This is relevant not only in the context of books and blogs, but also for the shorter forms of reading that we do more often like HN or Reddit comments, tweets etc.
One way to think about it is that reading on the internet, no matter how casual, is like scattering seeds on the soil of your mind. Some of these seeds will grow into trees and influence your actions and decisions, whether you're aware of it or not.
In context of RAG, the goal is not to have a pure vector DB but to have all the relevant data that we can gather for a user's prompt. This is where Cognitive Search and other existing DBs shine because they offer a combination of search strategies. Hybrid search on Cognitive Search performs both full text and vector queries in parallel and merges results which I find a better approach. Further, MS is rebranding Cognitive Search as Azure AI Search to bring it more in line with the overall Azure AI stack including Azure OpenAI.
One way to think about it is that reading on the internet, no matter how casual, is like scattering seeds on the soil of your mind. Some of these seeds will grow into trees and influence your actions and decisions, whether you're aware of it or not.