from bakery import assert_equal from dataclasses import dataclass @dataclass class Rectangle: length: int width: int def area(rect: Rectangle) -> int: return rect.length * rect.width box = Rectangle(5, 3) assert_equal(area(box), 15) I am getting confused which is function here and which is a class. What is the decorator here? Can somebody please explain this in simple words?

I need to build a rag based document retrieval llm, with the following features. I am having difficulty in coding it, because of the multiple dependencies and package versions, so if anyone has any reference code that I can use, it would be helpful. Features: - It must give information strictly from the documents only and not from the internet - It must give sources such as page number and document at the end of each response - It must be able to accept text, audio and images as input - It should very accurate in it's response giving information from the document chunks - The documents knowledge base should support PDF, audio and video formats as well. - This one I find very tricky, the bot must probe the user to explore more topics based on question, or probe the user if the query is very vague or hard to understand. - Other features like chat history, search through chats etc must also be there. I would appreciate the support!

hi all I'm trying to update my CV , I'm a data scientist for 3 years . and I need to update my CV I'm kinda confused😥 it would be helpful if someone could provide advice or a certain template to follow

Hey everyone nice to meet you all, I’m currently exploring a niche intersection between AI, healthcare, and sustainability—a high-demand area in the sustainability sector where I live. As a young entrepreneur, the challenge I'm facing involves managing diverse data systems within companies, especially when ensuring data security and compliance like HIPAA in healthcare. I'm considering developing an AI-driven service to streamline sustainability data management in healthcare businesses. This service would simplify data collection and reporting, reduce costs, and enhance compliance with regulations. However, the main challenge is the varied data storage practices across organizations, which complicates standardizing processes. I’m wondering if using AI to better integrate and interpret data might offer a more unified approach to managing sustainability metrics alongside patient data. Has anyone worked on or seen solutions in similar spaces? I'm interested in tools, technologies, or strategies that could assist in this integration, ensuring robust compliance while enhancing data operability. Any insights or suggestions would be greatly appreciated as I aim to develop this solution further. Thanks in advance for your help !!

Has anyone here read Automate Your Busywork by Aytekin Tank?