Building RAG Applications with PDFs Using LlamaIndex - Comparing Gemini Pro and Nvidia NIM Llama 3.2
The Machine Learning Engineer via YouTube
Master Windows Internals - Kernel Programming, Debugging & Architecture
Power BI Fundamentals - Create visualizations and dashboards from scratch
Overview
AI, Data Science & Cloud Certificates from Google, IBM & Meta — 40% Off
One plan covers every Professional Certificate on Coursera. 40% off Coursera Plus Annual.
Unlock All Certificates
Learn to build a RAG (Retrieval-Augmented Generation) agent for PDF document interaction in this comprehensive video tutorial. Explore the implementation of PymuPDF for text and image extraction, while comparing two powerful model variants: the Gemini SDK and NVIDIA NIM SDK with NVIDIA embeddings paired with LLama 3.2 3B language model. Follow along with practical demonstrations and access the complete implementation through the provided GitHub notebook to develop your own PDF-based conversational AI system using LlamaIndex framework.
Syllabus
RAG: Speak with PDFs: LLamaIndex , Gemini Pro vs Nvidia NIM Llama 3.2 #machinelearning #datascience
Taught by
The Machine Learning Engineer