Introduction

In this session, I built an AI-powered Notes application by integrating a local Large Language Model with FastAPI and MongoDB.

This project goes beyond basic APIs by combining model inference, backend logic, and persistent storage — similar to how real-world AI systems operate.

The goal was to create a system …

Introduction

In AI systems, data should not be lost after execution. Persistent storage helps store data permanently.

Use

• Store user inputs
• Save AI responses
• Maintain history

On Day 4, I connected my Python application to MongoDB to build a persistent backend system.

Understanding Persistent Storage

Persistent storage means saving data …

Introduction

In real-world AI systems, models alone are not enough. They need memory, structured outputs, and backend logic to work effectively.

To understand this, I built a simple end-to-end AI pipeline using a local model (llama.cpp) and MongoDB.

In this session, I focused on:

• Running a local AI model …