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POST
/
api
/
logs
/
{log_id}
/
scores
/
import requests

url = "https://api.keywordsai.co/api/logs/{log_id}/scores/"
api_key = "YOUR_KEY" # Replace with your actual Keywords AI API key
data = {
    "evaluator_slug": "my_custom_evaluator",
    "numerical_value": 4.5,
    "string_value": "Good response quality",
    "boolean_value": True
}
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

{
  "id": "eval_result_unique_id",
  "created_at": "2024-01-15T10:30:00Z",
  "type": "llm",
  "environment": "test",
  "numerical_value": 4.5,
  "string_value": "Good response quality",
  "boolean_value": true,
  "is_passed": false,
  "cost": 0.0,
  "evaluator_id": null,
  "evaluator_slug": "my_custom_evaluator",
  "log_id": null,
  "dataset_id": null
}
Creates a new evaluation score for a specific log. This ensures that only one score exists per evaluator per log.
​
evaluator_id
string
UUID of evaluator created in Keywords AI. Either this or evaluator_slug must be provided.
​
evaluator_slug
string
Custom string identifier for your evaluator. Either this or evaluator_id must be provided.
​
numerical_value
number
Optional numerical score value.
​
string_value
string
Optional string score value.
​
boolean_value
boolean
Optional boolean score value.
​
categorical_value
array
Optional categorical score value (array of strings). Use when evaluator’s score_value_type is categorical.
import requests

url = "https://api.keywordsai.co/api/logs/{log_id}/scores/"
api_key = "YOUR_KEY" # Replace with your actual Keywords AI API key
data = {
    "evaluator_slug": "my_custom_evaluator",
    "numerical_value": 4.5,
    "string_value": "Good response quality",
    "boolean_value": True
}
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

{
  "id": "eval_result_unique_id",
  "created_at": "2024-01-15T10:30:00Z",
  "type": "llm",
  "environment": "test",
  "numerical_value": 4.5,
  "string_value": "Good response quality",
  "boolean_value": true,
  "is_passed": false,
  "cost": 0.0,
  "evaluator_id": null,
  "evaluator_slug": "my_custom_evaluator",
  "log_id": null,
  "dataset_id": null
}
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