Add initial work from Codex

backend/microservices/persistence/main.py

@@ -0,0 +1,176 @@
+from __future__ import annotations
+
+import logging
+from contextlib import asynccontextmanager
+
+from fastapi import Depends, FastAPI, Query, Response
+from pydantic import BaseModel, Field
+
+from app.core.config import settings
+from app.core.otel import (
+    TelemetryProviders,
+    configure_otel,
+    instrument_fastapi,
+    instrument_sqlalchemy_engines,
+    shutdown_otel,
+)
+from app.core.security import InternalPrincipal, require_internal_principal
+from app.db.postgres import (
+    create_postgres_engine,
+    create_postgres_session_factory,
+    initialize_postgres_schema,
+)
+from app.services.persistence_service import PersistenceService
+from microservices.common.http import current_trace_headers
+
+logging.basicConfig(level=settings.log_level)
+LOGGER = logging.getLogger(__name__)
+
+
+class AuditLogIn(BaseModel):
+    method: str
+    path: str
+    query_string: str = ""
+    status_code: int
+    duration_ms: float
+    trace_id: str | None = None
+    span_id: str | None = None
+    client_ip: str | None = None
+    user_agent: str | None = None
+    details: dict = Field(default_factory=dict)
+
+
+class ForecastRunIn(BaseModel):
+    horizon_days: int
+    payload: list[dict]
+    trigger_source: str
+    trace_id: str | None = None
+    span_id: str | None = None
+
+
+class RankingRunIn(BaseModel):
+    top_n: int
+    payload: list[dict]
+    trigger_source: str
+    trace_id: str | None = None
+    span_id: str | None = None
+
+
+class RecommendationRunIn(BaseModel):
+    payload: list[dict]
+    trigger_source: str
+    trace_id: str | None = None
+    span_id: str | None = None
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    telemetry: TelemetryProviders = configure_otel(settings)
+    engine = create_postgres_engine()
+    initialize_postgres_schema(engine)
+    instrument_sqlalchemy_engines({"appdb": engine})
+    app.state.persistence_service = PersistenceService(
+        create_postgres_session_factory(engine)
+    )
+    LOGGER.info("Persistence service ready with PostgreSQL")
+    yield
+    engine.dispose()
+    shutdown_otel(telemetry)
+
+
+app = FastAPI(title="persistence-service", version="0.1.0", lifespan=lifespan)
+instrument_fastapi(app)
+
+
+def _service() -> PersistenceService:
+    return app.state.persistence_service
+
+
+@app.get("/internal/health")
+def health(response: Response) -> dict:
+    response.headers.update(current_trace_headers())
+    return {"status": "ok", "service": "persistence-service"}
+
+
+@app.post("/internal/audit-logs")
+def create_audit_log(
+    payload: AuditLogIn,
+    response: Response,
+    _auth: InternalPrincipal = Depends(require_internal_principal),
+) -> dict:
+    response.headers.update(current_trace_headers())
+    _service().record_audit_log(**payload.model_dump())
+    return {"status": "ok"}
+
+
+@app.post("/internal/forecast-runs")
+def create_forecast_run(
+    payload: ForecastRunIn,
+    response: Response,
+    _auth: InternalPrincipal = Depends(require_internal_principal),
+) -> dict:
+    response.headers.update(current_trace_headers())
+    _service().record_forecast_run(**payload.model_dump())
+    return {"status": "ok"}
+
+
+@app.post("/internal/ranking-runs")
+def create_ranking_run(
+    payload: RankingRunIn,
+    response: Response,
+    _auth: InternalPrincipal = Depends(require_internal_principal),
+) -> dict:
+    response.headers.update(current_trace_headers())
+    _service().record_ranking_run(**payload.model_dump())
+    return {"status": "ok"}
+
+
+@app.post("/internal/recommendation-runs")
+def create_recommendation_run(
+    payload: RecommendationRunIn,
+    response: Response,
+    _auth: InternalPrincipal = Depends(require_internal_principal),
+) -> dict:
+    response.headers.update(current_trace_headers())
+    _service().record_recommendation_run(**payload.model_dump())
+    return {"status": "ok"}
+
+
+@app.get("/internal/audit-logs")
+def list_audit_logs(
+    response: Response,
+    limit: int = Query(default=settings.storage_default_limit, ge=1, le=500),
+    _auth: InternalPrincipal = Depends(require_internal_principal),
+) -> list[dict]:
+    response.headers.update(current_trace_headers())
+    return _service().list_audit_logs(limit=limit)
+
+
+@app.get("/internal/forecast-runs")
+def list_forecast_runs(
+    response: Response,
+    limit: int = Query(default=settings.storage_default_limit, ge=1, le=500),
+    _auth: InternalPrincipal = Depends(require_internal_principal),
+) -> list[dict]:
+    response.headers.update(current_trace_headers())
+    return _service().list_forecast_runs(limit=limit)
+
+
+@app.get("/internal/ranking-runs")
+def list_ranking_runs(
+    response: Response,
+    limit: int = Query(default=settings.storage_default_limit, ge=1, le=500),
+    _auth: InternalPrincipal = Depends(require_internal_principal),
+) -> list[dict]:
+    response.headers.update(current_trace_headers())
+    return _service().list_ranking_runs(limit=limit)
+
+
+@app.get("/internal/recommendation-runs")
+def list_recommendation_runs(
+    response: Response,
+    limit: int = Query(default=settings.storage_default_limit, ge=1, le=500),
+    _auth: InternalPrincipal = Depends(require_internal_principal),
+) -> list[dict]:
+    response.headers.update(current_trace_headers())
+    return _service().list_recommendation_runs(limit=limit)