Skip to main content
result = evaluator.evaluate(
    eval_templates="is_json",
    inputs={
        "text": '''{"name": "Alice", "age": 30, "is_member": true}'''
      },
    model_name="turing_flash"
)

print(result.eval_results[0].output)
print(result.eval_results[0].reason)
Input
Required InputTypeDescription
textstringThe provided content to be evaluated for JSON validity.
Output
FieldDescription
ResultReturns Passed if the provided content is valid JSON, or Failed if it’s not.
ReasonProvides a detailed explanation of the evaluation.
What to do when JSON Validation Fails Identify common structural problems, such as missing commas, misplaced brackets, or incorrect key-value formatting, should be corrected accordingly. To prevent future errors, implementing automated checks within the system can help detect and resolve formatting issues before processing.
Differentiating Between Is JSON Eval with JSON Schema Validation “Is JSON” evaluation focuses solely on structural validity, ensuring that data follows basic JSON syntax, such as correct use of brackets, commas, and quotes. In contrast, JSON Schema Validation goes beyond syntax by checking adherence to predefined schema rules, including data types, required fields, and value constraints. The “Is JSON” check is simpler, as it only requires parsing the data, whereas JSON Schema Validation is more complex, involving validation against a structured schema.
⌘I