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How the parsing pipeline works
Keep the job in distinct stages so each failure is visible:
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- Choose a route. Recognize stable message templates with a deterministic parser or regex. Send unmatched or variable messages to the model.
- Constrain the output shape. Supply a JSON Schema to Ollama so the response is structured around the fields your application expects.
- Validate the response. Parse the returned JSON into a Pydantic model and reject values that violate its declared types or rules.
- Check the extracted facts. Compare the parsed fields with the notification text or known expected values in a representative test set before relying on them.
The first three stages address routing, format, and declared constraints. The fourth addresses extraction correctness. A response can pass the first three and still misread a transaction.
Define a schema that can represent uncertainty
Start by deciding which fields the application needs, such as amount, currency, merchant, date, and transaction type. Do not require a value when the notification may omit it or leave it ambiguous. Optional fields or an explicit status field let the model represent “not present” instead of encouraging a guess.
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The schema should describe the output your application can safely consume, not imply that every notification contains every field. The fields and validation rules are implementation decisions; there is no universal bank-notification schema established for this approach.
Request structured output with Ollama’s Python client
Ollama accepts a JSON Schema in the chat request’s format parameter. Its documented Python approach builds that schema from a Pydantic model’s model_json_schema(), then parses the response content with model_validate_json(). A minimal pattern is:
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from pydantic import BaseModel, ValidationError
from ollama import chat
class Transaction(BaseModel):
amount: str | None = None
currency: str | None = None
merchant: str | None = None
date: str | None = None
transaction_type: str | None = None
notification = "Your bank notification text goes here"
try:
response = chat(
model="your-local-model",
messages=[
{
"role": "system",
"content": (
"Extract only transaction details supported by the notification. "
"Use null when a field is absent or unclear. Return as JSON."
),
},
{"role": "user", "content": notification},
],
format=Transaction.model_json_schema(),
options={"temperature": 0},
)
transaction = Transaction.model_validate_json(response.message.content)
except ValidationError:
# Keep the source message available for review or fallback handling.
transaction = NoneThis is a pattern to adapt, not a bank-specific parser. Choose field types and validation rules deliberately: for example, an amount represented as text may need application-side normalization before arithmetic, while a date needs a defined format and timezone policy. Preserve the original notification when parsing fails so it can be reviewed or handled by another route.
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Ollama’s December 6, 2024 guidance recommends a Pydantic model or JSON Schema, adding “return as JSON” to the prompt, and setting temperature to 0 for more deterministic output. These choices can help keep responses consistent in format; they do not establish that a model will extract transaction facts correctly. See Ollama’s structured outputs documentation.
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Use regex for templates you understand
A regex or parser is useful when a bank repeatedly sends a stable, known message format. It makes the recognized text pattern and extraction rules explicit. The model can be a fallback for messages that do not match those templates, rather than the first tool applied to every notification.
- Match only patterns you have observed and tested; do not treat one bank’s template as universal.
- Route an unmatched message to the model or a review queue rather than silently returning partial or guessed data.
- Retain enough of the source text to investigate a mismatch, and test again when notification formats change.
This hybrid design is an engineering safeguard, not a measured accuracy improvement. The available documentation does not provide regex patterns or comparative results for bank alerts.
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Test correctness, not just JSON validity
Build a test set of representative notifications for the banks, languages, and formats your application actually handles. Include messages with missing, ambiguous, or unfamiliar fields. For each message, compare the returned values with expected values derived from the original text.
- Check each field independently: amount, currency, merchant, date, and transaction type can fail in different ways.
- Test both paths: recognized templates handled deterministically and unmatched messages sent to the model.
- Include malformed or unexpected model responses and confirm validation failures are handled without losing the original message.
- Track errors by field and message type; a valid Pydantic object alone is not an accuracy metric.
No bank-notification benchmark or accuracy figure is established for this workflow, and no particular small model, model version, or minimum hardware requirement is specified. Measure those properties in the environment and on the messages you intend to support.
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Understand what local processing means for privacy
Ollama’s privacy policy, last updated March 2026, says prompts and responses processed locally are not collected, stored, transmitted, or accessible to Ollama. Its policy distinguishes cloud-hosted model use, where prompts and responses are processed transiently. This is a statement about Ollama’s service, not a guarantee about your application’s logs, backups, host machine, or other dependencies. Review the data path of the complete deployment. See Ollama’s Privacy Policy.
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