Developed an end-to-end text chunking example script and document processing pipeline for the aigc-apps/PAI-RAG repository, focusing on scalable data preparation for downstream analysis and model training. The solution leveraged Python for data engineering and file handling, integrating custom PairaG file readers to process multiple document types efficiently. The pipeline included automated conversion to Markdown and image handling, with dependency management to ensure reliability and performance. By implementing custom reader logic, the work established a robust foundation for future content processing features, supporting large language model integration and advanced text processing workflows within the PAI-RAG project’s evolving infrastructure.