test

python/samples/getting_started/observability/advanced_zero_code.py

@@ -0,0 +1,101 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+from random import randint
+from typing import TYPE_CHECKING, Annotated
+
+from agent_framework.observability import get_tracer
+from agent_framework.openai import OpenAIResponsesClient
+from opentelemetry.trace import SpanKind
+from opentelemetry.trace.span import format_trace_id
+from pydantic import Field
+
+if TYPE_CHECKING:
+    from agent_framework import ChatClientProtocol
+
+
+"""
+This sample shows how you can configure observability of an application with zero code changes.
+It relies on the OpenTelemetry auto-instrumentation capabilities, and the observability setup
+is done via environment variables.
+
+Follow the install guidance from https://opentelemetry.io/docs/zero-code/python/ to install the OpenTelemetry CLI tool.
+
+And setup a local OpenTelemetry Collector instance to receive the traces and metrics (and update the endpoint below).
+
+Then you can run:
+```bash
+opentelemetry-enable_instrumentation \
+    --traces_exporter otlp \
+    --metrics_exporter otlp \
+    --service_name agent_framework \
+    --exporter_otlp_endpoint http://localhost:4317 \
+    python samples/getting_started/observability/advanced_zero_code.py
+```
+(or use uv run in front when you have did the install within your uv virtual environment)
+
+You can also set the environment variables instead of passing them as CLI arguments.
+
+"""
+
+
+async def get_weather(
+    location: Annotated[str, Field(description="The location to get the weather for.")],
+) -> str:
+    """Get the weather for a given location."""
+    await asyncio.sleep(randint(0, 10) / 10.0)  # Simulate a network call
+    conditions = ["sunny", "cloudy", "rainy", "stormy"]
+    return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
+
+
+async def run_chat_client(client: "ChatClientProtocol", stream: bool = False) -> None:
+    """Run an AI service.
+
+    This function runs an AI service and prints the output.
+    Telemetry will be collected for the service execution behind the scenes,
+    and the traces will be sent to the configured telemetry backend.
+
+    The telemetry will include information about the AI service execution.
+
+    Args:
+        stream: Whether to use streaming for the plugin
+
+    Remarks:
+        When function calling is outside the open telemetry loop
+        each of the call to the model is handled as a seperate span,
+        while when the open telemetry is put last, a single span
+        is shown, which might include one or more rounds of function calling.
+
+        So for the scenario below, you should see the following:
+
+        2 spans with gen_ai.operation.name=chat
+            The first has finish_reason "tool_calls"
+            The second has finish_reason "stop"
+        2 spans with gen_ai.operation.name=execute_tool
+
+    """
+    message = "What's the weather in Amsterdam and in Paris?"
+    print(f"User: {message}")
+    if stream:
+        print("Assistant: ", end="")
+        async for chunk in client.get_streaming_response(message, tools=get_weather):
+            if str(chunk):
+                print(str(chunk), end="")
+        print("")
+    else:
+        response = await client.get_response(message, tools=get_weather)
+        print(f"Assistant: {response}")
+
+
+async def main() -> None:
+    with get_tracer().start_as_current_span("Zero Code", kind=SpanKind.CLIENT) as current_span:
+        print(f"Trace ID: {format_trace_id(current_span.get_span_context().trace_id)}")
+
+        client = OpenAIResponsesClient()
+
+        await run_chat_client(client, stream=True)
+        await run_chat_client(client, stream=False)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())