An enterprise-inspired AI platform for intelligent commerce systems, semantic retail search, recommendation workflows, and Retrieval-Augmented Generation (RAG).

Designed to demonstrate how modern AI systems can power large-scale retail ecosystems across grocery, electronics, fashion, home, and general merchandise commerce platforms.

Modern commerce platforms require more than isolated machine learning models.

They require intelligent systems capable of:

• Understanding customer behavior
• Delivering personalized recommendations
• Generating AI-powered product content
• Retrieving retail knowledge semantically
• Assisting merchandising workflows
• Powering intelligent commerce experiences

This project demonstrates how multiple AI services can work together inside a scalable Retail AI platform architecture.

• 🧠 Retail AI RAG Assistant
• 🤖 Recommendation Intelligence Engine
• ✍️ AI-Powered Content Generation
• 🔎 Semantic Commerce Search
• 🗂️ ChromaDB Vector Database Integration
• ⚡ OpenAI Embeddings & Retrieval
• 🐳 Dockerized Microservices Platform
• 🛍️ Retail Knowledge Base Workflows
• 📊 Enterprise Retail Intelligence Architecture
• 🚀 React + FastAPI Production-Inspired Stack

flowchart TB
    UI[React + Vite Frontend Dashboard]

    UI --> REC[Recommendation Service<br/>FastAPI :8001]
    UI --> CONTENT[Content Intelligence Service<br/>FastAPI :8002]
    UI --> RAG[Retail AI RAG Assistant<br/>FastAPI :8003]

    REC --> PRODUCT[(Retail Product Catalog Dataset)]
    CONTENT --> OPENAI[OpenAI API]
    RAG --> CSV[(Retail AI Knowledge Base CSV)]
    RAG --> CHROMA[(ChromaDB Vector Store)]
    RAG --> OPENAI

    CSV --> INGEST[CSV Ingestion Pipeline]
    INGEST --> CHUNK[Chunking]
    CHUNK --> EMBED[OpenAI Embeddings]
    EMBED --> CHROMA
    CHROMA --> RETRIEVE[Semantic Retrieval]
    RETRIEVE --> ANSWER[AI Retail Answer Generation]
    ANSWER --> UI

    subgraph Services[FastAPI Microservices]
        REC
        CONTENT
        RAG
    end

    subgraph DataLayer[AI and Data Layer]
        OPENAI
        CHROMA
        PRODUCT
        CSV
    end

Retail AI Intelligence Platform
│
├── Frontend (React + Vite)
│
├── Recommendation Intelligence Service
│   ├── Product similarity search
│   ├── Recommendation scoring
│   ├── Category-aware discovery
│   └── Retail recommendation workflows
│
├── Content Intelligence Service
│   ├── OpenAI-powered product content
│   ├── SEO metadata generation
│   ├── Product merchandising workflows
│   └── Retail content intelligence
│
├── Retail AI RAG Assistant Service
│   ├── Retail knowledge ingestion
│   ├── OpenAI embeddings
│   ├── ChromaDB vector storage
│   ├── Semantic retrieval
│   ├── AI-powered retail Q&A
│   └── Commerce intelligence workflows
│
├── Customer Analytics Service (Planned)
│
└── Log Intelligence Service (Planned)

Retail Knowledge Base
        ↓
Document Ingestion
        ↓
Chunking Pipeline
        ↓
OpenAI Embeddings
        ↓
ChromaDB Vector Store
        ↓
Semantic Retrieval
        ↓
Context Injection
        ↓
LLM Response Generation
        ↓
Retail AI Assistant

AI-powered retail recommendation workflows for product discovery and similarity search.

• Product similarity scoring
• Category-aware recommendations
• Semantic recommendation workflows
• Retail product discovery
• Recommendation ranking engine

Generative AI workflows for retail product content and merchandising systems.

• AI-generated product titles
• Product descriptions
• SEO metadata generation
• Bullet point generation
• Merchandising content workflows
• OpenAI-powered content systems

A Retrieval-Augmented Generation (RAG) service designed for intelligent commerce retrieval workflows.

• Retail knowledge ingestion
• OpenAI embeddings
• ChromaDB vector search
• Semantic retrieval
• AI-powered retail Q&A
• Retail merchandising intelligence
• Commerce knowledge workflows
• RAG-ready retrieval pipelines

Future customer intelligence workflows.

• Customer segmentation
• Behavioral intelligence
• Engagement analysis
• AI-powered customer insights
• Retail analytics workflows

Operational AI workflows for monitoring and intelligence systems.

• AI-assisted log analysis
• Operational intelligence
• Intelligent monitoring workflows
• Production issue insights

This platform explores practical AI applications for modern commerce systems.

• Retrieval-Augmented Generation (RAG)
• Recommendation systems
• Semantic vector search
• AI-powered content generation
• Retail intelligence workflows
• Product discovery systems
• Semantic commerce retrieval
• AI merchandising assistants
• OpenAI embedding pipelines

This platform is connected with the Kaggle dataset:

A large-scale AI-ready dataset designed for:

• Semantic retrieval
• Recommendation systems
• RAG workflows
• Retail AI assistants
• Commerce intelligence systems

• 100K+ retail intelligence records
• Multi-category retail coverage
• AI use case mappings
• Semantic retrieval tags
• Merchandising strategies
• Customer segment intelligence

The project also includes a premium Kaggle notebook focused on:

• RAG workflows
• Semantic retrieval
• Retail AI intelligence
• Recommendation analysis
• Commerce AI insights
• AI-ready dataset engineering

Run the entire platform locally using Docker Compose.

docker compose up --build

https://hub.docker.com/r/noopur17/retail-ai-frontend

https://hub.docker.com/r/noopur17/retail-recommendation-service

https://hub.docker.com/r/noopur17/retail-content-intelligence-service

• React
• Vite
• JavaScript

• FastAPI
• Python
• REST APIs

• OpenAI
• ChromaDB
• Scikit-learn
• Pandas
• Vector Embeddings

• Docker
• Docker Compose
• Docker Hub

retail-ai-intelligence-platform/
│
├── docs/
│   └── screenshots/
│
├── frontend/
│
├── services/
│   ├── recommendation-service/
│   ├── content-intelligence-service/
│   ├── rag-assistant-service/
│   ├── customer-analytics-service/
│   └── log-intelligence-service/
│
├── datasets/
│
├── notebooks/
│
└── docker-compose.yml

http://localhost:8001/docs

http://localhost:8002/docs

http://localhost:8003/docs

cd services/recommendation-service
python -m uvicorn app.main:app --reload --port 8001

cd services/content-intelligence-service
python -m uvicorn app.main:app --reload --port 8002

cd services/rag-assistant-service

python3 -m venv venv
source venv/bin/activate

python -m pip install -r requirements.txt

export OPENAI_API_KEY="your_api_key_here"

python -m uvicorn app.main:app --reload --port 8003

cd frontend/frontend

npm install
npm run dev

This project explores practical applications of:

• Recommendation systems
• Retrieval-Augmented Generation (RAG)
• Semantic search systems
• Retail intelligence workflows
• Commerce AI systems
• Generative AI applications
• Intelligent retrieval pipelines
• Enterprise AI platform engineering

• Recommendation Intelligence API
• Content Intelligence Service
• OpenAI Integration
• Retail AI RAG Assistant
• ChromaDB Vector Search
• Dockerized Platform
• Enterprise-style React Dashboard
• Kaggle Retail AI Dataset
• Premium Kaggle Notebook

• Frontend RAG Chat Integration
• Customer Analytics Service
• Customer Review Ingestion
• Retail Analytics Dashboard
• Conversation Memory
• AI Shopping Assistant
• Recommendation Feedback Loop
• End-to-End Retail AI Simulation

AI & Full-Stack Engineer focused on:

• Retail AI Systems
• Retrieval-Augmented Generation (RAG)
• Recommendation Workflows
• Generative AI Applications
• Intelligent Commerce Platforms
• Semantic Retrieval Systems
• Scalable AI Services

The long-term vision of this project is to evolve into a production-inspired Retail AI ecosystem demonstrating how:

• recommendation systems,
• generative AI,
• semantic retrieval,
• vector search,
• intelligent merchandising,
• and commerce AI workflows

can work together inside modern enterprise retail platforms.