> ## Documentation Index
> Fetch the complete documentation index at: https://docs.chunkr.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Bulk Upload

> Learn how to efficiently process multiple files with Chunkr AI

Here's how to efficiently process multiple files using Chunkr AI's async capabilities.

## Process a Directory

Here's a simple script to process all files in a directory:

<CodeGroup>
  ```python Python theme={"system"}
  import asyncio
  from chunkr_ai import Chunkr
  import os
  from pathlib import Path

  chunkr = Chunkr()

  async def process_directory(input_dir: str, output_dir: str):
      try:
          # Create output directory if it doesn't exist
          os.makedirs(output_dir, exist_ok=True)
          
          # Get all files in directory
          files = list(Path(input_dir).glob('*.*'))
          print(f"Found {len(files)} files to process")
          
          # Process files concurrently
          tasks = []
          for file_path in files:
              task = asyncio.create_task(process_file(chunkr, file_path, output_dir))
              tasks.append(task)
          
          # Wait for all files to complete
          results = await asyncio.gather(*tasks)
          
          print(f"Completed processing {len(results)} files")
      except Exception as e:
          print(f"Error processing directory: {e}")

  async def process_file(chunkr, file_path, output_dir):
      try:
          # Upload file
          result = await chunkr.upload(file_path)
          
          # Check if upload was successful
          if result.status == "Failed":
              print(f"Failed to process file {file_path}: {result.message}")
              return None
          
          # Save result
          file_name = file_path.name
          output_file_path = Path(output_dir) / f"{file_name}.json"
          result.json(output_file_path)
          
          return file_name
      except Exception as e:
          print(f"Error processing file {file_path}: {e}")
          return None


  # Run the processor
  if __name__ == "__main__":
      INPUT_DIR = "/data/Chunkr/dataset/files"
      OUTPUT_DIR = "processed/"
      asyncio.run(process_directory(INPUT_DIR, OUTPUT_DIR))
  ```
</CodeGroup>