core[minor]: support pydantic v2 models in PydanticOutputParser (#18811)

As mentioned in #18322, the current PydanticOutputParser won't work for
anyone trying to parse to pydantic v2 models. This PR adds a separate
`PydanticV2OutputParser`, as well as a `langchain_core.pydantic_v2`
namespace that will fail on import to any projects using pydantic<2.
Happy to update the docs for output parsers if this is something we're
interesting in adding.

On a separate note, I also updated `check_pydantic.sh` to detect
pydantic imports with leading whitespace and excluded the internal
namespaces. That change can be separated into its own PR if needed.

---------

Co-authored-by: Jan Nissen <jan23@gmail.com>

libs/core/tests/unit_tests/output_parsers/test_pydantic_parser.py

@@ -0,0 +1,72 @@
+from typing import Literal
+
+import pydantic  # pydantic: ignore
+import pytest
+
+from langchain_core.exceptions import OutputParserException
+from langchain_core.language_models import ParrotFakeChatModel
+from langchain_core.output_parsers.pydantic import PydanticOutputParser, TBaseModel
+from langchain_core.prompts.prompt import PromptTemplate
+from langchain_core.utils.pydantic import PYDANTIC_MAJOR_VERSION
+
+V1BaseModel = pydantic.BaseModel
+if PYDANTIC_MAJOR_VERSION == 2:
+    from pydantic.v1 import BaseModel  # pydantic: ignore
+
+    V1BaseModel = BaseModel  # type: ignore
+
+
+class ForecastV2(pydantic.BaseModel):
+    temperature: int
+    f_or_c: Literal["F", "C"]
+    forecast: str
+
+
+class ForecastV1(V1BaseModel):
+    temperature: int
+    f_or_c: Literal["F", "C"]
+    forecast: str
+
+
+@pytest.mark.parametrize("pydantic_object", [ForecastV2, ForecastV1])
+def test_pydantic_parser_chaining(
+    pydantic_object: TBaseModel,
+) -> None:
+    prompt = PromptTemplate(
+        template="""{{
+        "temperature": 20,
+        "f_or_c": "C",
+        "forecast": "Sunny"
+    }}""",
+        input_variables=[],
+    )
+
+    model = ParrotFakeChatModel()
+
+    parser = PydanticOutputParser(pydantic_object=pydantic_object)  # type: ignore
+    chain = prompt | model | parser
+
+    res = chain.invoke({})
+    assert type(res) == pydantic_object
+    assert res.f_or_c == "C"
+    assert res.temperature == 20
+    assert res.forecast == "Sunny"
+
+
+@pytest.mark.parametrize("pydantic_object", [ForecastV2, ForecastV1])
+def test_pydantic_parser_validation(pydantic_object: TBaseModel) -> None:
+    bad_prompt = PromptTemplate(
+        template="""{{
+        "temperature": "oof",
+        "f_or_c": 1,
+        "forecast": "Sunny"
+    }}""",
+        input_variables=[],
+    )
+
+    model = ParrotFakeChatModel()
+
+    parser = PydanticOutputParser(pydantic_object=pydantic_object)  # type: ignore
+    chain = bad_prompt | model | parser
+    with pytest.raises(OutputParserException):
+        chain.invoke({})