tingwu_new

backend/ai_services/bailian_service.py

@@ -93,6 +93,54 @@ class BailianService:
         except Exception as e:
             logger.error(f"AI Evaluation failed: {e}")
             evaluation.status = AIEvaluation.Status.FAILED
-            evaluation.error_message = str(e)
+                evaluation.error_message = str(e)
-            evaluation.save()
+                evaluation.save()
-            return evaluation
+                return evaluation
+
+    def summarize_task(self, task):
+        """
+        使用 AI 模型总结转写和章节数据
+        """
+        if not self.client:
+            logger.error("DashScope client not initialized")
+            return
+
+        try:
+            summary_data = json.dumps(task.summary_data or {}, ensure_ascii=False)
+            chapters_data = json.dumps(task.auto_chapters_data or {}, ensure_ascii=False)
+            
+            prompt = f"""
+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。
+
+要求：
+1. 包含一个总体的“核心摘要”。
+2. 包含一个详细的“内容大纲”。
+3. 如果有问答或对话信息，请包含“关键问答”或“发言人观点”。
+4. 包含一个带有时间戳的“章节回顾”，格式为 [HH:MM:SS] 标题。
+5. 语言简练，重点突出。
+
+总结原始数据：
+{summary_data}
+
+章节原始数据：
+{chapters_data}
+"""
+            messages = [
+                {'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。'},
+                {'role': 'user', 'content': prompt}
+            ]
+
+            completion = self.client.chat.completions.create(
+                model="qwen-turbo",
+                messages=messages,
+                temperature=0.7
+            )
+            
+            ai_summary = completion.choices[0].message.content
+            if ai_summary:
+                task.summary = ai_summary
+                task.save()
+                logger.info(f"AI summary generated for task {task.id}")
+            
+        except Exception as e:
+            logger.error(f"Failed to generate AI summary: {e}")

backend/ai_services/services.py

@@ -272,62 +272,7 @@ class AliyunTingwuService:
         
         # 提取文本 (MindMapSummary)
         # 结构: {"MindMapSummary": [{"Title": "...", "Topic": [...]}]}
-        summary_text = []
+        # 移除了原先的 summary_text 拼接逻辑
-        
-        # 1. 优先提取段落标题和摘要
-        if 'ParagraphTitle' in summarization:
-             summary_text.append(f"### {summarization['ParagraphTitle']}")
-        if 'ParagraphSummary' in summarization:
-             summary_text.append(summarization['ParagraphSummary'])
-             summary_text.append("") # 空行分隔
-
-        # 2. 提取思维导图作为大纲
-        def parse_mindmap_topic(topic_list, level=0):
-            indent = "  " * level
-            for topic in topic_list:
-                title = topic.get('Title', '')
-                if title:
-                    summary_text.append(f"{indent}- {title}")
-                
-                sub_topics = topic.get('Topic', [])
-                if sub_topics:
-                    parse_mindmap_topic(sub_topics, level + 1)
-
-        if 'MindMapSummary' in summarization:
-            summary_text.append("### 内容大纲")
-            parse_mindmap_topic(summarization['MindMapSummary'])
-            summary_text.append("")
-
-        # 3. 提取对话总结 (ConversationalSummary)
-        if 'ConversationalSummary' in summarization and isinstance(summarization['ConversationalSummary'], list):
-            summary_text.append("### 对话总结")
-            for conv in summarization['ConversationalSummary']:
-                speaker = conv.get('SpeakerName', '发言人')
-                summary = conv.get('Summary', '')
-                if summary:
-                    summary_text.append(f"- **{speaker}**: {summary}")
-            summary_text.append("")
-
-        # 4. 提取问答总结 (QuestionsAnsweringSummary)
-        if 'QuestionsAnsweringSummary' in summarization and isinstance(summarization['QuestionsAnsweringSummary'], list):
-            summary_text.append("### 问答回顾")
-            for qa in summarization['QuestionsAnsweringSummary']:
-                question = qa.get('Question', '')
-                answer = qa.get('Answer', '')
-                if question and answer:
-                    summary_text.append(f"**Q: {question}**")
-                    summary_text.append(f"A: {answer}")
-                    summary_text.append("")
-
-        # 兼容旧逻辑：如果上述都为空，尝试 Text 或 Headline
-        if not summary_text:
-            if 'Text' in summarization:
-                summary_text.append(summarization['Text'])
-            elif 'Headline' in summarization:
-                summary_text.append(summarization['Headline'])
-             
-        if summary_text:
-            task.summary = "\n".join(summary_text)
         
         # --- C. 处理章节 (AutoChapters) ---
         auto_chapters = get_data_field(task_result, 'AutoChapters') or get_data_field(data_obj, 'AutoChapters') or []
@@ -346,34 +291,18 @@ class AliyunTingwuService:
         # 保存原始数据
         task.auto_chapters_data = auto_chapters
         
-        # 将章节信息追加到 summary
+        # 保存任务，确保原始数据已写入数据库
-        if auto_chapters and isinstance(auto_chapters, list):
-            if summary_text:
-                summary_text.append("\n\n### 章节速览")
-            else:
-                summary_text.append("### 章节速览")
-                
-            for chapter in auto_chapters:
-                headline = chapter.get('Headline', '')
-                summary = chapter.get('Summary', '')
-                start_time = chapter.get('Start', 0)
-                
-                # 格式化时间戳 (毫秒 -> HH:MM:SS)
-                seconds = int(start_time / 1000)
-                m, s = divmod(seconds, 60)
-                h, m = divmod(m, 60)
-                time_str = f"{h:02d}:{m:02d}:{s:02d}"
-                
-                chapter_text = f"- [{time_str}] {headline}"
-                if summary:
-                    chapter_text += f"\n  {summary}"
-                summary_text.append(chapter_text)
-
-        if summary_text:
-            task.summary = "\n".join(summary_text)
-        
         task.save()
 
+        # 调用大模型生成总结 (如果 summary_data 或 auto_chapters_data 存在)
+        if task.summary_data or task.auto_chapters_data:
+            try:
+                from .bailian_service import BailianService
+                bailian_service = BailianService()
+                bailian_service.summarize_task(task)
+            except Exception as e:
+                logger.error(f"Failed to trigger AI summarization: {e}")
+
         # 4. 自动触发 AI 评估 (如果任务首次成功且有启用的模板)
         if previous_status != 'SUCCEEDED' and task.status == 'SUCCEEDED' and task.transcription:
             self.trigger_ai_evaluations(task)