  This paper presents a novel method for parsing and vectorizing
semi-structured data to enhance the functionality of Retrieval-Augmented
Generation (RAG) within Large Language Models (LLMs). We developed a
comprehensive pipeline for converting various data formats into .docx, enabling
efficient parsing and structured data extraction. The core of our methodology
involves the construction of a vector database using Pinecone, which integrates
seamlessly with LLMs to provide accurate, context-specific responses,
particularly in environmental management and wastewater treatment operations.
Through rigorous testing with both English and Chinese texts in diverse
document formats, our results demonstrate a marked improvement in the precision
and reliability of LLMs outputs. The RAG-enhanced models displayed enhanced
ability to generate contextually rich and technically accurate responses,
underscoring the potential of vector knowledge bases in significantly boosting
the performance of LLMs in specialized domains. This research not only
illustrates the effectiveness of our method but also highlights its potential
to revolutionize data processing and analysis in environmental sciences,
setting a precedent for future advancements in AI-driven applications. Our code
is available at https://github.com/linancn/TianGong-AI-Unstructure.git.
