70 lines
2.7 KiB
Python
70 lines
2.7 KiB
Python
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"""LLM工厂类，用于创建和管理LLM实例"""
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from typing import Optional
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from langchain_openai import ChatOpenAI
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from langgraph.prebuilt import create_react_agent
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from loguru import logger
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from requests import Session
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from .config import get_settings
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async def new_llm(session: Session = None, model: Optional[str] = None,
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temperature: Optional[float] = None,
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streaming: bool = False) -> ChatOpenAI:
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"""创建LLM实例
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Args:
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model: 可选，指定使用的模型名称。如果不指定，将使用配置文件中的默认模型
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temperature: 可选，模型温度参数
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streaming: 是否启用流式响应，默认False
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Returns:
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ChatOpenAI实例
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"""
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settings = get_settings()
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llm_config = await settings.llm.get_current_config(session)
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if model:
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# 根据指定的模型获取对应配置
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if model.startswith('deepseek'):
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llm_config['model'] = settings.llm.deepseek_model
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llm_config['api_key'] = settings.llm.deepseek_api_key
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llm_config['base_url'] = settings.llm.deepseek_base_url
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elif model.startswith('doubao'):
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llm_config['model'] = settings.llm.doubao_model
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llm_config['api_key'] = settings.llm.doubao_api_key
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llm_config['base_url'] = settings.llm.doubao_base_url
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elif model.startswith('glm'):
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llm_config['model'] = settings.llm.zhipu_model
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llm_config['api_key'] = settings.llm.zhipu_api_key
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llm_config['base_url'] = settings.llm.zhipu_base_url
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elif model.startswith('moonshot'):
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llm_config['model'] = settings.llm.moonshot_model
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llm_config['api_key'] = settings.llm.moonshot_api_key
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llm_config['base_url'] = settings.llm.moonshot_base_url
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llm = ChatOpenAI(
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model=llm_config['model'],
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api_key=llm_config['api_key'],
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base_url=llm_config['base_url'],
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temperature=temperature if temperature is not None else llm_config['temperature'],
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max_tokens=llm_config['max_tokens'],
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streaming=streaming
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)
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return llm
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async def new_agent(session: Session = None, model: Optional[str] = None,
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temperature: Optional[float] = None,
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streaming: bool = False) -> ChatOpenAI:
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"""创建LLM实例
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Args:
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model: 可选，指定使用的模型名称。如果不指定，将使用配置文件中的默认模型
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temperature: 可选，模型温度参数
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streaming: 是否启用流式响应，默认False
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Returns:
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ChatOpenAI实例
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"""
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llm = await new_llm(session, model, temperature, streaming)
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return create_react_agent(llm, [])
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