# Source: https://docs.ollama.com/capabilities/structured-outputs.md

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ollama.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Structured Outputs

Structured outputs let you enforce a JSON schema on model responses so you can reliably extract structured data, describe images, or keep every reply consistent.

## Generating structured JSON

<Tabs>
  <Tab title="cURL">
    ```shell  theme={"system"}
    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"
    }'
    ```
  </Tab>

  <Tab title="Python">
    ```python  theme={"system"}
    from ollama import chat

    response = chat(
      model='gpt-oss',
      messages=[{'role': 'user', 'content': 'Tell me about Canada.'}],
      format='json'
    )
    print(response.message.content)
    ```
  </Tab>

  <Tab title="JavaScript">
    ```javascript  theme={"system"}
    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)
    ```
  </Tab>
</Tabs>

## Generating structured JSON with a schema

Provide a JSON schema to the `format` field.

<Note>
  It is ideal to also pass the JSON schema as a string in the prompt to ground the model's response.
</Note>

<Tabs>
  <Tab title="cURL">
    ```shell  theme={"system"}
    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"]
      }
    }'
    ```
  </Tab>

  <Tab title="Python">
    Use Pydantic models and pass `model_json_schema()` to `format`, then validate the response:

    ```python  theme={"system"}
    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)
    ```
  </Tab>

  <Tab title="JavaScript">
    Serialize a Zod schema with `zodToJsonSchema()` and parse the structured response:

    ```javascript  theme={"system"}
    import ollama from 'ollama'
    import { z } from 'zod'
    import { zodToJsonSchema } from 'zod-to-json-schema'

    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: zodToJsonSchema(Country),
    })

    const country = Country.parse(JSON.parse(response.message.content))
    console.log(country)
    ```
  </Tab>
</Tabs>

## Example: Extract structured data

Define the objects you want returned and let the model populate the fields:

```python  theme={"system"}
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 same `format` parameter, enabling deterministic descriptions of images:

```python  theme={"system"}
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='gemma3',
  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`