To turn a bank transaction notification into validated JSON, 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 Ollama’s returned text with model_validate_json(). This checks the response’s structure and types—not whether the notification was interpreted correctly or the transaction is genuine.
Build a schema that reflects what the notification actually says
There is no universal bank-notification format or canonical transaction schema in the Ollama and Pydantic documentation. Choose fields for your application, and represent information that may be missing as optional rather than treating an absent value as known.
For example, an application might need a merchant, amount, currency, and transaction date. Those are design choices, not fields guaranteed to appear in every bank’s messages. A Pydantic model is the contract your application expects; adjust it to the notifications and downstream use cases you support.
Pass the Pydantic-generated JSON Schema to Ollama
Ollama’s structured outputs feature accepts a JSON Schema through the chat API’s format parameter. Its official Python example generates the schema from a Pydantic model with model_json_schema() and supplies it as format=FriendList.model_json_schema(). The same pattern lets you request a response shaped for your own model.
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The following is a minimal pattern. Replace the illustrative model fields and notification text with the ones your application actually needs:
from pydantic import BaseModel, ValidationError
from ollama import chat
class TransactionNotification(BaseModel):
merchant: str | None = None
amount: float | None = None
currency: str | None = None
transaction_date: str | None = None
notification = "Your notification text here"
response = chat(
model="your-model",
messages=[
{
"role": "user",
"content": (
"Extract only transaction details stated in this notification. "
"Use null for details that are not stated.nn"
f"{notification}"
),
}
],
format=TransactionNotification.model_json_schema(),
)
try:
transaction = TransactionNotification.model_validate_json(
response.message.content
)
except ValidationError as exc:
# Handle invalid or incompatible output in your application.
raiseThis example illustrates the documented schema-generation and validation calls; it does not establish the right model, prompt, or field types for every bank. In particular, using a floating-point amount is a sample choice, not a recommendation for financial arithmetic. Select types and validation rules appropriate to your application.
Choose JSON mode or a specific schema
Ollama documents format as accepting either the string "json" or a JSON Schema object. JSON mode requests JSON generally; a schema object expresses a specific expected structure. For fields your downstream code depends on, the schema makes the requested shape more explicit.
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| Option | What it specifies | When it fits |
|---|---|---|
format="json" |
Requests JSON without supplying a particular field-level schema. | When JSON syntax is the main requirement and your application does not need a specified response shape from the request. |
A JSON Schema object, such as TransactionNotification.model_json_schema() |
Supplies the expected response structure through the schema. | When your application needs named fields and a defined data shape. |
Ollama’s API specification documents these format options, and its structured outputs guide demonstrates the Pydantic-generated schema pattern: Ollama structured outputs and the Ollama API specification.
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Validate the returned content before using it
Ollama’s Python example passes response.message.content to FriendList.model_validate_json(...). For a transaction pipeline, call the equivalent method on your own model before handing extracted data to application code. Pydantic validation checks whether the content can be parsed and conforms to the model’s requirements; it does not prove that a merchant, amount, or date matches the original notification.
Keep those checks conceptually separate:
- Structured generation: the schema guides Ollama toward the requested JSON shape.
- Application-side validation: Pydantic parses the returned content and checks it against your model.
- Factual verification: your application still needs a way to assess whether extracted values faithfully reflect the notification.
The cited documentation establishes the first two mechanisms, not a transaction-accuracy guarantee. Do not treat parseable, schema-valid output as independently verified financial data.
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Plan for missing details and validation failures
Bank alerts may not contain every field your application wants. Make absence explicit in the model and prompt, and decide how downstream code should handle unknown values. The sample uses optional fields and asks for null when a detail is not stated; tailor that behavior to your schema and verify that the resulting JSON validates as intended.
The Ollama and Pydantic examples do not prescribe a bank-notification retry policy, transaction-specific error categories, confidence threshold, or human-review process. Those are engineering decisions. A practical pipeline can distinguish a Pydantic validation failure from an extraction that parses successfully but needs additional verification, then route each case according to the consequences of using incorrect data.
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Check whether your Ollama deployment enforces the schema
Deployment affects how much you can rely on constrained generation. The Pydantic Ollama integration page says self-hosted Ollama v0.5.0 and later honors json_schema, while Ollama Cloud currently accepts the parameter without enforcing the schema. Check the documentation and behavior for your actual deployment, installed versions, model, and client interface before depending on schema enforcement: Pydantic’s Ollama integration documentation.
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| Deployment described by the cited documentation | Reported JSON Schema behavior | What to do |
|---|---|---|
| Self-hosted Ollama v0.5.0+ | The Pydantic page says it honors json_schema. |
Confirm your installed version and test the schema request with your model and client. |
| Ollama Cloud | The Pydantic page says it accepts the parameter but does not currently enforce the schema. | Do not assume the response is constrained; retain application-side validation and check for updated behavior. |
These statements describe the behavior reported by that integration page, not a guarantee for every future release or interface. Pydantic validation remains a separate application-side step in either case.
Account for notification privacy separately
The software documentation explains schema-constrained output and validation; it does not establish that a local deployment is automatically private, secure, or compliant. Nor does it settle which notification fields may be stored or processed under the rules that apply to your organization or jurisdiction. Review the relevant bank, device or operating-system, organizational, and regulator documentation before deciding how to capture, retain, or transmit notification text.
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