From the course: Build Your Own AI Lab

Introducing LlamaIndex

From the course: Build Your Own AI Lab

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Introducing LlamaIndex

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- [Instructor] Now we're going to explore LlamaIndex, which is another framework that is designed to enhance the capabilities of large language models, or llms, by connecting them to custom and private data sources. In other words, actually deploying virtual augmented generation and other implementations like chat bots, assistance and so on. It is an alternative to LangChain as well, because it acts as a bridge between the LLMS and a diverse data formats such as PDF databases, APIs, and more, and allows you to do a lot of function calling, tool calling, and so on for different automated cyclical workflows. At its core LlamaIndex provide tools for data ingestion, for indexing, for querying. In other words, you know, all the different components of a retrieval, augmented generation pipeline. It allows you to create chat bots and also to extract insights from your documents and also to evaluate your implementation. So definitely this is another alternative. We're not going to go into a lot of details about LlamaIndex in this course. We're going to concentrate a little bit more with LangChain later in the course with a few examples because of popularity. So both of them are pretty popular in the community. I will say that probably LangChain just is, is just a little bit more popular and just because of time and because of, you know, the ease of use. I'm going to concentrate a few examples later in the course using LangChain. But again, you know, there's a lot of documentation for LlamaIndex, a lot of tutorials, a lot of support from the community and so on. So it's another alternative for an orchestration library. So you can integrate vector databases, embedding models, and your LLMs and SLMs as well.

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