Python: Telemetry and observability follow-up (#833)

* updated telemetry work * updated telemetry * slight improvement * updated tests * fixes for telemetry * fixes for mypy * added settings setup to runner to avoid error * streamline usage * updated tests * updated tests * further refinement * fix dumped item for otel * removed enable_workflow_otel * final fixes * final fixes * updated samples * removed exporters * fix tests * fixed last import' * fixed devui
2026-06-16 21:04:09 +08:00 · 2025-09-23 08:21:56 +02:00
parent f93f16a9ad
commit 2576e7a091
52 changed files with 1625 additions and 1586 deletions
@@ -0,0 +1,82 @@
+# Copyright (c) Microsoft. All rights reserved.
+# type: ignore
+import asyncio
+from random import randint
+from typing import TYPE_CHECKING, Annotated
+
+from agent_framework.openai import OpenAIResponsesClient
+from pydantic import Field
+
+if TYPE_CHECKING:
+    from agent_framework import ChatClientProtocol
+
+
+"""
+This is the simplest sample of using the Agent Framework with telemetry.
+
+This relies on the environment setting up the telemetry, you can test this with
+by navigating to this folder and running:
+uv run --env-file=zero_code.env opentelemetry-instrument python 01-zero_code.py
+
+Check the zero_code.env file for the settings used in this example and to adapt it to your environment.
+"""
+
+
+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:
+    client = OpenAIResponsesClient()
+
+    # Scenarios where telemetry is collected in the SDK, from the most basic to the most complex.
+    await run_chat_client(client, stream=True)
+    await run_chat_client(client, stream=False)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())