2-Langchain Series-Building Chatbot Using Paid And Open Source LLM's using Langchain And Ollama

Summary

In this informative video, Krishn demonstrates how to create chatbot applications using both paid and open-source large language models (LLMs). He focuses on the Langchain ecosystem, showcasing practical implementations with OpenAI's API and integrating open-source LLMs locally using tools like AMA. The tutorial covers setting up environment variables, defining prompt templates, and utilizing Langchain's modules for streamlined development. Viewers are guided through coding a chatbot, monitoring with Langsmith, and leveraging AMA for cost-effective local model deployment, providing a comprehensive introduction to chatbot development.

Takeaways

• 😀 The video is part of a Lang chain series focused on creating chatbot applications using both paid and open-source LLMs (Large Language Models).
• 🔍 The presenter, Krishn, emphasizes the importance of understanding how to integrate open-source LLMs through platforms like Hugging Face and the Lang chain ecosystem.
• 📚 The tutorial aims to be practical, guiding viewers through the process of setting up a virtual environment and using specific Python packages for chatbot development.
• 💻 Environment variables are set up for the Lang chain API key, the open AI API key, and the Lang chain project name to facilitate monitoring and tracking of chatbot interactions.
• 🔑 The video demonstrates the coding process for a chatbot application, starting with foundational models and gradually increasing in complexity.
• 📝 The script mentions the use of 'chat prompt templates' which are essential for defining the initial prompt required for the chatbot to respond to user queries.
• 🔗 The integration of different components like model, prompt, output parser, and chain is discussed to show how they work together in creating a functional chatbot.
• 🛠️ The video highlights the use of 'Lang Smith' for monitoring and tracking the chatbot's performance and API costs, emphasizing the practical application of the tool.
• 🆓 The presenter introduces the use of 'AMA' (Ask Me Anything) for running large language models locally, which can be beneficial for developers without access to paid APIs.
• 🔄 The process of downloading and using open-source LLMs like 'Llama 2' and 'GMA' with AMA is explained, showing an alternative to paid API services.
• 📈 The video concludes with a demonstration of how to run the chatbot locally using the AMA model and how to track the interactions through the Lang chain dashboard.

Q & A

What is the main topic of the video?

The main topic of the video is creating chatbot applications using both paid APIs like OpenAI and open-source language models, with a focus on integrating these with the LangChain ecosystem.

What is LangChain?

LangChain is an ecosystem that provides components for developing AI applications, such as chatbots, and is focused on making it easier to integrate with various language models and APIs.

What is the purpose of the environment variables mentioned in the video?

The environment variables mentioned in the video, such as LangChain API key, OpenAI API key, and LangChain project, are used to store important information for accessing APIs and monitoring the application's performance.

What is the significance of the 'like target' mentioned by the presenter?

The 'like target' is a viewer engagement goal set by the presenter to encourage viewers to like the video, which helps in promoting the video and supporting the channel.

How does the presenter plan to monitor the chatbot application's performance?

The presenter plans to use the LangChain dashboard to monitor each call made to the chatbot application, allowing for tracking of performance and costs associated with API usage.

What is the role of the 'chat prompt template' in the chatbot application?

The 'chat prompt template' is used to define the initial prompt or system message that sets the context for the chatbot's responses, guiding how it interacts with users.

What is the importance of the 'output parser' in processing the chatbot's responses?

The 'output parser' is responsible for processing the responses from the language model. It can be customized to perform tasks such as splitting text or converting text to uppercase, and is essential for formatting the output before it is displayed to the user.

How does the presenter demonstrate the practical implementation of the chatbot?

The presenter demonstrates the practical implementation by writing code for the chatbot application, setting up the environment, defining the prompt template, and integrating with the OpenAI API and LangChain components.

What is the AMA mentioned in the video, and how does it relate to open-source language models?

AMA stands for 'Automatic Model Adapter', which is a tool that allows for the local running of large language models. It supports various open-source models and is used to demonstrate how to integrate these models with the chatbot application locally.

How can viewers support the presenter's channel?

Viewers can support the presenter's channel by subscribing, liking the videos, commenting, and taking a membership plan if available, which helps the presenter create more content.

Outlines

00:00

🌟 Introduction to Lang Chain Series

Krishn introduces his YouTube channel and the Lang Chain series, focusing on creating chatbot applications. He discusses integrating with both paid APIs and open-source LLMs, mentioning Hugging Face and the Langen ecosystem. The video aims to be practical, with a like target of 1,000 and 200 comments. Krishn encourages viewers to subscribe and support the channel for more content. He also outlines the steps to create a virtual environment and set up environment variables for Lang chain API key, open AI API key, and project name.

05:01

📚 Setting Up the Environment and Coding Basics

Krishn details the setup process for the chatbot application, including creating a virtual environment and defining environment variables. He imports necessary libraries from Lang chain, such as chat open AI, chat prompt template, and output parsers. The focus is on practical implementation, with a demonstration of how to write code for the chatbot application. Krishn emphasizes the importance of understanding the integration process with different LLMs and the use of Lang chain modules.

10:01

🤖 Building the Chatbot Application with Open AI API

Krishn demonstrates how to build a chatbot application using the Open AI API. He defines the prompt template, sets up the streamlet framework, and integrates the Open AI LLM. The video shows how to use the Lang chain components to create a functional chatbot that responds to user queries. Krishn also explains how to monitor and track the chatbot's performance using Lang Smith dashboard.

15:03

🔍 Exploring Open Source LLMs with AMA

Krishn introduces AMA (Ask Me Anything), a tool for running large language models locally. He explains how to download and install AMA and how it can be used to run open-source LLMs like Lama 2. The video covers the process of downloading models using AMA and integrating them into the chatbot application. Krishn also discusses the benefits of using AMA for local model execution and its compatibility with Lang chain.

20:05

🚀 Running Local LLMs with AMA and Lang Chain

Krishn shows how to run local LLMs using AMA and integrate them with Lang chain. He walks through the process of downloading the Lama 2 model, setting up the environment, and running the chatbot application locally. The video demonstrates how to use the Lang chain community library to call open-source models and how to monitor the application's performance. Krishn also discusses the importance of having a powerful system for running these models efficiently.

25:07

📈 Monitoring and Tracking Chatbot Performance

Krishn concludes the tutorial by demonstrating how to monitor and track the chatbot's performance using the Lang Smith dashboard. He shows how to track requests, costs, and response times for both Open AI API and open-source models. The video emphasizes the ease of monitoring with Lang chain and the ability to customize output parsers for better tracking. Krishn invites viewers to subscribe to the channel for more tutorials and thanks them for their support.

Keywords

💡Chatbot Applications

Chatbot Applications refer to software designed to mimic human-like conversation with users through text or voice interactions. In the video, the main theme revolves around creating chatbot applications using both paid and open-source language models, demonstrating the process from setting up the environment to coding the chatbot functionalities.

💡Lang Chain

Lang Chain is a term used in the video to refer to a specific ecosystem or set of tools for developing AI applications, particularly those involving language models. It is integral to the video's content, as the host focuses on using Lang Chain components to create and monitor chatbot applications.

💡Open Source LLMs

Open Source LLMs, or Large Language Models, are AI models that are publicly available and can be used without cost. The video discusses integrating these models into chatbot applications, emphasizing the use of tools like Hugging Face and the Lang Chain ecosystem to facilitate this integration.

💡Paid APIs

Paid APIs, such as the Open AI API mentioned in the script, are services that require payment for usage, often providing access to proprietary or specialized AI models. The video contrasts these with open-source models, showing how both can be used to create chatbot applications.

💡Environment Variables

Environment Variables are settings in a computer system that can affect the behavior of running processes. In the video, the host sets up environment variables for the Lang Chain API key, the Open AI API key, and the Lang Chain project name, which are crucial for configuring the development environment for the chatbot.

💡Virtual Environment

A Virtual Environment in Python is an isolated space that allows a project to have its own dependencies, separate from other projects. The video script includes instructions on how to create a virtual environment using the 'venv' module, which is a step in setting up the development environment for the chatbot application.

💡Prompt Template

A Prompt Template in the context of chatbots is a predefined text or set of instructions given to the AI model to guide its responses. The video discusses creating a prompt template for the chatbot, which includes system instructions and user queries to shape the interaction.

💡Output Parser

An Output Parser is a component that processes the output from an AI model to format it appropriately for the user. The script mentions using a string output parser as the default processor for the AI model's responses in the chatbot application.

💡Streamlit

Streamlit is an open-source library used to create custom web apps for machine learning and data science. The video script includes using Streamlit to build the user interface for the chatbot application, allowing users to interact with the chatbot through a web-based interface.

💡AMA (Ask Me Anything)

In the video, AMA refers to a tool that allows for the local running of large language models, which is crucial for offline functionality or when internet access to paid APIs is limited. The host demonstrates how to use AMA to download and utilize open-source models like Lama 2 for the chatbot.

💡Lama 2

Lama 2 is an open-source language model that can be run locally using the AMA tool. The video script provides an example of how to download and use Lama 2 as an alternative to paid APIs for the chatbot application, showcasing the flexibility of using open-source solutions.

Highlights

Introduction to creating chatbot applications using both paid and open-source language models.

Explanation of integrating open-source language models through Hugging Face and focusing on the LangChain ecosystem.

Demonstration of setting up a virtual environment for the project using Python 3.1.

Instructions on creating environment variables for LangChain API key, OpenAI API key, and project name.

Overview of practical implementation in the LangChain ecosystem for chatbot development.

Importing necessary libraries from LangChain for chatbot functionality.

Discussion on using paid language models like OpenAI and alternatives like Anthropic's Cloudy AI.

Introduction to using LangChain modules for chatbot development and their significance.

Explanation of dependencies required for developing a chatbot application.

Demonstration of defining a prompt template for the chatbot using LangChain's chat prompt template.

Setup of a Streamlit framework for the chatbot application.

Integration of OpenAI's GPT 3.5 Turbo model for chatbot responses.

Utilization of LangChain's output parser for processing model responses.

Introduction to monitoring and tracking chatbot interactions with LangSmith.

Explanation of the cost associated with using OpenAI's API for chatbot responses.

Demonstration of creating a local chatbot application using open-source models with the help of LangChain and AMA.

Instructions on downloading and installing AMA for running large language models locally.

Guide on using AMA to download and integrate open-source language models like Lama 2.

Completion of the chatbot application using local models with LangChain and AMA, showcasing a custom output parser.

Conclusion and summary of the tutorial, highlighting the versatility of LangChain for both paid and open-source models.

Transcripts

hello all my name is krishn and welcome

to my YouTube channel so guys welcome to

the fresh and updated Lang chain Series

in this video I will be showing you how

you can create chatbot applications with

the help of both paid API llm along with

that we'll also see how you can

integrate with open source llms now you

should definitely know both the specific

ways how you can actually do it one way

to basically integrate any open source

llm is through hugging face but as you

know that I'm focusing more on the L

chain ecosystem and with respect to

hugging face I've already uploaded a lot

of videos in my YouTube channel and how

you can actually call this kind of Open

Source llms but since we are working

with the langen ecosystem we will try to

use all the components that are

available in langen as you all know guys

uh this is a fresh playlist and

obviously my plan is that this month I

will be focusing entirely on langen many

more videos will be coming up many more

amazing videos along with endtoend

application fine-tuning many more things

is going to come up so please make sure

that we'll keep a like Target for every

video and for this video the like Target

is 1,000 and at least 200 comments and

please make sure that you watch this

video till the end because it is going

to be completely practical oriented okay

and uh if you really want to support

please make sure that you subscribe the

channel and take a membership plan from

my YouTube channel so that it'll help me

and with the help of those benefits I

will be able to create more videos as

such so let me quickly go ahead and

share my screen so here is my screen

over here and you'll be able to see in

the GitHub that you'll be finding in the

description of this particular video

you'll be having folders like this so

today is the third tutorial not third

second tutorial uh in the first and

second we just understood that what all

things we are going to learn but in this

is the real practical implementation

that is probably there so as usual the

first thing that we are going to do is

that create our V andv environment how

to create it cond create minus PV EnV

python is equal to 3 1 you can probably

take 3.10 version and I have already

shown you how to create virtual

environments in many number of videos

then you'll be using Dov file so this

will basically be my environment

variable um in this environment variable

I will be putting three important

information one is Lang chain API key uh

the second one is open a API key and

Lang chain project you might be thinking

this open AI API key I've kept it as

open no it is not I've changed some of

the numbers over here so don't try out

it'll be of no use okay and then the

third environment variable that I'm

actually going to create is my Lin

project name that is tutorial one I have

written it over here the reason why I

have written this because whenever I try

to go ahead and see in my lsmith right I

will be able to see observe the entire

I'll be able to monitor each and every

calls from the dashboard itself how we

will be using this everything I will be

discussing about it okay so all these

things will specifically get required

and uh all this will be used in our

environment variable so these are the

three parameters I have already created

myb file so let's go ahead and start the

coding okay and you have to make sure

that you code along with me because this

is the future AI engineering things are

basically coming up I'll just show you

initially with the foundation model

later on this complexity will keep on

increasing so let's go ahead and start

our first code now what is our main aim

what we are trying to do in our first

project let me just discuss about

because these are all the things that we

going to discuss in the future but first

thing that we will try to create is our

normal chat GPT application okay I'll

not say chat GPT but a normal chatbot

okay and this chatbot will be important

it will be helping you to probably

create chatbot with the help of both

paid and open open open source llm model

so this will be the chatbot that we will

be creating one way is that we will be

using some paid llms now paid llms one

example I can show it with the help of

open AI API okay open AI

API the second one that I will try to

probably show it uh or you can also use

cloudy API so that is from a company

called as anthropic okay that you can do

and one more I will try to use it with

the help of Open Source

llm see calling apis is a very easy task

okay but the major thing is that since

we have so many many modules we are

going to use Lang chain as suggested

right and in Lang chain we definitely

have so many modules how we can use this

modules for different different calls

and along with this whenever we are

developing any chatbot application what

all dependencies we have specifically

right

dependencies now if you probably see

this diagram here you'll be able to see

there will be model prompt output parcel

so in our video in this video I'm going

to to see some of the features with

respect to lsmith I'm going to see some

of the features with respect to chains

and agents and I'm also going to use

some of the feature present in model and

output parcel so all this combination we

are going to specifically use and that

is the reason how this is how I'm going

to create the all the projects that we

are doing entire videos that are

probably going to come up will be much

more practical oriented okay so now

let's start our first chatbot

application so here I will go ahead and

write from Lang

okay from Lang chain uncore open AI

since I'm going to use open AI

import chat open AI okay

chat open AI so this is the first one

that we're going to basically do from

Lang chain see this three things will

definitely be required then one is chat

openi or whatever openi you whatever

chat model that you're are going to use

how to call Open Source I will also be

discussing about that first of all we'll

start with opening API itself okay so

from

linore core do prompts I'm going to

import chat prompt template okay chat

prompt template so this is the next

thing that we are probably going to use

chat prompt template okay at any point

of time whenever you create a chat bot

right this chat prompt template will be

super important right here is what

you'll you'll basically give the initial

prompt template that is actually

required Okay the third library that I'm

actually going to import is from Lang

chain uncore core do output uncore

parsers okay

Import St

Str

output parsel okay now this three are

very important this string St Str output

processor is the default output

processor whenever your llm model gives

any kind of response you can also create

a custom output parser that also I will

be showing you in the upcoming videos

okay this custom output parser you can

do anything with respect to the output

that probably comes you want to do a

split you want to make it as a capital

letter anything right you can write your

own custom code with respect to this but

by default right now I'm going to use

just St Str output parser now along with

this the next thing that I'm actually

going to do is that I'm going to use

streamlet as St okay streamlet as St

then I'm going to also import OS and

since I'm also going to use from

EnV

import load uncore Dov so that we'll be

able to import all our libraries okay so

let's see whether everything is working

fine or

not okay U from EnV so here I'm going to

basically write

python load uncore dot sorry python app.

py I'm just running it so that

everything works fine and all our

libraries will also get imp cannot uh

python app.py okay I have to probably go

to my chatbot folder CD chatbot so now

I'll clear my screen

python

app. P oh

sorry from streamlet as St okay import

streamlet as St I have to write so that

is the reason it was coming all the

erors now let's see if everything is

working fine Lang chain core so here you

can probably see that there is a

spelling mistake okay but I'm just going

to keep all the errors like this so that

you'll be able to see it python m.p if

everything works fine uh do output

parser okay P Capital

now so I think my suggestion box is not

working well and that is reason now

everything is working fine uh here you

can see that I'm not getting any error

so let's start our coding and let's

continue it okay so we have imported all

these things right now now as I

suggested guys since we are going to use

three environment variables one is the

open API key Lang chain API key and

along with that I will also make sure

that the tracing to capture all the

monitoring results I will keep this

three environment variable one is open

API key Lang chain tracing version two

and Lang chain API key so lanin API key

will actually help us to know that where

the entire monitoring results needs to

be stored right so that dashboard you'll

be able to see all the monitoring

results will be over here and tracing we

have kept it as true so it is

automatically going to do the tracing

with respect to any code that I write

and this is not just with respect to

paid apis with open source llm also

you'll be able to do it now this is the

second step that I have actually done

now let's go ahead and Define my prompt

template simple so here I'm going to

write my prompt

template okay prompt template so here

I'm going to Define prompt is equal to

chat prom template dot okay from uncore

messages

okay and here I'm going to Define my

prom template in the form of list the

first thing that with respect to my prom

template that I'm going to give is

nothing but system and system here I say

that you

are a

helpful

assistant

please

respond to the queries okay please

respond to the questions or queries

please response to the user queries okay

whatever queries that I'm going to

specifically ask a simple prompt that

you can probably see over here the next

statement uh after this is

what

so this will be my next see if I'm

giving a system prompt I also have to

give a user prompt right user prompt

will be whatever question I ask so this

will be user and here I will define

something like question colon question I

can also give context if I want but

right now I'll just give it as a

question a simple chatbot application so

that you'll be able to start your

practice of creating all these chatbots

so now I will go ahead and Define my

streamlet framework okay see the

learning process will be in such a way

that I will try to create more projects

and use functionalities that are there

right and in this way you'll be able to

work it in an amazing way okay so here

I'm going to basically write st. title

Lang chain demo with the open API std.

textor input search the text topic you

want okay now let us go ahead and call

my open AI llms okay open AI llm so here

I'm going to basically write llm and

whenever we use openi API so it will be

nothing but chat open Ai and here I'm

going to give my model name the model

name will be nothing but GPT GPT 3.5

turbo so I'm going to use turbo because

the cost is less for this I've I've put

$5 in my open a account okay just to

teach you so please make sure that you

support so that I will be able to

explore all these tools and create

videos for all of you okay and finally

my output parser see always remember

Lang chain provides you features that

you can attach in the form of chain

right so here three main things we have

created one is the chat prom template

next one is the llm and next one is the

output parcel obviously this is the

first thing that we require after this

we integrate with our llm and then

finally we get our output so string

output parser is responsible in getting

the output itself finally chain is equal

to we will just combine all these things

so here I'm going to write prompt llm

and then finally my output parsel right

I will show you going forward how we can

customize this entire output parsel and

all and finally if I write if input

text if input undor text colon now

whenever I write any input and probably

press enter Then I should be able to get

this output so st. write and here I'm

going to just write chain. invoke and

finally I get I give my input as

question and that input is assigned to

my input text input text right so this

is what we are going to basically do

right st. write now this is what we are

doing a simple chatbot application but

along with this we have implemented this

this this feature is specifically for

Lang Smith Langs

Smith

Lang Smith tracking okay this will be

amazing for to use okay and this is the

recent updates that are there so

whatever code I'm writing will be

applicable going forward in various

things that are probably going to come

up okay now let's go ahead and run this

so in order to run it you'll just need

to write nothing but streamlet

Run app.py Okay oops that is an error

app.py and here I'll do allow access

okay so right now now you'll be able to

see over here Lang chain series test llm

but my my my project name was Project

one okay so now if I go ahead and hit

hey hi okay and just press enter you'll

be able to see that we'll be getting

this information over here and here you

can see my project something let me

reload

it tutorial one right so this is the

first request that is already been hit

and here you'll be able to see your

enable sequest chat prom template right

all the chat Brom template output

message your helpful assistance pleas

response to the user queries right along

with this you will be seeing chart open

AI API and with respect to this what was

the cost everything you are able to

track so

027 is the cost that actually took with

respect to this and finally my string

output parser how can you assist today

with respect to this output parser it is

just going to give me the response

clearly now when I develop my own custom

output parcel I'll be able to track

everything so here what you are able to

do you are able to monitor each and

everything that is there right all the

request that is probably coming up okay

so provide me a python

code a python code to swap two

numbers okay so once I execute this and

here you'll be able to see that I'm able

to get the output and answer everything

is over here and for this you'll be able

to see the cost will be little bit High

okay if you don't agree with me or let's

see with respect to tutorial one the

second request that I've actually got

4.80 seconds yes it took a little bit

more time and here the cost was

00211 so it is based on the token size

right for every token it is bearing some

kind of cost perfect uh this was the

first part of this particular tutorial

now let's go to the second part uh the

second part is more about making you

understand that how you can call um open

source llms in your local itself and how

you can actually use it so for this

first of all I will go ahead and

download AMA okay AMA is an amazing

thing because you'll be able to run all

the large language models locally uh the

best thing about AMA is that it

automatically does the compression and

probably in your local you'll be able to

run it let's say if you have 16 GB Ram

you will just have to wait for some

amount of time to get the response but

Lama 2 and code Lama you can

specifically use it over here all the

open source llm model and it supports a

lot of Open Source llm models and yes uh

in Lang chain ecosystem the integration

has also been provided over here so what

I'm actually going to do over here is

that I'll show you first of all just go

ahead and download it this is available

both in Mac Mac Linux and windows

wherever you want just download it after

you downloaded it what you really need

to do is just go ahead and install it it

is a simple exe file for Windows MSI

file for Mac OS and then Linux is a

different version so you just need to

double click it and start installing it

once you install it here uh somewhere in

the bottom this AMA will be start

running okay now once AMA installation

is done now what I will do over here I

will create another file inside my

chatbot okay and create another file

local

llama okay local Lama py now local Lama

py what we are going to basically do

over here is that uh with respect to the

local llama I will first of all go ahead

and import some of of the library see

code will be almost same right there

also I'll be using chat open API chat

prom template string output parser so

I'll copy the same thing over here I'll

paste it over here now along with this

what I'm going to do I have to import

AMA right because that is the reason why

we will be able to download all the

specific models okay so Lang chain

community. llm see over here whenever we

need to do the third party integration

so that will be available inside langin

Community okay so AMA is third party

cont configurations uh let's say you're

using some Vector embeddings that is

also third party so everything will be

available over here okay now this is

done langore community. LM import AMA

and then we have this output parser

string output parser core. prompts that

is nothing but chat prompt template and

everything is there okay now let's go

ahead and write import streamlet as St

so I'm going to going to use the

streamlet over here along with this

import

OS and not only that we will also go

ahead and import from

EnV

import load

uncore

dot loancore

dob

okay now we'll initialize it load

underscore

Dov okay once we initialize all this

random all this uh environment variables

as usual I will be importing this three

things now see in my previous code when

I was using open aipi prompt template we

have written it over here right same

promt template we'll also write it over

here because it we just need to repeat

it because the main thing is that you

really need to understand how with the

help of AMA I can call any open source

models okay so here it is and then

finally you'll be able to see where is

my uh code to call my open a llms that

we going to see over here so this is

done now stream late framework also I

will try to call it over here okay it's

more about copy past the same thing that

we have actually implemented and then

you will also be seeing this is the code

that we going to implement it okay but

here we are calling chat open AI okay I

specifically don't want chat open AI

instead I will be calling AMA okay so o

Lama whatever Library we have imported

so o Lama okay and then here we are

specifically going to call a Lama 2 okay

now before calling any models now which

all model are specific supported if you

go ahead and see in the GitHub right of

AMA you'll be seeing the list of

everything every every every libraries

that it supports like Lama 2 mral

dolphin F 52 neural chat code Lama all

are mostly open source GMA GMA is also

there but before calling this what you

really need to do is that just go to

your command prompt let's say that I

want to use GMA GMA model okay so what I

have to do or I have to use Lama model

right so in order to do this I have to

just write AMA run whatever model name

because initially it needs to download

it right uh this will get downloaded

from some open source some GitHub it can

be GitHub it can be hugging pH somewhere

right some location there will be there

we have to download that entire model so

let's say that I want to go ahead and

write AMA run gamma so this what will

happen it will pull the entire GMA model

right wherever it is so here you can see

pulling will basically happen now this

is right now 5.2 GB right for the first

instance you really need to do it now

since I I I am writing the code with

respect to Lama 2 I've already

downloaded that model so that is the

reason I'm showing you another example

over here run GMA now once this entire

downloading happens then only I'll be

able to use the gamma model in my local

with the help of AMA so I hope you have

got an idea about

it now what I'm actually going to do so

here I've called AMA model Lama 2 okay

then again output parser is this and I'm

combining prompt llm and output parser

and everything will be almost same and

that is the most amazing thing about

Lang chain the code will be only generic

now only you need to replace open a or

paid or open source it is up to you

again I'm saying you guys the system

that I'm currently working in has a 64GB

Ram uh it has Nvidia Titan RTX which was

gifted by Nvidia itself so with respect

to this uh amazing system I will be able

to run very very much quickly that is

what I feel so let's go ahead and run it

so here what I'm actually going to do

I'm going to write

python uh so it is streamlet so

streamlet run

run local Lama py so once I execute it

here you'll be able to see now now

instead of open AI API I should had okay

no module name Lang chain Community

let's see where is Lang chain Community

okay I have to also make sure that in my

requirement. txt I go ahead and use this

langin community and I need to import

this Library since I need to do that and

that is the reason I'm getting an error

so if I go ahead and write pip install

minus r requirement.

txt

oops

CD dot dot okay now if I go ahead and

write pip install minus r requirement.

txt so here you'll be able to see my

requirement. will get installed this

Lang chain Community will get installed

once I'm done with this then I can

probably go ahead and run my code okay

so this will take some amount of time so

if you liking this video please make

sure that you hit like uh there are many

things that are probably going to come

up and it'll be quite amazing when you

learn all these things okay so uh once

this is done then what will happen is

that we can and you can use any model up

to you okay and I don't want this open a

key also only this two information I

specifically want I'll be able to track

all these things okay and later on I'll

also show you how you can create this in

the form of apis again it some time

it'll take this but uh let me know uh

how do you think all these tutorials are

blank chain I see a lot of purpose for

this particular Library it's is quite

amazing that people are doing um the

company is doing amazingly well in this

open source world and it is developing

multiple things over there so now I will

go ahead and write CD chatbot I will go

inside my chatbot and then I will run

this python local Lama dopy once I

execute this now I don't think so it

should be an

error okay it should be streamlit come

on streamlit run local Lama oops local

Lama py not python run streamate run now

here you have again I'll be getting open

AI text over here let me change this

also so that I can make it

perfect with Lama

2 okay so I've executed it saved it I

will rerun it I'll say hey hi so once I

execute it you'll be seeing that it'll

take some amount of time in my system

even though I have a 64 GB Ram but I'll

get the output over here so assistant

says hello how can I help you today now

if I probably go ahead with respect to

this dashboard uh let's see where it is

so now tutorial one you'll be able to

see that this will increase okay there

will be one more over here right so I've

reloaded this

page okay and you'll be able to see it

okay you'll be able to see the new AMA

request see hey hi High 4.89 second

token 39 but there is no charges because

it is an open source model right so here

you'll be able to see if I extend this

there you'll be able to see chat prom

template ama ama is over here now this

AMA is specifically calling Lama 2 over

there and whatever open source libraries

that you specifically want just to call

this it is very much simple you have to

just go into the GitHub and download any

model first of all just by writing o

Lama run that particular model name once

it is downloaded it is good that you can

probably go ahead with and use it okay

now I will say uh provide me a python

code python code to swap two numbers

okay if you want more coding well chat

bot you can directly use code Lama if

you want okay so here you can see all

the examples are there and this was

quite fast right so this is good you

know so if you have the right kind of

things so here you can see 4 seconds it

has Pro taken okay AMA is over here all

the information is probably over here

prompt and completion and all right so I

hope uh you like this specific video I

hope you able to understand things uh I

said guys again uh if you're new in this

Channel please make sure that you

subscribe the channel there a lot of

tutorials that are probably going to

come up but here I've just shown you

multiple ways of creating chatbot

application using both uh open Ai apis

and open source models with the help of

langin so yes this was it for my side

I'll see you in the next video have a

great great day thank you and all take

care bye-bye