Advanced RAG Using LlamaIndex & Claude 3

Advanced RAG uses more sophisticated LLMs like Claude 3 and AI frameworks and functionalities from LlamaIndex & LangChain.
The chunking strategies will be applied based on the type of data source & documents size.
With LLMs like Claude 3, we see a new breed of advanced RAG known as 'Multimodal RAG'.
Multimodal Retrieval-Augmented Generation (MM-RAG) uses a combination of data types to generate a response to a user's query. It builds on the foundation of standard RAG by integrating data modalities beyond just text, such as images, audio, video, and even tactile or olfactory information.
And this has been possible with the rise of multimodal LLMs.
OpenAI’s GPT-4V(ision), Google’s Gemini and Anthropic’s Claude-3 series are some notable examples of multimodal models that are revolutionizing the AI industry.
Ready to see how multimodal RAG works?
I have written a tutorial to explain how this works. So, in the tutorial, we will be using LlamaIndex, an AI framework to build LLM-powered applications. We will be importing the libraries required, running the multimodal model from Anthropic [claude-3-haiku-20240307], storing the data in the SingleStore database and retrieving the data in multimodal format to see the power of multimodality through text and image.
Here is my complete article with code tutorial for you to try: / multimodal-rag-using-l...

[@limjuroy7078]

Do you think is it better to use LlamaParse to parse the documents?

[@pavanbelagatti]

Yes, of course. It can effectively parse documents such as PDFs, PPTs, etc.