Dify: Build Chatbots in Minutes using Open Source LLMOps Platform

--- type: transcript date: 2023-07-25 youtube_id: D2xJaLuJ_Vo --- # Transcript: Dify: Build Chatbots in Minutes using Open Source LLMOps Platform all right in this video I wanted to show you Diffy which is a very simple way that you can both create a chatbot with your own information whether it's text files or PDFs or other other types of files and be able to create and deploy a chatbot in just a few minutes now the thing with Diffy that is unique is it doesn't just give you the ability to create a one-off chatbot is it essentially gives you an llm Ops platform which is sort of like a backend as a service interface so if I just go ahead and show you what I mean by that is essentially once you deploy this to your own infrastructure so you you'll have a Docker container that you could apply to AWS or Azure and you can essentially uh have this type of experience uh set up on your own end and now because it's open source you'll be able to go ahead and change this and tweak it as you see fit now the thing with Diffy you can go ahead and look at their license it basically gives you the ability to use this more or less however you see fit so long as you're not competing on their core service so essentially uh from what I read if so long as you're not creating a cloud platform and essentially copying exactly what they have here for their their product offering you can use this how you see fit so if you want to change out The Branding and you know make it sort of your your own you know company or or what have you um you can go ahead and sort of tweak this as you see fit so now on their actual product offering it does show you a different a number of different templates that you can get started with for different chat bots so there's a very simple flow where it gives you the ability of having data sets and apps and these can tie in to one another so say if you have one data set but you want to play around with a number of different ch bot uh versions you could do that so it has that flexibility where the data sets and the app themselves aren't bound and tied together which is really nice now the data sets themselves this was the thing that I found most impressive because what this is doing behind the scenes having coded these things in Lang chain and and shown many of you on the channel how to set up certain aspects of this thing is this gives you the ability to manage all of it so it gives you the ability to embed them delete them uh manage the different documents you know if you have multiple documents so that's a common question that I get on this channel is how do I do this with my own PDF or I want to chat with my own PDF so here is one option for you uh if you're looking to create a uh chatbot with your own document so once you're within the data sets here and you're within the particular data set that you want to uh upload different files you can see here you just go ahead add files and then once you've uploaded the files it will break these up into chunks and on the back end here it will embed these so embedding is essentially giving your application the ability to quickly query the relatedness of what a user is asking within their llm prompt and returning results and giving you that natural Lang anguage at the end um with its response so there's a couple pieces to to all this working but you can see here that the nice thing with this is it gives you it all broken up within a visual guey so you can even go ahead and turn off different pieces of your PDF if say if there's something that's not pertinent so there's lots of like little features in here which is quite impressive for a project from my understanding that was just started a handful of months ago so once you have it all set up um I'll just show you a local version here so now to actually set up the local version it's really as simple as pulling down the repo making sure your system requirements are adequate which they almost certainly will be you can see you know how little uh you know CPU and RAM that you need to get this set up and then you simply go ahead make sure Docker is running and then you can compose that container so once you have that you'll have this view which is essentially or rather this view which is essentially the same thing of their product version but local on your machine so once you've gone ahead and configured this you know you could go ahead and deploy this on your Cloud infrastructure if you'd like and you can sort of have more or less control over uh the ability to you know change the different pieces and sort of make this your own so once you're in here you'll see that there is uh the same interface but in this example I actually set up an that same SEC filing and I plugged it into a demo for this video so if I go ahead here you can see I have a simple you know question what is this file in this case there's just one and you see it's a 10q from Apple and I can say how were the earnings this quarter so the unique part with this it almost goes without saying is it gives you the ability to use uh data that is up toate whereas on llms such as chat GPT it doesn't give you upto-date data because it's trained on data with quite a lag so it could be like a couple years lag from when they initially train that model so you can see here like you know it starts to give me an answer where it's you know it reported a net income of 24 billion and you can see here it has also suggestions that you can tie in so if I go within Diffy and I were to edit the application and I go into um the application here you can see that there's a few different options here so you see the context that I want to use is that data set and then in terms of the different features that I have turned on I have the follow-up questions which is what you see at the bottom and then there's also that ability to have uh speech to text so if I just go back here you can see that there is also the microphone icon so there's a whole lot within here that you know you can build a very impressive uh chatbot with your own data in just a handful of minutes and not only build your own chatbot but you can have a whole infrastructure so you could build and deploy a handful of these right you could essentially have a chatbot as a service or a bot uh agency if you will uh from something like this so a very cool project I encourage you to head over to GitHub check it out give it a star um and just explore it for for yourselves it's a very cool project I'll definitely be diving into it a little bit more if there's anything else that I find that's worth sharing I'll make sure to do that as well but otherwise if you found this video useful please like comment share and subscribe and until the next one