Build an AI Academic Tutor App with Local Ollama — Zero Cost

Learn how to download and install Ollama on your computer and prepare your local environment to run open-source Large Language Models (LLMs) without using paid cloud APIs.

Set up the development environment for the AI Academic Tutor application and create a simple Python script to connect with Ollama and test a locally running LLM.

Learn how to create the database structure and build a script to load and prepare academic content for use by the AI Academic Tutor application.

Build a reusable LLM service class to communicate with the local Ollama model and create prompt templates that guide the AI Tutor to generate consistent, relevant, and useful responses.

Build the Retrieval-Augmented Generation (RAG) service that retrieves relevant information from the academic knowledge base and provides it as context to the local LLM for more accurate and meaningful answers.

Build the chat API endpoint that connects the frontend with the AI Tutor, processes student questions, retrieves relevant context, and returns AI-generated responses from the local LLM.

Create the main application file for the FastAPI backend, configure the application structure, and connect the API routes to create the foundation of the AI Academic Tutor backend.

Learn how to organize the project into proper Python packages, configure package structure, and use __init__.py to ensure modules can be imported correctly throughout the application.

Test the completed backend API by sending requests to the chat endpoint and verify that the application successfully processes questions and returns responses generated by the local Ollama LLM.

Connect and test the frontend user interface with the FastAPI backend to verify that student questions are sent correctly and AI-generated responses are displayed in the application.