`;
});
clearInterval(interval);
}
attempts++;
if (attempts >= maxAttempts) {
clearInterval(interval);
}
}, 200);
return ;
}
};
`Structured Outputs` enables the model to generate responses that adhere to your supplied [JSON Schema](https://json-schema.org/specification).
## Supported Models
The following models support `Structured Outputs`:
## Quick Start Guide
This guide demonstrates how to use `Structured Outputs` ability to generate the JSON response via `json_schema` response format. We will walk through a complete Python code example.
### 1. Initialize the Client
First, you need to initialize the client with your Novita API key.
```python theme={"system"}
from openai import OpenAI
client = OpenAI(
base_url="https://api.novita.ai/openai",
# Get the Novita AI API Key from: https://novita.ai/settings/key-management.
api_key="",
)
# Go to the [Models](https://novita.ai/models) page to see the models that support `Structured Outputs`.
model = "mistralai/mistral-7b-instruct"
```
### 2. Define the JSON schema
This example creates a schema for extracting expense information from the user's input.
```python theme={"system"}
# Define 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 JSON schema for structured response.
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. Request chat completion API
Now, make the chat completion request to the Novita endpoint.
This request includes the `response_format` parameter, defining the JSON schema we defined in the previous step.
```python theme={"system"}
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 the 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={"system"}
{
"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
}
```
### The Complete Code
```python theme={"system"}
from openai import OpenAI
import json
client = OpenAI(
base_url="https://api.novita.ai/openai",
# Get the Novita AI API Key from: https://novita.ai/settings/key-management.
api_key="",
)
# Go to the [Models](https://novita.ai/models) page to see the models that support `Structured Outputs`.
model = "mistralai/mistral-7b-instruct"
# Example of using JSON Schema for structured output
# This example creates a schema for extracting expense information
# Define 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 JSON schema for structured response
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 the 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)
```