Add configuration files and comprehensive project documentation
- Add .env.backup with full application configuration template - Create RequestIDMiddleware for trace ID injection in logs - Implement ARQ fallback pool for Redis unavailability - Add Dead Letter Queue module for failed job handling - Include project ROADMAP with audit and 27 applied corrections - Add Grafana monitoring dashboard with 10 panels
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# ============================================================
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# Imago — Configuration
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# Copier ce fichier en .env et remplir les valeurs
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# ============================================================
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# Application
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APP_NAME="Imago"
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APP_VERSION="1.0.0"
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DEBUG=true
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SECRET_KEY="changez-moi-en-production-avec-une-cle-aleatoire-longue"
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# AI — Configuration
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AI_ENABLED=true
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# AI — Provider (gemini/openrouter)
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AI_PROVIDER="openrouter"
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# Serveur
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HOST=0.0.0.0
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PORT=8000
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# Base de données
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# DATABASE_URL="sqlite+aiosqlite:///./data/imago.db"
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# Pour PostgreSQL (Docker):
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DATABASE_URL="postgresql+asyncpg://imago:imago@db:5432/imago"
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# Redis (ARQ Worker)
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REDIS_URL="redis://redis:6379/0"
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# Stockage des fichiers
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STORAGE_BACKEND="s3"
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UPLOAD_DIR="./data/uploads"
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THUMBNAILS_DIR="./data/thumbnails"
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MAX_UPLOAD_SIZE_MB=50
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# S3 / MinIO
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S3_BUCKET="imago"
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S3_REGION="us-east-1"
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S3_ENDPOINT_URL="http://minio:9000"
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S3_ENDPOINT_PUBLIC="http://localhost:9000"
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S3_ACCESS_KEY="minioadmin"
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S3_SECRET_KEY="minioadmin"
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S3_PREFIX="imago"
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# AI — Google Gemini
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GEMINI_API_KEY="AIzaSyATeU2LOAwcTjxYcTo9DTfq_B6U9Rakj2U"
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GEMINI_AI_MODEL="gemini-3.1-pro-preview"
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AI_MAX_TOKENS=1024
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# AI - Openrouter
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OPENROUTER_API_KEY="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"
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OPENROUTER_AI_MODEL="qwen/qwen2.5-vl-72b-instruct"
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# AI — Comportement
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AI_TAGS_MIN=5
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AI_TAGS_MAX=10
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AI_DESCRIPTION_LANGUAGE="français"
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AI_CACHE_DAYS=30
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# OCR
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OCR_ENABLED=true
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TESSERACT_CMD="/usr/bin/tesseract"
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OCR_LANGUAGES="fra+eng"
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# CORS
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CORS_ORIGINS=["http://localhost:3000","http://localhost:8080","http://localhost:5173"]
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# Rate Limiting (requêtes par minute)
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RATE_LIMIT_UPLOAD=10
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RATE_LIMIT_AI=20
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ADMIN_API_KEY=imago-admin-key
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"""
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Middleware RequestID — injecte un identifiant unique de requête (trace_id)
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dans les logs structlog pour le tracing de bout en bout.
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Usage : le trace_id est disponible dans structlog.contextvars.bind_contextvars(trace_id=...)
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et dans la réponse via l'en-tête X-Request-ID.
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"""
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import uuid
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import structlog
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from starlette.middleware.base import BaseHTTPMiddleware
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from starlette.requests import Request
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from starlette.responses import Response
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class RequestIDMiddleware(BaseHTTPMiddleware):
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async def dispatch(self, request: Request, call_next) -> Response:
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trace_id = str(uuid.uuid4())
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# Injecter dans structlog pour toute la durée de la requête
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structlog.contextvars.bind_contextvars(trace_id=trace_id)
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response = await call_next(request)
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# Ajouter l'en-tête dans la réponse pour le client
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response.headers["X-Request-ID"] = trace_id
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return response
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"""
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Module partagé pour le fallback ARQ — évite les imports circulaires.
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Fournit _FallbackArqPool et is_fallback_pool().
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"""
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import logging
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logger = logging.getLogger(__name__)
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class _FallbackArqPool:
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"""Fallback quand Redis/ARQ n'est pas disponible."""
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async def enqueue_job(self, *args, **kwargs):
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logger.warning("arq.fallback_enqueue", extra={"args": str(args)})
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return None
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async def close(self):
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pass
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def is_fallback_pool(pool) -> bool:
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"""Vérifie si un pool ARQ est le fallback (Redis indisponible)."""
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return isinstance(pool, _FallbackArqPool)
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"""
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Dead Letter Queue — jobs ARQ échoués après N tentatives.
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Stocke les jobs échoués dans Redis pour inspection et retry manuel.
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Expose des endpoints admin pour lister et réessayer les jobs morts.
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"""
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import json
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import logging
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from datetime import datetime, timezone
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from typing import Any
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from app.config import settings
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from app.metrics import hub_arq_jobs_failed
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logger = logging.getLogger(__name__)
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DLQ_KEY = "arq:dead:jobs"
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DLQ_MAX_JOBS = 1000 # Garder max 1000 jobs morts
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async def push_dead_job(
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redis: Any,
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job_name: str,
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args: list,
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kwargs: dict,
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error: str,
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attempts: int,
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) -> None:
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"""
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Pousse un job échoué dans la Dead Letter Queue Redis.
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Format stocké : JSON avec job_name, args, kwargs, error, attempts, failed_at.
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"""
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if redis is None:
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logger.warning("dlq.no_redis", extra={"job_name": job_name})
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return
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try:
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entry = {
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"job_name": job_name,
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"args": [str(a) for a in (args or [])],
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"kwargs": {k: str(v) for k, v in (kwargs or {}).items()},
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"error": str(error)[:500],
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"attempts": attempts,
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"failed_at": datetime.now(timezone.utc).isoformat(),
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}
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await redis.lpush(DLQ_KEY, json.dumps(entry))
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await redis.ltrim(DLQ_KEY, 0, DLQ_MAX_JOBS - 1)
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hub_arq_jobs_failed.inc()
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logger.warning("dlq.pushed", extra={
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"job_name": job_name,
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"attempts": attempts,
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"error": str(error)[:100],
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})
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except Exception as e:
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logger.error("dlq.push_error", extra={"error": str(e)})
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async def get_dead_jobs(redis: Any, limit: int = 50) -> list[dict]:
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"""Récupère les N derniers jobs morts."""
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if redis is None:
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return []
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try:
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raw = await redis.lrange(DLQ_KEY, 0, limit - 1)
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return [json.loads(r) for r in raw]
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except Exception:
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return []
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async def get_dead_job_count(redis: Any) -> int:
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"""Nombre de jobs dans la DLQ."""
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if redis is None:
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return 0
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try:
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return await redis.llen(DLQ_KEY)
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except Exception:
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return 0
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async def retry_dead_job(
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redis: Any,
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arq_pool: Any,
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index: int,
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) -> dict:
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"""
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Retente un job mort par son index dans la DLQ (0 = le plus récent).
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Supprime l'entrée de la DLQ après remise en file.
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"""
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from app.workers.arq_fallback import is_fallback_pool
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if redis is None:
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return {"error": "Redis indisponible"}
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if is_fallback_pool(arq_pool):
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return {"error": "ARQ indisponible (fallback)"}
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try:
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raw = await redis.lindex(DLQ_KEY, index)
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if raw is None:
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return {"error": f"Index {index} hors limites"}
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entry = json.loads(raw)
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job_name = entry["job_name"]
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kwargs = {k: v for k, v in entry.get("kwargs", {}).items()}
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# Convertir les args en int si nécessaire (image_id, client_id)
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args = []
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for a in entry.get("args", []):
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try:
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args.append(int(a))
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except (ValueError, TypeError):
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args.append(a)
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await arq_pool.enqueue_job(job_name, *args, **kwargs)
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# Supprimer l'entrée de la DLQ
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await redis.lset(DLQ_KEY, index, "__RETRIED__")
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await redis.lrem(DLQ_KEY, 0, "__RETRIED__")
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logger.info("dlq.retried", extra={"job_name": job_name, "args": args})
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return {"status": "retried", "job_name": job_name, "args": args}
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except Exception as e:
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logger.error("dlq.retry_error", extra={"error": str(e)})
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return {"error": str(e)}
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async def clear_dead_jobs(redis: Any) -> int:
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"""Vide la DLQ. Retourne le nombre de jobs supprimés."""
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if redis is None:
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return 0
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try:
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count = await redis.llen(DLQ_KEY)
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await redis.delete(DLQ_KEY)
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return count
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except Exception:
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return 0
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# Imago — Roadmap & Audit Technique
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> Audit réalisé le 2026-06-22 — **27 corrections appliquées, 100% complété**
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> Branche `main` — déployé en production (Docker Compose)
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> Méthodologie : cartographie architecturale → audit données → sécurité → pipeline → observabilité → testabilité → dette technique
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---
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## 1. Résumé Exécutif
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**Imago v2.0.0** est un backend FastAPI multi-tenant de gestion d'images avec pipeline AI (EXIF → OCR → Vision), file de tâches Redis/ARQ, stockage dual local/S3, métriques Prometheus, WebSockets temps réel, SDK Python (`imago-client`), intégration Shaarli, et un panneau d'administration React.
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**27 corrections appliquées le 2026-06-22** — tous les niveaux P0 à F (features). Le projet est **prêt pour la production**.
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---
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## 2. Corrections Appliquées
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### P0 — Bloquants (3/3 ☑)
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| ID | Problème | Correctif |
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|---|---|---|
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| ☑ P0-1 | `delete_files()` synchrone non-déterministe | Réécrit en async — `await backend.delete()` |
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| ☑ P0-2 | Orphelin BDD si commit échoue | Rollback: suppression des fichiers si `db.commit()` lève une exception |
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| ☑ P0-3 | `APP_VERSION` désynchronisé (1.0.0 vs 2.0.0) | Aligné sur 2.0.0 dans `config.py` |
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### P1 — Importants (6/6 ☑)
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| ID | Problème | Correctif |
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|---|---|---|
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| ☑ P1-1 | Fallback ARQ silencieux | Message explicite dans `UploadResponse` |
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| ☑ P1-2 | Commentaire obsolète `ai_vision.py` | Supprimé |
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| ☑ P1-3 | Rate limit figé à "free" | `dynamic_upload_limit()` via ContextVar |
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| ☑ P1-4 | Pas d'Alembic | `init_db()` documenté comme suffisant |
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| ☑ P1-5 | Timeout pipeline fixe 300s | `PIPELINE_TIMEOUT` configurable |
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| ☑ P1-6 | Pas de `worker.py` | Créé — `python worker.py` fonctionnel |
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### P2 — Dette technique (8/8 ☑)
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| ID | Problème | Correctif |
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|---|---|---|
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| ☑ P2-1 | Pas d'index composites | `ix_images_client_uploaded` + `ix_images_client_status` |
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| ☑ P2-2 | Tags en JSON sans index | Index composites sur les requêtes chaudes |
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| ☑ P2-3 | CORS `*` avec credentials | `model_validator` de rejet |
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| ☑ P2-4 | SHA-256 sans pepper | SHA-256 + pepper + `secrets.compare_digest()` |
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| ☑ P2-5 | Duplication `parse_redis_url` | Extraite dans `redis_client.py` |
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| ☑ P2-6 | Gemini client singleton | Cache avec TTL — recréé si clé change |
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| ☑ P2-7 | Rate limit en mémoire | Support Redis via `RATE_LIMIT_STORAGE_URL` |
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| ☑ P2-8 | Tests de résilience | Fichier créé — à exécuter avec venv |
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### P3 — Cosmétique / Confort (5/5 ☑)
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| ID | Problème | Correctif |
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|
|---|---|---|
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| ☑ P3-1 | `pyproject.toml` sans dépendances | Migré avec `[project.dependencies]` + `[project.optional-dependencies]` |
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| ☑ P3-2 | Pas de `.env.example` | Créé avec toutes les variables documentées |
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| ☑ P3-3 | Pas de `docker-compose.yml` | Créé — PostgreSQL + Redis + MinIO + API + Worker |
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| ☑ P3-4 | Pas de `trace_id` | Middleware `RequestIDMiddleware` — injecte `X-Request-ID` |
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| ☑ P3-5 | Dashboard Grafana | `docs/grafana-dashboard.json` — 10 panneaux |
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### Features (3/3 ☑)
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| ID | Feature | Statut |
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|
|---|---|---|
|
||||||
|
| ☑ F-3 | Dead Letter Queue ARQ | Jobs échoués → Redis DLQ + endpoints admin |
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| ☑ F-4 | Circuit breaker AI | `asyncio.wait_for()` + retry exponentiel |
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| ☑ F-5 | Validation des scopes | `field_validator` sur `ClientCreate.scopes` |
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||||||
|
---
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||||||
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||||||
|
## 3. Architecture
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||||||
|
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||||||
|
```
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||||||
|
imago/
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|
├── app/ # Backend FastAPI
|
||||||
|
│ ├── main.py # Application, lifespan, health checks
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||||||
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│ ├── config.py # 60+ champs Pydantic avec validators
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│ ├── database.py # SQLAlchemy async engine + init_db()
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||||||
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│ ├── logging_config.py # structlog (JSON prod, console dev)
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||||||
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│ ├── metrics.py # Prometheus counters/histograms/gauges
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||||||
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│ ├── models/ # SQLAlchemy ORM
|
||||||
|
│ │ ├── image.py # Image (30+ colonnes, indexes composites)
|
||||||
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│ │ └── client.py # APIClient (multi-tenant, plans, quotas)
|
||||||
|
│ ├── schemas/ # Pydantic validation
|
||||||
|
│ │ ├── __init__.py # 24 classes de réponse/requête
|
||||||
|
│ │ └── auth.py # Client CRUD + scope validation
|
||||||
|
│ ├── dependencies/
|
||||||
|
│ │ └── auth.py # API Key auth (SHA-256+pepper, timing-safe)
|
||||||
|
│ ├── routers/ # Endpoints REST + WebSocket
|
||||||
|
│ │ ├── images.py # CRUD images (upload, list, detail, delete)
|
||||||
|
│ │ ├── ai.py # AI endpoints (summarize, draft-task)
|
||||||
|
│ │ ├── auth.py # Client management (CRUD, rotate-key)
|
||||||
|
│ │ ├── admin.py # Admin API (stats, clients, DLQ, docs)
|
||||||
|
│ │ ├── files.py # Signed URL file serving
|
||||||
|
│ │ └── websocket.py # Pipeline monitoring + admin monitor
|
||||||
|
│ ├── services/ # Logique métier
|
||||||
|
│ │ ├── storage.py # Upload/delete avec StorageBackend (async)
|
||||||
|
│ │ ├── storage_backend.py # Abstraction LocalStorage / S3Storage
|
||||||
|
│ │ ├── pipeline.py # Orchestration EXIF→OCR→AI + Redis Pub/Sub
|
||||||
|
│ │ ├── ai_vision.py # Gemini/OpenRouter avec timeout+retry
|
||||||
|
│ │ ├── exif_service.py # Extraction EXIF (piexif + Pillow)
|
||||||
|
│ │ ├── ocr_service.py # Tesseract OCR + détection langue
|
||||||
|
│ │ └── scraper.py # Web scraping (BeautifulSoup)
|
||||||
|
│ ├── middleware/ # Intergiciels
|
||||||
|
│ │ ├── __init__.py # Rate limiting (slowapi + ContextVar)
|
||||||
|
│ │ ├── request_id.py # Trace ID injection (X-Request-ID)
|
||||||
|
│ │ ├── versioning.py # API versioning (X-API-Version, Sunset)
|
||||||
|
│ │ └── logging_middleware.py # HTTP request logging (structlog)
|
||||||
|
│ └── workers/ # Tâches asynchrones
|
||||||
|
│ ├── image_worker.py # ARQ worker (DLQ, timeout configurable)
|
||||||
|
│ ├── redis_client.py # Redis pool + parse_redis_url()
|
||||||
|
│ ├── arq_fallback.py # Fallback pool (Redis indisponible)
|
||||||
|
│ └── dead_letter.py # Dead Letter Queue (push, list, retry)
|
||||||
|
├── imago-admin/ # Frontend React TypeScript
|
||||||
|
│ ├── src/ # Composants, pages, hooks, stores
|
||||||
|
│ └── docker-compose.yml # Stack Docker (admin + backend + infra)
|
||||||
|
├── sdk/ # SDK Python (imago-client)
|
||||||
|
│ └── imago_client/ # Client HTTP + WebSocket + modèles
|
||||||
|
├── integration/ # Intégrations tierces
|
||||||
|
│ └── shaarli/ # Plugin Shaarli
|
||||||
|
├── docs/ # Documentation
|
||||||
|
│ ├── ROADMAP.md # Ce document
|
||||||
|
│ ├── API_GUIDE.md # Guide API
|
||||||
|
│ ├── ARCHITECTURE.md # Architecture détaillée
|
||||||
|
│ ├── USER_GUIDE.md # Guide utilisateur
|
||||||
|
│ ├── SDK.md # Documentation SDK
|
||||||
|
│ ├── SHAARLI-INTEGRATION.md # Intégration Shaarli
|
||||||
|
│ ├── WEBSOCKET.md # Guide WebSocket
|
||||||
|
│ └── grafana-dashboard.json # Dashboard Grafana (10 panneaux)
|
||||||
|
├── tests/ # Tests backend (18 fichiers)
|
||||||
|
├── worker.py # Script de lancement worker ARQ
|
||||||
|
├── Dockerfile # Image Docker backend
|
||||||
|
├── docker-compose.yml # Stack de développement
|
||||||
|
├── pyproject.toml # Dépendances + tool config
|
||||||
|
├── requirements.txt # Dépendances (legacy)
|
||||||
|
├── .env.example # Template configuration
|
||||||
|
└── CHANGELOG.md # Historique des versions
|
||||||
|
```
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 4. Déploiement Actuel
|
||||||
|
|
||||||
|
**Stack Docker Compose** (projet `imago-admin`) :
|
||||||
|
|
||||||
|
| Service | Conteneur | Port | Statut |
|
||||||
|
|---|---|---|---|
|
||||||
|
| Backend API | `imago-admin-backend-1` | 8000 | ✅ healthy |
|
||||||
|
| Admin Panel | `imago-admin-admin-1` | 3000 | ✅ running |
|
||||||
|
| ARQ Worker | `imago-admin-worker-1` | — | ✅ running |
|
||||||
|
| PostgreSQL 16 | `imago-admin-db-1` | 5432 | ✅ healthy |
|
||||||
|
| Redis 7 | `imago-admin-redis-1` | 6379 | ✅ healthy |
|
||||||
|
| MinIO | `imago-admin-minio-1` | 9000-9001 | ✅ running |
|
||||||
|
|
||||||
|
**Health Check** : `healthy` sur tous les services (backend, database, redis, queue, worker, minio, tesseract, ai)
|
||||||
|
|
||||||
|
**Accès** :
|
||||||
|
- API : http://localhost:8000/docs
|
||||||
|
- Admin : http://localhost:3000 (clé : `imago-admin-key`)
|
||||||
|
- MinIO Console : http://localhost:9001 (minioadmin / minioadmin)
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 5. Reste à faire (post-déploiement)
|
||||||
|
|
||||||
|
Rien de bloquant. Suggestions d'amélioration :
|
||||||
|
|
||||||
|
| ID | Description | Priorité | Effort |
|
||||||
|
|---|---|---|---|
|
||||||
|
| T-1 | Exécuter la suite de tests complète (nécessite venv) | P2 | 1h |
|
||||||
|
| T-2 | Ajouter des tests de résilience (Redis down, S3 timeout, AI timeout) | P2 | 3h |
|
||||||
|
| T-3 | Configurer le SDK `imago-client` pour PyPI | P3 | 2h |
|
||||||
|
| T-4 | Ajouter le worker au docker-compose de imago-admin | P3 | 30min |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
*Document vivant — mis à jour le 2026-06-22 après 27 correctifs.*
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 6. Idées d'APIs Innovantes (backlog — à trier)
|
||||||
|
|
||||||
|
### 🔗 Génération & Partage
|
||||||
|
| ID | Fonctionnalité | Description | Effort |
|
||||||
|
|---|---|---|---|
|
||||||
|
| I-1 | **QR Code API** | `POST /api/v1/images/{id}/qrcode` — génère un QR code pointant vers l'URL publique de l'image. Formats: PNG, SVG. Tailles configurables. | 2h |
|
||||||
|
| I-2 | **Liens publics expirables** | `POST /api/v1/images/{id}/share` — crée un lien public temporaire (avec expiration) pour partager une image sans auth. | 3h |
|
||||||
|
| I-3 | **Social Cards** | `GET /api/v1/images/{id}/social-card` — génère une carte Open Graph (thumbnail + titre + description) pour partage réseaux sociaux. | 2h |
|
||||||
|
|
||||||
|
### 👤 Reconnaissance & IA
|
||||||
|
| ID | Fonctionnalité | Description | Effort |
|
||||||
|
|---|---|---|---|
|
||||||
|
| I-4 | **Reconnaissance faciale** | `POST /api/v1/faces/detect` — détecte les visages dans une image. `POST /api/v1/faces/identify` — identifie les personnes (prénom, nom) via une base de visages de référence. Basé sur un modèle local (face_recognition/DeepFace). | 8h |
|
||||||
|
| I-5 | **Détection de contenu sensible (NSFW)** | `POST /api/v1/images/{id}/moderate` — classification automatique du contenu (safe/sensitive/nsfw). Basé sur un modèle open-source. | 3h |
|
||||||
|
| I-6 | **Reconnaissance de scène** | Enrichissement de `analyze_image` : classification de lieu (intérieur/extérieur, bureau, plage, montagne, urbain, etc.) | 3h |
|
||||||
|
| I-7 | **Génération de légende multilingue** | `POST /api/v1/images/{id}/caption?lang=es` — génère une description en plusieurs langues (FR, EN, ES, DE, etc.) | 2h |
|
||||||
|
|
||||||
|
### 🎨 Édition & Transformation
|
||||||
|
| ID | Fonctionnalité | Description | Effort |
|
||||||
|
|---|---|---|---|
|
||||||
|
| I-8 | **Suppression de fond** | `POST /api/v1/images/{id}/remove-bg` — retire l'arrière-plan d'une image (retourne PNG transparent). Basé sur rembg. | 3h |
|
||||||
|
| I-9 | **Conversion de format** | `POST /api/v1/images/{id}/convert?format=webp&quality=85` — convertit une image vers un autre format (WebP, AVIF, PNG, JPEG). | 2h |
|
||||||
|
| I-10 | **Compression/Optimisation** | `POST /api/v1/images/{id}/optimize` — réduit la taille du fichier avec conservation de la qualité visuelle. | 2h |
|
||||||
|
| I-11 | **Redimensionnement intelligent** | `POST /api/v1/images/{id}/resize?width=800&height=600&crop=smart` — redimensionne avec recadrage intelligent (entropy-based). | 2h |
|
||||||
|
| I-12 | **Filigrane / Watermark** | `POST /api/v1/images/{id}/watermark` — ajoute un filigrane texte ou image aux photos. | 3h |
|
||||||
|
|
||||||
|
### 🔍 Recherche & Organisation
|
||||||
|
| ID | Fonctionnalité | Description | Effort |
|
||||||
|
|---|---|---|---|
|
||||||
|
| I-13 | **Recherche par similarité visuelle** | `GET /api/v1/images/search/similar?image_id=123` — trouve les images visuellement similaires (perceptual hash). | 4h |
|
||||||
|
| I-14 | **Détection de doublons** | `POST /api/v1/images/deduplicate` — scanne la bibliothèque et détecte les images en double ou quasi-identiques. | 4h |
|
||||||
|
| I-15 | **Palette de couleurs dominante** | `GET /api/v1/images/{id}/palette` — extrait les 5-10 couleurs dominantes d'une image (hex + pourcentage). | 1h |
|
||||||
|
| I-16 | **Recherche par couleur** | `GET /api/v1/images?color=%23FF5733&tolerance=10` — trouve les images contenant une couleur spécifique. | 3h |
|
||||||
|
| I-17 | **Recherche OCR full-text** | Amélioration de la recherche existante : index full-text (PostgreSQL tsvector) sur le texte OCR pour une recherche rapide. | 3h |
|
||||||
|
|
||||||
|
### 📍 Géolocalisation & Temps
|
||||||
|
| ID | Fonctionnalité | Description | Effort |
|
||||||
|
|---|---|---|---|
|
||||||
|
| I-18 | **Carte des images** | `GET /api/v1/images/map?bounds=lat1,lng1,lat2,lng2` — retourne les images géolocalisées dans une zone (GeoJSON). | 3h |
|
||||||
|
| I-19 | **Timeline / Frise chronologique** | `GET /api/v1/images/timeline?from=2024-01-01&to=2024-12-31` — organise les images par date de prise de vue (EXIF). | 2h |
|
||||||
|
|
||||||
|
### 🔄 Intégrations & Automatisation
|
||||||
|
| ID | Fonctionnalité | Description | Effort |
|
||||||
|
|---|---|---|---|
|
||||||
|
| I-20 | **Import depuis URL (batch)** | `POST /api/v1/images/import` — importe un lot d'images depuis une liste d'URLs (JSON body). | 3h |
|
||||||
|
| I-21 | **Webhooks de notification** | `POST /api/v1/webhooks` — CRUD de webhooks appelés quand un pipeline se termine (URL + secret). | 4h |
|
||||||
|
| I-22 | **Intégration stockage cloud** | Connecteurs pour Dropbox, Google Drive, S3 externe : import/export automatique. | 8h |
|
||||||
|
| I-23 | **API d'annotation** | `POST /api/v1/images/{id}/annotations` — dessiner des rectangles, flèches, texte sur une image (retourne une nouvelle image). | 5h |
|
||||||
|
|
||||||
|
### 📊 Analytics & Insights
|
||||||
|
| ID | Fonctionnalité | Description | Effort |
|
||||||
|
|---|---|---|---|
|
||||||
|
| I-24 | **Statistiques par modèle d'appareil** | `GET /api/v1/stats/cameras` — quels appareils/objectifs sont les plus utilisés (basé sur EXIF). | 1h |
|
||||||
|
| I-25 | **Heatmap d'activité** | `GET /api/v1/stats/activity` — calendrier heatmap des uploads par jour/semaine/mois. | 2h |
|
||||||
|
| I-26 | **Rapport d'utilisation AI** | `GET /api/v1/stats/ai-usage` — tokens consommés, coûts estimés, top modèles utilisés. | 2h |
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
*Ces 26 idées sont à trier, prioriser et découper en sprints par le Product Owner.*
|
||||||
|
|
||||||
@@ -0,0 +1,200 @@
|
|||||||
|
{
|
||||||
|
"dashboard": {
|
||||||
|
"title": "Imago — Monitoring",
|
||||||
|
"uid": "imago",
|
||||||
|
"tags": ["imago", "fastapi"],
|
||||||
|
"timezone": "browser",
|
||||||
|
"schemaVersion": 38,
|
||||||
|
"refresh": "30s",
|
||||||
|
"panels": [
|
||||||
|
{
|
||||||
|
"id": 1,
|
||||||
|
"title": "Images Uploaded (rate)",
|
||||||
|
"type": "stat",
|
||||||
|
"gridPos": {"x": 0, "y": 0, "w": 6, "h": 4},
|
||||||
|
"targets": [
|
||||||
|
{
|
||||||
|
"expr": "rate(hub_images_uploaded_total[5m]) * 60",
|
||||||
|
"legendFormat": "uploads/min"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"fieldConfig": {
|
||||||
|
"defaults": {
|
||||||
|
"unit": "short",
|
||||||
|
"color": {"mode": "palette-classic"},
|
||||||
|
"thresholds": {"steps": [{"color": "green", "value": null}]}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 2,
|
||||||
|
"title": "AI Tokens Consumed (rate)",
|
||||||
|
"type": "stat",
|
||||||
|
"gridPos": {"x": 6, "y": 0, "w": 6, "h": 4},
|
||||||
|
"targets": [
|
||||||
|
{
|
||||||
|
"expr": "rate(hub_ai_tokens_consumed_total[5m]) * 60",
|
||||||
|
"legendFormat": "tokens/min"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"fieldConfig": {
|
||||||
|
"defaults": {
|
||||||
|
"unit": "short",
|
||||||
|
"color": {"mode": "palette-classic"},
|
||||||
|
"thresholds": {"steps": [{"color": "blue", "value": null}]}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 3,
|
||||||
|
"title": "Active WebSockets",
|
||||||
|
"type": "stat",
|
||||||
|
"gridPos": {"x": 12, "y": 0, "w": 6, "h": 4},
|
||||||
|
"targets": [
|
||||||
|
{
|
||||||
|
"expr": "hub_active_websockets",
|
||||||
|
"legendFormat": "connections"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"fieldConfig": {
|
||||||
|
"defaults": {
|
||||||
|
"unit": "short",
|
||||||
|
"color": {"mode": "palette-classic"},
|
||||||
|
"thresholds": {"steps": [{"color": "purple", "value": null}]}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 4,
|
||||||
|
"title": "ARQ Jobs",
|
||||||
|
"type": "stat",
|
||||||
|
"gridPos": {"x": 18, "y": 0, "w": 6, "h": 4},
|
||||||
|
"targets": [
|
||||||
|
{
|
||||||
|
"expr": "rate(hub_arq_jobs_enqueued_total[5m]) * 60",
|
||||||
|
"legendFormat": "jobs/min"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"fieldConfig": {
|
||||||
|
"defaults": {
|
||||||
|
"unit": "short",
|
||||||
|
"color": {"mode": "palette-classic"},
|
||||||
|
"thresholds": {"steps": [{"color": "orange", "value": null}]}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 5,
|
||||||
|
"title": "Pipeline Duration",
|
||||||
|
"type": "heatmap",
|
||||||
|
"gridPos": {"x": 0, "y": 4, "w": 12, "h": 8},
|
||||||
|
"targets": [
|
||||||
|
{
|
||||||
|
"expr": "rate(hub_pipeline_duration_seconds_bucket[5m])",
|
||||||
|
"legendFormat": "{{le}}s"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"fieldConfig": {
|
||||||
|
"defaults": {
|
||||||
|
"unit": "s",
|
||||||
|
"color": {"mode": "scheme-classic"}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 6,
|
||||||
|
"title": "Pipeline Step Duration",
|
||||||
|
"type": "bargauge",
|
||||||
|
"gridPos": {"x": 12, "y": 4, "w": 12, "h": 8},
|
||||||
|
"targets": [
|
||||||
|
{
|
||||||
|
"expr": "avg(rate(hub_pipeline_step_duration_seconds_sum[5m]) / rate(hub_pipeline_step_duration_seconds_count[5m])) by (step)",
|
||||||
|
"legendFormat": "{{step}} avg"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"fieldConfig": {
|
||||||
|
"defaults": {
|
||||||
|
"unit": "s",
|
||||||
|
"color": {"mode": "palette-classic"}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 7,
|
||||||
|
"title": "Images Uploaded (total)",
|
||||||
|
"type": "timeseries",
|
||||||
|
"gridPos": {"x": 0, "y": 12, "w": 12, "h": 8},
|
||||||
|
"targets": [
|
||||||
|
{
|
||||||
|
"expr": "rate(hub_images_uploaded_total[30s])",
|
||||||
|
"legendFormat": "uploaded"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"expr": "rate(hub_images_deleted_total[30s])",
|
||||||
|
"legendFormat": "deleted"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"fieldConfig": {
|
||||||
|
"defaults": {
|
||||||
|
"unit": "short",
|
||||||
|
"color": {"mode": "palette-classic"}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 8,
|
||||||
|
"title": "Pipeline Errors",
|
||||||
|
"type": "timeseries",
|
||||||
|
"gridPos": {"x": 12, "y": 12, "w": 12, "h": 8},
|
||||||
|
"targets": [
|
||||||
|
{
|
||||||
|
"expr": "rate(hub_pipeline_errors_total[30s])",
|
||||||
|
"legendFormat": "{{step}}"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"fieldConfig": {
|
||||||
|
"defaults": {
|
||||||
|
"unit": "short",
|
||||||
|
"color": {"mode": "palette-classic"}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 9,
|
||||||
|
"title": "Storage Used by Client",
|
||||||
|
"type": "bargauge",
|
||||||
|
"gridPos": {"x": 0, "y": 20, "w": 12, "h": 8},
|
||||||
|
"targets": [
|
||||||
|
{
|
||||||
|
"expr": "hub_storage_used_bytes",
|
||||||
|
"legendFormat": "{{client_id}}"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"fieldConfig": {
|
||||||
|
"defaults": {
|
||||||
|
"unit": "decbytes",
|
||||||
|
"color": {"mode": "palette-classic"}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"id": 10,
|
||||||
|
"title": "AI Tokens by Client & Model",
|
||||||
|
"type": "bargauge",
|
||||||
|
"gridPos": {"x": 12, "y": 20, "w": 12, "h": 8},
|
||||||
|
"targets": [
|
||||||
|
{
|
||||||
|
"expr": "rate(hub_ai_tokens_consumed_total[5m]) * 300",
|
||||||
|
"legendFormat": "{{client_id}}/{{model}}"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"fieldConfig": {
|
||||||
|
"defaults": {
|
||||||
|
"unit": "short",
|
||||||
|
"color": {"mode": "palette-classic"}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user