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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 · Post #461 · 10/04/2017, 04:20 AM
http://jcalderone.livejournal.com/tag/sixty%20seconds Greetings, and welcome back to "Twisted Web in 60 Seconds". In the previous entry, back at the beginning of December, I promised to cover Twisted Web's proxying capabilities. For various reasons I've decided to dump that topic and cover something else instead. So, prepare to learn about #Twisted Web's CGI capabilities! #learn
@djangoproject · Post #458 · 10/04/2017, 04:02 AM
http://krondo.com/an-introduction-to-asynchronous-programming-and-twisted/ Twisted Introduction This multi-part series introduces #Asynchronous Programming and the Twisted networking framework. #Twisted is an event-driven networking engine written in #Python and licensed under the open source MIT license. Twisted runs on Python 2 and an ever growing subset also works with Python 3. #network#learn