And just to show you what is harder to do if we open this up and look for sheets and open up the Google
Sheets tool and go to Select Credentials and add a new credential.
You may remember that when we were on NHN cloud, it was relatively easy to connect to a sheet.
We pressed a button here, it launched a pop up and we logged into Google and we were done.
And that works because any N cloud has already had like a handshake with Google in the past.
Google knows who cloud is.
There's been some sort of OAuth two integration that's happened at that level, and as a result, it's
relatively quick to add yourself in.
But when we're running on our local box, Google Cloud has no idea who this is.
And so there's more hoops to jump through to be able to set this credentials in place.
It's a bit of a rigmarole, but it's perfectly doable.
We will do some of this tomorrow, but when you're running locally, you have to do it a lot.
Okay, let's come out of that close and back we go and we'll delete this new box.
The final thing I want to show you, I'm going to show you very quickly because it's super optional,
as I think I'd mentioned, and it is about running a models locally.
I do want to give you a demo of this, and for people that know about it, you can you can do this.
And for those that are that are new, you can just just get get some intuition for it.
But I do want to add in Olama.
So Olama Olama what is it is a product that lets you run models locally on your computer in a fast way,
and you can install it by pressing the download button here.
It will bring up a little chat box, and you can use that to chat with different models locally, and
it runs on your local computer.
Running those models, you're probably already familiar.
If you're listening to this part, you probably already have it.
If not, give it a shot, download it and run it and get get a sense for that.
We're about to hook up to that running locally.
Okay, once you've installed Olama just by pressing the download button and following the quick instructions
there, it should come up and it will look like this.
It's like a chat interface.
It might be white background or black background.
This is a llama.
And over here in this dropdown you pick one of the open source models that you could talk to.
And you can also find out more details about them by going to the models page on their website.
The ones with a cloud next to them are ones and which which say colon cloud in the name are not the
ones you want to pick.
They're the ones that would be remote.
We want the ones local.
On your computer, pick one of the ones that would be local on your computer.
This button here is how you download it, so be sure to download a few models and pick the smaller models
if you've got a smaller computer.
And again, look at the models page over here for more information about which models to pick.
Once you've picked one that's like, I could pick a GPT OS right here, you can then send a message
to it like, hi there!
And it will then think GPT OS is a big model that needs you to have at least 16GB of Ram on your GPU,
maybe.
Maybe more, or in your CPU.
It will just be even slower, but eventually this will reply.
Uh, you see, my screen just froze there because my computer is really, really working very hard on
this.
And there we go.
It finally it says, hello, how can I help you today?
And I think I jittered there because of course my computer is being slammed, as would yours.
But you could also pick a much smaller model.
I've got a really tiny one here.
Gemma from Google.
And if I say, uh.
Hi.
I'm great.
How are you now?
Going to Gemma three.
It's going to be a bit of a random but quick answer that we'll get from a tiny model that will hopefully
be somewhat coherent at the end of it, but don't expect too much.
But it's fine.
I'm doing well.
Thanks for asking.
All right, so download a few models, play with them.
And then coming up next we're going to connect them to Nan.
So the first thing I'm going to do is come back to the terminal which is running our n810 inside our
container here.
And I'm going to stop it and I'm going to clear my screen.
and now I'm going to run the same command as before to start it up again, but with one tiny difference.
If I show you this command, I've got it with the slashes again.
But but you can, uh, take them out.
This is the extra line I've put in there.
Now, what this is doing for people new to Docker is it's saying I also want to to make it so that there
is a new a web, a web address available inside my container called Host Internal.
And that gives me access to my host computer.
And that's going to mean that we're going to be able to run things within that can connect to the host,
which is where Obama is running.
So when I when I run this again, it's going to launch my container.
It's much quicker.
It's going to launch my my container based on the image.
It's already downloaded.
So it ran just like that.
And we can now go back in to to localhost five, six, seven eight.
And we will find that this web address is a web address that it will know about inside there.
Let's go see.
Okay, so I'm now back looking at my workflow.
And I'm going to refresh this screen, leave the site, refresh the screen because I want to bring it
back up again because we've stopped and started.
And in the meantime here we are.
Okay.
First up I'm going to delete this open router chat model.
Let's just just just bin that.
And I'm going to add in a new chat model.
The chat model that I'm picking it's going to be Olama Olama chat model okay.
And now we need to select a credential to connect with.
And that's kind of weird.
We don't have a credential.
Exactly.
Because we're going to be just connecting to the local computer.
It's not really a credential, it's the URL.
Now people that use Olama will recognize this because localhost 11434.
That is indeed where Olama runs and listens on your local computer.
If you've if you've launched Olama and you're chatting with it, it's listening on that port 11434 it's
ready for us.
So what can we do here?
Well, I'm going to paste in a different URL which is host internal and the same port 11434.
And that of course that is where we have told the container.
We've mapped that to our host computer.
Now again, if this is this is this is a too much, too much technical detail for you.
Don't worry about it.
Just get the gist.
I just want to show you this working.
And now when I press save we will hopefully see green the connection tested successfully.
This is hooked up to a llama running on my local machine.
So now I can I can exit out of here.
We now have this set up and when I go here, I'll see all of the models that I have downloaded locally
in llama and from within the UI you can you can download more models.
So we could pick GPT OSS, the DB version.
I want to shake things up.
I'm going to pick uh minstrel three.
This is the latest model for me from the French AI startup mistral.
That's very popular.
So minstrel three to have a bit of a different take.
Let's use that open source model running locally on my computer right here.
Uh, and, uh, so X escape out of here.
Here we are.
Back.
Ready?
Let's chat.
We're going to be chatting with Nan running locally to a model running locally on O llama.
Let's give it a try.
Okay.
Open chat.
Hi there.
It's not going to be fast because, uh, it's going to be chugging away on my computer.
I can see my GPU is, uh, is, uh, hammering up.
Hello.
How can I assist you today?
Uh, there we go.
It worked.
Okay.
Big moment for us.
What is the share price of Apple?
All right, off it goes.
I can see the tool just got called.
I can see my GPUs being used again.
There we go.
It already responded.
Current closing share price is $270.
There we have it.
That's a success.
Don't worry if you if you didn't do this part of it.
That doesn't matter.
If you're into open source models running on your computer, then for sure you should do this.
Run a llama, do this, give it a try, and, uh, hopefully you will see that working nicely.
As I say, this is this is a bit more of a technical point, but I think it's really cool to see everything
running locally on a machine and and running locally and the model running locally too.
And with that we can save this.
We can go back to the overview.
You'll notice a very familiar screen here.
And indeed you've got workflows.
The credentials of course shows us the different credentials that we've set up so far and executions
the same as before.
This is where we can see all the different executions that have happened for all of the workflows in
this account.
And that wraps up the tour of Nh10 running self-hosted.
I will see you for the wrap up.
All right.
Well, I hope that the technical people feel satisfied with the bit of technical detail that we got
to there.
The commercial people know that.
They've seen it got some perspective of what it's like.
And for the rest of it, we're going to be focusing on the commercial side.
But tech people, you can feel free to keep going in the self-hosted version and keep working with open
source models potentially running locally on your machine.
But that then, is a wrap today.
One of week three self-hosted and local.
We did all that.
We ran it in locally and it looked pretty much the same.
And we even briefly at the end there connected to Olama, which you can do two if you wish.
From tomorrow we go back to the cloud version to polish off the week with advanced integrations with
MCP, and then with lots of stuff leading up to the capstone project all about amplifying your business.
With that, you are 73% of the way through to being a pro.
See you tomorrow.