Structured Outputs is different from OpenAI’s JSON Mode as well as Function Calling. Check out this table for a quick comparison.
Obiguard SDKs for Python and JavaScript also make it easy to define object schemas using Pydantic and Zod respectively. Below, you can see how to extract information from unstructured text that conforms to a schema defined in code. 
from obiguard import Obiguard
from pydantic import BaseModel

class Step(BaseModel):
    explanation: str
    output: str

class MathReasoning(BaseModel):
    steps: list[Step]
    final_answer: str

client = Obiguard(
    obiguard_api_key="OBIGUARD_API_KEY",
)

completion = client.beta.chat.completions.parse(
    model="gpt-4o-2024-08-06",
    messages=[
        {"role": "system", "content": "You are a helpful math tutor. Guide the user through the solution step by step."},
        {"role": "user", "content": "how can I solve 8x + 7 = -23"}
    ],
    response_format=MathReasoning,
)

print(completion.choices[0].message)
print(completion.choices[0].message.parsed)
The second approach, shown in the subsequent examples, uses a JSON schema directly in the API call. This method is more portable across different languages and doesn’t require additional libraries, but lacks the integrated type checking of the Pydantic/Zod approach. Choose the method that best fits your project’s needs and language ecosystem. 
from obiguard import Obiguard

client = Obiguard(
    obiguard_api_key="OBIGUARD_API_KEY",
)

completion = client.chat.completions.create(
    model="gpt-4o-2024-08-06",
    messages=[
        {"role": "system", "content": "Extract the event information."},
        {"role": "user", "content": "A meteor the size of 1000 football stadiums will hit earth this Sunday"},
    ],
    response_format={
      "type": "json_schema",
      "json_schema": {
        "name": "math_reasoning",
        "schema": {
          "type": "object",
          "properties": {
            "steps": {
              "type": "array",
              "items": {
                "type": "object",
                "properties": {
                  "explanation": { "type": "string" },
                  "output": { "type": "string" }
                },
                "required": ["explanation", "output"],
                "additionalProperties": False
              }
            },
            "final_answer": { "type": "string" }
          },
          "required": ["steps", "final_answer"],
          "additionalProperties": False
        },
        "strict": True
      }
    },
)

print(completion.choices[0].message.content)

​
Difference Between Structured Outputs, JSON Mode, and Function Calling

  • If you are connecting the model to tools, functions, data, etc. in your system, then you should use function calling.
  • And if you want to structure the model’s output when it responds to the user, then you should use a structured response_format.
For more, refer to OpenAI’s detailed documentation on Structured Outputs here.