# 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
```bash
# 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:
```markdown
## 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
```python
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
```python
# 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
```python
# 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:
```python
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
```
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