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
Overview
Syllabus
Building Multimodal AI RAG with LlamaIndex, NVIDIA NIM, and Milvus | LLM App Development
Taught by
NVIDIA Developer
Tags
Related Courses
-
Building LLM Assistants with LlamaIndex, NVIDIA NIM, and Milvus - LLM App Development
-
Building Multimodal Search and RAG
-
Retrieval Augmented Generation (RAG) with LangChain
-
RAG Integration with Nvidia NIM and LlamaIndex - Building Document Query Applications
-
Fundamentals of AI Agents Using RAG and LangChain
-
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG
