Langchain vs LlamaIndex vs OpenAI GPTs: Which one should you use?

What's AI by Louis-François Bouchard
21 Dec 202308:59

Summary

TLDR本视频探讨了如何有效利用大型语言模型(LLMs)进行应用开发。比较了从零开始构建自己的框架与使用现成平台(如Lang chain、L index和Open AI)的路径。自建框架提供了最大的自由度和控制权,但需要大量的技术专长、时间和资源。而使用现成平台则可以快速部署,但可能缺乏独特价值。Lang chain和L index提供了定制化与易用性之间的平衡,支持与不同的LLMs和数据源集成,并具备强大的数据处理和检索能力,非常适合构建数据驱动的应用。

Takeaways

  • 🌟 构建自己的框架从零开始可以提供最大的自由度和控制权,但需要大量的技术专长、时间和资源。
  • 🛠️ 使用现成的平台如Lang chain和OpenAI助手可以快速部署应用程序,但可能难以提供独特价值,适合快速构建概念验证。
  • 🔧 采用开源方法并对其进行修改,可以有效地实现复杂的技术如合成文档生成和嵌入式检索。
  • 🔄 利用Lang chain等框架可以简化提示工程和数据解析过程,提高开发效率。
  • 📈 Lang chain的LCL编程语法和Lang serve功能可以加速原型设计和部署过程。
  • 🔍 Lama index擅长处理和检索复杂数据集,适合数据密集型应用程序。
  • 🔗 Lama index提供数据连接器、索引能力和高效的检索方法,便于将数据与语言模型连接。
  • 📚 通过Lang chain和Lama index的结合使用,可以构建各种领域的LLM应用程序。
  • 💡 选择构建框架的路径取决于项目目标、可用资源和特定需求。
  • 📈 通过课程和实践例子,可以更深入地了解Lang chain和Lama index的应用。
  • 🎯 针对不同的项目需求,合理选择使用从头构建、现成平台或介于两者之间的框架。

Q & A

  • 构建自己的框架与使用已建立平台(如Lang Chain、L Index和OpenAI助手)在LLM应用开发中的区别是什么?

    -构建自己的框架提供了无价的自由和控制,允许用户从头开始编码一切,适合追求完全拥有知识产权和更新的长期产品。而使用已建立的平台如OpenAI助手、Lang Chain和L Index,则提供了快速部署和易于使用的体验,适合那些寻求快速集成LLM功能且不需要深入技术参与的用户。

  • 什么是检索增强生成(RAG)系统,以及如何使用Buster库实现?

    -检索增强生成(RAG)系统结合了检索和生成技术,以提高语言模型的回答质量。通过使用Buster库,开发者可以方便地构建RAG系统,例如实现Hide技术,这种技术基于用户的提示生成合成文档,并使用这些文档的嵌入进行检索,以找到比原始查询更接近的数据点。

  • 什么是Lang Chain表达式语言(LCL),它如何简化LLM应用开发?

    -Lang Chain表达式语言(LCL)是一种编码语法,允许开发者通过使用管道符号将不同的组件简单地连接起来。这使得开发者能够快速原型化并尝试不同的组件组合,简化了LLM应用的开发过程。

  • 如何使用Lang Chain在应用中维护用户上下文?

    -Lang Chain提供了工具如提示模板和输出解析器,这些工具允许开发者构造有效的提示并将语言模型的文本响应转换成结构化数据,如JSON对象。这些特性非常适合需要在对话中维护用户上下文的应用,如医疗聊天机器人或教育辅导应用。

  • Lama Index在处理复杂数据集和高级查询技术方面的优势是什么?

    -Lama Index的优势在于其强大的数据管理和操控特性,使其成为数据密集型应用的有力工具。它提供了数据连接器、数据索引能力和有效的索引与检索方法,适合构建复杂的文档问答系统、知识代理和结构化分析等应用。

  • OpenAI助手(如GPT-3.5 Turbo和GPT-4)适合哪些类型的项目?

    -OpenAI助手适合需要快速部署和易于访问LLM功能的项目,特别是对于那些不需要深入技术参与或希望快速创建概念证明并展示给他人的开发者。这些助手提供了流畅且用户友好的体验,允许快速构建强大的应用。

  • 为什么说Lang Chain是在定制化和易用性之间的理想选择?

    -Lang Chain通过提供与不同LLM提供商和外部数据源的无缝集成、用户友好的提示工程工具和数据解析功能,为开发者提供了定制化与易用性之间的平衡。这些特性使Lang Chain成为构建各种LLM驱动应用的理想选择。

  • 在开发LLM应用时,如何选择适合的框架或平台?

    -选择适合的框架或平台取决于项目的具体需求、资源和约束。如果需要完全控制和拥有知识产权,从头开始构建可能是最佳选择。如果需要快速部署和简化开发过程,使用OpenAI助手或其他预建平台可能更合适。Lang Chain和Lama Index分别提供了定制化和数据处理能力的中间地带。

  • Lang Chain和Lama Index分别在哪些场景下最为适用?

    -Lang Chain适用于需要灵活性、定制化提示和维护对话上下文的应用,而Lama Index适合于数据密集型和需要高级数据检索技术的应用,如复杂的文档问答系统和知识增强的聊天机器人。

  • 开发LLM应用时面临的主要挑战是什么?

    -开发LLM应用时的主要挑战包括技术专业知识的需求、资源和时间的投入、以及在完全定制化与快速部署之间做出权衡。此外,还需要考虑数据处理能力、用户上下文维护和与外部数据源的集成等复杂性。

Outlines

00:00

🌟 自主开发与现有平台的比较

本段落讨论了在应用开发中使用大型语言模型(LLMs)时,自主构建框架与利用现有平台(如Lang chain、L index和Open AI助手)之间的比较。自主开发虽然技术要求高、耗时长,但提供了极大的自由度和控制权,适合长期产品开发和拥有完全知识产权。而现有平台则提供了快速部署和易用性,适合快速构建原型和展示,但长期依赖性较强。

05:02

🛠️ Lang chain和L index的特点与应用

本段落详细介绍了Lang chain和L index两个平台的特点和应用场景。Lang chain以其灵活性和易于使用的提示工程工具脱颖而出,适合构建各种LLM驱动的应用。L index则专注于复杂的数据处理和检索能力,特别适合数据密集型应用,如构建基于数据的检索增强型生成(RAG)系统。两者都提供了调试工具和优化功能,但L index是开源的,持续开发,而Lang chain则提供了更多的定制化选项和易于使用的数据处理工具。

Mindmap

Keywords

💡大型语言模型(LLMs)

大型语言模型(LLMs)是指能够理解和生成自然语言文本的复杂计算模型。在视频中,LLMs是讨论的核心,它们被用于开发各种应用程序,以提高其处理和生成语言的能力。例如,视频提到了使用LLMs构建检索增强型生成系统(RAG),这是一种结合了生成和检索技术的应用。

💡框架构建

框架构建是指创建一个基本的结构或系统,以支持特定的功能或应用程序。在视频脚本中,框架构建是从零开始创建自己的LLM应用程序的过程,这需要大量的技术专业知识、时间和资源投入。例如,构建一个RAG系统,需要实现生成合成文档的hide技术,并使用这些文档的嵌入进行检索。

💡Lang chain

Lang chain是一个强大的框架,专门用于使用LLMs构建应用程序。它支持与各种LLM提供商(如Open AI、Cohere和Hugging Face)以及数据源(如Google搜索和Wikipedia)的无缝集成。Lang chain的特点是支持提示工程,这是与LLMs合作的一个关键方面,可以通过构建有效的提示来显著影响模型输出的质量。

💡L index

L index是一个专注于复杂数据处理和检索的框架。它特别适合于需要处理复杂数据集并使用高级查询技术的项目。L index的强项在于其强大的数据管理和操作功能,使其成为数据密集型应用程序的强大工具。

💡Open AI助手

Open AI助手,包括GPT 3.5 Turbo和GPT-4,是预构建的平台,提供流线型和用户友好的体验。这些助手允许快速部署应用程序,但它们高度依赖于Open AI,且难以提供独特的价值。

💡提示工程

提示工程是指设计和优化输入到LLMs的提示,以提高模型输出质量的过程。在视频中,提示工程被视为与LLMs合作的一个关键方面,Lang chain通过提供提示模板等工具来简化这一过程。

💡数据源

数据源是指提供数据的原始位置或系统,可以是API、数据库、文档等。在视频中,Lang chain和L index都能够与各种数据源集成,以增强LLMs的功能和效率。

💡检索增强型生成系统(RAG)

检索增强型生成系统(RAG)是一种结合了生成和检索技术的应用,它可以根据用户的提示生成合成文档,并使用这些文档的嵌入进行检索。

💡代码解释器

代码解释器是一种软件工具,能够读取和执行编写的代码。在视频中,Open AI提供的代码解释器使得开发者可以构建相当强大的应用程序,尤其是如果他们能够编写自己的API或使用外部API。

💡自定义

自定义是指根据特定需求或偏好调整产品或服务的过程。在视频中,Lang chain提供了一个平衡点,既允许定制化,又易于使用,适合开发者在与LLMs交互时寻求灵活性。

💡开放源代码

开放源代码是指软件的源代码公开可用,允许任何人自由使用、修改和分发。在视频中,L index被描述为一个免费的开源框架,这意味着它可以被持续地开发和改进。

Highlights

构建自己的框架与使用现成平台如Lang chain和Open AI的比较

从零开始构建框架需要大量的技术专长、时间和资源

自定义框架可以轻松地编辑开源方法,如AI助手Buster

实施基于用户提示生成合成文档的hide技术

Lang chain和L index框架可以在一行代码中集成复杂的技术

Open AI助手如GPT 3.5 turbo和GP4提供流线型和用户友好的体验

Lang chain框架支持提示工程,这是与LLMs工作的关键方面

Lang chain提供的工具简化了提示构建和数据解析过程

Lang chain expression language (LCL) 允许通过简单的管道符号链接组件

Lang serve功能旨在简化链条部署过程

Lama index擅长复杂的数据处理和检索能力

Lama index提供数据连接器,集成多种数据源

Lama index支持高效的索引和检索方法,更好的分块策略和多模态

Lama index框架适合构建数据增强的聊天机器人和知识代理等应用

递归检索技术允许应用在多个数据块中导航以找到精确信息

Lama packs是一组基于现实世界的RAG应用,可快速部署

选择最佳框架取决于项目目标和可用资源

Transcripts

00:00

are you using large language models or

00:02

llms in your work and seeking the most

00:04

effective way to leverage the power for

00:06

your application then this video is for

00:09

you let's dive into llm application

00:11

development comparing the path of

00:13

building your own framework from scratch

00:16

with utilizing established platforms

00:18

like Lang chain L index and open AI

00:21

assistants the first obvious choice is

00:23

to construct your own framework from the

00:26

ground up you need to code everything

00:28

this route well demon in terms of

00:31

technical expertise time and resources

00:33

gives you invaluable freedom and control

00:36

plus you can easily fork and edit open

00:38

source approaches as we did with our AI

00:41

tutor with Buster a useful repository if

00:44

you aim to build retrieval augmented

00:46

generation or rag systems imagine

00:49

implementing the hide technique which

00:52

generates synthetic documents based on

00:54

the user's prompt and uses the generated

00:57

documents embedding for retrieval which

00:59

may be Clos closer to a data point in

01:01

the aming space than the original query

01:03

it's a challenging technique to

01:04

implement from scratch but it's possible

01:06

to incorporate it in Frameworks like

01:08

Lama index and Lang chain in one line of

01:11

code if you aim for a very long-term

01:13

product that you can fully own its IP

01:16

and updates then going from scratch is

01:19

the way to go the results will be the

01:21

perfect fit for your specific

01:22

requirements but you will encounter many

01:24

challenges that you didn't expect and it

01:26

will take much more time to develop if

01:28

you do not have unlimited time and

01:31

resources then you may want to take a

01:33

look at pre-built platforms like using

01:35

open

01:38

gpts if quick deployment and

01:40

accessibility are your priorities this

01:42

path is the go-to openi assistance

01:45

including GPT 3.5 turbo and gp4 provide

01:48

a streamlined and userfriendly

01:49

experience you can build very powerful

01:52

apps super quickly but they will be

01:54

quite dependent on open a and you will

01:56

hardly be able to bring unique value

01:58

this is definitely not an an ideal

02:00

long-term option but it's a powerful way

02:02

to quickly build a proof of concept and

02:04

show it to others they are perfect for

02:06

those eager to integrate llm

02:08

capabilities swiftly and efficiently

02:11

into their applications without the

02:13

complexities of building and training

02:14

models and Frameworks from scratch plus

02:17

the code interpreter noledge Retriever

02:20

and custom function code they provide

02:22

allow you to build a quite powerful app

02:24

especially if you can code your own apis

02:26

or use external ones the cost while

02:29

present is just generally more

02:30

manageable than undertaking the entire

02:32

development process on your own as well

02:34

it's going to cost you a few dollars to

02:36

make it and then it will depend on how

02:38

much you share it with others obviously

02:40

but what if you need something more

02:41

tailored than offir Solutions yet not as

02:45

time incentive as building from scratch

02:47

this is where Lang chain and L index

02:50

come into play but you need to

02:51

understand the difference between

02:54

both Lang chain offers a powerful and

02:57

flexible framework for building

02:59

applications with llms it stand out for

03:01

its ability to integrate seamlessly with

03:03

various llm providers like openi cohere

03:06

and hugging face or your own as well as

03:09

data sources such as Google search and

03:11

Wikipedia use l chain to create

03:13

application that can process user input

03:15

text and retrieve relevant responses

03:17

leveraging the latest NLP technology a

03:20

key advantage of Lang chain is its

03:22

support for prompt engineering a crucial

03:24

aspect of working with llms by

03:26

constructing effective prompts you can

03:28

significantly influence the quality of

03:30

the model's output Lang chain simplifies

03:32

this process with tools like prompt

03:34

templates which allow for the easy

03:36

integration of variables and context

03:39

into the prompts additionally output

03:41

parsers in L chain will transform the

03:43

language models text responses into

03:46

structured data like Json objects which

03:48

you don't have to code yourself L chain

03:50

is also quite useful for applications

03:53

requiring maintaining a user's context

03:55

throughout a conversation similar to

03:57

chat GPT like a medical chatbot or a ma

04:00

tutor for example they also recently

04:02

introduced Lang chain expression

04:04

language or LCL for short a coding

04:06

syntax where you can create chains by

04:08

simply piping them together using the

04:10

bar symbol it enables Swift prototyping

04:13

and trying different combinations of

04:15

components they also introduced The Lang

04:17

serve feature designed to facilitate

04:19

chains deployment process using fast API

04:22

they provide great features like

04:24

templates for different use cases and a

04:26

simple chat interface in summary Lang

04:29

chain is a nice middle ground for a

04:30

balance between customization and ease

04:32

of use its flexibility and integrating

04:34

with different llms and external data

04:36

sources coupled with its userfriendly

04:38

tools for prompt engineering and data

04:41

parsing make it an ideal choice for

04:43

building a wide range of llm powered

04:45

applications across various domains

04:47

another Advantage is their debugging

04:49

tools that simplify the development

04:51

process reducing the technical burden

04:54

significantly if you are curious about L

04:56

chain we shared two free courses using

04:58

it in the Gen 360 course series Linked

05:02

In the description below in contrast

05:04

Lama index excels in sophisticated data

05:07

handling and retrieval

05:10

capabilities it's particularly suited

05:12

for projects where you must handle

05:15

complex data sets and use Advanced

05:17

querying techniques Lama index's

05:19

strength lies in its robust data

05:22

management and manipulation features

05:24

making it a powerful tool for data

05:26

intensive applications practical terms l

05:29

IND offers key features such as data

05:31

connectors for integrating diverse data

05:34

sources including apis PDFs and SQL

05:37

databases it's data indexing capability

05:40

organizes data to make it readily

05:42

consumable by llms enhancing the

05:44

efficiency of data retrieval this

05:46

framework is particularly beneficial for

05:48

building rag applications where it acts

05:51

as a powerful data framework connecting

05:53

data with language models simplifying

05:55

programmers lives L index supports

05:58

efficient indexing and retrieval methods

06:00

better chunking strategies and

06:02

multimodality making it suitable for

06:04

various applications including Document

06:07

qna Systems data augmented chat buts

06:10

knowledge agents structured analytics

06:12

and Etc these tools also make it well

06:15

suited for advanced use cases like

06:17

multi-document analysis and querying

06:19

complex PDFs with embedded tables and

06:22

charts one example query tool is the sub

06:25

question query engine which breaks down

06:27

a complex query into several sub

06:29

questions and uses different data

06:31

sources to respond to each it then

06:33

complies all the retrieved documents to

06:36

construct the final answer as I

06:38

mentioned the Lama index framework

06:40

offers a wide range of advanced

06:42

retrieving techniques but more

06:44

specifically there's the recursive

06:46

retrieval enabling the application to

06:48

navigate through the graph of

06:50

interconnected nodes to locate precise

06:52

information in multiple chunks they also

06:54

introduced the concept of Lama packs a

06:57

collection of real world rag based

06:59

applications ready for deployment and

07:01

easy to build on top of these were just

07:03

a few concrete examples but there are

07:05

many other techniques that they can

07:07

facilitate for us which makes the

07:08

library really useful in essence Lama

07:11

index is your go-to for a rag based

07:14

application also offering fine-tuning

07:16

and embeding optimizations and the best

07:18

thing is that it's free open source and

07:21

continually

07:23

developed each of these paths offer its

07:25

unique set of advantages and challenges

07:28

building your own framework from scratch

07:30

gives you complete control but demands

07:33

substantial resources and expertise open

07:35

ey assistants offer an accessible and

07:38

quick to deploy option suitable for

07:40

those looking to integrate llms without

07:42

deep technical involvement or to create

07:44

a quick proof of concept L chain

07:46

provides a balance of customization and

07:49

ease of use ideal for developers seeking

07:51

flexibility in their llm interactions in

07:54

most cases Lama index stands out in its

07:57

robust data handling and retrieval

08:00

capabilities perfect for data Centric

08:02

applications like rag in the end the

08:04

choice boils down to your project and

08:06

the company's specific requirements and

08:08

constraints the key is to align your

08:10

decision with the projects goals and the

08:13

resources at your disposal they each

08:15

have a purpose and I personally used all

08:17

of them for different projects we also

08:20

have detailed lessons on Lang chain and

08:22

Lama index with practical examples in

08:24

the course we've built in collaboration

08:26

with 2di active Loop and the Intel

08:29

disruptor initiative I hope this video

08:31

was useful to help you choose the best

08:33

framework for your use case thank you

08:36

for

08:38

[Music]

08:45

[Music]

08:58

watching

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