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Source channel @githubtrending · Post #14826 · Jun 12

#jupyter_notebook#ai#llm#llms#multi_modal#openai#python#rag Retrieval-Augmented Generation (RAG) is a technique that helps improve the accuracy of large language models by fetching relevant information from databases or documents. This approach ensures that the model's responses are based on up-to-date and accurate data, reducing errors and "hallucinations" where the model might provide false information. For users, RAG offers more reliable and trustworthy responses, allowing them to verify the sources used to generate those responses. This method also saves resources by avoiding the need to retrain models with new data. https://github.com/FareedKhan-dev/all-rag-techniques

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djangoproject

@djangoproject · Post #521 · 12/13/2017, 07:57 PM

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https://www.infoworld.com/article/3230202/python/6-essential-libraries-for-every-python-developer.html 6 essential libraries for every Python developer Whether you're compiling Python for speed, building native UIs for Python desktop apps, or refining existing Python code, these Python projects have you covered Python has seen wide adoption across industries and disciplines by dint of being easy to work with. But it has also been aided by a wealth of third-party projects—libraries, add-ons, and complementary development efforts—that extend the language to an ever widening range of use cases. #PyPy #CFFI(C Foreign Function Interface library) #PyInstaller #PBR(Python Build Reasonableness) #WxPython #Mypy

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