List Metrics

from datetime import datetime, timezone
from opperai import Opper
import time

opper = Opper(http_bearer="YOUR_API_KEY")

# First, create a span and add multiple metrics to list
created_span = opper.spans.create(
    name="model_performance_analysis",
    start_time=datetime.now(timezone.utc),
    type="evaluation",
    input="Analyze the performance of our latest model deployment",
    output="Model shows 15% improvement in accuracy, 20% reduction in latency, and high user satisfaction scores.",
    meta={"model_version": "v2.1", "deployment_id": "deploy_456"},
)

print(f"Created span with ID: {created_span.id}")

# Add multiple metrics to demonstrate the list functionality
metrics_to_add = [
    ("accuracy_score", 0.92, "Model accuracy on test dataset"),
    ("latency_ms", 340.5, "Average response latency in milliseconds"),
    ("user_satisfaction", 8.7, "User satisfaction score (1-10)"),
    ("throughput_rps", 125.3, "Requests per second throughput"),
    ("error_rate", 0.02, "Error rate percentage"),
]

for dimension, value, comment in metrics_to_add:
    opper.span_metrics.create_metric(
        span_id=created_span.id, dimension=dimension, value=value, comment=comment
    )

print("Added performance metrics to span")

# List all metrics for the span
metrics = opper.span_metrics.list(span_id=created_span.id, offset=0, limit=20)

print(f"Total metrics for span: {metrics.meta.total_count}")
print(f"Showing {len(metrics.data)} metrics:")
print("=" * 50)

for metric in metrics.data:
    print(f"Dimension: {metric.dimension}")
    print(f"Value: {metric.value}")
    if metric.comment:
        print(f"Comment: {metric.comment}")
    print(f"Created: {metric.created_at}")
    print("---")

# Calculate average quality scores
quality_metrics = [m for m in metrics.data if "quality" in m.dimension.lower()]
if quality_metrics:
    avg_quality = sum(m.value for m in quality_metrics) / len(quality_metrics)
    print(f"\nAverage quality score: {avg_quality:.2f}")

{
  "meta": {
    "total_count": 1
  },
  "data": [
    {
      "dimension": "<string>",
      "value": 123,
      "comment": "<string>",
      "id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
      "span_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
      "created_at": "2023-11-07T05:31:56Z"
    }
  ]
}