Define the fields you need in a Pydantic model, send its generated JSON Schema to Ollama through the chat API’s format parameter, then validate the returned message with model_validate_json() before your application uses it. This gives you a repeatable data shape—not proof that the model correctly read the transaction.
What this pipeline can—and cannot—guarantee
Ollama’s structured outputs feature constrains a response to a specified JSON Schema; Ollama identifies document parsing as one use case in its December 6, 2024 announcement. Pydantic can define the expected fields, produce the schema, and validate the response content.
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Those controls address output structure and whether the result conforms to your model. They do not establish that an amount, date, merchant, or transaction type was extracted correctly from a notification. The cited documentation does not provide a bank-notification accuracy guarantee, a universal notification format, or a canonical set of transaction fields. Treat the result as extracted data that may need application checks or human review.
Choose fields that reflect what the notification actually says
Start with the data your application needs, then distinguish observed information from information that is absent or ambiguous. Do not make a field required just because it would be useful: a notification may not include it. The sources do not prescribe a universal transaction schema or bank-specific fields, so the model below is an illustrative starting point, not a standard.
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from pydantic import BaseModel
class TransactionNotification(BaseModel):
amount: str | None = None
currency: str | None = None
merchant: str | None = None
transaction_date: str | None = None
account_hint: str | None = None
transaction_type: str | None = NoneUsing optional fields allows the model to represent missing details instead of forcing it to invent them. This example keeps amounts and dates as text because notification formats and parsing rules vary; if your application needs numeric amounts or normalized dates, define and test those conversions against the formats you actually receive. Decide explicitly how to represent uncertainty and missing values in your own schema.
JSON mode or a Pydantic-generated JSON Schema?
Ollama’s API documents two relevant formats: the string "json" and a JSON Schema object supplied through format. JSON mode requests JSON generally; a schema expresses the particular response shape your application expects. Ollama’s API specification describes the parameter, and its structured outputs guide demonstrates the schema approach.
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| Choice | What you specify | When it fits |
|---|---|---|
format="json" |
That the response should be JSON. | When JSON syntax is sufficient and your application handles the shape separately. |
A JSON Schema object in format |
The expected fields and their schema-defined structure. | When you want the generated response guided by a specific data contract, such as one generated from a Pydantic model. |
Schema-constrained generation is not a replacement for application-side validation. The response still needs to be checked against the model before it enters downstream code.
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The following follows the documented Ollama Python pattern: generate a JSON Schema from the Pydantic model, provide it as format in a chat request, and pass the returned message content to Pydantic’s JSON validator. The official Python example demonstrates this flow.
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import ollama
from pydantic import BaseModel
class TransactionNotification(BaseModel):
amount: str | None = None
currency: str | None = None
merchant: str | None = None
transaction_date: str | None = None
account_hint: str | None = None
transaction_type: str | None = None
notification_text = "Your card purchase at Example Store was USD 12.34."
response = ollama.chat(
model="your-model",
messages=[
{
"role": "user",
"content": (
"Extract only information stated in this notification. "
"Use null for fields that are absent or unclear.nn"
f"Notification: {notification_text}"
),
}
],
format=TransactionNotification.model_json_schema(),
)
transaction = TransactionNotification.model_validate_json(
response.message.content
)
# Use the validated Pydantic instance in application code.
print(transaction)- Define the model. Include only fields your application needs, and make fields optional when the notification may omit them.
- Generate the schema. Call
TransactionNotification.model_json_schema()and pass the resulting schema object as the request’sformatvalue. - Ask for extraction, not completion. In the prompt, tell the model to use only information stated in the notification and to leave missing or unclear fields unset, in line with your schema’s representation.
- Validate before use. Pass
response.message.contenttoTransactionNotification.model_validate_json(). Use the returned Pydantic instance only after validation succeeds.
The example’s prompt and schema choices are application design decisions; the documented interface establishes how to request schema-shaped output and validate its JSON, not a transaction-specific prompt recipe.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Handle invalid output and questionable values separately
If validation raises an error, do not pass the unvalidated content downstream as though it were a transaction record. Log or quarantine the failure according to your application’s needs, and decide whether to retry, request review, or discard it. These are engineering recommendations: the cited Ollama and Pydantic sources do not define a bank-notification retry policy, transaction error taxonomy, confidence threshold, or review workflow.
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Even when validation succeeds, consider checks that address meaning rather than shape. For example, your application may need to reject implausible dates, preserve the original notification for audit or review, or require confirmation for consequential actions. Determine those rules from your own data, product requirements, and applicable obligations; schema validation alone cannot verify a transaction against a bank’s records.
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Check whether your Ollama deployment enforces the schema
Deployment affects how much the request’s JSON Schema constrains generation. The Pydantic Ollama integration documentation, accessed October 4, 2026, says self-hosted Ollama v0.5.0 and later honors json_schema, while Ollama Cloud currently accepts the parameter without enforcing the schema.
| Deployment | Schema enforcement stated in the cited documentation | Practical implication |
|---|---|---|
| Self-hosted Ollama v0.5.0+ | Honors json_schema, according to the Pydantic integration page accessed October 4, 2026. |
Confirm the installed version and client behavior, and still validate the returned content in your application. |
| Ollama Cloud | Currently accepts the parameter without enforcing the schema, according to that same page. | Do not assume the requested schema constrains generation; retain application-side validation and verify current service behavior. |
Behavior can depend on the deployment, installed versions, model, and client interface. Verify the actual combination you intend to run rather than treating schema enforcement as universal.
Account for notification privacy and format differences
The Ollama and Pydantic documentation cited here describes software interfaces, not bank-specific notification formats, device or operating-system capture behavior, privacy protections, or jurisdiction-specific compliance requirements. A self-hosted deployment is not, by itself, evidence that a system is private, secure, or compliant. Before collecting or processing real notifications, establish what data is captured, where it is sent and stored, who can access it, and which organizational and legal requirements apply.
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