feat: initial Skeen-CRM AI Agent architecture

- FastAPI + Python 3.12 backend
- Meta WhatsApp Business API client (official)
- OpenAI GPT-4o with function calling
- RAG vector store with pgvector
- ERPNext Frappe REST client
- Celery + Redis async task queue
- PostgreSQL with migrations (Alembic)
- Docker Compose full stack
- Enterprise logging, metrics, health checks

src/workers/celery_app.py

@@ -0,0 +1,61 @@
+"""Celery configuration for background task processing."""
+
+import os
+
+from celery import Celery
+from celery.signals import setup_logging
+
+from src.config import settings
+
+os.environ.setdefault("CELERY_CONFIG_MODULE", "src.workers.celery_app")
+
+celery_app = Celery(
+    "skeen_crm",
+    broker=str(settings.CELERY_BROKER_URL),
+    backend=str(settings.CELERY_RESULT_BACKEND),
+    include=["src.workers.tasks"],
+)
+
+celery_app.conf.update(
+    task_serializer="json",
+    accept_content=["json"],
+    result_serializer="json",
+    timezone="America/Tijuana",
+    enable_utc=True,
+    task_track_started=True,
+    task_time_limit=300,
+    worker_prefetch_multiplier=1,
+    worker_concurrency=settings.CELERY_WORKER_CONCURRENCY,
+    task_routes={
+        "src.workers.tasks.process_whatsapp_message_task": {"queue": "whatsapp"},
+        "src.workers.tasks.sync_erpnext_task": {"queue": "erpnext"},
+        "src.workers.tasks.generate_embedding_task": {"queue": "ai"},
+    },
+    task_default_queue="default",
+)
+
+
+@setup_logging.connect
+def config_loggers(*args, **kwargs) -> None:
+    """Configure structlog for Celery workers."""
+    import logging
+    import structlog
+
+    structlog.configure(
+        processors=[
+            structlog.stdlib.filter_by_level,
+            structlog.stdlib.add_logger_name,
+            structlog.stdlib.add_log_level,
+            structlog.stdlib.PositionalArgumentsFormatter(),
+            structlog.processors.TimeStamper(fmt="iso"),
+            structlog.processors.StackInfoRenderer(),
+            structlog.processors.format_exc_info,
+            structlog.processors.UnicodeDecoder(),
+            structlog.processors.JSONRenderer(),
+        ],
+        context_class=dict,
+        logger_factory=structlog.stdlib.LoggerFactory(),
+        wrapper_class=structlog.stdlib.BoundLogger,
+        cache_logger_on_first_use=True,
+    )
+    logging.basicConfig(format="%(message)s", level=logging.INFO)

src/workers/tasks.py

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+"""Celery background tasks."""
+
+import structlog
+
+from src.infrastructure.db import AsyncSessionLocal
+from src.use_cases.handle_incoming_message import process_incoming_message
+from src.workers.celery_app import celery_app
+
+logger = structlog.get_logger(__name__)
+
+
+@celery_app.task(bind=True, max_retries=3, default_retry_delay=10)
+def process_whatsapp_message_task(self, webhook_data: dict, client_ip: str = "unknown") -> dict:
+    """Process WhatsApp message asynchronously.
+
+    This task runs in a Celery worker and handles the full AI pipeline
+    so the webhook endpoint can return 200 immediately to Meta.
+    """
+    import asyncio
+
+    async def _process() -> dict:
+        async with AsyncSessionLocal() as db:
+            try:
+                result = await process_incoming_message(
+                    db=db,
+                    webhook_data=webhook_data,
+                    client_ip=client_ip,
+                )
+                return result
+            except Exception as exc:
+                logger.error("whatsapp_task_failed", error=str(exc), attempt=self.request.retries)
+                raise self.retry(exc=exc)
+
+    return asyncio.run(_process())
+
+
+@celery_app.task
+def sync_erpnext_patient_task(patient_data: dict) -> dict:
+    """Sync patient data to ERPNext in background."""
+    logger.info("syncing_patient_to_erpnext", patient=patient_data.get("name"))
+    # TODO: Implement ERPNext sync logic
+    return {"status": "synced", "patient": patient_data.get("name")}
+
+
+@celery_app.task
+def generate_embedding_task(document_id: str, content: str, category: str) -> dict:
+    """Generate embedding for a document chunk asynchronously."""
+    import asyncio
+
+    async def _generate() -> dict:
+        from src.infrastructure.ai.openai_client import get_openai_client
+        from src.infrastructure.db import AsyncSessionLocal
+        from src.infrastructure.ai.rag import RAGStore
+
+        async with AsyncSessionLocal() as db:
+            client = await get_openai_client()
+            embedding = await client.create_embedding(content)
+
+            store = RAGStore(db)
+            await store.add_document(
+                content=content,
+                category=category,
+                doc_id=document_id,
+            )
+            return {"document_id": document_id, "embedding_dims": len(embedding)}
+
+    return asyncio.run(_generate())