Build an AI-Powered App with FastAPI, Qdrant, NumPy, and Pydantic | Step-by-Step Tutorial #4

Welcome to today's tutorial! 🚀

In this video, we’ll kick off with the installation and setup of MedInsightsAPI, an AI-based project leveraging cutting-edge technologies. Here's what you'll learn:

🔧 Technologies Used:

Python: A powerhouse for AI and data projects, offering libraries like TensorFlow, PyTorch, scikit-learn, pandas, NumPy, and visualization tools such as Matplotlib and Seaborn.

FastAPI & Uvicorn: For creating high-performance, asynchronous APIs with automatic documentation.

NumPy: Enhancing numerical operations for machine learning and data processing.

Pydantic: Simplifying data validation and serialization with type-based models.

Qdrant: A vector database optimized for ANN (Approximate Nearest Neighbors) search and RAG (Retrieval-Augmented Generation) workflows.

🔍 What’s Inside:

File architecture explained with a clean and scalable structure for the API:

API/
├── app/
└── README.md

Step-by-step guide to setting up Pydantic models (Item and VectorData) for structured data validation and serialization.

Insights into Python’s inner classes (Config) and how they enhance integration with ORM models.
🌟 Why MedInsightsAPI? Harnessing these powerful tools and frameworks, this project is designed to showcase efficient development for AI-driven applications.

👉 Don’t forget to Like, Comment, and Subscribe for more tech insights and tutorials! 🔔

Let’s get started! 🎉

#MedInsightsAPI #AI #MachineLearning #FastAPI #Python #VectorDatabase #Qdrant
#Pydantic #Uvicorn #NumPy #APIDevelopment #DataScience #DeepLearning
#VectorSearch #AIIntegration #SemanticSearch #RetrievalAugmentedGeneration
#TechTutorial #PythonDevelopment #QdrantTutorial #CholakovIT