WEBVTT

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Oh, that was just a cheap move to get you to come to the next video.

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We're not going to do building.

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We've got one more slide to go.

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I just want to say one more thing, which is if you look back at this diagram that is showing you this

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idea of an endpoint request response HTTP to an endpoint, and then receiving an HTTP request to a webhook

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to notify us of something.

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There are nodes in that apply for each of those we've already discovered them.

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Like with slack showing here, we had a trigger that took a webhook and we had a, uh, like a, like

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a posting a message which which was a typical request response.

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And in many cases in HN, you'll find the sort of pair of nodes, one that takes a webhook and is for

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incoming events, and one that is about making a API calls.

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There are also generic nodes that are used to talk to platforms which haven't been explicitly built

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in N810 there hasn't.

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There isn't an integration component directly for that third party, but it has end points, so it has

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webhooks that it expects.

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And so you can sort of generically integrate with it based only on those URLs.

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And so there is a generic version of a request response node called the HTTP request node.

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And so it just calls a URL endpoint.

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You give it a URL.

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The node output is based on the results whatever comes back from that call.

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And then there is also a generic version on the other side there's a generic type of webhook and it

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is called the webhook node.

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And it has both a test URL and a production URL, just like the slack one did.

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It has a URL that can be called as a webhook for in test mode, or when you've actually deployed and

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you've activated that workflow, and it's triggered when someone else hits that URL, and you can configure

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it either to then respond immediately to the third party, or it can carry out a whole workflow and

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you can configure it so that it responds with whatever a later step returns comes up with.

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And those are some different configuration details we'll look at.

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But basically my point is that with some some external systems like slack, you get out of the box nodes

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like two nodes, one one for triggers and one for actually making making calls to it.

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And if you don't have an out of the box node like this, you can kind of make your own using these two

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generic nodes the HTTP request node and the webhook node.

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So hopefully that made sense.

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It's again something that will repeat a few times, so you'll get used to it.

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But that is how you build your own integration with a platform that isn't already sort of coming out

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of the box with any turn.

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Okay, it's time to be building stuff.

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I've teased you with this.

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Now we're gonna do it, and we're gonna start in 11 labs.

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You remember looking at 11 labs here?

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I've just signed in.

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You should as well.

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You've got this drop down here.

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Agents platform.

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Creative platform.

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Well, remember we're going to begin with technique one.

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N810 is going to be the boss.

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So we're not going to be setting up agents in 11 labs, but rather we're going to want to just use 11

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labs as a back end that can convert speech to text and back again.

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And the way we do that is using its API.

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And to do that, you need an API key.

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And this is how you get it.

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You go to developers which is down here, and you click on developers.

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And here we have a the section for developers.

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And there is a tab here API keys.

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And you go there and up it comes with some API keys like my one here Ed Key you are going to press Create

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key.

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I know you know how to make API keys, but I'm still going to show you it.

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It gives it a name and you're going to give it whatever name you want.

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Like I can call this key two.

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Uh, and it's good to have restrict key turned on.

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Now first thing it has here is monthly.

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And this looks like a monthly limit, which is a good thing to have.

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And you might think, like I originally did that typing ten in there would mean $10 a month, but it

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doesn't.

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It's talking about the number of credits and most calls we make take take 3060 credits or something

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like that.

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And you should have lots of free credits that came with your plan.

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And if you're on a pay plan like me, I've got tens of thousands of free credits baked in, so you should

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put whatever monthly number you want here, but I recommend a number like 500 is a good a good number

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for what we might want to do.

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And keep an eye on this.

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And you might need to come up and dial it up if you've if you've used up your monthly credit limit and

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then you want to be able to basically have access to all these things.

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So select access to this, access to this access access access.

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And then we don't need write access.

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I think read should be fine for all of these things here.

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And then uh, I don't think we're going to use these but access for everything anyway and then read

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for all of these things.

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And when you've done that, you press the create key button.

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It pops up with a key.

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You copy it into your clipboard and you say, okay.

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And you keep that there in your clipboard because we're going to need to paste it somewhere else, and

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maybe you keep a record of it safe and sound.

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And if you ever have a problem with your API key, you know what to do.

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You come back here and you create another one, and you can always come here, and you can disable a

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key if you're no longer using it.

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And you can also come in and edit it if you want to give it different permissions.

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So this is the thing to do.

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Always be careful with your API keys.

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Copy it carefully.

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Don't paste it into a word processor.

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You know the deal.

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Uh, and you can always create a second one.

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Okay.

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So here we are back into innate and I'm going to sign in.

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This is signing into our cloud accounts and into our instance.

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And here we are.

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And I'm going to press the Create Workflow button here to start our new workflow.

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And let's begin this time with with the with a chat the on chat message to get us started in the usual

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way.

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Click and escape to come back here.

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If you're seeing logs up here like this, remember you can just click there to get rid of them okay.

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And then let's click plus or press tab and then go to the AI agent selection.

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Here it is.

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Escape back we come the chat model.

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So so you can use as always open router or open AI.

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This time just just for this week to keep it different I'm going to look to use Google Gemini.

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Google Gemini is going to be our one for today.

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Uh, and for Google there's two different there's Google Gemini which is referring to the AI studio,

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uh, Google's AI studio.

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There's also vertex AI, which is a bigger deal, and which if you know it, you know it.

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Otherwise, stick with this one.

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And for the credential I'm going to go create new credential.

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And I have set up an API key.

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And I have set this up at AI studio google.com, which is where you set up the Or you just search AI

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studio, Gemini AI studio.

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And I have set up an API key and I've been careful and I paste it in here and I press save and I see

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the connection was tested successfully, as will you, because you are a pro with API keys and they

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never give you any trouble.

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All right.

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So with that we're going to be experimenting with Gemini.

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And uh, let's uh, let's just just finish this off.

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So we're not going to give it any, uh, tools or any memory for this, this one.

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Um, let me just see what I see.

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We've got Gemini 2.5 flash.

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Let's see if we can't give it Gemini three.

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Hang on.

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Um, models.

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Gemini three.

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Uh, there we go.

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They've got Gemini three.

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Flash preview.

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I imagine by the time you see this, it'll just be Gemini three flash.

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But you should pick the one you want.

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That's what I'll pick.

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And with that, let's come here, open the chat and say hi there.

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And off it goes.

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Hello.

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How can I help you today?

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Great.

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We have Gemini working.

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Not much to do with voice just yet.

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Okay, well, the next thing we're gonna do is add on something here, which is going to make an API

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call to 11 labs to turn this text into audio.

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So I'm going to press the plus there.

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And I'm going to look for 11 labs and up.

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It comes as a possible next node.

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Now when you click on this it will come up here.

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And I imagine for you you will see it as like not installed.

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And there'll be a button here to install it.

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And that is something you want to press that button to install it.

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If you're I assume you're using cloud, in which case it's just a matter of pressing that button and

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it gets installed.

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If you are actually using the self-hosted version, uh, then you have to go through a couple more steps,

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which you can just Google to find out about it.

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Super simple.

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It's like a community node that you just install, but it's very quick and then it will be installed

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like this.

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And now we can come down here to the possible actions, the node actions.

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And we want convert text to speech.

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That's what we want.

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And up comes this node for us to connect to 11 labs okay.

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And the first thing we got to do is select a credential, create a new credential.

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And it needs the 11 labs API key.

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The thing we created over in 11 labs.

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And I'm now gonna paste in that key that's in my clipboard.

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Obviously if you just copied one from Gemini then that won't work.

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It's got to be 11 labs API key.

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And I save that.

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And I'm a pro at this and that's why it says connected and tested successfully first time.

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And you got that too because you're a pro as well.

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Congrats.

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All right.

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If you didn't you're like uh but hopefully you did.

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All right.

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So now we choose the operation text to speech.

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We choose the voice from the list.

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Let's choose Ms..

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Walker Because she's warm and reassuring.

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And that's what we like.

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And now we have to choose the text to say this.

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This is where this this is the input that will be sent to 11 labs to turn into text.

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And you know, if we put the word bananas in there, then it would always generate audio for bananas.

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And that's not what we want.

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We want it to be different every time.

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And so we're talking about an expression.

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And you may be wondering why can't we say let the model decide.

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Remember you can only do that with tools.

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It's only if it were a tool that you could do that.

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And this isn't a tool.

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This is just the next step in the workflow, because we don't want the LLM to decide whether or not

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to generate audio.

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We always want to generate audio.

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So there's no there's no LLM decision here.

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It's a step in the workflow.

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So how do we how do we put the text here to say use whatever was the output from the LLM.

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Well of course we don't want something fixed.

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We want an expression.

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And you're a pro with expressions as well now.

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So hopefully I'll give you a moment to think what the expression is.

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First of all, there's curly braces around it and two of them like that.

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And then the way that you say, I want the, the, the, uh, the item from what came into me in the

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form of JSON, you do dollar, JSON.

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And what I want from that is the field you can see on the left there that's called output.

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So you do dot output and look at that.

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What you see in result is hello how can I help you today.

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Perfect.

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And of course the other way you can do it.

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You don't need to type it out like that.

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You can also just simply drag and drop over there like so.

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And if you have to, you have to drop properly.

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Try that again.

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There we go.

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And you get the same thing.

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You don't need to type the code.

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You can always just drag and drop.

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And so this will configure us so that whatever's coming in uh to this node, it looks into it, it looks

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at the output field.

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It takes that text and it sends that text to 11 labs for text to speech for Ms..

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Walker using our 11 labs API key.
