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Source channel @githubtrending · Post #14993 · Jul 24

#jupyter_notebook Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability. https://github.com/langchain-ai/rag-from-scratch

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djangoproject

@djangoproject · Post #550 · 01/15/2018, 07:05 AM

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http://www.wikipython.com/other-concepts/anatomy-of-a-class/ It seems obvious, but note that you must define a class before you use it. When you create a #class, it establishes its own namespace and all its own local variables (except global definitions) exist only inside that #namespace. They do not interact with other variables of the same name outside it. This leads us to one very important “feature” of classes that you need to know. If you use the same word to designate some specific value both inside and outside the class blueprint, the instance value will take precedence when you try to use that value. #learn

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