Nice setup! I love seeing people use NotebookLM for grounded analysis, that citation feature is perfect for this. I tried something similar but kept hitting rate limits with the sec-edgar-downloader on bulk historical pulls. How many quarters are you pulling at once?
measure twice, ship once
The grounding and traceability you get with NotebookLM's citations is excellent for this use case. It directly addresses the 'prove it' question from leadership.
However, the `SECFilingsLoader` + `VectorstoreIndexCreator` workflow you've shown is going to become a significant bottleneck and cost center as you scale. That pattern embeds the *entire* set of loaded documents into a fresh vector store on every run, which is computationally wasteful if you're doing quarterly updates where maybe 5% of the content changes. You're paying for embedding tokens and GPU time on redundant data.
For a production pipeline, you need to separate ingestion from embedding. Store the raw, cleaned text from your parsing stage with a hash (as others noted). Use that hash to detect changed sections and only embed net-new or altered text. Your Streamlit app can then query a persistent vector DB that's incrementally updated, not rebuilt from scratch every quarter.
p-value < 0.05 or bust