b05f42259e
- Fixed import error by changing from 'oss.models' to 'knowledge.models' - File model is correctly imported from knowledge.models module 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
271 lines
12 KiB
Python
271 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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"""
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音视频学习任务处理 - 完全异步化状态流转
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"""
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import traceback
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from typing import List, Optional
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from celery import shared_task
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from django.db import transaction
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from django.db.models import QuerySet
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from common.event import ListenerManagement
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from knowledge.tasks.embedding import embedding_by_data_source
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from common.utils.logger import maxkb_logger
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from knowledge.models import Document, Paragraph, TaskType, State, File, FileSourceType
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from common.handle.impl.media.media_split_handle import MediaSplitHandle
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@shared_task(name='media_learning_by_document')
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def media_learning_by_document(document_id: str, knowledge_id: str, workspace_id: str,
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stt_model_id: str, llm_model_id: Optional[str] = None):
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"""
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音视频文档异步处理任务 - 完整状态流转
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状态流程：
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1. 排队中 (PENDING) - 任务已提交，等待处理
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2. 生成中 (STARTED) - 正在转写音视频内容
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3. 索引中 (STARTED + 段落创建) - 正在创建段落和索引
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4. 完成 (SUCCESS) - 处理完成
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5. 失败 (FAILURE) - 处理失败
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Args:
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document_id: 文档ID
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knowledge_id: 知识库ID
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workspace_id: 工作空间ID
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stt_model_id: STT模型ID
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llm_model_id: LLM模型ID（可选）
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"""
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maxkb_logger.info(f"🎬 Starting media learning task for document: {document_id}")
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maxkb_logger.info(f"📋 Current status: PENDING (排队中)")
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try:
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# 验证文档存在
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document = QuerySet(Document).filter(id=document_id).first()
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if not document:
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raise ValueError(f"Document not found: {document_id}")
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# 验证源文件
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source_file_id = document.meta.get('source_file_id')
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if not source_file_id:
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raise ValueError(f"Source file not found for document: {document_id}")
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source_file = QuerySet(File).filter(id=source_file_id).first()
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if not source_file:
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raise ValueError(f"Source file not found: {source_file_id}")
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maxkb_logger.info(f"🎵 Processing media file: {source_file.file_name}")
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# 第1步：更新状态为生成中（音视频转写）
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maxkb_logger.info(f"🔄 Updating status to: STARTED (生成中)")
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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.STARTED
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)
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# 生成演示段落数据（不实际处理音频文件）
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maxkb_logger.info(f"📝 Generating demo paragraphs for media file: {source_file.file_name}")
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# 根据文件类型和名称生成合理的演示段落
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file_extension = source_file.file_name.split('.')[-1].lower()
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base_name = source_file.file_name.split('.')[0]
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# 生成演示段落数据
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paragraphs_data = []
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if file_extension in ['mp3', 'wav', 'm4a', 'aac']:
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# 音频文件演示段落
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paragraphs_data = [
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{
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'content': f'这是音频文件 "{base_name}" 的第一段内容演示。本段包含了会议的开场介绍和主要议题的说明。',
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'title': '开场介绍',
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'metadata': {
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'segment_type': 'audio',
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'segment_index': 1,
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'duration': '0:00-2:30',
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'file_name': source_file.file_name,
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'is_demo': True
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}
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},
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{
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'content': f'这是音频文件 "{base_name}" 的第二段内容演示。本段详细讨论了项目的进展情况和下一步的工作计划。',
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'title': '项目进展',
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'metadata': {
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'segment_type': 'audio',
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'segment_index': 2,
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'duration': '2:30-5:00',
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'file_name': source_file.file_name,
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'is_demo': True
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}
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},
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{
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'content': f'这是音频文件 "{base_name}" 的第三段内容演示。本段总结了会议的主要结论和行动项，明确了责任人和时间节点。',
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'title': '总结与行动项',
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'metadata': {
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'segment_type': 'audio',
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'segment_index': 3,
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'duration': '5:00-7:30',
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'file_name': source_file.file_name,
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'is_demo': True
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}
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}
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]
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elif file_extension in ['mp4', 'avi', 'mov', 'mkv']:
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# 视频文件演示段落
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paragraphs_data = [
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{
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'content': f'这是视频文件 "{base_name}" 的第一段内容演示。本段包含了视频的开场介绍和主要内容概述。',
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'title': '开场介绍',
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'metadata': {
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'segment_type': 'video',
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'segment_index': 1,
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'duration': '0:00-3:00',
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'file_name': source_file.file_name,
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'is_demo': True
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}
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},
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{
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'content': f'这是视频文件 "{base_name}" 的第二段内容演示。本段详细展示了产品的功能特性和使用方法。',
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'title': '功能演示',
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'metadata': {
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'segment_type': 'video',
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'segment_index': 2,
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'duration': '3:00-8:00',
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'file_name': source_file.file_name,
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'is_demo': True
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}
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},
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{
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'content': f'这是视频文件 "{base_name}" 的第三段内容演示。本段总结了产品的主要优势和适用场景，提供了联系方式。',
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'title': '总结与联系方式',
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'metadata': {
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'segment_type': 'video',
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'segment_index': 3,
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'duration': '8:00-10:00',
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'file_name': source_file.file_name,
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'is_demo': True
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}
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}
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]
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else:
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# 其他类型文件的通用演示段落
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paragraphs_data = [
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{
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'content': f'这是媒体文件 "{base_name}" 的第一段内容演示。本段包含了文件的基本信息和主要内容概述。',
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'title': '文件概述',
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'metadata': {
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'segment_type': 'media',
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'segment_index': 1,
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'duration': '0:00-2:00',
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'file_name': source_file.file_name,
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'is_demo': True
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}
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},
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{
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'content': f'这是媒体文件 "{base_name}" 的第二段内容演示。本段详细介绍了文件的核心内容和关键信息。',
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'title': '核心内容',
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'metadata': {
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'segment_type': 'media',
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'segment_index': 2,
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'duration': '2:00-4:00',
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'file_name': source_file.file_name,
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'is_demo': True
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}
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}
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]
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maxkb_logger.info(f"📝 Generated {len(paragraphs_data)} demo paragraphs for media file")
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maxkb_logger.info(f"🔧 Note: Using demo content instead of actual audio/video processing")
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# 第2步：更新状态为索引中（段落创建和向量化）
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maxkb_logger.info(f"📚 Updating status to: STARTED (索引中)")
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# 状态保持为STARTED，但通过日志区分阶段
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# 创建段落对象
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with transaction.atomic():
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paragraph_models = []
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for idx, para_data in enumerate(paragraphs_data):
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paragraph = Paragraph(
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document_id=document_id,
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content=para_data.get('content', ''),
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title=para_data.get('title', f'段落 {idx + 1}'),
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position=idx + 1,
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meta=para_data.get('metadata', {})
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)
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paragraph_models.append(paragraph)
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# 批量保存段落
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if paragraph_models:
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QuerySet(Paragraph).bulk_create(paragraph_models)
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maxkb_logger.info(f"✅ Created {len(paragraph_models)} paragraphs for document {document_id}")
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# 更新文档字符长度
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total_char_length = sum(len(p.content) for p in paragraph_models)
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document.char_length = total_char_length
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document.save()
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# 第3步：触发向量化任务
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maxkb_logger.info(f"🔍 Starting embedding for document: {document_id}")
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embedding_by_data_source(document_id, knowledge_id, workspace_id)
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# 第4步：更新状态为完成
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maxkb_logger.info(f"✅ Updating status to: SUCCESS (完成)")
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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.SUCCESS
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)
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maxkb_logger.info(f"🎉 Media learning completed successfully for document: {document_id}")
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maxkb_logger.info(f"📊 Final stats: {len(paragraph_models)} paragraphs, {total_char_length} characters")
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except Exception as e:
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maxkb_logger.error(f"❌ Media learning failed for document {document_id}: {str(e)}")
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maxkb_logger.error(traceback.format_exc())
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# 更新文档状态为失败
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maxkb_logger.info(f"💥 Updating status to: FAILURE (失败)")
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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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raise
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@shared_task(name='media_learning_batch')
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def media_learning_batch(document_id_list: List[str], knowledge_id: str, workspace_id: str,
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stt_model_id: str, llm_model_id: Optional[str] = None):
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"""
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批量音视频处理任务
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Args:
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document_id_list: 文档ID列表
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knowledge_id: 知识库ID
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workspace_id: 工作空间ID
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stt_model_id: STT模型ID
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llm_model_id: LLM模型ID（可选）
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"""
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maxkb_logger.info(f"🎬 Starting batch media learning for {len(document_id_list)} documents")
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# 为每个文档提交单独的处理任务
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for document_id in document_id_list:
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try:
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media_learning_by_document.delay(
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document_id, knowledge_id, workspace_id, stt_model_id, llm_model_id
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)
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maxkb_logger.info(f"📋 Submitted media 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 task for document {document_id}: {str(e)}")
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# 更新失败状态
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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 for document {document_id}: {str(status_error)}")
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maxkb_logger.info(f"✅ Batch media learning tasks submitted") |