158 lines
6.1 KiB
Python
158 lines
6.1 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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对转写任务进行总结
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"""
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if not self.client:
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logger.warning("BailianService not initialized, skipping summary.")
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return
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if not task.transcription:
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logger.warning(f"Task {task.id} has no transcription, skipping summary.")
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return
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try:
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content = task.transcription
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# 简单截断防止过长
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if len(content) > 15000:
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content = content[:15000] + "...(内容过长已截断)"
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# 准备上下文数据
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context_data = ""
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if task.summary_data:
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context_data += f"\n\n【总结原始数据】\n{json.dumps(task.summary_data, ensure_ascii=False, indent=2)}"
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if task.auto_chapters_data:
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context_data += f"\n\n【章节原始数据】\n{json.dumps(task.auto_chapters_data, ensure_ascii=False, indent=2)}"
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system_prompt = f"""你是一个专业的会议/内容总结助手。请根据提供的【转写文本】、【总结原始数据】和【章节原始数据】，生成一份结构清晰、内容详实的总结报告。
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请按照以下结构输出（Markdown格式）：
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1. **标题**：基于内容生成一个合适的标题。
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2. **核心摘要**：简要概括主要内容。
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3. **主要观点/话题**：结合思维导图数据，列出关键话题和层级。
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4. **章节速览**：结合章节数据，列出时间点和主要内容。[HH:MM:SS]格式来把章节列出来
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5. **问答精选**（如果有）：基于问答总结数据，列出重要问答。
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请确保语言通顺，重点突出，能够还原内容的逻辑结构。"""
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user_content = f"以下是需要总结的内容：\n\n【转写文本】\n{content}{context_data}"
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messages = [
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{'role': 'system', 'content': system_prompt},
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{'role': 'user', 'content': user_content}
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]
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# 使用 qwen-plus 作为默认模型
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completion = self.client.chat.completions.create(
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model="qwen-plus",
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messages=messages
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)
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summary_content = completion.choices[0].message.content
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task.summary = summary_content
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task.save(update_fields=['summary'])
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logger.info(f"Task {task.id} summary generated successfully.")
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except Exception as e:
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logger.error(f"Failed to generate summary for task {task.id}: {e}")
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