This repository contains a collection of projects leveraging Large Language Models (LLMs) for various applications. Each subdirectory is a standalone project with its own codebase, dependencies, and documentation. Below is an overview of each project:
An AI-powered grammar tutor that helps users improve their grammar skills. It features a Python backend and a simple frontend for user interaction.
- Backend: Python (main.py, src/)
- Frontend: HTML, CSS, JavaScript (frontend/)
- Usage: Run
main.pyto start the backend server. Openfrontend/index.htmlin a browser for the UI.
A chatbot interface for interacting with MySQL databases using natural language queries. It translates user questions into SQL and fetches results from the database.
- Backend: Python (app.py, src/)
- Database: SQL scripts for setup (database/)
- Usage: Run
app.pyto start the chatbot server. Ensure MySQL is running and the database is set up using the provided SQL script.
An application for researching and analyzing news articles using LLMs and retrieval-augmented generation (RAG) techniques.
- Backend: Python (app.py, src/)
- Data: Vector store for semantic search (vectorstore.pkl)
- Usage: Run
app.pyto start the analysis tool. Add news data to the vector store for improved results.
Generates creative restaurant names and menu items using LLMs, based on cuisine and style inputs.
- Backend: Python (app.py, src/)
- Data: Text files for cuisines and styles (data/)
- Usage: Run
app.pyand provide cuisine/style inputs to generate names and menus.
- Clone the repository:
git clone <repo-url> - Navigate to the desired project directory.
- Follow the instructions in each project's README.md for setup and usage.
Each project has its own requirements.txt or pyproject.toml. Install dependencies using pip install -r requirements.txt or pip install . as needed.
See individual project directories for license information.