>For all intents and purposes, the size of current context windows obviates all of that work.
Large context windows can make some problems easier or go away for sure. But you may still have the same issue of getting the right information to the model. If your data is much larger than e.g. 256k tokens you still need to filter it. Either way, it can still be beneficial (cost, performance, etc.) to filter out most of the irrelevant information.
>Reduced emphasis on vector search. People have found that for most purposes, having an agent grep your documents is cheaper and better than using a more complex rag pipeline
This has been obvious from the beginning for anyone familiar with information retrieval (R in RAG). It's very common that search queries are looking for exact matches, not just anything with similar meaning. Your linked example is code search. Exact matches/regex type of searches are generally what you are looking for there.
Large context windows can make some problems easier or go away for sure. But you may still have the same issue of getting the right information to the model. If your data is much larger than e.g. 256k tokens you still need to filter it. Either way, it can still be beneficial (cost, performance, etc.) to filter out most of the irrelevant information.
>Reduced emphasis on vector search. People have found that for most purposes, having an agent grep your documents is cheaper and better than using a more complex rag pipeline
This has been obvious from the beginning for anyone familiar with information retrieval (R in RAG). It's very common that search queries are looking for exact matches, not just anything with similar meaning. Your linked example is code search. Exact matches/regex type of searches are generally what you are looking for there.