Using semaphore to control parallel doc processing instead of batching.
This commit is contained in:
yangdx
@@ -841,8 +841,8 @@ class LightRAG:
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"job_name": "Default Job",
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"job_start": datetime.now().isoformat(),
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"docs": 0,
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"batchs": 0,
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"cur_batch": 0,
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"batchs": 0, # 将被重新定义为待处理的文件总数
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"cur_batch": 0, # 将被重新定义为当前处理的第几个文件
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"request_pending": False, # Clear any previous request
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"latest_message": "",
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}
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@@ -867,18 +867,13 @@ class LightRAG:
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pipeline_status["history_messages"].append(log_message)
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break
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# 2. split docs into chunks, insert chunks, update doc status
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docs_batches = [
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list(to_process_docs.items())[i : i + self.max_parallel_insert]
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for i in range(0, len(to_process_docs), self.max_parallel_insert)
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]
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log_message = f"Processing {len(to_process_docs)} document(s) in {len(docs_batches)} batches"
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log_message = f"Processing {len(to_process_docs)} document(s)"
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logger.info(log_message)
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# Update pipeline status with current batch information
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# 更新 pipeline_status，batchs 现在表示待处理的文件总数
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pipeline_status["docs"] = len(to_process_docs)
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pipeline_status["batchs"] = len(docs_batches)
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pipeline_status["batchs"] = len(to_process_docs)
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pipeline_status["cur_batch"] = 0 # 初始化为0，表示当前已处理的文件数量
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pipeline_status["latest_message"] = log_message
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pipeline_status["history_messages"].append(log_message)
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@@ -892,6 +887,11 @@ class LightRAG:
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job_name = f"{path_prefix}[{total_files} files]"
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pipeline_status["job_name"] = job_name
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# 创建一个计数器，用于跟踪已处理的文件数量
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processed_count = 0
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# 创建一个信号量，限制并发处理文件的数量
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semaphore = asyncio.Semaphore(self.max_parallel_insert)
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async def process_document(
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doc_id: str,
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status_doc: DocProcessingStatus,
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@@ -899,45 +899,95 @@ class LightRAG:
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split_by_character_only: bool,
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pipeline_status: dict,
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pipeline_status_lock: asyncio.Lock,
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semaphore: asyncio.Semaphore,
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) -> None:
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"""Process single document"""
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try:
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# Get file path from status document
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file_path = getattr(status_doc, "file_path", "unknown_source")
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# 使用信号量控制并发
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async with semaphore:
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nonlocal processed_count
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# 获取并保存当前文件的序号
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current_file_number = 0
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try:
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# Get file path from status document
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file_path = getattr(status_doc, "file_path", "unknown_source")
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async with pipeline_status_lock:
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log_message = f"Processing file: {file_path}"
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logger.info(log_message)
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pipeline_status["history_messages"].append(log_message)
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log_message = f"Processing d-id: {doc_id}"
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logger.info(log_message)
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pipeline_status["latest_message"] = log_message
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pipeline_status["history_messages"].append(log_message)
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async with pipeline_status_lock:
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# 更新已处理文件数量并保存当前文件序号
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processed_count += 1
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current_file_number = processed_count # 保存当前文件的序号
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pipeline_status["cur_batch"] = processed_count
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log_message = f"Processing file ({current_file_number}/{total_files}): {file_path}"
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logger.info(log_message)
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pipeline_status["history_messages"].append(log_message)
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log_message = f"Processing d-id: {doc_id}"
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logger.info(log_message)
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pipeline_status["latest_message"] = log_message
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pipeline_status["history_messages"].append(log_message)
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# Generate chunks from document
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chunks: dict[str, Any] = {
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compute_mdhash_id(dp["content"], prefix="chunk-"): {
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**dp,
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"full_doc_id": doc_id,
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"file_path": file_path, # Add file path to each chunk
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# Generate chunks from document
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chunks: dict[str, Any] = {
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compute_mdhash_id(dp["content"], prefix="chunk-"): {
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**dp,
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"full_doc_id": doc_id,
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"file_path": file_path, # Add file path to each chunk
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}
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for dp in self.chunking_func(
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self.tokenizer,
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status_doc.content,
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split_by_character,
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split_by_character_only,
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self.chunk_overlap_token_size,
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self.chunk_token_size,
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)
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}
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for dp in self.chunking_func(
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self.tokenizer,
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status_doc.content,
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split_by_character,
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split_by_character_only,
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self.chunk_overlap_token_size,
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self.chunk_token_size,
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)
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}
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# Process document (text chunks and full docs) in parallel
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# Create tasks with references for potential cancellation
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doc_status_task = asyncio.create_task(
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self.doc_status.upsert(
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# Process document (text chunks and full docs) in parallel
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# Create tasks with references for potential cancellation
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doc_status_task = asyncio.create_task(
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self.doc_status.upsert(
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{
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doc_id: {
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"status": DocStatus.PROCESSING,
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"chunks_count": len(chunks),
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"content": status_doc.content,
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"content_summary": status_doc.content_summary,
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"content_length": status_doc.content_length,
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"created_at": status_doc.created_at,
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"updated_at": datetime.now().isoformat(),
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"file_path": file_path,
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}
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}
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)
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)
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chunks_vdb_task = asyncio.create_task(
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self.chunks_vdb.upsert(chunks)
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)
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entity_relation_task = asyncio.create_task(
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self._process_entity_relation_graph(
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chunks, pipeline_status, pipeline_status_lock
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)
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)
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full_docs_task = asyncio.create_task(
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self.full_docs.upsert(
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{doc_id: {"content": status_doc.content}}
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)
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)
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text_chunks_task = asyncio.create_task(
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self.text_chunks.upsert(chunks)
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)
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tasks = [
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doc_status_task,
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chunks_vdb_task,
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entity_relation_task,
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full_docs_task,
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text_chunks_task,
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]
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await asyncio.gather(*tasks)
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await self.doc_status.upsert(
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{
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doc_id: {
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"status": DocStatus.PROCESSING,
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"status": DocStatus.PROCESSED,
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"chunks_count": len(chunks),
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"content": status_doc.content,
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"content_summary": status_doc.content_summary,
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@@ -948,112 +998,67 @@ class LightRAG:
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}
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}
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)
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)
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chunks_vdb_task = asyncio.create_task(
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self.chunks_vdb.upsert(chunks)
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)
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entity_relation_task = asyncio.create_task(
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self._process_entity_relation_graph(
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chunks, pipeline_status, pipeline_status_lock
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)
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)
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full_docs_task = asyncio.create_task(
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self.full_docs.upsert(
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{doc_id: {"content": status_doc.content}}
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)
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)
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text_chunks_task = asyncio.create_task(
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self.text_chunks.upsert(chunks)
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)
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tasks = [
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doc_status_task,
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chunks_vdb_task,
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entity_relation_task,
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full_docs_task,
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text_chunks_task,
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]
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await asyncio.gather(*tasks)
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await self.doc_status.upsert(
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{
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doc_id: {
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"status": DocStatus.PROCESSED,
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"chunks_count": len(chunks),
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"content": status_doc.content,
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"content_summary": status_doc.content_summary,
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"content_length": status_doc.content_length,
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"created_at": status_doc.created_at,
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"updated_at": datetime.now().isoformat(),
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"file_path": file_path,
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# 每处理完一个文件，就调用一次 _insert_done
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await self._insert_done()
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async with pipeline_status_lock:
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log_message = f"Completed processing file {current_file_number}/{total_files}: {file_path}"
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logger.info(log_message)
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pipeline_status["latest_message"] = log_message
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pipeline_status["history_messages"].append(log_message)
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except Exception as e:
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# Log error and update pipeline status
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error_msg = f"Failed to process document {doc_id}: {traceback.format_exc()}"
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logger.error(error_msg)
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async with pipeline_status_lock:
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pipeline_status["latest_message"] = error_msg
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pipeline_status["history_messages"].append(error_msg)
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# Cancel other tasks as they are no longer meaningful
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for task in [
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chunks_vdb_task,
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entity_relation_task,
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full_docs_task,
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text_chunks_task,
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]:
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if not task.done():
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task.cancel()
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# Update document status to failed
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await self.doc_status.upsert(
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{
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doc_id: {
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"status": DocStatus.FAILED,
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"error": str(e),
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"content": status_doc.content,
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"content_summary": status_doc.content_summary,
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"content_length": status_doc.content_length,
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"created_at": status_doc.created_at,
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"updated_at": datetime.now().isoformat(),
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"file_path": file_path,
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}
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}
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}
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)
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# 创建所有文档的处理任务
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doc_tasks = []
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for doc_id, status_doc in to_process_docs.items():
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doc_tasks.append(
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process_document(
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doc_id,
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status_doc,
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split_by_character,
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split_by_character_only,
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pipeline_status,
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pipeline_status_lock,
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semaphore,
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)
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except Exception as e:
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# Log error and update pipeline status
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error_msg = f"Failed to process document {doc_id}: {traceback.format_exc()}"
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logger.error(error_msg)
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async with pipeline_status_lock:
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pipeline_status["latest_message"] = error_msg
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pipeline_status["history_messages"].append(error_msg)
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# Cancel other tasks as they are no longer meaningful
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for task in [
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chunks_vdb_task,
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entity_relation_task,
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full_docs_task,
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text_chunks_task,
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]:
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if not task.done():
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task.cancel()
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# Update document status to failed
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await self.doc_status.upsert(
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{
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doc_id: {
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"status": DocStatus.FAILED,
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"error": str(e),
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"content": status_doc.content,
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"content_summary": status_doc.content_summary,
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"content_length": status_doc.content_length,
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"created_at": status_doc.created_at,
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"updated_at": datetime.now().isoformat(),
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"file_path": file_path,
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}
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}
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)
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# 3. iterate over batches
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total_batches = len(docs_batches)
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for batch_idx, docs_batch in enumerate(docs_batches):
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current_batch = batch_idx + 1
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log_message = (
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f"Start processing batch {current_batch} of {total_batches}."
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)
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logger.info(log_message)
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pipeline_status["cur_batch"] = current_batch
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pipeline_status["latest_message"] = log_message
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pipeline_status["history_messages"].append(log_message)
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doc_tasks = []
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for doc_id, status_doc in docs_batch:
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doc_tasks.append(
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process_document(
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doc_id,
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status_doc,
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split_by_character,
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split_by_character_only,
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pipeline_status,
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pipeline_status_lock,
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)
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)
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# Process documents in one batch parallelly
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await asyncio.gather(*doc_tasks)
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await self._insert_done()
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log_message = f"Completed batch {current_batch} of {total_batches}."
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logger.info(log_message)
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pipeline_status["latest_message"] = log_message
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pipeline_status["history_messages"].append(log_message)
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# 等待所有文档处理完成
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await asyncio.gather(*doc_tasks)
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# Check if there's a pending request to process more documents (with lock)
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has_pending_request = False
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