Llama Tutor: an AI tool that provides personalized tutoring, an open source AI personal tutor project built on Llama 3.1

General Introduction

Llama Tutor is an open source AI personal tutor project built on Llama 3.1, designed to provide users with a personalized learning experience. By integrating multiple technology stacks such as Together AI, Next.js, and Tailwind CSS, Llama Tutor is capable of real-time interaction and can generate tailored tutoring content based on the user's input of learning topics and education level to help them master knowledge faster.

Online experience: https://llamatutor.com/

Function List

• Personalized Tutoring: Generate customized tutoring content based on user input on learning topics and education levels.
• Multidisciplinary support: Covering a wide range of disciplines including basketball, machine learning, personal finance, American history, and more.
• open source project: Completely open source, users can freely view and modify the code.
• Real-time search: Integration with the Serper Search API to provide up-to-date learning resources.
• data analysis: Use Helicone for observability analysis to help users understand learning progress.

Using Help

Installation process

1. clone warehouse: fork or clone project repositories on GitHub.
2. Create an account: Create accounts on Together AI, SERP API or Azure (Bing Search API) and Helicone.
3. Configuration environment: Create the .env file (refer to .example.env) and replace the API key.
4. Installation of dependencies: Run npm install Install project dependencies.
5. Initiation of projects: Run npm run dev Start the local development server.

Function Operation Guide

1. Personalized Learning Experience::
   • Users can enter learning requirements and the system will generate customized learning content based on the requirements.
   • Instant Q&A through AI to help users solve their learning queries.
2. Real-time interactive teaching::
   • The system generates interactive content in real time based on user input, providing instant feedback.
   • Users can interact with the AI tutor through a dialog box to get instant help.
3. open source project::
   • Developers can access GitHub repositories to view and contribute code.
   • The project is under the MIT license, which allows free use and modification.
4. Multi-Technology Stack Support::
   • The project uses Llama 3.1 as the core AI model, providing powerful natural language processing capabilities.
   • Using Together AI for LLM inference, Next.js and Tailwind CSS to build the front-end interface.
   • Enhance your learning by getting search results through the Serper API or Bing Search API.
5. data analysis::
   • Use Plausible for website analytics to collect data on user behavior and optimize the user experience.
   • Developers can view analytics reports to understand user usage and make targeted improvements.