This paper presents an experience report on the development of Retrieval Augmented Generation (RAG) systems using PDF documents as the primary data source. The RAG architecture combines generative capabilities of Large Language Models (LLMs) with the precision of information retrieval. This approach has the potential to redefine how we interact with and augment both structured and unstructured knowledge in generative models to enhance transparency, accuracy, and contextuality of responses. The paper details the end-to-end pipeline, from data collection, preprocessing, to retrieval indexing and response generation, highlighting technical challenges and practical solutions. We aim to offer insights to researchers and practitioners developing similar systems using two distinct approaches: OpenAI's Assistant API with GPT Series and Llama's open-source models. The practical implications of this research lie in enhancing the reliability of generative AI systems in various sectors where domain-specific knowledge and real-time information retrieval is important. The Python code used in this work is also available at: https://github.com/GPT-Laboratory/RAG-LLM-Development-Guidebook-from-PDFs.

本文针对如何使用PDF文档作为主要数据源开发检索增强生成（RAG）系统进行了经验报告，填补了相关研究的空白。该研究提出了一种将大型语言模型的生成能力与信息检索的精确性相结合的新方法，并详细说明了从数据收集到响应生成的完整流程，强调了技术挑战及实用解决方案。研究结果显示，此方法能显著提升生成模型在处理特定领域知识和实时信息检索中的可靠性。

基于PDF的检索增强生成（RAG）大型语言模型系统开发：经验报告