Solutions: AI Workflows

Enterprise Retrieval-Augmented Generation

Build production-quality AI chatbots that can accurately answer questions about your enterprise data.

Build Enterprise Chatbots With Retrieval-Augmented Generation

Find the tools you need to develop generative AI-powered chatbots, run them in production, and transform data into valuable insights using retrieval-augmented generation (RAG)—a technique that connects large language models (LLMs) to a company’s enterprise data. Workflow examples offer an easy way to get started writing applications that integrate NVIDIA microservices with popular open-source LLM programming frameworks.

Try the Workflow Examples in GitHub

Explore the RAG Workflow Examples

NVIDIA AI workflow examples accelerate building and deploying enterprise solutions that accurately generate responses for a variety of use cases. You can use these examples to write RAG applications using the latest GPU-optimized LLMs and NVIDIA NeMo™ microservices.

NVIDIA RAG workflow examples include:

End-to-end sample RAG pipelines illustrating NVIDIA integration with LangChain and LlamaIndex
Open AI-compatible APIs for LLM answer generation
Example chatbot web user interfaces that can be customized for your application

Easy and fast deployment of the complete RAG pipeline via Docker on a single GPU
Optimized LLM inference performance and scaling through NVIDIA NIM
The flexibility to integrate models from the NVIDIA API catalog with self-hosted models

Get a high-level view of the AI workflows, from development to production.

Read the Technical Brief

Deploy Production RAG Pipelines

The NVIDIA RAG LLM operator makes it easy to deploy your RAG application into production. With an NVIDIA AI Enterprise subscription, the operator deploys RAG pipelines developed using the example workflows into production without rewriting any code.

The NVIDIA RAG LLM operator:

Simplifies production deployment of RAG applications into Kubernetes clusters
Uses the same Open AI-compatible APIs as the NVIDIA RAG workflow examples
Fully integrates with the NVIDIA GPU Operator to simplify infrastructure management

Is certified to run anywhere with workload portability across multi-and hybrid-cloud environments
Offers state-of-the-art model and data security with NVIDIA Confidential Computing support
Is optimized for GPU-accelerated performance at massive scale

Check out the Blog

Start Your Generative AI Journey on NVIDIA LaunchPad

Have an upcoming generative AI project? Get access to the AI chatbot using the RAG workflow with a free curated lab. Access a step-by-step guided lab with ready-to-use software, sample data, and applications.

Apply Now

Easily Build RAG-Based AI Applications Anywhere

Deliver Accurate Responses

LLM generates responses based on real-time information from a company’s enterprise data sources.

Generate Answers at Scale

Simplify the orchestration of scaling retrieval-augmented generation pods on Kubernetes in production.

Deploy Anywhere

The entire workflow can be deployed on your preferred on-premises or cloud platform.

Accelerate Development of AI Solutions

AI workflows accelerate the path to AI outcomes. The enterprise-ready RAG workflow gives developers a reference solution to start building an AI chatbot.

Reduce Development Time

Best-in-class AI software streamlines development and deployment of AI solutions.

Improve Accuracy And Performance

Frameworks and containers are performance-tuned and tested for NVIDIA GPUs.

Gain Confidence in AI Outcomes

NVIDIA AI Enterprise lets you move from pilot to production with the assurance of security, API stability, and support.

Get Started With Retrieval-Augmented Generation

Try It on NVIDIA LaunchPad

Have an existing generative AI project? Apply to access this free trial to get hands-on guided experience building a RAG-based solution.

Get Started

Build Production-Grade RAG

With an NVIDIA AI Enterprise subscription, unlock your business data with generative AI and enable better business insights in real time with enterprise-ready RAG.

Try Now

Get Early Access to RAG LLM Operator

Easily deploy your RAG applications into production without rewriting any code.