Initial monorepo scaffold for Duoc Thu RAG medical chatbot

docs/adr/0001-vector-db-qdrant.md

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+# 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.