Introduction to the LlamaIndex Playlist
Hello all. My name is Krish Naik and welcome to my YouTube channel. So guys, I'm starting a new playlist on an amazing framework which is called as LlamaIndex.
Now from past couple of months, I've been focusing more on generative AI, uploading amazing videos, implementing multiple end-to-end projects with the help of LLM models. So we have discussed already about OpenAI LLM models, we have discussed about Frameworks like LangChain, we have created a lot of end-to-end projects. Not only that, we have also seen open source LLM models like Llama 2 from Meta. We have also used Hugging Face libraries. We have seen how to do the deployment, and finally we have also implemented multiple end-to-end projects with the help of Google Gemini Pro and Google Gemini Pro Vision. All these things we have specifically done.
Now one more important framework that I'm going to discuss about is called as LlamaIndex. Again, in this playlist there will be a series of videos, and again we will see that how we can use LlamaIndex and even LangChain framework and create some amazing end-to-end projects and harness the power of the LLM models. All those things will be covered.
Video Agenda: LlamaIndex vs. LangChain
Now in this video, I'm going to discuss about two important things. What is LlamaIndex, right? And the second thing, which many people still have a lot of confusion, like what is the exact difference between LlamaIndex framework and LangChain. Not only that, we'll also discuss about one simple architecture of a project if you're combining LlamaIndex and LangChain, how your project architecture will look like.
And specifically, because many people think that LlamaIndex is also used to create an end-to-end GenAI project, it is... LangChain is also used to create an end-to-end GenAI projects or generative AI projects. Yes, it is used, but specifically in those architecture, where exactly is LlamaIndex used? Because LlamaIndex is very good at something which I will be discussing about it, and LangChain is super important and beneficial for some different applications in a specific project. So both these things will get covered.
Now let me go ahead and share my screen. Uh, just to keep a target for this particular video guys, please let's hit like and keep the target to 1,000 likes at least, because all these videos are completely for free. And as I said that, I want to democratize the entire AI education, so please do help me in that. So here exactly it is like, here is the LlamaIndex page itself. As I said, two important things I'm going to discuss, right? What exactly is LlamaIndex, why it is super beneficial, and the second thing is we'll compare the differences between LlamaIndex and LangChain. And with respect to a project architecture, where exactly is LlamaIndex used and where exactly LangChain used? Everything I'll be discussing about it.
The Core Functionality of LlamaIndex
Now first to first over here, this diagram will actually help you to understand where does LlamaIndex actually work. Whenever, guys, in your application, many people have different custom data, right? Let's say it be companies, it can be different data sources itself like YouTube, APIs, PDF, it can be Notions, it can be SQL. And this side you can probably see all the LLM models. Now if you have specific custom data... now let's see the definition. LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. Now to connect this entire data source to this large language models, right, that is where in those pipeline LlamaIndex can be used beneficially.
See, with the help of LlamaIndex, we can create an end-to-end project, but its core important feature is connecting the custom data to the LLM models. Now how do we connect this? You know, we take this entire data, we perform something called as indexing using LlamaIndex. And then once we create those indexing, right, then we will be able to query from that index because it creates the necessary metadata itself, this entire LlamaIndex library, and then when we're querying anything, we'll be able to get the response quickly. Right? We'll still discuss about multiple points with various features, what is the difference between LlamaIndex and LangChain. But here, I hope you got an idea. LlamaIndex is a simple, flexible data framework for connecting custom data source to large language models. Okay.
Some of the functionalities you'll be able to see, we'll be able to create document Q&A, data augmented chatbot, knowledge agents, structured analysis, many more things we'll be able to create. And the same thing we're also able to create with the help of LangChain. But what exactly is the difference? And in the entire project architecture, which pipeline we specifically use LlamaIndex, and that is where you'll be able to see over here in these three important steps we specifically use. One is the data ingestion, that basically means it provides you a lot of libraries to connect with your external data of different data sources like APIs, PDF, document, SQL, etc. Then after ingesting the data, it helps you perform data indexing. Again, store and index your data for different use cases, integrate with downstream vector store and database providers. And the third step is with respect to query interface. LlamaIndex provides a query interface that accepts any input prompt over your data and returns a knowledge augmented response. So from that index, whenever you put any queries, you'll be able to get a good response, not only good, quicker response. It is quite fast.
So if I consider LlamaIndex, if you tell me Chris, tomorrow if in a project if someone tells you to use LlamaIndex, where specifically you're going to use? In these three steps. Right now, once we get this response, we can further connect this to any LLM-powered app or LLM models itself and based on the prompt engineering that we do, a prompt template we can do, we get a specific response, and there we can specifically use LangChain. Again, I will be discussing as I go ahead. So I hope you got an idea with respect to this. Whenever we talk about LlamaIndex, these three kind of data you can easily connect to it. One is unstructured data, structured data, and semi-structured data. Right? It supports all these three different types of data itself.
LlamaIndex vs. LangChain: A Detailed Comparison
Now let me quickly go ahead and talk about some of the important differences between LlamaIndex and LangChain.
Now guys, these differences is super beneficial. Why I'm telling you? Because in interviews, in projects when you implement, you should know what is the thing that you should really use. And finally after discussing these differences, can we use LlamaIndex and LangChain together in a specific LLM-powered app? And that kind of architecture also I will discuss about.
So over here all the differences that you'll be seeing, and remember guys, this I have referred it from dair.ai website. So this is another blogging website over there where they put a lot of information regarding generative AI, so again the references from here. I've taken the screenshot from there, but I'll explain it from my way. Okay. So over here, based on the features, here you'll be able to see here you have LlamaIndex, here you have LangChain. As you know, both of them are frameworks, right? And these are specifically used with multiple LLM models. But if I talk about what is the primary focus of LlamaIndex, so here you'll be able to see intelligent search and data indexing along with retrieval. That three points that I specifically discussed about.
In case of LangChain, it helps you build a wide range of GenAI applications. Right? What all different functionalities... GenAI application will help you to create. If I talk with respect to data handling, LlamaIndex helps you in ingesting, structuring, and accessing private or domain-specific data. Right? It will be able to help you do all these functionalities. In case of LangChain, loading, processing, and indexing data for various use cases. See, here also you can do indexing, but with the help of LlamaIndex, this indexing that we are specifically doing, it is very much efficient over here. Right? Why? I will show you when I probably develop a project in my upcoming videos, upcoming series of videos. Okay.
Now if I talk with respect to customization, it offers tools for integrating private data into LLMs, whereas in LangChain you can see highly customizable. It allows users to chain multiple tools and components. Right? What is this multiple chain, multiple tools and components? I'll discuss about when I talk about the architecture. In case of flexibility, specialized for efficient and fast search. See, this is what is very much amazing in this, efficient and fast search. The kind of indexing, the kind of metadata that is created using LlamaIndex actually helps us to query that data efficiently and with less response time. In case of LangChain, general purpose framework with more flexibility in application behavior.
Let's see with respect to LLM model which all LLM models is being connected, uh, it can probably support. So it connects to almost most of the LLM providers like OpenAI, Anthropic, Hugging Face, AI21 Labs, right? In case of LangChain, it supports 60 LLM models, so it is pretty much good. It is more than when compared to the LlamaIndex.
Now you may be saying, Krish, I'm talking all good points about LangChain, what about LlamaIndex? Guys, exact distinguish... I'm trying to talk about the exact differences, right? And once I probably compare all these particular, uh, differences, then you'll be able to understand as soon as you see the architecture. So use cases, here you can probably see, best for application that require quick data lookup and retrieval. Again, quick data lookup and retrieval. Suitable for application that require complex interaction like chatbots, and need to remember the memory, QAs, summarization, many more. Then integration functions as a smart storage mechanism. Smart storage mechanism. Why this smart storage I'm saying? Because in the projects also, we will create this. Okay. Designed to bring multiple tools together and chain operations. Right? Again, it is Python-based. It is python-based. It is lamaindex.ts in case of front end. Here you have langchain.js. Focused on search-centric application. Now this is the most important thing. A broad range of application here. Whenever we talk about search-centric application, this is the thing. Right? Many people will be talking about RAG system. I hope everybody knows about RAG system. If you have an external custom data, how can you probably explore that data? If you have multiple PDFs document, how can you ask any question to that and probably retrieve the data itself? With the help of LangChain, we can do it, but if you're using LlamaIndex, we'll still get more efficient results. And I'm just not saying because it will just provide functionalities to load all the PDF, convert, uh, index those PDFs, take out all the text data, index in such a way that whenever you, uh, ask for any query, you'll be able to get the retrieval very much quicker.
And then for deployment, it is ideal for both of them. It is the deployment part.
Architecting a Hybrid LlamaIndex and LangChain Application
See, at the end of the day, our main aim is always to create an application using LlamaIndex plus LangChain.
As I told you guys, if I probably consider this, okay. Now let me show you an architecture here. So here is what if we are using LlamaIndex plus LangChain. Now this is what is the architecture that I'm talking about. So here you can see, let's go from here. This is your data, right? Structured, unstructured and programmatic. All this data is basically indexed. Right?
This indexing basically happens with the help of LlamaIndex. Right? Once this indexing is happened, right, this entire pipeline, this entire pipeline is implemented using LlamaIndex. Right? It is very much efficient when compared to using LangChain.
Then you will also be seeing this part. This entire application is built by LangChain framework. Now, LLM models can be anything, different LLM models. Now, let's understand this. So I have my structured, unstructured, programmatic data. This indexing will basically happen with the help of LlamaIndex. Now user, whenever they ask any query, now since we are indexing with the help of LlamaIndex, usually this query when we are asking, we will be quickly able to get the response. Now let's say we are getting some response over here. We will use this response along with one specific prompt, along with all the response that we have specifically getting, and then we'll send it to our LLM model, and from this LLM model we will get one another response. So this entire thing can be implemented with the help of LangChain because here the dependency will be with respect to multiple agents, right? Multi-chain agents. There may be one chain to other chain, there may be a requirement, right? So all these things will specifically be there.
Let's say if I want to use an application, create an application which is called Text to SQL. Now not Text to SQL, let's say I have a PDF document. I have multiple PDFs document, right? So PDF, I will index this data. Whenever I ask for any query, I will be able to get a response. Now along with this response, what I can do? I can create another prompt and give it to my LLM model and make this LLM model perform any functionality based on the response that I'm getting over here. Right? And then finally I'll get the response. So if I'm specifically using LangChain and LlamaIndex, I will be able to create some amazing application. But at the end of the day, if someone even asks you in the interview, where exactly is LlamaIndex used? You should specifically say in this pipeline. This is the pipeline where it will be used, still here. And this querying usually happens very much faster because the indexing technique that is used in LlamaIndex is completely different with respect to LangChain, the way that it is probably creating vectors, vector embeddings, the way that it is creating metadata, it is completely different when compared to LangChain. And this is a kind of expert in this matter. LangChain overall, it can do multiple things.
Conclusion and What's Next
So I hope you like this particular video. I hope you're able to understand this. Now in the upcoming video, we will try to create projects using LlamaIndex and LangChain. We'll try... I'll try to show you. I will talk about multiple PDFs. We'll talk about multiple PDF, how you can query it. We'll talk about amazing use cases that we are going to discuss. We'll talk about what all functionalities you have in LlamaIndex. We'll also be talking about vector embeddings in LlamaIndex. Uh, by using this LlamaIndex, many things will be probably coming. And what all applications you'll be able to develop, right?
So yes, this was it from my side. I think, I hope you like this particular video. I'll see you all in the next video. Have a great day. Thank you and all. Take care. Bye-bye.
