Retrieval-augmented generation (RAG) is the most popular approach for obtaining real-time data or updated data from a data source based on text input by users. Thus empowering all our search applications with state-of-the-art neural search. 

In RAG search systems, each user request is converted into a vector representation by embedding model, and this vector comparison is performed using various algorithms such as cosine similarity, longest common sub-sequence, etc., with existing vector representations stored in our vector-supporting database.

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