batchCooking/docker-compose.yml
Nicolas 065ef2a31a feat(recipes): rapatrie le corpus NLP cote Python et l'enrichit de 48 techniques
Changement d'architecture demande par l'utilisateur : le dataset
d'entrainement (TECH_STEP_TRAINING_DATA) quitte apps/api pour vivre
entierement dans services/tech-step-intent-service
(intent_service/training_data.py). Ce service est desormais autonome :
il s'entraine lui-meme une seule fois, a son propre demarrage
(PipelineRegistry.initialize, dans le lifespan FastAPI), sans plus
dependre d'un POST /v1/train pousse par apps/api (route supprimee).
apps/api ne connait plus aucune technique/synonyme, uniquement le
resultat de POST /v1/process.

Corpus enrichi avec les 48 techniques du lexique fourni (Arroser,
Appertiser, Braiser, Caraméliser, Confire, Julienne/Brunoise/Mirepoix/
Paysanne, Cuire à blanc/au bain-marie/à l'étouffée, Déglacer variantes,
Emulsionner, Glacer, Pocher, Réduire, Suer, Zester, etc.), soit 74
techniques au total (26 + 48). Integration complete bout en bout :
- reference-seed-data.ts : 48 nouvelles entrees TECH_STEPS
- apps/web/locales/fr/translation.json : libelles francais correspondants
- "Mitonner" fondu comme synonyme de simmer (pas une technique distincte,
  sa propre definition le dit)
- "Blanchir un oeuf" (whiskPale) distingue de "Blanchir un legume"
  (blanch, existant) via des synonymes en phrase complete plutot qu'au
  mot nu — filter_spans (deja en place) resout la collision par
  specificite

Impact performance mesure : le corpus elargi (74 classes vs 26) rend
l'entrainement bien plus lent a nombre d'iterations egal (150 iterations
depassait 17 minutes par run de test) — reduit a 40 iterations apres
mesures repetees en local (~200s/locale, ~400s pour fr+en combines).
docker-compose.yml (healthcheck start_period 600s), CI (timeout curl
600s) et le README du service documentent ce nouveau temps de demarrage.
CONFIDENCE_THRESHOLD recalibre a 0.2 par verification manuelle (0.75 puis
0.45 ne tenaient plus compte tenu du nombre de classes) — marque
explicitement comme placeholder en attendant une vraie repasse de
calibrate-tech-step-threshold.ts (necessite Postgres, indisponible dans
cet environnement).

Verifie : 28/28 tests pytest du service (suite complete re-ecrite pour
s'entrainer une seule fois par session sur le vrai corpus, fixture
partagee dans conftest.py), lint + build complets du monorepo. La suite
Mocha d'apps/api reste a confirmer via CI (le root hook mocha n'attend
plus l'entrainement, seulement CI's propre attente sur /health).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
2026-08-25 22:59:13 +02:00

130 lines
5.9 KiB
YAML

services:
postgres:
image: postgres:16-alpine
restart: unless-stopped
environment:
# No defaults on purpose: POSTGRES_USER/PASSWORD/DB must be set in your
# local, git-ignored .env (see .env.example). Compose fails loudly if
# they're missing instead of falling back to a guessable credential.
POSTGRES_USER: ${POSTGRES_USER:?set POSTGRES_USER in .env}
POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:?set POSTGRES_PASSWORD in .env}
POSTGRES_DB: ${POSTGRES_DB:?set POSTGRES_DB in .env}
ports:
- "${POSTGRES_PORT:-5432}:5432"
volumes:
- postgres_data:/var/lib/postgresql/data
healthcheck:
test: ["CMD-SHELL", "pg_isready -U $$POSTGRES_USER"]
interval: 5s
timeout: 5s
retries: 5
# Single service serving both the API and the built frontend (see
# apps/api/Dockerfile) — no separate nginx/web container, no cross-origin
# CORS_ORIGIN to keep in sync between two ports.
app:
build:
context: .
dockerfile: apps/api/Dockerfile
restart: unless-stopped
environment:
NODE_ENV: production
PORT: 3000
# Uses the "postgres" service name, not localhost/POSTGRES_PORT —
# container-to-container traffic stays on the compose network and
# always targets Postgres's internal port (5432).
DATABASE_URL: "postgresql://${POSTGRES_USER:?set POSTGRES_USER in .env}:${POSTGRES_PASSWORD:?set POSTGRES_PASSWORD in .env}@postgres:5432/${POSTGRES_DB:?set POSTGRES_DB in .env}?schema=public"
JWT_SECRET: ${JWT_SECRET:?set JWT_SECRET in .env}
# Unset by default (falls back to NODE_ENV === "production", i.e.
# Secure cookie required) — set COOKIE_SECURE=false in .env only if
# this deployment is reachable over plain HTTP (no TLS in front of
# it yet), otherwise the session cookie never comes back and every
# authenticated request 401s despite login succeeding. See its doc
# comment in apps/api/src/config/env.ts.
COOKIE_SECURE: ${COOKIE_SECURE:-}
# Shared with the `tech-step-llm-worker` service below — see
# requireInternalWorker's doc comment
# (apps/api/src/middlewares/require-internal-worker.ts). Unset by
# default: `/internal/tech-steps/*` fails closed rather than open
# for a deployment that doesn't run the worker at all.
INTERNAL_WORKER_SECRET: ${INTERNAL_WORKER_SECRET:-}
# Compose network service name, not localhost — same reasoning as
# DATABASE_URL above. Unlike INTERNAL_WORKER_SECRET, no `:-` fallback:
# tech-step-intent-service is a core dependency (see its own entry
# below), not an optional background job.
INTENT_SERVICE_BASE_URL: "http://tech-step-intent-service:8000"
INTENT_SERVICE_SECRET: ${INTENT_SERVICE_SECRET:?set INTENT_SERVICE_SECRET in .env}
ports:
- "${APP_PORT:-3000}:3000"
depends_on:
postgres:
condition: service_healthy
tech-step-intent-service:
condition: service_healthy
# spaCy-based NER + intent classification microservice
# (services/tech-step-intent-service) — `app` delegates all tech-step
# detection to it over HTTP (see `IntentServiceClient`,
# apps/api/src/lib/recipe-matching/intent-service-client.ts). Unlike
# `tech-step-llm-worker` below, **not optional**: without it, `app` can no
# longer detect any cooking technique in a recipe step at all. No exposed
# port — reachable only from `app` on the compose network, nothing ever
# calls into it from outside.
tech-step-intent-service:
build:
context: .
dockerfile: services/tech-step-intent-service/Dockerfile
restart: unless-stopped
environment:
INTENT_SERVICE_SECRET: ${INTENT_SERVICE_SECRET:?set INTENT_SERVICE_SECRET in .env}
healthcheck:
# No curl/wget in the python:3.12-slim base image — a one-line Python
# request is the healthcheck for a service that's already guaranteed
# to have Python (see this service's Dockerfile).
test:
[
"CMD",
"python",
"-c",
"import urllib.request; urllib.request.urlopen('http://localhost:8000/health', timeout=2)",
]
interval: 15s
timeout: 3s
retries: 5
# This service trains itself from scratch on every start (no model
# ever persisted to disk, see its own README) — `/health` only
# returns 200 once that's done, not just once the base spaCy models
# are loaded. Measured at ~200s per locale (~400s for fr+en combined)
# against the current ~74-technique corpus
# (`intent_service/training_data.py`) — `start_period` generous
# enough that failing checks during that whole window never count
# against `retries` (which would otherwise flip this container to
# "unhealthy" mid-training, blocking `app`'s own
# `depends_on: condition: service_healthy` indefinitely).
start_period: 600s
# Deliberately its own image, not built into `app`'s (see
# services/tech-step-llm-worker/Dockerfile's own doc comment) — a
# long-lived process with no exposed port (nothing ever calls *into* it,
# it only ever calls out to `app`). Optional: an `INTERNAL_WORKER_SECRET`-
# less deployment can omit this service entirely and `app` still runs
# fine, just without the offline audit/feedback-loop jobs.
tech-step-llm-worker:
build:
context: .
dockerfile: services/tech-step-llm-worker/Dockerfile
restart: unless-stopped
depends_on:
- app
environment:
API_BASE_URL: "http://app:3000"
INTERNAL_WORKER_SECRET: ${INTERNAL_WORKER_SECRET:?set INTERNAL_WORKER_SECRET in .env to run this service}
TECH_STEP_WORKER_CRON: ${TECH_STEP_WORKER_CRON:-0 3 * * 0}
volumes:
# GGUF weights persist across restarts — see this service's own
# Dockerfile doc comment on its VOLUME declaration.
- tech_step_llm_worker_models:/worker/models
volumes:
postgres_data:
tech_step_llm_worker_models: