147 lines
5.4 KiB
Python
147 lines
5.4 KiB
Python
import logging
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import json
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import os
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from django.conf import settings
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from openai import OpenAI
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from .models import AIEvaluation
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logger = logging.getLogger(__name__)
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class BailianService:
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def __init__(self):
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self.api_key = getattr(settings, 'DASHSCOPE_API_KEY', None)
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if not self.api_key:
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self.api_key = os.environ.get("DASHSCOPE_API_KEY")
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if self.api_key:
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self.client = OpenAI(
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api_key=self.api_key,
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
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)
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else:
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self.client = None
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logger.warning("DASHSCOPE_API_KEY not configured.")
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def evaluate_task(self, evaluation: AIEvaluation):
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"""
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执行AI评估
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"""
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if not self.client:
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evaluation.status = AIEvaluation.Status.FAILED
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evaluation.error_message = "服务未配置 (DASHSCOPE_API_KEY missing)"
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evaluation.save()
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return
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task = evaluation.task
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if not task.transcription:
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evaluation.status = AIEvaluation.Status.FAILED
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evaluation.error_message = "关联任务无逐字稿内容"
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evaluation.save()
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return
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evaluation.status = AIEvaluation.Status.PROCESSING
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evaluation.save()
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try:
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prompt = evaluation.prompt
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content = task.transcription
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# 截断过长的内容以防止超出Token限制 (简单处理，取前10000字)
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if len(content) > 10000:
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content = content[:10000] + "...(内容过长已截断)"
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# Construct messages
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messages = [
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{'role': 'system', 'content': 'You are a helpful assistant designed to output JSON.'},
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{'role': 'user', 'content': f"{prompt}\n\n以下是需要评估的内容：\n{content}"}
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]
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completion = self.client.chat.completions.create(
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model=evaluation.model_selection,
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messages=messages,
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response_format={"type": "json_object"}
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)
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response_content = completion.choices[0].message.content
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# Convert to dict for storage
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raw_response = completion.model_dump()
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evaluation.raw_response = raw_response
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# Parse JSON
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try:
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result = json.loads(response_content)
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evaluation.score = result.get('score')
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evaluation.evaluation = result.get('evaluation') or result.get('comment')
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# 尝试获取推理过程（如果模型返回了）
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evaluation.reasoning = result.get('reasoning') or result.get('analysis')
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if not evaluation.reasoning:
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# 如果JSON里没有，把整个JSON作为推理参考
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evaluation.reasoning = json.dumps(result, ensure_ascii=False, indent=2)
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evaluation.status = AIEvaluation.Status.COMPLETED
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except json.JSONDecodeError:
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evaluation.status = AIEvaluation.Status.FAILED
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evaluation.error_message = f"无法解析JSON响应: {response_content}"
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evaluation.reasoning = response_content
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evaluation.save()
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return evaluation
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except Exception as e:
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logger.error(f"AI Evaluation failed: {e}")
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evaluation.status = AIEvaluation.Status.FAILED
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evaluation.error_message = str(e)
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evaluation.save()
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return evaluation
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def summarize_task(self, task):
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"""
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使用 AI 模型总结转写和章节数据
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"""
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if not self.client:
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logger.error("DashScope client not initialized")
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return
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try:
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summary_data = json.dumps(task.summary_data or {}, ensure_ascii=False)
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chapters_data = json.dumps(task.auto_chapters_data or {}, ensure_ascii=False)
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prompt = f"""
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你是一个专业的会议摘要和内容分析助手。请根据以下提供的“总结原始数据”和“章节原始数据”，生成一个结构清晰、专业且易于阅读的 Markdown 格式总结。
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要求：
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1. 包含一个总体的“核心摘要”。
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2. 包含一个详细的“内容大纲”。
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3. 如果有问答或对话信息，请包含“关键问答”或“发言人观点”。
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4. 包含一个带有时间戳的“章节回顾”，格式为 [HH:MM:SS] 标题。
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5. 语言简练，重点突出。
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总结原始数据：
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{summary_data}
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章节原始数据：
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{chapters_data}
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"""
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messages = [
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{'role': 'system', 'content': '你是一个专业的文档总结助手。请直接返回 Markdown 格式的内容，不要包含任何引导性文字。'},
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{'role': 'user', 'content': prompt}
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]
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completion = self.client.chat.completions.create(
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model="qwen-turbo",
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messages=messages,
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temperature=0.7
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)
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ai_summary = completion.choices[0].message.content
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if ai_summary:
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task.summary = ai_summary
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task.save()
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logger.info(f"AI summary generated for task {task.id}")
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except Exception as e:
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logger.error(f"Failed to generate AI summary: {e}")
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