1.9 KiB
1.9 KiB
ADR 0001: Use Qdrant as the vector database
Status
Accepted
Context
The RAG pipeline needs a vector store for drug-monograph chunks. The main alternative considered was pgvector (a Postgres extension), which would let us reuse the Postgres instance already needed for users/chat history — one fewer moving part to operate.
The corpus is not free-flowing prose: it's a structured per-drug reference with rich per-chunk metadata (drug name, section type, page range). The common retrieval pattern this domain calls for is "vector similarity search, filtered by metadata" — e.g. "search only within chỉ định sections" or "filter to a specific drug the user named" combined with the semantic query.
Decision
Use Qdrant as a dedicated vector database, separate from Postgres.
Rationale
- Qdrant gives first-class combined payload-filtering + ANN search in a single query, which is exactly the retrieval pattern this structured corpus needs — pgvector supports filtering too, but it's a less natural fit layered on top of a general-purpose relational engine.
- Vector search becomes its own independent scaling axis, separate from the transactional Postgres workload (users/chat) — re-indexing or re-ingesting the formulary doesn't contend with transactional traffic.
- Mature standalone Docker image for local dev, a well-supported Python client, and a Helm chart for the production Kubernetes deployment target.
- Corpus size (tens of thousands of chunks) is trivial for Qdrant's HNSW indexing.
Consequences
- One additional service to operate/deploy/monitor compared to pgvector (which would ride on the existing Postgres).
- Revisit if operational overhead becomes a real burden at our actual scale, or if we want tighter transactional consistency between chat data and retrieval — pgvector remains a viable fallback documented here for that case.