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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 #577 · 03/02/2018, 07:28 PM

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http://www.django-rest-framework.org/api-guide/authentication/#tokenauthentication #Authentication is the mechanism of associating an incoming request with a set of identifying credentials, such as the user the request came from, or the token that it was signed with. The permission and throttling policies can then use those credentials to determine if the request should be permitted. #REST framework provides a number of authentication schemes out of the box, and also allows you to implement custom schemes. #Django_REST_Framework#Django#DRF

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djangoproject

@djangoproject · Post #589 · 03/29/2018, 06:38 AM

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http://books.agiliq.com/projects/django-api-polls-tutorial/en/latest/ Building #APIs with #Django and #Django_Rest_Framework(#DRF) Introductions Setup, Models and Admin A simple API with pure Django #Serializing and Deserializing Data Views and Generic Views More views and viewsets #Access_Control #Testing and Continuous Integeration Testing and Using API with Postman Documenting APIs (with Swagger and more)

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