219 lines
6.7 KiB
Python
219 lines
6.7 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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使用 LiteLLM 作为主要适配器，支持大多数 LLM 提供商。
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对于 API 格式特殊的提供商（百度、MiniMax、豆包），使用原生适配器。
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"""
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from typing import Dict, List
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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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LiteLLMAdapter,
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BaiduAdapter,
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MinimaxAdapter,
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DoubaoAdapter,
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)
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# 必须使用原生适配器的提供商（API 格式特殊）
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NATIVE_ONLY_PROVIDERS = {
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LLMProvider.BAIDU,
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LLMProvider.MINIMAX,
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LLMProvider.DOUBAO,
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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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# 对于必须使用原生适配器的提供商
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if config.provider in NATIVE_ONLY_PROVIDERS:
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return cls._create_native_adapter(config)
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# 其他提供商使用 LiteLLM
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if LiteLLMAdapter.supports_provider(config.provider):
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return LiteLLMAdapter(config)
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# 不支持的提供商
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raise ValueError(f"不支持的LLM提供商: {config.provider}")
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@classmethod
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def _create_native_adapter(cls, config: LLMConfig) -> BaseLLMAdapter:
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"""创建原生适配器（仅用于 API 格式特殊的提供商）"""
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native_adapter_map = {
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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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}
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adapter_class = native_adapter_map.get(config.provider)
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if not adapter_class:
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raise ValueError(f"不支持的原生适配器提供商: {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 (2025年最新)"""
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models = {
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LLMProvider.GEMINI: [
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"gemini-3-pro",
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"gemini-3.0-deep-think",
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"gemini-2.5-flash",
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"gemini-2.5-pro",
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"gemini-2.5-flash-lite",
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"gemini-2.5-flash-live-api",
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"veo-3.1",
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"veo-3.1-fast",
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],
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LLMProvider.OPENAI: [
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"gpt-5",
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"gpt-5.1",
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"gpt-5.1-instant",
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"gpt-5.1-codex-max",
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"gpt-4o",
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"gpt-4o-mini",
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"gpt-4.5",
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"o4-mini",
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"o3",
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"o3-mini",
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"gpt-oss-120b",
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"gpt-oss-20b",
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],
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LLMProvider.CLAUDE: [
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"claude-opus-4.5",
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"claude-sonnet-4.5",
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"claude-haiku-4.5",
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"claude-sonnet-4",
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"claude-opus-4",
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"claude-3.7-sonnet",
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"claude-3.5-sonnet",
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"claude-3.5-haiku",
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"claude-3-opus",
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],
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LLMProvider.QWEN: [
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"qwen3-max-instruct",
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"qwen3-235b-a22b",
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"qwen3-turbo",
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"qwen3-32b",
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"qwen3-4b",
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"qwen3-embedding-8b",
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"qwen-image",
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"qwen-vl",
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"qwen-audio",
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],
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LLMProvider.DEEPSEEK: [
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"deepseek-v3.1-terminus",
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"deepseek-r1-70b",
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"deepseek-r1-zero",
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"deepseek-v3.2-exp",
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"deepseek-chat",
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"deepseek-reasoner",
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"deepseek-ocr",
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],
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LLMProvider.ZHIPU: [
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"glm-4.6",
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"glm-4.6-reap-218b",
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"glm-4.5",
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"glm-4.5v",
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"glm-4.5-air-106b",
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"glm-4-flash",
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"glm-4v-flash",
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"glm-4.1v-thinking",
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],
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LLMProvider.MOONSHOT: [
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"kimi-k2",
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"kimi-k2-thinking",
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"kimi-k2-instruct-0905",
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"kimi-k1.5",
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"kimi-vl",
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"kimi-dev-72b",
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"kimi-researcher",
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"kimi-linear",
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],
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LLMProvider.BAIDU: [
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"ernie-4.5",
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"ernie-4.5-21b-a3b-thinking",
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"ernie-4.0-8k",
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"ernie-3.5-8k",
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"ernie-vl",
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],
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LLMProvider.MINIMAX: [
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"minimax-m2",
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"minimax-01-text",
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"minimax-01-vl",
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"minimax-m1",
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"speech-2.6",
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"hailuo-02",
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"music-1.5",
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],
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LLMProvider.DOUBAO: [
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"doubao-1.6-pro",
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"doubao-1.5-pro",
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"doubao-seed-code",
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"doubao-seed-1.6",
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"doubao-vision-language",
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],
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LLMProvider.OLLAMA: [
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"llama3.3-70b",
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"qwen3-8b",
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"gemma3-27b",
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"dolphin-3.0-llama3.1-8b",
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"cogito-v1",
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"deepseek-r1",
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"gpt-oss-120b",
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"llama3.1-405b",
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"mistral-nemo",
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"phi-3",
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],
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}
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return models.get(provider, [])
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