Offline batch pipeline (never part of the live ai-service request path): extract -> segment -> chunk -> embed -> load. Parses data/raw/duoc-thu-quoc-gia-viet-nam-2018.pdf into per-drug, per-section chunks and upserts embeddings into Qdrant. Run via python -m ingestion.cli run --pdf data/raw/duoc-thu-quoc-gia-viet-nam-2018.pdf.

This has already been run. Qdrant collection duocthu_v1 holds 15,100 points (14,949 prose + 151 block descriptors) embedded with cohere.embed-v4:0, and a duocthu_v1__manifest sidecar pins the corpus sha/model/dimensions that ai-service checks at startup.

Re-running the embed step costs real Bedrock spend on a personal AWS account, so do not start a corpus run without the owner's explicit go for that specific run. To move an existing corpus between machines, snapshot and restore the Qdrant collection instead — it is free and exact.

Scope: the corpus covers Part 2 monographs (printed pages 99-1496) only. Part 1 general chapters and Part 3 appendices (BSA table, IV preparation, ATC index) are deliberately excluded, so questions about them correctly abstain rather than being answered from a neighbouring section.