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.
Building RAG Applications with PDFs Using LlamaIndex - Comparing Gemini Pro and Nvidia NIM Llama 3.2
The Machine Learning Engineer via YouTube
Overview
Syllabus
RAG: Speak with PDFs: LLamaIndex , Gemini Pro vs Nvidia NIM Llama 3.2 #machinelearning #datascience
Taught by
The Machine Learning Engineer
Related Courses
-
RAG - Extracting Information from PDFs Using LlamaIndex, Gemini, and Nvidia NIM with Llama 3.2
-
RAG Integration with Nvidia NIM and LlamaIndex - Building Document Query Applications
-
RAG: Building Document Question-Answering with Nvidia NIM and LlamaIndex Integration
-
RAG Bot: Building a PDF Chatbot with LlamaIndex, Nvidia NIM, and Streamlit
-
Building a Conversational PDF Bot with LlamaIndex, Nvidia NIM, and Streamlit
-
Multimodal RAG with LlamaIndex, Google AI, Gemini Pro and Qdrant
