mirror of
https://github.com/hwchase17/langchain.git
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standard-tests: show right classes in api docs (#28591)
This commit is contained in:
Erick Friis
GitHub
@@ -11,7 +11,7 @@ import pytest
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from langchain_core.language_models import BaseChatModel
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from langchain_core.load import dumpd, load
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from langchain_core.runnables import RunnableBinding
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from langchain_core.tools import tool
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from langchain_core.tools import BaseTool, tool
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from pydantic import BaseModel, Field, SecretStr
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from pydantic.v1 import (
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BaseModel as BaseModelV1,
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@@ -28,15 +28,12 @@ from langchain_tests.base import BaseStandardTests
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from langchain_tests.utils.pydantic import PYDANTIC_MAJOR_VERSION
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class Person(BaseModel): # Used by some dependent tests. Should be deprecated.
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"""Record attributes of a person."""
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name: str = Field(..., description="The name of the person.")
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age: int = Field(..., description="The age of the person.")
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def generate_schema_pydantic_v1_from_2() -> Any:
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"""Use to generate a schema from v1 namespace in pydantic 2."""
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"""
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Use to generate a schema from v1 namespace in pydantic 2.
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:private:
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"""
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if PYDANTIC_MAJOR_VERSION != 2:
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raise AssertionError("This function is only compatible with Pydantic v2.")
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@@ -50,7 +47,11 @@ def generate_schema_pydantic_v1_from_2() -> Any:
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def generate_schema_pydantic() -> Any:
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"""Works with either pydantic 1 or 2"""
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"""
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Works with either pydantic 1 or 2
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:private:
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"""
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class PersonA(BaseModel):
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"""Record attributes of a person."""
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@@ -67,20 +68,153 @@ if PYDANTIC_MAJOR_VERSION == 2:
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TEST_PYDANTIC_MODELS.append(generate_schema_pydantic_v1_from_2())
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@tool
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def my_adder_tool(a: int, b: int) -> int:
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"""Takes two integers, a and b, and returns their sum."""
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return a + b
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def my_adder(a: int, b: int) -> int:
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"""Takes two integers, a and b, and returns their sum."""
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return a + b
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class ChatModelTests(BaseStandardTests):
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"""Base class for chat model tests.
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:private:
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""" # noqa: E501
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@property
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@abstractmethod
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def chat_model_class(self) -> Type[BaseChatModel]:
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"""The chat model class to test, e.g., `ChatParrotLink`."""
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...
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@property
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def chat_model_params(self) -> dict:
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"""Initialization parameters for the chat mobdel."""
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return {}
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@property
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def standard_chat_model_params(self) -> dict:
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""":meta private:"""
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return {
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"temperature": 0,
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"max_tokens": 100,
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"timeout": 60,
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"stop": [],
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"max_retries": 2,
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}
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@pytest.fixture
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def model(self) -> BaseChatModel:
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"""Fixture that returns an instance of the chat model. Should not be
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overridden."""
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return self.chat_model_class(
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**{**self.standard_chat_model_params, **self.chat_model_params}
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)
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@pytest.fixture
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def my_adder_tool(self) -> BaseTool:
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@tool
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def my_adder_tool(a: int, b: int) -> int:
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"""Takes two integers, a and b, and returns their sum."""
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return a + b
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return my_adder_tool
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@property
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def has_tool_calling(self) -> bool:
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"""Boolean property indicating whether the model supports tool calling."""
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return self.chat_model_class.bind_tools is not BaseChatModel.bind_tools
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@property
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def tool_choice_value(self) -> Optional[str]:
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"""Value to use for tool choice when used in tests."""
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return None
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@property
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def has_structured_output(self) -> bool:
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"""Boolean property indicating whether the chat model supports structured
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output."""
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return (
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self.chat_model_class.with_structured_output
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is not BaseChatModel.with_structured_output
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)
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@property
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def supports_image_inputs(self) -> bool:
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"""Boolean property indicating whether the chat model supports image inputs.
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Defaults to ``False``."""
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return False
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@property
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def supports_video_inputs(self) -> bool:
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"""Boolean property indicating whether the chat model supports image inputs.
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Defaults to ``False``. No current tests are written for this feature."""
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return False
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@property
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def returns_usage_metadata(self) -> bool:
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"""Boolean property indicating whether the chat model returns usage metadata
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on invoke and streaming responses."""
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return True
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@property
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def supports_anthropic_inputs(self) -> bool:
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"""Boolean property indicating whether the chat model supports Anthropic-style
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inputs."""
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return False
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@property
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def supports_image_tool_message(self) -> bool:
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"""Boolean property indicating whether the chat model supports ToolMessages
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that include image content."""
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return False
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@property
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def supported_usage_metadata_details(
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self,
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) -> Dict[
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Literal["invoke", "stream"],
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List[
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Literal[
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"audio_input",
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"audio_output",
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"reasoning_output",
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"cache_read_input",
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"cache_creation_input",
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]
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],
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]:
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"""Property controlling what usage metadata details are emitted in both invoke
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and stream. Only needs to be overridden if these details are returned by the
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model."""
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return {"invoke": [], "stream": []}
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class ChatModelUnitTests(ChatModelTests):
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"""Base class for chat model unit tests.
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Test subclasses must implement the ``chat_model_class`` and
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``chat_model_params`` properties to specify what model to test and its
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initialization parameters.
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Example:
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.. code-block:: python
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from typing import Type
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from langchain_tests.unit_tests import ChatModelUnitTests
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from my_package.chat_models import MyChatModel
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class TestMyChatModelUnit(ChatModelUnitTests):
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@property
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def chat_model_class(self) -> Type[MyChatModel]:
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# Return the chat model class to test here
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return MyChatModel
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@property
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def chat_model_params(self) -> dict:
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# Return initialization parameters for the model.
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return {"model": "model-001", "temperature": 0}
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.. note::
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API references for individual test methods include troubleshooting tips.
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Test subclasses must implement the following two properties:
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chat_model_class
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@@ -275,146 +409,6 @@ class ChatModelTests(BaseStandardTests):
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cached, audio, or reasoning.
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Only needs to be overridden if these details are supplied.
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""" # noqa: E501
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@property
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@abstractmethod
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def chat_model_class(self) -> Type[BaseChatModel]:
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"""The chat model class to test, e.g., `ChatParrotLink`."""
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...
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@property
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def chat_model_params(self) -> dict:
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"""Initialization parameters for the chat mobdel."""
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return {}
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@property
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def standard_chat_model_params(self) -> dict:
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""":meta private:"""
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return {
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"temperature": 0,
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"max_tokens": 100,
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"timeout": 60,
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"stop": [],
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"max_retries": 2,
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}
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@pytest.fixture
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def model(self) -> BaseChatModel:
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"""Fixture that returns an instance of the chat model. Should not be
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overridden."""
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return self.chat_model_class(
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**{**self.standard_chat_model_params, **self.chat_model_params}
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)
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@property
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def has_tool_calling(self) -> bool:
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"""Boolean property indicating whether the model supports tool calling."""
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return self.chat_model_class.bind_tools is not BaseChatModel.bind_tools
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@property
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def tool_choice_value(self) -> Optional[str]:
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"""Value to use for tool choice when used in tests."""
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return None
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@property
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def has_structured_output(self) -> bool:
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"""Boolean property indicating whether the chat model supports structured
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output."""
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return (
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self.chat_model_class.with_structured_output
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is not BaseChatModel.with_structured_output
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)
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@property
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def supports_image_inputs(self) -> bool:
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"""Boolean property indicating whether the chat model supports image inputs.
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Defaults to ``False``."""
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return False
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@property
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def supports_video_inputs(self) -> bool:
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"""Boolean property indicating whether the chat model supports image inputs.
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Defaults to ``False``. No current tests are written for this feature."""
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return False
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@property
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def returns_usage_metadata(self) -> bool:
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"""Boolean property indicating whether the chat model returns usage metadata
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on invoke and streaming responses."""
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return True
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@property
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def supports_anthropic_inputs(self) -> bool:
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"""Boolean property indicating whether the chat model supports Anthropic-style
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inputs."""
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return False
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@property
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def supports_image_tool_message(self) -> bool:
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"""Boolean property indicating whether the chat model supports ToolMessages
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that include image content."""
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return False
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@property
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def supported_usage_metadata_details(
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self,
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) -> Dict[
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Literal["invoke", "stream"],
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List[
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Literal[
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"audio_input",
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"audio_output",
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"reasoning_output",
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"cache_read_input",
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"cache_creation_input",
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]
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],
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]:
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"""Property controlling what usage metadata details are emitted in both invoke
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and stream. Only needs to be overridden if these details are returned by the
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model."""
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return {"invoke": [], "stream": []}
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class ChatModelUnitTests(ChatModelTests):
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"""Base class for chat model unit tests.
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Test subclasses must implement the ``chat_model_class`` and
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``chat_model_params`` properties to specify what model to test and its
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initialization parameters.
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Example:
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.. code-block:: python
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from typing import Type
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from langchain_tests.unit_tests import ChatModelUnitTests
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from my_package.chat_models import MyChatModel
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class TestMyChatModelUnit(ChatModelUnitTests):
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@property
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def chat_model_class(self) -> Type[MyChatModel]:
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# Return the chat model class to test here
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return MyChatModel
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@property
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def chat_model_params(self) -> dict:
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# Return initialization parameters for the model.
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return {"model": "model-001", "temperature": 0}
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.. note::
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API references for individual test methods include troubleshooting tips.
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.. note::
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Test subclasses can control what features are tested (such as tool
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calling or multi-modality) by selectively overriding the properties on the
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class. Relevant properties are mentioned in the references for each method.
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See this page for detail on all properties:
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https://python.langchain.com/api_reference/standard_tests/unit_tests/langchain_tests.unit_tests.chat_models.ChatModelTests.html
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Testing initialization from environment variables
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Some unit tests may require testing initialization from environment variables.
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@@ -526,6 +520,7 @@ class ChatModelUnitTests(ChatModelTests):
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def test_bind_tool_pydantic(
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self,
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model: BaseChatModel,
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my_adder_tool: BaseTool,
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) -> None:
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"""Test that chat model correctly handles Pydantic models that are passed
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into ``bind_tools``. Test is skipped if the ``has_tool_calling`` property
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@@ -542,6 +537,10 @@ class ChatModelUnitTests(ChatModelTests):
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if not self.has_tool_calling:
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return
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def my_adder(a: int, b: int) -> int:
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"""Takes two integers, a and b, and returns their sum."""
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return a + b
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tools = [my_adder_tool, my_adder]
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for pydantic_model in TEST_PYDANTIC_MODELS:
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