from langchain.text_splitter import CharacterTextSplitter, NLTKTextSplitter import argparse from pathlib import Path import os from tqdm import tqdm def fixed_size_chunking(text, chunk_size=256) -> list[str]: splitter = CharacterTextSplitter( separator=" ", chunk_size=chunk_size, chunk_overlap=20 ) return splitter.split_text(text) def content_aware_chunking(text, chunk_size=256) -> list[str]: splitter = NLTKTextSplitter( separator=".", chunk_size = chunk_size, chunk_overlap = 20 ) return splitter.split_text(text) def main(): parser = argparse.ArgumentParser() parser.add_argument("--input-dir", help="input directory with text files", type=str, default="docs") parser.add_argument("--output-dir", help="output directory to store chunked texts", type=str, default="chunked_docs") parser.add_argument("--chunk-size", help="chunk size", type=int, default=256) parser.add_argument("--chunking-type", help="fixed_size or content_aware", type=str, default="fixed_size") args = parser.parse_args() input_dir = Path(args.input_dir) output_dir = Path(args.output_dir) assert os.path.isdir(input_dir), "Input directory doesn't exist" os.makedirs(output_dir, exist_ok=True) for file in tqdm(input_dir.rglob("*")): if file.is_file(): with open(file, 'r', encoding='utf8') as f: text = f.read() if args.chunking_type == "fixed_size": chunked_text = fixed_size_chunking(text, args.chunk_size) elif args.chunking_type == "content_aware": chunked_text = content_aware_chunking(text, args.chunk_size) else: raise ValueError("Invalid chunking type. Choose from 'fixed_size' or 'content_aware'") for i, chunk in enumerate(chunked_text): with open(output_dir / f"{file.stem}_chunk_{i}.txt", "w", encoding='utf8') as f: f.write(chunk) if __name__ == "__main__": main()