JSON Schema Pydantic Converter

Common errors

• ModuleNotFoundError: No module named 'jsonschema_pydantic'

  cause The `jsonschema-pydantic-converter` package is either not installed or not accessible in the current Python environment.
  fix Install the package using `pip install jsonschema-pydantic-converter`.

• pydantic.error_wrappers.ValidationError: ... (for Pydantic v1) OR pydantic_core._pydantic_core.ValidationError: ... (for Pydantic v2) occurring during `build_pydantic_model` call.

  cause The input `schema_data` dictionary is not a valid JSON Schema or contains constructs that the converter cannot process into a Pydantic model.
  fix Review the `schema_data` against JSON Schema specification and library's capabilities. Debug the schema's structure and ensure all types and formats are correctly defined.

• AttributeError: 'GeneratedModel' object has no attribute 'model_dump_json'

  cause This typically happens when you are using Pydantic v1 (or an environment with v1 installed) but attempting to call Pydantic v2 methods like `model_dump_json` or `model_dump`.
  fix If Pydantic v1 is intended, use `.dict()` and `.json()` instead. If Pydantic v2 is required, ensure `pydantic>=2.0` is installed in your environment.

Warnings

• gotcha While the library supports both Pydantic v1 and v2, the behavior of the *generated* models (e.g., method names like `.dict()` vs `.model_dump()`, specific validator implementations) can differ. Ensure your application's Pydantic version aligns with the expected model behavior.
  Fix: Always test generated models with your target Pydantic version. For Pydantic v1, use `.dict()` and `.json()`. For Pydantic v2, use `.model_dump()` and `.model_dump_json()`.
• gotcha The library relies on well-formed and compliant JSON Schema definitions. Malformed schemas, unsupported keywords, or custom extensions not handled by the library can lead to conversion errors or unexpected Pydantic model structures.
  Fix: Validate your JSON Schema externally (e.g., using `jsonschema.validate`) before passing it to `build_pydantic_model`. Refer to the official JSON Schema specification for valid syntax and keywords.
• gotcha Schemas with deeply nested or complex `$ref` references, especially those involving circular dependencies or external files, might encounter resolution issues or performance degradation.
  Fix: Simplify complex `$ref` structures where possible. Ensure all referenced schemas are accessible and well-defined. For external `$ref`s, ensure the converter's environment can resolve them (though current versions focus on in-schema refs).

Install

• pip install jsonschema-pydantic-converter Install stable version

Imports

• build_pydantic_model
  wrong:

  from jsonschema_pydantic_converter import build_pydantic_model

  correct:

  from jsonschema_pydantic.builder import build_pydantic_model

  The primary conversion function is nested within the `jsonschema_pydantic.builder` submodule, not directly under the top-level package.

Quickstart

This quickstart demonstrates how to define a simple JSON Schema, use `build_pydantic_model` to generate a Pydantic model dynamically, and then instantiate and validate data against the newly created model. It also shows an example of a validation failure.

from jsonschema_pydantic.builder import build_pydantic_model

# Define a JSON Schema
schema = {
    "title": "User",
    "type": "object",
    "properties": {
        "name": {"type": "string", "minLength": 1},
        "age": {"type": "integer", "minimum": 0, "maximum": 150},
        "email": {"type": "string", "format": "email"}
    },
    "required": ["name", "age"]
}

# Build the Pydantic model from the schema
UserModel = build_pydantic_model(schema_data=schema)

# Instantiate and use the generated model
try:
    user_data = UserModel(name="Alice", age=30, email="alice@example.com")
    print("Valid user data:")
    print(user_data.model_dump_json(indent=2))
except Exception as e:
    print(f"Error creating valid user: {e}")

# Demonstrate validation failure
try:
    invalid_user_data = UserModel(name="", age=-5)
    print("Invalid user data (should not reach here):", invalid_user_data)
except Exception as e:
    print("\nValidation error for invalid user data:")
    print(e)