This sample project shows how a Python application can be configured to send Semantic Kernel telemetry to the Application Performance Management (APM) vendors of your choice.

In this sample, we provide options to send telemetry to Application Insights, Aspire Dashboard, and console output.

Note that it is also possible to use other Application Performance Management (APM) vendors. An example is Prometheus. Please refer to this link to learn more about exporters.

For more information, please refer to the following resources:

What to expect

The Semantic Kernel Python SDK is designed to efficiently generate comprehensive logs, traces, and metrics throughout the flow of function execution and model invocation. This allows you to effectively monitor your AI application's performance and accurately track token consumption.

Configuration

Required resources

OpenAI or Azure OpenAI

Optional resources

Dependencies

You will also need to install the following dependencies to your virtual environment to run this sample:

// For Azure ApplicationInsights/AzureMonitor
uv pip install azure-monitor-opentelemetry-exporter==1.0.0b24
// For OTLP endpoint
uv pip install opentelemetry-exporter-otlp-proto-grpc

Running the sample

Open a terminal and navigate to this folder: python/samples/demos/telemetry/. This is necessary for the .env file to be read correctly.
Create a .env file if one doesn't already exist in this folder. Please refer to the example file.

Note that TELEMETRY_SAMPLE_CONNECTION_STRING and OTLP_ENDPOINT are optional. If you don't configure them, everything will get outputted to the console.
Activate your python virtual environment, and then run python main.py.

This will output the Operation/Trace ID, which can be used later for filtering.

Scenarios

This sample is organized into scenarios where the kernel will generate useful telemetry data:

ai_service: This is when an AI service/connector is invoked directly (i.e. not via any kernel functions or prompts). Information about the call to the underlying model will be recorded.
kernel_function: This is when a kernel function is invoked. Information about the kernel function and the call to the underlying model will be recorded.
auto_function_invocation: This is when auto function invocation is triggered. Information about the auto function invocation loop, the kernel functions that are executed, and calls to the underlying model will be recorded.

By default, running python main.py will run all three scenarios. To run individual scenarios, use the --scenario command line argument. For example, python main.py --scenario ai_service. For more information, please run python main.py -h.

Application Insights/Azure Monitor

Logs and traces

Go to your Application Insights instance, click on Transaction search on the left menu. Use the operation id output by the program to search for the logs and traces associated with the operation. Click on any of the search result to view the end-to-end transaction details. Read more here.

Metrics

Running the application once will only generate one set of measurements (for each metrics). Run the application a couple times to generate more sets of measurements.

Note: Make sure not to run the program too frequently. Otherwise, you may get throttled.

Please refer to here on how to analyze metrics in Azure Monitor.

Aspire Dashboard

Make sure you have the dashboard running to receive telemetry data.

Once the the sample finishes running, navigate to http://localhost:18888 in a web browser to see the telemetry data. Follow the instructions here to authenticate to the dashboard and start exploring!

Console output

You won't have to deploy an Application Insights resource or install Docker to run Aspire Dashboard if you choose to inspect telemetry data in a console. However, it is difficult to navigate through all the spans and logs produced, so this method is only recommended when you are just getting started.

We recommend you to get started with the ai_service scenario as this generates the least amount of telemetry data. Below is similar to what you will see when you run python main.py --scenario ai_service:

{
    "name": "chat.completions gpt-4o",
    "context": {
        "trace_id": "0xbda1d9efcd65435653d18fa37aef7dd3",
        "span_id": "0xcd443e1917510385",
        "trace_state": "[]"
    },
    "kind": "SpanKind.INTERNAL",
    "parent_id": "0xeca0a2ca7b7a8191",
    "start_time": "2024-09-09T23:13:14.625156Z",
    "end_time": "2024-09-09T23:13:17.311909Z",
    "status": {
        "status_code": "UNSET"
    },
    "attributes": {
        "gen_ai.operation.name": "chat.completions",
        "gen_ai.system": "openai",
        "gen_ai.request.model": "gpt-4o",
        "gen_ai.response.id": "chatcmpl-A5hrG13nhtFsOgx4ziuoskjNscHtT",
        "gen_ai.response.finish_reason": "FinishReason.STOP",
        "gen_ai.response.prompt_tokens": 16,
        "gen_ai.response.completion_tokens": 28
    },
    "events": [
        {
            "name": "gen_ai.content.prompt",
            "timestamp": "2024-09-09T23:13:14.625156Z",
            "attributes": {
                "gen_ai.prompt": "[{\"role\": \"user\", \"content\": \"Why is the sky blue in one sentence?\"}]"
            }
        },
        {
            "name": "gen_ai.content.completion",
            "timestamp": "2024-09-09T23:13:17.311909Z",
            "attributes": {
                "gen_ai.completion": "[{\"role\": \"assistant\", \"content\": \"The sky appears blue because molecules in the Earth's atmosphere scatter shorter wavelengths of sunlight, such as blue, more effectively than longer wavelengths like red.\"}]"
            }
        }
    ],
    "links": [],
    "resource": {
        "attributes": {
            "telemetry.sdk.language": "python",
            "telemetry.sdk.name": "opentelemetry",
            "telemetry.sdk.version": "1.26.0",
            "service.name": "TelemetryExample"
        },
        "schema_url": ""
    }
}
{
    "name": "Scenario: AI Service",
    "context": {
        "trace_id": "0xbda1d9efcd65435653d18fa37aef7dd3",
        "span_id": "0xeca0a2ca7b7a8191",
        "trace_state": "[]"
    },
    "kind": "SpanKind.INTERNAL",
    "parent_id": "0x48af7ad55f2f64b5",
    "start_time": "2024-09-09T23:13:14.625156Z",
    "end_time": "2024-09-09T23:13:17.312910Z",
    "status": {
        "status_code": "UNSET"
    },
    "attributes": {},
    "events": [],
    "links": [],
    "resource": {
        "attributes": {
            "telemetry.sdk.language": "python",
            "telemetry.sdk.name": "opentelemetry",
            "telemetry.sdk.version": "1.26.0",
            "service.name": "TelemetryExample"
        },
        "schema_url": ""
    }
}
{
    "name": "main",
    "context": {
        "trace_id": "0xbda1d9efcd65435653d18fa37aef7dd3",
        "span_id": "0x48af7ad55f2f64b5",
        "trace_state": "[]"
    },
    "kind": "SpanKind.INTERNAL",
    "parent_id": null,
    "start_time": "2024-09-09T23:13:13.840481Z",
    "end_time": "2024-09-09T23:13:17.312910Z",
    "status": {
        "status_code": "UNSET"
    },
    "attributes": {},
    "events": [],
    "links": [],
    "resource": {
        "attributes": {
            "telemetry.sdk.language": "python",
            "telemetry.sdk.name": "opentelemetry",
            "telemetry.sdk.version": "1.26.0",
            "service.name": "TelemetryExample"
        },
        "schema_url": ""
    }
}
{
    "body": "OpenAI usage: CompletionUsage(completion_tokens=28, prompt_tokens=16, total_tokens=44)",
    "severity_number": "<SeverityNumber.INFO: 9>",
    "severity_text": "INFO",
    "attributes": {
        "code.filepath": "C:\\Users\\taochen\\Projects\\semantic-kernel-fork\\python\\semantic_kernel\\connectors\\ai\\open_ai\\services\\open_ai_handler.py",     
        "code.function": "store_usage",
        "code.lineno": 81
    },
    "dropped_attributes": 0,
    "timestamp": "2024-09-09T23:13:17.311909Z",
    "observed_timestamp": "2024-09-09T23:13:17.311909Z",
    "trace_id": "0xbda1d9efcd65435653d18fa37aef7dd3",
    "span_id": "0xcd443e1917510385",
    "trace_flags": 1,
    "resource": {
        "attributes": {
            "telemetry.sdk.language": "python",
            "telemetry.sdk.name": "opentelemetry",
            "telemetry.sdk.version": "1.26.0",
            "service.name": "TelemetryExample"
        },
        "schema_url": ""
    }
}

In the output, you will find three spans: main, Scenario: AI Service, and chat.completions gpt-4o, each representing a different layer in the sample. In particular, chat.completions gpt-4o is generated by the ai service. Inside it, you will find information about the call, such as the timestamp of the operation, the response id and the finish reason. You will also find sensitive information such as the prompt and response to and from the model (only if you have SEMANTICKERNEL_EXPERIMENTAL_GENAI_ENABLE_OTEL_DIAGNOSTICS_SENSITIVE set to true). If you use Application Insights or Aspire Dashboard, these information will be available to you in an interactive UI.