Epython Lab

When I build an AI application, choosing the backend framework is an important decision. There are several good options, but I usually look at 𝐅𝐚𝐬𝐭𝐀𝐏𝐈, 𝐃𝐣𝐚𝐧𝐠𝐨, and 𝐅𝐥𝐚𝐬𝐤 first. The choice really depends on what I'm building. ✔️ 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 makes a lot of sense when the application is mainly an AI/API backend. Since most AI tools I use are already in Python, I can keep the whole stack in one ecosystem, from LLMs and embeddings to document processing, RAG, databases, and the API itself. ✔️ 𝐃𝐣𝐚𝐧𝐠𝐨 is a strong choice when the AI functionality is part of a larger web application. Its built-in ORM, authentication, admin panel, and other features can save a lot of development time. ✔️ 𝐅𝐥𝐚𝐬𝐤 is still a great option when I want something simple, lightweight, and flexible, especially for smaller services or prototypes. For an AI application, I also need to think beyond the framework: ✔️ Authentication ✔️ Database and data persistence ✔️ Document processing ✔️ Embeddings and vector search ✔️ RAG ✔️ Background tasks ✔️ Testing ✔️ Docker ✔️ Monitoring ✔️ Deployment There isn't one framework that is "best" for every AI application. For the type of production AI backends I'm building, 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 is often a practical choice because it provides a clean API layer while keeping everything close to the Python AI ecosystem. The framework is only one piece of the puzzle. Good architecture matters more than the framework you choose. What do you normally use for AI applications: 𝐅𝐚𝐬𝐭𝐀𝐏𝐈, 𝐃𝐣𝐚𝐧𝐠𝐨, 𝐅𝐥𝐚𝐬𝐤, or something else? Here is the roadmap to build an AI application with FastAPI: https://www.youtube.com/watch?v=0SLLG2Z_Htw #FastAPI #Python #AIEngineering #GenerativeAI #RAG #BackendDevelopment #MachineLearning #Django #Flask #SoftwareArchitecture

𝐅𝐚𝐬𝐭𝐀𝐏𝐈 𝐯𝐬 𝐑𝐄𝐒𝐓 𝐀𝐏𝐈 — What’s the Difference? One thing I see quite often when people start building APIs with Python is confusion between FastAPI and REST API. In reality, they are not the same thing. 𝐑𝐄𝐒𝐓 𝐀𝐏𝐈 is an architectural approach for designing APIs around resources, HTTP methods, stateless communication, and standard HTTP responses. 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 is a Python web framework that helps you build APIs. For example, in an AI application, I might have: GET /documents 𝙶𝙴𝚃 /𝚍𝚘𝚌𝚞𝚖𝚎𝚗𝚝𝚜 𝙿𝙾𝚂𝚃 /𝚍𝚘𝚌𝚞𝚖𝚎𝚗𝚝𝚜 𝙶𝙴𝚃 /𝚍𝚘𝚌𝚞𝚖𝚎𝚗𝚝𝚜/{𝚒𝚍} 𝙿𝚄𝚃 /𝚍𝚘𝚌𝚞𝚖𝚎𝚗𝚝𝚜/{𝚒𝚍} 𝙳𝙴𝙻𝙴𝚃𝙴 /𝚍𝚘𝚌𝚞𝚖𝚎𝚗𝚝𝚜/{𝚒𝚍} These endpoints can follow 𝐑𝐄𝐒𝐓 principles. 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 is the tool I use to implement them in Python. So, a simple way to remember it: 𝐑𝐄𝐒𝐓 = how the API is designed 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 = the framework used to build it 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 also gives us useful features such as request validation, automatic API documentation, dependency injection, and strong support for asynchronous applications. Understanding this distinction makes it much easier to understand 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 and, more importantly, to design APIs properly. 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 Fundamentals: Build Your First AI API | Python FastAPI Course (An Overview of API): https://youtu.be/vvP9GIWSews #FastAPI #Python #RESTAPI #APIDevelopment #AI #MachineLearning #BackendDevelopment

This is the github starter for the project based FastAPI course tutorial https://github.com/epythonlab2/ai-document-api