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https://github.com/im-anhat/llama-latte
AI-powered coffee shop app built with React Native, featuring Llama 3 model integration, Retrieval-Augmented Generation (RAG) for personalized chatbot responses, agent-based task handling, and tailored product recommendations using Market Basket Analysis. Deployed with Docker and RunPod API for scalability and cross-platform compatibility.
https://github.com/im-anhat/llama-latte
Last synced: 3 days ago
JSON representation
AI-powered coffee shop app built with React Native, featuring Llama 3 model integration, Retrieval-Augmented Generation (RAG) for personalized chatbot responses, agent-based task handling, and tailored product recommendations using Market Basket Analysis. Deployed with Docker and RunPod API for scalability and cross-platform compatibility.
- Host: GitHub
- URL: https://github.com/im-anhat/llama-latte
- Owner: im-anhat
- Created: 2024-12-04T16:38:06.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-12-04T17:09:24.000Z (about 1 month ago)
- Last Synced: 2024-12-04T18:21:21.902Z (about 1 month ago)
- Language: Jupyter Notebook
- Size: 4.91 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome_ai_agents - Llama-Latte - AI-powered coffee shop app built with React Native, featuring Llama 3 model integration, Retrieval-Augmented Generation (RAG) for persona… (Building / Deployment)
README
# ☕ **Llama-Latte**
> **Llama-Latte** is an AI-powered coffee shop application designed to enhance customer experience through personalized chatbot interactions, real-time recommendations, and seamless order management. Built with **React Native** and **Llama 3 model**, it leverages **Retrieval-Augmented Generation (RAG)** and **Market Basket Analysis** for accurate, context-aware responses and tailored product suggestions.
---
## 📸 **Screenshots**
---
## 💻 **Technologies**
### **Frontend**
- **React Native**
- **Expo** for rapid app development
- **NativeWind** for styling
### **AI and Backend**
- **Llama 3 Model** for AI-driven chatbot
- **Retrieval-Augmented Generation (RAG)** for personalized responses
- **Firebase Firestore** for real-time database
- **RunPod API** for scalable model hosting
### **Data Science**
- **Market Basket Analysis** using Apriori algorithm for recommendation generation
### **DevOps**
- **Docker** for containerization
- **RunPod** for deployment and hosting
- **GitHub Actions** for CI/CD
---
## 🚀 **Features**
### 🤖 **AI-Driven Chatbot**
- Built with **Llama 3 Model** to handle customer queries, recommend products, and manage orders seamlessly.
- Enhanced with **RAG** for context-aware responses by retrieving real-time data from the coffee shop database.
### 🛒 **Personalized Recommendations**
- Utilizes **Market Basket Analysis** to provide tailored recommendations based on purchase patterns.
- Offers **popularity-based** and **category-specific** recommendations for drinks and pastries.
### 🎯 **Agent-Based System**
- Specialized agents handle:
- **Order-taking**: Validates menu items and calculates totals.
- **Product recommendations**: Suggests complementary products.
- **Query filtering**: Ensures only relevant questions are processed.
### 🖥️ **Cross-Platform Compatibility**
- Developed with **React Native** for smooth deployment on iOS and Android.
- Deployed on **RunPod** for scalable API and AI model integration.
---
## 🛠️ **Getting Started**
### 📋 **Prerequisites**
- **Node.js** (v14 or higher)
- **npm** or **yarn**
- **Expo CLI**
- **Firebase** account with Firestore configured
- **Docker** for local containerization (optional)
### 🚀 **Installation**
1. **Clone the Repository**:
```bash
git clone https://github.com/yourusername/llama-latte.git
cd llama-latte
```
2. **Install Dependencies**:
```bash
npm install
```
3. **Set Up Environment Variables**:
Create a .env file in the root directory with:
RUNPOD_TOKEN=your_runpod_token
RUNPOD_CHATBOT_URL=your_runpod_chatbot_url
MODEL_NAME=meta-llama/Meta-Llama-3-8B-Instruct
FIREBASE_API_KEY=your_firebase_api_key
4. **Start the Application**:
```bash
npx expo start --tunnel
```
## 📊 Architecture
Frontend
• React Native app powered by Expo
• NativeWind for consistent and flexible UI styling
Backend
• Llama 3 model deployed on RunPod API
• Firebase Firestore for real-time database management
Recommendation System
• Apriori algorithm for generating purchase-based recommendations
• Popularity-based recommendations categorized by product types
## 🛠️ Key Functionalities
🌟 AI-Driven Order Management
• Process orders with accurate validation and price calculation.
• Suggest complementary products based on the customer’s current selection.
🔍 Smart Recommendations
• Apriori Recommendations: Suggest items frequently purchased together.
• Popular Recommendations: Suggest trending items in the coffee shop.
• Category-Specific Recommendations: Suggest items within a specific category based on customer interest.
🔒 Secure and Scalable Deployment
• Deployed with Docker and RunPod, ensuring scalability and cross-platform compatibility.
• Sensitive configurations securely managed with environment variables.