299 lines
11 KiB
Python
299 lines
11 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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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 generate_meeting_summary_stream(self, meeting_id: int, user_prompt: str = "") -> Generator[str, None, None]:
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"""
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流式生成会议总结
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Args:
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meeting_id: 会议ID
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user_prompt: 用户额外提示词
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Yields:
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str: 流式输出的内容片段
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"""
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try:
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# 获取会议转录内容
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transcript_text = self._get_meeting_transcript(meeting_id)
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if not transcript_text:
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yield "error: 无法获取会议转录内容"
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return
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# 构建完整提示词
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full_prompt = self._build_prompt(transcript_text, user_prompt)
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# 调用大模型API进行流式生成
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full_content = ""
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for chunk in self._call_llm_api_stream(full_prompt):
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if chunk.startswith("error:"):
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yield chunk
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return
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full_content += chunk
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yield chunk
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# 保存完整总结到数据库
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if full_content:
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self._save_summary_to_db(meeting_id, full_content, user_prompt)
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except Exception as e:
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print(f"流式生成会议总结错误: {e}")
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yield f"error: {str(e)}"
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def generate_meeting_summary(self, meeting_id: int, user_prompt: str = "") -> Optional[Dict]:
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"""
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生成会议总结（非流式，保持向后兼容）
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Args:
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meeting_id: 会议ID
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user_prompt: 用户额外提示词
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Returns:
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包含总结内容的字典，如果失败返回None
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"""
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try:
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# 获取会议转录内容
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transcript_text = self._get_meeting_transcript(meeting_id)
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if not transcript_text:
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return {"error": "无法获取会议转录内容"}
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# 构建完整提示词
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full_prompt = self._build_prompt(transcript_text, user_prompt)
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# 调用大模型API
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response = self._call_llm_api(full_prompt)
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if response:
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# 保存总结到数据库
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summary_id = self._save_summary_to_db(meeting_id, response, user_prompt)
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return {
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"summary_id": summary_id,
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"content": response,
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"meeting_id": meeting_id
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}
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else:
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return {"error": "大模型API调用失败"}
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except Exception as e:
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print(f"生成会议总结错误: {e}")
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return {"error": str(e)}
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def _get_meeting_transcript(self, meeting_id: int) -> str:
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"""从数据库获取会议转录内容"""
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try:
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with get_db_connection() as connection:
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cursor = connection.cursor()
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query = """
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SELECT speaker_tag, start_time_ms, end_time_ms, text_content
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FROM transcript_segments
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WHERE meeting_id = %s
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ORDER BY start_time_ms
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"""
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cursor.execute(query, (meeting_id,))
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segments = cursor.fetchall()
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if not segments:
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return ""
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# 组装转录文本
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transcript_lines = []
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for speaker_tag, start_time, end_time, text in segments:
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# 将毫秒转换为分:秒格式
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start_min = start_time // 60000
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start_sec = (start_time % 60000) // 1000
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transcript_lines.append(f"[{start_min:02d}:{start_sec:02d}] 说话人{speaker_tag}: {text}")
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return "\n".join(transcript_lines)
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except Exception as e:
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print(f"获取会议转录内容错误: {e}")
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return ""
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def _build_prompt(self, transcript_text: str, user_prompt: str) -> str:
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"""构建完整的提示词"""
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prompt = f"{self.system_prompt}\n\n"
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if user_prompt:
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prompt += f"用户额外要求：{user_prompt}\n\n"
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prompt += f"会议转录内容：\n{transcript_text}\n\n请根据以上内容生成会议总结："
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return prompt
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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，保持向后兼容）"""
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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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def _save_summary_to_db(self, meeting_id: int, summary_content: str, user_prompt: str) -> Optional[int]:
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"""保存总结到数据库 - 更新meetings表的summary字段"""
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try:
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with get_db_connection() as connection:
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cursor = connection.cursor()
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# 更新meetings表的summary字段
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update_query = """
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UPDATE meetings
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SET summary = %s
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WHERE meeting_id = %s
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"""
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cursor.execute(update_query, (summary_content, meeting_id))
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connection.commit()
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print(f"成功保存会议总结到meetings表，meeting_id: {meeting_id}")
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return meeting_id
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except Exception as e:
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print(f"保存总结到数据库错误: {e}")
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return None
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def get_meeting_summaries(self, meeting_id: int) -> List[Dict]:
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"""获取会议的当前总结 - 从meetings表的summary字段获取"""
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try:
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with get_db_connection() as connection:
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cursor = connection.cursor()
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query = """
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SELECT summary
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FROM meetings
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WHERE meeting_id = %s
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"""
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cursor.execute(query, (meeting_id,))
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result = cursor.fetchone()
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# 如果有总结内容，返回一个包含当前总结的列表格式（保持API一致性）
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if result and result[0]:
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return [{
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"id": meeting_id,
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"content": result[0],
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"user_prompt": "", # meetings表中没有user_prompt字段
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"created_at": None # meetings表中没有单独的总结创建时间
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}]
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else:
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return []
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except Exception as e:
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print(f"获取会议总结错误: {e}")
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return []
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def get_current_meeting_summary(self, meeting_id: int) -> Optional[str]:
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"""获取会议当前的总结内容 - 从meetings表的summary字段获取"""
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try:
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with get_db_connection() as connection:
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cursor = connection.cursor()
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query = """
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SELECT summary
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FROM meetings
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WHERE meeting_id = %s
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"""
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cursor.execute(query, (meeting_id,))
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result = cursor.fetchone()
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return result[0] if result and result[0] else None
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except Exception as e:
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print(f"获取会议当前总结错误: {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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# 测试LLM服务
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test_meeting_id = 38
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test_user_prompt = "请重点关注决策事项和待办任务"
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print("--- 运行LLM服务测试 ---")
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llm_service = LLMService()
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# 生成总结
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result = llm_service.generate_meeting_summary(test_meeting_id, test_user_prompt)
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if result.get("error"):
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print(f"生成总结失败: {result['error']}")
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else:
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print(f"总结生成成功，ID: {result.get('summary_id')}")
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print(f"总结内容: {result.get('content')[:200]}...")
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# 获取历史总结
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summaries = llm_service.get_meeting_summaries(test_meeting_id)
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print(f"获取到 {len(summaries)} 个历史总结")
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print("--- LLM服务测试完成 ---") |