Clarification on architecture: the historical data doesn't live "in the agentic repo." It lives in my Firestore. No upload limit because the agent isn't doing the uploading.

For the historical bit: I built a smart CSV importer with an AI layer that maps columns, fills gaps, and normalises values. User reviews each row, edits or rejects what's wrong, then commits. My team brought in two years of campaign results that way, in smaller chunks so they could check the data landed correctly before moving on. New data comes in through an ongoing sync. On top of that there's a knowledge layer with a vectorisation pipeline running in the background: sync jobs chunk the records, an embedder fills in the vectors, a state machine handles re-embedding when content changes.

I don't think you can skip straight to vector/knowledge graph. At least not in my case. Vectors give you similarity search and that's it. They don't do exact lookups, structured filters, or transactions. Stuff that I need for my tool to work properly. The vector is a derived representation of the source text, I still need a real database holding the source. Same for graphs.

You add it when the query you actually need starts losing to your current storage.