- [Instructor] Before we can start building this agent,
we do need to set up our development environment.
And I'm going to be using two main tools
that I need to install.
The first is Ollama,
and that's going to run our large language model locally,
and we're also going to need to install Python
and some dependencies for our agents code.
But first, let's take a look at Ollama.
Now, I love Ollama.
It's a fantastic tool that lets you run powerful,
open source, large language models right on your machine.
So if you don't have it already,
open up your web browser and go to Ollama.com as I am here,
and then you can download it.
It's available for MacOS, Windows, and Linux.
So once you've downloaded it,
follow the instructions for your operating system.
If you're on Mac like I am right now,
you will see a dmg like this one.
You can just double click it to open it.
And you'll get this window that allows you to drag Ollama
over to your applications folder to install it.
Now that Ollama is running, you do need to download
a large language model for it to use.
And you can use any large language model you like,
of course, but I'm going to be using
the GPT-OSS 20 billion parameter model
in these videos and in this course.
So if you want to get that, first of all,
you're going to open up
the Ollama window that you've done here,
and you can start sending a message
and chatting with Ollama.
If the model isn't already there,
it's going to download the model for you
and then it'll be useful for you to be able to use.
Or if you prefer to use a terminal,
you can use a terminal window like this one,
and you can type Ollama run gpt-oss:20b.
Again, if you don't have the model,
it'll download it for you.
As you can see, I already have the model here
so I can start chatting with it.
So now Ollama is good, up and running, you're good to go.
Okay, next, let's get our Python environment ready.
Now, I find it's really good practice
to use a virtual environment
that keeps our project dependency separate.
So for example, on a Mac out of the box,
you're going to have Python three,
but we don't want to be using Python three,
I'm just going to be using Python,
and I'll show you how to set that up
with a virtual environment.
You're going to say Python3-m venv,
venv stands for virtual environment,
and then give you a virtual environment a name,
like I could say '.venv' here.
And that's going to create a virtual environment
so any dependencies, any other stuff
that you're going to need to install
to be able to run your application
will go into that virtual environment
and not mess up your main system.
And you're going to activate that with source.
It was called .venv/bin/activate.
And now you can actually say Python instead of Python three
to be able to do any Python command.
So you'll see as I'm going through this course,
I'll be typing Python a lot, and that's how.
And also any installments, any stuff that I need to install,
any requirements and all that kind of thing
are going to be installed
completely in the virtual environment.
And while they're in that virtual environment,
they're not going to impact anything else on your system.
So that's it.
The environment is now set up and is ready to go.
So next up, we're going to take a closer look
at what an AI agent actually is.