The [datamodel-code-generator](https://github.com/koxudaxi/datamodel-code-generator/) project is a library and command-line utility to generate pydantic models from just about any data source, including:

- OpenAPI 3 (YAML/JSON)
- JSON Schema
- JSON/YAML/CSV Data (which will be converted to JSON Schema)
- Python dictionary (which will be converted to JSON Schema)
- GraphQL schema

Whenever you find yourself with any data convertible JSON but without pydantic models, this tool will allow you to generate type-safe model hierarchies on demand.

## Installation

```bash
pip install datamodel-code-generator
```

## Example

In this case, datamodel-code-generator creates pydantic models from a JSON Schema file.

```bash
datamodel-codegen  --input person.json --input-file-type jsonschema --output model.py
```

person.json:

```json
{
  "$id": "person.json",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Person",
  "type": "object",
  "properties": {
    "first_name": {
      "type": "string",
      "description": "The person's first name."
    },
    "last_name": {
      "type": "string",
      "description": "The person's last name."
    },
    "age": {
      "description": "Age in years.",
      "type": "integer",
      "minimum": 0
    },
    "pets": {
      "type": "array",
      "items": [
        {
          "$ref": "#/definitions/Pet"
        }
      ]
    },
    "comment": {
      "type": "null"
    }
  },
  "required": [
      "first_name",
      "last_name"
  ],
  "definitions": {
    "Pet": {
      "properties": {
        "name": {
          "type": "string"
        },
        "age": {
          "type": "integer"
        }
      }
    }
  }
}
```

model.py:

```python
# generated by datamodel-codegen:
#   filename:  person.json
#   timestamp: 2020-05-19T15:07:31+00:00
from __future__ import annotations

from typing import Any

from pydantic import BaseModel, Field, conint


class Pet(BaseModel):
    name: str | None = None
    age: int | None = None


class Person(BaseModel):
    first_name: str = Field(description="The person's first name.")
    last_name: str = Field(description="The person's last name.")
    age: conint(ge=0) | None = Field(None, description='Age in years.')
    pets: list[Pet] | None = None
    comment: Any | None = None
```

More information can be found on the
[official documentation](https://datamodel-code-generator.koxudaxi.dev/).

## Catching drift from the source schema

A model generated from an OpenAPI or JSON Schema document is a snapshot of that contract. When the
upstream data stops matching it (a field changes type, a new required field appears), the mismatch
surfaces as a [`ValidationError`](/guides/pydantic-core-pydantic-core) at runtime, often the first sign the
source has drifted from the schema you generated against.

If you [record validations with Logfire](/guides/error-messages-troubleshooting), those failures are stored with
their structured errors and rejected values, so you can see _what_ changed and _when_ it started,
useful when you don't own the schema and can't regenerate the models until you know what moved.

## Related pages

- [API Documentation](./api-documentation-index.md)
- [Concepts](./concepts-index.md)
- [Dev Tools](./dev-tools-index.md)
- [Error Messages](./error-messages-index.md)
- [Examples](./examples-index.md)
- [Integrations](./integrations-index.md)
- [Internals](./internals-index.md)
- [Production Tools](./production-tools-index.md)
- [Pydantic](./pydantic-index.md)
- [Pydantic Core](./pydantic-core-index.md)

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