https://github.com/nvidia-ai-blueprints/retail-shopping-assistant
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.
https://github.com/nvidia-ai-blueprints/retail-shopping-assistant
agentic-ai agents embeddings genai llm rag
Last synced: about 1 month ago
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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.
- Host: GitHub
- URL: https://github.com/nvidia-ai-blueprints/retail-shopping-assistant
- Owner: NVIDIA-AI-Blueprints
- License: other
- Created: 2025-07-02T19:31:07.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-08-28T23:48:18.000Z (about 2 months ago)
- Last Synced: 2025-08-29T04:29:25.872Z (about 2 months ago)
- Topics: agentic-ai, agents, embeddings, genai, llm, rag
- Language: Jupyter Notebook
- Homepage:
- Size: 53.3 MB
- Stars: 8
- Watchers: 0
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
- Security: SECURITY.md
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README
# 🛍️ NVIDIA AI Blueprint: Retail Shopping Assistant
**AI-powered retail shopping assistant with multi-agent architecture**
[](LICENSE)
[](https://www.python.org/)
[](https://www.docker.com/)
[](https://github.com/NVIDIA-AI-Blueprints/retail-shopping-assistant/stargazers)
[](https://github.com/NVIDIA-AI-Blueprints/retail-shopping-assistant/issues)
[](https://github.com/NVIDIA-AI-Blueprints/retail-shopping-assistant/commits)
[](https://github.com/NVIDIA-AI-Blueprints/retail-shopping-assistant/graphs/contributors)
## 📋 Table of Contents
- [Overview](#overview)
- [Key Features](#key-features)
- [Architecture](#architecture)
- [Get Started](#get-started)
- [Prerequisites](#prerequisites)
- [Quick Start](#quick-start)
- [Documentation](#documentation)
- [Contribution Guidelines](#contribution-guidelines)
- [Community](#community)
- [References](#references)
- [License](#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](docs/README.md#architecture-overview).
## Get Started
### Prerequisites
- **Docker**: Version 20.10+ with Docker Compose plugin
- **NVIDIA NGC Account**: For API access ([Get API Key](https://ngc.nvidia.com/))
- **Hardware**: 4x H100 GPUs (preferred) or 4x A100 GPUs (minimum) for local deployment, or cloud access
### Quick Start
1. **Clone the repository**:
```bash
git clone https://github.com/NVIDIA-AI-Blueprints/retail-shopping-assistant.git
cd retail-shopping-assistant
```
2. **Authenticate with NVIDIA Container Registry**:
```bash
docker login nvcr.io
```
Use `$oauthtoken` as the username and your NGC API key as the password.
3. **Set up environment**:
```bash
export NGC_API_KEY=your_nvapi_key_here
export LLM_API_KEY=$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**:
```bash
# 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):
```bash
# 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**:
```bash
docker compose -f docker-compose.yaml -f docker-compose-nim-local.yaml down
```
**Option B: Cloud Deployment**:
```bash
docker compose -f docker-compose.yaml down
```
For detailed installation instructions, see [Deployment Guide](docs/DEPLOYMENT.md).
## 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](docs/BREV.md)** - 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](docs/USER_GUIDE.md)**: How to use the application
- **[API Documentation](docs/API.md)**: Complete API reference
- **[Deployment Guide](docs/DEPLOYMENT.md)**: Installation and setup instructions
- **[Documentation Hub](docs/README.md)**: Complete documentation index
## Contribution Guidelines
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) 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](https://github.com/NVIDIA-AI-Blueprints/retail-shopping-assistant/issues)
- **Documentation**: [Comprehensive guides and references](docs/README.md)
## References
### NVIDIA AI Blueprints
- [NVIDIA AI Blueprints](https://github.com/NVIDIA-AI-Blueprints): Collection of AI application blueprints
- [NVIDIA NIM](https://catalog.ngc.nvidia.com/orgs/nim): Containerized AI models
- [NVIDIA NGC](https://ngc.nvidia.com/): AI platform and container registry
### Technologies Used
- [LangGraph](https://github.com/langchain-ai/langgraph): Agent orchestration framework
- [FastAPI](https://fastapi.tiangolo.com/): Modern Python web framework
- [React](https://reactjs.org/): JavaScript library for building user interfaces
- [Milvus](https://milvus.io/): Vector database for similarity search
### Related Projects
- [NVIDIA Retrieval QA](https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/nv-embedqa-e5-v5): Embedding model for semantic search
- [NV-CLIP](https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/nvclip): Visual understanding model
- [Llama 3.1](https://catalog.ngc.nvidia.com/orgs/nim/teams/meta/containers/llama-3.1-70b-instruct): Large language model
## License
GOVERNING TERMS: Use of the blueprint software and materials and NIM containers are governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-specific Terms for AI products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of models is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).
ADDITIONAL INFORMATION: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/) 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](/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](/LICENSE-assets.txt) (15Aug2025).
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