WEBVTT

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Okay, so here we have our full workflow.

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Let's tidy it up.

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Uh, this thing has a red box here.

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Parameter table name is required.

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That does sound sensible, doesn't it?

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Uh, back we come.

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We need to tell it which table this is going in.

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You probably spotted that intentional uh, miss there.

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Uh, so we come back here to the super base vector store thing, and of course, it's asking, uh,

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what what table name should we pick here?

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We right click here and we'll see that knowledge base is the table that we created.

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That's the thing that our SQL script created called Knowledge Base.

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That's exactly what we want.

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Very good.

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Thank you for prompting me with that.

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And here we have our complete, uh, our workflow that's ready for data ingest.

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We should probably rename it to something that sounds important, like data ingest.

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And we should be ready to, uh.

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Now, give this a try.

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Let's just go back quickly to super base, uh, and go to our database table.

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Uh, and so we go to database over on the left here loading tables.

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Here it comes.

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Here's the knowledge base.

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Let's open that by clicking on that button on the top right here.

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And the table is empty.

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There's no data.

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Our knowledge base is empty and so should yours be.

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Let's go back over here.

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Let's hide the logs, give us a nice clean screen.

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And I'm simply going to press the Execute workflow button to kick this whole thing off.

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All right here we go.

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Execute workflow.

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It's loading in the 60 items.

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It's embedding them.

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It's working with super bass and tick workflow executed successfully.

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See the number 60 down there telling us that 60 documents were loaded.

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They've been vectorized.

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They've been chunked.

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Although we didn't actually need to chunk anything.

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Uh, each row has turned into a vector, has been put in super base.

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Everything is green.

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This would have been potentially a week of work or something if you were trying to code it, trying

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to stitch together all the pieces.

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But thanks to N810 and Super Base, we did this in a matter of minutes.

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Or did we?

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The way to find out is to go and look at the database.

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Okay, here we are.

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Our rag project that it says the table is empty.

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That's because it doesn't refresh.

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Oh, it did refresh automatically.

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Ah, here we go.

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Here it is.

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This is our knowledge base.

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What have we got here, everybody?

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Let's see.

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So we have, uh, we have 60 items.

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I think you'll find, uh, and, uh, in these 60 items, each one of them has an ID, it has content,

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which is text, it has metadata, and it has embedding, which is a bunch of numbers.

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If I double click on this, let's have a look.

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This is one of the contents.

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It is the stuff that you remember.

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We wrote this very nicely.

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This is stuff which would be useful to give an LLM should there be a question that is related to it.

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Uh, and uh, over here is, uh, um, here is the, the, uh, the details of the metadata, which

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has a, a category in it.

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There you see it, there's the category.

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It got added into the metadata just as we wanted.

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There's also some some metadata that is automatically added in there.

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And over here is the vector.

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I almost double click on this and escape out of that.

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This would have the the 1536.

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Is that right?

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1536 numbers, which represents a point in that 1536 dimensional space that best reflects the meaning

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of this content.

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But while I was showing you that, I realized I made a silly mistake.

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Just a typo.

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But it gives a chance for me to quickly show how easy it is to do this.

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So I'm going to embrace the typo.

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You may have noticed this yourself.

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I bet you didn't make this mistake in here.

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This looks all very nice.

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Fancy text that we constructed for the LM.

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Says the price is $1.59 .99, and somehow I put in an extra dollar sign there as well.

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The description is dollar vertical design.

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And if we look at another one here you'll see the description is dollar compact.

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Oops.

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Well luckily this is so simple just to rerun it thanks to the the how easy and streamlined these workflows

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are.

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So first up we need to, uh, probably delete what we've got here.

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Let's just start again.

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So I'm just going to click here and I'm going to go over here and press delete.

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60 rows can't be undone.

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Oh, well.

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Off it goes.

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The 60 rows are gone.

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We've now got an empty database again.

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Let's come back to Edit fields.

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If I double click on here, you will see here I'm an idiot.

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Look, there's an extra dollar sign in there.

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The description as a dollar sign.

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I don't know how I did that, but I will remove it, I will save, I will come back to here again and

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then we will kick this off.

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It's also a nice point to mention that yellow line and that triangle there is telling us that that this

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node has changed since we last ran it as the as the pop up tells you, uh, so that that the things

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aren't consistent right now.

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So I press this again to run it again.

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Everything runs.

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This is again putting it in the vector data store.

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60 things are in there.

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Fabulous.

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Let's go back here, see if it refreshes automatically like it did last time.

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Yes it does.

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Here it is.

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Let's double click on one of these and the dollar sign is gone.

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No more nonsense.

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We've got good data here.

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And again it's it's just terrific that it's so easy to rerun the pipeline with a small mistake like

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that fixed quickly.

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Excellent.

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We're in great shape.

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We've built our data, ingest pipes, and they work.

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And so to wrap up first, to put this in front of you again, we talked about the two different phases

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the data ingest that they're doing today.

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And the pipeline question answering will do tomorrow.

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And in the steps of that ingest, the data source represents the fact that we had a Google sheet with

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product data in it extract that was the node in N810 that that connected to Google and pulled in the

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Google Sheet transform.

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That was the the field, the node, the field settings node that did a field mapping and allowed us

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to create the new thing with the content and the category as the output, we transform the data.

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Fairly simple transformation.

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It can be a lot more involved than that, but it was a good way to learn about it.

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And then chunking and vectorizing that happened because of those two extra sub nodes that we had there.

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Did you notice that that whole thing was was another node cluster?

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The AI agent is a node cluster.

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And then the thing we had to to connect to super base.

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That was another node cluster.

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Uh, and then we did the loading and put it in super base in a Postgres database that we set up in Super

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Base.

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So that was the whole of the flow of our data pipes.

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And we have a dedicated workflow to handle our data pipes.

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And it's triggered right now by pressing a button.

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And you could turn that to be something that's triggered on a schedule.

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You could do it with something that's triggered when the spreadsheet changes.

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You can experiment with different triggers to see what could kick off your data pipes.

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All right.

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And that is a wrap on data ingest and connecting to super base and working with vectors.

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It was uh, hopefully it was uh, it was it was pretty satisfying.

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But I think that definitely we hit the bullseye tomorrow when we pull this together and we complete

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our project for week two, all about accelerating, building with voice agents and with Rag.

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And that brings us to to the final slide, to say that you have reached the 60% point 60% tomorrow.

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Conclusion of week two.

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You don't want to miss it.
