JSON is one of the most widely used formats in the world for applications to exchange data.
Structured Outputs is a feature that ensures the model will always generate responses that adhere to your supplied JSON Schema, so you don’t need to worry about the model omitting a required key, or hallucinating an invalid enum value.
Some benefits of Structured Outputs include:
- Reliable type-safety: No need to validate or retry incorrectly formatted responses
- Explicit refusals: Safety-based model refusals are now programmatically detectable
- Simpler prompting: No need for strongly worded prompts to achieve consistent formatting
In addition to supporting JSON Schema in the REST API, the OpenAI 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.
Getting a structured response
Python
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25import OpenAI from "openai";
import { zodResponseFormat } from "openai/helpers/zod";
import { z } from "zod";
const openai = new OpenAI();
const CalendarEvent = z.object({
name: z.string(),
date: z.string(),
participants: z.array(z.string()),
});
const completion = await openai.chat.completions.parse({
model: "gpt-5.6",
messages: [
{ role: "system", content: "Extract the event information." },
{
role: "user",
content: "Alice and Bob are going to a science fair on Friday.",
},
],
response_format: zodResponseFormat(CalendarEvent, "event"),
});
const event = completion.choices[0].message.parsed;1
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25from pydantic import BaseModel
from openai import OpenAI
client = OpenAI()
class CalendarEvent(BaseModel):
name: str
date: str
participants: list[str]
completion = client.chat.completions.parse(
model="gpt-5.6",
messages=[
{"role": "system", "content": "Extract the event information."},
{
"role": "user",
"content": "Alice and Bob are going to a science fair on Friday.",
},
],
response_format=CalendarEvent,
)
event = completion.choices[0].message.parsed1
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41package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/shared"
)
func main() {
client := openai.NewClient()
schema := map[string]any{
"type": "object",
"properties": map[string]any{
"name": map[string]any{"type": "string"},
"date": map[string]any{"type": "string"},
"participants": map[string]any{"type": "array", "items": map[string]any{"type": "string"}},
},
"required": []string{"name", "date", "participants"},
"additionalProperties": false,
}
completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
Model: "gpt-5.6",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.SystemMessage("Extract the event information."),
openai.UserMessage("Alice and Bob are going to a science fair on Friday."),
},
ResponseFormat: openai.ChatCompletionNewParamsResponseFormatUnion{
OfJSONSchema: &shared.ResponseFormatJSONSchemaParam{JSONSchema: shared.ResponseFormatJSONSchemaJSONSchemaParam{
Name: "event", Schema: schema, Strict: openai.Bool(true),
}},
},
})
if err != nil {
panic(err)
}
fmt.Println(completion.Choices[0].Message.Content)
}1
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39import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.List;
import java.util.Map;
Map<String, Object> schema =
Map.of(
"type",
"object",
"properties",
Map.of(
"name", Map.of("type", "string"),
"date", Map.of("type", "string"),
"participants", Map.of("type", "array", "items", Map.of("type", "string"))),
"required",
List.of("name", "date", "participants"),
"additionalProperties",
false);
ChatCompletionCreateParams params =
ChatCompletionCreateParams.builder()
.model("gpt-5.6")
.addSystemMessage("Extract the event information.")
.addUserMessage("Alice and Bob are going to a science fair on Friday.")
.putAdditionalBodyProperty(
"response_format",
JsonValue.from(
Map.of(
"type",
"json_schema",
"json_schema",
Map.of("name", "event", "strict", true, "schema", schema))))
.build();
client.chat().completions().create(params).choices().stream()
.flatMap(choice -> choice.message().content().stream())
.forEach(System.out::println);1
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27require "openai"
client = OpenAI::Client.new
event_schema = {
type: :object,
properties: {
name: {type: :string},
date: {type: :string},
participants: {type: :array, items: {type: :string}}
},
required: %w[name date participants],
additionalProperties: false
}
completion = client.chat.completions.create(
model: "gpt-5.6",
messages: [
{role: :system, content: "Extract the event information."},
{role: :user, content: "Alice and Bob are going to a science fair on Friday."}
],
response_format: {
type: :json_schema,
json_schema: {name: "event", strict: true, schema: event_schema}
}
)
puts(completion.choices.fetch(0).message.content)Getting a structured response
Python
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27import OpenAI from "openai";
import { zodTextFormat } from "openai/helpers/zod";
import { z } from "zod";
const openai = new OpenAI();
const CalendarEvent = z.object({
name: z.string(),
date: z.string(),
participants: z.array(z.string()),
});
const response = await openai.responses.parse({
model: "gpt-5.6",
input: [
{ role: "system", content: "Extract the event information." },
{
role: "user",
content: "Alice and Bob are going to a science fair on Friday.",
},
],
text: {
format: zodTextFormat(CalendarEvent, "event"),
},
});
const event = response.output_parsed;1
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25from openai import OpenAI
from pydantic import BaseModel
client = OpenAI()
class CalendarEvent(BaseModel):
name: str
date: str
participants: list[str]
response = client.responses.parse(
model="gpt-5.6",
input=[
{"role": "system", "content": "Extract the event information."},
{
"role": "user",
"content": "Alice and Bob are going to a science fair on Friday.",
},
],
text_format=CalendarEvent,
)
event = response.output_parsed1
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45package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/responses"
)
func main() {
client := openai.NewClient()
schema := map[string]any{
"type": "object",
"properties": map[string]any{
"name": map[string]any{"type": "string"},
"date": map[string]any{"type": "string"},
"participants": map[string]any{"type": "array", "items": map[string]any{"type": "string"}},
},
"required": []string{"name", "date", "participants"},
"additionalProperties": false,
}
response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
Model: "gpt-5.6",
Input: responses.ResponseNewParamsInputUnion{OfInputItemList: responses.ResponseInputParam{
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("Extract the event information.")},
responses.EasyInputMessageRoleSystem,
),
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("Alice and Bob are going to a science fair on Friday.")},
responses.EasyInputMessageRoleUser,
),
}},
Text: responses.ResponseTextConfigParam{Format: responses.ResponseFormatTextConfigUnionParam{
OfJSONSchema: &responses.ResponseFormatTextJSONSchemaConfigParam{Name: "event", Schema: schema, Strict: openai.Bool(true)},
}},
})
if err != nil {
panic(err)
}
fmt.Println(response.OutputText())
}1
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58import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.responses.EasyInputMessage;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.ResponseFormatTextJsonSchemaConfig;
import com.openai.models.responses.ResponseInputItem;
import com.openai.models.responses.ResponseTextConfig;
import java.util.List;
import java.util.Map;
Map<String, Object> schema =
Map.of(
"type",
"object",
"properties",
Map.of(
"name", Map.of("type", "string"),
"date", Map.of("type", "string"),
"participants", Map.of("type", "array", "items", Map.of("type", "string"))),
"required",
List.of("name", "date", "participants"),
"additionalProperties",
false);
ResponseCreateParams params =
ResponseCreateParams.builder()
.model("gpt-5.6")
.inputOfResponse(
List.of(
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.SYSTEM)
.content("Extract the event information.")
.build()),
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.USER)
.content("Alice and Bob are going to a science fair on Friday.")
.build())))
.text(
ResponseTextConfig.builder()
.format(
ResponseFormatTextJsonSchemaConfig.builder()
.name("event")
.strict(true)
.schema(
JsonValue.from(schema)
.convert(ResponseFormatTextJsonSchemaConfig.Schema.class))
.build())
.build())
.build();
client.responses().create(params).output().stream()
.flatMap(item -> item.message().stream())
.flatMap(message -> message.content().stream())
.flatMap(content -> content.outputText().stream())
.forEach(text -> System.out.println(text.text()));1
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31require "openai"
client = OpenAI::Client.new
event_schema = {
type: :object,
properties: {
name: {type: :string},
date: {type: :string},
participants: {type: :array, items: {type: :string}}
},
required: %w[name date participants],
additionalProperties: false
}
response = client.responses.create(
model: "gpt-5.6",
input: [
{role: :system, content: "Extract the event information."},
{role: :user, content: "Alice and Bob are going to a science fair on Friday."}
],
text: {
format: {
type: :json_schema,
name: "event",
strict: true,
schema: event_schema
}
}
)
puts(response.output_text)Supported models
Structured Outputs is available in our latest large language models, starting with GPT-4o. For new projects, start with gpt-5.6. Older models like gpt-4-turbo and earlier may use JSON mode instead.
When to use Structured Outputs via function calling vs via response_format
When to use Structured Outputs via function calling vs via text.format
Structured Outputs is available in two forms in the OpenAI API:
- When using function calling
- When using a
json_schemaresponse format
Function calling is useful when you are building an application that bridges the models and functionality of your application.
For example, you can give the model access to functions that query a database in order to build an AI assistant that can help users with their orders, or functions that can interact with the UI.
Conversely, Structured Outputs via response_format are more suitable when you want to indicate a structured schema for use when the model responds to the user, rather than when the model calls a tool.
For example, if you are building a math tutoring application, you might want the assistant to respond to your user using a specific JSON Schema so that you can generate a UI that displays different parts of the model’s output in distinct ways.
Put simply:
- If you are connecting the model to tools, functions, data, etc. in your
system, then you should use function calling - If you want to structure the
model’s output when it responds to the user, then you should use a structured
response_format
- If you are connecting the model to tools, functions, data, etc. in your
system, then you should use function calling - If you want to structure the
model’s output when it responds to the user, then you should use a structured
text.format
The remainder of this guide will focus on non-function calling use cases in the Chat Completions API. To learn more about how to use Structured Outputs with function calling, check out the
Function Calling
guide.
The remainder of this guide will focus on non-function calling use cases in the Responses API. To learn more about how to use Structured Outputs with function calling, check out the
Function Calling
guide.
Structured Outputs vs JSON mode
Structured Outputs is the evolution of JSON mode. While both ensure valid JSON is produced, only Structured Outputs ensure schema adherence. Both Structured Outputs and JSON mode are supported in the Responses API, Chat Completions API, Assistants API, Fine-tuning API and Batch API.
We recommend always using Structured Outputs instead of JSON mode when possible.
However, Structured Outputs with response_format: {type: "json_schema", ...} is only supported with the gpt-4o-mini, gpt-4o-mini-2024-07-18, and gpt-4o-2024-08-06 model snapshots and later.
| Structured Outputs | JSON Mode | |
|---|---|---|
| Outputs valid JSON | Yes | Yes |
| Adheres to schema | Yes (see supported schemas) | No |
| Compatible models | gpt-4o-mini, gpt-4o-2024-08-06, and later | gpt-3.5-turbo, gpt-4-*, gpt-4o-*, and compatible GPT-5 models |
| Enabling | response_format: { type: "json_schema", json_schema: {"strict": true, "schema": ...} } | response_format: { type: "json_object" } |
| Structured Outputs | JSON Mode | |
|---|---|---|
| Outputs valid JSON | Yes | Yes |
| Adheres to schema | Yes (see supported schemas) | No |
| Compatible models | gpt-4o-mini, gpt-4o-2024-08-06, and later | gpt-3.5-turbo, gpt-4-*, gpt-4o-*, and compatible GPT-5 models |
| Enabling | text: { format: { type: "json_schema", "strict": true, "schema": ... } } | text: { format: { type: "json_object" } } |
Examples
Chain of thought
Chain of thought
You can ask the model to output an answer in a structured, step-by-step way, to guide the user through the solution.
Structured Outputs for chain-of-thought math tutoring
Python
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30import OpenAI from "openai";
import { z } from "zod";
import { zodResponseFormat } from "openai/helpers/zod";
const openai = new OpenAI();
const Step = z.object({
explanation: z.string(),
output: z.string(),
});
const MathReasoning = z.object({
steps: z.array(Step),
final_answer: z.string(),
});
const completion = await openai.chat.completions.parse({
model: "gpt-5.6",
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: zodResponseFormat(MathReasoning, "math_reasoning"),
});
const math_reasoning = completion.choices[0].message.parsed;1
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29from pydantic import BaseModel
from openai import OpenAI
client = OpenAI()
class Step(BaseModel):
explanation: str
output: str
class MathReasoning(BaseModel):
steps: list[Step]
final_answer: str
completion = client.chat.completions.parse(
model="gpt-5.6",
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,
)
math_reasoning = completion.choices[0].message.parsed1
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49package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/shared"
)
func main() {
client := openai.NewClient()
step := map[string]any{
"type": "object",
"properties": map[string]any{
"explanation": map[string]any{"type": "string"},
"output": map[string]any{"type": "string"},
},
"required": []string{"explanation", "output"},
"additionalProperties": false,
}
schema := map[string]any{
"type": "object",
"properties": map[string]any{
"steps": map[string]any{"type": "array", "items": step},
"final_answer": map[string]any{"type": "string"},
},
"required": []string{"steps", "final_answer"},
"additionalProperties": false,
}
completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
Model: "gpt-5.6",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.SystemMessage("You are a helpful math tutor. Guide the user through the solution step by step."),
openai.UserMessage("how can I solve 8x + 7 = -23"),
},
ResponseFormat: openai.ChatCompletionNewParamsResponseFormatUnion{
OfJSONSchema: &shared.ResponseFormatJSONSchemaParam{JSONSchema: shared.ResponseFormatJSONSchemaJSONSchemaParam{
Name: "math_reasoning", Schema: schema, Strict: openai.Bool(true),
}},
},
})
if err != nil {
panic(err)
}
fmt.Println(completion.Choices[0].Message.Content)
}1
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54import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.List;
import java.util.Map;
Map<String, Object> schema =
Map.of(
"type",
"object",
"properties",
Map.of(
"steps",
Map.of(
"type",
"array",
"items",
Map.of(
"type",
"object",
"properties",
Map.of(
"explanation", Map.of("type", "string"),
"output", Map.of("type", "string")),
"required",
List.of("explanation", "output"),
"additionalProperties",
false)),
"final_answer", Map.of("type", "string")),
"required",
List.of("steps", "final_answer"),
"additionalProperties",
false);
ChatCompletionCreateParams params =
ChatCompletionCreateParams.builder()
.model("gpt-5.6")
.addSystemMessage(
"You are a helpful math tutor. Guide the user through the solution step by step.")
.addUserMessage("How can I solve 8x + 7 = -23?")
.putAdditionalBodyProperty(
"response_format",
JsonValue.from(
Map.of(
"type",
"json_schema",
"json_schema",
Map.of("name", "math_reasoning", "strict", true, "schema", schema))))
.build();
client.chat().completions().create(params).choices().stream()
.flatMap(choice -> choice.message().content().stream())
.forEach(System.out::println);1
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38require "openai"
client = OpenAI::Client.new
step_schema = {
type: :object,
properties: {
explanation: {type: :string},
output: {type: :string}
},
required: %w[explanation output],
additionalProperties: false
}
math_schema = {
type: :object,
properties: {
steps: {type: :array, items: step_schema},
final_answer: {type: :string}
},
required: %w[steps final_answer],
additionalProperties: false
}
completion = client.chat.completions.create(
model: "gpt-5.6",
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: {
type: :json_schema,
json_schema: {name: "math_reasoning", strict: true, schema: math_schema}
}
)
puts(completion.choices.fetch(0).message.content)1
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43curl https://api.openai.com/v1/chat/completions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.6",
"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": {
"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
}
}
}'Structured Outputs for chain-of-thought math tutoring
Python
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32import OpenAI from "openai";
import { zodTextFormat } from "openai/helpers/zod";
import { z } from "zod";
const openai = new OpenAI();
const Step = z.object({
explanation: z.string(),
output: z.string(),
});
const MathReasoning = z.object({
steps: z.array(Step),
final_answer: z.string(),
});
const response = await openai.responses.parse({
model: "gpt-5.6",
input: [
{
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" },
],
text: {
format: zodTextFormat(MathReasoning, "math_reasoning"),
},
});
const math_reasoning = response.output_parsed;1
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29from openai import OpenAI
from pydantic import BaseModel
client = OpenAI()
class Step(BaseModel):
explanation: str
output: str
class MathReasoning(BaseModel):
steps: list[Step]
final_answer: str
response = client.responses.parse(
model="gpt-5.6",
input=[
{
"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"},
],
text_format=MathReasoning,
)
math_reasoning = response.output_parsed1
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53package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/responses"
)
func main() {
client := openai.NewClient()
step := map[string]any{
"type": "object",
"properties": map[string]any{
"explanation": map[string]any{"type": "string"},
"output": map[string]any{"type": "string"},
},
"required": []string{"explanation", "output"},
"additionalProperties": false,
}
schema := map[string]any{
"type": "object",
"properties": map[string]any{
"steps": map[string]any{"type": "array", "items": step},
"final_answer": map[string]any{"type": "string"},
},
"required": []string{"steps", "final_answer"},
"additionalProperties": false,
}
response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
Model: "gpt-5.6",
Input: responses.ResponseNewParamsInputUnion{OfInputItemList: responses.ResponseInputParam{
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("You are a helpful math tutor. Guide the user through the solution step by step.")},
responses.EasyInputMessageRoleSystem,
),
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("how can I solve 8x + 7 = -23")},
responses.EasyInputMessageRoleUser,
),
}},
Text: responses.ResponseTextConfigParam{Format: responses.ResponseFormatTextConfigUnionParam{
OfJSONSchema: &responses.ResponseFormatTextJSONSchemaConfigParam{Name: "math_reasoning", Schema: schema, Strict: openai.Bool(true)},
}},
})
if err != nil {
panic(err)
}
fmt.Println(response.OutputText())
}1
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73import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.responses.EasyInputMessage;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.ResponseFormatTextJsonSchemaConfig;
import com.openai.models.responses.ResponseInputItem;
import com.openai.models.responses.ResponseTextConfig;
import java.util.List;
import java.util.Map;
Map<String, Object> schema =
Map.of(
"type",
"object",
"properties",
Map.of(
"steps",
Map.of(
"type",
"array",
"items",
Map.of(
"type",
"object",
"properties",
Map.of(
"explanation", Map.of("type", "string"),
"output", Map.of("type", "string")),
"required",
List.of("explanation", "output"),
"additionalProperties",
false)),
"final_answer", Map.of("type", "string")),
"required",
List.of("steps", "final_answer"),
"additionalProperties",
false);
ResponseCreateParams params =
ResponseCreateParams.builder()
.model("gpt-5.6")
.inputOfResponse(
List.of(
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.SYSTEM)
.content(
"You are a helpful math tutor. Guide the user through the solution step by step.")
.build()),
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.USER)
.content("How can I solve 8x + 7 = -23?")
.build())))
.text(
ResponseTextConfig.builder()
.format(
ResponseFormatTextJsonSchemaConfig.builder()
.name("math_reasoning")
.strict(true)
.schema(
JsonValue.from(schema)
.convert(ResponseFormatTextJsonSchemaConfig.Schema.class))
.build())
.build())
.build();
client.responses().create(params).output().stream()
.flatMap(item -> item.message().stream())
.flatMap(message -> message.content().stream())
.flatMap(content -> content.outputText().stream())
.forEach(text -> System.out.println(text.text()));1
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42require "openai"
client = OpenAI::Client.new
step_schema = {
type: :object,
properties: {
explanation: {type: :string},
output: {type: :string}
},
required: %w[explanation output],
additionalProperties: false
}
math_schema = {
type: :object,
properties: {
steps: {type: :array, items: step_schema},
final_answer: {type: :string}
},
required: %w[steps final_answer],
additionalProperties: false
}
response = client.responses.create(
model: "gpt-5.6",
input: [
{
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?"}
],
text: {
format: {
type: :json_schema,
name: "math_reasoning",
strict: true,
schema: math_schema
}
}
)
puts(response.output_text)1
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43curl https://api.openai.com/v1/responses \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.6",
"input": [
{
"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"
}
],
"text": {
"format": {
"type": "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
}
}
}'Example response
{
"steps": [
{
"explanation": "Start with the equation 8x + 7 = -23.",
"output": "8x + 7 = -23"
},
{
"explanation": "Subtract 7 from both sides to isolate the term with the variable.",
"output": "8x = -23 - 7"
},
{
"explanation": "Simplify the right side of the equation.",
"output": "8x = -30"
},
{
"explanation": "Divide both sides by 8 to solve for x.",
"output": "x = -30 / 8"
},
{
"explanation": "Simplify the fraction.",
"output": "x = -15 / 4"
}
],
"final_answer": "x = -15 / 4"
}Structured data extraction
Structured data extraction
You can define structured fields to extract from unstructured input data, such as research papers.
Extracting data from research papers using Structured Outputs
Python
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30import OpenAI from "openai";
import { z } from "zod";
import { zodResponseFormat } from "openai/helpers/zod";
const openai = new OpenAI();
const ResearchPaperExtraction = z.object({
title: z.string(),
authors: z.array(z.string()),
abstract: z.string(),
keywords: z.array(z.string()),
});
const completion = await openai.chat.completions.parse({
model: "gpt-5.6",
messages: [
{
role: "system",
content:
"You are an expert at structured data extraction. You will be given unstructured text from a research paper and should convert it into the given structure.",
},
{ role: "user", content: "..." },
],
response_format: zodResponseFormat(
ResearchPaperExtraction,
"research_paper_extraction"
),
});
const research_paper = completion.choices[0].message.parsed;1
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36from pydantic import BaseModel
from openai import OpenAI
client = OpenAI()
class ResearchPaperExtraction(BaseModel):
title: str
authors: list[str]
abstract: str
keywords: list[str]
completion = client.chat.completions.parse(
model="gpt-5.6",
messages=[
{
"role": "system",
"content": "You are an expert at structured data extraction. You will be given unstructured text from a research paper and should convert it into the given structure.",
},
{
"role": "user",
"content": (
"Attention Is All You Need by Ashish Vaswani, Noam Shazeer, "
"Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, "
"Łukasz Kaiser, and Illia Polosukhin. We propose the "
"Transformer, a sequence transduction architecture based "
"entirely on attention. Keywords: transformers, attention, "
"sequence transduction."
),
},
],
response_format=ResearchPaperExtraction,
)
research_paper = completion.choices[0].message.parsed1
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48package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/shared"
)
const researchPaperText = "Attention Is All You Need by Ashish Vaswani, Noam Shazeer, " +
"Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, " +
"Łukasz Kaiser, and Illia Polosukhin. We propose the Transformer, " +
"a sequence transduction architecture based entirely on attention. " +
"Keywords: transformers, attention, sequence transduction."
func main() {
client := openai.NewClient()
schema := map[string]any{
"type": "object",
"properties": map[string]any{
"title": map[string]any{"type": "string"},
"authors": map[string]any{"type": "array", "items": map[string]any{"type": "string"}},
"abstract": map[string]any{"type": "string"},
"keywords": map[string]any{"type": "array", "items": map[string]any{"type": "string"}},
},
"required": []string{"title", "authors", "abstract", "keywords"},
"additionalProperties": false,
}
completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
Model: "gpt-5.6",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.SystemMessage("You are an expert at structured data extraction. You will be given unstructured text from a research paper and should convert it into the given structure."),
openai.UserMessage(researchPaperText),
},
ResponseFormat: openai.ChatCompletionNewParamsResponseFormatUnion{
OfJSONSchema: &shared.ResponseFormatJSONSchemaParam{JSONSchema: shared.ResponseFormatJSONSchemaJSONSchemaParam{
Name: "research_paper_extraction", Schema: schema, Strict: openai.Bool(true),
}},
},
})
if err != nil {
panic(err)
}
fmt.Println(completion.Choices[0].Message.Content)
}1
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54import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.List;
import java.util.Map;
Map<String, Object> schema =
Map.of(
"type",
"object",
"properties",
Map.of(
"title", Map.of("type", "string"),
"authors", Map.of("type", "array", "items", Map.of("type", "string")),
"abstract", Map.of("type", "string"),
"keywords", Map.of("type", "array", "items", Map.of("type", "string"))),
"required",
List.of("title", "authors", "abstract", "keywords"),
"additionalProperties",
false);
ChatCompletionCreateParams params =
ChatCompletionCreateParams.builder()
.model("gpt-5.6")
.addSystemMessage(
"You are an expert at structured data extraction. You will be given unstructured"
+ " text from a research paper and should convert it into the given structure.")
.addUserMessage(
"Attention Is All You Need by Ashish Vaswani, Noam Shazeer, Niki Parmar,"
+ " Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and"
+ " Illia Polosukhin."
+ " We propose the Transformer, a sequence transduction architecture based"
+ " entirely on attention. Keywords: transformers, attention, sequence"
+ " transduction.")
.putAdditionalBodyProperty(
"response_format",
JsonValue.from(
Map.of(
"type",
"json_schema",
"json_schema",
Map.of(
"name",
"research_paper_extraction",
"strict",
true,
"schema",
schema))))
.build();
client.chat().completions().create(params).choices().stream()
.flatMap(choice -> choice.message().content().stream())
.forEach(System.out::println);1
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42require "openai"
client = OpenAI::Client.new
research_paper = <<~TEXT
Attention Is All You Need by Ashish Vaswani, Noam Shazeer, Niki Parmar,
Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia
Polosukhin. We propose the Transformer, a sequence transduction architecture
based entirely on attention. Keywords: transformers, attention, sequence
transduction.
TEXT
paper_schema = {
type: :object,
properties: {
title: {type: :string},
authors: {type: :array, items: {type: :string}},
abstract: {type: :string},
keywords: {type: :array, items: {type: :string}}
},
required: %w[title authors abstract keywords],
additionalProperties: false
}
completion = client.chat.completions.create(
model: "gpt-5.6",
messages: [
{
role: :system,
content: "Extract structured data from the supplied research paper text."
},
{role: :user, content: research_paper}
],
response_format: {
type: :json_schema,
json_schema: {
name: "research_paper_extraction",
strict: true,
schema: paper_schema
}
}
)
puts(completion.choices.fetch(0).message.content)1
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40curl https://api.openai.com/v1/chat/completions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.6",
"messages": [
{
"role": "system",
"content": "You are an expert at structured data extraction. You will be given unstructured text from a research paper and should convert it into the given structure."
},
{
"role": "user",
"content": "..."
}
],
"response_format": {
"type": "json_schema",
"json_schema": {
"name": "research_paper_extraction",
"schema": {
"type": "object",
"properties": {
"title": { "type": "string" },
"authors": {
"type": "array",
"items": { "type": "string" }
},
"abstract": { "type": "string" },
"keywords": {
"type": "array",
"items": { "type": "string" }
}
},
"required": ["title", "authors", "abstract", "keywords"],
"additionalProperties": false
},
"strict": true
}
}
}'Extracting data from research papers using Structured Outputs
Python
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29import OpenAI from "openai";
import { zodTextFormat } from "openai/helpers/zod";
import { z } from "zod";
const openai = new OpenAI();
const ResearchPaperExtraction = z.object({
title: z.string(),
authors: z.array(z.string()),
abstract: z.string(),
keywords: z.array(z.string()),
});
const response = await openai.responses.parse({
model: "gpt-5.6",
input: [
{
role: "system",
content:
"You are an expert at structured data extraction. You will be given unstructured text from a research paper and should convert it into the given structure.",
},
{ role: "user", content: "..." },
],
text: {
format: zodTextFormat(ResearchPaperExtraction, "research_paper_extraction"),
},
});
const research_paper = response.output_parsed;1
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36from openai import OpenAI
from pydantic import BaseModel
client = OpenAI()
class ResearchPaperExtraction(BaseModel):
title: str
authors: list[str]
abstract: str
keywords: list[str]
response = client.responses.parse(
model="gpt-5.6",
input=[
{
"role": "system",
"content": "You are an expert at structured data extraction. You will be given unstructured text from a research paper and should convert it into the given structure.",
},
{
"role": "user",
"content": (
"Attention Is All You Need by Ashish Vaswani, Noam Shazeer, "
"Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, "
"Łukasz Kaiser, and Illia Polosukhin. We propose the "
"Transformer, a sequence transduction architecture based "
"entirely on attention. Keywords: transformers, attention, "
"sequence transduction."
),
},
],
text_format=ResearchPaperExtraction,
)
research_paper = response.output_parsed1
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52package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/responses"
)
const researchPaperText = "Attention Is All You Need by Ashish Vaswani, Noam Shazeer, " +
"Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, " +
"Łukasz Kaiser, and Illia Polosukhin. We propose the Transformer, " +
"a sequence transduction architecture based entirely on attention. " +
"Keywords: transformers, attention, sequence transduction."
func main() {
client := openai.NewClient()
schema := map[string]any{
"type": "object",
"properties": map[string]any{
"title": map[string]any{"type": "string"},
"authors": map[string]any{"type": "array", "items": map[string]any{"type": "string"}},
"abstract": map[string]any{"type": "string"},
"keywords": map[string]any{"type": "array", "items": map[string]any{"type": "string"}},
},
"required": []string{"title", "authors", "abstract", "keywords"},
"additionalProperties": false,
}
response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
Model: "gpt-5.6",
Input: responses.ResponseNewParamsInputUnion{OfInputItemList: responses.ResponseInputParam{
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("You are an expert at structured data extraction. You will be given unstructured text from a research paper and should convert it into the given structure.")},
responses.EasyInputMessageRoleSystem,
),
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText(researchPaperText)},
responses.EasyInputMessageRoleUser,
),
}},
Text: responses.ResponseTextConfigParam{Format: responses.ResponseFormatTextConfigUnionParam{
OfJSONSchema: &responses.ResponseFormatTextJSONSchemaConfigParam{Name: "research_paper_extraction", Schema: schema, Strict: openai.Bool(true)},
}},
})
if err != nil {
panic(err)
}
fmt.Println(response.OutputText())
}1
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69import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.responses.EasyInputMessage;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.ResponseFormatTextJsonSchemaConfig;
import com.openai.models.responses.ResponseInputItem;
import com.openai.models.responses.ResponseTextConfig;
import java.util.List;
import java.util.Map;
Map<String, Object> schema =
Map.of(
"type",
"object",
"properties",
Map.of(
"title", Map.of("type", "string"),
"authors", Map.of("type", "array", "items", Map.of("type", "string")),
"abstract", Map.of("type", "string"),
"keywords", Map.of("type", "array", "items", Map.of("type", "string"))),
"required",
List.of("title", "authors", "abstract", "keywords"),
"additionalProperties",
false);
ResponseCreateParams params =
ResponseCreateParams.builder()
.model("gpt-5.6")
.inputOfResponse(
List.of(
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.SYSTEM)
.content(
"You are an expert at structured data extraction. You will be given"
+ " unstructured text from a research paper and should convert"
+ " it into the given structure.")
.build()),
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.USER)
.content(
"Attention Is All You Need by Ashish Vaswani, Noam Shazeer,"
+ " Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez,"
+ " Łukasz Kaiser, and Illia Polosukhin. We propose the"
+ " Transformer, a"
+ " sequence transduction architecture based entirely on"
+ " attention. Keywords: transformers, attention, sequence"
+ " transduction.")
.build())))
.text(
ResponseTextConfig.builder()
.format(
ResponseFormatTextJsonSchemaConfig.builder()
.name("research_paper_extraction")
.strict(true)
.schema(
JsonValue.from(schema)
.convert(ResponseFormatTextJsonSchemaConfig.Schema.class))
.build())
.build())
.build();
client.responses().create(params).output().stream()
.flatMap(item -> item.message().stream())
.flatMap(message -> message.content().stream())
.flatMap(content -> content.outputText().stream())
.forEach(text -> System.out.println(text.text()));1
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42require "openai"
client = OpenAI::Client.new
research_paper = <<~TEXT
Attention Is All You Need by Ashish Vaswani, Noam Shazeer, Niki Parmar,
Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia
Polosukhin. We propose the Transformer, a sequence transduction architecture
based entirely on attention. Keywords: transformers, attention, sequence
transduction.
TEXT
paper_schema = {
type: :object,
properties: {
title: {type: :string},
authors: {type: :array, items: {type: :string}},
abstract: {type: :string},
keywords: {type: :array, items: {type: :string}}
},
required: %w[title authors abstract keywords],
additionalProperties: false
}
response = client.responses.create(
model: "gpt-5.6",
input: [
{
role: :system,
content: "Extract structured data from the supplied research paper text."
},
{role: :user, content: research_paper}
],
text: {
format: {
type: :json_schema,
name: "research_paper_extraction",
strict: true,
schema: paper_schema
}
}
)
puts(response.output_text)1
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40curl https://api.openai.com/v1/responses \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.6",
"input": [
{
"role": "system",
"content": "You are an expert at structured data extraction. You will be given unstructured text from a research paper and should convert it into the given structure."
},
{
"role": "user",
"content": "..."
}
],
"text": {
"format": {
"type": "json_schema",
"name": "research_paper_extraction",
"schema": {
"type": "object",
"properties": {
"title": { "type": "string" },
"authors": {
"type": "array",
"items": { "type": "string" }
},
"abstract": { "type": "string" },
"keywords": {
"type": "array",
"items": { "type": "string" }
}
},
"required": ["title", "authors", "abstract", "keywords"],
"additionalProperties": false
},
"strict": true
}
}
}'Example response
{
"title": "Application of Quantum Algorithms in Interstellar Navigation: A New Frontier",
"authors": ["Dr. Stella Voyager", "Dr. Nova Star", "Dr. Lyra Hunter"],
"abstract": "This paper investigates the utilization of quantum algorithms to improve interstellar navigation systems. By leveraging quantum superposition and entanglement, our proposed navigation system can calculate optimal travel paths through space-time anomalies more efficiently than classical methods. Experimental simulations suggest a significant reduction in travel time and fuel consumption for interstellar missions.",
"keywords": [
"Quantum algorithms",
"interstellar navigation",
"space-time anomalies",
"quantum superposition",
"quantum entanglement",
"space travel"
]
}UI generation
UI Generation
You can generate valid HTML by representing it as recursive data structures with constraints, like enums.
Generating HTML using Structured Outputs
Python
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33import OpenAI from "openai";
import { z } from "zod";
import { zodResponseFormat } from "openai/helpers/zod";
const openai = new OpenAI();
const UI = z.lazy(() =>
z.object({
type: z.enum(["div", "button", "header", "section", "field", "form"]),
label: z.string(),
children: z.array(UI),
attributes: z.array(
z.object({
name: z.string(),
value: z.string(),
})
),
})
);
const completion = await openai.chat.completions.parse({
model: "gpt-5.6",
messages: [
{
role: "system",
content: "You are a UI generator AI. Convert the user input into a UI.",
},
{ role: "user", content: "Make a User Profile Form" },
],
response_format: zodResponseFormat(UI, "ui"),
});
const ui = completion.choices[0].message.parsed;1
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50from enum import Enum
from typing import List
from pydantic import BaseModel
from openai import OpenAI
client = OpenAI()
class UIType(str, Enum):
div = "div"
button = "button"
header = "header"
section = "section"
field = "field"
form = "form"
class Attribute(BaseModel):
name: str
value: str
class UI(BaseModel):
type: UIType
label: str
children: List["UI"]
attributes: List[Attribute]
UI.model_rebuild() # This is required to enable recursive types
class Response(BaseModel):
ui: UI
completion = client.chat.completions.parse(
model="gpt-5.6",
messages=[
{
"role": "system",
"content": "You are a UI generator AI. Convert the user input into a UI.",
},
{"role": "user", "content": "Make a User Profile Form"},
],
response_format=Response,
)
ui = completion.choices[0].message.parsed
print(ui)1
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42package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/shared"
)
func main() {
client := openai.NewClient()
schema := map[string]any{
"type": "object",
"properties": map[string]any{
"type": map[string]any{"type": "string", "enum": []string{"div", "button", "header", "section", "field", "form"}},
"label": map[string]any{"type": "string"},
"children": map[string]any{"type": "array", "items": map[string]any{"$ref": "#"}},
"attributes": map[string]any{"type": "array", "items": map[string]any{"type": "object", "properties": map[string]any{"name": map[string]any{"type": "string"}, "value": map[string]any{"type": "string"}}, "required": []string{"name", "value"}, "additionalProperties": false}},
},
"required": []string{"type", "label", "children", "attributes"},
"additionalProperties": false,
}
completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
Model: "gpt-5.6",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.SystemMessage("You are a UI generator AI. Convert the user input into a UI."),
openai.UserMessage("Make a User Profile Form"),
},
ResponseFormat: openai.ChatCompletionNewParamsResponseFormatUnion{
OfJSONSchema: &shared.ResponseFormatJSONSchemaParam{JSONSchema: shared.ResponseFormatJSONSchemaJSONSchemaParam{
Name: "ui", Description: openai.String("Dynamically generated UI"), Schema: schema, Strict: openai.Bool(true),
}},
},
})
if err != nil {
panic(err)
}
fmt.Println(completion.Choices[0].Message.Content)
}1
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68import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.List;
import java.util.Map;
Map<String, Object> schema =
Map.of(
"type",
"object",
"properties",
Map.of(
"type",
Map.of(
"type",
"string",
"enum",
List.of("div", "button", "header", "section", "field", "form")),
"label", Map.of("type", "string"),
"children", Map.of("type", "array", "items", Map.of("$ref", "#")),
"attributes",
Map.of(
"type",
"array",
"items",
Map.of(
"type",
"object",
"properties",
Map.of(
"name", Map.of("type", "string"),
"value", Map.of("type", "string")),
"required",
List.of("name", "value"),
"additionalProperties",
false))),
"required",
List.of("type", "label", "children", "attributes"),
"additionalProperties",
false);
ChatCompletionCreateParams params =
ChatCompletionCreateParams.builder()
.model("gpt-5.6")
.addSystemMessage("Convert the user request into a UI definition.")
.addUserMessage("Make a user profile form.")
.putAdditionalBodyProperty(
"response_format",
JsonValue.from(
Map.of(
"type",
"json_schema",
"json_schema",
Map.of(
"name",
"ui",
"description",
"A dynamically generated UI",
"strict",
true,
"schema",
schema))))
.build();
client.chat().completions().create(params).choices().stream()
.flatMap(choice -> choice.message().content().stream())
.forEach(System.out::println);1
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47require "openai"
client = OpenAI::Client.new
ui_schema = {
type: :object,
properties: {
type: {
type: :string,
enum: %w[div button header section field form]
},
label: {type: :string},
children: {type: :array, items: {"$ref" => "#"}},
attributes: {
type: :array,
items: {
type: :object,
properties: {
name: {type: :string},
value: {type: :string}
},
required: %w[name value],
additionalProperties: false
}
}
},
required: %w[type label children attributes],
additionalProperties: false
}
completion = client.chat.completions.create(
model: "gpt-5.6",
messages: [
{role: :system, content: "Convert the user request into a UI definition."},
{role: :user, content: "Make a user profile form."}
],
response_format: {
type: :json_schema,
json_schema: {
name: "ui",
description: "A dynamically generated UI",
strict: true,
schema: ui_schema
}
}
)
puts(completion.choices.fetch(0).message.content)1
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64curl https://api.openai.com/v1/chat/completions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.6",
"messages": [
{
"role": "system",
"content": "You are a UI generator AI. Convert the user input into a UI."
},
{
"role": "user",
"content": "Make a User Profile Form"
}
],
"response_format": {
"type": "json_schema",
"json_schema": {
"name": "ui",
"description": "Dynamically generated UI",
"schema": {
"type": "object",
"properties": {
"type": {
"type": "string",
"description": "The type of the UI component",
"enum": ["div", "button", "header", "section", "field", "form"]
},
"label": {
"type": "string",
"description": "The label of the UI component, used for buttons or form fields"
},
"children": {
"type": "array",
"description": "Nested UI components",
"items": {"$ref": "#"}
},
"attributes": {
"type": "array",
"description": "Arbitrary attributes for the UI component, suitable for any element",
"items": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "The name of the attribute, for example onClick or className"
},
"value": {
"type": "string",
"description": "The value of the attribute"
}
},
"required": ["name", "value"],
"additionalProperties": false
}
}
},
"required": ["type", "label", "children", "attributes"],
"additionalProperties": false
},
"strict": true
}
}
}'Generating HTML using Structured Outputs
Python
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38import OpenAI from "openai";
import { zodTextFormat } from "openai/helpers/zod";
import { z } from "zod";
const openai = new OpenAI();
const UI = z.lazy(() =>
z.object({
type: z.enum(["div", "button", "header", "section", "field", "form"]),
label: z.string(),
children: z.array(UI),
attributes: z.array(
z.object({
name: z.string(),
value: z.string(),
})
),
})
);
const response = await openai.responses.parse({
model: "gpt-5.6",
input: [
{
role: "system",
content: "You are a UI generator AI. Convert the user input into a UI.",
},
{
role: "user",
content: "Make a User Profile Form",
},
],
text: {
format: zodTextFormat(UI, "ui"),
},
});
const ui = response.output_parsed;1
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50from enum import Enum
from typing import List
from openai import OpenAI
from pydantic import BaseModel
client = OpenAI()
class UIType(str, Enum):
div = "div"
button = "button"
header = "header"
section = "section"
field = "field"
form = "form"
class Attribute(BaseModel):
name: str
value: str
class UI(BaseModel):
type: UIType
label: str
children: List["UI"]
attributes: List[Attribute]
UI.model_rebuild() # This is required to enable recursive types
class Response(BaseModel):
ui: UI
response = client.responses.parse(
model="gpt-5.6",
input=[
{
"role": "system",
"content": "You are a UI generator AI. Convert the user input into a UI.",
},
{"role": "user", "content": "Make a User Profile Form"},
],
text_format=Response,
)
ui = response.output_parsed1
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46package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/responses"
)
func main() {
client := openai.NewClient()
schema := map[string]any{
"type": "object",
"properties": map[string]any{
"type": map[string]any{"type": "string", "enum": []string{"div", "button", "header", "section", "field", "form"}},
"label": map[string]any{"type": "string"},
"children": map[string]any{"type": "array", "items": map[string]any{"$ref": "#"}},
"attributes": map[string]any{"type": "array", "items": map[string]any{"type": "object", "properties": map[string]any{"name": map[string]any{"type": "string"}, "value": map[string]any{"type": "string"}}, "required": []string{"name", "value"}, "additionalProperties": false}},
},
"required": []string{"type", "label", "children", "attributes"},
"additionalProperties": false,
}
response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
Model: "gpt-5.6",
Input: responses.ResponseNewParamsInputUnion{OfInputItemList: responses.ResponseInputParam{
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("You are a UI generator AI. Convert the user input into a UI.")},
responses.EasyInputMessageRoleSystem,
),
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("Make a User Profile Form")},
responses.EasyInputMessageRoleUser,
),
}},
Text: responses.ResponseTextConfigParam{Format: responses.ResponseFormatTextConfigUnionParam{
OfJSONSchema: &responses.ResponseFormatTextJSONSchemaConfigParam{Name: "ui", Description: openai.String("Dynamically generated UI"), Schema: schema, Strict: openai.Bool(true)},
}},
})
if err != nil {
panic(err)
}
fmt.Println(response.OutputText())
}1
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87import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.responses.EasyInputMessage;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.ResponseFormatTextJsonSchemaConfig;
import com.openai.models.responses.ResponseInputItem;
import com.openai.models.responses.ResponseTextConfig;
import java.util.List;
import java.util.Map;
ResponseCreateParams params =
ResponseCreateParams.builder()
.model("gpt-5.6")
.inputOfResponse(
List.of(
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.SYSTEM)
.content("Convert the user request into a UI definition.")
.build()),
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.USER)
.content("Make a user profile form.")
.build())))
.text(
ResponseTextConfig.builder()
.format(
ResponseFormatTextJsonSchemaConfig.builder()
.name("ui")
.description("A dynamically generated UI")
.strict(true)
.schema(
ResponseFormatTextJsonSchemaConfig.Schema.builder()
.putAdditionalProperty("type", JsonValue.from("object"))
.putAdditionalProperty(
"properties",
JsonValue.from(
Map.of(
"type",
Map.of(
"type",
"string",
"enum",
List.of(
"div", "button", "header", "section",
"field", "form")),
"label", Map.of("type", "string"),
"children",
Map.of(
"type",
"array",
"items",
Map.of("$ref", "#")),
"attributes",
Map.of(
"type",
"array",
"items",
Map.of(
"type",
"object",
"properties",
Map.of(
"name", Map.of("type", "string"),
"value", Map.of("type", "string")),
"required",
List.of("name", "value"),
"additionalProperties",
false)))))
.putAdditionalProperty(
"required",
JsonValue.from(
List.of("type", "label", "children", "attributes")))
.putAdditionalProperty(
"additionalProperties", JsonValue.from(false))
.build())
.build())
.build())
.build();
client.responses().create(params).output().stream()
.flatMap(item -> item.message().stream())
.flatMap(message -> message.content().stream())
.flatMap(content -> content.outputText().stream())
.forEach(text -> System.out.println(text.text()));1
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47require "openai"
client = OpenAI::Client.new
ui_schema = {
type: :object,
properties: {
type: {
type: :string,
enum: %w[div button header section field form]
},
label: {type: :string},
children: {type: :array, items: {"$ref" => "#"}},
attributes: {
type: :array,
items: {
type: :object,
properties: {
name: {type: :string},
value: {type: :string}
},
required: %w[name value],
additionalProperties: false
}
}
},
required: %w[type label children attributes],
additionalProperties: false
}
response = client.responses.create(
model: "gpt-5.6",
input: [
{role: :system, content: "Convert the user request into a UI definition."},
{role: :user, content: "Make a user profile form."}
],
text: {
format: {
type: :json_schema,
name: "ui",
description: "A dynamically generated UI",
strict: true,
schema: ui_schema
}
}
)
puts(response.output_text)1
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64curl https://api.openai.com/v1/responses \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.6",
"input": [
{
"role": "system",
"content": "You are a UI generator AI. Convert the user input into a UI."
},
{
"role": "user",
"content": "Make a User Profile Form"
}
],
"text": {
"format": {
"type": "json_schema",
"name": "ui",
"description": "Dynamically generated UI",
"schema": {
"type": "object",
"properties": {
"type": {
"type": "string",
"description": "The type of the UI component",
"enum": ["div", "button", "header", "section", "field", "form"]
},
"label": {
"type": "string",
"description": "The label of the UI component, used for buttons or form fields"
},
"children": {
"type": "array",
"description": "Nested UI components",
"items": {"$ref": "#"}
},
"attributes": {
"type": "array",
"description": "Arbitrary attributes for the UI component, suitable for any element",
"items": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "The name of the attribute, for example onClick or className"
},
"value": {
"type": "string",
"description": "The value of the attribute"
}
},
"required": ["name", "value"],
"additionalProperties": false
}
}
},
"required": ["type", "label", "children", "attributes"],
"additionalProperties": false
},
"strict": true
}
}
}'Example response
{
"type": "form",
"label": "User Profile Form",
"children": [
{
"type": "div",
"label": "",
"children": [
{
"type": "field",
"label": "First Name",
"children": [],
"attributes": [
{
"name": "type",
"value": "text"
},
{
"name": "name",
"value": "firstName"
},
{
"name": "placeholder",
"value": "Enter your first name"
}
]
},
{
"type": "field",
"label": "Last Name",
"children": [],
"attributes": [
{
"name": "type",
"value": "text"
},
{
"name": "name",
"value": "lastName"
},
{
"name": "placeholder",
"value": "Enter your last name"
}
]
}
],
"attributes": []
},
{
"type": "button",
"label": "Submit",
"children": [],
"attributes": [
{
"name": "type",
"value": "submit"
}
]
}
],
"attributes": [
{
"name": "method",
"value": "post"
},
{
"name": "action",
"value": "/submit-profile"
}
]
}Moderation
Moderation
You can classify inputs on multiple categories, which is a common way of doing moderation.
Moderation using Structured Outputs
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26import OpenAI from "openai";
import { z } from "zod";
import { zodResponseFormat } from "openai/helpers/zod";
const openai = new OpenAI();
const ContentCompliance = z.object({
is_violating: z.boolean(),
category: z.enum(["violence", "sexual", "self_harm"]).nullable(),
explanation_if_violating: z.string().nullable(),
});
const completion = await openai.chat.completions.parse({
model: "gpt-5.6",
messages: [
{
role: "system",
content:
"Determine if the user input violates specific guidelines and explain if they do.",
},
{ role: "user", content: "How do I prepare for a job interview?" },
],
response_format: zodResponseFormat(ContentCompliance, "content_compliance"),
});
const compliance = completion.choices[0].message.parsed;1
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33from enum import Enum
from typing import Optional
from pydantic import BaseModel
from openai import OpenAI
client = OpenAI()
class Category(str, Enum):
violence = "violence"
sexual = "sexual"
self_harm = "self_harm"
class ContentCompliance(BaseModel):
is_violating: bool
category: Optional[Category]
explanation_if_violating: Optional[str]
completion = client.chat.completions.parse(
model="gpt-5.6",
messages=[
{
"role": "system",
"content": "Determine if the user input violates specific guidelines and explain if they do.",
},
{"role": "user", "content": "How do I prepare for a job interview?"},
],
response_format=ContentCompliance,
)
compliance = completion.choices[0].message.parsed1
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43package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/shared"
)
func main() {
client := openai.NewClient()
schema := contentComplianceSchema()
completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
Model: "gpt-5.6",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.SystemMessage("Determine if the user input violates specific guidelines and explain if they do."),
openai.UserMessage("How do I prepare for a job interview?"),
},
ResponseFormat: openai.ChatCompletionNewParamsResponseFormatUnion{
OfJSONSchema: &shared.ResponseFormatJSONSchemaParam{JSONSchema: shared.ResponseFormatJSONSchemaJSONSchemaParam{
Name: "content_compliance", Description: openai.String("Determines if content is violating specific moderation rules"), Schema: schema, Strict: openai.Bool(true),
}},
},
})
if err != nil {
panic(err)
}
fmt.Println(completion.Choices[0].Message.Content)
}
func contentComplianceSchema() map[string]any {
return map[string]any{
"type": "object",
"properties": map[string]any{
"is_violating": map[string]any{"type": "boolean", "description": "Indicates if the content is violating guidelines"},
"category": map[string]any{"type": []string{"string", "null"}, "description": "Type of violation, if the content is violating guidelines. Null otherwise.", "enum": []any{"violence", "sexual", "self_harm", nil}},
"explanation_if_violating": map[string]any{"type": []string{"string", "null"}, "description": "Explanation of why the content is violating"},
},
"required": []string{"is_violating", "category", "explanation_if_violating"},
"additionalProperties": false,
}
}1
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61import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
Map<String, Object> schema =
Map.of(
"type",
"object",
"properties",
Map.of(
"is_violating",
Map.of(
"type", "boolean",
"description", "Whether the content violates the guidelines"),
"category",
Map.of(
"type", List.of("string", "null"),
"enum", Arrays.asList("violence", "sexual", "self_harm", null),
"description", "The violation category, or null when content is allowed"),
"explanation_if_violating",
Map.of(
"type",
List.of("string", "null"),
"description",
"Why the content violates the guidelines, or null")),
"required",
List.of("is_violating", "category", "explanation_if_violating"),
"additionalProperties",
false);
ChatCompletionCreateParams params =
ChatCompletionCreateParams.builder()
.model("gpt-5.6")
.addSystemMessage(
"Determine whether the user input violates the guidelines and explain any violation.")
.addUserMessage("How do I prepare for a job interview?")
.putAdditionalBodyProperty(
"response_format",
JsonValue.from(
Map.of(
"type",
"json_schema",
"json_schema",
Map.of(
"name",
"content_compliance",
"description",
"Determines whether content violates moderation rules",
"strict",
true,
"schema",
schema))))
.build();
client.chat().completions().create(params).choices().stream()
.flatMap(choice -> choice.message().content().stream())
.forEach(System.out::println);1
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45require "openai"
client = OpenAI::Client.new
compliance_schema = {
type: :object,
properties: {
is_violating: {
type: :boolean,
description: "Whether the content violates the guidelines"
},
category: {
type: %i[string null],
enum: ["violence", "sexual", "self_harm", nil],
description: "The violation category, or null when the content is allowed"
},
explanation_if_violating: {
type: %i[string null],
description: "Why the content violates the guidelines, or null"
}
},
required: %w[is_violating category explanation_if_violating],
additionalProperties: false
}
completion = client.chat.completions.create(
model: "gpt-5.6",
messages: [
{
role: :system,
content: "Determine whether the user input violates the guidelines and explain any violation."
},
{role: :user, content: "How do I prepare for a job interview?"}
],
response_format: {
type: :json_schema,
json_schema: {
name: "content_compliance",
description: "Determines whether content violates moderation rules",
strict: true,
schema: compliance_schema
}
}
)
puts(completion.choices.fetch(0).message.content)1
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44curl https://api.openai.com/v1/chat/completions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.6",
"messages": [
{
"role": "system",
"content": "Determine if the user input violates specific guidelines and explain if they do."
},
{
"role": "user",
"content": "How do I prepare for a job interview?"
}
],
"response_format": {
"type": "json_schema",
"json_schema": {
"name": "content_compliance",
"description": "Determines if content is violating specific moderation rules",
"schema": {
"type": "object",
"properties": {
"is_violating": {
"type": "boolean",
"description": "Indicates if the content is violating guidelines"
},
"category": {
"type": ["string", "null"],
"description": "Type of violation, if the content is violating guidelines. Null otherwise.",
"enum": ["violence", "sexual", "self_harm"]
},
"explanation_if_violating": {
"type": ["string", "null"],
"description": "Explanation of why the content is violating"
}
},
"required": ["is_violating", "category", "explanation_if_violating"],
"additionalProperties": false
},
"strict": true
}
}
}'Moderation using Structured Outputs
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31import OpenAI from "openai";
import { zodTextFormat } from "openai/helpers/zod";
import { z } from "zod";
const openai = new OpenAI();
const ContentCompliance = z.object({
is_violating: z.boolean(),
category: z.enum(["violence", "sexual", "self_harm"]).nullable(),
explanation_if_violating: z.string().nullable(),
});
const response = await openai.responses.parse({
model: "gpt-5.6",
input: [
{
role: "system",
content:
"Determine if the user input violates specific guidelines and explain if they do.",
},
{
role: "user",
content: "How do I prepare for a job interview?",
},
],
text: {
format: zodTextFormat(ContentCompliance, "content_compliance"),
},
});
const compliance = response.output_parsed;1
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34from enum import Enum
from typing import Optional
from openai import OpenAI
from pydantic import BaseModel
client = OpenAI()
class Category(str, Enum):
violence = "violence"
sexual = "sexual"
self_harm = "self_harm"
class ContentCompliance(BaseModel):
is_violating: bool
category: Optional[Category]
explanation_if_violating: Optional[str]
response = client.responses.parse(
model="gpt-5.6",
input=[
{
"role": "system",
"content": "Determine if the user input violates specific guidelines and explain if they do.",
},
{"role": "user", "content": "How do I prepare for a job interview?"},
],
text_format=ContentCompliance,
)
compliance = response.output_parsed1
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43package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/responses"
)
func main() {
client := openai.NewClient()
schema := contentComplianceSchema()
response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
Model: "gpt-5.6",
Input: responses.ResponseNewParamsInputUnion{OfInputItemList: responses.ResponseInputParam{
responses.ResponseInputItemParamOfMessage("Determine if the user input violates specific guidelines and explain if they do.", responses.EasyInputMessageRoleSystem),
responses.ResponseInputItemParamOfMessage("How do I prepare for a job interview?", responses.EasyInputMessageRoleUser),
}},
Text: responses.ResponseTextConfigParam{Format: responses.ResponseFormatTextConfigUnionParam{
OfJSONSchema: &responses.ResponseFormatTextJSONSchemaConfigParam{
Name: "content_compliance", Description: openai.String("Determines if content is violating specific moderation rules"), Schema: schema, Strict: openai.Bool(true),
},
}},
})
if err != nil {
panic(err)
}
fmt.Println(response.OutputText())
}
func contentComplianceSchema() map[string]any {
return map[string]any{
"type": "object",
"properties": map[string]any{
"is_violating": map[string]any{"type": "boolean", "description": "Indicates if the content is violating guidelines"},
"category": map[string]any{"type": []string{"string", "null"}, "description": "Type of violation, if the content is violating guidelines. Null otherwise.", "enum": []any{"violence", "sexual", "self_harm", nil}},
"explanation_if_violating": map[string]any{"type": []string{"string", "null"}, "description": "Explanation of why the content is violating"},
},
"required": []string{"is_violating", "category", "explanation_if_violating"},
"additionalProperties": false,
}
}1
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73import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.responses.EasyInputMessage;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.ResponseFormatTextJsonSchemaConfig;
import com.openai.models.responses.ResponseInputItem;
import com.openai.models.responses.ResponseTextConfig;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
Map<String, Object> schema =
Map.of(
"type",
"object",
"properties",
Map.of(
"is_violating",
Map.of(
"type", "boolean",
"description", "Whether the content violates the guidelines"),
"category",
Map.of(
"type", List.of("string", "null"),
"enum", Arrays.asList("violence", "sexual", "self_harm", null),
"description", "The violation category, or null when content is allowed"),
"explanation_if_violating",
Map.of(
"type",
List.of("string", "null"),
"description",
"Why the content violates the guidelines, or null")),
"required",
List.of("is_violating", "category", "explanation_if_violating"),
"additionalProperties",
false);
ResponseCreateParams params =
ResponseCreateParams.builder()
.model("gpt-5.6")
.inputOfResponse(
List.of(
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.SYSTEM)
.content(
"Determine whether the user input violates the guidelines and explain any violation.")
.build()),
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.USER)
.content("How do I prepare for a job interview?")
.build())))
.text(
ResponseTextConfig.builder()
.format(
ResponseFormatTextJsonSchemaConfig.builder()
.name("content_compliance")
.description("Determines whether content violates moderation rules")
.strict(true)
.schema(
JsonValue.from(schema)
.convert(ResponseFormatTextJsonSchemaConfig.Schema.class))
.build())
.build())
.build();
client.responses().create(params).output().stream()
.flatMap(item -> item.message().stream())
.flatMap(message -> message.content().stream())
.flatMap(content -> content.outputText().stream())
.forEach(text -> System.out.println(text.text()));1
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45require "openai"
client = OpenAI::Client.new
compliance_schema = {
type: :object,
properties: {
is_violating: {
type: :boolean,
description: "Whether the content violates the guidelines"
},
category: {
type: %i[string null],
enum: ["violence", "sexual", "self_harm", nil],
description: "The violation category, or null when the content is allowed"
},
explanation_if_violating: {
type: %i[string null],
description: "Why the content violates the guidelines, or null"
}
},
required: %w[is_violating category explanation_if_violating],
additionalProperties: false
}
response = client.responses.create(
model: "gpt-5.6",
input: [
{
role: :system,
content: "Determine whether the user input violates the guidelines and explain any violation."
},
{role: :user, content: "How do I prepare for a job interview?"}
],
text: {
format: {
type: :json_schema,
name: "content_compliance",
description: "Determines whether content violates moderation rules",
strict: true,
schema: compliance_schema
}
}
)
puts(response.output_text)1
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44curl https://api.openai.com/v1/responses \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.6",
"input": [
{
"role": "system",
"content": "Determine if the user input violates specific guidelines and explain if they do."
},
{
"role": "user",
"content": "How do I prepare for a job interview?"
}
],
"text": {
"format": {
"type": "json_schema",
"name": "content_compliance",
"description": "Determines if content is violating specific moderation rules",
"schema": {
"type": "object",
"properties": {
"is_violating": {
"type": "boolean",
"description": "Indicates if the content is violating guidelines"
},
"category": {
"type": ["string", "null"],
"description": "Type of violation, if the content is violating guidelines. Null otherwise.",
"enum": ["violence", "sexual", "self_harm"]
},
"explanation_if_violating": {
"type": ["string", "null"],
"description": "Explanation of why the content is violating"
}
},
"required": ["is_violating", "category", "explanation_if_violating"],
"additionalProperties": false
},
"strict": true
}
}
}'Example response
{
"is_violating": false,
"category": null,
"explanation_if_violating": null
}How to use Structured Outputs with response_format
You can use Structured Outputs with the new SDK helper to parse the model’s output into your desired format, or you can specify the JSON schema directly.
Note: for fine tuned models, the first request you make with any schema will have additional latency as our API processes the schema, but subsequent requests with the same schema will not have additional latency. Other models do not have this limitation.
SDK objects
Step 1: Define your object
First you must define an object or data structure to represent the JSON Schema that the model should be constrained to follow. See the examples at the top of this guide for reference.
While Structured Outputs supports much of JSON Schema, some features are unavailable either for performance or technical reasons. See here for more details.
For example, you can define an object like this:
Python
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12import { z } from "zod";
import { zodResponseFormat } from "openai/helpers/zod";
const Step = z.object({
explanation: z.string(),
output: z.string(),
});
const MathResponse = z.object({
steps: z.array(Step),
final_answer: z.string(),
});1
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11from pydantic import BaseModel
class Step(BaseModel):
explanation: str
output: str
class MathResponse(BaseModel):
steps: list[Step]
final_answer: strTips for your data structure
To maximize the quality of model generations, we recommend the following:
- Name keys clearly and intuitively
- Create clear titles and descriptions for important keys in your structure
- Create and use evals to determine the structure that works best for your use case
Step 2: Supply your object in the API call
You can use the parse method to automatically parse the JSON response into the object you defined.
Under the hood, the SDK takes care of supplying the JSON schema corresponding to your data structure, and then parsing the response as an object.
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12const completion = await openai.chat.completions.parse({
model: "gpt-5.6",
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: zodResponseFormat(MathResponse, "math_response"),
});1
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11completion = client.chat.completions.parse(
model="gpt-5.6",
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=MathResponse,
)Step 3: Handle edge cases
In some cases, the model might not generate a valid response that matches the provided JSON schema.
This can happen in the case of a refusal, if the model refuses to answer for safety reasons, or if for example you reach a max tokens limit and the response is incomplete.
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70try {
const completion = await openai.chat.completions.create({
model: "gpt-5.6",
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",
},
],
store: true,
response_format: {
type: "json_schema",
json_schema: {
name: "math_response",
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,
},
},
max_completion_tokens: 50,
});
if (completion.choices[0].finish_reason === "length") {
// Handle the case where the model did not return a complete response
throw new Error("Incomplete response");
}
const math_response = completion.choices[0].message;
if (math_response.refusal) {
// handle refusal
console.log(math_response.refusal);
} else if (math_response.content) {
console.log(math_response.content);
} else {
throw new Error("No response content");
}
} catch (e) {
// Handle edge cases
console.error(e);
}1
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54try:
response = client.chat.completions.create(
model="gpt-5.6",
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={
"type": "json_schema",
"json_schema": {
"name": "math_response",
"strict": True,
"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,
},
},
},
max_completion_tokens=50,
)
if response.choices[0].finish_reason == "length":
raise Exception("Incomplete response")
math_response = response.choices[0].message
if math_response.refusal:
print(math_response.refusal)
elif math_response.content:
print(math_response.content)
else:
raise Exception("No response content")
except Exception as e:
# handle errors like finish_reason, refusal, content_filter, etc.
print(e)1
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56package main
import (
"context"
"errors"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/shared"
)
func main() {
client := openai.NewClient()
completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
Model: "gpt-5.6",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.SystemMessage("You are a helpful math tutor. Guide the user through the solution step by step."),
openai.UserMessage("how can I solve 8x + 7 = -23"),
},
Store: openai.Bool(true),
MaxCompletionTokens: openai.Int(1024),
ResponseFormat: openai.ChatCompletionNewParamsResponseFormatUnion{
OfJSONSchema: &shared.ResponseFormatJSONSchemaParam{JSONSchema: shared.ResponseFormatJSONSchemaJSONSchemaParam{
Name: "math_response", Schema: mathSchema(), Strict: openai.Bool(true),
}},
},
})
if err != nil {
panic(err)
}
choice := completion.Choices[0]
if choice.FinishReason == "length" {
panic(errors.New("incomplete response"))
}
if choice.Message.Refusal != "" {
fmt.Println(choice.Message.Refusal)
return
}
if choice.Message.Content == "" {
panic(errors.New("no response content"))
}
fmt.Println(choice.Message.Content)
}
func mathSchema() map[string]any {
return map[string]any{
"type": "object",
"properties": map[string]any{
"steps": map[string]any{"type": "array", "items": map[string]any{"type": "object", "properties": map[string]any{"explanation": map[string]any{"type": "string"}, "output": map[string]any{"type": "string"}}, "required": []string{"explanation", "output"}, "additionalProperties": false}},
"final_answer": map[string]any{"type": "string"},
},
"required": []string{"steps", "final_answer"},
"additionalProperties": false,
}
}1
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63import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.chat.completions.ChatCompletion;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.List;
import java.util.Map;
Map<String, Object> stepSchema =
Map.of(
"type",
"object",
"properties",
Map.of(
"explanation", Map.of("type", "string"),
"output", Map.of("type", "string")),
"required",
List.of("explanation", "output"),
"additionalProperties",
false);
Map<String, Object> mathSchema =
Map.of(
"type",
"object",
"properties",
Map.of(
"steps", Map.of("type", "array", "items", stepSchema),
"final_answer", Map.of("type", "string")),
"required",
List.of("steps", "final_answer"),
"additionalProperties",
false);
ChatCompletionCreateParams params =
ChatCompletionCreateParams.builder()
.model("gpt-5.6")
.addSystemMessage(
"You are a helpful math tutor. Guide the user through the solution step by step.")
.addUserMessage("How can I solve 8x + 7 = -23?")
.maxCompletionTokens(1024)
.store(true)
.putAdditionalBodyProperty(
"response_format",
JsonValue.from(
Map.of(
"type",
"json_schema",
"json_schema",
Map.of("name", "math_response", "strict", true, "schema", mathSchema))))
.build();
var choice = client.chat().completions().create(params).choices().get(0);
if (choice.finishReason().equals(ChatCompletion.Choice.FinishReason.LENGTH)) {
System.out.println("Incomplete response");
} else if (choice.message().refusal().isPresent()) {
System.out.println(choice.message().refusal().orElseThrow());
} else {
System.out.println(
choice
.message()
.content()
.orElseThrow(() -> new IllegalStateException("No response content")));
}1
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48require "openai"
client = OpenAI::Client.new
step_schema = {
type: :object,
properties: {
explanation: {type: :string},
output: {type: :string}
},
required: %w[explanation output],
additionalProperties: false
}
math_schema = {
type: :object,
properties: {
steps: {type: :array, items: step_schema},
final_answer: {type: :string}
},
required: %w[steps final_answer],
additionalProperties: false
}
completion = client.chat.completions.create(
model: "gpt-5.6",
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?"}
],
max_completion_tokens: 1_024,
store: true,
response_format: {
type: :json_schema,
json_schema: {name: "math_response", strict: true, schema: math_schema}
}
)
choice = completion.choices.fetch(0)
if choice.finish_reason == OpenAI::Chat::ChatCompletion::Choice::FinishReason::LENGTH
raise "Incomplete response"
elsif choice.message.refusal
puts(choice.message.refusal)
else
content = choice.message.content or raise "No response content"
puts(content)
endManual schema
Step 1: Define your schema
First you must design the JSON Schema that the model should be constrained to follow. See the examples at the top of this guide for reference.
While Structured Outputs supports much of JSON Schema, some features are unavailable either for performance or technical reasons. See here for more details.
Tips for your JSON Schema
To maximize the quality of model generations, we recommend the following:
- Name keys clearly and intuitively
- Create clear titles and descriptions for important keys in your structure
- Create and use evals to determine the structure that works best for your use case
Step 2: Supply your schema in the API call
To use Structured Outputs, simply specify
response_format: { "type": "json_schema", "json_schema": … , "strict": true }text: { format: { type: "json_schema", "strict": true, "schema": … } }For example:
Python
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41const response = await openai.chat.completions.create({
model: "gpt-5.6",
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" },
],
store: true,
response_format: {
type: "json_schema",
json_schema: {
name: "math_response",
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,
},
},
});
console.log(response.choices[0].message.content);1
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39response = client.chat.completions.create(
model="gpt-5.6",
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={
"type": "json_schema",
"json_schema": {
"name": "math_response",
"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(response.choices[0].message.content)1
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43package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/shared"
)
func main() {
client := openai.NewClient()
schema := mathSchema()
completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
Model: "gpt-5.6",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.SystemMessage("You are a helpful math tutor. Guide the user through the solution step by step."),
openai.UserMessage("how can I solve 8x + 7 = -23"),
},
Store: openai.Bool(true),
ResponseFormat: openai.ChatCompletionNewParamsResponseFormatUnion{
OfJSONSchema: &shared.ResponseFormatJSONSchemaParam{JSONSchema: shared.ResponseFormatJSONSchemaJSONSchemaParam{
Name: "math_response", Schema: schema, Strict: openai.Bool(true),
}},
},
})
if err != nil {
panic(err)
}
fmt.Println(completion.Choices[0].Message.Content)
}
func mathSchema() map[string]any {
return map[string]any{
"type": "object",
"properties": map[string]any{
"steps": map[string]any{"type": "array", "items": map[string]any{"type": "object", "properties": map[string]any{"explanation": map[string]any{"type": "string"}, "output": map[string]any{"type": "string"}}, "required": []string{"explanation", "output"}, "additionalProperties": false}},
"final_answer": map[string]any{"type": "string"},
},
"required": []string{"steps", "final_answer"},
"additionalProperties": false,
}
}1
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52import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.List;
import java.util.Map;
Map<String, Object> stepSchema =
Map.of(
"type",
"object",
"properties",
Map.of(
"explanation", Map.of("type", "string"),
"output", Map.of("type", "string")),
"required",
List.of("explanation", "output"),
"additionalProperties",
false);
Map<String, Object> mathSchema =
Map.of(
"type",
"object",
"properties",
Map.of(
"steps", Map.of("type", "array", "items", stepSchema),
"final_answer", Map.of("type", "string")),
"required",
List.of("steps", "final_answer"),
"additionalProperties",
false);
ChatCompletionCreateParams params =
ChatCompletionCreateParams.builder()
.model("gpt-5.6")
.addSystemMessage(
"You are a helpful math tutor. Guide the user through the solution step by step.")
.addUserMessage("How can I solve 8x + 7 = -23?")
.store(true)
.putAdditionalBodyProperty(
"response_format",
JsonValue.from(
Map.of(
"type",
"json_schema",
"json_schema",
Map.of("name", "math_response", "strict", true, "schema", mathSchema))))
.build();
client.chat().completions().create(params).choices().stream()
.flatMap(choice -> choice.message().content().stream())
.forEach(System.out::println);1
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41require "openai"
client = OpenAI::Client.new
math_schema = {
type: :object,
properties: {
steps: {
type: :array,
items: {
type: :object,
properties: {
explanation: {type: :string},
output: {type: :string}
},
required: %w[explanation output],
additionalProperties: false
}
},
final_answer: {type: :string}
},
required: %w[steps final_answer],
additionalProperties: false
}
completion = client.chat.completions.create(
model: "gpt-5.6",
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?"}
],
store: true,
response_format: {
type: :json_schema,
json_schema: {name: "math_response", strict: true, schema: math_schema}
}
)
puts(completion.choices.fetch(0).message.content)1
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43curl https://api.openai.com/v1/chat/completions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.6",
"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": {
"type": "json_schema",
"json_schema": {
"name": "math_response",
"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
}
}
}'Python
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40const response = await openai.responses.create({
model: "gpt-5.6",
input: [
{
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" },
],
text: {
format: {
type: "json_schema",
name: "math_response",
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,
},
},
});
console.log(response.output_text);1
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39response = client.responses.create(
model="gpt-5.6",
input=[
{
"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"},
],
text={
"format": {
"type": "json_schema",
"name": "math_response",
"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(response.output_text)1
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45package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/responses"
)
func main() {
client := openai.NewClient()
response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
Model: "gpt-5.6",
Input: responses.ResponseNewParamsInputUnion{OfInputItemList: responses.ResponseInputParam{
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("You are a helpful math tutor. Guide the user through the solution step by step.")},
responses.EasyInputMessageRoleSystem,
),
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("how can I solve 8x + 7 = -23")},
responses.EasyInputMessageRoleUser,
),
}},
Text: responses.ResponseTextConfigParam{Format: responses.ResponseFormatTextConfigUnionParam{
OfJSONSchema: &responses.ResponseFormatTextJSONSchemaConfigParam{Name: "math_response", Schema: mathSchema(), Strict: openai.Bool(true)},
}},
})
if err != nil {
panic(err)
}
fmt.Println(response.OutputText())
}
func mathSchema() map[string]any {
return map[string]any{
"type": "object",
"properties": map[string]any{
"steps": map[string]any{"type": "array", "items": map[string]any{"type": "object", "properties": map[string]any{"explanation": map[string]any{"type": "string"}, "output": map[string]any{"type": "string"}}, "required": []string{"explanation", "output"}, "additionalProperties": false}},
"final_answer": map[string]any{"type": "string"},
},
"required": []string{"steps", "final_answer"},
"additionalProperties": false,
}
}1
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73import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.responses.EasyInputMessage;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.ResponseFormatTextJsonSchemaConfig;
import com.openai.models.responses.ResponseInputItem;
import com.openai.models.responses.ResponseTextConfig;
import java.util.List;
import java.util.Map;
Map<String, Object> schema =
Map.of(
"type",
"object",
"properties",
Map.of(
"steps",
Map.of(
"type",
"array",
"items",
Map.of(
"type",
"object",
"properties",
Map.of(
"explanation", Map.of("type", "string"),
"output", Map.of("type", "string")),
"required",
List.of("explanation", "output"),
"additionalProperties",
false)),
"final_answer", Map.of("type", "string")),
"required",
List.of("steps", "final_answer"),
"additionalProperties",
false);
ResponseCreateParams params =
ResponseCreateParams.builder()
.model("gpt-5.6")
.inputOfResponse(
List.of(
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.SYSTEM)
.content(
"You are a helpful math tutor. Guide the user through the solution step by step.")
.build()),
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.USER)
.content("How can I solve 8x + 7 = -23?")
.build())))
.text(
ResponseTextConfig.builder()
.format(
ResponseFormatTextJsonSchemaConfig.builder()
.name("math_response")
.strict(true)
.schema(
JsonValue.from(schema)
.convert(ResponseFormatTextJsonSchemaConfig.Schema.class))
.build())
.build())
.build();
client.responses().create(params).output().stream()
.flatMap(item -> item.message().stream())
.flatMap(message -> message.content().stream())
.flatMap(content -> content.outputText().stream())
.forEach(text -> System.out.println(text.text()));1
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44require "openai"
client = OpenAI::Client.new
math_schema = {
type: :object,
properties: {
steps: {
type: :array,
items: {
type: :object,
properties: {
explanation: {type: :string},
output: {type: :string}
},
required: %w[explanation output],
additionalProperties: false
}
},
final_answer: {type: :string}
},
required: %w[steps final_answer],
additionalProperties: false
}
response = client.responses.create(
model: "gpt-5.6",
input: [
{
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?"}
],
text: {
format: {
type: :json_schema,
name: "math_response",
strict: true,
schema: math_schema
}
}
)
puts(response.output_text)1
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43curl https://api.openai.com/v1/responses \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.6",
"input": [
{
"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"
}
],
"text": {
"format": {
"type": "json_schema",
"name": "math_response",
"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
}
}
}'Note: the first request you make with any schema will have additional latency as our API processes the schema, but subsequent requests with the same schema will not have additional latency.
Step 3: Handle edge cases
In some cases, the model might not generate a valid response that matches the provided JSON schema.
This can happen in the case of a refusal, if the model refuses to answer for safety reasons, or if for example you reach a max tokens limit and the response is incomplete.
Python
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70try {
const completion = await openai.chat.completions.create({
model: "gpt-5.6",
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",
},
],
store: true,
response_format: {
type: "json_schema",
json_schema: {
name: "math_response",
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,
},
},
max_completion_tokens: 50,
});
if (completion.choices[0].finish_reason === "length") {
// Handle the case where the model did not return a complete response
throw new Error("Incomplete response");
}
const math_response = completion.choices[0].message;
if (math_response.refusal) {
// handle refusal
console.log(math_response.refusal);
} else if (math_response.content) {
console.log(math_response.content);
} else {
throw new Error("No response content");
}
} catch (e) {
// Handle edge cases
console.error(e);
}1
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54try:
response = client.chat.completions.create(
model="gpt-5.6",
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={
"type": "json_schema",
"json_schema": {
"name": "math_response",
"strict": True,
"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,
},
},
},
max_completion_tokens=50,
)
if response.choices[0].finish_reason == "length":
raise Exception("Incomplete response")
math_response = response.choices[0].message
if math_response.refusal:
print(math_response.refusal)
elif math_response.content:
print(math_response.content)
else:
raise Exception("No response content")
except Exception as e:
# handle errors like finish_reason, refusal, content_filter, etc.
print(e)1
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56package main
import (
"context"
"errors"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/shared"
)
func main() {
client := openai.NewClient()
completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
Model: "gpt-5.6",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.SystemMessage("You are a helpful math tutor. Guide the user through the solution step by step."),
openai.UserMessage("how can I solve 8x + 7 = -23"),
},
Store: openai.Bool(true),
MaxCompletionTokens: openai.Int(1024),
ResponseFormat: openai.ChatCompletionNewParamsResponseFormatUnion{
OfJSONSchema: &shared.ResponseFormatJSONSchemaParam{JSONSchema: shared.ResponseFormatJSONSchemaJSONSchemaParam{
Name: "math_response", Schema: mathSchema(), Strict: openai.Bool(true),
}},
},
})
if err != nil {
panic(err)
}
choice := completion.Choices[0]
if choice.FinishReason == "length" {
panic(errors.New("incomplete response"))
}
if choice.Message.Refusal != "" {
fmt.Println(choice.Message.Refusal)
return
}
if choice.Message.Content == "" {
panic(errors.New("no response content"))
}
fmt.Println(choice.Message.Content)
}
func mathSchema() map[string]any {
return map[string]any{
"type": "object",
"properties": map[string]any{
"steps": map[string]any{"type": "array", "items": map[string]any{"type": "object", "properties": map[string]any{"explanation": map[string]any{"type": "string"}, "output": map[string]any{"type": "string"}}, "required": []string{"explanation", "output"}, "additionalProperties": false}},
"final_answer": map[string]any{"type": "string"},
},
"required": []string{"steps", "final_answer"},
"additionalProperties": false,
}
}1
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63import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.chat.completions.ChatCompletion;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.List;
import java.util.Map;
Map<String, Object> stepSchema =
Map.of(
"type",
"object",
"properties",
Map.of(
"explanation", Map.of("type", "string"),
"output", Map.of("type", "string")),
"required",
List.of("explanation", "output"),
"additionalProperties",
false);
Map<String, Object> mathSchema =
Map.of(
"type",
"object",
"properties",
Map.of(
"steps", Map.of("type", "array", "items", stepSchema),
"final_answer", Map.of("type", "string")),
"required",
List.of("steps", "final_answer"),
"additionalProperties",
false);
ChatCompletionCreateParams params =
ChatCompletionCreateParams.builder()
.model("gpt-5.6")
.addSystemMessage(
"You are a helpful math tutor. Guide the user through the solution step by step.")
.addUserMessage("How can I solve 8x + 7 = -23?")
.maxCompletionTokens(1024)
.store(true)
.putAdditionalBodyProperty(
"response_format",
JsonValue.from(
Map.of(
"type",
"json_schema",
"json_schema",
Map.of("name", "math_response", "strict", true, "schema", mathSchema))))
.build();
var choice = client.chat().completions().create(params).choices().get(0);
if (choice.finishReason().equals(ChatCompletion.Choice.FinishReason.LENGTH)) {
System.out.println("Incomplete response");
} else if (choice.message().refusal().isPresent()) {
System.out.println(choice.message().refusal().orElseThrow());
} else {
System.out.println(
choice
.message()
.content()
.orElseThrow(() -> new IllegalStateException("No response content")));
}1
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48require "openai"
client = OpenAI::Client.new
step_schema = {
type: :object,
properties: {
explanation: {type: :string},
output: {type: :string}
},
required: %w[explanation output],
additionalProperties: false
}
math_schema = {
type: :object,
properties: {
steps: {type: :array, items: step_schema},
final_answer: {type: :string}
},
required: %w[steps final_answer],
additionalProperties: false
}
completion = client.chat.completions.create(
model: "gpt-5.6",
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?"}
],
max_completion_tokens: 1_024,
store: true,
response_format: {
type: :json_schema,
json_schema: {name: "math_response", strict: true, schema: math_schema}
}
)
choice = completion.choices.fetch(0)
if choice.finish_reason == OpenAI::Chat::ChatCompletion::Choice::FinishReason::LENGTH
raise "Incomplete response"
elsif choice.message.refusal
puts(choice.message.refusal)
else
content = choice.message.content or raise "No response content"
puts(content)
endPython
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77try {
const response = await openai.responses.create({
model: "gpt-5.6",
input: [
{
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",
},
],
max_output_tokens: 50,
text: {
format: {
type: "json_schema",
name: "math_response",
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,
},
},
});
if (
response.status === "incomplete" &&
response.incomplete_details.reason === "max_output_tokens"
) {
// Handle the case where the model did not return a complete response
throw new Error("Incomplete response");
}
const message = response.output.find((item) => item.type === "message");
const math_response = message?.content[0];
if (!math_response) {
throw new Error("No response content");
}
if (math_response.type === "refusal") {
// handle refusal
console.log(math_response.refusal);
} else if (math_response.type === "output_text") {
console.log(math_response.text);
} else {
throw new Error("No response content");
}
} catch (e) {
// Handle edge cases
console.error(e);
}1
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61try:
response = client.responses.create(
model="gpt-5.6",
input=[
{
"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"},
],
text={
"format": {
"type": "json_schema",
"name": "math_response",
"strict": True,
"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,
},
},
},
max_output_tokens=50,
)
if (
response.status == "incomplete"
and response.incomplete_details.reason == "max_output_tokens"
):
raise Exception("Incomplete response")
message = next((item for item in response.output if item.type == "message"), None)
math_response = message.content[0] if message and message.content else None
if not math_response:
raise Exception("No response content")
if math_response.type == "refusal":
print(math_response.refusal)
elif math_response.type == "output_text":
print(math_response.text)
else:
raise Exception("No response content")
except Exception as e:
# handle errors like finish_reason, refusal, content_filter, etc.
print(e)1
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66package main
import (
"context"
"errors"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/responses"
)
func main() {
client := openai.NewClient()
response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
Model: "gpt-5.6",
Input: responses.ResponseNewParamsInputUnion{OfInputItemList: responses.ResponseInputParam{
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("You are a helpful math tutor. Guide the user through the solution step by step.")},
responses.EasyInputMessageRoleSystem,
),
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("how can I solve 8x + 7 = -23")},
responses.EasyInputMessageRoleUser,
),
}},
MaxOutputTokens: openai.Int(1024),
Text: responses.ResponseTextConfigParam{Format: responses.ResponseFormatTextConfigUnionParam{
OfJSONSchema: &responses.ResponseFormatTextJSONSchemaConfigParam{Name: "math_response", Schema: mathSchema(), Strict: openai.Bool(true)},
}},
})
if err != nil {
panic(err)
}
if response.Status == "incomplete" {
panic(errors.New("incomplete response"))
}
for _, output := range response.Output {
if output.Type != "message" {
continue
}
for _, content := range output.AsMessage().Content {
if content.Type == "refusal" {
fmt.Println(content.AsRefusal().Refusal)
return
}
if content.Type == "output_text" {
fmt.Println(content.AsOutputText().Text)
return
}
}
}
panic(errors.New("no response content"))
}
func mathSchema() map[string]any {
return map[string]any{
"type": "object",
"properties": map[string]any{
"steps": map[string]any{"type": "array", "items": map[string]any{"type": "object", "properties": map[string]any{"explanation": map[string]any{"type": "string"}, "output": map[string]any{"type": "string"}}, "required": []string{"explanation", "output"}, "additionalProperties": false}},
"final_answer": map[string]any{"type": "string"},
},
"required": []string{"steps", "final_answer"},
"additionalProperties": false,
}
}1
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93import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.responses.EasyInputMessage;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.ResponseFormatTextJsonSchemaConfig;
import com.openai.models.responses.ResponseInputItem;
import com.openai.models.responses.ResponseStatus;
import com.openai.models.responses.ResponseTextConfig;
import java.util.List;
import java.util.Map;
ResponseCreateParams params =
ResponseCreateParams.builder()
.model("gpt-5.6")
.inputOfResponse(
List.of(
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.SYSTEM)
.content(
"You are a helpful math tutor. Guide the user through the solution step by step.")
.build()),
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.USER)
.content("How can I solve 8x + 7 = -23?")
.build())))
.text(
ResponseTextConfig.builder()
.format(
ResponseFormatTextJsonSchemaConfig.builder()
.name("math_response")
.strict(true)
.schema(
ResponseFormatTextJsonSchemaConfig.Schema.builder()
.putAdditionalProperty("type", JsonValue.from("object"))
.putAdditionalProperty(
"properties",
JsonValue.from(
Map.of(
"steps",
Map.of(
"type",
"array",
"items",
Map.of(
"type",
"object",
"properties",
Map.of(
"explanation",
Map.of("type", "string"),
"output",
Map.of("type", "string")),
"required",
List.of("explanation", "output"),
"additionalProperties",
false)),
"final_answer",
Map.of("type", "string"))))
.putAdditionalProperty(
"required",
JsonValue.from(List.of("steps", "final_answer")))
.putAdditionalProperty(
"additionalProperties", JsonValue.from(false))
.build())
.build())
.build())
.maxOutputTokens(1_024L)
.build();
var response = client.responses().create(params);
if (response.status().filter(ResponseStatus.INCOMPLETE::equals).isPresent()) {
throw new IllegalStateException("Incomplete response");
}
var content =
response.output().stream()
.flatMap(item -> item.message().stream())
.flatMap(message -> message.content().stream())
.findFirst()
.orElseThrow(() -> new IllegalStateException("No response content"));
if (content.refusal().isPresent()) {
System.out.println(content.refusal().orElseThrow().refusal());
} else {
System.out.println(
content
.outputText()
.orElseThrow(() -> new IllegalStateException("No response content"))
.text());
}1
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59require "openai"
client = OpenAI::Client.new
step_schema = {
type: :object,
properties: {
explanation: {type: :string},
output: {type: :string}
},
required: %w[explanation output],
additionalProperties: false
}
math_schema = {
type: :object,
properties: {
steps: {type: :array, items: step_schema},
final_answer: {type: :string}
},
required: %w[steps final_answer],
additionalProperties: false
}
response = client.responses.create(
model: "gpt-5.6",
input: [
{
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?"}
],
max_output_tokens: 1_024,
text: {
format: {
type: :json_schema,
name: "math_response",
strict: true,
schema: math_schema
}
}
)
if response.status == OpenAI::Responses::ResponseStatus::INCOMPLETE
raise "Incomplete response"
end
message = response.output.find do |item|
item.is_a?(OpenAI::Models::Responses::ResponseOutputMessage)
end
unless message.is_a?(OpenAI::Models::Responses::ResponseOutputMessage)
raise "No response message"
end
content = message.content.fetch(0)
if content.is_a?(OpenAI::Models::Responses::ResponseOutputRefusal)
puts(content.refusal)
else
puts(content.text)
endStep 4: Parse and use the generated structured data
Once you have confirmed that the response contains JSON matching your schema, parse it into your language’s native data structures. In typed languages, you can also model the data with a corresponding type or class.
For example:
Python
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5// The request that produces `response` appears earlier in this guide.
const content = response.choices[0].message.content;
if (!content) throw new Error("The response did not contain JSON output.");
const solution = JSON.parse(content);1
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20from typing import List
from pydantic import BaseModel, ValidationError
class Step(BaseModel):
explanation: str
output: str
class Solution(BaseModel):
steps: List[Step]
final_answer: str
try:
solution = Solution.model_validate_json(response.choices[0].message.content)
print(solution)
except ValidationError as error:
print(error.json())1System.out.println(new ObjectMapper().readTree(content));How to use Structured Outputs with text.format
Step 1: Define your schema
First you must design the JSON Schema that the model should be constrained to follow. See the examples at the top of this guide for reference.
While Structured Outputs supports much of JSON Schema, some features are unavailable either for performance or technical reasons. See here for more details.
Tips for your JSON Schema
To maximize the quality of model generations, we recommend the following:
- Name keys clearly and intuitively
- Create clear titles and descriptions for important keys in your structure
- Create and use evals to determine the structure that works best for your use case
Step 2: Supply your schema in the API call
To use Structured Outputs, simply specify
response_format: { "type": "json_schema", "json_schema": … , "strict": true }text: { format: { type: "json_schema", "strict": true, "schema": … } }For example:
Python
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41const response = await openai.chat.completions.create({
model: "gpt-5.6",
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" },
],
store: true,
response_format: {
type: "json_schema",
json_schema: {
name: "math_response",
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,
},
},
});
console.log(response.choices[0].message.content);1
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39response = client.chat.completions.create(
model="gpt-5.6",
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={
"type": "json_schema",
"json_schema": {
"name": "math_response",
"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(response.choices[0].message.content)1
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43package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/shared"
)
func main() {
client := openai.NewClient()
schema := mathSchema()
completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
Model: "gpt-5.6",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.SystemMessage("You are a helpful math tutor. Guide the user through the solution step by step."),
openai.UserMessage("how can I solve 8x + 7 = -23"),
},
Store: openai.Bool(true),
ResponseFormat: openai.ChatCompletionNewParamsResponseFormatUnion{
OfJSONSchema: &shared.ResponseFormatJSONSchemaParam{JSONSchema: shared.ResponseFormatJSONSchemaJSONSchemaParam{
Name: "math_response", Schema: schema, Strict: openai.Bool(true),
}},
},
})
if err != nil {
panic(err)
}
fmt.Println(completion.Choices[0].Message.Content)
}
func mathSchema() map[string]any {
return map[string]any{
"type": "object",
"properties": map[string]any{
"steps": map[string]any{"type": "array", "items": map[string]any{"type": "object", "properties": map[string]any{"explanation": map[string]any{"type": "string"}, "output": map[string]any{"type": "string"}}, "required": []string{"explanation", "output"}, "additionalProperties": false}},
"final_answer": map[string]any{"type": "string"},
},
"required": []string{"steps", "final_answer"},
"additionalProperties": false,
}
}1
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52import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.List;
import java.util.Map;
Map<String, Object> stepSchema =
Map.of(
"type",
"object",
"properties",
Map.of(
"explanation", Map.of("type", "string"),
"output", Map.of("type", "string")),
"required",
List.of("explanation", "output"),
"additionalProperties",
false);
Map<String, Object> mathSchema =
Map.of(
"type",
"object",
"properties",
Map.of(
"steps", Map.of("type", "array", "items", stepSchema),
"final_answer", Map.of("type", "string")),
"required",
List.of("steps", "final_answer"),
"additionalProperties",
false);
ChatCompletionCreateParams params =
ChatCompletionCreateParams.builder()
.model("gpt-5.6")
.addSystemMessage(
"You are a helpful math tutor. Guide the user through the solution step by step.")
.addUserMessage("How can I solve 8x + 7 = -23?")
.store(true)
.putAdditionalBodyProperty(
"response_format",
JsonValue.from(
Map.of(
"type",
"json_schema",
"json_schema",
Map.of("name", "math_response", "strict", true, "schema", mathSchema))))
.build();
client.chat().completions().create(params).choices().stream()
.flatMap(choice -> choice.message().content().stream())
.forEach(System.out::println);1
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41require "openai"
client = OpenAI::Client.new
math_schema = {
type: :object,
properties: {
steps: {
type: :array,
items: {
type: :object,
properties: {
explanation: {type: :string},
output: {type: :string}
},
required: %w[explanation output],
additionalProperties: false
}
},
final_answer: {type: :string}
},
required: %w[steps final_answer],
additionalProperties: false
}
completion = client.chat.completions.create(
model: "gpt-5.6",
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?"}
],
store: true,
response_format: {
type: :json_schema,
json_schema: {name: "math_response", strict: true, schema: math_schema}
}
)
puts(completion.choices.fetch(0).message.content)1
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43curl https://api.openai.com/v1/chat/completions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.6",
"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": {
"type": "json_schema",
"json_schema": {
"name": "math_response",
"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
}
}
}'Python
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40const response = await openai.responses.create({
model: "gpt-5.6",
input: [
{
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" },
],
text: {
format: {
type: "json_schema",
name: "math_response",
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,
},
},
});
console.log(response.output_text);1
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39response = client.responses.create(
model="gpt-5.6",
input=[
{
"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"},
],
text={
"format": {
"type": "json_schema",
"name": "math_response",
"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(response.output_text)1
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45package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/responses"
)
func main() {
client := openai.NewClient()
response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
Model: "gpt-5.6",
Input: responses.ResponseNewParamsInputUnion{OfInputItemList: responses.ResponseInputParam{
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("You are a helpful math tutor. Guide the user through the solution step by step.")},
responses.EasyInputMessageRoleSystem,
),
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("how can I solve 8x + 7 = -23")},
responses.EasyInputMessageRoleUser,
),
}},
Text: responses.ResponseTextConfigParam{Format: responses.ResponseFormatTextConfigUnionParam{
OfJSONSchema: &responses.ResponseFormatTextJSONSchemaConfigParam{Name: "math_response", Schema: mathSchema(), Strict: openai.Bool(true)},
}},
})
if err != nil {
panic(err)
}
fmt.Println(response.OutputText())
}
func mathSchema() map[string]any {
return map[string]any{
"type": "object",
"properties": map[string]any{
"steps": map[string]any{"type": "array", "items": map[string]any{"type": "object", "properties": map[string]any{"explanation": map[string]any{"type": "string"}, "output": map[string]any{"type": "string"}}, "required": []string{"explanation", "output"}, "additionalProperties": false}},
"final_answer": map[string]any{"type": "string"},
},
"required": []string{"steps", "final_answer"},
"additionalProperties": false,
}
}1
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73import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.responses.EasyInputMessage;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.ResponseFormatTextJsonSchemaConfig;
import com.openai.models.responses.ResponseInputItem;
import com.openai.models.responses.ResponseTextConfig;
import java.util.List;
import java.util.Map;
Map<String, Object> schema =
Map.of(
"type",
"object",
"properties",
Map.of(
"steps",
Map.of(
"type",
"array",
"items",
Map.of(
"type",
"object",
"properties",
Map.of(
"explanation", Map.of("type", "string"),
"output", Map.of("type", "string")),
"required",
List.of("explanation", "output"),
"additionalProperties",
false)),
"final_answer", Map.of("type", "string")),
"required",
List.of("steps", "final_answer"),
"additionalProperties",
false);
ResponseCreateParams params =
ResponseCreateParams.builder()
.model("gpt-5.6")
.inputOfResponse(
List.of(
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.SYSTEM)
.content(
"You are a helpful math tutor. Guide the user through the solution step by step.")
.build()),
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.USER)
.content("How can I solve 8x + 7 = -23?")
.build())))
.text(
ResponseTextConfig.builder()
.format(
ResponseFormatTextJsonSchemaConfig.builder()
.name("math_response")
.strict(true)
.schema(
JsonValue.from(schema)
.convert(ResponseFormatTextJsonSchemaConfig.Schema.class))
.build())
.build())
.build();
client.responses().create(params).output().stream()
.flatMap(item -> item.message().stream())
.flatMap(message -> message.content().stream())
.flatMap(content -> content.outputText().stream())
.forEach(text -> System.out.println(text.text()));1
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44require "openai"
client = OpenAI::Client.new
math_schema = {
type: :object,
properties: {
steps: {
type: :array,
items: {
type: :object,
properties: {
explanation: {type: :string},
output: {type: :string}
},
required: %w[explanation output],
additionalProperties: false
}
},
final_answer: {type: :string}
},
required: %w[steps final_answer],
additionalProperties: false
}
response = client.responses.create(
model: "gpt-5.6",
input: [
{
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?"}
],
text: {
format: {
type: :json_schema,
name: "math_response",
strict: true,
schema: math_schema
}
}
)
puts(response.output_text)1
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43curl https://api.openai.com/v1/responses \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.6",
"input": [
{
"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"
}
],
"text": {
"format": {
"type": "json_schema",
"name": "math_response",
"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
}
}
}'Note: the first request you make with any schema will have additional latency as our API processes the schema, but subsequent requests with the same schema will not have additional latency.
Step 3: Handle edge cases
In some cases, the model might not generate a valid response that matches the provided JSON schema.
This can happen in the case of a refusal, if the model refuses to answer for safety reasons, or if for example you reach a max tokens limit and the response is incomplete.
Python
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70try {
const completion = await openai.chat.completions.create({
model: "gpt-5.6",
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",
},
],
store: true,
response_format: {
type: "json_schema",
json_schema: {
name: "math_response",
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,
},
},
max_completion_tokens: 50,
});
if (completion.choices[0].finish_reason === "length") {
// Handle the case where the model did not return a complete response
throw new Error("Incomplete response");
}
const math_response = completion.choices[0].message;
if (math_response.refusal) {
// handle refusal
console.log(math_response.refusal);
} else if (math_response.content) {
console.log(math_response.content);
} else {
throw new Error("No response content");
}
} catch (e) {
// Handle edge cases
console.error(e);
}1
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54try:
response = client.chat.completions.create(
model="gpt-5.6",
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={
"type": "json_schema",
"json_schema": {
"name": "math_response",
"strict": True,
"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,
},
},
},
max_completion_tokens=50,
)
if response.choices[0].finish_reason == "length":
raise Exception("Incomplete response")
math_response = response.choices[0].message
if math_response.refusal:
print(math_response.refusal)
elif math_response.content:
print(math_response.content)
else:
raise Exception("No response content")
except Exception as e:
# handle errors like finish_reason, refusal, content_filter, etc.
print(e)1
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56package main
import (
"context"
"errors"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/shared"
)
func main() {
client := openai.NewClient()
completion, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
Model: "gpt-5.6",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.SystemMessage("You are a helpful math tutor. Guide the user through the solution step by step."),
openai.UserMessage("how can I solve 8x + 7 = -23"),
},
Store: openai.Bool(true),
MaxCompletionTokens: openai.Int(1024),
ResponseFormat: openai.ChatCompletionNewParamsResponseFormatUnion{
OfJSONSchema: &shared.ResponseFormatJSONSchemaParam{JSONSchema: shared.ResponseFormatJSONSchemaJSONSchemaParam{
Name: "math_response", Schema: mathSchema(), Strict: openai.Bool(true),
}},
},
})
if err != nil {
panic(err)
}
choice := completion.Choices[0]
if choice.FinishReason == "length" {
panic(errors.New("incomplete response"))
}
if choice.Message.Refusal != "" {
fmt.Println(choice.Message.Refusal)
return
}
if choice.Message.Content == "" {
panic(errors.New("no response content"))
}
fmt.Println(choice.Message.Content)
}
func mathSchema() map[string]any {
return map[string]any{
"type": "object",
"properties": map[string]any{
"steps": map[string]any{"type": "array", "items": map[string]any{"type": "object", "properties": map[string]any{"explanation": map[string]any{"type": "string"}, "output": map[string]any{"type": "string"}}, "required": []string{"explanation", "output"}, "additionalProperties": false}},
"final_answer": map[string]any{"type": "string"},
},
"required": []string{"steps", "final_answer"},
"additionalProperties": false,
}
}1
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63import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.chat.completions.ChatCompletion;
import com.openai.models.chat.completions.ChatCompletionCreateParams;
import java.util.List;
import java.util.Map;
Map<String, Object> stepSchema =
Map.of(
"type",
"object",
"properties",
Map.of(
"explanation", Map.of("type", "string"),
"output", Map.of("type", "string")),
"required",
List.of("explanation", "output"),
"additionalProperties",
false);
Map<String, Object> mathSchema =
Map.of(
"type",
"object",
"properties",
Map.of(
"steps", Map.of("type", "array", "items", stepSchema),
"final_answer", Map.of("type", "string")),
"required",
List.of("steps", "final_answer"),
"additionalProperties",
false);
ChatCompletionCreateParams params =
ChatCompletionCreateParams.builder()
.model("gpt-5.6")
.addSystemMessage(
"You are a helpful math tutor. Guide the user through the solution step by step.")
.addUserMessage("How can I solve 8x + 7 = -23?")
.maxCompletionTokens(1024)
.store(true)
.putAdditionalBodyProperty(
"response_format",
JsonValue.from(
Map.of(
"type",
"json_schema",
"json_schema",
Map.of("name", "math_response", "strict", true, "schema", mathSchema))))
.build();
var choice = client.chat().completions().create(params).choices().get(0);
if (choice.finishReason().equals(ChatCompletion.Choice.FinishReason.LENGTH)) {
System.out.println("Incomplete response");
} else if (choice.message().refusal().isPresent()) {
System.out.println(choice.message().refusal().orElseThrow());
} else {
System.out.println(
choice
.message()
.content()
.orElseThrow(() -> new IllegalStateException("No response content")));
}1
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48require "openai"
client = OpenAI::Client.new
step_schema = {
type: :object,
properties: {
explanation: {type: :string},
output: {type: :string}
},
required: %w[explanation output],
additionalProperties: false
}
math_schema = {
type: :object,
properties: {
steps: {type: :array, items: step_schema},
final_answer: {type: :string}
},
required: %w[steps final_answer],
additionalProperties: false
}
completion = client.chat.completions.create(
model: "gpt-5.6",
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?"}
],
max_completion_tokens: 1_024,
store: true,
response_format: {
type: :json_schema,
json_schema: {name: "math_response", strict: true, schema: math_schema}
}
)
choice = completion.choices.fetch(0)
if choice.finish_reason == OpenAI::Chat::ChatCompletion::Choice::FinishReason::LENGTH
raise "Incomplete response"
elsif choice.message.refusal
puts(choice.message.refusal)
else
content = choice.message.content or raise "No response content"
puts(content)
endPython
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77try {
const response = await openai.responses.create({
model: "gpt-5.6",
input: [
{
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",
},
],
max_output_tokens: 50,
text: {
format: {
type: "json_schema",
name: "math_response",
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,
},
},
});
if (
response.status === "incomplete" &&
response.incomplete_details.reason === "max_output_tokens"
) {
// Handle the case where the model did not return a complete response
throw new Error("Incomplete response");
}
const message = response.output.find((item) => item.type === "message");
const math_response = message?.content[0];
if (!math_response) {
throw new Error("No response content");
}
if (math_response.type === "refusal") {
// handle refusal
console.log(math_response.refusal);
} else if (math_response.type === "output_text") {
console.log(math_response.text);
} else {
throw new Error("No response content");
}
} catch (e) {
// Handle edge cases
console.error(e);
}1
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61try:
response = client.responses.create(
model="gpt-5.6",
input=[
{
"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"},
],
text={
"format": {
"type": "json_schema",
"name": "math_response",
"strict": True,
"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,
},
},
},
max_output_tokens=50,
)
if (
response.status == "incomplete"
and response.incomplete_details.reason == "max_output_tokens"
):
raise Exception("Incomplete response")
message = next((item for item in response.output if item.type == "message"), None)
math_response = message.content[0] if message and message.content else None
if not math_response:
raise Exception("No response content")
if math_response.type == "refusal":
print(math_response.refusal)
elif math_response.type == "output_text":
print(math_response.text)
else:
raise Exception("No response content")
except Exception as e:
# handle errors like finish_reason, refusal, content_filter, etc.
print(e)1
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66package main
import (
"context"
"errors"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/responses"
)
func main() {
client := openai.NewClient()
response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
Model: "gpt-5.6",
Input: responses.ResponseNewParamsInputUnion{OfInputItemList: responses.ResponseInputParam{
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("You are a helpful math tutor. Guide the user through the solution step by step.")},
responses.EasyInputMessageRoleSystem,
),
responses.ResponseInputItemParamOfMessage(
responses.ResponseInputMessageContentListParam{responses.ResponseInputContentParamOfInputText("how can I solve 8x + 7 = -23")},
responses.EasyInputMessageRoleUser,
),
}},
MaxOutputTokens: openai.Int(1024),
Text: responses.ResponseTextConfigParam{Format: responses.ResponseFormatTextConfigUnionParam{
OfJSONSchema: &responses.ResponseFormatTextJSONSchemaConfigParam{Name: "math_response", Schema: mathSchema(), Strict: openai.Bool(true)},
}},
})
if err != nil {
panic(err)
}
if response.Status == "incomplete" {
panic(errors.New("incomplete response"))
}
for _, output := range response.Output {
if output.Type != "message" {
continue
}
for _, content := range output.AsMessage().Content {
if content.Type == "refusal" {
fmt.Println(content.AsRefusal().Refusal)
return
}
if content.Type == "output_text" {
fmt.Println(content.AsOutputText().Text)
return
}
}
}
panic(errors.New("no response content"))
}
func mathSchema() map[string]any {
return map[string]any{
"type": "object",
"properties": map[string]any{
"steps": map[string]any{"type": "array", "items": map[string]any{"type": "object", "properties": map[string]any{"explanation": map[string]any{"type": "string"}, "output": map[string]any{"type": "string"}}, "required": []string{"explanation", "output"}, "additionalProperties": false}},
"final_answer": map[string]any{"type": "string"},
},
"required": []string{"steps", "final_answer"},
"additionalProperties": false,
}
}1
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93import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.core.JsonValue;
import com.openai.models.responses.EasyInputMessage;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.ResponseFormatTextJsonSchemaConfig;
import com.openai.models.responses.ResponseInputItem;
import com.openai.models.responses.ResponseStatus;
import com.openai.models.responses.ResponseTextConfig;
import java.util.List;
import java.util.Map;
ResponseCreateParams params =
ResponseCreateParams.builder()
.model("gpt-5.6")
.inputOfResponse(
List.of(
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.SYSTEM)
.content(
"You are a helpful math tutor. Guide the user through the solution step by step.")
.build()),
ResponseInputItem.ofEasyInputMessage(
EasyInputMessage.builder()
.role(EasyInputMessage.Role.USER)
.content("How can I solve 8x + 7 = -23?")
.build())))
.text(
ResponseTextConfig.builder()
.format(
ResponseFormatTextJsonSchemaConfig.builder()
.name("math_response")
.strict(true)
.schema(
ResponseFormatTextJsonSchemaConfig.Schema.builder()
.putAdditionalProperty("type", JsonValue.from("object"))
.putAdditionalProperty(
"properties",
JsonValue.from(
Map.of(
"steps",
Map.of(
"type",
"array",
"items",
Map.of(
"type",
"object",
"properties",
Map.of(
"explanation",
Map.of("type", "string"),
"output",
Map.of("type", "string")),
"required",
List.of("explanation", "output"),
"additionalProperties",
false)),
"final_answer",
Map.of("type", "string"))))
.putAdditionalProperty(
"required",
JsonValue.from(List.of("steps", "final_answer")))
.putAdditionalProperty(
"additionalProperties", JsonValue.from(false))
.build())
.build())
.build())
.maxOutputTokens(1_024L)
.build();
var response = client.responses().create(params);
if (response.status().filter(ResponseStatus.INCOMPLETE::equals).isPresent()) {
throw new IllegalStateException("Incomplete response");
}
var content =
response.output().stream()
.flatMap(item -> item.message().stream())
.flatMap(message -> message.content().stream())
.findFirst()
.orElseThrow(() -> new IllegalStateException("No response content"));
if (content.refusal().isPresent()) {
System.out.println(content.refusal().orElseThrow().refusal());
} else {
System.out.println(
content
.outputText()
.orElseThrow(() -> new IllegalStateException("No response content"))
.text());