https://github.com/jeethanxx01/retrieval-augmented-generation
Retrieval-Augmented Generation (RAG) model with a vector database and language model (LLM) responses, leveraging Flask for server-side scripting and Astra DB for efficient semantic search and vector-based retrieval.
https://github.com/jeethanxx01/retrieval-augmented-generation
astradb aws aws-ec2 flask retrieval-augmented-generation
Last synced: 3 months ago
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Retrieval-Augmented Generation (RAG) model with a vector database and language model (LLM) responses, leveraging Flask for server-side scripting and Astra DB for efficient semantic search and vector-based retrieval.
- Host: GitHub
- URL: https://github.com/jeethanxx01/retrieval-augmented-generation
- Owner: Jeethanxx01
- License: mit
- Created: 2025-05-26T04:09:22.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-05-27T07:33:00.000Z (4 months ago)
- Last Synced: 2025-06-06T08:07:50.831Z (4 months ago)
- Topics: astradb, aws, aws-ec2, flask, retrieval-augmented-generation
- Language: Python
- Homepage:
- Size: 773 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🛍️ Retrieval-Augmented Generation Ecommerce Chatbot
[](https://www.python.org/)
[](https://flask.palletsprojects.com/)
[](LICENSE)
[]()
## 📂 Quick Navigation
- [View Project Images](Images/)
- [OpenAI Version of the Project](OpenAI_version/)
## 🚀 Overview
AI-powered E-commerce Chatbot application leveraging Retrieval-Augmented Generation architecture that combines
LLM capabilities to enable context-aware product recommendations and semantic search from real time vector Database.
Built with Python, Flask, Google's Gemini API, and DataStax Astra DB, and deployed on AWS EC2, this project marks a
revolution in e-commerce product recommendation domain.
## ✨ Key Features
### 🤖 Intelligent Conversational Interface
- Real-time chat interactions
- Natural language understanding and processing
### 🎯 Advanced Product Intelligence
- AI-powered product recommendation engine
- Dynamic pricing and availability updates
### 🔄 Seamless Database Integration
- Real-time product catalog synchronization
- Efficient data retrieval and scalable data management
### 🏢 Enterprise-Grade Architecture
- High-performance request handling
- Scalable cloud infrastructure
## 🛠️ Installation
1. **Clone the repository**
```bash
git clone https://github.com/your-username/ecommerce-chatbot.git
cd ecommerce-chatbot
```
2. **Install dependencies**
```bash
pip install -r requirements.txt
```
3. **Configure environment variables**
Create a `.env` file with the following structure:
```ini
GOOGLE_API_KEY=your_google_api_key_here
ASTRA_DB_API_ENDPOINT=your_astra_db_endpoint
ASTRA_DB_APPLICATION_TOKEN=your_astra_db_token
ASTRA_DB_KEYSPACE=your_astra_db_keyspace
```
4. **Run the application**
```bash
python app.py
```
## ☁️ AWS Deployment
### Prerequisites
- AWS Account
- EC2 Instance
- Security Group Configuration
### Deployment Steps
1. **EC2 Instance Setup**
```bash
sudo apt-get update
sudo apt install git curl unzip tar make sudo vim wget -y
git clone
```
2. **Environment Configuration**
```bash
touch .env
# Add your environment variables
```
3. **Install Dependencies**
```bash
sudo apt install python3-pip
pip3 install -r requirements.txt --break-system-packages
```
4. **Security Configuration**
- Configure Security Group for port 5000
- Allow inbound traffic from 0.0.0.0/0
5. **Launch Application**
```bash
python3 app.py
```
## 🔑 API Configuration
### Google Gemini API
1. Visit [Google AI Studio](https://makersuite.google.com/app/apikey)
2. Create API Key
3. Add to `.env`:
```ini
GOOGLE_API_KEY=your_api_key_here
```
### Astra DB Setup
1. Create account at [DataStax Astra](https://astra.datastax.com/)
2. Create new database
3. Generate token
4. Configure `.env`:
```ini
ASTRA_DB_API_ENDPOINT=your_endpoint
ASTRA_DB_APPLICATION_TOKEN=your_token
ASTRA_DB_KEYSPACE=your_keyspace
```
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 👥 Authors
- **Jeethan Joel Crasta**- [GitHub](https://github.com/Jeethanxx01)
## 🤝 Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
Contributions are welcome! Please feel free to submit a Pull Request.