Buildantic: A library for JSON schema generation and data validation, with native support for LLM function-calling formats.

Buildantic streamlines the process of generating schemas from types and OpenAPI specification operations, as well as validating data against these schemas.

Beyond standard JSON Schema generation, It facilitates the creation of schema formats tailored for Large Language Model (LLM) function calling. The supported formats include OpenAI (compatible with most function-calling LLMs), Anthropic, and Gemini.

• Type support
• OpenAPI specification support

Buildantic is highly inspired from the talk "Pydantic is all you need" by Jason Liu, author of Instructor library.

Getting Started

pip install -U buildantic

Working with types

TypeDescriptor utilizes pydantic's TypeAdapter internally. The schema generated by the adapter is updated with docstring recursively. Any type supported by pydantic will work with this descriptor.

Descripting a simple type

import typing as t

from buildantic import TypeDescriptor

descriptor = TypeDescriptor(t.List[str])

• Get standard JsON schema

  print(descriptor.schema)
  """{'items': {'type': 'string'}, 'type': 'array'}"""
  

• Get function calling schema

  As function-calling only accepts object input, the simple type is transformed into object type with input being the only property key.

  print(descriptor.openai_schema)
  """
  {
      'name': 'List',
      'parameters': {
          'type': 'object',
          'properties': {
              'input': {
                  'items': {'type': 'string'}, 'type': 'array'
              }
          }
      }
  }
  """
  

• Validating a python object

  print(descriptor.validate_python(["name", "age"]))
  # OR output generated from function-calling schema
  print(descriptor.validate_python({"input": ["name", "age"]}))
  """['name', 'age']"""
  

• Validating a JsON object

  print(descriptor.validate_json('["name", "age"]'))
  # OR output generated from function-calling schema
  print(descriptor.validate_json('{"input": ["name", "age"]}'))
  """['name', 'age']"""
  

Descripting a simple type with custom name and description

Annonate the simple type (non-object type) with pydantic's FieldInfo to add name and description

import typing as t

from buildantic import TypeDescriptor
from pydantic.fields import Field

descriptor = TypeDescriptor[t.List[str]](
    t.Annotated[t.List[str], Field(alias="strings", description="List of string")]
)
print(descriptor.schema)
"""{'items': {'type': 'string'}, 'type': 'array'}"""

print(descriptor.openai_schema)
"""
{
    "name": "strings",
    "description": "List of string",
    "parameters": {
        "type": "object",
        "properties": {
            "input": {"type": "array", "items": {"type": "string"}}
        },
        "required": ["input"]
    }
}
"""

print(descriptor.validate_python(["name", "age"]))
"""['name', 'age']"""

print(descriptor.validate_json('{"input": ["name", "age"]}'))
"""['name', 'age']"""

Descripting an object type

An object type refers to type with properties. TypedDict, pydantic model, dataclasses and functions are some examples of it.

TypeDescriptor aliased as descript can be used as a decorator.

from buildantic import descript
from typing import Any, Dict, Literal, Tuple

@descript # same as TypeDescriptor(create_user)
async def create_user(
    name: str, age: int, role: Literal["developer", "tester"] = "tester"
) -> Tuple[bool, Dict[str, Any]]:
    """
    Create a new user

    :param name: Name of the user
    :param age: Age of the user
    :param role: Role to assign.
    """
    return (True, {"metadata": [name, age, role]})

print(create_user.gemini_schema)
"""
{
    "name": "create_user",
    "description": "Create a new user",
    "parameters": {
        "type": "object",
        "properties": {
            "name": {
                "type": "string", "description": "Name of the user"
            },
            "age": {
                "type": "integer", "description": "Age of the user"
            },
            "role": {
                "type": "string",
                "description": "Role to assign.",
                "enum": ["developer", "tester"],
                "format": "enum"
            }
        },
        "required": ["name", "age"]
    }
}
"""

import asyncio

print(asyncio.run(create_user.validate_python({
    "name": "synacktra", "age": 21, "role": "developer"
})))
"""(True, {'metadata': ['synacktra', 21, 'developer']})"""

Creating a registry of type descriptors

from typing import Tuple, Literal

from pydantic import BaseModel
from buildantic import Registry

registry = Registry()

@registry.register
class UserInfo(BaseModel):
    """
    User Information

    :param name: Name of the user
    :param age: Age of the user
    :param role: Role to assign.
    """
    name: str
    age: int
    role: Literal["developer", "tester"] = "tester"


@registry.register
def get_coordinates(location: str) -> Tuple[float, float]:
    """Get coordinates of a location."""
    return (48.858370, 2.2944813)

• Getting schema list in different formats

  print(registry.schema)
  print(registry.openai_schema)
  print(registry.anthropic_schema)
  print(registry.gemini_schema)
  

• Validating a python object

  print(registry.validate_python(id="UserInfo", obj={"name": "synacktra", "age": 21}))
  """name='synacktra' age=21 role='tester'"""
  print(registry.validate_python(id="get_coordinates", obj={"location": "eiffeltower"}))
  """(48.85837, 2.2944813)"""
  

• Validating a JsON object

  print(registry.validate_json(id="UserInfo", data='{"name": "synacktra", "age": 21}'))
  """name='synacktra' age=21 role='tester'"""
  print(registry.validate_json(id="get_coordinates", data='{"location": "eiffeltower"}'))
  """(48.85837, 2.2944813)"""
  

• Accessing descriptor from registry instance

  get_coords_descriptor = registry["get_coordinates"]
  

Working with OpenAPI Specification

OpenAPI operations are loaded as operation descriptors in the OpenAPIRegistry.

Validation methods returns a RequestModel, after which you can use your favorite http client library to finally make request to the API.

• Loading the specification as a registyr

  from buildantic.registry import OpenAPIRegistry
  openapi_registry = OpenAPIRegistry.from_file("/path/to/petstore-v3.json_or_yml")
  # or
  openapi_registry = OpenAPIRegistry.from_url(
      "https://raw.githubusercontent.com/OAI/OpenAPI-Specification/refs/heads/main/examples/v3.0/petstore.json"
  )
  

• Get list of operations

  print(openapi_registry.ids)
  """['listPets', 'createPets', 'showPetById']"""
  

• Accessing specific operation descriptor from registry

  print(openapi_registry["listPets"].schema)
  """
  {
      'type': 'object',
      'description': 'List all pets',
      'properties': {
          'limit': {
              'type': 'integer',
              'maximum': 100,
              'format': 'int32',
              'description': 'How many items to return at one time (max 100)'
          }
      }
  }
  """
  
  print(openapi_registry["createPets"].schema)
  {
      'type': 'object',
      'description': 'Create a pet',
      'properties': {
          'requestBody': {
              'type': 'object',
              'properties': {
                  'id': {'type': 'integer', 'format': 'int64'},
                  'name': {'type': 'string'},
                  'tag': {'type': 'string'}
              }
          }
      },
      'required': ['requestBody']
  }
  

• Getting schema list in different formats

  print(registry.schema)
  print(registry.openai_schema)
  print(registry.anthropic_schema)
  print(registry.gemini_schema)
  

• Validating a python object

  print(openapi_registry.validate_python(id="listPets", obj={"limit": 99}))
  """
  path='/pets' method='get' queries={'limit': 99} encoded_query='limit=99' headers=None cookies=None body=None
  """
  print(openapi_registry.validate_python(id="listPets", obj={"limit": 101}))
  # This will raise `jsonschema.exceptions.ValidationError` exception
  

• Validating a JsON object

  print(openapi_registry.validate_json(
      id="createPets",
      data='{"requestBody": {"id": 12, "name": "rocky", "tag": "dog"}}'
  ))
  """
  path='/pets' method='post' queries=None encoded_query=None headers=None cookies=None body={'id': 12, 'name': 'rocky', 'tag': 'dog'}
  """