用PDF构建RAG系统，提升大模型回答的准确与可信度
Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report
- 结合大模型生成能力与检索技术，从PDF中提取精准信息
- 实现端到端流程，解决数据处理、索引与响应生成难题
- 提供开源代码，适合需要领域知识的AI开发者参考
本文报告了基于PDF文档开发检索增强生成（RAG）系统的过程。RAG架构融合大型语言模型（LLMs）的生成能力与信息检索的精确性，有望重新定义生成模型对结构化与非结构化知识的交互方式，从而提升回答的透明性、准确性和上下文相关性。论文详述了从数据收集、预处理、检索索引到响应生成的全流程，揭示了关键技术挑战与实际解决方案。研究对比了两种实现路径：使用OpenAI Assistant API与GPT系列模型，以及采用Llama开源模型。该工作的实践意义在于提升生成式AI在需领域知识和实时信息检索场景中的可靠性。相关Python代码已公开于GitHub：https://github.com/GPT-Laboratory/RAG-LLM-Development-Guidebook-from-PDFs。
原文摘要 · Abstract (English)
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.
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