import requests
url = "https://api.keywordsai.co/api/logs/{log_id}/scores/{score_id}/"
api_key = "YOUR_KEY" # Replace with your actual Keywords AI API key
data = {
"numerical_value": 4.8,
"string_value": "Excellent quality"
}
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
response = requests.patch(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": 4.8,
"string_value": "Excellent quality",
"boolean_value": true,
"is_passed": false,
"cost": 0.0,
"evaluator_id": null,
"evaluator_slug": "quality_evaluator",
"log_id": "log_unique_id",
"dataset_id": null
}
import requests
url = "https://api.keywordsai.co/api/logs/{log_id}/scores/{score_id}/"
api_key = "YOUR_KEY" # Replace with your actual Keywords AI API key
data = {
"numerical_value": 4.8,
"string_value": "Excellent quality"
}
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
response = requests.patch(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": 4.8,
"string_value": "Excellent quality",
"boolean_value": true,
"is_passed": false,
"cost": 0.0,
"evaluator_id": null,
"evaluator_slug": "quality_evaluator",
"log_id": "log_unique_id",
"dataset_id": null
}
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import requests
url = "https://api.keywordsai.co/api/logs/{log_id}/scores/{score_id}/"
api_key = "YOUR_KEY" # Replace with your actual Keywords AI API key
data = {
"numerical_value": 4.8,
"string_value": "Excellent quality"
}
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
response = requests.patch(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": 4.8,
"string_value": "Excellent quality",
"boolean_value": true,
"is_passed": false,
"cost": 0.0,
"evaluator_id": null,
"evaluator_slug": "quality_evaluator",
"log_id": "log_unique_id",
"dataset_id": null
}