diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 45891ea37..1a7f2070f 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -10,6 +10,7 @@ on: - main - dev - feat/* + - 0.*.* # Allows you to run this workflow manually from the Actions tab workflow_dispatch: @@ -27,12 +28,6 @@ jobs: with: python-version: ${{ matrix.python-version }} - # - name: Poetry cache - # uses: actions/cache@v3 - # with: - # path: ~/.cache/pypoetry - # key: poetry-cache-${{ runner.os }}-${{ matrix.python-version }}-${{ env.POETRY_VERSION }} - - name: Install Poetry uses: snok/install-poetry@v1 @@ -50,8 +45,6 @@ jobs: strategy: matrix: python-version: ["3.8", "3.9", "3.10", "3.11"] - pydantic-version: ["==1.10.9", ">=2.x"] - openai-version: [">=1.x"] steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} @@ -59,12 +52,6 @@ jobs: with: python-version: ${{ matrix.python-version }} - # - name: Poetry cache - # uses: actions/cache@v3 - # with: - # path: ~/.cache/pypoetry - # key: poetry-cache-${{ runner.os }}-${{ matrix.python-version }}-${{ env.POETRY_VERSION }}-${{ matrix.pydantic-version }} - - name: Install Poetry uses: snok/install-poetry@v1 @@ -72,8 +59,6 @@ jobs: # TODO: fix errors so that we can run `make dev` instead run: | make full - poetry run pip install "pydantic${{ matrix.pydantic-version }}" - poetry run pip install "openai${{ matrix.openai-version }}" - name: Static analysis with pyright run: | @@ -86,9 +71,7 @@ jobs: python-version: ["3.8", "3.9", "3.10", "3.11"] # TODO: fix errors so that we can run both `make dev` and `make full` # dependencies: ['dev', 'full'] - dependencies: ["full"] - pydantic-version: ["==1.10.9", ">=2.x"] - openai-version: [">=1.x"] + # dependencies: ["full"] steps: - uses: actions/checkout@v4 @@ -102,20 +85,12 @@ jobs: with: python-version: ${{ matrix.python-version }} - # - name: Poetry cache - # uses: actions/cache@v3 - # with: - # path: ~/.cache/pypoetry - # key: poetry-cache-${{ runner.os }}-${{ matrix.python-version }}-${{ env.POETRY_VERSION }}-${{ matrix.pydantic-version }}-${{ matrix.openai-version }} - - name: Install Poetry uses: snok/install-poetry@v1 - name: Install Dependencies run: | - make ${{ matrix.dependencies }} - poetry run pip install "pydantic${{ matrix.pydantic-version }}" - poetry run pip install "openai${{ matrix.openai-version }}" + make full - name: Run Pytests run: | diff --git a/guardrails/schema/pydantic_schema.py b/guardrails/schema/pydantic_schema.py index 96399399b..b0cfbe399 100644 --- a/guardrails/schema/pydantic_schema.py +++ b/guardrails/schema/pydantic_schema.py @@ -27,7 +27,7 @@ from guardrails.validator_base import Validator -def _resolve_alias(alias: str | AliasPath | AliasChoices) -> List[str]: +def _resolve_alias(alias: Union[str, AliasPath, AliasChoices]) -> List[str]: aliases = [] if isinstance(alias, str): aliases.append(alias) @@ -41,7 +41,7 @@ def _resolve_alias(alias: str | AliasPath | AliasChoices) -> List[str]: def _collect_aliases( - field: FieldInfo | AliasGenerator, field_name: str, model: Type[BaseModel] + field: Union[FieldInfo, AliasGenerator], field_name: str, model: Type[BaseModel] ) -> List[str]: aliases = [] diff --git a/guardrails/utils/dataclass.py b/guardrails/utils/dataclass.py deleted file mode 100644 index 92bdb81ce..000000000 --- a/guardrails/utils/dataclass.py +++ /dev/null @@ -1,11 +0,0 @@ -import pydantic.version - -PYDANTIC_VERSION = pydantic.version.VERSION - -if PYDANTIC_VERSION.startswith("1"): - - def dataclass(cls): # type: ignore - return cls - -else: - from dataclasses import dataclass # type: ignore # noqa diff --git a/guardrails/utils/pydantic_utils/v2.py b/guardrails/utils/pydantic_utils.py similarity index 100% rename from guardrails/utils/pydantic_utils/v2.py rename to guardrails/utils/pydantic_utils.py diff --git a/guardrails/utils/pydantic_utils/__init__.py b/guardrails/utils/pydantic_utils/__init__.py deleted file mode 100644 index e7006493c..000000000 --- a/guardrails/utils/pydantic_utils/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -import pydantic.version - -PYDANTIC_VERSION = pydantic.version.VERSION - -if PYDANTIC_VERSION.startswith("1"): - from .v1 import ( - convert_pydantic_model_to_openai_fn, - ) -else: - from .v2 import ( - convert_pydantic_model_to_openai_fn, - ) - - -__all__ = [ - "convert_pydantic_model_to_openai_fn", -] diff --git a/guardrails/utils/pydantic_utils/v1.py b/guardrails/utils/pydantic_utils/v1.py deleted file mode 100644 index 0ddfe86a7..000000000 --- a/guardrails/utils/pydantic_utils/v1.py +++ /dev/null @@ -1,132 +0,0 @@ -"""Utilities for working with Pydantic models.""" - -from copy import deepcopy -from typing import ( - Any, - Dict, - List, - Type, - Union, - get_args, - get_origin, -) - -from pydantic import BaseModel -from guardrails.utils.safe_get import safe_get - - -def create_bare_model(model: Type[BaseModel]): - class BareModel(BaseModel): - __annotations__ = getattr(model, "__annotations__", {}) - - return BareModel - - -def reduce_to_annotations(type_annotation: Any) -> Type[Any]: - if ( - type_annotation - and isinstance(type_annotation, type) - and issubclass(type_annotation, BaseModel) - ): - return create_bare_model(type_annotation) - return type_annotation - - -def find_models_in_type(type_annotation: Any) -> Type[Any]: - type_origin = get_origin(type_annotation) - inner_types = get_args(type_annotation) - if type_origin == Union: - data_types = tuple([find_models_in_type(t) for t in inner_types]) - return Type[Union[data_types]] # type: ignore - elif type_origin == list: - if len(inner_types) > 1: - raise ValueError("List data type must have exactly one child.") - # No List[List] support; we've already declared that in our types - item_type = safe_get(inner_types, 0) - return Type[List[find_models_in_type(item_type)]] - elif type_origin == dict: - # First arg is key which must be primitive - # Second arg is potentially a model - key_value_type = safe_get(inner_types, 1) - value_value_type = safe_get(inner_types, 1) - return Type[Dict[key_value_type, find_models_in_type(value_value_type)]] - else: - return reduce_to_annotations(type_annotation) - - -def schema_to_bare_model(model: Type[BaseModel]) -> Type[BaseModel]: - copy = deepcopy(model) - for field_key in copy.__fields__: - field = copy.__fields__.get(field_key) - if field: - extras = field.field_info.extra - if "validators" in extras: - extras["format"] = list( - v.to_prompt() - for v in extras.pop("validators", []) - if hasattr(v, "to_prompt") - ) - - field.field_info.extra = extras - - value_type = find_models_in_type(field.annotation) - field.annotation = value_type - copy.__fields__[field_key] = field - - # root_model = reduce_to_annotations(model) - - # for key in root_model.__annotations__: - # value = root_model.__annotations__.get(key) - # print("value.field_info: ", value.field_info) - # value_type = find_models_in_type(value) - # root_model.__annotations__[key] = value_type - - return copy - - -def convert_pydantic_model_to_openai_fn( - model: Union[Type[BaseModel], Type[List[Type[BaseModel]]]], -) -> Dict: - """Convert a Pydantic BaseModel to an OpenAI function. - - Args: - model: The Pydantic BaseModel to convert. - - Returns: - OpenAI function paramters. - """ - return {} - - # schema_model = model - - # type_origin = get_origin(model) - # if type_origin == list: - # item_types = get_args(model) - # if len(item_types) > 1: - # raise ValueError("List data type must have exactly one child.") - # # No List[List] support; we've already declared that in our types - # schema_model = safe_get(item_types, 0) - - # # Create a bare model with no extra fields - # bare_model = schema_to_bare_model(schema_model) - - # # Convert Pydantic model to JSON schema - # json_schema = bare_model.schema() - # json_schema["title"] = schema_model.__name__ - - # if type_origin == list: - # json_schema = { - # "title": f"Array<{json_schema.get('title')}>", - # "type": "array", - # "items": json_schema, - # } - - # # Create OpenAI function parameters - # fn_params = { - # "name": json_schema["title"], - # "parameters": json_schema, - # } - # if "description" in json_schema and json_schema["description"] is not None: - # fn_params["description"] = json_schema["description"] - - # return fn_params diff --git a/guardrails/validator_base.py b/guardrails/validator_base.py index f92261a96..419397814 100644 --- a/guardrails/validator_base.py +++ b/guardrails/validator_base.py @@ -32,7 +32,7 @@ from guardrails.constants import hub from guardrails.errors import ValidationError from guardrails.types.on_fail import OnFailAction -from guardrails.utils.dataclass import dataclass +from dataclasses import dataclass VALIDATOR_IMPORT_WARNING = """Accessing `{validator_name}` using `from guardrails.validators import {validator_name}` is deprecated and diff --git a/poetry.lock b/poetry.lock index 4b5b69721..6af2335f4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -137,13 +137,13 @@ 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c2f009443..a24326916 100644 --- a/tests/integration_tests/test_assets/pydantic/validated_response_reask.py +++ b/tests/integration_tests/test_assets/pydantic/validated_response_reask.py @@ -3,7 +3,6 @@ from pydantic import BaseModel, Field -from guardrails.utils.pydantic_utils import PYDANTIC_VERSION from guardrails.actions.reask import FieldReAsk from guardrails.validator_base import OnFailAction from guardrails.validators import ( @@ -61,33 +60,22 @@ class Person(BaseModel): """ name: str - if PYDANTIC_VERSION.startswith("1"): - age: int = Field( - ..., validators=[AgeMustBeBetween0And150(on_fail=OnFailAction.REASK)] - ) - zip_code: str = Field( - ..., - validators=[ + + age: int = Field( + ..., + json_schema_extra={ + "validators": [AgeMustBeBetween0And150(on_fail=OnFailAction.REASK)] + }, + ) + zip_code: str = Field( + ..., + json_schema_extra={ + "validators": [ ZipCodeMustBeNumeric(on_fail=OnFailAction.REASK), ZipCodeInCalifornia(on_fail=OnFailAction.REASK), ], - ) - else: - age: int = Field( - ..., - json_schema_extra={ - "validators": [AgeMustBeBetween0And150(on_fail=OnFailAction.REASK)] - }, - ) - zip_code: str = Field( - ..., - json_schema_extra={ - "validators": [ - ZipCodeMustBeNumeric(on_fail=OnFailAction.REASK), - ZipCodeInCalifornia(on_fail=OnFailAction.REASK), - ], - }, - ) + }, + ) class ListOfPeople(BaseModel): diff --git a/tests/integration_tests/test_python_rail.py b/tests/integration_tests/test_python_rail.py index 6d4e83db6..50e643e70 100644 --- a/tests/integration_tests/test_python_rail.py +++ b/tests/integration_tests/test_python_rail.py @@ -3,7 +3,7 @@ from typing import List, Literal, Union import pytest -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, field_validator, model_validator import guardrails as gd from guardrails.classes.llm.llm_response import LLMResponse @@ -11,7 +11,6 @@ get_static_openai_chat_create_func, get_static_openai_create_func, ) -from guardrails.utils.pydantic_utils import PYDANTIC_VERSION from guardrails.validator_base import OnFailAction from guardrails.validators import ( FailResult, @@ -45,6 +44,64 @@ def validate(self, value, metadata) -> ValidationResult: return PassResult() +class BoxOfficeRevenue(BaseModel): + revenue_type: Literal["box_office"] + gross: float + opening_weekend: float + + # Field-level validation using Pydantic (not Guardrails) + @field_validator("gross") + def validate_gross(cls, gross): + if gross <= 0: + raise ValueError("Gross revenue must be a positive value") + return gross + + +class StreamingRevenue(BaseModel): + revenue_type: Literal["streaming"] + subscriptions: int + subscription_fee: float + + +class Details(BaseModel): + release_date: date + duration: time + budget: float + is_sequel: bool = Field(default=False) + website: str = Field( + json_schema_extra={ + "validators": [ValidLength(min=9, max=100, on_fail=OnFailAction.REASK)] + } + ) + + # Root-level validation using Pydantic (Not in Guardrails) + @model_validator(mode="before") + def validate_budget_and_gross(cls, values): + budget = values.get("budget") + revenue = values.get("revenue") + if revenue["revenue_type"] == "box_office": + gross = revenue["gross"] + if budget >= gross: + raise ValueError("Budget must be less than gross revenue") + return values + + contact_email: str + revenue: Union[BoxOfficeRevenue, StreamingRevenue] = Field( + ..., discriminator="revenue_type" + ) + + +class Movie(BaseModel): + rank: int + title: str + details: Details + + +class Director(BaseModel): + name: str = Field(validators=[IsValidDirector()]) + movies: List[Movie] + + def test_python_rail(mocker): mock_invoke_llm = mocker.patch( "guardrails.llm_providers.OpenAIChatCallable._invoke_llm" @@ -62,89 +119,6 @@ def test_python_rail(mocker): ), ] - class BoxOfficeRevenue(BaseModel): - revenue_type: Literal["box_office"] - gross: float - opening_weekend: float - - # Field-level validation using Pydantic (not Guardrails) - if PYDANTIC_VERSION.startswith("1"): - from pydantic import validator - - decorator = validator("gross") - else: - from pydantic import field_validator - - decorator = field_validator("gross") - - @decorator - def validate_gross(cls, gross): - if gross <= 0: - raise ValueError("Gross revenue must be a positive value") - return gross - - class StreamingRevenue(BaseModel): - revenue_type: Literal["streaming"] - subscriptions: int - subscription_fee: float - - class Details(BaseModel): - release_date: date - duration: time - budget: float - is_sequel: bool = Field(default=False) - - # Root-level validation using Pydantic (Not in Guardrails) - if PYDANTIC_VERSION.startswith("1"): - website: str = Field( - validators=[ValidLength(min=9, max=100, on_fail=OnFailAction.REASK)] - ) - from pydantic import root_validator - - @root_validator - def validate_budget_and_gross(cls, values): - budget = values.get("budget") - revenue = values.get("revenue") - if isinstance(revenue, BoxOfficeRevenue): - gross = revenue.gross - if budget >= gross: - raise ValueError("Budget must be less than gross revenue") - return values - - else: - website: str = Field( - json_schema_extra={ - "validators": [ - ValidLength(min=9, max=100, on_fail=OnFailAction.REASK) - ] - } - ) - from pydantic import model_validator - - @model_validator(mode="before") - def validate_budget_and_gross(cls, values): - budget = values.get("budget") - revenue = values.get("revenue") - if revenue["revenue_type"] == "box_office": - gross = revenue["gross"] - if budget >= gross: - raise ValueError("Budget must be less than gross revenue") - return values - - contact_email: str - revenue: Union[BoxOfficeRevenue, StreamingRevenue] = Field( - ..., discriminator="revenue_type" - ) - - class Movie(BaseModel): - rank: int - title: str - details: Details - - class Director(BaseModel): - name: str = Field(validators=[IsValidDirector()]) - movies: List[Movie] - guard = gd.Guard.from_pydantic( output_class=Director, prompt=( @@ -176,15 +150,10 @@ class Director(BaseModel): # Check that the guard state object has the correct number of re-asks. assert call.iterations.length == 2 - if PYDANTIC_VERSION.startswith("1"): - assert ( - call.compiled_prompt == python_rail.COMPILED_PROMPT_1_WITHOUT_INSTRUCTIONS - ) - else: - assert ( - call.compiled_prompt - == python_rail.COMPILED_PROMPT_1_PYDANTIC_2_WITHOUT_INSTRUCTIONS - ) + assert ( + call.compiled_prompt + == python_rail.COMPILED_PROMPT_1_PYDANTIC_2_WITHOUT_INSTRUCTIONS + ) assert ( call.iterations.first.raw_output @@ -201,26 +170,13 @@ class Director(BaseModel): == python_rail.LLM_OUTPUT_2_SUCCEED_GUARDRAILS_BUT_FAIL_PYDANTIC_VALIDATION ) - if PYDANTIC_VERSION.startswith("1"): - with pytest.raises(ValueError): - Director.parse_raw( - python_rail.LLM_OUTPUT_2_SUCCEED_GUARDRAILS_BUT_FAIL_PYDANTIC_VALIDATION - ) - - # The user can take corrective action based on the failed validation. - # Either manipulating the output themselves, taking corrective action - # in their application, or upstreaming their validations into Guardrails. - - # The fixed output should pass validation using Pydantic - Director.parse_raw(python_rail.LLM_OUTPUT_3_SUCCEED_GUARDRAILS_AND_PYDANTIC) - else: - with pytest.raises(ValueError): - Director.model_validate_json( - python_rail.LLM_OUTPUT_2_SUCCEED_GUARDRAILS_BUT_FAIL_PYDANTIC_VALIDATION - ) + with pytest.raises(ValueError): Director.model_validate_json( - python_rail.LLM_OUTPUT_3_SUCCEED_GUARDRAILS_AND_PYDANTIC + python_rail.LLM_OUTPUT_2_SUCCEED_GUARDRAILS_BUT_FAIL_PYDANTIC_VALIDATION ) + Director.model_validate_json( + python_rail.LLM_OUTPUT_3_SUCCEED_GUARDRAILS_AND_PYDANTIC + ) def test_python_string(mocker): diff --git a/tests/unit_tests/utils/pydantic_utils/v1.py b/tests/unit_tests/utils/pydantic_utils/v1.py deleted file mode 100644 index a9322c8d2..000000000 --- a/tests/unit_tests/utils/pydantic_utils/v1.py +++ /dev/null @@ -1,84 +0,0 @@ -from copy import deepcopy -from typing import List -from warnings import warn - -import pydantic.version -import pytest -from pydantic import BaseModel, Field - -from guardrails.utils.pydantic_utils.v1 import convert_pydantic_model_to_openai_fn - -PYDANTIC_VERSION = pydantic.version.VERSION - - -class Foo(BaseModel): - bar: str = Field(description="some string value") - - -# fmt: off -foo_schema = { - "title": "Foo", - "type": "object", - "properties": { - "bar": { - "title": "Bar", - "description": "some string value", - "type": "string" - } - }, - "required": [ - "bar" - ] -} -# fmt: on - - -# This test is descriptive, not prescriptive. -@pytest.mark.skipif( - not PYDANTIC_VERSION.startswith("1"), - reason="Tests function calling syntax for Pydantic v1", -) -class TestConvertPydanticModelToOpenaiFn: - def test_object_schema(self): - expected_schema = deepcopy(foo_schema) - # When pushed through BareModel it loses the description on any properties. - del expected_schema["properties"]["bar"]["description"] - - # fmt: off - expected_fn_params = { # noqa - "name": "Foo", - "parameters": expected_schema - } - # fmt: on - - actual_fn_params = convert_pydantic_model_to_openai_fn(Foo) - - # assert actual_fn_params == expected_fn_params - warn("Function calling is disabled for pydantic 1.x") - assert actual_fn_params == {} - - def test_list_schema(self): - expected_schema = deepcopy(foo_schema) - # When pushed through BareModel it loses the description on any properties. - del expected_schema["properties"]["bar"]["description"] - - # fmt: off - expected_schema = { - "title": f"Array<{expected_schema.get('title')}>", - "type": "array", - "items": expected_schema - } - # fmt: on - - # fmt: off - expected_fn_params = { # noqa - "name": "Array", - "parameters": expected_schema - } - # fmt: on - - actual_fn_params = convert_pydantic_model_to_openai_fn(List[Foo]) - - # assert actual_fn_params == expected_fn_params - warn("Function calling is disabled for pydantic 1.x") - assert actual_fn_params == {} diff --git a/tests/unit_tests/utils/pydantic_utils/v2.py b/tests/unit_tests/utils/pydantic_utils/v2.py index 32f6e98d9..b4c063ab0 100644 --- a/tests/unit_tests/utils/pydantic_utils/v2.py +++ b/tests/unit_tests/utils/pydantic_utils/v2.py @@ -5,7 +5,7 @@ import pytest from pydantic import BaseModel, Field -from guardrails.utils.pydantic_utils.v2 import convert_pydantic_model_to_openai_fn +from guardrails.utils.pydantic_utils import convert_pydantic_model_to_openai_fn PYDANTIC_VERSION = pydantic.version.VERSION