57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
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from typing import Optional
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from langchain_core.language_models import BaseChatModel
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from langchain_openai import ChatOpenAI
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from langchain_deepseek import ChatDeepSeek
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from langchain_ollama import OllamaLLM
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from app.core.config import settings
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class LLMFactory:
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@staticmethod
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def create(
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provider: Optional[str] = None,
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temperature: float = 0,
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streaming: bool = True,
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) -> BaseChatModel:
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"""
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Create a LLM instance based on the provider
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"""
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# If no provider specified, use the one from settings
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provider = provider or settings.CHAT_PROVIDER
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if provider.lower() == "openai":
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return ChatOpenAI(
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temperature=temperature,
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streaming=streaming,
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model=settings.OPENAI_MODEL,
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openai_api_key=settings.OPENAI_API_KEY,
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openai_api_base=settings.OPENAI_API_BASE
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)
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elif provider.lower() == "deepseek":
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return ChatDeepSeek(
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temperature=temperature,
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streaming=streaming,
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model=settings.DEEPSEEK_MODEL,
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api_key=settings.DEEPSEEK_API_KEY,
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api_base=settings.DEEPSEEK_API_BASE
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)
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elif provider.lower() == "dashscope":
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return ChatOpenAI(
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temperature=temperature,
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streaming=streaming,
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model=settings.DASH_SCOPE_CHAT_MODEL,
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openai_api_key=settings.DASH_SCOPE_API_KEY,
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openai_api_base=settings.DASH_SCOPE_API_BASE,
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)
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elif provider.lower() == "ollama":
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# Initialize Ollama model
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return OllamaLLM(
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model=settings.OLLAMA_MODEL,
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base_url=settings.OLLAMA_API_BASE,
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temperature=temperature,
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streaming=streaming
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)
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# Add more providers here as needed
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# elif provider.lower() == "anthropic":
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# return ChatAnthropic(...)
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else:
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raise ValueError(f"Unsupported LLM provider: {provider}")
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