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As a Software Engineering Agent, I will focus on implementing the Google Cloud Platform (GCP) Observability exporters for your FastAPI application using OpenTelemetry and Application Default Credentials (ADC).

Here is the implementation plan to enable Tracing and Metrics export to GCP.

1. Update Dependencies

Add the following OpenTelemetry packages to your backend/requirements.txt:

opentelemetry-api
opentelemetry-sdk
opentelemetry-instrumentation-fastapi
opentelemetry-exporter-gcp-trace
opentelemetry-exporter-gcp-monitoring

2. Configure Settings

Update backend/config/settings.py to include the Project ID and a toggle for telemetry.

# backend/config/settings.py

class Settings(BaseSettings):
    # ... existing settings ...

    # Telemetry
    GCP_PROJECT_ID: str = "gemini-cli-frank-20260120"
    ENABLE_TELEMETRY: bool = True

3. Create Telemetry Utility

Create a new file backend/utils/telemetry.py to handle the initialization of the exporters. This keeps main.py clean.

# backend/utils/telemetry.py
import logging
from opentelemetry import trace, metrics
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry.exporter.cloud_trace import CloudTraceSpanExporter
from opentelemetry.exporter.cloud_monitoring import CloudMonitoringMetricsExporter
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor

logger = logging.getLogger(__name__)

def setup_telemetry(app, project_id: str):
    """
    Sets up OpenTelemetry tracing and metrics with Google Cloud exporters.
    Uses Application Default Credentials (ADC) automatically.
    """
    try:
        # --- Tracing Setup ---
        tracer_provider = TracerProvider()
        trace.set_tracer_provider(tracer_provider)

        # Initialize GCP Trace Exporter
        trace_exporter = CloudTraceSpanExporter(project_id=project_id)
        span_processor = BatchSpanProcessor(trace_exporter)
        tracer_provider.add_span_processor(span_processor)

        # --- Metrics Setup ---
        # Initialize GCP Monitoring (Metrics) Exporter
        metric_exporter = CloudMonitoringMetricsExporter(project_id=project_id)
        reader = PeriodicExportingMetricReader(metric_exporter)
        meter_provider = MeterProvider(metric_readers=[reader])
        metrics.set_meter_provider(meter_provider)

        # --- Instrument FastAPI ---
        # This automatically captures HTTP requests, latencies, and status codes
        FastAPIInstrumentor.instrument_app(app, tracer_provider=tracer_provider, meter_provider=meter_provider)
        
        logger.info(f"✅ GCP Telemetry initialized for project: {project_id}")
        
    except Exception as e:
        logger.error(f"❌ Failed to initialize GCP telemetry: {e}")

4. Integrate into Application

Modify backend/main.py to initialize telemetry during the application startup lifespan.

# backend/main.py

# ... imports ...
from backend.utils.telemetry import setup_telemetry  # <--- Import this

@asynccontextmanager
async def lifespan(app: FastAPI):
    global orchestrator, scheduler, db_client
    settings = get_settings()

    try:
        logger.info("🚀 Starting News AI Application...")
        
        # ... Database and Agent initialization ...

        # 5. Init Telemetry (New)
        if settings.ENABLE_TELEMETRY:
            setup_telemetry(app, settings.GCP_PROJECT_ID)

        # ... rest of startup logic ...

Deployment Note

Since you requested ADC (Application Default Credentials), ensure that the environment where this runs (your remote server or Docker container) is authenticated.

• Local/Dev: Run gcloud auth application-default login.
• GCE/Cloud Run: It will pick up the service account automatically.
• Docker: Ensure the GOOGLE_APPLICATION_CREDENTIALS environment variable is set and the JSON key file is mounted if not running on GCP infrastructure.

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