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So guys, we are going to continue the discussion of building the simple LM application using LCL.

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Now in this video we are going to talk about Lang serve.

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Right.

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And what other things we have learned in the past couple of videos.

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We are going to probably deploy that entire Lang application as Rest APIs.

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So here when I say deploy Lang chain Runnables and chain as Rest API, that basically means how with

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the help of Lang serve, we will be able to create Rest APIs, see deployment parts and all where to

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deploy, how to deploy.

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I will be talking about that in the upcoming videos.

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But here, before doing that specific deployment, how you can convert this entire Lang chain, complete

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chains and runnables as Rest API is super important.

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Okay, so for this, uh, when we are using Lang serve, this is basically integrated with fast API.

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So let's go ahead and do this specific coding.

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But this is a another amazing module that has been brought by Liang Chen.

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And it really makes your application, uh, like it will be very much easier to probably go ahead and

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create your entire chain.

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Okay.

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So I will be discussing about that.

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So first of all, what I will do, I will just go ahead and install one more library.

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So let's say that I'm in my V and V environment.

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So I'll say pip install.

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Um and here I will say fast API Okay.

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Let me just go ahead and install this.

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So I definitely require fast API over here.

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So here you can see the requirement is already installed because I have done that particular installation

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so that I don't want to waste any time.

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So once you do the fast API installation make sure that you update in the requirement dot txt.

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Also along with this we will also go ahead and install something called as Uvicorn.

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Okay, so this is also one thing that is basically required.

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So I will go over here.

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Uh, and here you can see requirement already satisfied with respect to Uvicorn also.

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Okay.

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So this is perfect.

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Now let's go ahead and start implementing it as you all remember.

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Uh, what all things we have already added so quickly.

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Uh, first of all, I will go ahead and import from now I'm using this.py file.

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See, this is a different file altogether in the same folder right before we were executing step by

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step over here in this particular file.

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Simple lm LCL right now in the same folder location that is basics of lang chain.

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Here I have created one another file which is called as server dot p y.

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Okay.

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And here is what we are going to implement.

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Everything whatever we have implemented over here.

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Right.

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And finally we'll be able to see that how the chain API looks like.

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So I'll write from fast API import fast API okay.

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And then here I will go ahead and write from long chain underscore core dot prompts import chat prompt

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template okay.

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And similarly from long chain underscore core dot output parser.

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We had also used something called as string output parser right.

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Chat prompt lit chat prompt template we had used.

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We had also used this, uh, string output parser.

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And along with this we had also used from lang chain underscore grok.

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We have imported chat Grok.

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Right along with this we'll go ahead and say import OS.

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And then I will also go ahead and write from dot env import load underscore dot env.

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We will go ahead and initialize load underscore dot env.

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So to load all the environment variables and you know that uh how to get our uh grok API key.

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So for that I will go ahead and write grok underscore API underscore key.

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This is the variable that name that I'm actually going to create.

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And I'll write OS dot get env.

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And here will basically be my grok underscore API underscore key okay.

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Now quickly I will go ahead and initialize my grok LM model.

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So quickly.

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Let's go over here.

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And you know that.

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How did we go ahead and initialize our grok LM model.

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So this was it.

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So I will just go ahead and copy this entire thing.

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Model Chad Grok model this one.

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And I'll write it over here.

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Right now my model is specifically ready.

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And this is what we have learned already in our previous video.

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Let me go ahead and import one more library now, which is called as Lang serve.

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Okay.

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Now before going ahead you need to also install Lang serve right.

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And without installing Lang serve it will be of no use.

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So since this is a new module, we will again go ahead and right pip install.

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And here I'm going to right lang serve okay lang serve.

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And with respect to the lang serve I will also give all if you want all the components that is specifically

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required.

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Otherwise I'll just go ahead and install Lang serve okay.

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Okay, so this is for the first time I'm installing because I did not install it.

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I wanted to show you, uh, so here you can see I'm having this particular length of 0.222.2 version.

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Right.

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So once I install it right you can just go ahead and right pip install.

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But please make sure that you update in the requirement dot txt okay.

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So after installing it over here I've installed the lang so or any libraries that you install.

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Please make sure that you update in the Lang sir.

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Okay uh sorry requirement dot txt.

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Now after importing lang stuff I will quickly go ahead and uh import from lang.

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So I will be importing add routes.

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Okay.

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You need to have a basic idea with respect to fast API.

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Okay.

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Now this add underscore routes will actually help you to create your APIs.

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right?

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So now let us go ahead.

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And as usual what I will do I will go ahead and create my entire prompt template.

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So here the same code, whatever we had in our ipynb file I have copied and pasted it over here.

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Right.

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This is my system template.

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Translate the following into this particular language.

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Then chat prompt template user with text.

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Okay, then I have already created my model.

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I will go ahead and also create my string output parser.

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So I will write.

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Parser is equal to string output parser.

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Okay, now after creating the string output parser let's create chain.

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So for creating chain what I need to do I need to combine these three things right.

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One is the prompt template.

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Another one is the model and the other one is something called as parser.

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Okay.

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So we'll go ahead and combine this.

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And finally we have this app definition that we have.

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Okay.

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Now for this app definition what I will do is that now we are going to use this add routes.

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So if I really want to add any kind of route over here I will go ahead and use this add route.

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And inside this add route which is coming from Lang save Lang.

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So I have to give my app the first app which I've actually created and still I have not defined the

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app also.

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So let me just go ahead and define the app.

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So I will go ahead and create my app over here app definition.

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And I'll say, hey, this will be my first API app.

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It is almost like flask, but there is some more additional functionalities that you get in fast API.

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But if you have seen this flask tutorials, I think you should be able to understand it.

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So I will give my title saying that this is my Lang chain server.

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Okay.

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And the second parameter that I'm actually going to give is nothing but version is equal to 1.0.

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And finally I will also go ahead and give my description.

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Description will be nothing, but I'll go ahead and write a simple API server using long chain runnable

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interfaces.

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Okay, so this, uh, I've actually given it.

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So this will basically be my first parameter in this route.

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Which app?

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I'm actually going to give that second thing I want to give my chain the chain uh, which has the combination

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of prompt template, LM model, string output parser, and the third one I have to actually specify

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my path.

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So here I'm going to specify slash chain as my path okay.

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You'll be understanding what exactly this route will basically do.

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So here, uh, this is basically adding chain routes.

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Okay.

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Perfect.

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Now it's time that, uh, we can go ahead and execute this.

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Uh, so I will write from underscore underscore name underscore underscore is double equal to underscore

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underscore main underscore underscore.

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This is how we start the entry of the program I will go ahead and import Uvicorn.

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Okay.

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Uvicorn.

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And then I will say Uvicorn dot run.

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This is how we run the fast API.

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We'll tell the app name I will give the host.

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Host will be my localhost okay local host.

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And finally I'll also give my port if I want.

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So port will be 8000 okay so these are all the information.

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Now see what we have exactly done.

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We are loading our chat graph model.

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Uh here we have created our prompt template.

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Then we have created our chain and output parser.

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We have created a fast API app.

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We have added the route write slash chain.

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And finally we are executing this.

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So now let's quickly go ahead and execute it okay.

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Don't forget to save the file over here.

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Now if you remember this particular uh file is present in the location 1.1 Basics of link chain.

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So let me just go ahead and write CD 1.1 basics of long chain.

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And here I'm just going to write Python serve.py.

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This is how you go ahead and exit your execute a fast API app.

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Once I execute it, I think it should get executed.

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But here I'm getting an error saying that hey pip install se starlet.

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Okay, I need this particular dependency so quickly I will go ahead and write it over here.

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Okay So quickly I will go ahead and execute this.

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Let me do one thing.

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Uh, instead of writing it over here, I'll open a new command prompt.

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Okay.

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And let me write it over here.

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Pip install.

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SSH.

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Starlet.

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This is one of the dependencies that we have.

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So once I have, uh, written or created the routes, now, what we are going to specifically do over

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here is that I will give the entry point and I'll start the execution.

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Right.

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So first of all, what I will do, I will write if uh underscore underscore name underscore underscore

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is double equal to underscore underscore main underscore underscore.

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So this is the entry point I will go ahead and import uvicorn okay.

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And along with this particular uvicorn uh I will go ahead and write Uvicorn.

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Uh, unicorn Unicorn.

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Unicorn.

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Dot.

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Run and app.

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Comma.

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Host is equal to.

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I will give my host as 127 .0.1.

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This will be my local host and here I will give my port as 8000.

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Okay.

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You can also replace this particular values to local host okay.

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Now let's go ahead and execute it.

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Remember I'm inside this particular folder 1.1 basics of long chain here.

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Uh I will just go ahead and write Python serve.py.

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Okay.

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And once we execute this here you'll be able to see that it is running over here.

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Let's click on this particular link okay.

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So here is the page that is visible.

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Uh right now in the home page nothing is basically visible.

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So what I will do I'll just go ahead and write slash docs Once I write slash docs.

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So this is what, uh, you know, uh, fast API uses pedantic rules.

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Okay.

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So that actually gives you all the APIs that is over there.

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Okay.

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So here, uh, it gives you this entire thing.

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You just need to write this particular URL slash docs.

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You'll be able to see that I have this chain invoke.

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Okay.

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Um, here I can pass any values.

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Then let's, let's discuss about this, uh, single by single.

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Right.

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So this basically has created this entire chain configuration, uh, with all the information that I

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have over here.

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Right.

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So, uh, if I just write slash chain, right.

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Uh, from that lang serve, you'll be able to see that here.

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You have this input schema, right?

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Uh, it says that you have to provide the input as string.

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Here is your output.

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You will be getting in the form of JSON.

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Okay.

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then you have this invoke function.

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Invoke basically means you will provide some value okay.

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And based on that value what all values will be providing like language and text okay.

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And uh, this is what is the kind of response that you are going to get.

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And this is all are there.

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Right.

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So all this information like let's say I want to go ahead and try out this invoke.

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Okay.

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So here uh, let's say language is called string.

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Uh string and text is equal to string.

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This is the request body.

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Okay, let me, uh, just go ahead and pass this values over here.

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Okay.

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So I'll say, hey, language is French and, uh, text is basically hi.

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And if I go ahead and execute it, you'll be able to see that it has got executed.

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I'm getting the output as salute okay.

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So guys, now let's explore more example from this particular documentation.

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See I have just created one chain that is slash chain slash invoke.

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You can create any number of chains over there.

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Let's say I will go ahead and write.

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Hello.

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My name is Krish okay.

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And this is what is the post request that needs to go.

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And if I just go ahead and click on execute it here you'll be able to see Bon Jour de la creche.

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Right.

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This is the output that I'm actually getting.

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So this is how you can actually go ahead and create chain.

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And along with this documentation, you'll also be seeing that there are some other information like

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slash chain slash input schema.

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See if I just go ahead and execute this since this is a Get request.

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So I'll say slash chain uh slash chain slash input Input schema.

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Input underscore schema.

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So if I execute it.

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So what is the information that I am able to get right.

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This is the prompt Im told this is the language.

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The text should be a string type and all the information.

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Along with this I can also go ahead and see some more information.

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So this is with respect to the input schema.

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Now how the output schema should look like I can go ahead and hit this URL right.

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And that is what is beautiful about, uh, fast API, you know, so I can go ahead and write like this.

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I will copy this same thing.

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I will go ahead and write it over here.

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And let me just go ahead and see my output schema.

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Okay.

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So this is the entire documentation, right.

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Uh, like the swagger documentation we see in fast API.

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So here it says that, hey, your string, uh, the output is a string output parse and the type is

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string.

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That entire information will be seen.

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So this is one Post request.

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You also have something like batch okay.

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You can give the let's let's try this out okay.

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So let's try it out over here.

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Let's say here I'm going to give language as Hindi I want to convert text into Hindi English into Hindi.

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So I'll say hello.

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Hello how are you.

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Okay.

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So I'll go ahead and execute it.

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Let's see what kind of output I will get.

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So here you can see uh output like Namaste.

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Right.

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How are you in Hindi.

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All the information over here with some more metadata information you are getting so that there is one

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more, uh, by default, one more Post request.

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When you created slash chain, right.

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So many things have got created like slash and slash invoke slash chain slash batch.

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And you can also use postman and you can probably use all these things.

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Right.

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Um, uh, so this is what, uh, you specifically get from this?

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Uh, I hope you're able to understand this particular video.

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00:15:14,000 --> 00:15:17,000
Let me just go ahead and show you with the help of postman also.

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Right.

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So if you don't know about postman, postman actually helps you to interact with any kind of APIs.

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Right.

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So let's open this and I will create one get uh, post request, let's say.

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So the post request uh, that I really want to get is slash chain slash batch.

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Okay.

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So here I will say okay, I'll dismiss this.

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I'll make it as a Post request.

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It is 127 point this the 8000, uh, port address is there.

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And here, let me just go ahead and write it over here.

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What it is slash n slash batch.

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Okay.

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Slash chain slash batch.

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Now, I know, uh, in my body I have to make this as my raw.

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Select my JSON.

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Okay?

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And here is what I'm actually going to give my entire body right now how my body should look like.

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Same this way.

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00:16:06,000 --> 00:16:07,000
Uh, where is it?

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00:16:07,000 --> 00:16:08,000
Okay.

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00:16:08,000 --> 00:16:09,000
This is the Get request okay.

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00:16:09,000 --> 00:16:11,000
So I just now tried it out, right.

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00:16:11,000 --> 00:16:16,000
So I will keep this entire is my body in the form of JSON.

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00:16:16,000 --> 00:16:17,000
I'll paste it over here.

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00:16:17,000 --> 00:16:19,000
Let's, uh, execute this.

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00:16:19,000 --> 00:16:24,000
So let me just keep this in string because I need to send it in the form of body.

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00:16:24,000 --> 00:16:28,000
And once I click on send here you can see invalid JSON body.

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00:16:28,000 --> 00:16:28,000
Okay.

327
00:16:28,000 --> 00:16:31,000
Let me just remove this I think this is invalid.

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00:16:32,000 --> 00:16:37,000
Now see once I send it here is what is my output along with my metadata.

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00:16:37,000 --> 00:16:41,000
So this way also you can go ahead and test each and every request.

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00:16:41,000 --> 00:16:42,000
And from the postman also you can do this.

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00:16:42,000 --> 00:16:45,000
You can just go ahead and download postman.

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00:16:45,000 --> 00:16:47,000
Go ahead and set the post request.

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00:16:47,000 --> 00:16:52,000
What is the URL and what should be your body right and you'll be able to get it.

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00:16:52,000 --> 00:16:55,000
So I hope you like this particular video.

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00:16:55,000 --> 00:16:55,000
You know.

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00:16:55,000 --> 00:17:00,000
So in this video what all things we basically did, we developed a simple LM application.

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00:17:00,000 --> 00:17:04,000
First of all I'll try to close this up okay I don't require it more.

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00:17:04,000 --> 00:17:08,000
First of all, we saw how to develop an LM application with the help of LCL.

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00:17:08,000 --> 00:17:13,000
Then we also saw how we used Lang server and probably created this entire application in the form of

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00:17:13,000 --> 00:17:15,000
chains and APIs, right?

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00:17:15,000 --> 00:17:20,000
That is the most amazing thing because after this, we can take this entire application and we can deploy

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00:17:20,000 --> 00:17:21,000
it wherever you want.

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00:17:21,000 --> 00:17:22,000
Okay.

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00:17:22,000 --> 00:17:26,000
And, uh, in the upcoming videos, you'll also be seeing, you know, in the upcoming modules, you'll

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00:17:26,000 --> 00:17:31,000
also be seeing that I'll be showing you various deployment mechanisms and techniques.

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00:17:31,000 --> 00:17:31,000
Right.

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00:17:31,000 --> 00:17:33,000
So yes, this was it from my side.

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00:17:33,000 --> 00:17:34,000
I hope you liked this particular video.

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I will see you all in the next video.

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Thank you.

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Take care.