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Rasa-based e-commerce chatbot with React frontend for intelligent customer support and order management

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Rasa E-commerce Assistant

Python 3.8+ Rasa 3.x React

Overview

An AI-powered chatbot for e-commerce platforms built using the Rasa framework. Handles customer queries related to order tracking, product information, and general support through natural language conversations.

Features

  • Order tracking with ID validation
  • Product search and inventory checking
  • Return and refund policy assistance
  • Payment and shipping information
  • Context-aware and intent-based conversation flows

Tech Stack

  • AI/NLP: Rasa Open Source 3.x, spaCy
  • Frontend: React.js (18+)
  • Backend: Node.js + Express
  • Language: Python 3.8+

Setup Instructions

# Clone the repository
git clone <repo-url>
cd rasa-ecommerce-assistant

# Python environment
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install Python dependencies
pip install -r requirements.txt

# Install frontend dependencies
cd src
npm install
cd ..

# Train and run the assistant
rasa train
rasa run --enable-api --port 5005 &
rasa run actions --port 5055 &
cd src
npm start

Supported Intents

  • track_order: Track orders by order ID
  • return_policy: Provide return/refund details
  • product_search: Discover available products
  • shipping_info: Offer shipping and delivery details
  • payment_methods: List accepted payment options

Entity Recognition

  • Order IDs
  • Product names
  • Shipping types
  • Payment methods

Project Structure

rasa-ecommerce-assistant/
├── actions/            # Custom action code
├── data/               # NLU training data, stories, and rules
├── models/             # Trained Rasa models
├── src/                # React frontend
├── config.yml          # Rasa pipeline and policies
├── domain.yml          # Intents, entities, slots, responses
├── endpoints.yml       # Action server and tracker store config
└── requirements.txt    # Python dependencies

Performance Metrics

  • Response latency: < 200ms
  • Intent classification accuracy: 95%+
  • Handles up to 50 concurrent users (test environment)

License

This project is licensed under the MIT License.

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Rasa-based e-commerce chatbot with React frontend for intelligent customer support and order management

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