The first private-corpus decision is not which vector database to buy. Define data classes, trust zones, policy enforcement points, and freshness SLAs so every index, model, and observability system receives only the minimum data it is allowed to process.
Private-corpus sync is not periodic refetching. It requires stable canonical IDs, source versions plus checksums for change detection, idempotent upserts, and tombstones that propagate deletion through every index.
Qdrant is not just a place to store embeddings: define the vector schema, index frequently filtered payload fields, then add dense+sparse queries, tenant boundaries, snapshots, and monitoring to build an operable retrieval service.
Vector database selection is more constrained by deployment platform than LLM selection. Determine your platform and scale requirements first, then evaluate features — don't just look at benchmarks.
Next.js + Expo frontend, Node.js + Python dual backend, PostgreSQL + Redis core — plus a social notification system and LLM recommendation engine. Here's how DaoDao builds a learning community platform with a modern tech stack.