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Using semaphore to control parallel doc processing instead of batching.

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This commit is contained in:
yangdx
2025-04-24 13:45:44 +08:00
parent 47c4520a65
commit 4f68f3e410
1 changed files with 149 additions and 144 deletions
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293
lightrag/lightrag.py
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@@ -841,8 +841,8 @@ class LightRAG:
"job_name": "Default Job", "job_name": "Default Job",
"job_start": datetime.now().isoformat(), "job_start": datetime.now().isoformat(),
"docs": 0, "docs": 0,
"batchs": 0, "batchs": 0, # 将被重新定义为待处理的文件总数
"cur_batch": 0, "cur_batch": 0, # 将被重新定义为当前处理的第几个文件
"request_pending": False, # Clear any previous request "request_pending": False, # Clear any previous request
"latest_message": "", "latest_message": "",
} }
@@ -867,18 +867,13 @@ class LightRAG:
pipeline_status["history_messages"].append(log_message) pipeline_status["history_messages"].append(log_message)
break break
# 2. split docs into chunks, insert chunks, update doc status log_message = f"Processing {len(to_process_docs)} document(s)"
docs_batches = [
list(to_process_docs.items())[i : i + self.max_parallel_insert]
for i in range(0, len(to_process_docs), self.max_parallel_insert)
]
log_message = f"Processing {len(to_process_docs)} document(s) in {len(docs_batches)} batches"
logger.info(log_message) logger.info(log_message)
# Update pipeline status with current batch information # 更新 pipeline_status，batchs 现在表示待处理的文件总数
pipeline_status["docs"] = len(to_process_docs) pipeline_status["docs"] = len(to_process_docs)
pipeline_status["batchs"] = len(docs_batches) pipeline_status["batchs"] = len(to_process_docs)
pipeline_status["cur_batch"] = 0 # 初始化为0，表示当前已处理的文件数量
pipeline_status["latest_message"] = log_message pipeline_status["latest_message"] = log_message
pipeline_status["history_messages"].append(log_message) pipeline_status["history_messages"].append(log_message)
@@ -892,6 +887,11 @@ class LightRAG:
job_name = f"{path_prefix}[{total_files} files]" job_name = f"{path_prefix}[{total_files} files]"
pipeline_status["job_name"] = job_name pipeline_status["job_name"] = job_name
# 创建一个计数器，用于跟踪已处理的文件数量
processed_count = 0
# 创建一个信号量，限制并发处理文件的数量
semaphore = asyncio.Semaphore(self.max_parallel_insert)
async def process_document( async def process_document(
doc_id: str, doc_id: str,
status_doc: DocProcessingStatus, status_doc: DocProcessingStatus,
@@ -899,45 +899,95 @@ class LightRAG:
split_by_character_only: bool, split_by_character_only: bool,
pipeline_status: dict, pipeline_status: dict,
pipeline_status_lock: asyncio.Lock, pipeline_status_lock: asyncio.Lock,
semaphore: asyncio.Semaphore,
) -> None: ) -> None:
"""Process single document""" """Process single document"""
try: # 使用信号量控制并发
# Get file path from status document async with semaphore:
file_path = getattr(status_doc, "file_path", "unknown_source") nonlocal processed_count
# 获取并保存当前文件的序号
current_file_number = 0
try:
# Get file path from status document
file_path = getattr(status_doc, "file_path", "unknown_source")
async with pipeline_status_lock: async with pipeline_status_lock:
log_message = f"Processing file: {file_path}" # 更新已处理文件数量并保存当前文件序号
logger.info(log_message) processed_count += 1
pipeline_status["history_messages"].append(log_message) current_file_number = processed_count # 保存当前文件的序号
log_message = f"Processing d-id: {doc_id}" pipeline_status["cur_batch"] = processed_count
logger.info(log_message)
pipeline_status["latest_message"] = log_message log_message = f"Processing file ({current_file_number}/{total_files}): {file_path}"
pipeline_status["history_messages"].append(log_message) logger.info(log_message)
pipeline_status["history_messages"].append(log_message)
log_message = f"Processing d-id: {doc_id}"
logger.info(log_message)
pipeline_status["latest_message"] = log_message
pipeline_status["history_messages"].append(log_message)
# Generate chunks from document # Generate chunks from document
chunks: dict[str, Any] = { chunks: dict[str, Any] = {
compute_mdhash_id(dp["content"], prefix="chunk-"): { compute_mdhash_id(dp["content"], prefix="chunk-"): {
**dp, **dp,
"full_doc_id": doc_id, "full_doc_id": doc_id,
"file_path": file_path, # Add file path to each chunk "file_path": file_path, # Add file path to each chunk
}
for dp in self.chunking_func(
self.tokenizer,
status_doc.content,
split_by_character,
split_by_character_only,
self.chunk_overlap_token_size,
self.chunk_token_size,
)
} }
for dp in self.chunking_func(
self.tokenizer,
status_doc.content,
split_by_character,
split_by_character_only,
self.chunk_overlap_token_size,
self.chunk_token_size,
)
}
# Process document (text chunks and full docs) in parallel # Process document (text chunks and full docs) in parallel
# Create tasks with references for potential cancellation # Create tasks with references for potential cancellation
doc_status_task = asyncio.create_task( doc_status_task = asyncio.create_task(
self.doc_status.upsert( self.doc_status.upsert(
{
doc_id: {
"status": DocStatus.PROCESSING,
"chunks_count": len(chunks),
"content": status_doc.content,
"content_summary": status_doc.content_summary,
"content_length": status_doc.content_length,
"created_at": status_doc.created_at,
"updated_at": datetime.now().isoformat(),
"file_path": file_path,
}
}
)
)
chunks_vdb_task = asyncio.create_task(
self.chunks_vdb.upsert(chunks)
)
entity_relation_task = asyncio.create_task(
self._process_entity_relation_graph(
chunks, pipeline_status, pipeline_status_lock
)
)
full_docs_task = asyncio.create_task(
self.full_docs.upsert(
{doc_id: {"content": status_doc.content}}
)
)
text_chunks_task = asyncio.create_task(
self.text_chunks.upsert(chunks)
)
tasks = [
doc_status_task,
chunks_vdb_task,
entity_relation_task,
full_docs_task,
text_chunks_task,
]
await asyncio.gather(*tasks)
await self.doc_status.upsert(
{ {
doc_id: { doc_id: {
"status": DocStatus.PROCESSING, "status": DocStatus.PROCESSED,
"chunks_count": len(chunks), "chunks_count": len(chunks),
"content": status_doc.content, "content": status_doc.content,
"content_summary": status_doc.content_summary, "content_summary": status_doc.content_summary,
@@ -948,112 +998,67 @@ class LightRAG:
} }
} }
) )
)
chunks_vdb_task = asyncio.create_task( # 每处理完一个文件，就调用一次 _insert_done
self.chunks_vdb.upsert(chunks) await self._insert_done()
)
entity_relation_task = asyncio.create_task( async with pipeline_status_lock:
self._process_entity_relation_graph( log_message = f"Completed processing file {current_file_number}/{total_files}: {file_path}"
chunks, pipeline_status, pipeline_status_lock logger.info(log_message)
) pipeline_status["latest_message"] = log_message
) pipeline_status["history_messages"].append(log_message)
full_docs_task = asyncio.create_task(
self.full_docs.upsert( except Exception as e:
{doc_id: {"content": status_doc.content}} # Log error and update pipeline status
) error_msg = f"Failed to process document {doc_id}: {traceback.format_exc()}"
)
text_chunks_task = asyncio.create_task( logger.error(error_msg)
self.text_chunks.upsert(chunks) async with pipeline_status_lock:
) pipeline_status["latest_message"] = error_msg
tasks = [ pipeline_status["history_messages"].append(error_msg)
doc_status_task,
chunks_vdb_task, # Cancel other tasks as they are no longer meaningful
entity_relation_task, for task in [
full_docs_task, chunks_vdb_task,
text_chunks_task, entity_relation_task,
] full_docs_task,
await asyncio.gather(*tasks) text_chunks_task,
await self.doc_status.upsert( ]:
{ if not task.done():
doc_id: { task.cancel()
"status": DocStatus.PROCESSED, # Update document status to failed
"chunks_count": len(chunks), await self.doc_status.upsert(
"content": status_doc.content, {
"content_summary": status_doc.content_summary, doc_id: {
"content_length": status_doc.content_length, "status": DocStatus.FAILED,
"created_at": status_doc.created_at, "error": str(e),
"updated_at": datetime.now().isoformat(), "content": status_doc.content,
"file_path": file_path, "content_summary": status_doc.content_summary,
"content_length": status_doc.content_length,
"created_at": status_doc.created_at,
"updated_at": datetime.now().isoformat(),
"file_path": file_path,
}
} }
} )
# 创建所有文档的处理任务
doc_tasks = []
for doc_id, status_doc in to_process_docs.items():
doc_tasks.append(
process_document(
doc_id,
status_doc,
split_by_character,
split_by_character_only,
pipeline_status,
pipeline_status_lock,
semaphore,
) )
except Exception as e:
# Log error and update pipeline status
error_msg = f"Failed to process document {doc_id}: {traceback.format_exc()}"
logger.error(error_msg)
async with pipeline_status_lock:
pipeline_status["latest_message"] = error_msg
pipeline_status["history_messages"].append(error_msg)
# Cancel other tasks as they are no longer meaningful
for task in [
chunks_vdb_task,
entity_relation_task,
full_docs_task,
text_chunks_task,
]:
if not task.done():
task.cancel()
# Update document status to failed
await self.doc_status.upsert(
{
doc_id: {
"status": DocStatus.FAILED,
"error": str(e),
"content": status_doc.content,
"content_summary": status_doc.content_summary,
"content_length": status_doc.content_length,
"created_at": status_doc.created_at,
"updated_at": datetime.now().isoformat(),
"file_path": file_path,
}
}
)
# 3. iterate over batches
total_batches = len(docs_batches)
for batch_idx, docs_batch in enumerate(docs_batches):
current_batch = batch_idx + 1
log_message = (
f"Start processing batch {current_batch} of {total_batches}."
) )
logger.info(log_message)
pipeline_status["cur_batch"] = current_batch
pipeline_status["latest_message"] = log_message
pipeline_status["history_messages"].append(log_message)
doc_tasks = [] # 等待所有文档处理完成
for doc_id, status_doc in docs_batch: await asyncio.gather(*doc_tasks)
doc_tasks.append(
process_document(
doc_id,
status_doc,
split_by_character,
split_by_character_only,
pipeline_status,
pipeline_status_lock,
)
)
# Process documents in one batch parallelly
await asyncio.gather(*doc_tasks)
await self._insert_done()
log_message = f"Completed batch {current_batch} of {total_batches}."
logger.info(log_message)
pipeline_status["latest_message"] = log_message
pipeline_status["history_messages"].append(log_message)
# Check if there's a pending request to process more documents (with lock) # Check if there's a pending request to process more documents (with lock)
has_pending_request = False has_pending_request = False