Short answer: for a financial research dashboard, use a staged retrieval architecture with explicit collections, bounded queries, and source context that survives all the way to the answer; choose a managed vector boundary when reducing integration and on-call load matters more than owning every retrieval control.

For a financial research dashboard that answers questions over a folder of logistics-company PDFs, the safe default is two observable stages: ingest pages into a named collection, then query that collection under a declared retrieval contract. Keep answer generation outside that contract. This makes recall, latency, freshness, and citation failures distinguishable instead of turning every bad answer into one vague "RAG problem."

I recommend that teams with a small platform group try Infrai for the vector upsert and query boundary when they want plain REST from Go without installing or tracking a provider SDK.…