Moonflour_Bean – AI Chatbot for Coffee Shop Apps

Welcome to the Moonflour_Bean Chat Agent repository! This project contains the full-stack implementation of an AI-powered Multi-agent app designed to enhance customer experiences in a coffee shop application by simulating real-world interactions.

The chatbot utilizes Large Language Models (LLMs), Natural Language Processing (NLP), RunPod’s infrastructure, and Firebase to assist customers with:

✅ Taking orders
✅ Answering detailed menu-related queries
✅ Providing personalized product recommendations

This chatbot is integrated within a React Native mobile app (currently under development).

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🎯 Project Goals

The goal of this project is to develop an intelligent, agent-based chatbot capable of:

1. Handling real-time customer interactions – Allowing customers to place orders seamlessly.
2. Answering menu-related questions – Providing details on ingredients, allergens, and other menu information using a Retrieval-Augmented Generation (RAG) system.
3. Offering personalized recommendations – Utilizing a market basket analysis recommendation engine to suggest complementary items.
4. Ensuring structured order placement – Guiding customers through an efficient ordering process for accuracy.
5. Filtering inappropriate queries – Using a Guard Agent to block irrelevant or harmful inputs.

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🛠️ Core Agents in the Chatbot System

1. Guard Agent (Content Filtering)

• Serves as the first line of defense by monitoring all incoming queries.
• Blocks inappropriate, harmful, or irrelevant messages before they reach other agents.
• Ensures safe and meaningful interactions with the chatbot.

2. Order Taking Agent (Order Assistance)

• Guides users through the entire order placement process using chain-of-thought reasoning.
• Captures all customer preferences and order details in a structured, logical manner.
• Ensures orders are complete and accurate before processing.

3. Details Agent (RAG System) (Menu Information)

• Uses Retrieval-Augmented Generation (RAG) to provide detailed answers on menu items, including:
  • Ingredients
  • Allergens
  • Nutritional information
• Retrieves relevant data from a vector database and generates accurate responses.

4. Recommendation Agent (Personalized Suggestions)

• Works alongside the Order Taking Agent to suggest complementary products based on the user’s order history.
• Uses a market basket analysis model to enhance upselling opportunities and help users discover new products.

5. Classification Agent (Query Routing)

• Analyzes user intent and directs queries to the appropriate agent.
• Ensures queries are handled efficiently, whether they relate to:
  • Order placement
  • Menu details
  • Product recommendations

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⚙️ How the Agents Work Together

1. User Query Processing:

   • A customer sends a message to the chatbot.
   • The Guard Agent filters out any harmful or irrelevant content.

2. Intent Classification:

   • The Classification Agent determines the purpose of the query (e.g., ordering, menu details, recommendations).

3. Task Delegation:

   • The query is sent to the most relevant agent:
     • Order Taking Agent for order-related queries.
     • Details Agent for menu-related questions.
     • Recommendation Agent for product suggestions.

4. Upselling Process:

   • Before finalizing an order, the Order Taking Agent may request recommendations from the Recommendation Agent to enhance the customer’s experience.

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📂 Folder Structure

coffee_shop_customer_service_chatbot/
│   ├── Frontend/                # React Native app code (Coming soon)  
│   ├── python/ (Backend)  
│       ├── API/                # Backend API for the agent-based chatbot system  
│       ├── dataset/            # Dataset for training the recommendation engine  
│       ├── products/           # Product details (names, prices, descriptions, images)  
│       ├── vector_database.ipynb  # Builds the vector database for the RAG model  
│       ├── firebase_uploader.ipynb  # Uploads product data to Firebase  
│       ├── recommendation_engine.ipynb  # Trains the recommendation system  

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🚀 Getting Started

Prerequisites

Ensure you have the following installed on your system:

• Python 3.8+
• Node.js & npm
• React Native
• Firebase CLI (for data management)
• RunPod API Key (for cloud-based inference)

Installation

Backend Setup

# Clone the repository
git https://github.com/buriihenry/Moonflour_Bean.git

# Install dependencies
pip install -r requirements.txt

Frontend Setup

# Navigate to the frontend directory
cd ../Frontend

# Install dependencies
npm install

Running the Project

Start the Backend

cd python
python main.py

Start the Frontend

cd ../Frontend
npm start

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📌 Contributing

Contributions are welcome! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature-name).
3. Commit your changes (git commit -m 'Add feature').
4. Push to your branch (git push origin feature-name).
5. Open a pull request.

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📜 License

This project is licensed under the MIT License – see the LICENSE file for details.

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📞 Contact

For any inquiries or support, feel free to reach out: 📌 GitHub: buriihenry

Happy coding! ☕🤖