# ingestion

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.