78 lines
2.1 KiB
Python
78 lines
2.1 KiB
Python
"""
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LLM服务 - 兼容层，使用多模型服务
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"""
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from typing import Optional, List, Dict, Any
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from app.services.multi_llm_service import multi_llm_service
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from app.utils.logger import logger
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class LLMService:
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"""LLM服务类（兼容层）"""
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def __init__(self):
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"""初始化LLM服务"""
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self.multi_service = multi_llm_service
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self.client = multi_llm_service.current_model # 兼容性
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def chat(
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self,
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messages: List[Dict[str, str]],
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model: str = None,
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temperature: float = 0.7,
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max_tokens: int = 2000,
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model_override: str = None
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) -> Optional[str]:
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"""
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调用LLM进行对话
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Args:
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messages: 消息列表
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model: 模型名称（已废弃，使用 model_override）
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temperature: 温度参数
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max_tokens: 最大token数
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model_override: 指定使用的模型 (zhipu/deepseek)
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Returns:
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LLM响应文本
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"""
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return self.multi_service.chat(
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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model_override=model_override
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)
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def analyze_intent(self, user_message: str) -> Dict[str, Any]:
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"""使用LLM分析用户意图"""
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return self.multi_service.analyze_intent(user_message)
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def chat_stream(
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self,
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messages: List[Dict[str, str]],
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temperature: float = 0.7,
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max_tokens: int = 2000,
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model_override: str = None
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):
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"""
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流式调用LLM进行对话
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Args:
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messages: 消息列表
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temperature: 温度参数
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max_tokens: 最大token数
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model_override: 指定使用的模型 (zhipu/deepseek)
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Yields:
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LLM响应的文本片段
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"""
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return self.multi_service.chat_stream(
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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model_override=model_override
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)
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# 创建全局实例
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llm_service = LLMService()
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