Oh, that was just a cheap move to get you to come to the next video.
We're not going to do building.
We've got one more slide to go.
I just want to say one more thing, which is if you look back at this diagram that is showing you this
idea of an endpoint request response HTTP to an endpoint, and then receiving an HTTP request to a webhook
to notify us of something.
There are nodes in that apply for each of those we've already discovered them.
Like with slack showing here, we had a trigger that took a webhook and we had a, uh, like a, like
a posting a message which which was a typical request response.
And in many cases in HN, you'll find the sort of pair of nodes, one that takes a webhook and is for
incoming events, and one that is about making a API calls.
There are also generic nodes that are used to talk to platforms which haven't been explicitly built
in N810 there hasn't.
There isn't an integration component directly for that third party, but it has end points, so it has
webhooks that it expects.
And so you can sort of generically integrate with it based only on those URLs.
And so there is a generic version of a request response node called the HTTP request node.
And so it just calls a URL endpoint.
You give it a URL.
The node output is based on the results whatever comes back from that call.
And then there is also a generic version on the other side there's a generic type of webhook and it
is called the webhook node.
And it has both a test URL and a production URL, just like the slack one did.
It has a URL that can be called as a webhook for in test mode, or when you've actually deployed and
you've activated that workflow, and it's triggered when someone else hits that URL, and you can configure
it either to then respond immediately to the third party, or it can carry out a whole workflow and
you can configure it so that it responds with whatever a later step returns comes up with.
And those are some different configuration details we'll look at.
But basically my point is that with some some external systems like slack, you get out of the box nodes
like two nodes, one one for triggers and one for actually making making calls to it.
And if you don't have an out of the box node like this, you can kind of make your own using these two
generic nodes the HTTP request node and the webhook node.
So hopefully that made sense.
It's again something that will repeat a few times, so you'll get used to it.
But that is how you build your own integration with a platform that isn't already sort of coming out
of the box with any turn.
Okay, it's time to be building stuff.
I've teased you with this.
Now we're gonna do it, and we're gonna start in 11 labs.
You remember looking at 11 labs here?
I've just signed in.
You should as well.
You've got this drop down here.
Agents platform.
Creative platform.
Well, remember we're going to begin with technique one.
N810 is going to be the boss.
So we're not going to be setting up agents in 11 labs, but rather we're going to want to just use 11
labs as a back end that can convert speech to text and back again.
And the way we do that is using its API.
And to do that, you need an API key.
And this is how you get it.
You go to developers which is down here, and you click on developers.
And here we have a the section for developers.
And there is a tab here API keys.
And you go there and up it comes with some API keys like my one here Ed Key you are going to press Create
key.
I know you know how to make API keys, but I'm still going to show you it.
It gives it a name and you're going to give it whatever name you want.
Like I can call this key two.
Uh, and it's good to have restrict key turned on.
Now first thing it has here is monthly.
And this looks like a monthly limit, which is a good thing to have.
And you might think, like I originally did that typing ten in there would mean $10 a month, but it
doesn't.
It's talking about the number of credits and most calls we make take take 3060 credits or something
like that.
And you should have lots of free credits that came with your plan.
And if you're on a pay plan like me, I've got tens of thousands of free credits baked in, so you should
put whatever monthly number you want here, but I recommend a number like 500 is a good a good number
for what we might want to do.
And keep an eye on this.
And you might need to come up and dial it up if you've if you've used up your monthly credit limit and
then you want to be able to basically have access to all these things.
So select access to this, access to this access access access.
And then we don't need write access.
I think read should be fine for all of these things here.
And then uh, I don't think we're going to use these but access for everything anyway and then read
for all of these things.
And when you've done that, you press the create key button.
It pops up with a key.
You copy it into your clipboard and you say, okay.
And you keep that there in your clipboard because we're going to need to paste it somewhere else, and
maybe you keep a record of it safe and sound.
And if you ever have a problem with your API key, you know what to do.
You come back here and you create another one, and you can always come here, and you can disable a
key if you're no longer using it.
And you can also come in and edit it if you want to give it different permissions.
So this is the thing to do.
Always be careful with your API keys.
Copy it carefully.
Don't paste it into a word processor.
You know the deal.
Uh, and you can always create a second one.
Okay.
So here we are back into innate and I'm going to sign in.
This is signing into our cloud accounts and into our instance.
And here we are.
And I'm going to press the Create Workflow button here to start our new workflow.
And let's begin this time with with the with a chat the on chat message to get us started in the usual
way.
Click and escape to come back here.
If you're seeing logs up here like this, remember you can just click there to get rid of them okay.
And then let's click plus or press tab and then go to the AI agent selection.
Here it is.
Escape back we come the chat model.
So so you can use as always open router or open AI.
This time just just for this week to keep it different I'm going to look to use Google Gemini.
Google Gemini is going to be our one for today.
Uh, and for Google there's two different there's Google Gemini which is referring to the AI studio,
uh, Google's AI studio.
There's also vertex AI, which is a bigger deal, and which if you know it, you know it.
Otherwise, stick with this one.
And for the credential I'm going to go create new credential.
And I have set up an API key.
And I have set this up at AI studio google.com, which is where you set up the Or you just search AI
studio, Gemini AI studio.
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
the connection was tested successfully, as will you, because you are a pro with API keys and they
never give you any trouble.
All right.
So with that we're going to be experimenting with Gemini.
And uh, let's uh, let's just just finish this off.
So we're not going to give it any, uh, tools or any memory for this, this one.
Um, let me just see what I see.
We've got Gemini 2.5 flash.
Let's see if we can't give it Gemini three.
Hang on.
Um, models.
Gemini three.
Uh, there we go.
They've got Gemini three.
Flash preview.
I imagine by the time you see this, it'll just be Gemini three flash.
But you should pick the one you want.
That's what I'll pick.
And with that, let's come here, open the chat and say hi there.
And off it goes.
Hello.
How can I help you today?
Great.
We have Gemini working.
Not much to do with voice just yet.
Okay, well, the next thing we're gonna do is add on something here, which is going to make an API
call to 11 labs to turn this text into audio.
So I'm going to press the plus there.
And I'm going to look for 11 labs and up.
It comes as a possible next node.
Now when you click on this it will come up here.
And I imagine for you you will see it as like not installed.
And there'll be a button here to install it.
And that is something you want to press that button to install it.
If you're I assume you're using cloud, in which case it's just a matter of pressing that button and
it gets installed.
If you are actually using the self-hosted version, uh, then you have to go through a couple more steps,
which you can just Google to find out about it.
Super simple.
It's like a community node that you just install, but it's very quick and then it will be installed
like this.
And now we can come down here to the possible actions, the node actions.
And we want convert text to speech.
That's what we want.
And up comes this node for us to connect to 11 labs okay.
And the first thing we got to do is select a credential, create a new credential.
And it needs the 11 labs API key.
The thing we created over in 11 labs.
And I'm now gonna paste in that key that's in my clipboard.
Obviously if you just copied one from Gemini then that won't work.
It's got to be 11 labs API key.
And I save that.
And I'm a pro at this and that's why it says connected and tested successfully first time.
And you got that too because you're a pro as well.
Congrats.
All right.
If you didn't you're like uh but hopefully you did.
All right.
So now we choose the operation text to speech.
We choose the voice from the list.
Let's choose Ms..
Walker Because she's warm and reassuring.
And that's what we like.
And now we have to choose the text to say this.
This is where this this is the input that will be sent to 11 labs to turn into text.
And you know, if we put the word bananas in there, then it would always generate audio for bananas.
And that's not what we want.
We want it to be different every time.
And so we're talking about an expression.
And you may be wondering why can't we say let the model decide.
Remember you can only do that with tools.
It's only if it were a tool that you could do that.
And this isn't a tool.
This is just the next step in the workflow, because we don't want the LLM to decide whether or not
to generate audio.
We always want to generate audio.
So there's no there's no LLM decision here.
It's a step in the workflow.
So how do we how do we put the text here to say use whatever was the output from the LLM.
Well of course we don't want something fixed.
We want an expression.
And you're a pro with expressions as well now.
So hopefully I'll give you a moment to think what the expression is.
First of all, there's curly braces around it and two of them like that.
And then the way that you say, I want the, the, the, uh, the item from what came into me in the
form of JSON, you do dollar, JSON.
And what I want from that is the field you can see on the left there that's called output.
So you do dot output and look at that.
What you see in result is hello how can I help you today.
Perfect.
And of course the other way you can do it.
You don't need to type it out like that.
You can also just simply drag and drop over there like so.
And if you have to, you have to drop properly.
Try that again.
There we go.
And you get the same thing.
You don't need to type the code.
You can always just drag and drop.
And so this will configure us so that whatever's coming in uh to this node, it looks into it, it looks
at the output field.
It takes that text and it sends that text to 11 labs for text to speech for Ms..
Walker using our 11 labs API key.