55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
from typing import Tuple, Dict, Any
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from src.common.logger import get_logger
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config
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logger = get_logger("llm_api")
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class LLMAPI:
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"""LLM API模块
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提供了与LLM模型交互的功能
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"""
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def get_available_models(self) -> Dict[str, Any]:
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"""获取所有可用的模型配置
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Returns:
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Dict[str, Any]: 模型配置字典，key为模型名称，value为模型配置
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"""
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if not hasattr(global_config, "model"):
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logger.error(f"{self.log_prefix} 无法获取模型列表：全局配置中未找到 model 配置")
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return {}
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models = global_config.model
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return models
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async def generate_with_model(
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self, prompt: str, model_config: Dict[str, Any], request_type: str = "plugin.generate", **kwargs
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) -> Tuple[bool, str, str, str]:
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"""使用指定模型生成内容
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Args:
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prompt: 提示词
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model_config: 模型配置（从 get_available_models 获取的模型配置）
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request_type: 请求类型标识
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**kwargs: 其他模型特定参数，如temperature、max_tokens等
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Returns:
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Tuple[bool, str, str, str]: (是否成功, 生成的内容, 推理过程, 模型名称)
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"""
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try:
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logger.info(f"{self.log_prefix} 使用模型生成内容，提示词: {prompt[:100]}...")
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llm_request = LLMRequest(model=model_config, request_type=request_type, **kwargs)
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response, (reasoning, model_name) = await llm_request.generate_response_async(prompt)
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return True, response, reasoning, model_name
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except Exception as e:
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error_msg = f"生成内容时出错: {str(e)}"
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logger.error(f"{self.log_prefix} {error_msg}")
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return False, error_msg, "", ""
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