What is generativeaiexamples?

nvidia/generativeaiexamples — explained in plain English

Analysis updated 2026-06-26

★ 4,037Jupyter NotebookAudience · developerComplexity · 3/5LicenseSetup · moderate

Mindmap

mindmap
  root((NVIDIA GenAI))
    What it does
      RAG examples
      Agentic workflows
      Vision tasks
      Safety guardrails
    Tech Stack
      LangChain LlamaIndex
      NVIDIA NIM
      Docker
      Jupyter notebooks
    Use Cases
      Document chatbots
      Model fine-tuning
      Video monitoring
      Image search
    Audience
      AI developers
      ML engineers

mindmap root((NVIDIA GenAI)) What it does RAG examples Agentic workflows Vision tasks Safety guardrails Tech Stack LangChain LlamaIndex NVIDIA NIM Docker Jupyter notebooks Use Cases Document chatbots Model fine-tuning Video monitoring Image search Audience AI developers ML engineers

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What do people build with it?

USE CASE 1

Build a document Q&A chatbot that searches a knowledge base before answering, using NVIDIA NIMs and LangChain.

USE CASE 2

Add safety guardrails to an AI assistant to block unsafe prompts and audit the model for known vulnerabilities.

USE CASE 3

Fine-tune a smaller NVIDIA model on your own task data and evaluate whether it improved using NeMo microservices.

USE CASE 4

Monitor a video stream for events or search an image library with plain-English descriptions using NVIDIA vision models.

What is it built with?

PythonJupyter NotebookLangChainLlamaIndexDockerNVIDIA NIM

How does it compare?

	nvidia/generativeaiexamples	xitu/tensorflow-docs	justmarkham/scikit-learn-videos
Stars	4,037	3,794	3,789
Language	Jupyter Notebook	Jupyter Notebook	Jupyter Notebook
Setup difficulty	moderate	moderate	easy
Complexity	3/5	1/5	2/5
Audience	developer	data	general

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires an NVIDIA API key for cloud NIMs, some advanced examples need a local GPU and Docker.

Use freely for any purpose including commercial, as long as you keep the copyright and license notices.

So what is it?

This repository is a collection of working examples and reference code for building AI applications using NVIDIA's software and models. It focuses on two broad patterns: retrieval-augmented generation, where an AI assistant answers questions by searching a document collection before responding, and agentic workflows, where an AI model calls external tools or APIs to complete tasks rather than answering from memory alone. The examples are written as Jupyter notebooks and runnable code projects. They use popular developer frameworks such as LangChain, LlamaIndex, and Haystack to connect to NVIDIA's inference services, called NIMs. A NIM is a packaged AI model that you can run locally or access through NVIDIA's cloud API. The quickest way to try the basic retrieval example is to get an API key from NVIDIA's catalog, clone this repository, and run a single Docker command that starts a local chat interface in your browser. Beyond the basics, the repository covers more advanced topics including multi-turn conversations, working with images alongside text, querying structured data from spreadsheets, breaking complex questions into sub-questions before answering, and building knowledge graphs from large datasets. There are also sections on safety, showing how to add guardrails that block unsafe prompts and how to audit a model for known vulnerabilities. A section called Data Flywheel shows how to fine-tune a smaller model on real task data, evaluate whether it improved, and add safety constraints, all using NVIDIA's NeMo platform of microservices. Vision-specific workflows cover tasks like monitoring video streams for events, searching image libraries using plain-English descriptions, and extracting text from images. The project is open to community contributions and is licensed under Apache 2.0.

Copy-paste prompts

Prompt 1

Using the NVIDIA generativeaiexamples RAG notebook as a starting point, help me adapt it to answer questions from a PDF collection I have stored locally, using an NVIDIA NIM as the inference backend.

Prompt 2

I want to add NeMo Guardrails to my NVIDIA-based chatbot so it blocks off-topic questions. Show me how to configure a guardrails policy based on the examples in this repo.

Prompt 3

Walk me through the Data Flywheel example in nvidia/generativeaiexamples: how do I collect real user queries, fine-tune a smaller model on them, and evaluate whether it outperforms the base model?

Prompt 4

Using the vision workflow notebooks in this repo, write code that takes a folder of product images and returns a JSON list of each image's contents described in plain English using an NVIDIA vision NIM.

Prompt 5

I want to run the basic RAG example from nvidia/generativeaiexamples with a single Docker command. Give me the exact steps from cloning the repo to seeing the chat interface in my browser.

Frequently asked questions