1- Lets Learn About Langchain-What We Will Learn And Demo Projects
Summary
TLDRKrishak 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
🚀 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.
🤖 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.
Mindmap
Keywords
💡Lang chain
💡Generative AI
💡APIs
💡Lang Smith
💡Lang Server
💡Documentation
💡Cognitive Architectures
💡Chains
💡AMA
💡Vector Embeddings
💡Lang Chain Core
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
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