HYBIM-395 | Ensure metric attributes are present on errors (#25)

* Add error type attribute to metrics on failure in MetricsEmitter

* Add examples for LLM invocation and evaluation error attributes

* fix linting and type checking in metric emitter

Author: keith-decker

util/opentelemetry-util-genai/examples/evaluation_error_attr_output

@@ -0,0 +1,133 @@
+#!/usr/bin/env python3
+"""
+Example: Evaluation Error Example
+
+Install deepeval for evaluation:
+    pip install deepeval
+
+This example demonstrates:
+1. Successful LLM call, with a failed evaluation
+"""
+
+import os
+
+# overwrite OTEL_INSTRUMENTATION_GENAI_EVALS_EVALUATORS=deepeval
+os.environ["OTEL_INSTRUMENTATION_GENAI_EVALS_EVALUATORS"] = "deepeval"
+# set the openai key to an invalid key which will cause the evaluation to fail
+os.environ["OPENAI_API_KEY"] = "invalid_key"
+os.environ["OTEL_RESOURCE_ATTRIBUTES"] = (
+    "deployment.environment=example_evaluation_errors"
+)
+os.environ["OTEL_SERVICE_NAME"] = "demo-app-util-genai-dev"
+os.environ["OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT"] = "true"
+os.environ["OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT_MODE"] = (
+    "SPAN_AND_EVENT"
+)
+os.environ["OTEL_INSTRUMENTATION_GENAI_EVALS_RESULTS_AGGREGATION"] = "true"
+os.environ["OTEL_INSTRUMENTATION_GENAI_EMITTERS"] = "span_metric_event"
+
+import time
+
+from opentelemetry import _logs as logs
+from opentelemetry import metrics, trace
+from opentelemetry.sdk._logs import LoggerProvider
+from opentelemetry.sdk._logs.export import (
+    ConsoleLogExporter,
+    SimpleLogRecordProcessor,
+)
+from opentelemetry.sdk.metrics import MeterProvider
+from opentelemetry.sdk.metrics.export import (
+    ConsoleMetricExporter,
+    PeriodicExportingMetricReader,
+)
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import (
+    ConsoleSpanExporter,
+    SimpleSpanProcessor,
+)
+from opentelemetry.util.genai.handler import get_telemetry_handler
+from opentelemetry.util.genai.types import (
+    Error,
+    InputMessage,
+    LLMInvocation,
+    OutputMessage,
+    Text,
+)
+
+
+def setup_telemetry():
+    trace_provider = TracerProvider()
+    trace_provider.add_span_processor(
+        SimpleSpanProcessor(ConsoleSpanExporter())
+    )
+    trace.set_tracer_provider(trace_provider)
+
+    metric_reader = PeriodicExportingMetricReader(
+        ConsoleMetricExporter(), export_interval_millis=5000
+    )
+    meter_provider = MeterProvider(metric_readers=[metric_reader])
+    metrics.set_meter_provider(meter_provider)
+
+    logger_provider = LoggerProvider()
+    logger_provider.add_log_record_processor(
+        SimpleLogRecordProcessor(ConsoleLogExporter())
+    )
+    logs.set_logger_provider(logger_provider)
+
+    return trace_provider, meter_provider, logger_provider
+
+
+def successful_llm_call():
+    handler = get_telemetry_handler()
+    print("Starting successful LLM invocation...")
+    llm = LLMInvocation(
+        request_model="gpt-4",
+        input_messages=[
+            InputMessage(
+                role="user",
+                parts=[Text(content="Hello, how can I track my order?")],
+            ),
+        ],
+    )
+    handler.start_llm(llm)
+    time.sleep(0.1)
+    llm.output_messages = [
+        OutputMessage(
+            role="assistant",
+            parts=[
+                Text(
+                    content="You can track your order using the tracking link sent to your email."
+                )
+            ],
+            finish_reason="stop",
+        )
+    ]
+    llm.input_tokens = 12
+    llm.output_tokens = 18
+    handler.stop_llm(llm)
+    print("LLM invocation completed successfully.\n")
+
+
+def failed_llm_call():
+    handler = get_telemetry_handler()
+    print("Starting failed LLM invocation...")
+    llm = LLMInvocation(
+        request_model="gpt-4",
+        input_messages=[
+            InputMessage(
+                role="user",
+                parts=[Text(content="Tell me about the weather in Atlantis.")],
+            ),
+        ],
+    )
+    handler.start_llm(llm)
+    time.sleep(0.1)
+    error = Error(message="Model unavailable", type=RuntimeError)
+    handler.fail_llm(llm, error)
+    print("LLM invocation failed.\n")
+
+
+if __name__ == "__main__":
+    setup_telemetry()
+    successful_llm_call()
+    time.sleep(6)  # Wait for metrics export