Well, I was thinking about something more complex in the context of "Chat with your notes".
Agents could abstract the Jarvis "command block".
The user would ask a question and the first agent would decide what to parse into the "context". If you start "chat with your notes" in an empty note, you will get a very good selection of notes related to your first question. However, for each subsequent question, the results get worse as it does a semantic search on the whole note. You can partially fix this by always adding your last question to "Context"; that way you'll only get notes related to the last question, which is mostly fine if you don't need to take the context of the conversation into account.
I think the optimal solution would be for an agent to customise the user's last question to include some keywords or some kind of summary from the chat history that would give the semantic search relevant contextual information, but only relevant to that question. In this way, the semantic search query is improved to give the most relevant results.
The next level could then be an agent that reasoned step by step what information it needed to answer the user's question, then performed multiple semantic searches with a summary of results for each query, and finally combined the summaries into one answer.
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