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Request Recompute Metrics

POST /api/v2/metrics/model/request_recompute_metrics​

Description​

The request_recompute_metrics endpoint initiates the re-computation of metrics for a specific Arcanna model. This endpoint is useful when you want to refresh model performance statistics after new training data has been added or when you suspect metrics may be outdated. The re-computation process runs asynchronously in the background.

Quick Start Examples​

Request metrics recomputation for a specific model​

curl -X POST "https://your.arcanna.ai/api/v2/metrics/model/request_recompute_metrics?job_id=1234&model_id=generic-1-1234-model-1747385873.hdf5" \
  -H "X-Arcanna-Api-Key: your-api-key-here" \
  -H "Content-Type: application/json"

Request Parameters​

Query Parameters​

ParameterTypeRequiredDescription
job_idinteger or stringYesThe ID or name of the job associated with the model
model_idstringYesThe unique identifier of the model to recompute metrics for

Response​

Success Response (200 OK)​

{
  "message": "Metrics recomputation initiated successfully",
  "job_id": 1234,
  "model_id": "generic-1-1234-model-1747385873.hdf5",
  "status": "queued",
  "requested_at": "2025-01-15T14:30:00Z"
}

Error Response (404 Not Found)​

{
  "error": "Model not found",
  "details": "Model 'generic-1-1234-model-1747385873.hdf5' not found for job '1234'"
}

Error Response (422 Validation Error)​

{
  "detail": [
    {
      "loc": ["query", "model_id"],
      "msg": "Model ID is required",
      "type": "value_error"
    }
  ]
}

Response Fields​

Success Response Fields​

  • message: Confirmation that the recomputation request was successful
  • job_id: The ID of the job associated with the model
  • model_id: The unique identifier of the model
  • status: Current status of the recomputation request ('queued', 'in_progress', 'completed', 'failed')
  • requested_at: Timestamp when the recomputation was requested

Important Notes​

Asynchronous Processing​

  • The metrics recomputation runs asynchronously in the background
  • The endpoint returns immediately after queuing the request
  • Use the Model Metrics endpoint to check the is_recomputing_metrics flag and last_recomputed_timestamp

When to Use​

  • After adding significant amounts of new training data
  • When model metrics appear outdated or inconsistent
  • Before important model evaluations or reports
  • After manual corrections to the knowledge base
  • As part of regular maintenance schedules

Use Cases​

  • Refresh model performance metrics after training data updates
  • Ensure accurate metrics before model deployment decisions
  • Maintain up-to-date performance statistics for reporting

Monitoring Recomputation Status​

You can monitor the status of metrics recomputation using the Model Metrics endpoint:

curl -X GET "https://your.arcanna.ai/api/v2/metrics/model?job_id=1234&model_id=generic-1-1234-model-1747385873.hdf5" \
  -H "X-Arcanna-Api-Key: your-api-key-here"

Check the is_recomputing_metrics field in the response:

  • true: Recomputation is currently in progress
  • false: Recomputation is complete or not running