48 lines
1.4 KiB
Markdown
48 lines
1.4 KiB
Markdown
---
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title: Working with Decimal Types in Instructor
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description: Learn how to use Python Decimal types for precise financial calculations and numeric data extraction with Instructor.
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---
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## See Also
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- [Types](../concepts/types.md) - Working with different data types
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- [Fields](../concepts/fields.md) - Customizing field validation
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- [Field Validation](../learning/patterns/field_validation.md) - Field-level validation patterns
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- [Validation](../concepts/validation.md) - Core validation concepts
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# Using Decimals
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Extract precise decimal values for financial calculations using Python's `Decimal` type.
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```python
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from decimal import Decimal
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from pydantic import BaseModel, field_validator
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import instructor
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class Receipt(BaseModel):
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item: str
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price: Decimal
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@field_validator('price', mode='before')
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@classmethod
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def parse_price(cls, v):
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if isinstance(v, str):
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return Decimal(v)
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return v
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client = instructor.from_provider("openai/gpt-4.1-mini")
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receipt = client.create(
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messages=[{"role": "user", "content": "Coffee costs $4.99"}],
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response_model=Receipt,
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)
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print(f"Item: {receipt.item}")
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print(f"Price: {receipt.price}") # Decimal('4.99')
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print(f"Type: {type(receipt.price)}") # <class 'decimal.Decimal'>
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```
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The `field_validator` ensures string values from LLM responses are properly converted to Decimal objects for precise financial calculations.
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