--- title: "GenerativeAIExamples" type: "tool" slug: "nvidia-generativeaiexamples" canonical_url: "https://www.graphcanon.com/tools/nvidia-generativeaiexamples" github_url: "https://github.com/NVIDIA/GenerativeAIExamples" homepage_url: null stars: 4106 forks: 1091 primary_language: "Jupyter Notebook" license: "Apache-2.0" categories: ["model-training", "llm-frameworks", "data-retrieval", "inference-serving", "ai-agents"] tags: ["nemo", "llm", "gpu-acceleration", "large-language-models", "microservice", "rag", "retrieval-augmented-generation", "llm-inference"] updated_at: "2026-07-07T18:41:46.333202+00:00" --- # GenerativeAIExamples > Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture This repository offers optimized workflows for Generative AI systems using NVIDIA's software ecosystem, focusing on GPU-acceleration, RAG pipelines, agentic workflows, model fine-tuning, and integration with tools like NeMo, Triton Inference Server, and TensorRT. ## Facts - Repository: https://github.com/NVIDIA/GenerativeAIExamples - Stars: 4,106 · Forks: 1,091 · Open issues: 84 · Watchers: 89 - Primary language: Jupyter Notebook - License: Apache-2.0 - Last pushed: 2026-05-29T00:05:55+00:00 ## Categories - [Model Training](/categories/model-training.md) - [LLM Frameworks](/categories/llm-frameworks.md) - [Data & Retrieval](/categories/data-retrieval.md) - [Inference & Serving](/categories/inference-serving.md) - [AI Agents](/categories/ai-agents.md) ## Tags nemo, llm, gpu-acceleration, large language models, microservice, rag, retrieval-augmented-generation, llm-inference ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,962) - [hermes-agent](/tools/nousresearch-hermes-agent.md) - The self-improving AI agent built by Nous Research (★ 210,880) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,417) - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. 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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?](#whats-new) * [Data Flywheel](#data-flywheel) * [Safer Agentic AI](#safer-agentic-ai) * [Knowledge Graph RAG](#knowledge-graph-rag) * [Agentic Workflows with Llama 3.1](#agentic-workflows-with-llama-31) * [RAG with Local NIM Deployment and LangChain](#rag-with-local-nim-deployment-and-langchain) * [Vision NIM Workflows](#vision-nim-workflows) * [Try it Now!](#try-it-now) * [Data Flywheel](#data-flywheel) * [Tool-Calling Notebooks](#tool-calling-notebooks) * [RAG](#rag) * [RAG Notebooks](#rag-notebooks) * [RAG Examples](#rag-examples) * [RAG Tools](#rag-tools) * [RAG Projects](#rag-projects) * [Documentation](#documentation) * [Getting Started](#getting-started) * [How To's](#how-tos) * [Reference](#reference) * [Community](#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](./nemo/data-flywheel/tool-calling) - [Embedding Fine-tuning, Inference, and Evaluation with NVIDIA NeMo Microservices and NIMs](./nemo/data-flywheel/embedding-finetuning/) ### 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](./nemo/NeMo-Auditor/Getting_Started_With_NeMo_Auditor.ipynb) - [Inference with Parallel Rails](./nemo/NeMo-Guardrails/Parallel_Rails_Tutorial.ipynb) ### 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](community/knowledge_graph_rag) ### Agentic Workflows with Llama 3.1 - Build an Agentic RAG Pipeline with Llama 3.1 and NVIDIA NeMo Retriever NIM microservices [[Blog](https://developer.nvidia.com/blog/build-an-agentic-rag-pipeline-with-llama-3-1-and-nvidia-nemo-retriever-nims/), [Notebook](RAG/notebooks/langchain/agentic_rag_with_nemo_retriever_nim.ipynb)] - [NVIDIA Morpheus, NIM microservices, and RAG pipelines integrated to create LLM-based agent pipelines](https://github.com/NVIDIA/GenerativeAIExamples/blob/v0.7.0/experimental/event-driven-rag-cve-analysis) ### RAG with Local NIM Deployment and LangChain - Tips for Building a RAG Pipeline with NVIDIA AI LangChain AI Endpoints by Amit Bleiweiss. [[Blog](https://developer.nvidia.com/blog/tips-for-building-a-rag-pipeline-with-nvidia-ai-langchain-ai-endpoints/), [Notebook](https://github.com/NVIDIA/GenerativeAIExamples/blob/v0.7.0/notebooks/08_RAG_Langchain_with_Local_NIM.ipynb)] For more information, refer to the [Generative AI Example releases](https://github.com/NVIDIA/GenerativeAIExamples/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](vision_workflows/README.md) Follow the links below to learn more: - [Learn how t ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/nvidia-generativeaiexamples`](/api/graphcanon/tools/nvidia-generativeaiexamples) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_