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You can use Ollama’s structured-output feature to ask a local model for transaction fields, then validate the returned JSON with Pydantic. For safer handling, use a deterministic parser for notification formats you already understand, route unmatched messages to the model, and check extracted values against the original text before relying on them. A valid schema-conforming response is not proof that the model identified the transaction correctly.
What this approach can—and cannot—guarantee
There are three separate checks in this workflow:
- Output shape: A JSON Schema supplied to Ollama constrains the response structure.
- Validation: Pydantic can parse the JSON and reject values that violate the model’s declared types or validation rules.
- Extraction correctness: Neither valid JSON nor a valid Pydantic object proves that the merchant, amount, currency, date, or transaction type was read accurately from the notification.
Ollama’s documentation describes structured output but does not report accuracy for bank-notification extraction or evaluate a particular small model on that task. Build and test against representative messages before using extracted fields in consequential workflows.
Define a schema that can represent uncertainty
Start with the fields your application actually needs—for example, amount, currency, merchant, date, transaction type, and a status indicating whether the message was understood. Design the schema so missing or ambiguous details can be represented, such as with optional fields or an explicit status. Do not require the model to fill every field if the notification does not contain the information.
The field choices below are an example of design guidance, not a bank-specific schema prescribed by Ollama. Adapt names, types, and validation rules to your messages and downstream use.
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from datetime import date
from decimal import Decimal
from typing import Literal
from pydantic import BaseModel
class TransactionNotice(BaseModel):
status: Literal["parsed", "incomplete", "unclear"]
amount: Decimal | None = None
currency: str | None = None
merchant: str | None = None
transaction_date: date | None = None
transaction_type: str | None = NoneUse validation rules that match your data rather than assuming every bank uses the same currency notation, date format, or transaction labels. If a notification leaves a value ambiguous, your model design should permit that ambiguity instead of encouraging a guess.
Request structured output and validate it in Python
Ollama accepts a JSON Schema through the chat request’s format parameter. Its Python example generates that schema from a Pydantic model with model_json_schema(), then parses the returned message content with model_validate_json(). The following illustrates that documented path; select and configure a model that is available in your own Ollama installation.
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import ollama
from pydantic import ValidationError
schema = TransactionNotice.model_json_schema()
notice_text = "Your card was charged 24.50 USD at Example Market on 2026-09-30."
try:
response = ollama.chat(
model="your-local-model",
messages=[
{
"role": "system",
"content": (
"Extract transaction details only when they are supported by "
"the notification. Use null for missing or unclear fields. "
"Return as JSON."
),
},
{"role": "user", "content": notice_text},
],
format=schema,
options={"temperature": 0},
)
parsed = TransactionNotice.model_validate_json(
response["message"]["content"]
)
except (ValidationError, KeyError, TypeError) as exc:
# Keep the original notice available for review or fallback handling.
parsed = None
print(f"Could not validate the response: {exc}")Ollama’s December 6, 2024 structured-output post recommends using Pydantic or a JSON Schema to define the response structure, adding “return as JSON” to the prompt, and setting temperature to 0 for more deterministic output. These measures can help keep the format consistent; they do not establish that the extracted facts are correct. See Ollama’s structured outputs documentation.
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Use regex for stable formats and the model for the rest
If a bank’s notification template is stable and you can reliably identify its fields, a conventional parser or regular expression may be easier to inspect and reason about for that format. A practical routing design is:
- Identify notification formats you know and have tested.
- Parse those formats with deterministic rules, and check that required fields were actually matched.
- Route messages that do not match a known template to the local model’s structured-output path.
- Compare either path’s extracted values with the source message or known expected values before consequential use.
This is an engineering safeguard, not a documented Ollama feature or a measured claim that regex will improve accuracy. Regex can be brittle when layouts change; a model can return plausible but incorrect values. Keep the original text available so a mismatch or uncertain result can be investigated.
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Test the complete extraction path
Create a test set of representative messages from the banks, locales, and notification variations your application expects. Include straightforward examples as well as missing fields, ambiguous dates or amounts, unfamiliar merchants, and template changes. For each case, record the correct expected values and compare them with the complete pipeline’s output—not just whether the response is valid JSON.
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- Check field-level correctness against the original notification or known expected data.
- Verify how absent and ambiguous information is represented.
- Exercise both the known-template parser and the model fallback, including validation failures.
- Repeat testing when notification formats, prompts, schema rules, or model versions change.
No accuracy percentage, preferred small model, minimum hardware specification, or bank-specific regex pattern is established here. Those depend on the implementation and messages being processed, so measure them in your own environment rather than assuming a general result.
Understand the privacy boundary
Ollama’s privacy policy, last updated March 2026, says prompts and responses processed locally are not collected, stored, transmitted, or accessible to Ollama. It describes cloud-hosted model prompts and responses separately as processed transiently. The local-processing statement concerns Ollama’s service; it does not guarantee how your application, host machine, logs, backups, or other dependencies handle notification data. Review the full Ollama privacy policy and your own deployment’s data flow.
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