音视频支持分段

apps/common/handle/impl/media/media_split_handle.py

@@ -31,6 +31,7 @@ class MediaSplitHandle(BaseSplitHandle):
         """处理音视频文件"""
 
         maxkb_logger.info(f"MediaSplitHandle.handle called with file: {file.name}")
+        maxkb_logger.info(f"Split parameters - limit: {limit}, patterns: {pattern_list}, with_filter: {with_filter}")
 
         # 检查是否需要实际处理
         use_actual_processing = kwargs.get('use_actual_processing', False)
@@ -38,10 +39,23 @@ class MediaSplitHandle(BaseSplitHandle):
 
         if use_actual_processing and stt_model_id:
             # 进行实际处理
-            return self._handle_actual_processing(file, get_buffer, **kwargs)
+            result = self._handle_actual_processing(file, get_buffer, **kwargs)
+
+            # 应用智能分块（如果需要）
+            chunk_limit = limit if limit > 0 else 1000  # 默认1000字符
+            if len(result.get('content', [])) > 0:
+                result = self._apply_smart_split(result, chunk_limit, with_filter)
+
+            return result
         else:
             # 使用默认文本
-            return self._handle_default_text(file, **kwargs)
+            result = self._handle_default_text(file, **kwargs)
+
+            # 即使是默认文本也应用分块（如果有limit参数）
+            if limit > 0:
+                result = self._apply_smart_split(result, limit, with_filter)
+
+            return result
     
     def _get_audio_default_segments(self, file_name: str) -> List[dict]:
         """生成音频文件的默认段落"""
@@ -297,3 +311,122 @@ class MediaSplitHandle(BaseSplitHandle):
             return f"{hours:02d}:{minutes:02d}:{secs:02d}"
         else:
             return f"{minutes:02d}:{secs:02d}"
+
+    def smart_split_transcription(self, text, limit=1000, overlap=100):
+        """智能分割转录文本"""
+        if len(text) <= limit:
+            return [text]
+
+        # 预处理：移除过多的空行
+        text = '\n'.join(line for line in text.split('\n') if line.strip())
+
+        chunks = []
+        start = 0
+
+        while start < len(text):
+            # 计算当前块的结束位置
+            end = min(start + limit, len(text))
+
+            # 如果不是最后一块，尝试在句号处断开
+            if end < len(text):
+                # 寻找最佳断点
+                best_end = end
+
+                # 优先在句号处断开
+                last_period = text.rfind('。', start, end)
+                if last_period > start + limit // 2:
+                    best_end = last_period + 1
+                else:
+                    # 其次在换行符处断开
+                    last_newline = text.rfind('\n', start, end)
+                    if last_newline > start + limit // 3:
+                        best_end = last_newline + 1
+
+                end = best_end
+
+            # 提取当前块
+            chunk = text[start:end].strip()
+            if chunk:
+                chunks.append(chunk)
+
+            # 计算下一块的开始位置（考虑重叠）
+            start = max(end - overlap, len(chunks[-1]) if chunks else 0)
+
+            # 避免无限循环
+            if start >= len(text):
+                break
+
+        return chunks
+
+    def _apply_smart_split(self, result: dict, limit: int = 1000, with_filter: bool = False):
+        """应用智能分块到转录结果"""
+        overlap = 100  # 前后重叠字符数
+
+        new_paragraphs = []
+        for paragraph in result.get('content', []):
+            content = paragraph.get('content', '')
+
+            # 应用文本过滤（如果需要）
+            if with_filter:
+                content = self._clean_text(content)
+
+            if content:
+                # 应用智能分块
+                chunks = self.smart_split_transcription(content, limit, overlap)
+
+                # 如果只有一个块且没有变化，保持原样
+                if len(chunks) == 1 and chunks[0] == content:
+                    new_paragraphs.append(paragraph)
+                else:
+                    # 创建新的段落
+                    for idx, chunk in enumerate(chunks):
+                        # 保留原始元数据，但更新分段相关信息
+                        metadata = paragraph.get('metadata', {}).copy()
+                        metadata.update({
+                            'chunk_index': idx,
+                            'total_chunks': len(chunks),
+                            'split_method': 'smart_transcription',
+                            'split_limit': limit,
+                            'split_overlap': overlap,
+                            'with_filter': with_filter
+                        })
+
+                        new_paragraph = {
+                            'content': chunk,
+                            'title': f"{paragraph.get('title', '段落')} - 第{idx + 1}部分" if len(chunks) > 1 else paragraph.get('title', '段落'),
+                            'metadata': metadata
+                        }
+                        new_paragraphs.append(new_paragraph)
+            else:
+                new_paragraphs.append(paragraph)
+
+        # 更新结果
+        result['content'] = new_paragraphs
+
+        # 更新元数据
+        metadata = result.get('metadata', {})
+        metadata['smart_split_applied'] = True
+        metadata['total_chunks'] = len(new_paragraphs)
+        result['metadata'] = metadata
+
+        maxkb_logger.info(f"Applied smart transcription split: {len(new_paragraphs)} chunks")
+
+        return result
+
+    def _clean_text(self, text):
+        """清理文本：去掉重复多余符号空格、空行、制表符"""
+        import re
+
+        # 移除多余的空白字符
+        text = re.sub(r'\s+', ' ', text)
+
+        # 移除开头和结尾的空白
+        text = text.strip()
+
+        # 移除重复的标点符号
+        text = re.sub(r'([。！？，])\1+', r'\1', text)
+
+        # 移除多余的换行
+        text = re.sub(r'\n{3,}', '\n\n', text)
+
+        return text

apps/knowledge/serializers/document.py

@@ -249,15 +249,33 @@ def submit_advanced_learning_task(document_id, knowledge_id, workspace_id, llm_m
 def submit_media_learning_task(document_id, knowledge_id, workspace_id, stt_model_id, llm_model_id):
     """提交音视频学习任务的辅助函数"""
     try:
+        # 获取文档实例以访问分块参数
+        from knowledge.models import Document
+        document = QuerySet(Document).filter(id=document_id).first()
+
+        # 获取分块参数
+        limit = None
+        patterns = None
+        with_filter = None
+
+        if document and document.meta:
+            limit = document.meta.get('split_limit')
+            patterns = document.meta.get('split_patterns', [])
+            with_filter = document.meta.get('split_with_filter')
+
         from knowledge.tasks.media_learning import media_learning_by_document
         media_learning_by_document.delay(
             document_id,
             knowledge_id,
             workspace_id,
             stt_model_id,
-            llm_model_id
+            llm_model_id,
+            limit=limit,
+            patterns=patterns,
+            with_filter=with_filter
         )
         maxkb_logger.info(f"Media learning task submitted for document: {document_id}")
+        maxkb_logger.info(f"Split parameters - limit: {limit}, patterns: {patterns}, with_filter: {with_filter}")
     except Exception as e:
         maxkb_logger.error(f"Failed to submit media learning task: {str(e)}")
         ListenerManagement.update_status(

apps/knowledge/tasks/media_learning.py

@@ -17,7 +17,10 @@ from common.handle.impl.media.media_split_handle import MediaSplitHandle
 
 @shared_task(name='media_learning_by_document')
 def media_learning_by_document(document_id: str, knowledge_id: str, workspace_id: str,
-                               stt_model_id: str, llm_model_id: Optional[str] = None):
+                               stt_model_id: str, llm_model_id: Optional[str] = None,
+                               limit: Optional[int] = None,
+                               patterns: Optional[List[str]] = None,
+                               with_filter: Optional[bool] = None):
     """
     音视频文档异步处理任务 - 完整状态流转
 
@@ -34,9 +37,13 @@ def media_learning_by_document(document_id: str, knowledge_id: str, workspace_id
         workspace_id: 工作空间ID
         stt_model_id: STT模型ID
         llm_model_id: LLM模型ID（可选）
+        limit: 分段长度（可选）
+        patterns: 分段正则列表（可选）
+        with_filter: 是否清除特殊字符（可选）
     """
     maxkb_logger.info(f"🎬 Starting media learning task for document: {document_id}")
     maxkb_logger.info(f"📋 Current status: PENDING (排队中)")
+    maxkb_logger.info(f"📝 Split parameters - limit: {limit}, patterns: {patterns}, with_filter: {with_filter}")
     
     try:
         # 验证文档存在
@@ -81,12 +88,12 @@ def media_learning_by_document(document_id: str, knowledge_id: str, workspace_id
             def get_buffer(file_obj):
                 return file_obj.read()
             
-            # 处理音视频文件（禁用默认文本模式）
+            # 处理音视频文件（传递分块参数）
             result = handler.handle(
                 file=temp_file,
-                pattern_list=[],
-                with_filter=False,
-                limit=0,  # 不限制段落数量
+                pattern_list=patterns or [],  # 使用传入的分段模式
+                with_filter=with_filter if with_filter is not None else False,
+                limit=limit if limit is not None else 0,  # 0表示使用默认值（在handle中会转为1000）
                 get_buffer=get_buffer,
                 save_image=False,
                 stt_model_id=stt_model_id,
@@ -180,7 +187,10 @@ def media_learning_by_document(document_id: str, knowledge_id: str, workspace_id
 
 @shared_task(name='media_learning_batch')
 def media_learning_batch(document_id_list: List[str], knowledge_id: str, workspace_id: str,
-                         stt_model_id: str, llm_model_id: Optional[str] = None):
+                         stt_model_id: str, llm_model_id: Optional[str] = None,
+                         limit: Optional[int] = None,
+                         patterns: Optional[List[str]] = None,
+                         with_filter: Optional[bool] = None):
     """
     批量音视频处理任务
 
@@ -190,6 +200,9 @@ def media_learning_batch(document_id_list: List[str], knowledge_id: str, workspa
         workspace_id: 工作空间ID
         stt_model_id: STT模型ID
         llm_model_id: LLM模型ID（可选）
+        limit: 分段长度（可选）
+        patterns: 分段正则列表（可选）
+        with_filter: 是否清除特殊字符（可选）
     """
     maxkb_logger.info(f"🎬 Starting batch media learning for {len(document_id_list)} documents")
 
@@ -197,7 +210,8 @@ def media_learning_batch(document_id_list: List[str], knowledge_id: str, workspa
     for document_id in document_id_list:
         try:
             media_learning_by_document.delay(
-                document_id, knowledge_id, workspace_id, stt_model_id, llm_model_id
+                document_id, knowledge_id, workspace_id, stt_model_id, llm_model_id,
+                limit, patterns, with_filter
             )
             maxkb_logger.info(f"📋 Submitted media learning task for document: {document_id}")
         except Exception as e: