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，旨在提升大型语言模型中检索增强生成的功能。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文件，从而实现高效的解析与结构化数据提取。该方法的核心是利用Pinecone构建向量数据库，该数据库与大型语言模型无缝集成，可在环境管理与污水处理操作等特定场景中提供精准且上下文相关的响应。通过对多种文档格式的英文与中文文本进行严格测试，结果表明该方法显著提升了大型语言模型输出的准确性与可靠性。经检索增强生成增强的模型展现出更强的能力，能够生成语义丰富且技术准确的响应，这凸显了向量知识库在显著提升大型语言模型在特定领域性能方面的潜力。本研究不仅验证了我们方法的有效性，还强调了其在环境科学领域革新数据处理与分析流程的潜力，为人工智能驱动应用的未来发展树立了典范。我们的代码开源于 https://github.com/linancn/TianGong-AI-Unstructure.git。