Structured Outputs

Structured Output Schema

You can set the response_format parameter to your defined schema to ensure the model produces a JSON object that matches your specified structure.

import openai
import json

# Define the OpenAI client
client = openai.OpenAI(
    base_url="https://api.scx.ai/v1", 
    api_key="your-scx-api-key"
)

MODEL = "DeepSeek-V3.1"

# Define the schema
response_format = {
    "type": "json_schema",
    "json_schema": {
        "name": "data_extraction",
        "schema": {
            "type": "object",
            "properties": {
                "section": {
                    "type": "string"
                },
                "products": {
                    "type": "array",
                    "items": {
                        "type": "string"
                    }
                }
            },
            "required": ["section", "products"],
            "additionalProperties": False
        },
        "strict": False 
    }
}

# Call the API
completion = client.chat.completions.create(
    model=MODEL,
    messages=[
        {
            "role": "system",
            "content": "You are an expert at structured data extraction. You will be given unstructured text and should convert it into the given structure."
        },
        {
            "role": "user",
            "content": "the section 24 has appliances, and videogames"
        }
    ],
    response_format=response_format
)

# Print the parsed result
print(completion)

import openai
import json

# Define the OpenAI client
client = openai.OpenAI(
    base_url="https://api.scx.ai/v1", 
    api_key="your-scx-api-key"
)

MODEL = "DeepSeek-V3.1"

# Define the schema
response_format = {
    "type": "json_schema",
    "json_schema": {
        "name": "data_extraction",
        "schema": {
            "type": "object",
            "properties": {
                "section": {
                    "type": "string"
                },
                "products": {
                    "type": "array",
                    "items": {
                        "type": "string"
                    }
                }
            },
            "required": ["section", "products"],
            "additionalProperties": False
        },
        "strict": False 
    }
}

# Call the API
completion = client.chat.completions.create(
    model=MODEL,
    messages=[
        {
            "role": "system",
            "content": "You are an expert at structured data extraction. You will be given unstructured text and should convert it into the given structure."
        },
        {
            "role": "user",
            "content": "the section 24 has appliances, and videogames"
        }
    ],
    response_format=response_format
)

# Print the parsed result
print(completion)

Note

Ensure to set the "strict" parameter to false, as true isn’t supported yet. When it is available, it will ensure the model strictly follows your function schema instead of making a best-effort attempt.

JSON mode

You can set the response_format parameter to json_object in your request to ensure that the model outputs a valid JSON. In case the mode is not able to generate a valid JSON, an error will be returned.

Note

In case the model fails to generate a valid JSON, you will get an error message Model did not output valid JSON.

import openai

# Define the OpenAI client
client = openai.OpenAI(
    base_url="https://api.scx.ai/v1", 
    api_key="your-scx-api-key"
)

MODEL = 'Meta-Llama-3.3-70B-Instruct'


def run_conversation(user_prompt):
    # Initial conversation with user input
    messages = [
        {
            "role": "system",
            "content": "Always provide the response in this JSON format: {\"country\": \"name\", \"capital\": \"xx\"}"
        },

        {
            "role": "user",
            "content": user_prompt,
        }
    ]

    # First API call to get model's response
    response = client.chat.completions.create(
        model=MODEL,
        messages=messages,
        max_tokens=500,
        response_format = { "type": "json_object"},
        # stream = True
    )
    
    response_message = response.choices[0].message
    print(response_message)


run_conversation('what is the capital of Austria')

import openai

# Define the OpenAI client
client = openai.OpenAI(
    base_url="https://api.scx.ai/v1", 
    api_key="your-scx-api-key"
)

MODEL = 'Meta-Llama-3.3-70B-Instruct'


def run_conversation(user_prompt):
    # Initial conversation with user input
    messages = [
        {
            "role": "system",
            "content": "Always provide the response in this JSON format: {\"country\": \"name\", \"capital\": \"xx\"}"
        },

        {
            "role": "user",
            "content": user_prompt,
        }
    ]

    # First API call to get model's response
    response = client.chat.completions.create(
        model=MODEL,
        messages=messages,
        max_tokens=500,
        response_format = { "type": "json_object"},
        # stream = True
    )
    
    response_message = response.choices[0].message
    print(response_message)


run_conversation('what is the capital of Austria')

javascript

ChatCompletionMessage(content='{"country": "Austria", "capital": "Vienna"}', role='assistant', function_call=None, tool_calls=None)

ChatCompletionMessage(content='{"country": "Austria", "capital": "Vienna"}', role='assistant', function_call=None, tool_calls=None)

Other methods of structured outputs

Beyond JSON mode, structured outputs can be generated using the Instructor library. Learn more on the Instructor integration page.