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NVIDIA Generative AI Examples

Introduction

State-of-the-art Generative AI examples that are easy to deploy, test, and extend. All examples run on the high performance NVIDIA CUDA-X software stack and NVIDIA GPUs.

NVIDIA NGC

Generative AI Examples uses resources from the NVIDIA NGC AI Development Catalog.

Sign up for a free NGC developer account to access:

  • GPU-optimized containers used in these examples
  • Release notes and developer documentation

Retrieval Augmented Generation (RAG)

A RAG pipeline embeds multimodal data -- such as documents, images, and video -- into a database connected to a LLM. RAG lets users chat with their data!

Developer RAG Examples

The developer RAG examples run on a single VM. They demonstrate how to combine NVIDIA GPU acceleration with popular LLM programming frameworks using NVIDIA's open source connectors. The examples are easy to deploy via Docker Compose.

Examples support local and remote inference endpoints. If you have a GPU, you can inference locally via TensorRT-LLM. If you don't have a GPU, you can inference and embed remotely via NVIDIA AI Foundations endpoints.

Model Embedding Framework Description Multi-GPU TRT-LLM NVIDIA AI Foundation Triton Vector Database
llama-2 e5-large-v2 Llamaindex Canonical QA Chatbot YES YES No YES Milvus/PGVector
mixtral_8x7b nvolveqa_40k Langchain Nvidia AI foundation based QA Chatbot No No YES YES FAISS
llama-2 all-MiniLM-L6-v2 Llama Index QA Chatbot, GeForce, Windows NO YES NO NO FAISS
llama-2 nvolveqa_40k Langchain QA Chatbot, Task Decomposition Agent No No YES YES FAISS
mixtral_8x7b nvolveqa_40k Langchain Minimilastic example showcasing RAG using Nvidia AI foundation models No No YES YES FAISS

Enterprise RAG Examples

The enterprise RAG examples run as microservies distributed across multiple VMs and GPUs. They show how RAG pipelines can be orchestrated with Kubernetes and deployed with Helm.

Enterprise RAG examples include a Kubernetes operator for LLM lifecycle management. It is compatible with the NVIDIA GPU operator that automates GPU discovery and lifecycle management in a Kubernetes cluster.

Enterprise RAG examples also support local and remote inference via TensorRT-LLM and NVIDIA AI Foundations endpoints.

Model Embedding Framework Description Multi-GPU Multi-node TRT-LLM NVIDIA AI Foundation Triton Vector Database
llama-2 NV-Embed-QA-003 Llamaindex QA Chatbot, Helm, k8s NO NO YES NO YES Milvus

Tools

Example tools and tutorials to enhance LLM development and productivity when using NVIDIA RAG pipelines.

Name Description Deployment Tutorial
Evaluation Example open source RAG eval tool that uses synthetic data generation and LLM-as-a-judge Docker compose file README
Observability Observability serves as an efficient mechanism for both monitoring and debugging RAG pipelines. Docker compose file README

Open Source Integrations

These are open source connectors for NVIDIA-hosted and self-hosted API endpoints. These open source connectors are maintained and tested by NVIDIA engineers.

Name Framework Chat Text Embedding Python Description
NVIDIA AI Foundation Endpoints Langchain YES YES YES Easy access to NVIDIA hosted models. Supports chat, embedding, code generation, steerLM, multimodal, and RAG.
NVIDIA Triton + TensorRT-LLM Langchain YES YES YES This connector allows Langchain to remotely interact with a Triton inference server over GRPC or HTTP tfor optimized LLM inference.
NVIDIA Triton Inference Server LlamaIndex YES YES NO Triton inference server provides API access to hosted LLM models over gRPC.
NVIDIA TensorRT-LLM LlamaIndex YES YES NO TensorRT-LLM provides a Python API to build TensorRT engines with state-of-the-art optimizations for LLM inference on NVIDIA GPUs.

NVIDIA support

In each example README we indicate the level of support provided.

Feedback / Contributions

We're posting these examples on GitHub to support the NVIDIA LLM community, facilitate feedback. We invite contributions via GitHub Issues or pull requests!

Known issues

  • In each of the READMEs, we indicate any known issues and encourage the community to provide feedback.
  • The datasets provided as part of this project is under a different license for research and evaluation purposes.
  • This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.