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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# SPDX-License-Identifier: Apache-2.0
"""Azure telemetry sample - Application Insights tracing with Genkit.
This sample demonstrates how to export Genkit telemetry to Azure Application
Insights for distributed tracing, monitoring, and debugging.
Key Concepts (ELI5)::
┌─────────────────────┬────────────────────────────────────────────────────┐
│ Concept │ ELI5 Explanation │
├─────────────────────┼────────────────────────────────────────────────────┤
│ Application Insights│ Azure's monitoring service. Shows you what your │
│ │ app is doing - like a fitness tracker for code. │
├─────────────────────┼────────────────────────────────────────────────────┤
│ Connection String │ Your App Insights "address". Contains the key │
│ │ and endpoint to send traces to. │
├─────────────────────┼────────────────────────────────────────────────────┤
│ Traces │ Records of what happened during a request. │
│ │ Like breadcrumbs showing where your code went. │
├─────────────────────┼────────────────────────────────────────────────────┤
│ PII Redaction │ Model inputs/outputs are hidden by default. │
│ │ Your users' data stays private. │
└─────────────────────┴────────────────────────────────────────────────────┘
Data Flow::
┌─────────────────────────────────────────────────────────────────────────┐
│ AZURE TELEMETRY DATA FLOW │
│ │
│ Your Genkit App │
│ │ │
│ │ (1) You call a flow │
│ ▼ │
│ ┌─────────┐ ┌─────────┐ │
│ │say_hello│ ──▶ │ Gemini │ Each creates a "span" (timing record) │
│ │ (flow) │ │ (model) │ │
│ └─────────┘ └─────────┘ │
│ │ │ │
│ └───────┬───────┘ │
│ │ │
│ │ (2) Spans sent to Azure │
│ ▼ │
│ ┌─────────────────────────┐ │
│ │ Azure App Insights │ View in Azure Portal │
│ │ • Transaction search │ Debug performance issues │
│ │ • Application map │ See dependencies │
│ └─────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
Testing This Sample:
1. Set your Application Insights connection string:
export APPLICATIONINSIGHTS_CONNECTION_STRING="InstrumentationKey=..."
2. Set your Google AI API key:
export GOOGLE_GENAI_API_KEY="your-key"
3. Run the sample:
./run.sh
4. Open the DevUI at http://localhost:4000
5. Run the "say_hello" flow with a name
6. View traces in Azure Portal → Application Insights → Transaction search
"""
import asyncio
from pydantic import BaseModel, Field
from rich.traceback import install as install_rich_traceback
install_rich_traceback(show_locals=True, width=120, extra_lines=3)
from genkit.ai import Genkit
from genkit.plugins.azure import add_azure_telemetry
from genkit.plugins.google_genai import GoogleAI
# Configure Azure telemetry FIRST (before creating Genkit instance)
# Uses APPLICATIONINSIGHTS_CONNECTION_STRING environment variable
add_azure_telemetry()
# Initialize Genkit with Google AI
ai = Genkit(
plugins=[GoogleAI()],
model='googleai/gemini-2.0-flash',
)
class HelloInput(BaseModel):
"""Input for the say_hello flow."""
name: str = Field(default='World', description='Name to greet')
@ai.flow()
async def say_hello(input: HelloInput) -> str:
"""Say hello to someone.
This flow demonstrates Azure Application Insights tracing.
The request/response will be traced and visible in App Insights.
Args:
input: Contains the name to greet.
Returns:
A personalized greeting from the AI model.
"""
response = await ai.generate(prompt=f'Say a friendly hello to {input.name}!')
return response.text
async def main() -> None:
"""Run the sample and keep the server alive."""
await asyncio.Event().wait()
if __name__ == '__main__':
ai.run_main(main())
