Building Multimodal AI RAG with LlamaIndex, NVIDIA NIM, and Milvus - LLM App Development

Explore the process of building a multimodal AI retrieval-augmented generation (RAG) application in this 17-minute video tutorial. Learn how to convert documents into text using vision language models like NeVA 22B and DePlot, utilize GPU-accelerated Milvus for efficient embedding storage and retrieval, leverage NVIDIA NIM API's Llama 3 model for handling user queries, and seamlessly integrate all components with LlamaIndex. Gain practical insights into document processing, vector database management, inference techniques, and orchestration for creating a smooth Q&A experience. Access the accompanying notebook for hands-on practice and join the NVIDIA Developer Program for additional resources. Discover how to combine cutting-edge technologies such as LangChain, Mixtral, and NIM APIs to develop advanced LLM applications.

Building Multimodal AI RAG with LlamaIndex, NVIDIA NIM, and Milvus | LLM App Development