Architecture — Dược Thư RAG Medical Chatbot

Overview

A medical chatbot grounded in the Vietnamese National Drug Formulary (Dược thư quốc gia Việt Nam 2018), built as a microservices monorepo. Users ask drug-related questions through a web chat UI; answers are generated via retrieval-augmented generation (RAG) over the formulary content, always citing the source drug monograph/section, and always carrying a medical disclaimer.

Service responsibilities & communication

Service	Owns	Talks to
api-gateway (NestJS)	Single public entry point; request routing, JWT validation, rate limiting	Routes to auth-service, user-service, chat-service, ai-service over internal REST
auth-service (NestJS)	Signup/login, password hashing, JWT issuance/refresh	Postgres (users); no dependency on other services
user-service (NestJS)	Profile data, preferences, account settings	Postgres (profiles), called by gateway
chat-service (NestJS)	Chat session lifecycle, message history persistence	Postgres (chat_sessions, chat_messages); calls ai-service per user message, persists both turns
ai-service (Python/FastAPI)	RAG orchestration: embed query → vector search in Qdrant → build grounded prompt → call OpenAI → return answer + citations	Qdrant (vector search), OpenAI API; stateless, does not own chat history
ingestion (Python, offline batch)	One-time/periodic job: parse PDF → monographs → chunks → embeddings → upsert to Qdrant	Qdrant (write), OpenAI embeddings API; runs as CLI/CI/k8s Job, never in the live request path
web (Next.js)	Chat UI, auth UI, citation/disclaimer rendering, session list	Calls api-gateway only

Sync vs async: the live chat path (web → gateway → chat-service → ai-service → Qdrant + OpenAI → back) is synchronous request/response. Ingestion is fully decoupled, offline, batch — it populates Qdrant ahead of time and is never triggered by a chat request, since parsing the 37MB PDF and embedding thousands of chunks takes minutes. Internal protocol is REST/JSON for v1; a future gRPC migration is a documented option (see ADRs), not needed now.

Data stores

Vector DB: Qdrant. Chosen over pgvector because retrieval quality here depends on metadata-filtered ANN search (filter by drug name / section type combined with vector similarity) over a highly structured corpus — Qdrant makes that a first-class, single query. It also scales independently from the transactional Postgres and has a mature Helm chart for the production k8s target. See docs/adr/0001-vector-db-qdrant.md.
Relational DB: PostgreSQL. One instance, logically separated per service (users/credentials, profiles, chat sessions+messages).
Redis. Session/refresh-token cache, rate-limit counters, and reserved as the future job-queue backend (BullMQ/Celery) if async admin-triggered re-ingestion or background jobs are added later.

RAG ingestion pipeline (PDF-specific)

The formulary is a structured per-drug reference, not free prose — the pipeline exploits that structure instead of naive fixed-size chunking. This section reflects an actual empirical investigation of the real PDF (not assumptions) — see docs/adr/0003-pdf-parsing-strategy.md for the full methodology, cross-tool comparison, and validation numbers.

Extraction: PyMuPDF (fitz) as primary extractor. This document has no bookmark/outline (doc.get_toc() returns 0 entries — confirmed, do not rely on it) and is a tagged PDF with only a shallow, unusable structure tree (~29 generic H1/P elements covering a fraction of 1668 pages — also confirmed dead-end, not a data source). PyMuPDF's reading order was cross-validated against pdfplumber and opendataloader-pdf on real sample pages: pdfplumber's default text order is unreliable for this layout (scrambles paragraph order, leaks marked-content artifacts) — use it only for its dedicated table-extraction API, never for body text. Raw per-page extraction is persisted to ingestion/data/interim/ so re-segmentation doesn't require re-running the expensive extraction step.
Segmentation: drug-entry boundaries are detected via bold-font spans (PyMuPDF span font containing "Bold"), not font-size alone — font size for title/heading spans varies between monographs (confirmed: 10.0pt and 9.5pt both occur for genuine drug-title headings), so bold is the reliable signal, all-caps + short length narrows it to monograph titles specifically. Section headings inside a monograph are also bold spans, cross-checked against a canonical taxonomy (chi_dinh, chong_chi_dinh, lieu_dung, tac_dung_phu, tuong_tac_thuoc, plus real observed extras like ten_thuong_mai "Tên thương mại" not in the book's own documented 19-field list — treat the taxonomy as open/ extensible, not a fixed enum). Multi-line wrapped titles/headings (long Vietnamese names/vaccine names) must be merged across consecutive bold+all-caps lines before matching — this was the single largest source of missed detections in validation. Output: {drug_id, drug_name, source_page_range, sections: {...}} per drug, persisted to ingestion/data/processed/monographs.jsonl and validated both automatically (see ADR 0003) and via manual spot-check in ingestion/notebooks/.
Chunking (monograph range only, pp. 99-1496 — see docs/adr/0004-chunking-strategy.md for the full measured rationale): each (drug_id, section_key) pair is the chunk unit; a section stays one chunk if it's under an 800-token ceiling (chars/4 estimate — a validated line, not a guess: whole-corpus measurement across 682 monographs shows ~16 of 18 section types clear it comfortably at their p90). Two sections routinely exceed it — dược lý và cơ chế tác dụng (35.7% of monographs that have it) and liều lượng và cách dùng (29.6%) — sub-chunking is the routine path for those two, not a rare edge case. Oversized sections are split with a sentence-boundary-aware sliding window (~600-700 tokens/sub-chunk, ~1 sentence/50-80 token overlap), never a blind character/line window — PDF line-wrap points are not safe cut points, and a mid-sentence split risks separating an adult/child dosing instruction (a measured, common pattern — outlier catalog item 17) into two chunks. Every chunk carries chunk_id, drug_id, drug_name, section_key, section_display_name, atc_codes, source_page_range, part_index/part_count as Qdrant payload — this is what makes citations possible. Known open gaps (see ADR 0004): sub-compound tagging inside class-level/multi-ATC monographs (25.5% of the corpus) is not yet solved; source_page_range is monograph-level, not sub-chunk-exact; chunking for general chapters/ appendices is a separate, not-yet-designed task; a confirmed header/footer-boilerplate leak into section text (98.4% of monographs affected) must be fixed upstream before this design runs against real data.
Embedding + load: OpenAI text-embedding-3-small in batches, upserted into a versioned Qdrant collection (drug_monographs_v1) keyed by chunk_id for idempotent re-runs; collection aliasing allows re-ingesting with a changed chunking strategy without downtime.
Batch job, not synchronous: runs as a CLI command locally, and as a Kubernetes Job/CronJob in production — never inside the ai-service request path.

Safety / guardrails

System prompt instructs the model to answer only from retrieved context, never state a dosage/contraindication/interaction not present in it, always append a disclaimer, and say "not found in the formulary" rather than guess when retrieval is irrelevant.
Retrieval-confidence gate: below a similarity threshold, skip the LLM call entirely and return a canned "consult a professional" response.
Citations from metadata, not LLM prose: the citations list is built directly from retrieved-chunk metadata, independent of what the LLM says, so the frontend can always show verifiable sources.
Disclaimer enforced at multiple layers: system prompt + a non-LLM-generated static string always appended to the API response + a persistent, non-dismissible UI banner.
Scoped refusal: out-of-scope questions (e.g. general symptom diagnosis) get a scoped refusal directing to a professional, not an ungrounded general-knowledge answer.

Build roadmap

Ingestion pipeline + populated, queryable vector DB. Done when a CLI run populates Qdrant and a test script retrieves the correct drug/section chunk for a sample query — no API, no LLM call yet.
ai-service (FastAPI) wrapping RAG + OpenAI. Done when a curl to /query returns a grounded answer with a traceable citation and an always-present disclaimer.
auth/user/chat services + api-gateway. Done when register → login → chat message flows end-to-end through the gateway only, persisted in Postgres.
Next.js frontend chat UI. Done when a browser user can log in, ask a question, and see a grounded answer with citation + disclaimer banner.
Containerize + docker-compose local. Done when docker compose up from a clean checkout brings up the full stack and the Phase 4 flow works.
Kubernetes/Helm + Terraform + CI + ArgoCD (GitOps) deployment. Done when CI builds/tests/pushes an image and bumps the target environment's Helm values file, the team's ArgoCD instance (see infra/argocd/, docs/adr/0002-argocd-gitops.md) picks up the change and syncs the cluster, and the Phase 4 flow works against the k8s-hosted stack. CI never runs kubectl/helm directly against a cluster. Cloud provider choice (AWS/GCP/Azure) only affects the Terraform module implementations, not this repo's structure.

See docs/adr/ for architecture decision records and docs/runbooks/ for operational runbooks (added as they're needed).

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