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“ shoes .” The vector search should deliver a mix of results that match the search idea , not just the words involved , and provide that customer with a better set of results in context .
“ To implement RAG effectively , you have to pick the right embedding model , data chunking strategy and index approach . Each of these approaches is tailored to specific scenarios and types of texts . With multiple different chunking approaches to consider , you can experiment with different methods to determine which best suits the specific needs of your RAG application .”
Cauldwell ’ s ‘ Day 2 ’ problems explained “ Beyond getting your data prepared , the next problem to consider is how to run at production levels . While you might be able to use data in test implementations and get relevant results , how does that same implementation scale up to thousands or millions of requests coming in ? The complexity of accessing structured and unstructured data can introduce latency that impacts the user experience .
“ Up to 40 % of that latency can come from calls to the embedding service and vector search service . Reducing
128 May 2024