From the course: Enterprise AI Development with GitHub Models and Azure

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Retrieval-augmented generation

Retrieval-augmented generation

From the course: Enterprise AI Development with GitHub Models and Azure

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Retrieval-augmented generation

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- [Instructor] We have now seen the basic examples like Multiturn, Streaming, and sending an image with your prompt. Then we went on with Embeddings and Tools, and now it is time to tie some things together and see some more advanced examples. There are very nice examples with a lot of explanation in the cookbooks directory of the Models Repository. The examples have been grouped in the SDK that they use. LangChain is a framework designed to simplify the development of applications using large language models. As the name says, you focus on chaining several tools together to get the desired output of the prompt. The LlamaIndex framework is designed to integrate with various data sources, and create indices optimized for efficient querying. This framework is well-suited for analyzing documents to create an index using embeddings, and then matching the user prompt with the right document. The Mistral AI folder has a broader explanation on how the standard use cases for a large language…

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