148 lines
4.9 KiB
Python
148 lines
4.9 KiB
Python
import json
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import dashscope
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from http import HTTPStatus
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from typing import Optional, Dict, List, Generator
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import app.core.config as config_module
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from app.core.database import get_db_connection
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class LLMService:
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"""LLM服务 - 专注于大模型API调用和提示词管理"""
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def __init__(self):
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# 设置dashscope API key
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dashscope.api_key = config_module.QWEN_API_KEY
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@property
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def model_name(self):
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"""动态获取模型名称"""
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return config_module.LLM_CONFIG["model_name"]
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@property
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def system_prompt(self):
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"""动态获取系统提示词"""
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return config_module.LLM_CONFIG["system_prompt"]
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@property
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def time_out(self):
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"""动态获取超时时间"""
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return config_module.LLM_CONFIG["time_out"]
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@property
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def temperature(self):
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"""动态获取temperature"""
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return config_module.LLM_CONFIG["temperature"]
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@property
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def top_p(self):
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"""动态获取top_p"""
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return config_module.LLM_CONFIG["top_p"]
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def get_task_prompt(self, task_name: str, cursor=None) -> str:
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"""
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统一的提示词获取方法
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Args:
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task_name: 任务名称，如 'MEETING_TASK', 'KNOWLEDGE_TASK' 等
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cursor: 数据库游标，如果传入则使用，否则创建新连接
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Returns:
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str: 提示词内容，如果未找到返回默认提示词
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"""
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query = """
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SELECT p.content
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FROM prompt_config pc
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JOIN prompts p ON pc.prompt_id = p.id
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WHERE pc.task_name = %s
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"""
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if cursor:
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cursor.execute(query, (task_name,))
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result = cursor.fetchone()
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if result:
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return result['content'] if isinstance(result, dict) else result[0]
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else:
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with get_db_connection() as connection:
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cursor = connection.cursor(dictionary=True)
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cursor.execute(query, (task_name,))
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result = cursor.fetchone()
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if result:
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return result['content']
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# 返回默认提示词
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return self._get_default_prompt(task_name)
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def _get_default_prompt(self, task_name: str) -> str:
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"""获取默认提示词"""
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default_prompts = {
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'MEETING_TASK': self.system_prompt, # 使用配置文件中的系统提示词
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'KNOWLEDGE_TASK': "请根据提供的信息生成知识库文章。",
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}
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return default_prompts.get(task_name, "请根据提供的内容进行总结和分析。")
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def _call_llm_api_stream(self, prompt: str) -> Generator[str, None, None]:
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"""流式调用阿里Qwen3大模型API"""
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try:
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responses = dashscope.Generation.call(
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model=self.model_name,
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prompt=prompt,
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stream=True,
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timeout=self.time_out,
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temperature=self.temperature,
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top_p=self.top_p,
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incremental_output=True # 开启增量输出模式
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)
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for response in responses:
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if response.status_code == HTTPStatus.OK:
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# 增量输出内容
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new_content = response.output.get('text', '')
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if new_content:
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yield new_content
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else:
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error_msg = f"Request failed with status code: {response.status_code}, Error: {response.message}"
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print(error_msg)
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yield f"error: {error_msg}"
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break
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except Exception as e:
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error_msg = f"流式调用大模型API错误: {e}"
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print(error_msg)
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yield f"error: {error_msg}"
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def _call_llm_api(self, prompt: str) -> Optional[str]:
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"""调用阿里Qwen3大模型API（非流式）"""
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try:
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response = dashscope.Generation.call(
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model=self.model_name,
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prompt=prompt,
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timeout=self.time_out,
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temperature=self.temperature,
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top_p=self.top_p
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)
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if response.status_code == HTTPStatus.OK:
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return response.output.get('text', '')
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else:
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print(f"API调用失败: {response.status_code}, {response.message}")
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return None
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except Exception as e:
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print(f"调用大模型API错误: {e}")
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return None
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# 测试代码
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if __name__ == '__main__':
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print("--- 运行LLM服务测试 ---")
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llm_service = LLMService()
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# 测试获取任务提示词
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meeting_prompt = llm_service.get_task_prompt('MEETING_TASK')
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print(f"会议任务提示词: {meeting_prompt[:100]}...")
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knowledge_prompt = llm_service.get_task_prompt('KNOWLEDGE_TASK')
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print(f"知识库任务提示词: {knowledge_prompt[:100]}...")
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print("--- LLM服务测试完成 ---")
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