So here we are in the edit fields node.
And and this is such a core node.
This node used to be called the set uh node is this is where a data engineer lives.
This is all about mapping data from one format to another.
It's got nothing to do with AI, although of course working with data is all about AI.
But, uh, but it's absolutely crucial for this kind of, of data engineering work.
And you can see that there are some, some special, clever ways to do it with JSON.
But we're going to do manual mapping, which I think is most most common.
And I'm going to to explain that what we're going to want to do is create a data set which has two fields,
a field called content, which is going to contain text, which is going to be best to go in our knowledge
base language, which can be vectorized.
And then another field called category which will say, what category of thing is this?
And that's because that feels like that's useful metadata that we'd like to tag everything with.
However it gets vectorized.
Um, and that's just we're not we're probably not going to make huge use of that, but it's a great
practice to have that kind of metadata, and it could be used to filter on the results in different
ways.
So add field I press and this allows me to to come up with with some fields.
And I said we're going to have one field that's called content.
And we're going to have another field that's called category.
So that everything that comes out, every every record, every record in the inputs, every, every
one of these items is going to end up with a content and a category being built on the right hand side.
Okay.
Let's do content first.
Okay.
So again we're writing here the what should go in a field called content which is going to come over
here.
And we can choose what type it is.
Is it a string a number true or false, an array a list, an object or just some some something else,
some something like a PDF document or something.
And we are going to say string.
And what's going to go in here is going to be the text associated with this information.
And, and really this is up to us.
We want to describe something that if this ends up going to an MLM, it will be it will be useful.
So we might as well just try and turn this information here into something which will be handy.
So we will we will say something like a product name colon.
And now we want and sorry I want this to be an expression not not fixed.
So product name.
And then we will have the name of the product.
So remember how we're going to do this.
It's dollar JSON dot name.
And that you can see in the result below that says product name colon.
And then the the keyboard.
All right let's put an empty line let's say category.
And let's put in the category dollar Jason.
And then category spelt with a capital C because that's how it is in the table that's coming in.
And then we will.
Why not.
We'll just we'll just put everything basically we'll say this the SKU which is dollar Jason dot SKU
and then we'll give it the price.
Dollar Jason Price.
Now let's see how that comes out.
Do you see if you look, if you look down at the at the result, you'll see the prices come there just
as it came in without a dollar sign.
Because that's how it is in the table.
And maybe it will be helpful for us to put a dollar sign there so that that's what goes to the model.
And it knows in this content that that it's getting something that is that is a dollar value.
Uh, okay.
And then finally and most importantly description, uh, spell it right.
Description.
And this is where I'm going to put in the, um, the description of it, of course coming in from the
input.
There it goes.
Okay.
And so this is a field called content.
And this is what, what what it evaluates to it comes to this bunch of text down here.
Now to someone that's used to data engineering and mapping things carefully, this might seem a bit
janky.
We're just sort of describe some text here, and I came up with product name because I thought it sounded
good for an LLM.
And and this is really the sort of where data data engineering meets data science, where it becomes
more of an AI concern.
We want to try and come up with a string here.
That's going to be most reasonable if that's sent to an LLM.
So it gives it the most relevant context to answer a question about this product.
And so, you know, there's no right or wrong way of doing it.
You want to experiment.
You want to try different ways that you can make this content be rich for the LLM and get that right.
And the best way to find out is to experiment, trial and error until you get good outcomes.
But this seems like a perfectly reasonable starting point to me.
Okay.
And now next up we've got this category field that I have a feeling is going to be useful.
And we want that to be basically the contents of this field right here, which is also called category,
but with a capital C, because that's how it is in the table.
Okay.
So uh, we should be able to do this quite easily.
Now we click on expression and we could drag and drop from here.
But now we're getting more familiar with this.
We know it's JSON dot category with a capital C.
There it is.
Keyboard that seems to work perfectly.
We've got ourselves a mapping we have mapped from this stuff here on the left to something new, which
is going to have two fields content and category.
Content is going to be some text that we feel is going to be useful for an LLM category is going to
be whether it's a keyboard, whether it's a monitor or whatever it happens to be.
All right, let's give this a try so we can simply press the execute step button right here.
Just press that and look what happens on the right.
It shows us what's the result of mapping this.
Through this we get this.
We get something that's perfectly civilized.
Uh, product name, category, keyboard, price, description.
That's what we'll be providing to the LLM as context.
And we also have this useful extra piece of information category is keyboard.
Excellent.
We have just done some data mapping.
And if I now go back to the prior screen and I press Execute Workflow.
It does it for the whole lot.
All 60 rows have just been mapped to the new data format, and that is the beginning of some data pipes.
Okay.
At this point I'd like you please to go to Super Bass.
This is Super bass.com, and if you haven't already, please set up an account.
You need to to create an organization.
As I said yesterday my mine is called Donna Research.
Feel free to call it whatever you want.
Don't call it Donna research though.
Uh, and then once you've set it up, you should find yourself pretty quickly at like a projects page.
You won't have any projects yet.
If not, then click on this projects in the left nav or click on the name of your organization and come
here.
You should be able to find your way to this screen, where there should be a new project button that
you can press and come on in and let's call the project right as the name of the project.
The compute size can be tiny.
You should be able to be on the free plan.
If you're on a paid plan, then this costs a cent an hour.
And of course you can bring this down.
Delete it after we're done with this activity.
Um, and I will.
Then I will come up with a database password region.
You should pick the region closest to you.
Mine would normally be in the Americas, but I am in the UK today.
So Europe is, I guess, right for me right now.
Uh, but I think I'm going to go Americas anyway because I probably want to be closest to where my An
is running.
Um, and, uh, once you're ready with that, we'll press create new project.
Okay.
So I've just put in a strong password there.
I've kept a record of my password, but safely in a password manager, as should you.
And now I'm pressing Create New Project.
And it's telling me that that because I'm on a paid plan, it will increase my costs.
But.
But I think this should be something that will be free for you as part of the the trial free account.
And it's been set up and here we go.
Uh, it's called rag.
It's getting set up.
It will take a few minutes while it while it gets things together, including your API endpoints.
This is terminology that you're so familiar with.
And then we will be the proud owners of our first super base project.