RAG Frameworks · NVIDIA

GenerativeAIExamples

NVIDIA's Generative AI Examples is a curated collection of reference workflows and notebooks for building RAG pipelines, agentic AI systems, and LLM applications using NVIDIA's GPU-optimized infrastructure (NIM microservices, TensorRT, Triton). It provides production-ready patterns for LangChain, LlamaIndex, and Haystack integrations with Docker-based deployment templates.

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Source: GitHub — github.com/NVIDIA/GenerativeAIExamples

4.1k

GitHub stars

1.1k

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Jupyter Notebook

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	NVIDIA/GenerativeAIExamples
Owner	NVIDIA
Primary language	Jupyter Notebook
License	Apache-2.0 — OSI-approved
Stars	4.1k
Forks	1.1k
Open issues	84
Latest release	v0.8.0 (2024-08-21)
Last updated	2026-05-29
Source	https://github.com/NVIDIA/GenerativeAIExamples

What GenerativeAIExamples is

Reference implementation repository covering RAG pipelines, tool-calling workflows, vision VLM integrations, and agentic systems using NVIDIA NIM microservices, NeMo platform components (Datastore, Guardrails, Auditor, Customizer), and GPU-accelerated inference servers. Supports local NIM deployment and cloud-based API endpoints; includes Jupyter notebooks, Docker Compose examples, and Kubernetes-ready microservice architectures.

Quickstart

Get the GenerativeAIExamples source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/NVIDIA/GenerativeAIExamples.git2cd GenerativeAIExamples3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Building production RAG pipelines with NVIDIA infrastructure

Accelerated semantic search and retrieval workflows using NIM microservices, with ready-to-deploy Docker Compose and Kubernetes configurations for LangChain/LlamaIndex stacks.

Agentic AI systems with tool calling and guardrails

Reference patterns for LLM agents that invoke external APIs, combined with NeMo Guardrails and NeMo Auditor for safety auditing and constraint enforcement at scale.

Knowledge graphs and multimodal search workflows

GPU-accelerated knowledge graph construction, vision NIM workflows (VLM alerts, image search, text extraction), and embedding fine-tuning with RAPIDS integration for large-scale processing.

Implementation considerations

Requires NVIDIA API key for cloud NIM endpoints or local GPU infrastructure + NIM deployment knowledge; both paths entail procurement and ops complexity.
Notebook-based examples are reference material, not production code; adapt error handling, logging, observability, and security policies before shipping.
Microservice architecture (Datastore, Entity Store, Customizer, Guardrails, Auditor) introduces operational overhead; plan for monitoring, scaling, and version management.
Integration points with LangChain/LlamaIndex/Haystack are documented but expect version drift; test connectors against your target framework versions.
Data and model governance (fine-tuning, evaluation, guardrailing) require clear ownership and audit trails; examples show patterns but not compliance automation.

When to avoid it — and what to weigh

Non-NVIDIA GPU infrastructure — Repository is heavily optimized for NVIDIA hardware. Workflows assume access to NVIDIA GPUs, TensorRT compilation, and Triton inference server; adaptation to other accelerators requires significant rework.
Strict on-premises-only requirements without cloud fallback — Many examples use NVIDIA API Catalog cloud endpoints by default. Local NIM deployment is supported but requires additional infrastructure setup; no guidance for air-gapped deployments.
Minimal operational overhead or learning curve — Requires familiarity with microservices, Docker/Kubernetes, LangChain/LlamaIndex APIs, and NeMo platform components. Not a plug-and-play solution; expects hands-on integration work.
Small-scale prototyping with minimal dependencies — Architecture assumes containerized microservice deployment (Docker Compose minimum, Kubernetes recommended). Overkill for simple single-model inference or small dataset scenarios.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution under the same license terms. SPDX identifier clearly stated in repository files.

Apache-2.0 permits commercial use without explicit permission or royalties. However, the repository provides reference patterns and workflows; commercial deployment typically also requires licensing of NVIDIA NIM microservices, GPU infrastructure, and any proprietary models (e.g., Llama 3.1). Verify licensing of all dependencies (LangChain, LlamaIndex, vector databases, embedding models) in your supply chain.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	High

Security considerations

Repository itself does not contain known CVEs or hardcoded secrets in provided excerpts. Security posture depends on NIM microservice implementations, container images, and your deployment environment. Considerations: (1) NeMo Guardrails and Auditor are provided as patterns but require active configuration; (2) API key management (NVIDIA_API_KEY) must follow secure credential handling; (3) Data in-flight and at-rest in microservices (Datastore, Entity Store) requires encryption policies in your infrastructure; (4) No explicit supply-chain security or signed artifacts mentioned.

Alternatives to consider

LangChain / LlamaIndex standalone

Both frameworks support multiple LLM providers (OpenAI, Anthropic, AWS Bedrock) and vector stores without NVIDIA lock-in. Lower operational overhead for cloud-hosted models; less control over inference optimization on GPU.

Hugging Face TGI (Text Generation Inference) + vLLM

Open-source inference servers with broad GPU support and no vendor lock-in. Lower abstraction layer; requires more custom integration work; fewer enterprise features like Guardrails and Auditor.

LLamaIndex + Vertex AI or Bedrock

Managed RAG platforms with built-in observability and guardrails. Simpler ops; locked into cloud provider pricing and SLAs; less fine-tuning and custom model control than local NIM deployment.

Software development agency

Build on GenerativeAIExamples with DEV.co software developers

Explore NVIDIA Generative AI Examples on GitHub. Start with the basic_rag Docker Compose example or dive into advanced microservice patterns with NeMo platform.

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Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

rag RAG-Driven-Generative-AI generative-ai

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GenerativeAIExamples FAQ

Can I run these examples without NVIDIA GPUs?

Cloud-based NIM endpoints (NVIDIA API Catalog) work from any machine with internet access and an API key. Local NIM deployment requires NVIDIA GPUs and significant infrastructure setup. CPU-only inference is not a supported path in these examples.

Do I need to modify the code for production deployment?

Yes. Examples are reference implementations. Production requires: error handling, observability (logging, tracing, metrics), security hardening (credential management, encryption, network policies), performance tuning, and compliance automation. Treat notebooks and Docker Compose as starting templates, not production-ready code.

What vector databases are supported?

Examples show integration with Milvus and generic LangChain/LlamaIndex vector store abstractions. Specific support depends on the framework version; consult the connectors' documentation. Custom implementations are straightforward via LangChain/LlamaIndex plugin APIs.

How is this licensed for commercial use?

Apache-2.0 permits commercial use. However, verify licensing of NVIDIA NIM microservices (may require separate agreement), embedding models (Llama, others), and all third-party dependencies (LangChain, databases, etc.) in your deployment.

Software developers & web developers for hire

Adopting GenerativeAIExamples is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate rag frameworks software in production.

Ready to build GPU-accelerated RAG pipelines?

Explore NVIDIA Generative AI Examples on GitHub. Start with the basic_rag Docker Compose example or dive into advanced microservice patterns with NeMo platform.

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