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 guys! I’ve completed the Kaggle first tutorial on programming and I feel like all the info went right over my head. Anyone feel the same way? Or have constructive learning tips?

Someone posted this image on LinkedIn. I found it hilarious.

A fun way to understand false positives and false negatives: - False Positive: This occurs when a model incorrectly predicts something as true when it is not. (Type I error, like predicting a man is pregnant.) - False Negative: This happens when the model fails to detect something that is actually true. (Type II error, like telling a pregnant woman she is not pregnant.) Read LinkedIn post

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