Translations:FACTS About Building Retrieval Augmented Generation-based Chatbots/67/zh: Difference between revisions

Latest revision as of 08:52, 19 February 2025

Information about message (contribute)

This message has no documentation. If you know where or how this message is used, you can help other translators by adding documentation to this message.

Message definition (FACTS About Building Retrieval Augmented Generation-based Chatbots)

Our work can be compared with RAG papers on various topics dealing with RAG quality along all the FACTS dimensions we presented (freshness, architecture, costs, testing, and security). Due to lack of space, we contrast our work with selective works. Barnett ''et. al.'' ([[#bib.bib3|3]]) presented seven failure points when engineering RAG systems. In their work, they highlight the challenges of getting retrieval augmented generation right by presenting their findings from having built three chatbots. Wenqi Glantz ([[#bib.bib6|6]]) elaborated 12 RAG pain points and presented solutions. We experienced similar challenges first-hand when building our chatbots. However, none of these works discuss the challenges with complex queries, testing, dealing with document security, and the need for flexible architectures. In our work, we not only build on failure/pain points of RAGs as mentioned above, but also present our 15 control points in RAG pipelines and offer specific solutions for each stage. Also, we extend our insights and present practical techniques for handling complex queries, testing, and security. We present a reference architecture for one of the implementations of agentic architectures for complex query handling, strategies for testing and evaluating subjective query responses, and raised awareness for dealing with document ACLs and security. Furthermore, we present a reference architecture for a flexible generative-AI based Chatbot platform.

我们的工作可以与RAG论文进行比较，这些论文涉及我们所提出的所有FACTS维度（新鲜度、架构、成本、测试和安全性）的RAG质量。由于篇幅限制，我们选择性地对比了一些工作。Barnett等人（3）在工程RAG系统时提出了七个失败点。在他们的工作中，他们通过展示构建三个聊天机器人的经验，强调了正确实现检索增强生成的挑战。Wenqi Glantz（6）详细阐述了12个RAG痛点并提出了解决方案。在构建我们的聊天机器人时，我们也亲身经历了类似的挑战。然而，这些工作都没有讨论复杂查询、测试、处理文档安全性以及灵活架构需求的挑战。在我们的工作中，我们不仅基于上述RAG的失败/痛点，还提出了RAG管道中的15个控制点，并为每个阶段提供了具体的解决方案。此外，我们扩展了我们的见解，提出了处理复杂查询、测试和安全性的实用技术。我们为复杂查询处理的代理架构实现之一提供了参考架构，提出了测试和评估主观查询响应的策略，并提高了处理文档ACL和安全性的意识。此外，我们还为基于生成式AI的灵活聊天机器人平台提供了参考架构。