duocthu/docs-legacy/15-configuration.md

15 — Configuration

Where configuration is defined

apps/ai-service/config.py is the single authority for the Python service: every setting is a field on the Pydantic Settings class, loaded from the environment or from a .env file next to the process, with extra="ignore". get_settings() is @lru_cached, so values are read once per process.

There is no .env.example anywhere in the repository. The only env file is apps/ai-service/.env, which is gitignored and local; production uses apps/ai-service/.env.prod, which is also gitignored and lives only on the EC2 host. A new engineer therefore has no committed template to copy — see 27-technical-debt.md.

ai-service settings

Variable	Required	Default	Purpose	Secret
APP_NAME	no	vsf-duoc-thu-ai-service	FastAPI title	no
ENVIRONMENT	no	local	Label; sent as deployment.environment on OTel resource	no
QDRANT_URL	effectively yes	http://localhost:6333	Vector store	no
QDRANT_COLLECTION	no	duocthu_v1	Collection name; the manifest sidecar is <name>__manifest	no
QDRANT_API_KEY	no	None	Qdrant auth	yes
POSTGRES_DSN	no	postgresql://duoc_thu:duoc_thu@localhost:5432/duoc_thu	Traces, turns, feedback. Declared repr=False so it is not echoed	yes
EMBEDDING_PROVIDER	no	cohere-v4	cohere-v4 or disabled. Any other value raises at startup	no
EMBEDDING_DIMENSIONS	no	1024	Must match the corpus manifest or startup fails	no
EVIDENCE_MINIMUM_SCORE	no	0.12	Dense-route score floor	no
AWS_REGION	no	us-east-1	Bedrock region	no
ANSWER_PROVIDER	no	disabled	disabled | stub | bedrock-converse | bedrock-claude. Chooses the operating mode	no
ANSWER_MODEL_ID	no	deepseek.v3.2	Bedrock model id	no
RERANK_ENABLED	no	false	Enables cohere.rerank-v3-5:0 on the fallback route	no
METRICS_ENABLED	no	true	Builds the Prometheus exporter	no
METRICS_TOKEN	no	""	Bearer token for GET /metrics; empty = unauthenticated	yes
OTEL_ENABLED	no	false	Turns on OTLP export	no
OTEL_SERVICE_NAME	no	ai-service		no
OTEL_EXPORTER_OTLP_ENDPOINT	no	http://localhost:4318/v1/traces	OTLP/HTTP traces endpoint	no
OTEL_SAMPLE_RATIO	no	1.0 (0.0–1.0)	TraceIdRatioBased sampler	no
ENTITIES_PATH	in the container	repo-relative ingestion/data/verified/drug_entities.json	Drug alias catalog	no
MAX_WALL_CLOCK_MS	no	40000	Per-turn budget	no
MAX_LLM_CALLS_PER_TURN	no	8	Per-turn budget	no

ENTITIES_PATH needs an explicit value in the container: config.py's default resolves two parents up from apps/ai-service/config.py, and the image flattens apps/ai-service/ into /app, so the depth is wrong. The Dockerfile bakes the file to ./ingestion_data/drug_entities.json and .env.prod points at it.

Settings that change behaviour, not just tuning

Three values are mode switches rather than knobs:

Setting	Effect
EMBEDDING_PROVIDER=disabled	build_runtime returns no answer service and no agent. /v1/rag/query answers 503, while /ready still answers 200.
ANSWER_PROVIDER=disabled	No RagAgent, no understanding, no multi-turn. Retrieval-only, single-turn, verbatim quotes.
EMBEDDING_DIMENSIONS ≠ manifest	Startup raises ManifestMismatch and the process does not come up.

web settings

Variable	Required	Default	Purpose
API_GATEWAY_URL	no	—	Preferred upstream; accepts a base URL or a full /v1/rag/... URL
AI_SERVICE_URL	no	http://localhost:8000	Fallback; set to http://ai-service:8000 in prod Compose

Rate-limit rules are hard-coded constants in middleware.ts, not configuration: /api/chat 12/min and 120/hour; /api/suggest 120/min.

ingestion settings

ingestion takes no environment variables. Everything is a CLI flag (--pdf, --out, --tables, --monographs, --chunks, --provider, --collection, --region, --qdrant-url, --slice-size, --attempts, --embed-only). AWS credentials come from the standard boto3 chain.

Deployment-layer configuration

Layer	File	Notes
Production Compose	infra/docker/docker-compose.prod.yml	ai-service reads env_file: ../../apps/ai-service/.env.prod (not in the repo)
Observability overlay	infra/docker/docker-compose.observability.yml	Sets OTEL_ENABLED=true, OTEL_EXPORTER_OTLP_ENDPOINT=http://otel-collector:4318/v1/traces, ENVIRONMENT=compose; reads GRAFANA_ADMIN_USER / GRAFANA_ADMIN_PASSWORD from the shell
Helm	values.yaml + values-{dev,staging,prod}.yaml	Maps to a ConfigMap of the same env vars; POSTGRES_DSN comes from a Secret
CI	.github/workflows/deploy.yml	Uses EC2_HOST, EC2_SSH_KEY, GRAFANA_ADMIN_PASSWORD GitHub secrets

Helm chart defaults are not production defaults

infra/helm/medical-chatbot/values.yaml ships aiService.config.embeddingProvider: disabled and answerProvider: disabled, i.e. a deployment of the chart as-is answers 503 on /v1/rag/query. It also ships secret.postgresPassword: duoc_thu and secret.grafanaAdminPassword: change-me as literal defaults.

Secrets inventory

Secret	Where it lives	Committed?
PostgreSQL password	docker-compose.prod.yml env (duoc_thu/duoc_thu), Helm secret.postgresPassword	Yes — a default credential is in the repository
Grafana admin password	GRAFANA_ADMIN_PASSWORD GitHub secret → shell env; Helm default change-me	Secret value not committed; the placeholder default is
AWS credentials	EC2 instance IAM role	No — deliberately; the Compose header comment says so
QDRANT_API_KEY	Unset (Qdrant is not exposed)	No
METRICS_TOKEN	Unset	No
EC2 host + SSH key	GitHub Actions secrets	No

git ls-files shows no .env file tracked, and the two IAM documents under infra/aws/iam/ are policy JSON, not credentials. The one real issue is the PostgreSQL default credential, which is committed in two places — see 16-security.md.

Configuration verified this session

apps/ai-service/.env (local, gitignored) contains:

EMBEDDING_PROVIDER=cohere-v4
ANSWER_PROVIDER=bedrock-converse
ANSWER_MODEL_ID=qwen.qwen3-next-80b-a3b
RERANK_ENABLED=true
AWS_REGION=us-east-1
QDRANT_COLLECTION=duocthu_v1
QDRANT_URL=<local>

Note the drift: the code default for ANSWER_MODEL_ID is deepseek.v3.2, the local .env uses qwen.qwen3-next-80b-a3b, and the production value is unverifiable from the repository because .env.prod is not committed. Any statement about which model production runs would be a guess.