GenerativeAIExamples: what it is, what problem it solves & why it's gaining traction

What it solves

It provides a comprehensive set of starting points and reference implementations for developers to integrate NVIDIA's software ecosystem into generative AI systems. It specifically addresses the complexity of building production-ready RAG pipelines, agentic workflows, and model fine-tuning processes using NVIDIA's specialized infrastructure.

How it works

The repository serves as a catalog of Jupyter notebooks, sample code, and reference applications. It leverages NVIDIA NIM (NeMo Inference Microservices) and the NeMo microservices platform to provide modular infrastructure for inference, evaluation, and guardrailing. It integrates with popular frameworks like LangChain, LlamaIndex, and Haystack, and provides GPU-accelerated pipelines for tasks such as knowledge graph creation and vision-based workflows.

Who it’s for

Developers and AI engineers who want to build generative AI applications using NVIDIA hardware and software, specifically those looking to implement RAG, agentic AI, or fine-tuned LLMs within the NVIDIA ecosystem.

Highlights

• RAG Implementations: Includes basic and advanced RAG examples (multi-turn, multimodal, and structured data) and tools for evaluation and observability.
• Agentic Workflows: Tutorials for building agentic RAG pipelines and implementing tool-calling capabilities for LLMs.
• Data Flywheel: Workflows for continuous model improvement through fine-tuning, inference, and evaluation using NeMo microservices.
• Vision NIM Workflows: Reference applications for VLM-based video monitoring, multimodal search with NV-CLIP, and text extraction pipelines.
• Safety and Auditing: Tools like NeMo Auditor and NeMo Guardrails to identify vulnerabilities and ensure safe AI behavior.