Highlight.io

To send OpenTelemetry metrics and traces generated by Shield360 from your AI Application to Highlight.io, follow the below steps.

1. Get your Credentials

1. Sign in to your Highlight.io account
2. Navigate to Project Settings:
   • Go to your project dashboard
   • Click on Settings → Project Settings
3. Get your Project ID:
   • Copy your Project ID from the settings page
   • This will be used in the OTLP endpoint URL
4. Generate API Key (if needed):
   • Navigate to API Keys section
   • Generate a new API key for OpenTelemetry ingestion
   • Copy the API key for authentication

2. Instrument your application

SDK

For direct integration into your Python applications:

Function Arguments

import shield360

shield360.init(
  otlp_endpoint="https://otel.highlight.io:4318/v1/traces",
  otlp_headers="x-highlight-project=YOUR_PROJECT_ID"
)

Replace:

1. YOUR_PROJECT_ID with your Highlight.io Project ID from Step 1.
   • Example: x-highlight-project=1jdkoe52

Environment Variables

import shield360

shield360.init()

Set these environment variables:

export OTEL_EXPORTER_OTLP_ENDPOINT="https://otel.highlight.io:4318/v1/traces"
export OTEL_EXPORTER_OTLP_HEADERS="x-highlight-project=YOUR_PROJECT_ID"

Replace:

1. YOUR_PROJECT_ID with your Highlight.io Project ID from Step 1.
   • Example: x-highlight-project=1jdkoe52

Refer to the Shield360 SDK configuration reference for more advanced configurations and use cases.

CLI

For zero-code auto-instrumentation via command line:

CLI Arguments

# Using CLI arguments
shield360-instrument \
  --otlp-endpoint "https://otel.highlight.io:4318/v1/traces" \
  --otlp-headers "x-highlight-project=YOUR_PROJECT_ID" \
  --service-name "my-ai-service" \
  --deployment-environment "production" \
  python app.py

Replace:

1. YOUR_PROJECT_ID with your Highlight.io Project ID from Step 1.
   • Example: x-highlight-project=1jdkoe52

Environment Variables

# Set environment variables (takes precedence over CLI args)
export OTEL_EXPORTER_OTLP_ENDPOINT="https://otel.highlight.io:4318/v1/traces"
export OTEL_EXPORTER_OTLP_HEADERS="x-highlight-project=YOUR_PROJECT_ID"
export OTEL_SERVICE_NAME="my-ai-service"
export OTEL_DEPLOYMENT_ENVIRONMENT="production"

# Run your application
shield360-instrument python app.py

Replace:

1. YOUR_PROJECT_ID with your Highlight.io Project ID from Step 1.
   • Example: x-highlight-project=1jdkoe52

Refer to the Shield360 SDK configuration reference for more advanced configurations and use cases.

3. Visualize in Highlight.io

Once your LLM application is instrumented, you can explore the telemetry data in Highlight.io:

1. Navigate to Traces: Go to your Highlight.io project dashboard and click on Traces
2. Explore AI Operations: View your AI application traces including:
   • LLM request traces with detailed timing
   • Token usage and cost information
   • Vector database operations
   • Model performance analytics
   • Request/response payloads (if enabled)
3. Session Monitoring: Link traces to user sessions for full-stack observability
4. Error Tracking: Monitor and debug AI application errors and exceptions
5. Performance Analysis: Analyze latency, throughput, and resource usage

Your Shield360-instrumented AI applications will appear automatically in Highlight.io with comprehensive observability including LLM costs, token usage, model performance, and integration with your existing application monitoring.