Pydantic A discriminated union field received a value that does not match any of the union types.
Understanding Pydantic and Its Purpose
Pydantic is a data validation and settings management library for Python, based on Python type annotations. It is widely used for ensuring that data conforms to specified types and constraints, making it an essential tool for developers working with data models. Pydantic is particularly popular for its ability to parse and validate data efficiently, providing clear error messages when validation fails.
Identifying the Symptom: value_error.discriminated_union
When working with Pydantic, you might encounter the error code value_error.discriminated_union. This error typically arises when a discriminated union field receives a value that does not match any of the union types defined in your Pydantic model. The symptom is usually a validation error message indicating that the provided data does not fit any of the expected types.
Exploring the Issue: What is a Discriminated Union?
A discriminated union in Pydantic is a way to define a field that can accept multiple types, each distinguished by a specific discriminator field. This allows for more flexible data models where a field can be one of several types, but each type is identified by a unique value in the discriminator field. The error occurs when the discriminator value does not match any of the defined types in the union.
Example of a Discriminated Union
from pydantic import BaseModel, Field
from typing import Union
class Cat(BaseModel):
type: str = Field("cat", const=True)
name: str
class Dog(BaseModel):
type: str = Field("dog", const=True)
name: str
Pet = Union[Cat, Dog]
In this example, Pet is a discriminated union that can be either a Cat or a Dog, distinguished by the type field.
Steps to Fix the Issue
To resolve the value_error.discriminated_union, follow these steps:
Step 1: Verify the Discriminator Field
Ensure that the discriminator field in your data matches one of the expected values. In the example above, the type field must be either "cat" or "dog". Check your input data to confirm that the discriminator field is correctly set.
Step 2: Validate Against the Model
Use Pydantic's model validation to ensure your data conforms to the expected structure. You can do this by creating an instance of the model with your data:
data = {"type": "cat", "name": "Whiskers"}
pet = Pet.parse_obj(data)
If the data is valid, this will create a Pet instance without errors. If not, Pydantic will raise a validation error.
Step 3: Review the Union Definition
Double-check the union definition in your model to ensure all types are correctly defined and include the necessary discriminator fields. Make sure each type in the union has a unique and correctly specified discriminator value.
Additional Resources
For more information on Pydantic and discriminated unions, you can refer to the official Pydantic documentation. Additionally, the section on Unions provides detailed examples and explanations.
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