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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 · 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