NVIDIA Generative AI Examples

This repository is a starting point for developers looking to integrate with the NVIDIA software ecosystem to speed up their generative AI systems. Whether you are building RAG pipelines, agentic workflows, or fine-tuning models, this repository will help you integrate NVIDIA, seamlessly and natively, with your development stack.

Table of Contents

• What's New?
  • Data Flywheel
  • Safer Agentic AI
  • Knowledge Graph RAG
  • Agentic Workflows with Llama 3.1
  • RAG with Local NIM Deployment and LangChain
  • Vision NIM Workflows
• Try it Now!
• Data Flywheel
  • Tool-Calling Notebooks
• RAG
  • RAG Notebooks
  • RAG Examples
  • RAG Tools
  • RAG Projects
• Documentation
  • Getting Started
  • How To's
  • Reference
• Community

What's New?

Data Flywheel

These tutorials demonstrate Data Flywheel workflows that use NVIDIA NeMo Microservices. They include components such as NVIDIA NeMo Datastore, NeMo Entity Store, NeMo Customizer, NeMo Evaluator, NeMo Guardrails microservices, and NVIDIA NIMs.

• Tool Calling Fine-tuning, Inference, Evaluation, and Guardrailing with NVIDIA NeMo Microservices and NIMs
• Embedding Fine-tuning, Inference, and Evaluation with NVIDIA NeMo Microservices and NIMs

Safer Agentic AI

The following tutorials illustrate how to audit your large language models with NeMo Auditor to identify vulnerabilities to unsafe prompts, and how to run inference with multiple rails in parallel to reduce latency and improve throughput.

• Audit your LLMs
• Inference with Parallel Rails

Knowledge Graph RAG

This example implements a GPU-accelerated pipeline for creating and querying knowledge graphs using RAG by leveraging NIM microservices and the RAPIDS ecosystem to process large-scale datasets efficiently.

• Knowledge Graphs for RAG with NVIDIA AI Foundation Models and Endpoints

Agentic Workflows with Llama 3.1

• Build an Agentic RAG Pipeline with Llama 3.1 and NVIDIA NeMo Retriever NIM microservices [Blog, Notebook]
• NVIDIA Morpheus, NIM microservices, and RAG pipelines integrated to create LLM-based agent pipelines

RAG with Local NIM Deployment and LangChain

• Tips for Building a RAG Pipeline with NVIDIA AI LangChain AI Endpoints by Amit Bleiweiss. [Blog, Notebook]

For more information, refer to the Generative AI Example releases.

Vision NIM Workflows

A collection of Jupyter notebooks, sample code and reference applications built with Vision NIMs.

To pull the vision NIM workflows, clone this repository recursively:

git clone https://github.com/nvidia/GenerativeAIExamples --recurse-submodules

The workflows will then be located at GenerativeAIExamples/vision_workflows

Follow the links below to learn more:

• [Learn how t