To send OpenTelemetry metrics and traces generated by ARMS from your AI Application to OpenObserve, follow the below steps.

1. Get your Credentials

1. Log into your OpenObserve instance
2. Navigate to Ingestion:
   • Click Ingestion in the side navigation
   • Select Traces (OpenTelemetry) option
3. Copy OTLP Configuration:
   • Under the OTLP HTTP section, copy:
     • Ingestion HTTP Endpoint
     • Ingestion Authorization key (the part after Basic)
   • Example endpoint: http://127.0.0.1:5080/api/default
   • Example auth: Authorization=Basic%20<Ingestion Authorization key>

2. Instrument your application

SDK

For direct integration into your Python applications:

Function Arguments

import elsai_arms

elsai_arms.init(
  otlp_endpoint="YOUR_OPENOBSERVE_HTTP_ENDPOINT",
  otlp_headers="YOUR_OPENOBSERVE_AUTH_HEADER"
)

Replace:

1. YOUR_OPENOBSERVE_HTTP_ENDPOINT with the Ingestion HTTP Endpoint value you copied in Step 1.
   • Example: http://127.0.0.1:5080/api/default
2. YOUR_OPENOBSERVE_AUTH_HEADER with the Ingestion Authorization key value you copied in Step 1.
   • Example: Authorization=Basic%20<Ingestion Authorization key>

Environment Variables

import elsai_arms

elsai_arms.init()

Set these environment variables:

export OTEL_EXPORTER_OTLP_ENDPOINT="YOUR_OPENOBSERVE_HTTP_ENDPOINT"
export OTEL_EXPORTER_OTLP_HEADERS="YOUR_OPENOBSERVE_AUTH_HEADER"

Replace:

1. YOUR_OPENOBSERVE_HTTP_ENDPOINT with the Ingestion HTTP Endpoint value you copied in Step 1.
   • Example: http://127.0.0.1:5080/api/default
2. YOUR_OPENOBSERVE_AUTH_HEADER with the Ingestion Authorization key value you copied in Step 1.
   • Example: Authorization=Basic%20<Ingestion Authorization key>

See the ARMS SDK configuration docs for more advanced options.

CLI

For zero-code auto-instrumentation via command line:

CLI Arguments

# Using CLI arguments
elsai-arms-instrument \
  --otlp-endpoint "YOUR_OPENOBSERVE_HTTP_ENDPOINT" \
  --otlp-headers "YOUR_OPENOBSERVE_AUTH_HEADER" \
  --service-name "my-ai-service" \
  --deployment-environment "production" \
  python app.py

Replace:

1. YOUR_OPENOBSERVE_HTTP_ENDPOINT with the Ingestion HTTP Endpoint value you copied in Step 1.
   • Example: http://127.0.0.1:5080/api/default
2. YOUR_OPENOBSERVE_AUTH_HEADER with the Ingestion Authorization key value you copied in Step 1.
   • Example: Authorization=Basic%20<Ingestion Authorization key>

Environment Variables

# Set environment variables (takes precedence over CLI args)
export OTEL_EXPORTER_OTLP_ENDPOINT="YOUR_OPENOBSERVE_HTTP_ENDPOINT"
export OTEL_EXPORTER_OTLP_HEADERS="YOUR_OPENOBSERVE_AUTH_HEADER"
export OTEL_SERVICE_NAME="my-ai-service"
export OTEL_DEPLOYMENT_ENVIRONMENT="production"

# Run your application
elsai-arms-instrument python app.py

Replace:

1. YOUR_OPENOBSERVE_HTTP_ENDPOINT with the Ingestion HTTP Endpoint value you copied in Step 1.
   • Example: http://127.0.0.1:5080/api/default
2. YOUR_OPENOBSERVE_AUTH_HEADER with the Ingestion Authorization key value you copied in Step 1.
   • Example: Authorization=Basic%20<Ingestion Authorization key>

See the ARMS SDK configuration docs for more advanced options.

3. Import the pre-built Dashboard

1. Log into your OpenObserve Instance
2. Navigate to Dashboards:
   • Click Dashboards in the side navigation menu
   • Click Import on the top right corner
3. Import Dashboard:
   • Copy the dashboard JSON provided in the accordion below
   • Paste the dashboard JSON text directly under the Import Dashboard from JSON section
   • Click Import
   • Save the dashboard

Dashboard

{
  "version": 4,
  "dashboardId": "7232616793020301352",
  "title": "GenAI Observability",
  "description": "This dashboard tracks the performance of LLM and VectorDB, recording metrics like latency, tokens used, and cost. It also logs input and output data, including request IDs, timestamps, user IDs, and model versions. This helps identify how well the systems are working and where improvements can be made.",
  "role": "",
  "owner": "",
  "created": "2024-08-23T05:46:38.299Z",
  "tabs": [
    {
      "tabId": "default",
      "name": "GenAI",
      "panels": [
        {
          "id": "Panel_ID4678610",
          "type": "metric",
          "title": "Total Successful GenAI Requests",
          "description": "",
          "config": {
            "show_legends": true,
            "legend_position": "bottom",
            "unit": "short",
            "unit_custom": "",
            "decimals": 2
          },
          "queryType": "promql",
          "queries": [
            {
              "query": "sum(gen_ai_total_requests{telemetry_sdk_name=\"elsai-arms\"})",
              "customQuery": true,
              "fields": {
                "stream": "default",
                "stream_type": "metrics",
                "x": [],
                "y": [],
                "z": [],
                "filter": []
              }
            }
          ],
          "layout": {
            "x": 0,
            "y": 0,
            "w": 8,
            "h": 6,
            "i": 0,
            "panelType": "metric"
          }
        },
        {
          "id": "Panel_ID8765432",
          "type": "metric",
          "title": "Total VectorDB Requests",
          "description": "",
          "config": {
            "show_legends": true,
            "legend_position": "bottom",
            "unit": "short",
            "unit_custom": "",
            "decimals": 2
          },
          "queryType": "promql",
          "queries": [
            {
              "query": "sum(db_total_requests{telemetry_sdk_name=\"elsai-arms\"})",
              "customQuery": true,
              "fields": {
                "stream": "default",
                "stream_type": "metrics",
                "x": [],
                "y": [],
                "z": [],
                "filter": []
              }
            }
          ],
          "layout": {
            "x": 8,
            "y": 0,
            "w": 8,
            "h": 6,
            "i": 1,
            "panelType": "metric"
          }
        }
      ]
    }
  ]
}