standard-tests: rename langchain_standard_tests to langchain_tests, release 0.3.2 (#28203)

libs/standard-tests/langchain_tests/unit_tests/chat_models.py

@@ -0,0 +1,269 @@
+"""Unit tests for chat models."""
+
+import os
+from abc import abstractmethod
+from typing import Any, Dict, List, Literal, Optional, Tuple, Type
+from unittest import mock
+
+import pytest
+from langchain_core.language_models import BaseChatModel
+from langchain_core.load import dumpd, load
+from langchain_core.runnables import RunnableBinding
+from langchain_core.tools import tool
+from pydantic import BaseModel, Field, SecretStr
+from pydantic.v1 import (
+    BaseModel as BaseModelV1,
+)
+from pydantic.v1 import (
+    Field as FieldV1,
+)
+from pydantic.v1 import (
+    ValidationError as ValidationErrorV1,
+)
+from syrupy import SnapshotAssertion
+
+from langchain_tests.base import BaseStandardTests
+from langchain_tests.utils.pydantic import PYDANTIC_MAJOR_VERSION
+
+
+class Person(BaseModel):  # Used by some dependent tests. Should be deprecated.
+    """Record attributes of a person."""
+
+    name: str = Field(..., description="The name of the person.")
+    age: int = Field(..., description="The age of the person.")
+
+
+def generate_schema_pydantic_v1_from_2() -> Any:
+    """Use to generate a schema from v1 namespace in pydantic 2."""
+    if PYDANTIC_MAJOR_VERSION != 2:
+        raise AssertionError("This function is only compatible with Pydantic v2.")
+
+    class PersonB(BaseModelV1):
+        """Record attributes of a person."""
+
+        name: str = FieldV1(..., description="The name of the person.")
+        age: int = FieldV1(..., description="The age of the person.")
+
+    return PersonB
+
+
+def generate_schema_pydantic() -> Any:
+    """Works with either pydantic 1 or 2"""
+
+    class PersonA(BaseModel):
+        """Record attributes of a person."""
+
+        name: str = Field(..., description="The name of the person.")
+        age: int = Field(..., description="The age of the person.")
+
+    return PersonA
+
+
+TEST_PYDANTIC_MODELS = [generate_schema_pydantic()]
+
+if PYDANTIC_MAJOR_VERSION == 2:
+    TEST_PYDANTIC_MODELS.append(generate_schema_pydantic_v1_from_2())
+
+
+@tool
+def my_adder_tool(a: int, b: int) -> int:
+    """Takes two integers, a and b, and returns their sum."""
+    return a + b
+
+
+def my_adder(a: int, b: int) -> int:
+    """Takes two integers, a and b, and returns their sum."""
+    return a + b
+
+
+class ChatModelTests(BaseStandardTests):
+    @property
+    @abstractmethod
+    def chat_model_class(self) -> Type[BaseChatModel]: ...
+
+    @property
+    def chat_model_params(self) -> dict:
+        return {}
+
+    @property
+    def standard_chat_model_params(self) -> dict:
+        return {
+            "temperature": 0,
+            "max_tokens": 100,
+            "timeout": 60,
+            "stop": [],
+            "max_retries": 2,
+        }
+
+    @pytest.fixture
+    def model(self) -> BaseChatModel:
+        return self.chat_model_class(
+            **{**self.standard_chat_model_params, **self.chat_model_params}
+        )
+
+    @property
+    def has_tool_calling(self) -> bool:
+        return self.chat_model_class.bind_tools is not BaseChatModel.bind_tools
+
+    @property
+    def tool_choice_value(self) -> Optional[str]:
+        """Value to use for tool choice when used in tests."""
+        return None
+
+    @property
+    def has_structured_output(self) -> bool:
+        return (
+            self.chat_model_class.with_structured_output
+            is not BaseChatModel.with_structured_output
+        )
+
+    @property
+    def supports_image_inputs(self) -> bool:
+        return False
+
+    @property
+    def supports_video_inputs(self) -> bool:
+        return False
+
+    @property
+    def returns_usage_metadata(self) -> bool:
+        return True
+
+    @property
+    def supports_anthropic_inputs(self) -> bool:
+        return False
+
+    @property
+    def supports_image_tool_message(self) -> bool:
+        return False
+
+    @property
+    def supported_usage_metadata_details(
+        self,
+    ) -> Dict[
+        Literal["invoke", "stream"],
+        List[
+            Literal[
+                "audio_input",
+                "audio_output",
+                "reasoning_output",
+                "cache_read_input",
+                "cache_creation_input",
+            ]
+        ],
+    ]:
+        return {"invoke": [], "stream": []}
+
+
+class ChatModelUnitTests(ChatModelTests):
+    @property
+    def standard_chat_model_params(self) -> dict:
+        params = super().standard_chat_model_params
+        params["api_key"] = "test"
+        return params
+
+    @property
+    def init_from_env_params(self) -> Tuple[dict, dict, dict]:
+        """Return env vars, init args, and expected instance attrs for initializing
+        from env vars."""
+        return {}, {}, {}
+
+    def test_init(self) -> None:
+        model = self.chat_model_class(
+            **{**self.standard_chat_model_params, **self.chat_model_params}
+        )
+        assert model is not None
+
+    def test_init_from_env(self) -> None:
+        env_params, model_params, expected_attrs = self.init_from_env_params
+        if env_params:
+            with mock.patch.dict(os.environ, env_params):
+                model = self.chat_model_class(**model_params)
+            assert model is not None
+            for k, expected in expected_attrs.items():
+                actual = getattr(model, k)
+                if isinstance(actual, SecretStr):
+                    actual = actual.get_secret_value()
+                assert actual == expected
+
+    def test_init_streaming(
+        self,
+    ) -> None:
+        model = self.chat_model_class(
+            **{
+                **self.standard_chat_model_params,
+                **self.chat_model_params,
+                "streaming": True,
+            }
+        )
+        assert model is not None
+
+    def test_bind_tool_pydantic(
+        self,
+        model: BaseChatModel,
+    ) -> None:
+        if not self.has_tool_calling:
+            return
+
+        tools = [my_adder_tool, my_adder]
+
+        for pydantic_model in TEST_PYDANTIC_MODELS:
+            model_schema = (
+                pydantic_model.model_json_schema()
+                if hasattr(pydantic_model, "model_json_schema")
+                else pydantic_model.schema()
+            )
+            tools.extend([pydantic_model, model_schema])
+
+        # Doing a mypy ignore here since some of the tools are from pydantic
+        # BaseModel 2 which isn't typed properly yet. This will need to be fixed
+        # so type checking does not become annoying to users.
+        tool_model = model.bind_tools(tools, tool_choice="any")  # type: ignore
+        assert isinstance(tool_model, RunnableBinding)
+
+    @pytest.mark.parametrize("schema", TEST_PYDANTIC_MODELS)
+    def test_with_structured_output(
+        self,
+        model: BaseChatModel,
+        schema: Any,
+    ) -> None:
+        if not self.has_structured_output:
+            return
+
+        assert model.with_structured_output(schema) is not None
+
+    def test_standard_params(self, model: BaseChatModel) -> None:
+        class ExpectedParams(BaseModelV1):
+            ls_provider: str
+            ls_model_name: str
+            ls_model_type: Literal["chat"]
+            ls_temperature: Optional[float]
+            ls_max_tokens: Optional[int]
+            ls_stop: Optional[List[str]]
+
+        ls_params = model._get_ls_params()
+        try:
+            ExpectedParams(**ls_params)  # type: ignore
+        except ValidationErrorV1 as e:
+            pytest.fail(f"Validation error: {e}")
+
+        # Test optional params
+        model = self.chat_model_class(
+            max_tokens=10,
+            stop=["test"],
+            **self.chat_model_params,  # type: ignore
+        )
+        ls_params = model._get_ls_params()
+        try:
+            ExpectedParams(**ls_params)  # type: ignore
+        except ValidationErrorV1 as e:
+            pytest.fail(f"Validation error: {e}")
+
+    def test_serdes(self, model: BaseChatModel, snapshot: SnapshotAssertion) -> None:
+        if not self.chat_model_class.is_lc_serializable():
+            return
+        env_params, model_params, expected_attrs = self.init_from_env_params
+        with mock.patch.dict(os.environ, env_params):
+            ser = dumpd(model)
+            assert ser == snapshot(name="serialized")
+            assert model.dict() == load(dumpd(model)).dict()