参考高级学习处理流程修改音视频文档处理
- 修改批量插入段落逻辑，跳过音视频文档的段落插入 - 音视频文档段落由异步任务处理，不在此处插入 - 保持与高级学习文档相同的处理模式 - 添加详细的日志记录用于调试 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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@ -1205,73 +1205,12 @@ class DocumentSerializers(serializers.Serializer):
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document['paragraphs'] = [] # 清空段落，等待异步处理
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document['paragraphs'] = [] # 清空段落，等待异步处理
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# 检查是否是音视频类型的文档
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# 检查是否是音视频类型的文档
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elif stt_model_id:
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elif stt_model_id:
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maxkb_logger.info(f"Document {document.get('name')} is media type, processing synchronously")
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maxkb_logger.info(f"Document {document.get('name')} is media type, will process asynchronously")
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# 音视频类型的文档，直接处理
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# 音视频类型的文档，设置为异步处理
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source_file_id = document.get('source_file_id')
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# 清空段落，等待异步任务处理
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if source_file_id:
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document['paragraphs'] = []
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try:
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# 标记为异步音视频文档，用于后续异步处理
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source_file = QuerySet(File).filter(id=source_file_id).first()
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document['is_media_async'] = True
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if source_file:
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workspace_id_value = self.data.get('workspace_id', '')
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maxkb_logger.info(f"Processing media file: {source_file.file_name}")
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maxkb_logger.info(f" - STT model ID: {stt_model_id}")
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maxkb_logger.info(f" - Workspace ID: {workspace_id_value}")
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maxkb_logger.info(f" - LLM model ID: {llm_model_id}")
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# 使用MediaSplitHandle处理音视频文件
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from common.handle.impl.media.media_split_handle import MediaSplitHandle
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media_handler = MediaSplitHandle()
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# 准备文件对象
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class FileWrapper:
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def __init__(self, file_obj):
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self.file_obj = file_obj
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self.name = file_obj.file_name
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self.size = file_obj.file_size
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def read(self):
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return self.file_obj.get_bytes()
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def seek(self, pos):
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pass
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file_wrapper = FileWrapper(source_file)
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# 处理音视频文件
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result = media_handler.handle(
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file_wrapper,
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pattern_list=[],
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with_filter=False,
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limit=0,
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get_buffer=lambda f: f.read(),
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save_image=lambda x: None,
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workspace_id=workspace_id_value,
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stt_model_id=stt_model_id,
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llm_model_id=llm_model_id
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)
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# 将处理结果添加到文档
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if result and result.get('content'):
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document['paragraphs'] = result.get('content', [])
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maxkb_logger.info(f"Media file processed, got {len(document['paragraphs'])} paragraphs")
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else:
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maxkb_logger.warning(f"No content extracted from media file, using default")
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document['paragraphs'] = [{
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'content': f'[音视频文件: {source_file.file_name}]',
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'title': '音视频内容'
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}]
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except Exception as e:
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maxkb_logger.error(f"Failed to process media file: {str(e)}")
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import traceback
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maxkb_logger.error(traceback.format_exc())
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# 如果处理失败，创建一个默认段落
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document['paragraphs'] = [{
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'content': f'[音视频文件处理失败: {str(e)}]',
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'title': '处理失败'
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}]
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else:
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maxkb_logger.warning(f"No source file for media document")
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document['paragraphs'] = []
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# 插入文档
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# 插入文档
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for document in instance_list:
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for document in instance_list:
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@ -1373,10 +1312,25 @@ class DocumentSerializers(serializers.Serializer):
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State.FAILURE
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State.FAILURE
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)
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)
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# 批量插入段落（只为非高级学习文档）
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# 批量插入段落（只为非高级学习文档和非音视频文档）
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if len(paragraph_model_list) > 0:
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if len(paragraph_model_list) > 0:
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maxkb_logger.info(f"Total paragraphs to insert: {len(paragraph_model_list)}")
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maxkb_logger.info(f"Total paragraphs to insert: {len(paragraph_model_list)}")
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# 获取音视频文档ID列表
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media_document_ids = []
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for idx, document in enumerate(instance_list):
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stt_model_id = document.get('stt_model_id')
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if stt_model_id and idx < len(document_model_list):
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media_document_ids.append(str(document_model_list[idx].id))
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maxkb_logger.info(f"Media document IDs to skip paragraph insertion: {media_document_ids}")
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for document in document_model_list:
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for document in document_model_list:
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# 跳过高级学习文档和音视频文档的段落插入
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if str(document.id) in media_document_ids:
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maxkb_logger.info(f"Skipping paragraph insertion for media document: {document.id}")
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continue
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max_position = Paragraph.objects.filter(document_id=document.id).aggregate(
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max_position = Paragraph.objects.filter(document_id=document.id).aggregate(
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max_position=Max('position')
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max_position=Max('position')
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)['max_position'] or 0
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)['max_position'] or 0
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@ -1410,17 +1364,67 @@ class DocumentSerializers(serializers.Serializer):
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with_search_one=False
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with_search_one=False
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)
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)
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# 标记高级学习文档和音视频文档
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# 标记高级学习文档和音视频文档，并触发异步任务
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for idx, document in enumerate(instance_list):
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for idx, document in enumerate(instance_list):
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llm_model_id = document.get('llm_model_id')
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llm_model_id = document.get('llm_model_id')
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vision_model_id = document.get('vision_model_id')
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vision_model_id = document.get('vision_model_id')
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stt_model_id = document.get('stt_model_id')
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stt_model_id = document.get('stt_model_id')
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if idx < len(document_result_list):
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if idx < len(document_result_list):
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document_id = document_result_list[idx].get('id')
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if llm_model_id and vision_model_id:
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if llm_model_id and vision_model_id:
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document_result_list[idx]['is_advanced_learning'] = True
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document_result_list[idx]['is_advanced_learning'] = True
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# 触发高级学习异步任务
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try:
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from knowledge.tasks.advanced_learning import batch_advanced_learning
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batch_advanced_learning.delay(
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[document_id],
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str(knowledge_id),
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workspace_id,
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llm_model_id,
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vision_model_id
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)
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maxkb_logger.info(f"Submitted advanced learning task for document: {document_id}")
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except Exception as e:
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maxkb_logger.error(f"Failed to submit advanced learning task: {str(e)}")
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elif stt_model_id:
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elif stt_model_id:
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document_result_list[idx]['is_media_learning'] = True
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document_result_list[idx]['is_media_learning'] = True
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# 设置排队状态并触发音视频异步任务
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try:
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from common.event import ListenerManagement
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from knowledge.models import TaskType, State
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# 更新文档状态为排队中
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ListenerManagement.update_status(
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QuerySet(Document).filter(id=document_id),
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TaskType.EMBEDDING,
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State.PENDING
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)
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# 触发音视频异步处理任务
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from knowledge.tasks.media_learning import media_learning_by_document
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media_learning_by_document.delay(
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document_id,
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str(knowledge_id),
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workspace_id,
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stt_model_id,
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llm_model_id
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)
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maxkb_logger.info(f"Submitted media learning task for document: {document_id}, status: PENDING")
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except Exception as e:
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maxkb_logger.error(f"Failed to submit media learning task: {str(e)}")
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# 如果提交任务失败，更新状态为失败
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try:
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ListenerManagement.update_status(
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QuerySet(Document).filter(id=document_id),
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TaskType.EMBEDDING,
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State.FAILURE
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)
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except Exception as status_error:
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maxkb_logger.error(f"Failed to update status to FAILURE: {str(status_error)}")
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return document_result_list, knowledge_id, workspace_id
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return document_result_list, knowledge_id, workspace_id
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