Matrices App Clone

This project is a full-stack application designed to replicate the functionality of Matrices.app, "The spreadsheet that fills itself". It's an excellent tool for anyone looking to quickly generate structured data from natural language inputs.

Features

• AI-Powered Data Generation: Utilizes OpenAI's GPT-3.5 to interpret user prompts and generate relevant table names and fields.
• Real-Time Data Streaming: Streams AI-generated data to the client in real-time using Server-Sent Events (SSE), providing an interactive and responsive user experience.
• Dynamic Table Rendering: Dynamically renders tables on the web page based on the AI-generated output, allowing for immediate visualization and editing of the data.

Stack

• Frontend: Vanilla JavaScript, HTML, CSS, Vite for bundling, and AG-Grid for dynamic table rendering.
• Backend: Node.js with Express for the server and OpenAI API for data generation.

Getting Started

Prerequisites

• Node.js installed on your machine.
• An OpenAI API key.

Setup

1. Clone the repository

git clone https://github.com/CocoaPriest/matrices-clone.git

2. Install dependencies

Navigate to both the server and client directories in separate terminal windows and run:

npm install

3. Set up environment variables

Create a .env file in the root of the server directory and add your OpenAI API key:

OPENAI_API_KEY=your_openai_api_key_here

Running the Application

1. Start the server

In the server directory, run:

npm run dev

This will start the backend server on http://localhost:3002.

2. Start the client

In the client directory, run:

npm run dev

This will start the frontend development server, typically on http://localhost:3000. Open your browser to this URL to interact with the application.

Usage

1. Enter a Prompt: In the provided text area on the webpage, enter a natural language prompt describing the data table you wish to generate.
2. Process: Click the "Process" button to send the prompt to the server, which then interacts with the OpenAI API to generate table names and fields based on your input.
3. View Results: The application will dynamically render a table based on the AI's response, allowing you to view and edit the generated data.

TODO

1. Detect what data is needed (using prompts)
2. Do a google search & scrape data
3. Do RAG ✨✨
4. Fill the table

Contributing

Contributions are welcome! Please feel free to submit a pull request or create an issue if you have any suggestions, improvements, or find any bugs.

License

This project is open-sourced under the MIT License. See the LICENSE file for more details.