Okay, so here we have our full workflow.
Let's tidy it up.
Uh, this thing has a red box here.
Parameter table name is required.
That does sound sensible, doesn't it?
Uh, back we come.
We need to tell it which table this is going in.
You probably spotted that intentional uh, miss there.
Uh, so we come back here to the super base vector store thing, and of course, it's asking, uh,
what what table name should we pick here?
We right click here and we'll see that knowledge base is the table that we created.
That's the thing that our SQL script created called Knowledge Base.
That's exactly what we want.
Very good.
Thank you for prompting me with that.
And here we have our complete, uh, our workflow that's ready for data ingest.
We should probably rename it to something that sounds important, like data ingest.
And we should be ready to, uh.
Now, give this a try.
Let's just go back quickly to super base, uh, and go to our database table.
Uh, and so we go to database over on the left here loading tables.
Here it comes.
Here's the knowledge base.
Let's open that by clicking on that button on the top right here.
And the table is empty.
There's no data.
Our knowledge base is empty and so should yours be.
Let's go back over here.
Let's hide the logs, give us a nice clean screen.
And I'm simply going to press the Execute workflow button to kick this whole thing off.
All right here we go.
Execute workflow.
It's loading in the 60 items.
It's embedding them.
It's working with super bass and tick workflow executed successfully.
See the number 60 down there telling us that 60 documents were loaded.
They've been vectorized.
They've been chunked.
Although we didn't actually need to chunk anything.
Uh, each row has turned into a vector, has been put in super base.
Everything is green.
This would have been potentially a week of work or something if you were trying to code it, trying
to stitch together all the pieces.
But thanks to N810 and Super Base, we did this in a matter of minutes.
Or did we?
The way to find out is to go and look at the database.
Okay, here we are.
Our rag project that it says the table is empty.
That's because it doesn't refresh.
Oh, it did refresh automatically.
Ah, here we go.
Here it is.
This is our knowledge base.
What have we got here, everybody?
Let's see.
So we have, uh, we have 60 items.
I think you'll find, uh, and, uh, in these 60 items, each one of them has an ID, it has content,
which is text, it has metadata, and it has embedding, which is a bunch of numbers.
If I double click on this, let's have a look.
This is one of the contents.
It is the stuff that you remember.
We wrote this very nicely.
This is stuff which would be useful to give an LLM should there be a question that is related to it.
Uh, and uh, over here is, uh, um, here is the, the, uh, the details of the metadata, which
has a, a category in it.
There you see it, there's the category.
It got added into the metadata just as we wanted.
There's also some some metadata that is automatically added in there.
And over here is the vector.
I almost double click on this and escape out of that.
This would have the the 1536.
Is that right?
1536 numbers, which represents a point in that 1536 dimensional space that best reflects the meaning
of this content.
But while I was showing you that, I realized I made a silly mistake.
Just a typo.
But it gives a chance for me to quickly show how easy it is to do this.
So I'm going to embrace the typo.
You may have noticed this yourself.
I bet you didn't make this mistake in here.
This looks all very nice.
Fancy text that we constructed for the LM.
Says the price is $1.59 .99, and somehow I put in an extra dollar sign there as well.
The description is dollar vertical design.
And if we look at another one here you'll see the description is dollar compact.
Oops.
Well luckily this is so simple just to rerun it thanks to the the how easy and streamlined these workflows
are.
So first up we need to, uh, probably delete what we've got here.
Let's just start again.
So I'm just going to click here and I'm going to go over here and press delete.
60 rows can't be undone.
Oh, well.
Off it goes.
The 60 rows are gone.
We've now got an empty database again.
Let's come back to Edit fields.
If I double click on here, you will see here I'm an idiot.
Look, there's an extra dollar sign in there.
The description as a dollar sign.
I don't know how I did that, but I will remove it, I will save, I will come back to here again and
then we will kick this off.
It's also a nice point to mention that yellow line and that triangle there is telling us that that this
node has changed since we last ran it as the as the pop up tells you, uh, so that that the things
aren't consistent right now.
So I press this again to run it again.
Everything runs.
This is again putting it in the vector data store.
60 things are in there.
Fabulous.
Let's go back here, see if it refreshes automatically like it did last time.
Yes it does.
Here it is.
Let's double click on one of these and the dollar sign is gone.
No more nonsense.
We've got good data here.
And again it's it's just terrific that it's so easy to rerun the pipeline with a small mistake like
that fixed quickly.
Excellent.
We're in great shape.
We've built our data, ingest pipes, and they work.
And so to wrap up first, to put this in front of you again, we talked about the two different phases
the data ingest that they're doing today.
And the pipeline question answering will do tomorrow.
And in the steps of that ingest, the data source represents the fact that we had a Google sheet with
product data in it extract that was the node in N810 that that connected to Google and pulled in the
Google Sheet transform.
That was the the field, the node, the field settings node that did a field mapping and allowed us
to create the new thing with the content and the category as the output, we transform the data.
Fairly simple transformation.
It can be a lot more involved than that, but it was a good way to learn about it.
And then chunking and vectorizing that happened because of those two extra sub nodes that we had there.
Did you notice that that whole thing was was another node cluster?
The AI agent is a node cluster.
And then the thing we had to to connect to super base.
That was another node cluster.
Uh, and then we did the loading and put it in super base in a Postgres database that we set up in Super
Base.
So that was the whole of the flow of our data pipes.
And we have a dedicated workflow to handle our data pipes.
And it's triggered right now by pressing a button.
And you could turn that to be something that's triggered on a schedule.
You could do it with something that's triggered when the spreadsheet changes.
You can experiment with different triggers to see what could kick off your data pipes.
All right.
And that is a wrap on data ingest and connecting to super base and working with vectors.
It was uh, hopefully it was uh, it was it was pretty satisfying.
But I think that definitely we hit the bullseye tomorrow when we pull this together and we complete
our project for week two, all about accelerating, building with voice agents and with Rag.
And that brings us to to the final slide, to say that you have reached the 60% point 60% tomorrow.
Conclusion of week two.
You don't want to miss it.