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https://github.com/niklasvonm/pydantic-modelgen
Create Pydantic BaseModels from JSON Schema at runtime.
https://github.com/niklasvonm/pydantic-modelgen
json-schema pydantic pydantic-v2
Last synced: 2 months ago
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Create Pydantic BaseModels from JSON Schema at runtime.
- Host: GitHub
- URL: https://github.com/niklasvonm/pydantic-modelgen
- Owner: NiklasvonM
- License: mit
- Created: 2024-06-08T12:26:59.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-06-12T20:06:47.000Z (7 months ago)
- Last Synced: 2024-10-14T03:22:00.864Z (2 months ago)
- Topics: json-schema, pydantic, pydantic-v2
- Language: Python
- Homepage: https://niklasvonm.github.io/pydantic-modelgen/
- Size: 308 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# pydantic-modelgen
Create Pydantic `BaseModel`s from JSON Schema at runtime.
[](https://www.python.org/downloads/)
[](https://pydantic.dev)
[](https://opensource.org/licenses/MIT)
[](https://github.com/NiklasvonM/pydantic-modelgen/actions/workflows/mypy.yml)
[](https://github.com/NiklasvonM/pydantic-modelgen/actions/workflows/ruff.yml)
[](https://github.com/NiklasvonM/pydantic-modelgen/actions/workflows/tests.yml)
[](https://github.com/NiklasvonM/pydantic-modelgen/actions/workflows/bandit.yml)
See [documentation](https://niklasvonm.github.io/pydantic-modelgen/).
## Usage
```python
from pydanticmodelgen import generate_basemodel
json_schema = {
"title": "Person Information",
"type": "object",
"properties": {
"name": {"type": "string"},
"age": {"type": "integer", "minimum": 0},
"gender": {"enum": ["male", "female", "other"]}
}
}
model = generate_basemodel(json_schema, validate_schema=True)
print(model)
#
print(model.model_fields)
# {'name': FieldInfo(annotation=str, required=False, default=None), 'age': FieldInfo(annotation=int, required=False, default=None, metadata=[Ge(ge=0)]), 'gender': FieldInfo(annotation=genderEnum, required=False, default=None)}
instance = model(**{"name": "John Doe", "age": 30, "gender": "male"})
print(instance)
# name='John Doe' age=30 gender='male'
```
## Motivation
The motivation for this project is to create dynamic Swagger API documentations for FastAPI apps, which rely on Pydantic, from JSON Schema. This is useful when the application logic does not depend on the exact schema of the JSON, but the consumers of the REST API do. For example, this may be the case if the JSON is populated via an LLM.
If data validation is your only concern, [python-jsonschema](https://github.com/python-jsonschema/jsonschema) is recommended.
Demo FastAPI App
```python
import uvicorn
from fastapi import FastAPI
from pydanticmodelgen import generate_basemodel
app = FastAPI(title="Pydantic Modelgen Demo")
json_schema = {
"title": "Person Information",
"type": "object",
"properties": {
"name": {"type": "string"},
"age": {"type": "integer", "minimum": 0},
"gender": {"enum": ["male", "female", "other"]},
},
}
Model = generate_basemodel(json_schema)
@app.get("/", response_model=Model)
async def root():
return Model(name="Alice", age=30)
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000)
```
While running this FastAPI app, the following documentation can be accessed under localhost:8000/docs:
