Explore a 24-minute Spanish-language tutorial demonstrating how to build a RAG (Retrieval-Augmented Generation) application for querying private documents using LlamaIndex integration with Nvidia NIM microservices. Learn to implement two popular Large Language Models - LLama 3.2 3B Instruct and Phi3 3.5 Small 128k Instruct - while following along with a practical implementation available through the provided GitHub notebook. Master the fundamentals of combining document retrieval with language model capabilities to create an intelligent document query system.
RAG: Building Document Question-Answering with Nvidia NIM and LlamaIndex Integration
The Machine Learning Engineer via YouTube
Overview
Syllabus
RAG: Nvidia NIM Integracion con LLamaIndex . Habla con tus Documentos #machinelearning #datascience
Taught by
The Machine Learning Engineer
Related Courses
-
RAG Integration with Nvidia NIM and LlamaIndex - Building Document Query Applications
-
RAG - Extracting Information from PDFs Using LlamaIndex, Gemini, and Nvidia NIM with Llama 3.2
-
Building RAG Applications with PDFs Using LlamaIndex - Comparing Gemini Pro and Nvidia NIM Llama 3.2
-
RAG Bot: Building a PDF Chatbot with LlamaIndex, Nvidia NIM, and Streamlit
-
RAG: Nvidia NIM Integration with Langchain for HTML Web Page Parsing
-
Building Multimodal AI RAG with LlamaIndex, NVIDIA NIM, and Milvus - LLM App Development
