开篇: 介绍(Introduction) 中英文字幕
Summary
TLDR本视频课程介绍了构建高质量的Retrieval Augmented Generation (RAG)系统的关键方法,包括高级检索技术如句子窗口检索和自动合并检索,以及评估RAG系统的有效框架。课程由LX的联合创始人Jerry和Truera的联合创始人兼首席科学家Anupam主讲,他们将分享如何通过系统的方法构建可靠的问答系统,并提供实践操作经验,帮助学习者掌握构建生产就绪的RAG应用的技巧。
Takeaways
- 📚 检索增强生成(RAG)是一种关键方法,用于在用户数据上获取答案。
- 🚀 构建高质量的RAG系统需要有效的检索技术和评估框架。
- 🔍 课程介绍了两种高级检索方法:句子窗口检索和自适应合并检索。
- 🌳 自适应合并检索通过将文档组织成树状结构来提供更连贯的文本块。
- 📈 三元组度量(RAG Triad)是评估RAG基于LLM应用的有效方法。
- 🔎 通过计算上下文相关性来识别和调试系统检索上下文的问题。
- 🔧 课程还包括其他评估指标,如基础性和答案相关性。
- 🛠️ 通过系统方法分析QA系统的不同部分,以改进系统。
- 🎯 课程目标是帮助建立生产就绪的基于RAG的应用。
- 📊 后期课程将实践使用检索方法和评估方法进行迭代。
- 📈 课程还将分享基于经验的调整检索方法的建议。
- 🙏 感谢多位专家和贡献者共同创建此课程。
Q & A
什么是RAG技术?
-RAG(Retrieval Augmented Generation)技术是一种通过检索增强的生成方法,用于在用户自己的数据上回答未解决的问题。
为什么建立高质量的RAG系统需要有效的检索技术?
-有效的检索技术可以为语言模型(LM)提供高度相关的上下文,以生成准确的答案,这对于构建高质量的RAG系统至关重要。
RAG系统的生产化过程中,评估框架的作用是什么?
-评估框架有助于高效迭代和改进RAG系统,无论是在初始开发阶段还是在部署后的维护过程中。
课程中提到的两种高级检索方法是什么?
-课程中提到的两种高级检索方法是句子窗口检索和自动合并检索,它们都能为LM提供比简单方法更好的上下文。
句子窗口检索是如何工作的?
-句子窗口检索通过检索不仅包括最相关句子,还包括其前后句子的窗口,从而为LM提供更好的上下文。
自动合并检索如何组织文档?
-自动合并检索将文档组织成类似树状的结构,其中父节点的文本被分配给其子节点。当足够多的子节点被识别为与用户问题相关时,父节点的整个文本就被作为上下文提供给LM。
RAG Triad是用于评估什么的?
-RAG Triad是一组用于评估基于RAG的LLM应用程序的三个主要步骤的指标,包括上下文相关性、接地性和答案相关性。
如何计算上下文相关性?
-上下文相关性衡量检索到的文本块与用户问题的关联程度,有助于识别和调试系统在为LLM检索上下文时可能存在的问题。
除了上下文相关性,还有哪些评估指标?
-除了上下文相关性,还有其他评估指标如接地性和答案相关性,它们让你能够系统地分析系统哪些部分工作良好,哪些还需要改进。
本课程的目标是什么?
-本课程的目标是帮助你构建生产就绪的基于RAG的应用程序,并在课程的后半部分通过实践学习如何迭代改进系统。
课程中会提供哪些实践操作?
-课程中会提供使用句子窗口检索和自动合并检索方法进行迭代的实践操作,并展示如何使用系统性实验跟踪来建立基线并快速改进。
课程中提到的两位特邀讲师是谁?
-课程中提到的两位特邀讲师是LX的联合创始人兼CEO Jerry,以及Truena的联合创始人兼首席科学家Anupam。
Outlines
🤖 介绍RAG系统及其重要性
本段介绍了检索增强生成(RAG)系统的重要性,它是一种关键的方法,用于在用户自己的数据上回答LS问题。为了构建和生产高质量的RAG系统,需要有效的检索技术为语言模型(LM)提供高度相关的上下文以生成答案,以及有效的评估框架来帮助在初始开发和部署后维护期间高效迭代和改进RAG系统。课程涵盖了两种先进的检索方法:句子窗口检索和自动合并检索,这些方法比简单方法更好地为LM提供上下文。此外,还介绍了如何使用三个评估指标(上下文相关性、淹没度和答案相关性)来评估LM问答系统。
Mindmap
Keywords
💡检索增强生成(RAG)
💡语言模型(LM)
💡检索技术
💡评估框架
💡句子窗口检索
💡自动合并检索
💡上下文相关性
💡置信度
💡答案相关性
💡迭代
💡系统性实验跟踪
Highlights
检索增强生成(RAG)已成为获取用户数据中问题答案的关键方法。
构建和生产化高质量的RAG系统需要有效的检索技术为语言模型(LM)提供高度相关的上下文以生成答案。
有效的评估框架有助于高效迭代和改进RAG系统,包括初始开发和部署后维护。
本课程涵盖两种先进的检索方法:句子窗口检索和自动合并检索,它们能为LM提供比简单方法更好的上下文。
句子窗口检索通过检索最相关句子及其前后句子的窗口来为LM提供更好的上下文。
自动合并检索通过将文档组织成树状结构,动态检索更连贯的文本块。
RAG Triad是评估基于RAG的LLM应用的有效方法,包括三个主要步骤的度量:上下文相关性、接地性和答案相关性。
计算上下文相关性有助于识别和调试系统在为LLM检索上下文时可能存在的问题。
除了上下文相关性,还需评估接地性和答案相关性,系统地分析系统各部分的工作情况。
本课程旨在帮助您构建生产就绪的基于RAG的应用,并通过系统迭代提高系统性能。
后续课程中,您将通过实践这些检索方法和评估方法进行迭代,并学习如何使用系统实验跟踪来建立基线并快速改进。
本课程还将分享基于经验调整这两种检索方法的建议,以帮助合作伙伴构建RAG应用。
感谢多位人员共同创建本课程,包括LX的联合创始人兼CEO Jerry,以及Truera的联合创始人兼首席科学家Anupam。
Anupam是CMU的教授,十多年来一直致力于可信赖AI的研究,包括如何监控、评估和优化AI的有效性。
下一课将概述课程的其余部分,您将尝试使用句子窗口检索或自动合并检索的问题回答系统,并比较它们在RAG Triad中的性能。
Transcripts
retrieval augmented generation or rag
has become a key method for getting LS
answered questions over a user's own
data but to actually build and
productionize a high quality rag system
it helps a lot to have effective
retrieval techniques to give the LM
highly relevant context to generate his
answer and also to have an effective
evaluation frameware to help you
efficiently iterate and improve your rag
system both through initial development
and during post deployment maintenance
this course covers two Advanced
retrieval methods sentence window
retrieval at Auto emerging retrieval
that deliver a significantly better
context of the LM than simpler methods
it also covers how to evaluate your LM
question answering system with three
evaluation metrics context relevance
drowned and answer relevance I'm excited
to introduce Jer new co-founder and CEO
of LX and anupam data co-founder and
chief scientist of true era for a long
time I've enjoyed following Jerry and
Lama index on social media and getting
tips on evolving rap practices so
looking forward to him teaching this
body of knowledge more systematically
here at an new pump has been a professor
at CMU and has done research for over a
decade on trustworthy Ai and how to
monitor evaluate and optimize a
Effectiveness thanks Andrew it's great
to be here great to be with you Andrew
sentence window retrieval gives an Ln
better context by retrieving not just
the most relevant sentence but the
window of sentences that occur before
and after it in the document autom
merging retrieval organizes the document
into a tree likee structure where each
parent nodes text is divided among its
child nodes when enough child nodes are
identified as relevant to a user's
question then the entire text of the
parent node is provided as context for
the Ln I know this sounds like a lot of
steps but don't worry we'll go over it
in detail on code later but the main
takeaway is that this provides a way to
dynamically retrieve more coherent
chunks of text than simpler methods to
evaluate rag based llm apps the rag
Triad a Triad of metrics for the three
main steps of a Rags execution is quite
effective for example we'll cover in
detail how to compute context relevance
which measures how relevant the retrieve
chunks of text are to the user's
question
this helps you identify and debug
possible issues with how your system is
retrieving context for the llm in the QA
system but that's only part of the
overall QA system we'll also cover
additional evaluation metrics such as
groundedness and answer relevance that
let you systematically analyze what
parts of your system are or are not yet
working well so that you can go in in a
targeted way to improve whatever part
part needs the most work if you're
familiar with the concept of error
analysis and machine learning this has
similarities and I found that taking
this sort of systematic approach helps
you be much more efficient in building a
reliable QA system the goal of this
course is to help you build production
ready ride based om apps and important
part of getting production ready is to
iterate in a systematic way on the
system in the later half of this course
you gain Hands-On practice iterating
using these retrieval methods and
evaluation methods and you also see how
to use systematics experiment tracking
to establish a Baseline and then quickly
improve on that we'll also share some
suggestions for tuning these two
retrieval methods based on our
experience assisting Partners who are
building rag apps many people have
worked to create this course I'd like to
thank on the larm index side
Logan makit and on the trer side shyen
joshu Ry and barara Lewis from de. a
Eddie Shu and diala aine also
contributed to this course the next
lesson will give you an overview of what
you'll see in the rest of the course
you'll try out question answering
systems that use sentence window
retrieval or autom merging retrieval and
compare their performance on the rag
Triad context relevance groundedness and
answer relevance sounds great let's get
started and I think you be really clean
up with this rag
stuff laughed on
it
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