Files are now processed in batches in auto scan
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@@ -472,11 +472,30 @@ async def run_scanning_process(rag: LightRAG, doc_manager: DocumentManager):
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total_files = len(new_files)
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logger.info(f"Found {total_files} new files to index.")
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if new_files:
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await pipeline_index_files(rag, new_files)
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if not new_files:
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return
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# Get MAX_PARALLEL_INSERT from global_args
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max_parallel = global_args["max_parallel_insert"]
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# Calculate batch size as 2 * MAX_PARALLEL_INSERT
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batch_size = 2 * max_parallel
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# Process files in batches
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for i in range(0, total_files, batch_size):
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batch_files = new_files[i:i+batch_size]
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batch_num = i // batch_size + 1
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total_batches = (total_files + batch_size - 1) // batch_size
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logger.info(f"Processing batch {batch_num}/{total_batches} with {len(batch_files)} files")
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await pipeline_index_files(rag, batch_files)
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# Log progress
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processed = min(i + batch_size, total_files)
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logger.info(f"Processed {processed}/{total_files} files ({processed/total_files*100:.1f}%)")
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except Exception as e:
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logger.error(f"Error during scanning process: {str(e)}")
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logger.error(traceback.format_exc())
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def create_document_routes(
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@@ -365,6 +365,9 @@ def parse_args(is_uvicorn_mode: bool = False) -> argparse.Namespace:
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"LIGHTRAG_VECTOR_STORAGE", DefaultRAGStorageConfig.VECTOR_STORAGE
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)
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# Get MAX_PARALLEL_INSERT from environment
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global_args["max_parallel_insert"] = get_env_value("MAX_PARALLEL_INSERT", 2, int)
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# Handle openai-ollama special case
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if args.llm_binding == "openai-ollama":
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args.llm_binding = "openai"
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