Ollama’s Cloud currently does not support structured outputs.
Generating structured JSON
- cURL
- Python
- JavaScript
curl -X POST http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
"model": "gpt-oss",
"messages": [{"role": "user", "content": "Tell me about Canada in one line"}],
"stream": false,
"format": "json"
}'
from ollama import chat
response = chat(
model='gpt-oss',
messages=[{'role': 'user', 'content': 'Tell me about Canada.'}],
format='json'
)
print(response.message.content)
import ollama from 'ollama'
const response = await ollama.chat({
model: 'gpt-oss',
messages: [{ role: 'user', content: 'Tell me about Canada.' }],
format: 'json'
})
console.log(response.message.content)
Generating structured JSON with a schema
Provide a JSON schema to theformat field.
It is ideal to also pass the JSON schema as a string in the prompt to ground the model’s response.
- cURL
- Python
- JavaScript
curl -X POST http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
"model": "gpt-oss",
"messages": [{"role": "user", "content": "Tell me about Canada."}],
"stream": false,
"format": {
"type": "object",
"properties": {
"name": {"type": "string"},
"capital": {"type": "string"},
"languages": {
"type": "array",
"items": {"type": "string"}
}
},
"required": ["name", "capital", "languages"]
}
}'
Use Pydantic models and pass
model_json_schema() to format, then validate the response:from ollama import chat
from pydantic import BaseModel
class Country(BaseModel):
name: str
capital: str
languages: list[str]
response = chat(
model='gpt-oss',
messages=[{'role': 'user', 'content': 'Tell me about Canada.'}],
format=Country.model_json_schema(),
)
country = Country.model_validate_json(response.message.content)
print(country)
Serialize a Zod schema with
z.toJSONSchema() and parse the structured response:import ollama from 'ollama'
import * as z from 'zod'
const Country = z.object({
name: z.string(),
capital: z.string(),
languages: z.array(z.string()),
})
const response = await ollama.chat({
model: 'gpt-oss',
messages: [{ role: 'user', content: 'Tell me about Canada.' }],
format: z.toJSONSchema(Country),
})
const country = Country.parse(JSON.parse(response.message.content))
console.log(country)
Example: Extract structured data
Define the objects you want returned and let the model populate the fields:from ollama import chat
from pydantic import BaseModel
class Pet(BaseModel):
name: str
animal: str
age: int
color: str | None
favorite_toy: str | None
class PetList(BaseModel):
pets: list[Pet]
response = chat(
model='gpt-oss',
messages=[{'role': 'user', 'content': 'I have two cats named Luna and Loki...'}],
format=PetList.model_json_schema(),
)
pets = PetList.model_validate_json(response.message.content)
print(pets)
Example: Vision with structured outputs
Vision models accept the sameformat parameter, enabling deterministic descriptions of images:
from ollama import chat
from pydantic import BaseModel
from typing import Literal, Optional
class Object(BaseModel):
name: str
confidence: float
attributes: str
class ImageDescription(BaseModel):
summary: str
objects: list[Object]
scene: str
colors: list[str]
time_of_day: Literal['Morning', 'Afternoon', 'Evening', 'Night']
setting: Literal['Indoor', 'Outdoor', 'Unknown']
text_content: Optional[str] = None
response = chat(
model='gemma4',
messages=[{
'role': 'user',
'content': 'Describe this photo and list the objects you detect.',
'images': ['path/to/image.jpg'],
}],
format=ImageDescription.model_json_schema(),
options={'temperature': 0},
)
image_description = ImageDescription.model_validate_json(response.message.content)
print(image_description)
Tips for reliable structured outputs
- Define schemas with Pydantic (Python) or Zod (JavaScript) so they can be reused for validation.
- Lower the temperature (e.g., set it to
0) for more deterministic completions. - Structured outputs work through the OpenAI-compatible API via
response_format