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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 #254 · 02/02/2017, 06:31 PM

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https://groups.google.com/forum/#!topic/pyside-dev/pqwzngAGLWE Dear #Pyside2 contributors, As you might know, Pyside was originally developed for Nokia while it was the owner of the #Qt technology. When Nokia sold Qt to Digia (and now The Qt Company), all the copyrights over the original Pyside code for Qt 4 got transferred to The Qt Company as well. For different reasons, it was not possible for The Qt Company to push Pyside forward as much as we would have wished over the last few years. Fortunately this changed now, and The Qt Company is today in a position, where it can and will invest into Pyside. The goal is to ensure Pyside becomes a fully supported part of the Qt product family, with a similar development and licensing model as the rest of Qt. We want to make sure Pyside works on new Qt releases when they come out and are committed to invest long term into the technology.

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