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.

翻译：本文提出了一种新颖的半结构化数据解析与向量化方法，旨在增强大语言模型（LLMs）中检索增强生成（RAG）的功能。我们开发了一套完整的流水线，用于将多种数据格式转换为.docx格式，从而实现高效的解析和结构化数据提取。该方法的核心是利用Pinecone构建向量数据库，该数据库与LLMs无缝集成，尤其在环境管理和废水处理运营中能够提供精准、上下文相关的回答。通过对英文和中文文本在多种文档格式下的严格测试，我们的结果显示LLMs输出的精度和可靠性显著提升。经过RAG增强的模型展现出生成上下文丰富且技术准确回答的能力，突显了向量知识库在专门领域显著提升LLMs性能的潜力。本研究不仅证明了该方法的有效性，还揭示了其在环境科学领域革新数据处理与分析流程的潜力，为未来AI驱动应用的进步树立了先例。我们的代码已发布于https://github.com/linancn/TianGong-AI-Unstructure.git。