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api_key: API密钥（如未提供，从环境变量获取）
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model: 模型名称（如未提供，使用默认模型）
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base_url: API基础URL
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max_tokens: 最大输出token数
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temperature: 温度参数
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**kwargs: 其他配置参数
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Returns:
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BaseLLMClient: LLM客户端实例
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"""
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provider_enum = self._parse_provider(provider)
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# 获取配置
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api_key = api_key or self._get_api_key(provider_enum)
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model = model or DEFAULT_MODELS.get(provider_enum)
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base_url = base_url or DEFAULT_BASE_URLS.get(provider_enum)
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if not api_key:
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raise ValueError(f"缺少API密钥，请设置环境变量或传入api_key参数")
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# 检查全局缓存
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cache_key = f"{provider}_{model}"
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if cache_key in LLMFactory._global_instances:
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logger.debug(f"使用缓存的LLM客户端: {cache_key}")
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return LLMFactory._global_instances[cache_key]
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config = LLMConfig(
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provider=provider_enum,
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model=model,
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api_key=api_key,
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base_url=base_url,
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max_tokens=max_tokens,
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temperature=temperature,
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**kwargs
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)
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# 创建客户端
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client = self._create_client(config)
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# 缓存到全局实例
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LLMFactory._global_instances[cache_key] = client
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logger.info(f"LLM客户端创建成功并缓存: {provider} - {model}")
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return client
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def _parse_provider(self, provider: str) -> LLMProvider:
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"""解析提供商名称"""
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provider_map = {
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"deepseek": LLMProvider.DEEPSEEK,
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"deepseek-v3": LLMProvider.DEEPSEEK,
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"deepseek_chat": LLMProvider.DEEPSEEK,
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"qwen": LLMProvider.QWEN,
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"qwen-turbo": LLMProvider.QWEN,
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"qwen-plus": LLMProvider.QWEN,
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"qwen-max": LLMProvider.QWEN,
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"qwen3.5-flash": LLMProvider.QWEN,
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"qwen3.5-plus": LLMProvider.QWEN,
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"qwen_vl": LLMProvider.QWEN_VL,
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"qwen-vl": LLMProvider.QWEN_VL,
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"qwen-vl-plus": LLMProvider.QWEN_VL,
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"qwen-vl-max": LLMProvider.QWEN_VL
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}
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provider_lower = provider.lower()
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if provider_lower not in provider_map:
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raise ValueError(f"不支持的提供商: {provider}，支持的: {list(provider_map.keys())}")
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return provider_map[provider_lower]
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def _get_api_key(self, provider: LLMProvider) -> Optional[str]:
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"""从环境变量获取API密钥"""
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import os
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key_map = {
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LLMProvider.DEEPSEEK: ["DEEPSEEK_API_KEY", "OPENAI_API_KEY"],
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LLMProvider.QWEN: ["QWEN_API_KEY", "DASHSCOPE_API_KEY", "ALIBABA_API_KEY"],
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LLMProvider.QWEN_VL: ["QWEN_API_KEY", "DASHSCOPE_API_KEY", "ALIBABA_API_KEY"]
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}
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for key_name in key_map.get(provider, []):
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api_key = os.getenv(key_name)
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if api_key:
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return api_key
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return None
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def _create_client(self, config: LLMConfig) -> BaseLLMClient:
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"""创建具体客户端"""
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client_map = {
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LLMProvider.DEEPSEEK: DeepSeekClient,
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LLMProvider.QWEN: QwenClient,
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LLMProvider.QWEN_VL: QwenVLClient
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}
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client_class = client_map.get(config.provider)
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if not client_class:
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raise ValueError(f"不支持的提供商: {config.provider}")
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return client_class(config)
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def get_cached(self, provider: str, model: Optional[str] = None) -> Optional[BaseLLMClient]:
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"""获取缓存的客户端"""
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provider_enum = self._parse_provider(provider)
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model = model or DEFAULT_MODELS.get(provider_enum)
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cache_key = f"{provider}_{model}"
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return LLMFactory._global_instances.get(cache_key)
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def list_available_providers(self) -> Dict[str, list]:
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"""列出可用的提供商和模型"""
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return {
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"deepseek": DeepSeekClient.SUPPORTED_MODELS,
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"qwen": QwenClient.SUPPORTED_MODELS,
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"qwen_vl": QwenVLClient.SUPPORTED_MODELS
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}
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@classmethod
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def preload_clients(cls, providers: list = None):
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"""
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预加载LLM客户端（应用启动时调用）
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Args:
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providers: 要预加载的提供商列表，默认加载qwen和deepseek
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"""
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if providers is None:
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providers = ["qwen", "deepseek"]
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factory = cls()
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for provider in providers:
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try:
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client = factory.create(provider)
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logger.success(f"预加载LLM客户端成功: {provider}")
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except Exception as e:
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logger.warning(f"预加载LLM客户端失败: {provider} - {e}")
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@classmethod
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def get_global_client(cls, provider: str, model: Optional[str] = None) -> Optional[BaseLLMClient]:
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"""获取全局缓存的客户端"""
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provider_lower = provider.lower()
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# 处理模型名作为provider的情况（如 qwen3.5-flash）
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if provider_lower.startswith("qwen"):
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provider_lower = "qwen"
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model = model or DEFAULT_MODELS.get(LLMProvider.QWEN if provider_lower == "qwen" else LLMProvider.DEEPSEEK)
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cache_key = f"{provider_lower}_{model}"
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return cls._global_instances.get(cache_key)
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@classmethod
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def cleanup(cls):
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"""清理所有缓存的客户端"""
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for cache_key, client in cls._global_instances.items():
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try:
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client.close()
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logger.debug(f"关闭LLM客户端: {cache_key}")
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except Exception as e:
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logger.warning(f"关闭LLM客户端失败: {cache_key} - {e}")
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cls._global_instances.clear()
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logger.info("所有LLM客户端已清理")
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@lru_cache
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def get_llm_factory() -> LLMFactory:
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"""获取LLM工厂实例（缓存）"""
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return LLMFactory()
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def get_llm_client(
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provider: str = "qwen",
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model: Optional[str] = None,
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**kwargs
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) -> BaseLLMClient:
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"""
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便捷函数：获取LLM客户端（优先使用缓存）
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Args:
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provider: 提供商名称
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model: 模型名称
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**kwargs: 其他配置
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Returns:
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BaseLLMClient: LLM客户端实例
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"""
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factory = get_llm_factory()
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# 先尝试获取缓存的实例
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cached = factory.get_cached(provider, model)
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if cached:
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return cached
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return factory.create(provider, model=model, **kwargs)
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