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https://github.com/elcaiseri/survey-analysis-rag-system
A web application using Retrieval-Augmented Generation (RAG) to analyze and compare survey datasets. Built with FastAPI, Nginx, and OpenAI’s models. Fully containerized with Docker for easy deployment.
https://github.com/elcaiseri/survey-analysis-rag-system
ai docker docker-compose fastapi javascript machine-learning nginx nlp openai python rag semantic-search survey-analysis
Last synced: 18 days ago
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A web application using Retrieval-Augmented Generation (RAG) to analyze and compare survey datasets. Built with FastAPI, Nginx, and OpenAI’s models. Fully containerized with Docker for easy deployment.
- Host: GitHub
- URL: https://github.com/elcaiseri/survey-analysis-rag-system
- Owner: elcaiseri
- License: mit
- Created: 2024-10-17T21:21:36.000Z (29 days ago)
- Default Branch: main
- Last Pushed: 2024-10-18T22:54:38.000Z (28 days ago)
- Last Synced: 2024-10-20T08:18:31.384Z (26 days ago)
- Topics: ai, docker, docker-compose, fastapi, javascript, machine-learning, nginx, nlp, openai, python, rag, semantic-search, survey-analysis
- Language: Python
- Homepage:
- Size: 603 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AI-Powered Survey Insights
## Project Description
AI-Powered Survey Insights is a web application that leverages advanced AI models to process and analyze user queries related to two survey datasets using Retrieval-Augmented Generation (RAG). This tool provides actionable insights from:
- **Dataset 1:** Sustainability Research Results
- **Dataset 2:** Christmas Research Results
The project consists of:
- **Backend:** A FastAPI-based service that processes user queries and interacts with the OpenAI API.
- **Frontend:** A simple HTML/CSS/JavaScript interface served using Nginx.
- **Dockerized Deployment:** Both backend and frontend are containerized for easy deployment using Docker and Docker Compose.
## Table of Contents
- [Features](#features)
- [Prerequisites](#prerequisites)
- [Installation and Setup](#installation-and-setup)
- [Usage Instructions](#usage-instructions)
- [API Documentation](#api-documentation)
- [Docker Instructions](#docker-instructions)
- [Deployment](#deployment)
- [License](#license)
- [Acknowledgements](#acknowledgements)
## Features
- **Interactive Query Interface:** Users can ask questions about the survey datasets in natural language.
- **AI-Generated Insights:** Retrieves precise answers using OpenAI’s models.
- **Dataset Comparison:** Compare insights between Sustainability and Christmas survey datasets.
- **User-Friendly Interface:** Clean, intuitive UI for easy interaction.
- **Dockerized Setup:** Simple container-based deployment with Docker Compose.
## Prerequisites
- **Docker:** Install Docker.
- **Docker Compose:** Comes bundled with Docker Desktop.
- **OpenAI API Key:** Obtain an API key from OpenAI.
## Installation and Setup
1. **Clone the Repository:**
```sh
git clone https://github.com/elcaiseri/Survey-Analysis-RAG-System.git
cd Survey-Analysis-RAG-System
```
2. **Set Up Environment Variables:**
Create a `.env` file in the project root:
```plaintext
OPENAI_API_KEY=your-openai-api-key
APP_TOKEN=your-app-token
BACKEND_URL=http://localhost:8000
FRONTEND_URL=http://localhost:5500
```
*Note: Replace the placeholders with actual values. Do not commit this file to version control.*
3. **Prepare the Data:**
Run the exploratory data analysis script to prepare the datasets:
```sh
python eda.py
```
4. **Build and Run the Containers:**
Use Docker Compose to build and start the services:
```sh
docker-compose up --build
```
This will build both the frontend and backend containers and start them.
## Usage Instructions
1. **Access the Application:**
Open your browser and go to `http://localhost:5500`.
2. **Interact with the Application:**
- Enter a query in the input field (e.g., “How important is sustainability to consumers?”).
- Select the relevant dataset from the dropdown (Sustainability or Christmas).
- Click **Get Insights** to submit your query.
3. **View Results:**
- The AI-generated response will be displayed below the form.
- If there’s an error, it will be shown in the error message section.
## API Documentation
The backend exposes a POST endpoint for querying datasets.
- **Endpoint:** `/query`
- **Method:** POST
- **Description:** Processes a user query and returns AI-generated insights.
**Request Body Example:**
```json
{
"query": "How important is sustainability to consumers?",
"dataset": "sustainability"
}
```
**Response Example:**
```json
{
"answer": "Sustainability is highly important to consumers, with 75% preferring eco-friendly products."
}
```
**Error Handling:**
- If the request fails, the API returns an appropriate HTTP status code and an error message.
## Docker Instructions
### Building and Running the Containers
1. **Build the Docker Containers:**
```sh
docker-compose build
```
2. **Start the Containers:**
```sh
docker-compose up
```
3. **Run in Detached Mode (Optional):**
```sh
docker-compose up -d
```
### Stopping the Containers
```sh
docker-compose down
```
### Rebuilding After Code Changes
If you make changes to the code, rebuild the containers:
```sh
docker-compose up --build
```
## Deployment
To deploy the application to a production environment, consider using one of these services:
- **AWS Elastic Beanstalk**
- **Google Cloud Run**
- **Azure App Service**
- **Heroku**
### Deployment Checklist:
- Securely manage environment variables (e.g., using AWS Secrets Manager).
- Configure CORS policies to allow frontend-backend communication.
- Use HTTPS in production to ensure secure data transmission.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.
## Acknowledgements
- **OpenAI:** For providing the API and language models.
- **FastAPI:** For the robust and lightweight web framework.
- **Docker:** For containerization and simplified deployment.
- **Nginx:** For serving static files efficiently.
- **Community Resources:** Tutorials and documentation that helped shape this project.
Feel free to contribute to this project by opening issues or submitting pull requests. If you have any questions, contact us at [email protected].