What Is LangChain? - LangChain + ChatGPT Overview

Summary

このビデオシリーズは、AIと外部ソースをつなぐライブラリであるLangchainについて学ぶことを目的としています。LangchainはOpenAIやHugging FaceのAIモデルと、GoogleドライブやWolfram Alphaなどの外部サービスを繋げることができます。チュートリアルではLangchainのドキュメントを通して、チャットボットの作成や質問応答、要約生成など、様々な機能を学んでいきます。データに基づく実践的な応用を重視することで、Langchainを使って世界に良い影響を与えられることを目指します。

Takeaways

• 👀 This tutorial series will explain what Langchain is and why it's important to learn
• 🎥 The series will involve short videos explaining Langchain documentation with real code examples
• 🔗 Langchain connects AI models like GPT to outside data sources
• 🤖 It allows chaining commands to leverage both AI and your personal data
• ✨ Applications are endless - new integrations added daily
• ❓ Langchain can answer personal questions GPT can't access
• 🚀 Choosing to learn Langchain is betting openAI won't build its own integrations
• 🌎 Langchain allows small teams to create more impact
• 📝 Series will cover modules like chatbots, question answering, summarization
• 🎓 Focus is on real-world application rather than just theory

Q & A

What are the key problems that Langchain aims to solve?

Langchain aims to solve the problem of AI models like ChatGPT not having access to a user's personal information or data sources. It connects AI models to outside data sources to enable more personalized and context-aware responses.

How does Langchain integrate with other AI models?

Langchain can connect to AI models like those from OpenAI and Hugging Face. It acts as a layer above these models, chaining together commands and accessing outside data sources to provide responses.

What are some real-world use cases for Langchain?

Potential use cases include personal assistants that can access your calendar and email, chatbots that are aware of user data and context, writing assistants with access to your previous work, and more.

Outlines

00:00

Lanechainの概要説明 😃

<paragraph1>について、このパラグラフはLanechainとは何かについて説明しています。😃 LanechainはOpenAIなどのAIモデルを、GoogleやNotionなどのサービスとつなげるためのライブラリです。これにより、OpenAIにGoogle Driveのファイル数を聞いて答えてもらったり、天気を聞いて答えてもらったりすることができます。 Lanechainの重要性は、Direct integrationをOpenAI側が構築しないことに賭けている点にあります。コミュニティが異なる抽象化レイヤーをつなぎ合わせる部分を担うことが期待されています。

05:00

チュートリアルの概要 😃

<paragraph2>について、このパラグラフはチュートリアルの概要について説明しています。😃 このシリーズではLanechainのドキュメントに沿って説明していきます。実際のコード例を示しながら、ビジネスや個人的な活用方法を紹介していきます。 インストラクターの立場としては理論よりも実用性を重視し、少人数で大きな社会的インパクトを生み出すことを目指します。

Keywords

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Highlights

このチュートリアルシリーズの目標について説明しています

Langchainは最近リリースされた新しいライブラリであることを説明しています

LangchainはAIモデルと外部ソースをつなぐことができることを説明しています

Langchainを学ぶことは、抽象化レイヤーが分離するという賭けをしていることを説明しています

Langchainのドキュメントを確認することを勧めています

このチュートリアルシリーズではLangchainのドキュメントを実行していくことを説明しています

Langchainには多くの便利なモジュールとユースケースがあることを説明しています

新しいドキュメントが出たら、新しいビデオを作成すると述べています

このチャンネルやメールリストを通じて新しい動画を入手できると説明しています

受講生と一緒にLangchainを学習することを楽しみにしていると述べています

実世界での応用に重点を置いていることを強調しています

これらのツールは世界に大きな影響を与えることができると信じていることを共有しています

Langchainとの学習を楽しみにしていて、一緒に学習することを勧めています

このチャンネルを購読して、新しいビデオの通知を受け取ることを勧めています

dataindependent.comを訪問して、新しい動画の更新を受け取ることができると述べています

Transcripts

hello good people and welcome to a

tutorial series on Lane chain in this

introductory video we're going to talk

about our goals for this series we're

going to talk about what Lane chain is

and we're going to talk about why I

think it's a crucial investment to uh

put energy into learning what Lane chain

is and how you can use it now first off

how the series is going to work we're

going to run through the documentation

that they've provided and I'm going to

speak over the documentation and provide

some real world code samples and extend

them just a little bit so you can see

some some functionality that you may

find applicable in your business or your

personal life now how is the series

going to be going well it's mostly going

to be around short tutorial videos

however make it crazy and go say roaring

kitty style and do some three hour

videos but we will see

now why is Lang chain important and what

is it well first before I go and explain

that I want to kind of set up what the

problem is now what we're looking at

here is we're looking at chat GPT and

this is something that we all know and

I'm going to first ask chat GPT what is

LinkedIn uh what is lynching and we'll

see what it says for us

and says oh shoot I'm sorry but I'm not

familiar with the term Lang chain now

why could this be well it's because Lang

chain is a more recent library that just

came out I think I was just looking at

uh or Harrison was speaking and he said

he went full time on it three weeks ago

so this is something like in January of

23 he went full time on it that is one

of the creators of line chain now this

is new and chat gbt has not been trained

on data more recent than I think 2021.

now open AI is definitely going to get

there eventually but they haven't done

it yet so that's problem number one

problem number two is how many Google

Docs did I write last year now if I were

to ask Chad DBT how many Google Docs did

I write last year let's see what it says

let's give it a second

it's going a little bit slow

I'm gonna pause it here just until it

comes back for us

and there it goes so it says I'm sorry

but as an AI model I do not have access

to your personal information which makes

complete sense because I'm not linked my

Google Drive to it and it has no

understanding about who I am well not in

the explicit sense and it can't do that

for me so what's really cool is that

that is where Lane chain likes to play

now if I were to represent this in a

different way rather than just questions

what we're doing here is I'm this cool

person with the sunglasses and I'm

accessing the open AI AP uh uh the

artificial intelligence models

I just did it through my browser which

is over here on the left but you could

also do this through the API and they

have an API that you can go in and

there's plenty of tutorials about how to

use that which is cool however it's not

connected to my outside information and

this is where Lang chain starts to come

in let me move my

little guy over here this is where Lane

chain starts to come in so if I put

myself right in the middle there

what Lang chain does is it's going to

connect your AI models that you want to

use whether it's open AI or something on

hugging face and it's going to connect

to outside sources

and it's going to do some really cool

things without those outside sources so

all of a sudden you can start to chain

together commands which is the chain in

link chain and you can say hey what is

the weather today to open Ai and it is

going to come back and those language

models are going to understand what are

the series of questions that it needs to

do to understand how to get the

information that you want now the

applications of this are almost endless

and they're building even more

Integrations every single day if you

follow them on Twitter or there are

GitHub chains log you can see some

really cool things that they're putting

out so I think that this is an amazing

place to start to learn because by

learning this or by choosing to learn

this you're betting that open Ai and

hugging face are not going to build

direct Integrations to the tools that

you use every day

now in my opinion open III open AI wants

to remain on the API at the very bottom

layer and just be an API for certain

machine learning models they do not want

to build direct Integrations into Google

into notion into wolfen Alpha they want

the community to do that so by Learning

Land chain you're taking the bet that

there's going to be a separation and

different abstraction layers and this is

the piece that is going to connect those

different layers now is Lang chain going

to be the library that wins it all and

going to be very ubiquitous who knows I

don't know but I guarantee that by

learning the functionality in this

Library whichever one does come around

you're going to find extremely valuable

now that's Lang chain and I encourage

you to go check out their documentation

now in fact what we're going to do in a

lot of this tutorial series let me just

make this a little smaller what we're

going to do in this tutorial series is

we're going to run through their

documentation and I'm going to start to

explain what you can do with it they

have a lot of cool modules and they have

a lot of cool use cases like starting to

create agents that can do things off

your on your behalf how to create chat

Bots how to do some generations and

question answering summarization we're

going to do some really cool stuff here

evaluation and etc etc now as more

documentation comes out we'll do more

videos but this is just a quick overview

about how this is going to run I

encourage you to subscribe to this

channel to see when other videos come

out or if you want to get them via email

go to data independent.com which this

channel is associated with and you can

sign up for the email uh newsletter not

a newsletter but just get updated when

new videos come out there now I hope

that you're having fun I hope you're

going to enjoy this with me because I'm

really excited to learn this alongside

of you and build some really cool things

the last thing that I will say is my

emphasis as a instructor is always about

real world applications I don't really

care about Theory I'm not a hardcore

machine learning stats person who's

going to show you some academic papers I

really care about having you make impact

in the B2B environment or in your

personal life because I believe that

these tools can leverage up the amount

of impact that we can have so that one

person or a small team of people can

create a lot more good in the world and

impact on the world so I'll wrap up with

that very excited to learn with you

let's have some fun