What Is LangChain? - LangChain + ChatGPT Overview

Summary

This introductory video to a tutorial series on LangChain focuses on the importance and potential of LangChain as a crucial investment for those looking to integrate AI models with various external sources. The host explains that LangChain is a newer library designed to connect AI models like OpenAI with outside information, allowing users to chain together commands and perform complex tasks seamlessly. The series will explore LangChain's documentation, real-world code samples, and extend functionalities to show practical applications in business and personal life. The host emphasizes learning LangChain as a bet on the separation of abstraction layers in AI, preparing for a future where direct integrations are less common. The series aims to provide valuable insights and practical skills, focusing on real-world applications to maximize impact.

Takeaways

• 🎓 **Introduction to Lane Chain**: The tutorial series aims to introduce and explore Lane Chain, a new library for connecting AI models with external sources.
• 📈 **Investment in Learning**: The presenter believes that learning Lane Chain is a crucial investment due to its potential applications and growing integrations.
• 🔗 **Connecting AI with External Data**: Lane Chain's primary function is to connect AI models like Open AI with external data sources, enabling more complex and personalized interactions.
• 🚀 **Real-World Applications**: The series will focus on real-world applications, showing how Lane Chain can be used in business and personal life to increase impact.
• 📚 **Documentation and Code Samples**: The tutorial will go through Lane Chain's documentation and provide real-world code samples to demonstrate functionality.
• 🔍 **AI Limitations**: The script highlights the limitations of current AI models, such as lack of access to personal data or more recent information.
• 🌐 **Community-Driven Integrations**: Open AI is likely to stay at the API level and encourage the community to build integrations with everyday tools, which is where Lane Chain comes in.
• 🛠️ **Building Abstraction Layers**: Learning Lane Chain is a bet on the existence of different abstraction layers in AI, which will be necessary to connect various tools and services.
• 🤖 **Creating Agents and Bots**: The series will cover creating agents and bots using Lane Chain, which can perform tasks on behalf of users.
• 📝 **Content Generation and QA**: The library enables content generation, question answering, and summarization, which will be explored in the tutorial series.
• ⚙️ **Continuous Learning and Updates**: The presenter encourages subscribing to the channel or signing up for updates to learn as more documentation and integrations become available.

Q & A

What is the primary goal of the tutorial series on LangChain?

The primary goal of the tutorial series is to educate viewers on what LangChain is, its importance, and how it can be used in various applications. The series will cover the documentation, provide real-world code samples, and extend them to show functionality applicable in business or personal life.

How does LangChain differ from AI models like those from OpenAI?

LangChain differs from standalone AI models by connecting AI models to external sources and enabling the chaining of commands. This allows for a series of questions and commands to be executed to gather the desired information, which is not possible with AI models that are not connected to outside information.

Why is LangChain considered crucial for investment in learning?

LangChain is considered crucial because it represents a new and emerging technology that allows for integration with various tools and platforms. Learning LangChain is a bet on the separation of different abstraction layers in technology, where LangChain acts as a connector.

What is one of the limitations of current AI models like Chat GPT?

One limitation is that they do not have access to personal or external data unless explicitly linked. They also may not be trained on the most recent data, which can lead to gaps in knowledge about newer technologies or concepts like LangChain.

What is the format of the tutorial videos in the series?

The tutorial videos are mostly short, focused sessions that cover specific aspects of LangChain. However, there might be longer, in-depth videos as well, depending on the complexity of the topic.

How can viewers stay updated with new videos in the series?

Viewers can subscribe to the channel for notifications when new videos are released. Alternatively, they can sign up for updates via email at dataindependent.com, which is associated with the channel.

What is the focus of the instructor when teaching about LangChain?

The instructor focuses on real-world applications rather than theory. The aim is to help learners make an impact in the B2B environment or in their personal lives by leveraging these tools to increase their capacity for good in the world.

What are some potential applications of LangChain?

Potential applications of LangChain include creating agents to perform tasks on behalf of users, developing chatbots, generating content, and performing question-answering, summarization, and evaluation tasks.

Who is one of the creators of LangChain?

Harrison is mentioned as one of the creators of LangChain, who went full-time on the project in January of 2023.

How does LangChain aim to position itself in the technology ecosystem?

LangChain aims to position itself as a connector between AI models and various tools or platforms. It does not intend to build direct integrations but rather allows the community to create these connections, maintaining a separation between different abstraction layers.

What is the significance of LangChain's ability to chain together commands?

The ability to chain together commands allows for a more natural and efficient interaction with AI models. It enables users to perform a series of related tasks or inquiries in a sequential manner, which can lead to more sophisticated and contextually aware applications.

How does LangChain address the issue of AI models not having access to personal data?

LangChain addresses this by acting as an intermediary that can connect AI models with external sources, including personal data repositories like Google Drive, thus enabling AI models to access and utilize personal data when given explicit permission.

Outlines

00:00

🚀 Introduction to LangChain: A Tutorial Series

This introductory video sets the stage for a tutorial series on LangChain, a recent library for connecting AI models with outside sources. The host outlines the goals for the series, which include discussing LangChain, its importance, and providing real-world code samples to demonstrate its functionality. The series will consist of short tutorial videos, with a focus on practical applications rather than theory. The host emphasizes the potential of LangChain to integrate AI models with everyday tools, predicting a future where different abstraction layers are connected, and LangChain could play a crucial role.

05:00

🤖 LangChain's Applications and Learning Opportunities

The second paragraph delves into the various applications of LangChain, such as creating agents, chatbots, and performing tasks like question-answering, summarization, and evaluation. The host expresses excitement about the ongoing development and integration of LangChain, encouraging viewers to stay updated by subscribing to the channel or signing up for email notifications. The emphasis is on real-world applications, with the belief that these tools can significantly enhance the impact individuals or small teams can have in the business or personal spheres. The host concludes with a note of enthusiasm for the learning journey ahead and the potential to build impactful solutions.

Keywords

💡LangChain

LangChain is a recent library that enables the connection of AI models, such as those from OpenAI or Hugging Face, with external sources. It allows users to chain together commands and perform complex tasks that require integration with various tools and services. In the video, LangChain is presented as a crucial tool for those who want to leverage AI in their daily operations without waiting for direct integrations from the AI providers themselves.

💡AI Models

AI models refer to artificial intelligence systems designed to perform tasks that typically require human intelligence, such as understanding natural language or recognizing patterns. In the context of the video, AI models are used in conjunction with LangChain to execute a series of commands and access external information, which is essential for practical applications.

💡OpenAI

OpenAI is a company that creates and maintains AI models, which are used for various applications, including natural language processing. The video mentions OpenAI as one of the AI sources that can be connected and utilized with LangChain, highlighting its role in the ecosystem of AI tools available for developers and users.

💡Hugging Face

Hugging Face is an organization known for developing AI models, particularly in the field of natural language processing. The video suggests that, similar to OpenAI, Hugging Face's models can be integrated with LangChain to extend their capabilities and interact with other services and data sources.

💡Integration

Integration in the video refers to the process of combining different software systems or tools to work together. LangChain facilitates integration by connecting AI models with external services, which is a key aspect of its functionality. The video emphasizes the growing number of integrations being developed, showcasing the potential for a wide range of applications.

💡Chaining Commands

Chaining commands is the process of linking multiple commands or actions together to perform a complex task. In the context of LangChain, this involves using AI models to understand and execute a series of related queries or instructions, which can automate and streamline workflows.

💡Documentation

Documentation in this video refers to the official guides and resources provided by LangChain that explain how to use its features and functionalities. The tutorial series plans to go through this documentation, offering insights and real-world examples to help viewers understand and apply LangChain in practical scenarios.

💡Real World Applications

The video emphasizes the importance of real-world applications, which means using AI and LangChain to solve practical problems or enhance daily operations. The instructor's focus is on showing how these tools can be applied to have a tangible impact in business or personal life, rather than just discussing theoretical concepts.

💡Personal Information

Personal information, as mentioned in the video, refers to private data that pertains to an individual. The video uses the example of Google Docs to illustrate the limitations of AI models when it comes to accessing personal data, which is where LangChain can provide a solution by enabling secure and controlled integration with personal accounts and services.

💡API

API stands for Application Programming Interface, which is a set of protocols and tools that allow different software applications to communicate with each other. In the video, the presenter mentions using the OpenAI API to interact with their AI models, and LangChain can also be used to create custom integrations with various APIs.

💡GitHub

GitHub is a platform for software development and version control, where developers can share and collaborate on projects. The video encourages viewers to follow LangChain on GitHub to stay updated with the latest integrations and features, indicating that the community and open-source nature of the platform play a significant role in the development and adoption of LangChain.

Highlights

Introduction to a tutorial series on LangChain, a crucial investment for learning and using AI models.

LangChain connects AI models like OpenAI with external sources to perform complex tasks.

The series will cover real-world code samples and extend functionalities from LangChain documentation.

LangChain allows chaining together commands for more advanced AI functionalities.

LangChain is a recent library, not yet included in the training data for models like Chat GPT.

The importance of LangChain lies in its ability to access and utilize personal data sources.

LangChain is expected to remain an API layer, encouraging community to build integrations.

Learning LangChain is a bet on the separation of different abstraction layers in AI tools.

The tutorial series will explore creating agents, chatbots, and other practical applications of LangChain.

LangChain's applications are expanding with new integrations being built daily.

The instructor emphasizes real-world applications over theoretical knowledge.

LangChain's documentation will be a primary resource for the tutorial series.

The series will provide updates on LangChain's modules and use cases as new documentation is released.

The instructor aims to help learners make an impact in the B2B environment and personal life through AI.

Encouragement for subscribers to stay updated for new videos in the series.

The instructor's excitement about learning and building with the audience using LangChain.

The series will focus on practical applications to leverage AI for greater good and impact.

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