🛍️ NVIDIA AI Blueprint: Retail Shopping Assistant

AI-powered retail shopping assistant with multi-agent architecture

📋 Table of Contents

• Overview
  • Key Features
  • Architecture
• Get Started
  • Prerequisites
  • Quick Start
• Documentation
• Contribution Guidelines
• Community
• References
• License

Overview

The Retail Shopping Assistant is an AI-powered blueprint that provides a comprehensive interface for an intelligent retail shopping advisor. Built with LangGraph for agent orchestration, it features multi-agent architecture, real-time streaming responses, image-based search, and intelligent shopping cart management.

Key Features

• 🤖 Intelligent Product Search: Find products using natural language or images
• 🛒 Smart Cart Management: Add, remove, and manage shopping cart items
• 🖼️ Visual Search: Upload images to find similar products
• 💬 Conversational AI: Natural language interactions
• 🔒 Content Safety: Built-in moderation and safety checks
• ⚡ Real-time Streaming: Live response generation
• 📱 Responsive UI: Modern, mobile-friendly interface

Architecture

The application follows a microservices architecture with specialized agents for different tasks:

• Chain Server: Main API with LangGraph orchestration
• Catalog Retriever: Product search and recommendations
• Memory Retriever: User context and cart management
• Guardrails: Content safety and moderation
• UI: React-based frontend interface

For detailed architecture information, see Architecture Overview.

Get Started

Prerequisites

• Docker: Version 20.10+ with Docker Compose plugin
• NVIDIA NGC Account: For API access (Get API Key)
• Hardware: 4x H100 GPUs (preferred) or 4x A100 GPUs (minimum) for local deployment, or cloud access

Quick Start

1. Clone the repository:

   git clone https://github.com/NVIDIA-AI-Blueprints/retail-shopping-assistant.git
   cd retail-shopping-assistant

2. Authenticate with NVIDIA Container Registry:

   docker login nvcr.io

   Use $oauthtoken as the username and your NGC API key as the password.

3. Set up environment:

   export NGC_API_KEY=your_nvapi_key_here
   export LLM_API_KEY=$NGC_API_KEY
   export EMBED_API_KEY=$NGC_API_KEY
   export RAIL_API_KEY=$NGC_API_KEY
   export LOCAL_NIM_CACHE=~/.cache/nim
   mkdir -p "$LOCAL_NIM_CACHE"
   chmod a+w "$LOCAL_NIM_CACHE"

4. Launch the application:

   Option A: Local Deployment:

   # Start local NIMs (requires 4x H100 GPUs)
   docker compose -f docker-compose-nim-local.yaml up -d
   
   # Build and launch the application
   docker compose -f docker-compose.yaml up -d --build

   Option B: Cloud Deployment (no local GPUs required):

   # Configure to use NVIDIA API Catalog endpoints
   export CONFIG_OVERRIDE=config-build.yaml
   
   # Build and launch the application
   docker compose -f docker-compose.yaml up -d --build

5. Access the application: Open your browser to http://localhost:3000

6. Stop the containers:

   Option A: Local Deployment:

   docker compose -f docker-compose.yaml -f docker-compose-nim-local.yaml down

   Option B: Cloud Deployment:

   docker compose -f docker-compose.yaml down

For detailed installation instructions, see Deployment Guide.

Deploy on NVIDIA Brev

For a streamlined cloud deployment experience, you can deploy the Retail Shopping Assistant on NVIDIA Brev using GPU Environment Templates (Launchables):

NVIDIA Brev Deployment Guide - Complete step-by-step instructions for deploying on Brev

Why Choose NVIDIA Brev?

• One-Click Deployment: Pre-configured GPU environments with automatic setup
• Managed Infrastructure: No need to manage servers or GPU clusters
• Secure Access: Built-in secure tunneling for web interface access
• Flexible Resources: Choose from H100, A100, and other GPU configurations
• Cost-Effective: Pay only for actual usage time

The Brev deployment guide walks you through the entire process from creating a Launchable to accessing your fully functional retail shopping assistant.

Documentation

• User Guide: How to use the application
• API Documentation: Complete API reference
• Deployment Guide: Installation and setup instructions
• Documentation Hub: Complete documentation index

Contribution Guidelines

We welcome contributions! Please see our Contributing Guide for details on:

• Development setup and environment configuration
• Coding standards and best practices
• Testing guidelines and examples
• Pull request process and code review guidelines

Community

• GitHub Issues: Report bugs and feature requests
• Documentation: Comprehensive guides and references

References

NVIDIA AI Blueprints

• NVIDIA AI Blueprints: Collection of AI application blueprints
• NVIDIA NIM: Containerized AI models
• NVIDIA NGC: AI platform and container registry

Technologies Used

• LangGraph: Agent orchestration framework
• FastAPI: Modern Python web framework
• React: JavaScript library for building user interfaces
• Milvus: Vector database for similarity search

Related Projects

• NVIDIA Retrieval QA: Embedding model for semantic search
• NV-CLIP: Visual understanding model
• Llama 3.1: Large language model

License

GOVERNING TERMS: Use of the blueprint software and materials and NIM containers are governed by the NVIDIA Software License Agreement and Product-specific Terms for AI products; and the use of models is governed by the NVIDIA Community Model License.

ADDITIONAL INFORMATION: Llama 3.1 Community License Agreement for Llama 3.1 70B Instruct NIM, Llama 3.1 NemoGuard 8B - Content Safety and Llama 3.1 NemoGuard 8B - Topic Control models, built with Llama, (ii) MIT license for NV-EmbedQA-E5-v5.

This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use, found in License-3rd-party.txt.

Use of the product catalog data in the retail shopping assistant is governed by the terms of the NVIDIA Data License for Retail Shopping Assistant (15Aug2025).

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