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Build the schema and validate the response
Define the fields and types your application expects with a Pydantic model. Ollama’s structured outputs documentation shows supplying that model’s JSON Schema as format and parsing the returned message content with model_validate_json(). The example below is a pattern to adapt, not a guarantee that a particular model or installation will behave identically.
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from ollama import chat
from pydantic import BaseModel
class Item(BaseModel):
name: str
quantity: int
response = chat(
model="your-installed-model",
messages=[
{
"role": "user",
"content": "Extract the item and quantity from: ...",
}
],
format=Item.model_json_schema(),
options={"temperature": 0},
)
item = Item.model_validate_json(response.message.content)
print(item)Replace your-installed-model with a model available in your Ollama environment, and provide the actual source text in the prompt. Make the extraction rules explicit: say what to do when a value is missing, unclear, or expressed in a way that does not fit the requested type. Ollama recommends including the schema as a string in the prompt as additional guidance; the schema passed through format is the machine-readable constraint. See the Ollama structured outputs documentation and the official Python client examples.
Choose JSON mode or a schema
Ollama supports both format="json" and a JSON Schema object in format. JSON mode asks for a valid JSON object but does not declare the application-specific fields and types your code expects. When the next step depends on known properties, use a schema and validate the result against the corresponding model. The chat API documentation describes the format parameter.
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Handle complete and streamed replies differently
With a complete, non-streamed reply, validate the assistant message content after the call returns, as in the example. Streamed replies arrive in pieces: collect the complete assistant content first, then parse and validate it. A partial fragment is not a completed JSON extraction and should not be passed to the validator as if it were one. Ollama documents both response modes in its chat API reference.
Separate valid structure from correct extraction
Pydantic validation checks whether the response can be parsed into the declared model and whether its fields meet that model’s requirements. It cannot determine whether the model interpreted the source correctly. For important data, add application-level checks that compare values with the input and define how your workflow handles missing, ambiguous, or implausible results.
The official Python example sets temperature to 0 to make responses more deterministic. Treat that as a way to reduce variability, not as a promise of identical output or factual accuracy. Keep validation and any domain-specific checks in place.
Troubleshoot format errors with current documentation
A reported issue opened on December 7, 2024, described a format type error with ollama-python 0.4.3. That report is historical; it does not establish a current minimum version or show that the same problem affects current releases. If the example fails, check the current structured outputs guide and the Python client documentation for syntax and compatibility applicable to your installation.
The structured outputs documentation also states that Ollama Cloud does not currently support structured outputs. Because cloud capabilities can change, verify the current documentation if you plan to use that deployment path.
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