Practical overview
When it fits
Use it when a project needs implementation references for NVIDIA ecosystem integration and accelerated generative AI workflows.
Who it is for
- Developers building generative AI applications.
- Teams exploring NVIDIA-based RAG, agentic, fine-tuning or inference workflows.
- Practitioners working with GPU-accelerated or microservice-oriented AI infrastructure.
Documented uses
- Prototype a retrieval-augmented generation pipeline.
- Explore agentic workflows and tool-calling patterns.
- Create and query knowledge graphs with RAG.
- Fine-tune, evaluate and run inference with NVIDIA NeMo microservices and NIMs.
- Experiment with vision-language and multimodal workflows.
Requirements & limits
The repository is public and open source under the Apache-2.0 license. The documented basic RAG quick start requires an NVIDIA API key, Docker Compose and a local clone.
- The collection is centered on NVIDIA software, NIM microservices, NeMo components and accelerated infrastructure.
- Examples span both notebook-based exploration and microservice-oriented reference architectures.
- The basic RAG quick start provides a documented local Docker Compose path.
- Vision workflows are obtained through recursive repository cloning.
- Specific corpus-ingestion, tool-interface and production-deployment details are not documented in the supplied material.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Launch the basic local RAG Playground
Build and open the repository’s documented basic RAG pipeline.
Before you start
- An NVIDIA API key obtained from the NVIDIA API Catalog.
- A cloned copy of the public repository.
- Docker Compose.
Steps
Export the NVIDIA API key as
NVIDIA_API_KEY.Clone the repository.
Change to
RAG/examples/basic_rag/langchain/.Run the command below.
docker compose up -d --buildOpen
https://localhost:8090/and submit queries; run the command below when finished.docker compose down
Expected outcome
A locally accessible sample RAG Playground for submitting queries.
Guide limitations
- The cited instructions document the sample pipeline only; they do not specify organization-specific corpus ingestion or deployment configuration.
Retrieve the repository’s vision workflows
Clone the repository recursively so the documented vision workflow directory is available.
Before you start
- Git.
- Access to the public repository.
Steps
Clone the repository with
--recurse-submodules.Open the
vision_workflowsdirectory.Select a documented workflow, such as video-event monitoring, natural-language image search, vision text extraction or few-shot classification.
Expected outcome
The vision workflows are placed in the repository’s `vision_workflows` directory.
Guide limitations
- The cited material identifies the workflows but does not provide their individual execution steps or requirements.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 1 October 2026
- Original website title
- NVIDIA/GenerativeAIExamples · Official homepage title · retained in its source language