Now I know what you're thinking.
We've been building environments for so long.
Are we not done yet?
We're almost done.
There's two more steps to go, and then we are through with environment setup and onto projects.
So the next thing we have to do is set up our keys to use open AI so that we can connect from our JupyterLab
environment to run with some of the most powerful models on the planet.
Uh, if you go to the Readme, uh, in our GitHub repository and you scroll down a little bit, we'll
get to the point where there are the links to the major providers, and we're just going to be using
open AI for this week.
And if you click on that link, it will take us to the open AI page.
If you've never been before, you will have to sign up or sign in using Google credentials or creating
an account.
Now, before we go further, I have to explain something that's pretty confusing, which does sometimes,
uh, throw people all of the major closed source providers like OpenAI and anthropic for Cloud and Google
for Gemini have two different types of plan that are completely separate and don't have any relationship
with each other.
One of them relates to their web tools, the front end tools, which you can use to chat with like famously
ChatGPT that we all know well.
ChatGPT has a pro plan that you can pay for if you want to, to be able to use some of the latest models
that we'll look at later, like, oh one preview and GPT four with canvas and others.
Uh, that needs a pro plan in some cases.
Uh, and the Pro plan costs about $20 a month in the US and similar pricing in other regions.
Um, a fixed monthly price, and then you have almost unlimited access to those models.
The same is true for anthropic.
Uh, same same kind of thing.
And, uh, Google Gemini also.
But this is entirely separate to the API world.
The API world is where you can use.
You can call out to the model directly using code, and it's running on the cloud, and it will respond
with answers to your questions.
It's a different type of pricing plan.
It is there's no monthly subscription.
There's no no payment whatsoever monthly.
But you do have to pay per API request.
It's a pay per request situation, and the payment per request will talk more detail about API costs
later.
But it's really tiny for any project that we work on in this course.
For most of the projects, except where I will mention otherwise, it is a fraction of a cent it is
tiny, so there is really no harm in trying out some of these APIs and it gets you such power, gives
you access to such incredible models, even though some of the models will use their open source are
very powerful.
These frontier closed source models are super powerful, as you will see, and so it makes sense that
you have to pay a small amount for the compute that goes behind the processing to to generate these
responses.
But having said that, there is a hitch.
There is something about this pricing, uh, which is uh, in some cases it's uh, it's new and it may
be different in different regions, but certainly for me and for, for OpenAI, uh, they have a minimum
that you have to put down to use the API.
And in my case right now it is a $5 minimum.
So you have to put in at least $5.
And then as you use the API, you'll start to charge down against that $5.
We're not going to use we're going to use a fraction of that and this entire course, uh, but there'll
be plenty of other ways that you can use that $5.
I assure you.
There are so many projects will build.
There'll be so many exercises for you for real, useful things, ways that you'll be able to spend that
and have a good outcome from it.
So my sales pitch to you would be that it is well worth it.
I would do it, but it is, of course a personal choice.
If you don't feel comfortable putting down the $5 or you'd rather not, that's fine.
You can watch me as I go through the exercises, and what I'll do is I'll show you a way that you can
use open source models.
Instead, we'll be able to use a llama.
The thing that we did at the very, very start, and you'll be able to use that as an alternative to
using a frontier model.
Now, the results, of course, are not going to be the same.
The it's a, uh, the llama models will be using as like a 2 billion parameter model, uh, compared
to GPT four, that is rumored to be about 10 trillion parameters, uh, perhaps like 10,000 times as
many.
So it is a different quality of model, but you would have to put down that $5 up front.
So that's the decision for you.
And again I my my take is that it is worth it.
So in order to do this though, what you need to do is once you've come into the OpenAI platform, you've
logged in, you've got to this point.
There's just two things you have to do.
You first have to go to the settings button up here.
And on settings over here you go to billing and on billing.
This is where you need to have some credit balance to be able to use the GPT four zero.
And the minimum that it will take is $5.
You have to press add to credit balance and then punch in a credit card.
And it will take $5 and keep auto recharge off.
You don't want them snapping from your card without your permission.
As I say this, this has been it.
I use it all the time, and I barely manage to get beyond a few cents a day because we use the GPT four
mini, the cheap version, almost exclusively on this project, on this course.
So.
So this $5 is more than enough and you'll be able to put it to fine use, I assure you.
So if you are comfortable doing this then go ahead, go to billing, put on your $5.
And then the next thing is to go to dashboard over here and then down to API keys here.
And then you have to come here and press this create new secret key button right here.
Uh, you have it set to you.
You can give it a name if you wish.
You give permissions all and then you press the Create Secret key button.
And when you do that, it's going to show you a new secret key that you will have created.
And it will give you a chance to copy that secret key into your clipboard.
And you need to do that because it won't let you ever see it again.
This is the most you'll ever see of that secret key again.
It is your secret and you have to copy it and keep it somewhere safe.
We will use it in just a second.
Once you've done that, we'll be ready to actually put this key to use.
And we are almost there.
We are almost done.
So with any luck, you have now got a secret key copied.
You've forked out $5 or local currency equivalent and you're ready to go.
And I will see you in the next video for the very final step of setting up our environment.