166 lines
4.9 KiB
Python
166 lines
4.9 KiB
Python
"""
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LLM工厂类 - 统一创建和管理LLM适配器
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"""
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from typing import Dict, List, Optional
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from .types import LLMConfig, LLMProvider, DEFAULT_MODELS
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from .base_adapter import BaseLLMAdapter
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from .adapters import (
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OpenAIAdapter,
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GeminiAdapter,
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ClaudeAdapter,
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DeepSeekAdapter,
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QwenAdapter,
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ZhipuAdapter,
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MoonshotAdapter,
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BaiduAdapter,
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MinimaxAdapter,
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DoubaoAdapter,
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OllamaAdapter,
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)
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class LLMFactory:
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"""LLM工厂类"""
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_adapters: Dict[str, BaseLLMAdapter] = {}
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@classmethod
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def create_adapter(cls, config: LLMConfig) -> BaseLLMAdapter:
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"""创建LLM适配器实例"""
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cache_key = cls._get_cache_key(config)
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# 从缓存中获取
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if cache_key in cls._adapters:
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return cls._adapters[cache_key]
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# 创建新的适配器实例
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adapter = cls._instantiate_adapter(config)
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# 缓存实例
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cls._adapters[cache_key] = adapter
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return adapter
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@classmethod
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def _instantiate_adapter(cls, config: LLMConfig) -> BaseLLMAdapter:
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"""根据提供商类型实例化适配器"""
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# 如果未指定模型，使用默认模型
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if not config.model:
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config.model = DEFAULT_MODELS.get(config.provider, "gpt-4o-mini")
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adapter_map = {
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LLMProvider.OPENAI: OpenAIAdapter,
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LLMProvider.GEMINI: GeminiAdapter,
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LLMProvider.CLAUDE: ClaudeAdapter,
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LLMProvider.DEEPSEEK: DeepSeekAdapter,
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LLMProvider.QWEN: QwenAdapter,
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LLMProvider.ZHIPU: ZhipuAdapter,
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LLMProvider.MOONSHOT: MoonshotAdapter,
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LLMProvider.BAIDU: BaiduAdapter,
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LLMProvider.MINIMAX: MinimaxAdapter,
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LLMProvider.DOUBAO: DoubaoAdapter,
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LLMProvider.OLLAMA: OllamaAdapter,
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}
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adapter_class = adapter_map.get(config.provider)
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if not adapter_class:
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raise ValueError(f"不支持的LLM提供商: {config.provider}")
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return adapter_class(config)
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@classmethod
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def _get_cache_key(cls, config: LLMConfig) -> str:
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"""生成缓存键"""
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api_key_prefix = config.api_key[:8] if config.api_key else "no-key"
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return f"{config.provider.value}:{config.model}:{api_key_prefix}"
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@classmethod
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def clear_cache(cls) -> None:
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"""清除缓存"""
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cls._adapters.clear()
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@classmethod
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def get_supported_providers(cls) -> List[LLMProvider]:
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"""获取支持的提供商列表"""
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return list(LLMProvider)
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@classmethod
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def get_default_model(cls, provider: LLMProvider) -> str:
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"""获取提供商的默认模型"""
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return DEFAULT_MODELS.get(provider, "gpt-4o-mini")
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@classmethod
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def get_available_models(cls, provider: LLMProvider) -> List[str]:
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"""获取提供商的可用模型列表"""
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models = {
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LLMProvider.GEMINI: [
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"gemini-2.5-flash",
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"gemini-2.5-pro",
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"gemini-1.5-flash",
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"gemini-1.5-pro",
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],
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LLMProvider.OPENAI: [
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"gpt-4o",
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"gpt-4o-mini",
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"gpt-4-turbo",
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"gpt-4",
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"gpt-3.5-turbo",
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"o1-preview",
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"o1-mini",
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],
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LLMProvider.CLAUDE: [
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"claude-3-5-sonnet-20241022",
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"claude-3-opus-20240229",
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"claude-3-sonnet-20240229",
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"claude-3-haiku-20240307",
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],
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LLMProvider.QWEN: [
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"qwen-turbo",
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"qwen-plus",
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"qwen-max",
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"qwen-max-longcontext",
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],
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LLMProvider.DEEPSEEK: [
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"deepseek-chat",
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"deepseek-coder",
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],
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LLMProvider.ZHIPU: [
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"glm-4-flash",
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"glm-4",
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"glm-4-plus",
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"glm-4-long",
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],
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LLMProvider.MOONSHOT: [
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"moonshot-v1-8k",
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"moonshot-v1-32k",
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"moonshot-v1-128k",
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],
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LLMProvider.BAIDU: [
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"ERNIE-3.5-8K",
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"ERNIE-4.0-8K",
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"ERNIE-Speed-8K",
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],
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LLMProvider.MINIMAX: [
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"abab6.5-chat",
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"abab6.5s-chat",
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"abab5.5-chat",
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],
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LLMProvider.DOUBAO: [
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"doubao-pro-32k",
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"doubao-pro-128k",
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"doubao-lite-32k",
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],
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LLMProvider.OLLAMA: [
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"llama3",
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"llama3.1",
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"llama3.2",
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"codellama",
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"mistral",
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"deepseek-coder-v2",
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"qwen2.5-coder",
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],
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}
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return models.get(provider, [])
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