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Structured Outputs ​

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Force Mercury 2 responses to conform to a specific JSON schema.

How It Works ​

Set response_format with type: "json_schema" and provide a strict schema definition.

Example: Sentiment Analysis ​

bash
curl https://api.inceptionlabs.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $INCEPTION_API_KEY" \
  -d '{
    "model": "mercury-2",
    "messages": [
      {"role": "user", "content": "Analyze the sentiment: I love this product!"}
    ],
    "response_format": {
      "type": "json_schema",
      "json_schema": {
        "name": "sentiment_analysis",
        "strict": true,
        "schema": {
          "type": "object",
          "properties": {
            "sentiment": {
              "type": "string",
              "enum": ["positive", "negative", "neutral"]
            },
            "confidence": {
              "type": "number",
              "minimum": 0,
              "maximum": 1
            },
            "key_phrases": {
              "type": "array",
              "items": {"type": "string"}
            }
          },
          "required": ["sentiment", "confidence", "key_phrases"]
        }
      }
    }
  }'

Python Example ​

python
import os
import requests
import json

response = requests.post(
    "https://api.inceptionlabs.ai/v1/chat/completions",
    headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {os.environ['INCEPTION_API_KEY']}"
    },
    json={
        "model": "mercury-2",
        "messages": [
            {"role": "user", "content": "Extract entities: Apple released the new MacBook Pro in Cupertino."}
        ],
        "response_format": {
            "type": "json_schema",
            "json_schema": {
                "name": "entity_extraction",
                "strict": True,
                "schema": {
                    "type": "object",
                    "properties": {
                        "entities": {
                            "type": "array",
                            "items": {
                                "type": "object",
                                "properties": {
                                    "name": {"type": "string"},
                                    "type": {"type": "string", "enum": ["person", "org", "location", "product"]}
                                },
                                "required": ["name", "type"]
                            }
                        }
                    },
                    "required": ["entities"]
                }
            }
        }
    }
)
result = json.loads(response.json()["choices"][0]["message"]["content"])
print(json.dumps(result, indent=2))

Schema Requirements ​

  • Set "strict": true in the schema definition
  • Define all required properties
  • Use standard JSON Schema types: string, number, boolean, array, object
  • Enums are supported for constrained values

See Also ​