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

This video explains how to create a multimodal AI retrieval-augmented generation (RAG) application, including the following steps:

1. Document processing: Convert documents into text form using vision language models, specifically NeVA 22B for general image understanding and DePlot for charts and plots.

2. Vector database: Explore the power of GPU-accelerated Milvus for efficient storage and retrieval of your embeddings.

3. Inference with Llama 3: Leverage the NVIDIA NIM API Llama 3 model to handle user queries and generate accurate responses.

4. Orchestration with LlamaIndex: Integrate and manage all components seamlessly with LlamaIndex for a smooth Q&A experience.

📗 Learn more with this notebook: github.com/NVIDIA/GenerativeAIExamples/tree/main/c…

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NVIDIA NIM, code review, LangChain, llamaIndex, Llama 3, Milvus, NIM APIs, Mixtral, NeVA/DePlo