1- Lets Learn About Langchain-What We Will Learn And Demo Projects

Summary

Krishak introduces an updated Lang Chain series on his YouTube channel, focusing on building generative AI applications using both paid and open-source LLM APIs. He plans to cover everything from scratch to advanced, demonstrating end-to-end projects, deployment, and ecosystem utilization. The series will also explore custom output functions, data injection techniques, vector embeddings, and local LLM model execution with AMA.

Takeaways

• 😀 Krishak introduces an updated Lang chain series aimed at covering updates and teaching how to build generative AI applications.
• 🔍 The series will cover content from scratch to advanced, focusing on using both paid and open-source LLM APIs and models.
• 🛠️ Krishak emphasizes the importance of the Lang chain ecosystem for deployment and will demonstrate its use throughout the series.
• 📚 Documentation will be a key part of the series, with Krishak using a diagram to simplify complex concepts for beginners.
• 🔑 Projects will incorporate Lang Smith for monitoring, debugging, evaluation, and annotation, and Lang Serve for deployment.
• 🤖 The series will explore cognitive architectures, chains, agent retrieval strategies, and the Lang chain community for third-party integration.
• 📝 Custom output functions will be taught, allowing users to tailor responses from LLM models to fit their specific product needs.
• 📈 Data injection techniques for various formats like CSV and PDF will be discussed, along with vector embeddings using both paid and open-source APIs.
• 💻 AMA (Align Machine) will be highlighted as a crucial library for running LLM models locally, requiring a high-configuration system.
• 🔧 Lang chain core will delve into the Lang chain expression language, covering techniques like paralyzation, fallback, tracing, and composition.
• 🚀 Krishak will demonstrate the entire ecosystem in action, including monitoring and debugging, with practical examples and projects.

Q & A

What is the main aim of the updated Lang Chain series?

The main aim of the updated Lang Chain series is to cover the new updates from Lang Chain and demonstrate how to build generative AI-powered applications using both paid LLM APIs and open-source LLM models.

What will the series cover besides building AI-powered applications?

The series will also cover creating end-to-end projects and using the Lang Chain ecosystem for deployment purposes.

Why does Krishak emphasize the importance of documentation in the video?

Krishak emphasizes documentation to help viewers understand how to use Lang Chain's documentation effectively and to provide clarity on the concepts and components involved in the projects.

What example does Krishak give to explain the usage of Lang Smith and Lang Server?

Krishak explains that Lang Smith is used for monitoring, debugging, evaluation, and annotation, while Lang Server is used for deployment with respect to a REST API. These components will be used in each project and technique demonstrated in the series.

What are the three main components of Lang Chain mentioned in the video?

The three main components mentioned are cognitive architectures (chains, agents, retrieval strategies), Lang Chain Community (for third-party integration), and model IO retrieval and agent tooling.

How does Krishak plan to demonstrate the usage of Lang Chain components?

Krishak plans to demonstrate the usage by combining prompt templates, chains, and custom output functions for specific tasks, as well as showing data injection techniques and vector embeddings using both paid and open-source LLM APIs.

What is AMA, and why is it important in the series?

AMA is a library that helps run large language models locally. It is important because it allows viewers to execute LLM models without needing high-end cloud-based APIs, provided they have a good system configuration.

What kind of project does Krishak showcase as an example using AMA and Lang Chain?

Krishak showcases a simple chatbot project created with Streamlit, which demonstrates the use of Llama 2 model executed locally via AMA, integrated with Lang Smith for monitoring and debugging.

What does Krishak demonstrate with the simple chatbot project?

He demonstrates how to execute LLM models locally, interact with the chatbot, and monitor the LLM calls using Lang Smith, highlighting response times, latency, and other details.

What does Krishak promise to cover in the next video of the series?

In the next video, Krishak promises to cover environment setup, creating API keys, using open-source LLM models, and various other foundational aspects needed to start building projects with Lang Chain.

Outlines

00:00

🚀 Introduction to Lang Chain Series

Krishak introduces the Lang Chain series on his YouTube channel, focusing on updates from Lang Chain and covering topics from scratch to advanced levels. The series aims to demonstrate building generative AI applications using both paid and open-source LLM APIs. Krishak emphasizes the importance of the Lang Chain ecosystem for deployment and will use a diagram to simplify the documentation process. He mentions the use of Lang Smith for monitoring and debugging, and Lang Server for deployment. The first project, a simple chatbot, will showcase the use of these components.

05:00

🤖 Demonstrating Lang Chain with a Chatbot Project

In this paragraph, Krishak provides a live demonstration of a chatbot project using Lang Chain. He executes the chatbot locally using the AMA model, which allows for running large language models on a local system with a high-configuration setup. The demonstration includes interaction with the chatbot, asking for the user's name and requesting a Python code for the Fibonacci series. Krishak also shows how to track and monitor the LLM calls using Lang Smith, and he discusses the importance of system configuration for quick responses. He concludes by expressing excitement about the future development of the Lang Chain ecosystem and invites viewers to join the upcoming sessions covering environment setup, API key creation, and the use of open-source LLM models.

Keywords

💡Lang chain

Lang chain is the central theme of the video, referring to a suite of tools and technologies designed for building and deploying generative AI applications. It is mentioned as having components like Lang Smith and Lang serve, which are integral to the projects discussed in the video. The script discusses using Lang chain for everything from scratch to advanced applications, emphasizing its comprehensive ecosystem for AI development.

💡Generative AI

Generative AI refers to artificial intelligence systems that can create new content, such as text, images, or music. In the context of the video, the host plans to demonstrate how to build applications powered by this technology using both paid and open-source models, highlighting the creative and versatile nature of generative AI.

💡APIs

APIs, or Application Programming Interfaces, are sets of rules and protocols that allow different software applications to communicate with each other. The script mentions using both paid LLM (Large Language Model) APIs and open-source LLM models, indicating that the video will cover how to integrate these APIs to build AI applications.

💡Lang Smith

Lang Smith is mentioned as a tool within the Lang chain ecosystem used for monitoring, debugging, evaluation, and annotation of AI models. It is positioned as an essential component in the workflow for developing and refining AI applications, as it helps in tracking and improving model performance.

💡Lang Server

Lang Server is another component of the Lang chain ecosystem, specifically used for the deployment of AI models. The script suggests that it will be used in conjunction with Lang Smith to create end-to-end projects, emphasizing the importance of deployment in the full lifecycle of AI application development.

💡Documentation

The script discusses the importance of understanding the documentation of Lang chain to navigate its features effectively. The host mentions using a diagram to simplify the documentation for viewers, aiming to make the complex information more accessible and easier to follow.

💡Cognitive Architectures

Cognitive architectures are systems that simulate human-like thought processes in AI. In the script, they are part of the Lang chain core and are used to demonstrate how to build complex AI applications. The term is used to illustrate the advanced capabilities of the Lang chain ecosystem.

💡Chains

In the context of the video, 'chains' likely refers to a series of processes or steps within the Lang chain ecosystem that are used to create and execute AI models. The script mentions using chains in combination with other techniques to build AI applications, indicating a modular approach to AI development.

💡AMA

AMA, as mentioned in the script, is a library or tool that allows for the local running of large language models. It is highlighted as a way to execute models without relying on external APIs, which requires a high-configuration system for optimal performance. The script uses AMA to demonstrate the practical application of local model execution.

💡Vector Embeddings

Vector embeddings are a method of representing words or phrases as points in a multi-dimensional space, which can be used by AI models to understand and process language. The script mentions discussing vector embeddings using both paid and open-source APIs, indicating their importance in the development of AI applications.

💡Lang Chain Core

Lang Chain Core is the foundational component of the Lang chain ecosystem, which includes features like Lang chain expressions and techniques such as parallelization, fallback, tracing, and composition. The script positions it as a critical part of the video's教程, showing how these advanced features can be applied in projects.

Highlights

Introduction to the updated Lang chain series aimed at covering new updates and building generative AI applications.

Coverage of both paid LLM APIs and open source LLM models in the series.

Discussion on creating end-to-end projects using the Lang chain ecosystem for deployment.

Emphasis on the importance of understanding the Lang chain documentation.

Use of a diagram to simplify understanding of complex concepts in Lang chain.

Introduction to Lang server for deployment and Lang Smith for monitoring and debugging.

Explanation of the use of Langmi and Lang serve in projects.

Description of cognitive architectures, chains, and agent retrieval strategies in Lang chain.

Introduction to Lang community for third-party integration.

Discussion on model IO retrieval and agent tooling.

Teaching approach involving prompt templates and multiple chains for specific tasks.

Introduction to custom output parsing for LLM model responses.

Exploration of data injection techniques for CSV and PDF.

Discussion on vector embeddings using paid APIs and open source APIs.

Introduction to the AMA library for running LLM models locally.

Requirement of a high configuration system for running AMA.

Demonstration of a simple chatbot project using AMA and Lang chain.

Explanation of Lang chain core and the Langen expression language.

Discussion on techniques like paralyzation, fallback, tracing, and composition in Lang chain core.

Introduction to Lang Smith for debugging and evaluating projects.

Plans for future videos covering environment setup, API keys, and use of open source LLM models.

Invitation to support the channel and excitement for upcoming content.

Transcripts

hello all my name is krishak and welcome

to my YouTube channel so guys finally uh

welcome to the updated Lang chain series

uh and again the main aim of this

specific series is to make sure that

whatever new updates are specifically

coming from Lang chain and I'm also

going to cover it from scratch to

Advanced and mainly the main aim of this

series is to show you how you can build

generative AI powered applications uh

with the help of both paid apis

specifically llm apis or open source llm

models also so we're going to see along

with that how we can actually create an

end to-end project not only that we are

also going to see that how we will be

using the entire ecosystem that is

provided by langin for the deployment

purpose and all now before I go ahead uh

I really want to talk about the entire

documentation of the Lang chain so in

this video it's mostly about

documentation how things are going to

cover how I'm going to use this

particular documentation now for any

starters who are probably starting this

if you go ahead and read all the

documentation step by step right it

becomes very difficult to understand

things okay so that is the reason what I

am actually going to do is that I'm

going to use this important diagram and

with respect to any concepts that I

explain okay it will be a combination of

all these things that is basically used

over here let me give you an example I

will forget like let's say oh yeah Lang

Smith and Lang server is there Lang

server is specifically used for

deployment with respect to arrest API

lsmith is for monitoring debugging

evaluation annotation and all okay so if

I probably consider langmi and Lang

serve I'm going to use for this two

techniques in each and every projects

and each and every techniques that I

probably explain you that basically

means first day like that is tomorrow

let's say if I upload my next video

which is my practical oriented video

here already the code is also ready I

will just show you this thing and is

everything is ready in short I've

actually created all these things for

you itself so uh the main aim is that

whenever I create a project by default

Lang Smith and Lang Lang of are going to

be used over there and over here you'll

be able to see these are the three main

components one is the cognitive

architectures right so over here you'll

be able to see in langin you have chains

agent retrieval strategies in langin

community so langin Community

specifically used for thirdparty

integration okay and here you'll be able

to see model IO retrieval and agent

tooling so what I'm actually going to do

you know whenever I teach I'm going to

use this combinations right you'll be

seeing that there will be one specific

prompt template there will be multiple

chains that will be involved how to

invoke those specific chains for uh any

specific task not only that how how you

can also create your own custom output

parsel that basically means let's say A

Lang your llm model is giving some kind

of response out there and with respect

to this particular response you want to

provide provide your own custom output

function you know so that uh you know

you you write your own code so that you

get a output that is feasible to your

product itself so that is also what I'll

be showing in retrieval I will be

talking about various data injection

techniques with respect to CSV PDF and

many more things we'll be discussing

about Vector embeddings we'll be

discussing about vector embeddings by

using both uh paid apis llm apis and

along with that open source apis also

okay and we also going to use one very

important important uh uh model or

Library which I specifically want to say

like AMA which will be used to actually

run this all the llm models in your

local system itself but one thing that

you really need to do is that you really

need to have a good configuration with

respect to the system also and finally

here you'll be able to see Lang chain

core now in Lang Chen core we are going

to use something called as langen

expression um uh langin expression

language so here techniques like

paralyzation fall back tracing

composition how we can specifically use

this where it is used in a project that

all will be covered so over here if you

go step by step obviously you can go

ahead and practice things but my main

aim is to show you that how this entire

ecosystem works and that ecosystem when

I specifically say is a combination of

multiple things okay let me give you an

example and let me show you okay so

let's say that I am going to use some

techniques okay over here you'll be able

to see that I'm going to use some some

Lin racing technique and all and here

I've used AMA okay how to write this

code and all I will show you everything

uh from scratch okay so if you probably

go down over here let me just show you

so here I have imported AMA now olama if

you don't know what exactly is AMA I

will run and show it to you so it'll be

quite

amazing so AMA actually helps you to it

helps you to run large language models

locally the only thing that you

specifically require is a good high

configuration system right so one of the

project that I've just created for you

you know simple chatbot okay and here

you'll be understanding the importance

of it and what I will do is that this

will be the this will be the level of

project okay so let me just execute this

streamlet run local llmp I have actually

created this entire app in streamlet

okay so this is the simple streamlet app

now here if I ask any question okay so I

written open AI open API let me just

change this okay

um

with this is actually Lama to okay and

with the help of AMA I will be able to

execute this so if I execute this

now now see lanen demo with llama 2 okay

I'll say hey hi okay and I'll execute it

until then what I will do I will just go

ahead and open my Lang chain Lang chain

I'll go ahead and sign up now I'll go to

the dashboard also because as I said

right what is my main aim I'll be using

Langs Smith and Lang serve everywhere

right now if you don't know about Langs

Smith we will be

debugging uh using this playground

feature evaluating and all okay now in

Langs Smith if I go to projects okay now

now you know that I'm going to hit the

AMA model okay so I'm going to use the

AMA model techniques and I'm going to

call the Llama 2 models itself and I'm

going to do it in locally right many

people say that hey Krish we don't have

that open a API Keys also so once I

execute this let's see

okay so I will say hey hello hi so I've

got this particular input then I will

say tell me your name okay I'm just

asking some minimal information okay so

name name name name name name your name

please let me know which would like to

use okay fine oh it is telling me to use

their name okay

please provide me a code

on python code

on Fibonacci series okay I'm just asking

something okay I'll I don't care about

the spelling also it's okay so if I

execute this you'll be able to see that

now Lama 2 is basically getting used and

yes I have a high configuration system

then also I'll be able to get the

response in some time right I have 64GB

RAM and all uh with respect to all the

other simple token size right like the

let the token size is very less at that

point of time you'll be getting the

quick response but if you have a low

configuration system if you have just 8

GB Ram I think it is going to take time

so here you'll be able to see all the

code everything is given over here right

conclusion everything and all now if I

go to this lsmith if I go inside this

Lang chain series you'll be able to see

that yes this is my thing right I I have

given this code itself pre provide a

python code on Fibonacci series and I'm

able to get the answer now if I go ahead

and click on llm calls so here you can

see ama is basically getting hit right

so all the information is also getting

tracked and this is the kind of level of

project that I am planning to do right

it will be more about monitor and all

and I see in the future what is

basically going to happen and right now

Lang chain they're developing many

things they're developing that entire

ecosystem and I think there is there

will be one place where you can actually

create your entire gen power application

along with deployments right so here

you'll be able to see everything and

with respect to this monitoring calls

everything is over here like how much

response how much latency is basically

taking over here you can see cost is

zero because cost is not required to

call Llama to model right and just

imagine once you do this deployment in

any server with the help of olama and

Lang chin is going to come up with that

there is amazing thing called as Langer

and Lang Ser is still coming I'm in

still in the beta list um once I get

this particular waiting list approved I

will also be able to do the deployment

and show it to you right so this is what

is the entire plan I hope you all are

excited and if you are excited please

make sure that you hit like for this

specific video share with all of your

friends because from the next video

onwards I'm going to start it and in the

first instance there there will be a 1

hour session uh 1 hour session where I

will be discussing about each and

everything from environment setting to

how you can create your API Keys along

with that how you can use open source

llm models and all not only just opening

API key I'm going to use Lama 2 mral

different different models which is

basically available as an open source

everything will be available to you

right so I hope you're excited if you're

excited please do hit like share with

all your friends and yes if you want to

support please go ahead and take a

membership of my channel and I will be

coming up with much more interesting

videos so yeah this was it for my side

I'll see you in the next video have a

great day thank you one all take care

bye-bye