Okay, let's do this.
What tool do we think we're going to pick?
We press plus and of course it is going to be exactly the same.
We do super bass.
We want a super bass vector store as our tool.
Up it comes.
Super bass account operation.
Uh, retrieve documents as tool for AI agent.
That sounds like a good one to me.
Uh, let's see the other things.
That's definitely the one, isn't it?
Um, so now a description.
So we need to, to use this to tell the agent when it would want to use use it.
So use this tool to look up any product information that relates to products offered by the company.
Use this tool to look up any information that relates to products offered by the company.
Keep it a bit generic so that this could apply to whatever your company is.
This time I will try not to forget to select a table name that I did last time.
It pops up and we're on knowledge base.
Uh, and uh, um, we'll include the metadata in the, in the result.
There's, there's like a limit thing here.
We might as well allow it to bring back ten documents.
I don't see why not.
Uh, and, uh.
Yeah.
Rerank results.
That's one of the fancy things.
If you want to use an LLM to decide how to reorder what comes back, which which you can look at, uh,
at some of that and experiment with it.
But we're not going to do that.
We're going to keep it keep it vanilla out of the box.
Um, and uh, so there we go.
Uh, here here is our tool.
It looks great.
It's got a red box, presumably because we haven't yet told it what embedding to use.
And you might be thinking, hang on, why do we need to use an embedding?
Haven't we already taken our data and turned it all into vectors?
And we got into the vector store.
But hopefully you know the answer to this.
You're like, ah, come on, we get this.
The reason you need an embedding is because when the user asks a question, we need to vectorize that
question.
Do you remember that like the first line on that on that diagram.
So, uh, yes, it needs to know what embedding model to use, what Vectorizer to use.
And we're using OpenAI embeddings.
There it is.
We are using text embedding three small uh, and uh, there we go.
That's all we need.
And now the angry red thing has gone away.
Everything looks good.
So, you know, presumably there's there's a there's a lot more complex than this.
This is just us getting started.
But no, this is it.
This should be it.
This.
This is what it takes to build a gigantic rag.
And I love the way that this this diagram looks quite similar to the diagram.
I drew the schematic on how it works.
That's the beautiful thing about Nw10 is that you can really sort of draw out there in this canvas,
just, just the way that you would think about it naturally.
Anyway.
It's it's very expressive and simple in that way.
All right.
So I think we should try I think we should try it.
All right, let's do it.
All right.
Here we go.
Hi there.
Everything's off.
Hello.
If you're looking for specific products or have questions about what we have in stock, feel free to
ask.
All right.
Because it read the tool description so it knows what we're dealing with.
Everything seems to work okay.
So let me say uh, do you, uh, sell any keyboards.
Let's see what happens.
You can see it's doing a query, a lookup a lookup.
Stuff's coming back.
It's going to answer us.
Haha.
Yes, yes, we have a wide selection of keyboards to suit different needs.
Here are some of our popular options.
Gaming and Performance Pro RGB gaming keyboard.
And here they all are.
Wow.
There's a lot of them.
And see that it tells us the price.
I was going to ask it for the price, but it's got the price and I do.
Luckily I was gonna go back and check at the database.
It's really there, but I actually recognize a couple of these.
So it's nice and it's given it a description, but it's grouped it and organized it its way.
And it's clear that because we've got that many, uh, coming back that, that, uh, it's able to give
us the full range of keyboards, which is terrific.
So, um, uh, how much does it cost to purchase more memory for my computer?
Let's see what it says.
It's looking it up.
And here we go.
The cost from the desktop or laptop, whether your system is blah, blah, blah, blah, blah.
And here it is.
And this is all the results that have come from the knowledge base.
And so here you have it.
I don't know if you thought this part was going to be complicated.
It's really not this.
I feel like this is when the simplicity of nw10 really comes to the forefront.
Building this rag pipeline was so easy.
And again, remember the big aha moment is when you realize how scalable this is.
It wouldn't be such a big deal if I told you that I could build a question answerer that could answer
questions about 60 products we've got, because you could just paste that into ChatGPT.
The point is, it doesn't need to be 60.
It could be 60,000, it could be 600,000.
This would work.
It would ingest, it would vectorize.
It would pick the nearest neighbors, and it would use this to be able to answer questions as if it
knew the whole lot.
And that that is the the true power of Rag and the power of Nan is that it made it all so easy.
But you know the deal.
You know that we're not here to to chat in this chat box right here.
That's not the point of week two, is it?
Not at all.
Let's hide the chat.
Let's come here and delete that.
We don't have any interest whatsoever.
It's time for us to build our voice agent.