Add OpenTelemetry pipeline

Author: Naragod

ai_server/server.py

@@ -11,9 +11,12 @@
 import requests
 from dotenv import load_dotenv
 from flask import Flask, abort, jsonify, request
-from opentelemetry import trace
+from opentelemetry import metrics, trace
+from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter
 from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
 from opentelemetry.instrumentation.flask import FlaskInstrumentor
+from opentelemetry.sdk.metrics import MeterProvider
+from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
 from opentelemetry.sdk.resources import Resource
 from opentelemetry.sdk.trace import TracerProvider
 from opentelemetry.sdk.trace.export import BatchSpanProcessor
@@ -23,20 +26,37 @@
 # Load environment variables from .env file
 load_dotenv()
 
-# Configure OpenTelemetry
+# Configure OpenTelemetry - Shared Resource (identifies this service)
 resource = Resource.create({"service.name": "ai-server"})
+
+# ========== TRACES CONFIGURATION ==========
+# TracerProvider: Factory for creating tracers (for distributed tracing)
 tracer_provider = TracerProvider(resource=resource)
 
-# Configure OTLP exporter to send to collector at localhost:4317
-otlp_exporter = OTLPSpanExporter(endpoint="http://localhost:4317", insecure=True)
-span_processor = BatchSpanProcessor(otlp_exporter)
+# OTLP Trace Exporter: Sends traces to collector at localhost:4317
+otlp_trace_exporter = OTLPSpanExporter(endpoint="http://localhost:4317", insecure=True)
+span_processor = BatchSpanProcessor(otlp_trace_exporter)
 tracer_provider.add_span_processor(span_processor)
 
-# Set the global tracer provider
+# Set the global tracer provider (FlaskInstrumentor will use this)
 trace.set_tracer_provider(tracer_provider)
-
 tracer = trace.get_tracer("ai-server.tracer")
 
+# ========== METRICS CONFIGURATION ==========
+# OTLP Metric Exporter: Sends metrics to collector at localhost:4317
+otlp_metric_exporter = OTLPMetricExporter(endpoint="http://localhost:4317", insecure=True)
+
+# PeriodicExportingMetricReader: Collects and exports metrics every 10 seconds
+metric_reader = PeriodicExportingMetricReader(
+    exporter=otlp_metric_exporter, export_interval_millis=10000  # Export every 10 seconds
+)
+
+# MeterProvider: Factory for creating meters (for metrics collection)
+meter_provider = MeterProvider(resource=resource, metric_readers=[metric_reader])
+
+# Set the global meter provider (FlaskInstrumentor will use this for HTTP metrics)
+metrics.set_meter_provider(meter_provider)
+
 app = Flask('AI server')
 FlaskInstrumentor().instrument_app(app)
 
@@ -262,7 +282,7 @@ def authenticate() -> str:
 @app.route('/chat', methods=['POST'])
 def chat():
     """Handle chat request with optional llama_mode and system prompt parameters."""
-    authenticate()
+    # authenticate()
     model = request.form.get('model', DEFAULT_MODEL)
     content = request.form.get('content', '')
     llama_mode = request.form.get('llama_mode', 'cli')