ntt-ai-observability-exporter 0.1.5

NTT AI Observability Exporter for Azure Monitor OpenTelemetry in AI Foundry projects

NTT AI Observability Exporter

A specialized telemetry exporter for NTT AI Foundry projects using Azure Monitor OpenTelemetry. This package simplifies telemetry setup for AI applications built with Azure services.

Features

• Automatic instrumentation of Azure SDK libraries
• Simplified configuration for Azure Monitor OpenTelemetry
• Specialized support for Semantic Kernel telemetry
• Compatible with Azure AI Foundry projects

Installation

# Using pip
pip install ntt-ai-observability-exporter

# Using uv
uv pip install ntt-ai-observability-exporter

Updating Your Package Documentation

Make sure to add a note in your package documentation (such as README.md) about the dependencies:

## Dependencies

This package depends on:
- azure-monitor-opentelemetry (>=1.0.0)
- opentelemetry-sdk (>=1.15.0)

These dependencies will be automatically installed when you install the package via pip.

```bash
# Using pip
pip install ntt-ai-observability-exporter

# Using uv
uv pip install ntt-ai-observability-exporter

Usage

Simple Usage - One Line Setup

from ntt_ai_observability_exporter import configure_telemetry

# That's it! This single line configures all telemetry
configure_telemetry()

# Now you can use your AI components normally - telemetry is automatic

Configuration Options

# Explicit configuration
configure_telemetry(
    connection_string="InstrumentationKey=your-key;IngestionEndpoint=your-endpoint",
    customer_name="your-customer",
    agent_name="your-agent"
)

What Gets Instrumented Automatically

The Azure Monitor OpenTelemetry package automatically instruments:

• Azure SDK libraries (including azure.ai.openai)
• HTTP client libraries (requests, aiohttp)

This means when you use Azure AI Foundry components, telemetry is captured without any additional code.

Configuration Parameters

• connection_string: Azure Monitor connection string
• customer_name: Maps to service.name in OpenTelemetry resource
• agent_name: Maps to service.instance.id in OpenTelemetry resource

Environment Variables

You can set these environment variables:

• AZURE_MONITOR_CONNECTION_STRING: The connection string for Azure Monitor
• CUSTOMER_NAME: Maps to service.name in OpenTelemetry resource
• AGENT_NAME: Maps to service.instance.id in OpenTelemetry resource

Telemetry Types Captured

The configuration captures:

• Traces: Request flows and operations
• Metrics: Performance measurements
• Logs: When integrated with Python logging

Example in Azure AI Foundry Project

# Import the NTT AI Observability Exporter
from ntt_ai_observability_exporter import configure_telemetry

# Configure telemetry with your project details
configure_telemetry(
    connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://westeurope-5.in.applicationinsights.azure.com/",
    customer_name="customer-name-foundry",
    agent_name="ai-foundry-agent"
)

# Now use Azure AI components as normal - telemetry is automatic
from azure.ai.assistant import AssistantClient

client = AssistantClient(...)
# All operations are automatically instrumented

Semantic Kernel Telemetry Support

For applications using Semantic Kernel, use the specialized configuration function:

from ntt_ai_observability_exporter import configure_semantic_kernel_telemetry

# Configure Semantic Kernel telemetry BEFORE creating any Kernel instances
configure_semantic_kernel_telemetry(
    connection_string="your_connection_string",
    customer_name="your_customer_name",
    agent_name="your_agent_name"
)

# Then create and use your Semantic Kernel
from semantic_kernel import Kernel
kernel = Kernel()
# ... rest of your code

Development and Testing

Installation for Development

Install the package with development dependencies:

# Install with development dependencies
pip install -e ".[dev]"

# Or install with just testing dependencies
pip install -e ".[test]"

# Or install from requirements-dev.txt
pip install -r requirements-dev.txt

Running Tests

# Run all tests
pytest

# Run tests with coverage
pytest --cov=src/ntt_ai_observability_exporter

# Run tests with detailed coverage report
pytest --cov=src/ntt_ai_observability_exporter --cov-report=term-missing

# Run specific test file
pytest tests/test_semantic_kernel_telemetry.py -v

Test Coverage

The package includes comprehensive unit tests with:

• 93% overall test coverage
• 100% coverage for semantic kernel telemetry module
• 17 test cases covering all functionality
• Validation of configuration, error handling, and Azure Monitor integration

Code Quality

The project uses:

• pytest for testing framework
• black for code formatting
• flake8 for linting
• mypy for type checking
• isort for import sorting