# agents/core-logic/app.py # OSVauco-NMTMD-GCOS — FastAPI HTTP entrypoint for Cloud Run import os import sys import logging from contextlib import asynccontextmanager from fastapi import FastAPI, HTTPException, Request from fastapi.responses import JSONResponse from pydantic import BaseModel logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Gjor ml-pakken tilgjengelig uansett cwd (Cloud Run starter i agents/core-logic/) sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..')) try: from google.adk.runners import Runner from google.adk.sessions import InMemorySessionService from google.genai.types import Content, Part from agent import root_agent from agents.recommendations_engine import get_recommendations except ImportError as e: logger.error(f"Failed to import ADK dependencies: {e}") raise session_service = InMemorySessionService() APP_NAME = os.environ.get("CLOUD_RUN_SERVICE", "gcp-orchestrator") PROJECT_ID = os.environ.get("GOOGLE_CLOUD_PROJECT", "propane-will-491900-m5") BQ_BILLING_DATASET = os.environ.get("BQ_BILLING_DATASET", "billing_data") @asynccontextmanager async def lifespan(app: FastAPI): logger.info(f"OSVauco agent '{APP_NAME}' starting up") yield logger.info(f"OSVauco agent '{APP_NAME}' shutting down") app = FastAPI( title="OSVauco GCP Agent", description="ADK-based multi-agent orchestrator on Cloud Run", version="1.0.0", lifespan=lifespan, ) class RunRequest(BaseModel): user_id: str session_id: str message: str class RunResponse(BaseModel): user_id: str session_id: str response: str async def _ensure_session(user_id: str, session_id: str): try: session = await session_service.get_session( app_name=APP_NAME, user_id=user_id, session_id=session_id ) if session is not None: return session except Exception: pass session = await session_service.create_session( app_name=APP_NAME, user_id=user_id, session_id=session_id ) logger.info(f"Created new session: {session_id} for user: {user_id}") return session # ── helse ────────────────────────────────────────────────────────────────────── @app.get("/health") async def health(): return JSONResponse({"status": "ok", "service": APP_NAME}) # ── agent run ────────────────────────────────────────────────────────────────── @app.post("/run", response_model=RunResponse) async def run(req: RunRequest): try: await _ensure_session(req.user_id, req.session_id) runner = Runner(agent=root_agent, app_name=APP_NAME, session_service=session_service) user_content = Content(role="user", parts=[Part(text=req.message)]) final_response = "" async for event in runner.run_async( user_id=req.user_id, session_id=req.session_id, new_message=user_content ): if event.is_final_response() and event.content: for part in event.content.parts: if part.text: final_response += part.text logger.info(f"[{req.user_id}/{req.session_id}] Response length: {len(final_response)}") return RunResponse(user_id=req.user_id, session_id=req.session_id, response=final_response) except Exception as e: logger.error(f"Agent run failed: {e}", exc_info=True) raise HTTPException(status_code=500, detail=str(e)) # ── billing: tokens ──────────────────────────────────────────────────────────── @app.get("/billing/tokens/summary") async def billing_tokens_summary(): """ CG3e — LLM token-bruk og estimert kostnad per agent siste 30 dager. """ try: from google.cloud import bigquery client = bigquery.Client(project=PROJECT_ID) query = f""" SELECT agent_name, model_name, SUM(input_tokens) AS total_input_tokens, SUM(output_tokens) AS total_output_tokens, SUM(total_tokens) AS total_tokens, SUM(estimated_cost_usd) AS total_cost_usd FROM `{PROJECT_ID}.{BQ_BILLING_DATASET}.llm_token_usage` WHERE timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY) GROUP BY agent_name, model_name ORDER BY total_cost_usd DESC """ rows = [ { "agent_name": row.agent_name, "model_name": row.model_name, "total_input_tokens": row.total_input_tokens, "total_output_tokens": row.total_output_tokens, "total_tokens": row.total_tokens, "total_cost_usd": round(float(row.total_cost_usd), 6), } for row in client.query(query).result() ] return JSONResponse({"period_days": 30, "rows": rows}) except Exception as e: logger.error(f"[/billing/tokens/summary] Failed: {e}", exc_info=True) raise HTTPException(status_code=500, detail=str(e)) # ── billing: anbefalinger ────────────────────────────────────────────────────── @app.get("/billing/recommendations") async def billing_recommendations(budget: float = 500.0): """ CG4 — Anbefalings- og anomali-motor. """ try: recommendations = get_recommendations(budget) return JSONResponse(recommendations) except Exception as e: logger.error(f"[/billing/recommendations] Failed: {e}", exc_info=True) raise HTTPException(status_code=500, detail=str(e)) # ── billing: tjenester med SKU-detaljer (CG5) ─────────────────────────────────── @app.get("/billing/by-service") async def billing_by_service(days: int = 30): """ CG5 — Kostnad per tjeneste med SKU-detaljer for drill-down i dashbordet. Returnerer liste sortert etter total_cost DESC. Hvert element: {service, total_cost, skus: [{sku, sku_cost}]} Fallback: hvis BillingAgent feiler returneres en tom liste (dashbordet viser da fallback-visning istedenfor å krasje). """ try: from ml.billing_agent import BillingAgent agent = BillingAgent() data = agent.get_service_totals(days) return JSONResponse(data) except Exception as e: logger.error(f"[/billing/by-service] Failed: {e}", exc_info=True) # Returner tom liste istedenfor 500 — dashbordet faller da tilbake til # den enkle buildSvc-visningen uten å miste all annen data. return JSONResponse([])