How to Build Custom AI Chatbots 🔥(No Code)
Summary
TLDRThis tutorial offers a comprehensive guide to creating and deploying AI chatbots using the Flowwise AI platform. It starts with the installation process, including setting up Node.js and Flowwise AI on a local machine. The video then demonstrates building a chatbot with a custom knowledge base, utilizing a drag-and-drop interface. It covers the use of templates, configuring the Open AI API, and testing the chatbot. The tutorial also explains how to deploy the chatbot to the cloud using GitHub and Render.com for a persistent online presence. Finally, it shows how to embed the chatbot into a website, providing a hands-on approach for non-coders to create interactive AI chatbot experiences.
Takeaways
- 🌟 The tutorial introduces Flowwise AI, a drag-and-drop visual AI chatbot builder that allows users to create unique chatbot experiences.
- 🛠️ The tool is entirely free to install and use, as demonstrated through the provided installation instructions.
- 💻 The first step in the installation process is downloading Node.js from nodejs.org, which is essential for non-coders and coders alike.
- 🔍 The tutorial provides a step-by-step guide on how to access and use Flowwise AI, even for those without coding experience.
- 📝 The process involves using the command prompt on a computer to install and run Flowwise AI locally on port 3000.
- 🔗 Flowwise AI offers a user-friendly interface with a dashboard for adding chat flows and deploying chatbots to websites or web apps.
- 📚 The platform includes a marketplace with numerous templates for various use cases to help build complex chatbot models and flows.
- 📑 The tutorial focuses on creating a simple conversation chain using a document module to build a website FAQ-style chatbot.
- 🔑 To function, the chatbot requires an OpenAI API key, which can be obtained from the OpenAI website.
- 🚀 The video also covers deploying the chatbot to the cloud using GitHub and a hosting service like Render.com for wider accessibility.
- 🔒 For security, the tutorial explains how to set up a username and password to protect the chatbot instance in the cloud.
Q & A
What is the purpose of the tutorial in the video?
-The tutorial aims to guide viewers on how to create and deploy AI chatbots using a drag and drop visual AI chatbot builder called Flowwise AI.
Is Flowwise AI a free tool?
-Yes, Flowwise AI is entirely free to use for creating chatbots.
What is the first step in installing Flowwise AI?
-The first step is to download Node.js from nodejs.org.
How do non-coders access and use Flowwise AI?
-Non-coders can access Flowwise AI by using the command prompt on their computer to install and run the software.
What does 'npm install -g flowwise' command do in the installation process?
-This command installs the Flowwise AI software onto the user's computer.
How can users access the locally installed Flowwise AI?
-Users can access Flowwise AI locally by going to localhost:3000 in their web browser.
What is the main dashboard in Flowwise AI used for?
-The main dashboard is used to add and manage chat flows, which are individual chatbots with specific functionalities.
What is a chat flow in the context of Flowwise AI?
-A chat flow represents a specific chatbot with a certain functionality or ability within Flowwise AI.
How can users get their chatbot online for others to use?
-Users can deploy their chatbot to the cloud using services like Render and then embed it into their website or web app.
What is the importance of using 'gpt-3.5-turbo' instead of 'gpt-4' in the tutorial?
-Using 'gpt-3.5-turbo' is recommended for testing and playing around as it is cheaper than 'gpt-4', thus avoiding high costs.
How does the tutorial help in building a customer service type chatbot for a website?
-The tutorial guides users through building a chatbot that can answer questions about a website's services by using a text document as its knowledge base.
What is GitHub and how is it used in the deployment process?
-GitHub is a code repository where developers store and share code. It is used to create a copy of the Flowwise AI application for deployment.
What is Render and how does it relate to deploying Flowwise AI?
-Render is an app for hosting web services. It is used to deploy the Flowwise AI application online, making it accessible on the internet.
Why is persistence important when deploying Flowwise AI on Render?
-Persistence ensures that the data and chat flows created in the Flowwise AI application are saved and do not get lost when the app is restarted or redeployed.
How can users protect their Flowwise AI cloud instance from unauthorized access?
-Users can set up a username and password within the Render environment to protect their Flowwise AI cloud instance from unauthorized access.
How can the chat flow created locally be uploaded to the cloud version of Flowwise AI?
-The chat flow can be exported from the local environment and then imported into the cloud version of Flowwise AI through the 'add new' option.
What is the final step to integrate the deployed chatbot into a website?
-The final step is to copy the provided HTML snippet from the cloud-hosted version of Flowwise AI and embed it into the website's code.
Outlines
🤖 Introduction to Flowwise AI Chatbot Builder
This paragraph introduces the tutorial's focus on Flowwise AI, a drag and drop visual AI chatbot builder. The speaker will guide viewers on creating unique AI chatbot experiences, installing the free tool, and deploying it on personal websites or web projects. The tutorial aims to reduce the learning curve for non-coders by providing step-by-step instructions on setting up Flowwise AI, starting with downloading Node.js and using the command prompt to install the necessary software. The goal is to empower users to build and deploy chatbots without coding knowledge.
📚 Building a Knowledge Base AI Chatbot
The second paragraph delves into the process of building a custom knowledge base AI-powered chatbot using Flowwise AI's drag and drop interface. The speaker discusses the workflow, starting with accessing the marketplace for templates and selecting a simple conversation chain. The template consists of a language model, memory module for context, and a document loader for additional data. The tutorial then moves on to configuring the OpenAI API key and testing the chatbot within the workspace. The aim is to create a chatbot that can answer user queries by referencing a provided document, such as a website's FAQ.
🚀 Deploying the Chatbot to a Web Hosted Platform
In this paragraph, the focus shifts to deploying the locally built chatbot to a web-hosted platform. The speaker outlines the process of setting up a GitHub account to store and share code, and then using Render.com to deploy the Flowwise AI application online. The tutorial covers creating a GitHub fork of Flowwise, connecting it to Render, and configuring the web service with the appropriate settings for deployment. The speaker emphasizes the importance of persistence, opting for a paid plan to ensure that the chatbot's data and progress are saved across sessions.
🔐 Ensuring Security and Persistence in the Cloud
The fourth paragraph addresses the importance of security and data persistence when deploying the chatbot in the cloud. The speaker explains how to set up persistence by adding a disk and configuring environment variables in Render. This ensures that the chat flows and user data are saved and retained even when not actively using the application. Additionally, the tutorial covers how to protect the cloud instance by setting up a username and password, preventing unauthorized access to the chatbot's workings.
🛠️ Embedding the Chatbot into a Live Web Page
The final paragraph concludes the tutorial by demonstrating how to embed the deployed chatbot into a live web page. The speaker shows how to use the embed pop-up HTML snippet provided by Flowwise AI to integrate the chatbot into a website. The process involves copying the HTML code and pasting it into a custom code block within the website's content management system. The result is a functional chatbot that can interact with website visitors, providing information and assistance based on the chat flows created during the tutorial.
Mindmap
Keywords
💡Drag and Drop
💡AI Chatbot
💡Flowwise AI
💡Node.js
💡Command Prompt
💡NPM
💡Knowledge Base
💡Deployment
💡GitHub
💡Persistence
💡Render.com
Highlights
Introduction to Flowwise AI, a drag and drop visual AI chatbot builder.
How to create unique AI chatbot experiences for users.
Installation of Flowwise AI tool, which is entirely free.
Deployment and embedding of the chatbot into websites and web projects.
Accessing the Flowwise AI website for installation instructions.
Downloading Node.js as the first step in the installation process.
Instructions for non-coders on how to access and use Flowwise AI.
Using the command prompt to install npm and Flowwise AI.
Running the software on port 3000 to access it through a web browser.
Building a custom knowledge base AI-powered chatbot step by step.
Overview of the Flowwise AI interface and its main dashboard.
Using marketplace templates to build complex chatbot models and flows.
Creating a simple conversation chain with a document and language model.
Using Airtable to feed data into the large language model.
Building a large knowledge base around a specific topic.
Importing chat flows from the desktop version to the cloud version.
Deploying the chatbot to a web-hosted platform using GitHub and Render.
Ensuring persistence of data in the cloud instance of Flowwise AI.
Securing the cloud instance with a username and password.
Embedding the chatbot into a webpage using the provided HTML snippet.
Transcripts
in this tutorial I'm going to show you
everything you need to know about this
amazing drag and drop visual AI chatbot
Builder I'm going to show you how you
can create really unique AI chatbot
experiences for your users how to
install this tool which is entirely free
and most importantly how to get it
deployed and embedded into your own
websites and Web projects
so the two we're going to be using is
called flowwise AI if you come over to
flowwiseai.com and there's a link in the
description of the video below you'll be
able to find the installation
instructions but luckily I'm going to go
through that for you quickly now in the
next minute or so so that you'll be up
set up and ready to go and cut the
learning curve in half so if you just
come over to nodejs.org and again the
link will be in description of the video
and download node.js that's your first
step in this installation process if
you're not a coder and if you're a no
coder like myself you probably will
never have installed node.js if you're a
coder you probably already have this
installed if we now come back to
flowwise AI it gives you very brief
instructions as to how to get started
for me as a no coder I didn't find this
overly useful because I didn't know even
where to go to put these these Snippets
in but luckily I'm here to give a little
bit more information for no coders out
there into how to actually access this
really powerful tool the way you need to
go is on your computer you need to find
the command prompt and where you find
that is by going into your search bar
and searching for command prompt and
we'll get out the command prompt you'll
open that up
and this is where you basically follow
these instructions so I'm gonna install
npm
oops npm install Dash G flowwise now
I've already installed limo system but
what you do is when you type that in you
simply hit enter and you're probably
gonna need to wait a few minutes there's
gonna be a big load of text that flows
down through this the screen here and
it's just basically installing the
flowwise software onto your computer and
when that process is finished it will
let you know that it's finished and then
all you need to do is hit npx slowwise
start and once you hit that what this is
doing there is is running the software
that you've just installed and it is
um
it's going to run it on on a port 3000
essentially and all that means is that
you can access it through your web
browser by simply going to localhost or
colon 3000 and going there and now we've
got flowwise running locally on our
computer this is our own private instant
that is unique to your computer or your
machine that you've installed on and
you're now able to use this software
completely free of charge to build as
many chat Bots as you like and in the
next part of this video that's exactly
what we're going to do we're going to
show you step by step how to build your
very own custom knowledge base AI
powered chatbot and we're going to go
through this workflow here and show you
how to use the glowwise drag and drop
user interface get this all set up and
running and make sure you stick around
to the end because we're going to show
you when you have this build out how you
can then take it from your private
machine and push it into the cloud so
that you can make a version of it
available not just for your own private
use but so that you can make it
available for people on your website
site or inside of a web app that you
might be building or anywhere online
that you might want to use as opposed to
just being local and personal to your
use so first a brief overview of the
flowers AI interface this is your main
dashboard you can add chat flows here
each of these boxes represent an
individual chat flow and a chat flow is
essentially a specific chat bot with a
certain functionality or a certain
ability that you any of these chatbots
you can use personally on your local
machine and you can interact with the
chatbot as you have built it and you can
interact with it in real time as you're
building it and then also you can use
any of these chatbots to deploy them and
to have them embedded on your own web
page or inside your own web app and
again I'll show you how to do that later
on in the video where I'd normally start
is come into marketplaces because it's
got a huge amount of template in here
and I show the sample of the various use
cases that you can use this tool and to
help you build really complex chatbot
models and flows today we're just going
to start with this simple conversation
chain so this lets us preview the
template in this particular template
there's just three components so each
card here represents essentially a
module or a component this chain
component is really what brings it all
together and the chain is basically you
can have various different types of
chains this one is a conversational
chain and it's basically allowing us to
attach a language model or an open AI
language model in this in this context
but you can use other language models
and we'll show you what you have on
offer inside of flowwise when we get
into it and it allows you to attach a
memory in this case it's just using the
buffer memory module and the buffer
memory is basically whereby it just
stores the chat history so that means
usually it would save sort of the last
five messages in the conversation and
send that into the language model every
single time that a user puts in a
message so that it has the context of
the conversation and it can respond as
if it is having a conversation with you
and then this one all also allows you to
attach in a document and so we'll use
that today in our use case we're going
to use we're going to use this to build
essentially a website fac type Chopper
so that you can install it on your
website and you can go to your website
instead of reading your entire website
they can just ask questions directly to
your chatbot and it's a pretty good
starter use case we'll then in future
videos show more complex builds inside
of flowwise and would love to hear if
you've got any particular use cases or
any particular things that you're trying
to build a chatbot around one thing that
I'd love to show next is how to actually
chat with your airtable base for example
and how to use an airtable component in
here to basically feed data from
airtable into your large language model
as and when needed so it pulls only the
relevant bits based on the user's prompt
and other use case will be building up a
large data store a large knowledge base
around a particular topic and it could
be anything from sort of help to
legislation into anything where there's
just Reams and reams of information that
you don't necessarily want to feed the
entirety of it in a single document like
in this use case where you want to have
a system so that you can query that data
use open AI features to basically find
the relevant bits of information inside
of your knowledge base and then feed
that into your into your language model
at the point of the prompt being asked
anyway going on a tangent next step is
to click this use template button okay
so that now creates a working version of
the template into the workspace here
that we can now configure for the
purposes of building our chatbot and
then so what we'll do first is we will
bring in the document module because
it's the only one that's not yet
connected and it'll give us an
opportunity to show you just the menu
over here and the different options that
you have we'll then configure the open
AI API key and I'll show you where to
get that and we'll then show you how to
troubleshoot and to actually test the
chatbot in the workspace environment
here and then after that we'll move on
to the deployment and actually getting
it into a live web page and getting it
functioning so if we come over here
we're going to look for a document
loader component when we click this plus
it shows you all of the different types
of categories of components that you can
have so you can see that agents those
chains there's chat models there's
document loaders and that's where we're
going to need to go and you know and
these are all of the different
components that can be used in a chatbot
build and as you can imagine they can
get relatively complex over time in this
one we're just going to have four
modules so if we come down to document
loaders and I'm gonna try and find just
the text file loader just as sort of a
very basic working example here where
every time you see one of these cards
it's gonna have something down in the
bottom right hand corner and if it's
blue it means that it's available to
attach to something else and so you'll
see here if we just zoom in there
and this one is looking to be attached
to a document node up here so we're
going to actually just click and drag
that up and connect it to our chain so
at the moment now our chain has our
language model the memory and then the
document down here and what we're going
to do is we're going to go to my home
page here and we're actually just going
to copy all of this and put it into a
text file so I've opened up notepad here
and I'm just going to paste it in there
it doesn't really matter about
formatting at all and I'm just going to
save this as a txt file so I've saved
the file as no code homepage.txt and I
can now close out of it and I've got to
come back into the flowwise workspace
here and if I click upload file I found
the file on my desktop and I clicked it
and it's now uploaded here and the only
thing that now won't let this work is
that I don't yet have the open AI API
key so I'm going to come over to open AI
you're going to need to log in here I'm
going to go into the API selection
and then if you come over here to the
top right and find your profile it's
going to have view API Keys here and I'm
going to come in here and I'm going to
create a new secret key and I'll just
call this the flowwise five create
secret key I'm going to copy that and
I'm going to come back into flowwise
here and I'm going to drop that in there
I'm not going to change any of the
settings here I'm definitely going to
keep it on 3.5 turbo because it's far
far cheaper than the gbt4 and when
you're just playing around and testing
with things you definitely don't want to
be using gbt4 because it will start to
to rack up costs whereas gbt 3.5 turbo
you can feed a huge amount of prompting
into it and only incur sort of sense or
you know not not too much of a spend so
you can you can play at it and then be
be sure that you won't be breaking the
bank and so now that we've done all of
that your Chapel won't work yet over
here until you've actually clicked save
so I'm going to click save I'm actually
asking me for the name of the chat flow
and I'm just going to call it simple
a conversation with text Doc and just
same as that
and it's now saved so now you can test
as you're building your chat bot okay so
we're gonna ask it a question that I
suppose proves that it's using the
information that we just drew from the
web page so I'm gonna ask it something
that a user to the web page might ask
like what services do you offer
and see what it comes up with training
consultancy one-to-one online community
so that looks pretty spot on if we go to
the website we have this section here we
offer training wonderful consultancy and
we're running online community so yeah
that seems to be doing a really good job
there and um you know this is a really
effective and simple way to build a
customer service type chatbot for your
website
so we'll move on to the deployment now
of this chatbot of out of our sort of
local environment and onto a web hosted
platform and then from there we're going
to show you how to take anything that
you've built from the local environment
and how to put it into your now web
hosted environment so that you're able
to utilize whatever scenarios or models
that you build locally on the internet
essentially or anywhere that you want to
use it because we can't use it directly
from our local machine because there's
no server there's no API access to this
local instance Overton that's why we
need to deploy and use a web instance so
to deploy the app you're going to need
to come and set up a GitHub account
GitHub is a repository essentially
whereby coders come in here and they
store all of their code and they allow
other coders and developers to share the
code we won't need to know anything more
about GitHub other than how to set up
and sign up an account on it and then
how to take the application that we want
to use in the next step so if you come
here click sign up and you're logged in
you can then just type and what we're
looking for is flowwise and that should
come up top here so this one here click
on flowwise and then what you're going
to want to do is just click this button
here called fork and what that's going
to do is it's actually just going to
make a complete copy of flowwise onto
your
in onto your GitHub so that you can now
use the flowey software wherever it is
that you would like to use it so if we
just go create Fork
and it's now running down in the
background well that's running I'll show
you the next tool that we're going to
need to use to actually get the app
deployed and so that we can actually use
it online as this app called render.com
this app again we will sign up and start
using on the free plan so if we go get
started I'm gonna sign in with my Google
account and I'm going to hit complete
sign up so when you log into your render
account for the first time what we're
going to want to do is create a web
service so if we click new web service
and I'm going to connect the GitHub that
we just set up so connect GitHub I'm
already logged into my GitHub account
and so I'm just gonna go authorize
render you are being redirected I've
connected my thing and it's going to
save which repositories I'm going to say
select repositories and I'm going to
find flowwise and click that I'm then
going to click install
brings it back to the screen and you
just go new web servers again we're now
connected to our flowwise repository so
click connect here
and you need to just give it a name and
a region so I'm just going to call it no
code IE no code Ireland
chatbot
region I'm just going to choose noted
either overly close to me EU Central
probably is the closest and I got that
if you're in the US or if you're in Asia
probably choose one of the other options
that'd probably be quicker for you to
come down here now to your runtime and
you will need to change this to node and
then if we just change it to yarn
install here leave yarn build as and
then down here yarn start and that
should all be fine exactly as it is
there and I'm gonna actually sign up on
the starter plan here at seven dollars a
month and the reason I'm going for a
paid plan is for persistence so
persistence is the concept whereby you
want the data that you actually create
inside of your flowwise application to
persist meaning that it will live on
every single time that you come back to
that app to use it again it will have
everything that you have created
previously saved in there and it won't
be deleted every time that you return to
the app essentially if you go for the
free plan currently render doesn't allow
the persistence feature under the free
plan so that's why I'm going with the
seven dollars per month if you do want
to just use flowwise for just building
things purely without actually
publishing it or deploying it online you
can always install it locally on your
machine and use it as much as you want
and it will process for as long as you
have that computer and then when you are
ready to deploy something you can then
come to a service like render and deploy
it to the cloud and start paying seven
dollars a month or you could find a
different service for example replit
Amazon web services there's lots of
other hosting providers and you might
have one that you prefer already or you
might not have heard of any of these
types of tools before and in which case
render is a great option in terms of
Simplicity and in terms of it working
well with flowwise so after a few
minutes your instance will show us live
and that's when you know everything has
worked and that you're up and running
and now to access your flowwise instance
in the cloud all you need to do is Click
your url here and it should open your
flowwise in interface you can see this
is a brand new version of flowwise AI so
anything that we've built already in the
desktop version is not yet presented
here however it's very easy to import
chat flows from your desktop version
into the cloud version and when you load
it into Cloud version you can now use it
for deployment online in any web project
or web app that you would like to use it
in before we do that what I think we
should do is talk about the persistence
point that I spoke about earlier which
is whereby any chat flow that you have
inside of your cloudflowwise AI instance
you don't want that chat flow to be lost
every time that you go outside of render
and you log out or render and then you
come back into render and log back in
and you'd start backup flowwise or you
click on this URL and you're back into
flowwise there's a risk that your chat
flow the memory that stores the chat
flows might actually be lost so the
actual chat flows that you've built may
be lost and thus this whole important
concept of persistence so we should go
and set that up now with that now that
we're on a paid plan and if you come
into disks this is where you can do it
and you simply click add disk we'll just
leave it as my disk and then the path
here you're going to want to use this
specific path leave the size at 10
gigabits and click save we now need to
go into the environment section here and
we need to add three new environment
variables these are the three
environment variables that we need
database path API keypath and log path
and the values for each of them are opt
render dot flowwise same for this one
for API keypath and then the last one
did the same but we add on logs at the
end once we've done that we'll hit save
changes the other thing that's really
important to update if you're going to
use flowwise AI in the cloud rather than
locally on your machine is that anybody
that now has this URL can essentially
access your instance or your Cloud
instance of flow wise and so if you've
been starting to build out chat flows in
here you know when you have API Keys
possibly in those chat flows anybody
that comes along to this URL address
will be able to get into your workings
and see what you're doing they'll be
able to basically act as if they are you
at this website so what we should do
there is go back into render and we'll
actually create a username and a
password so that anybody that lands on
this web URL will need to know the
password to be able to log in to start
actually working on your check flows or
looking at your chat flows so we come
back over to render and we come down and
go into environment so these are the two
additional environment variables that
we'll need the first one being flowwise
underscore password and the cycle being
flowwise underscore username and you
just fill in whatever you want so I've
made password password and username
Jamie and you use whatever details you
would like and that's what you're going
to be asked to import every time you go
to your Cloud instance of flowwise
you're going to be asked for whatever
username and password that you've input
here and only you will know that so only
you can work in your flowwise account
and hit save changes the last step is to
go to manual deploy click shared build
cash and deploy it is now redeploying so
you'll have to wait just another few
minutes while it redeploys and we'll
come back in just a sec so the instance
is updated now and I've clicked back
into the link and it asked me for a
username and passwords I've entered my
details and I hit login and I'm now back
into my flowwise AI dashboard in the
cloud and so now all we need to do is
take the chat flow that we built in our
local environment and upload it into our
Cloud environment and the way to do that
is if you come back to the local
environment here on localhost we'll find
our chat which is here simple
conversation with tech stock and what
we're going to do is go to settings here
and go export chat flow okay so that's
exported the chat flow now so we're
going to come back into the cloud
version of it and rather than clicking
on settings we're actually going to
click on add new because this is how you
actually add a an individual chat flow
and if we go over the settings now up
here and go low chat flow we found our
file and we double clicked it and it is
now uploaded as an title chat flow so
we'll just save this as
simple
chat with text
Dock and click save
okay and now that is saved and if we
click save here and I'll just double
check that everything is working okay
class services do you offer
okay so it didn't work and it's
obviously because I need to actually
input the open AI API key again and I
also need to upload the document again
so bear with me one second
okay I've done both of those things now
so I'm going to come in here and I'm
just going to clear the the chat clear
and I'm gonna come back in and I'm gonna
ask it the same question what services
do you offer and let's see here we go
so it seems to be working Consulting
service looking to learn our goals teach
business owners how to use no code build
okay and that all looks really good and
it looks like it's coming directly from
the web page so that's brilliant so if
we just hit save and
um that's essentially it so now if you
want to actually use it inside of a web
page what you do is you click this
little blue button here
in your Cloud hosted version and I
usually just use this embed pop-up HTML
version and so you literally just copy
this
snippet here and I'm my website is built
inside of software so I'm going to go
into the software studio and I'm going
to add a custom code block the custom
code block is under the static category
here here's the custom code block I'm
going to add it in it's going to appear
up here and I'll paste in the exact
snippet that came directly from the flow
it's Cloud version from this blue
section here and then if I come back and
if I just hit publish
and in software this is probably
software specific I'm just going to get
rid of the padding on that code block
actually so that you don't see a big
white space at the top of the screen and
if we hit publish and if we come back
here and there's currently no chat bot
on the side so if we refresh it
and let's just see it's now showing on
the site and if we just go over what
services do you offer
so here it goes for training multiple
consultancy in online community looks
absolutely perfect so look that brings
to see into today's tutorial if you
enjoyed the video I would really
appreciate if you'd click like and the
video consider subscribing to the
channel also if you'd like to see any
future content in this area or any
future tutorials around chatbots or no
code or anything like that let me know
and please leave a comment below thank
you very much for watching and talk to
you soon
浏览更多相关视频
Build AI Apps in 5 Minutes: Dify AI + Docker Setup
Build An E-Commerce AI Chatbot | STEP-BY-STEP
Come creare un Chatbot AI in 5 minuti (Tutorial Completo)
Ollama-Run large language models Locally-Run Llama 2, Code Llama, and other models
Come creare un AI Chatbot Come creare un assistente virtuale in pochi minuti con MerlinAI
2-Langchain Series-Building Chatbot Using Paid And Open Source LLM's using Langchain And Ollama
5.0 / 5 (0 votes)