core[minor]: support pydantic V2 for JSONOutputParser, allow for other sources of JSON schemas (#19716)

This PR supports using Pydantic v2 objects to generate the schema for
the JSONOutputParser (#19441). This also adds a `json_schema` parameter
to allow users to pass any JSON schema to validate with, not just
pydantic.

libs/core/langchain_core/output_parsers/json.py

@@ -3,15 +3,27 @@ from __future__ import annotations
 import json
 import re
 from json import JSONDecodeError
-from typing import Any, Callable, List, Optional, Type
+from typing import Any, Callable, List, Optional, Type, TypeVar, Union
 
 import jsonpatch  # type: ignore[import]
+import pydantic  # pydantic: ignore
 
 from langchain_core.exceptions import OutputParserException
 from langchain_core.output_parsers.format_instructions import JSON_FORMAT_INSTRUCTIONS
 from langchain_core.output_parsers.transform import BaseCumulativeTransformOutputParser
 from langchain_core.outputs import Generation
-from langchain_core.pydantic_v1 import BaseModel
+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=PydanticBaseModel)
 
 
 def _replace_new_line(match: re.Match[str]) -> str:
@@ -200,11 +212,19 @@ class JsonOutputParser(BaseCumulativeTransformOutputParser[Any]):
     describing the difference between the previous and the current object.
     """
 
-    pydantic_object: Optional[Type[BaseModel]] = None
+    pydantic_object: Optional[Type[TBaseModel]] = None  # type: ignore
 
     def _diff(self, prev: Optional[Any], next: Any) -> Any:
         return jsonpatch.make_patch(prev, next).patch
 
+    def _get_schema(self, pydantic_object: Type[TBaseModel]) -> dict[str, Any]:
+        if PYDANTIC_MAJOR_VERSION == 2:
+            if issubclass(pydantic_object, pydantic.BaseModel):
+                return pydantic_object.model_json_schema()
+            elif issubclass(pydantic_object, pydantic.v1.BaseModel):
+                return pydantic_object.schema()
+        return pydantic_object.schema()
+
     def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
         text = result[0].text
         text = text.strip()
@@ -228,7 +248,7 @@ class JsonOutputParser(BaseCumulativeTransformOutputParser[Any]):
             return "Return a JSON object."
         else:
             # Copy schema to avoid altering original Pydantic schema.
-            schema = {k: v for k, v in self.pydantic_object.schema().items()}
+            schema = {k: v for k, v in self._get_schema(self.pydantic_object).items()}
 
             # Remove extraneous fields.
             reduced_schema = schema

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

@@ -5,6 +5,7 @@ import pytest
 
 from langchain_core.exceptions import OutputParserException
 from langchain_core.language_models import ParrotFakeChatModel
+from langchain_core.output_parsers.json import JsonOutputParser
 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
@@ -70,3 +71,28 @@ def test_pydantic_parser_validation(pydantic_object: TBaseModel) -> None:
     chain = bad_prompt | model | parser
     with pytest.raises(OutputParserException):
         chain.invoke({})
+
+
+# JSON output parser tests
+@pytest.mark.parametrize("pydantic_object", [ForecastV2, ForecastV1])
+def test_json_parser_chaining(
+    pydantic_object: TBaseModel,
+) -> None:
+    prompt = PromptTemplate(
+        template="""{{
+        "temperature": 20,
+        "f_or_c": "C",
+        "forecast": "Sunny"
+    }}""",
+        input_variables=[],
+    )
+
+    model = ParrotFakeChatModel()
+
+    parser = JsonOutputParser(pydantic_object=pydantic_object)  # type: ignore
+    chain = prompt | model | parser
+
+    res = chain.invoke({})
+    assert res["f_or_c"] == "C"
+    assert res["temperature"] == 20
+    assert res["forecast"] == "Sunny"