Description:
Learn to build a Retrieval-Augmented Generation (RAG) application for querying private documents in this 20-minute tutorial that demonstrates the integration between LlamaIndex and Nvidia NIM microservices. Explore practical implementation using two leading Large Language Models - LLama 3.2 3B Instruct and Phi3 3.5 Small 128k Instruct. Follow along with hands-on examples and access the complete implementation through the provided Jupyter notebook to develop your own document interrogation system using machine learning and data science techniques.
RAG Integration with Nvidia NIM and LlamaIndex - Building Document Query Applications
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#Computer Science
#Machine Learning
#Retrieval Augmented Generation (RAG)
#Artificial Intelligence
#Natural Language Processing (NLP)
#LLM (Large Language Model)
#LLaMA (Large Language Model Meta AI)
#Programming
#Databases
#Vector Databases
#Data Science
#Information Retrieval
#LlamaIndex
#NVIDIA NIM
#Phi-3