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 <[email protected]>

libs/core/langchain_core/output_parsers/pydantic.py

@@ -1,34 +1,64 @@
 import json
-from typing import Generic, List, Type, TypeVar
+from typing import Generic, List, Type, TypeVar, Union
+
+import pydantic  # pydantic: ignore
 
 from langchain_core.exceptions import OutputParserException
 from langchain_core.output_parsers import JsonOutputParser
 from langchain_core.outputs import Generation
-from langchain_core.pydantic_v1 import BaseModel, ValidationError
+from langchain_core.utils.pydantic import PYDANTIC_MAJOR_VERSION
+
+if PYDANTIC_MAJOR_VERSION < 2:
+    PydanticBaseModel = pydantic.BaseModel
+
+else:
+    from pydantic.v1 import BaseModel  # pydantic: ignore
+
+    # Union type needs to be last assignment to PydanticBaseModel to make mypy happy.
+    PydanticBaseModel = Union[BaseModel, pydantic.BaseModel]  # type: ignore
 
-TBaseModel = TypeVar("TBaseModel", bound=BaseModel)
+TBaseModel = TypeVar("TBaseModel", bound=PydanticBaseModel)
 
 
 class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
     """Parse an output using a pydantic model."""
 
-    pydantic_object: Type[TBaseModel]
-    """The pydantic model to parse.
-    
-    Attention: To avoid potential compatibility issues, it's recommended to use
-        pydantic <2 or leverage the v1 namespace in pydantic >= 2.
-    """
+    pydantic_object: Type[TBaseModel]  # type: ignore
+    """The pydantic model to parse."""
+
+    def _parse_obj(self, obj: dict) -> TBaseModel:
+        if PYDANTIC_MAJOR_VERSION == 2:
+            try:
+                if issubclass(self.pydantic_object, pydantic.BaseModel):
+                    return self.pydantic_object.model_validate(obj)
+                elif issubclass(self.pydantic_object, pydantic.v1.BaseModel):
+                    return self.pydantic_object.parse_obj(obj)
+                else:
+                    raise OutputParserException(
+                        f"Unsupported model version for PydanticOutputParser: \
+                            {self.pydantic_object.__class__}"
+                    )
+            except (pydantic.ValidationError, pydantic.v1.ValidationError) as e:
+                raise self._parser_exception(e, obj)
+        else:  # pydantic v1
+            try:
+                return self.pydantic_object.parse_obj(obj)
+            except pydantic.ValidationError as e:
+                raise self._parser_exception(e, obj)
+
+    def _parser_exception(
+        self, e: Exception, json_object: dict
+    ) -> OutputParserException:
+        json_string = json.dumps(json_object)
+        name = self.pydantic_object.__name__
+        msg = f"Failed to parse {name} from completion {json_string}. Got: {e}"
+        return OutputParserException(msg, llm_output=json_string)
 
     def parse_result(
         self, result: List[Generation], *, partial: bool = False
     ) -> TBaseModel:
         json_object = super().parse_result(result)
-        try:
-            return self.pydantic_object.parse_obj(json_object)
-        except ValidationError as e:
-            name = self.pydantic_object.__name__
-            msg = f"Failed to parse {name} from completion {json_object}. Got: {e}"
-            raise OutputParserException(msg, llm_output=json_object)
+        return self._parse_obj(json_object)
 
     def parse(self, text: str) -> TBaseModel:
         return super().parse(text)

libs/core/scripts/check_pydantic.sh

@@ -14,7 +14,10 @@ fi
 repository_path="$1"
 
 # Search for lines matching the pattern within the specified repository
-result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
+result=$(
+  git -C "$repository_path" grep -E '^[[:space:]]*import pydantic|^[[:space:]]*from pydantic' \
+  -- ':!langchain_core/pydantic_*' ':!langchain_core/utils' | grep -v 'pydantic: ignore'
+)
 
 # Check if any matching lines were found
 if [ -n "$result" ]; then
@@ -23,5 +26,6 @@ if [ -n "$result" ]; then
   echo "Please replace the code with an import from langchain_core.pydantic_v1."
   echo "For example, replace 'from pydantic import BaseModel'"
   echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
+  echo "If this was intentional, you can add #  pydantic: ignore after the import to ignore this error."
   exit 1
 fi

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({})