I want to dynamically generate a Pydantic model at runtime. I can do this by calling create_model. For example,
from pydantic import create_model
create_model("MyModel", i=(int,...), s=(str...))
does this same thing as
from pydantic import BaseModel
class MyModel(BaseModel):
i: int
s: str
I want to serialize these Pydantic schemas as JSON. It's easy to write code to parse JSON into create_model arguments, and it would make sense to use the output of BaseModel.schema_json() since that already defines a serialization format. That makes me think that there should already be some sort of BaseModel.from_json_schema classmethod that could dynamically create a model like so
from pydantic import BaseModel
class MyModel(BaseModel):
i: int
s: str
my_model = BaseModel.from_json_schema(MyModel.schema_json())
my_model(i=5, s="s") # returns MyModel(i=5, s="s")
I can't find any such function in the documentation. Am I overlooking something, or do I have to write my own own JSON schema deserialization code?
This has been discussed some time ago and Samuel Colvin said he didn't want to pursue this as a feature for Pydantic.
If you are fine with code generation instead of actual runtime creation of models, you can use the datamodel-code-generator.
To be honest, I struggle to see the use case for generating complex models at runtime, seeing as their main purpose is validation, implying that you think about correct schema before running your program. But that is just my view.
For simple models I guess you can throw together your own logic for this fairly quickly.
If do you need something more sophisticated, the aforementioned library does offer some extensibility. You should be able to import and inherit from some of their classes like the JsonSchemaParser. Maybe that will get you somewhere.
Ultimately I think this becomes non-trivial very quickly, which is why Pydantic's maintainer didn't want to deal with it and why there is a whole separate project for this.