`;
});
clearInterval(interval);
}
attempts++;
if (attempts >= maxAttempts) {
clearInterval(interval);
}
}, 200);
return ;
}
};
## Use Cases
Structured Outputs enables models to generate responses that conform to the [JSON Schema](https://json-schema.org/specification) you provide, making generated results more controllable and easier to parse. This feature facilitates parsing and processing in downstream logic, and also helps integrate results into business systems. It is suitable for a variety of automation and data processing scenarios.
## Supported Models
The following models support structured outputs:
## Usage
Add the following information to your request:
* **Set parameters**: Specify your defined JSON Schema through the `response_format` parameter.
* **Prompt guidance**: Instruct the model in your prompt to produce structured output.
## Example
The following provides a complete Python code example demonstrating how to use Structured Outputs to generate a JSON response that conforms to the JSON Schema you provide.
### 1. Initialize the Client
You need to initialize the client with your JieKou AI API key.
```python theme={null}
from openai import OpenAI
client = OpenAI(
base_url="https://api.highwayapi.ai/openai",
api_key="",
)
model = "qwen/qwen-2.5-72b-instruct"
```
### 2. Define the JSON Schema
You need to define a JSON Schema. The following example creates a JSON Schema that extracts expense information from user input.
```python theme={null}
# Define the system prompt for expense tracking.
system_prompt = """You are an expense tracking assistant.
Extract expense information from the user's input and format it according to the provided schema."""
# Define the JSON Schema for structured responses.
response_format = {
"type": "json_schema",
"json_schema": {
"name": "expense_tracking_schema",
"schema": {
"type": "object",
"properties": {
"expenses": {
"type": "array",
"items": {
"type": "object",
"properties": {
"description": {
"type": "string",
"description": "Description of the expense"
},
"amount": {
"type": "number",
"description": "Amount spent in dollars"
},
"date": {
"type": "string",
"description": "When the expense occurred"
},
"category": {
"type": "string",
"description": "Category of expense (e.g., food, office, travel)"
}
},
"required": [
"description",
"amount"
]
}
},
"total": {
"type": "number",
"description": "Total amount of all expenses"
}
},
"required": [
"expenses",
"total"
],
},
},
}
```
### 3. Make an API Request
Create an API request. This request includes the `response_format` parameter, which specifies the JSON schema defined in the previous step.
```python theme={null}
chat_completion = client.chat.completions.create(
model=model,
messages=[
{
"role": "system",
"content": system_prompt,
},
{
"role": "user",
"content": """I spent $120 on dinner at an Italian restaurant last Friday with my colleagues.
Also bought office supplies for $45 on Monday.""",
},
],
max_tokens=1024,
temperature=0.8,
stream=False,
response_format=response_format,
)
response_content = chat_completion.choices[0].message.content
# Parse and prettify JSON
try:
json_response = json.loads(response_content)
prettified_json = json.dumps(json_response, indent=2)
print(prettified_json)
except json.JSONDecodeError:
print("Could not parse response as JSON. Raw response:")
print(response_content)
```
**Output**:
```json theme={null}
{
"expenses": [
{
"date": "2023-03-17",
"description": "Dinner at Italian restaurant",
"amount": 120,
"category": "Food & Dining"
},
{
"date": "2023-03-13",
"description": "Office supplies",
"amount": 45,
"category": "Office Supplies"
}
],
"total": 165
}
```
## Complete Code
```python theme={null}
from openai import OpenAI
import json
client = OpenAI(
base_url="https://api.highwayapi.ai/openai",
api_key="",
)
model = "qwen/qwen-2.5-72b-instruct"
# Example of using JSON Schema for structured outputs
# This example creates a schema for extracting expense information
# Define the system prompt for expense tracking
system_prompt = """You are an expense tracking assistant.
Extract expense information from the user's input and format it according to the provided schema."""
# Define the JSON schema for structured responses
response_format = {
"type": "json_schema",
"json_schema": {
"name": "expense_tracking_schema",
"schema": {
"type": "object",
"properties": {
"expenses": {
"type": "array",
"items": {
"type": "object",
"properties": {
"description": {
"type": "string",
"description": "Description of the expense"
},
"amount": {
"type": "number",
"description": "Amount spent in dollars"
},
"date": {
"type": "string",
"description": "When the expense occurred"
},
"category": {
"type": "string",
"description": "Category of expense (e.g., food, office, travel)"
}
},
"required": [
"description",
"amount"
]
}
},
"total": {
"type": "number",
"description": "Total amount of all expenses"
}
},
"required": [
"expenses",
"total"
],
},
},
}
chat_completion = client.chat.completions.create(
model=model,
messages=[
{
"role": "system",
"content": system_prompt,
},
{
"role": "user",
"content": """I spent $120 on dinner at an Italian restaurant last Friday with my colleagues.
Also bought office supplies for $45 on Monday.""",
},
],
max_tokens=1024,
temperature=0.8,
stream=False,
response_format=response_format,
)
response_content = chat_completion.choices[0].message.content
# Parse and prettify JSON
try:
json_response = json.loads(response_content)
prettified_json = json.dumps(json_response, indent=2)
print(prettified_json)
except json.JSONDecodeError:
print("Could not parse response as JSON. Raw response:")
print(response_content)
```