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core/providers.py

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+"""
+LLM Provider 抽象层
+
+支持多种 LLM 提供商的统一接口
+"""
+
+from typing import Any, Dict, List, Optional, Protocol, Type, runtime_checkable
+from abc import ABC, abstractmethod
+import logging
+
+from langchain_core.language_models import BaseChatModel
+
+from config import Config
+from .registry import ProviderRegistry, register_provider
+
+logger = logging.getLogger(__name__)
+
+
+@runtime_checkable
+class LLMProvider(Protocol):
+    """
+    LLM Provider 协议
+    
+    定义所有 LLM 提供商必须实现的接口
+    """
+    
+    @property
+    def name(self) -> str:
+        """Provider 名称"""
+        ...
+    
+    @property
+    def supported_models(self) -> List[str]:
+        """支持的模型列表"""
+        ...
+    
+    def create_model(self, config: Dict[str, Any]) -> BaseChatModel:
+        """
+        创建 LLM 模型实例
+        
+        Args:
+            config: 模型配置
+            
+        Returns:
+            BaseChatModel 实例
+        """
+        ...
+
+
+class BaseLLMProvider(ABC):
+    """LLM Provider 基类"""
+    
+    def __init__(self, name: str, supported_models: List[str]):
+        self._name = name
+        self._supported_models = supported_models
+    
+    @property
+    def name(self) -> str:
+        return self._name
+    
+    @property
+    def supported_models(self) -> List[str]:
+        return self._supported_models
+    
+    @abstractmethod
+    def create_model(self, config: Dict[str, Any]) -> BaseChatModel:
+        """子类实现具体的模型创建逻辑"""
+        pass
+
+
+class OpenAICompatibleProvider(BaseLLMProvider):
+    """
+    OpenAI 兼容的 Provider
+    
+    支持所有兼容 OpenAI API 的服务：
+    - OpenAI
+    - Azure OpenAI
+    - 本地部署的兼容服务
+    - 国产大模型（通义千问、文心一言等）
+    """
+    
+    def __init__(self, name: str = "openai_compatible", supported_models: Optional[List[str]] = None):
+        super().__init__(name, supported_models or [])
+    
+    def create_model(self, config: Dict[str, Any]) -> BaseChatModel:
+        from langchain_openai import ChatOpenAI
+        
+        return ChatOpenAI(
+            model=config.get("model_name") or config.get("MODEL_NAME"),
+            openai_api_key=config.get("api_key") or config.get("OPENAI_API_KEY"),
+            openai_api_base=config.get("base_url") or config.get("URL"),
+            temperature=config.get("temperature", 0.7),
+            max_tokens=config.get("max_tokens"),
+            timeout=config.get("timeout", 30),
+            max_retries=config.get("max_retries", 3),
+        )
+
+
+class AzureOpenAIProvider(BaseLLMProvider):
+    """Azure OpenAI Provider"""
+    
+    def __init__(self):
+        super().__init__("azure", ["gpt-4", "gpt-35-turbo"])
+    
+    def create_model(self, config: Dict[str, Any]) -> BaseChatModel:
+        from langchain_openai import AzureChatOpenAI
+        
+        return AzureChatOpenAI(
+            azure_deployment=config.get("deployment_name"),
+            openai_api_version=config.get("api_version", "2024-02-15-preview"),
+            azure_endpoint=config.get("endpoint"),
+            openai_api_key=config.get("api_key"),
+            temperature=config.get("temperature", 0.7),
+        )
+
+
+class LLMFactory:
+    """
+    LLM 工厂类
+    
+    统一管理 LLM 实例的创建，支持：
+    - 多种 Provider
+    - 配置驱动
+    - 单例缓存
+    """
+    
+    _instances: Dict[str, BaseChatModel] = {}
+    _default_provider: str = "openai_compatible"
+    
+    @classmethod
+    def register_provider(cls, name: str, provider: LLMProvider) -> None:
+        """注册 Provider"""
+        ProviderRegistry._entries[name] = type(
+            "RegistryEntry",
+            (),
+            {"instance": provider, "metadata": {}}
+        )()
+        logger.info(f"Registered LLM provider: {name}")
+    
+    @classmethod
+    def get_provider(cls, name: str) -> Optional[LLMProvider]:
+        """获取 Provider"""
+        entry = ProviderRegistry.get_entry(name)
+        if entry:
+            return entry.instance
+        return None
+    
+    @classmethod
+    def create(
+        cls,
+        provider: Optional[str] = None,
+        config: Optional[Dict[str, Any]] = None,
+        model_section: Optional[str] = None,
+        use_cache: bool = True,
+    ) -> BaseChatModel:
+        """
+        创建 LLM 实例
+        
+        Args:
+            provider: Provider 名称，默认使用 openai_compatible
+            config: 模型配置
+            model_section: 配置文件中的模型段名
+            use_cache: 是否使用缓存
+            
+        Returns:
+            BaseChatModel 实例
+        """
+        provider_name = provider or cls._default_provider
+        
+        if model_section:
+            config = Config.get_section(model_section)
+            cache_key = f"{provider_name}:{model_section}"
+        else:
+            config = config or {}
+            cache_key = f"{provider_name}:{hash(frozenset(config.items()))}"
+        
+        if use_cache and cache_key in cls._instances:
+            return cls._instances[cache_key]
+        
+        provider_instance = cls.get_provider(provider_name)
+        
+        if provider_instance is None:
+            provider_instance = OpenAICompatibleProvider()
+        
+        model = provider_instance.create_model(config)
+        
+        if use_cache:
+            cls._instances[cache_key] = model
+        
+        return model
+    
+    @classmethod
+    def clear_cache(cls) -> None:
+        """清空缓存"""
+        cls._instances.clear()
+    
+    @classmethod
+    def list_providers(cls) -> List[str]:
+        """列出所有注册的 Provider"""
+        return ProviderRegistry.list_names()
+
+
+OpenAICompatibleProvider()
+ProviderRegistry._entries["openai_compatible"] = type(
+    "RegistryEntry",
+    (),
+    {"instance": OpenAICompatibleProvider(), "metadata": {}}
+)()
+
+
+def create_chat_model(model_section: Optional[str] = None) -> BaseChatModel:
+    """
+    创建聊天模型（兼容现有代码）
+    
+    这是现有 llm_factory.create_chat_model 的替代实现，
+    使用新的 Provider 架构但保持接口兼容
+    """
+    return LLMFactory.create(model_section=model_section)