Large Language Models (LLMs) are revolutionizing how we interact with information, but they sometimes struggle with accuracy, especially in specialized fields. Imagine an LLM that could instantly access and process complex data from scientific papers, legal documents, or even environmental reports. That's the promise of Retrieval Augmented Generation (RAG), a technique that empowers LLMs by connecting them to external databases. But building these databases from messy, real-world data isn't easy. This new research introduces a streamlined method for parsing and vectorizing semi-structured data, making it easier to build the knowledge bases that fuel RAG. The researchers developed a pipeline that converts various file formats (like PDFs and HTML) into a standardized .docx format, then uses clever techniques to extract key information, including text, images, and tables. This structured data is then transformed into vectors – numerical representations that LLMs can easily understand and use. These vectors are stored in a specialized database called Pinecone, which allows LLMs to quickly find the most relevant information when answering questions. Tests with both English and Chinese texts showed significant improvements in the accuracy and reliability of LLM responses, especially in complex domains like environmental management and wastewater treatment. This breakthrough opens exciting possibilities for using LLMs in fields that require highly specific, accurate information. By making it easier to create and access specialized knowledge bases, this research paves the way for more powerful and reliable AI applications across various industries.