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Learn how to leverage the Pydantic library and the dictionary data type to create dynamic models that adjust based on provided input data structures.
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How to Create Dynamic Models Using Pydantic and a Dict Data Type

Creating dynamic models can greatly streamline the process of working with various data types in Python. One powerful library you can use for this purpose is Pydantic. In this guide, we’ll explore how to automate the creation of Pydantic models based on the keys and values of a dictionary that you load from a YAML file. This can save you from having to manually define each attribute of your model.

The Problem: Loading a Dictionary from YAML

Let’s say you have a YAML file with the following content:

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When you load this file using the pyyaml library, it automatically converts the content into a dictionary:

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Here, key1 has a string value, while key2 has an integer value. The challenge arises when you want to create a Pydantic model that reflects this dictionary's structure without manually specifying each key and type.

The Goal

We want to create a dynamic class (Test) such that its attributes correspond to the dictionary keys, and their types and default values reflect the values from the dictionary:

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In Pydantic, you could achieve this using the create_model function, but you have to specify each key and its corresponding type manually. This is where automation becomes beneficial.

The Solution: Using Pydantic’s create_model Function

Pydantic’s create_model function can be called dynamically — you can create a recursive function that constructs the model based on the dictionary's contents. Here’s how you can do it:

Step 1: Understanding the Recursive Function

The recursive function will check if the current value is a dictionary:

If it is, it will call itself recursively to extract keys and value types and build the model.

If it is not a dictionary, it will just return the type of the value and the value itself.

Step 2: Implementation

Here’s a complete Python implementation that will help you create the dynamic model based on the dictionary:

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Step 3: Example Output

With the code above, you will get output indicating the dynamically created model:

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Conclusion

By utilizing Pydantic’s powerful features along with some Python magic, you can create dynamic models that adapt based on the input data. This not only simplifies your code but also makes your models much more flexible and easier to maintain.

Now that you understand how to effectively use Pydantic to create dynamic models from a dictionary, you can adapt this approach to suit your specific needs, enabling smart data validation and type checking.

If you have any questions or further useful tips, feel free to share them in the comments below!