core,groq,openai,mistralai,robocorp,fireworks,anthropic[patch]: Update BaseModel subclass and instance checks to handle both v1 and proper namespaces (#24417)

After this PR chat models will correctly handle pydantic 2 with
bind_tools and with_structured_output.


```python
import pydantic
print(pydantic.__version__)
```
2.8.2

```python
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field

class Add(BaseModel):
    x: int
    y: int

model = ChatOpenAI().bind_tools([Add])
print(model.invoke('2 + 5').tool_calls)

model = ChatOpenAI().with_structured_output(Add)
print(type(model.invoke('2 + 5')))
```

```
[{'name': 'Add', 'args': {'x': 2, 'y': 5}, 'id': 'call_PNUFa4pdfNOYXxIMHc6ps2Do', 'type': 'tool_call'}]
<class '__main__.Add'>
```


```python
from langchain_openai import ChatOpenAI
from pydantic.v1 import BaseModel, Field

class Add(BaseModel):
    x: int
    y: int

model = ChatOpenAI().bind_tools([Add])
print(model.invoke('2 + 5').tool_calls)

model = ChatOpenAI().with_structured_output(Add)
print(type(model.invoke('2 + 5')))
```

```python
[{'name': 'Add', 'args': {'x': 2, 'y': 5}, 'id': 'call_hhiHYP441cp14TtrHKx3Upg0', 'type': 'tool_call'}]
<class '__main__.Add'>
```

Addresses issues: https://github.com/langchain-ai/langchain/issues/22782

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>

libs/langchain/langchain/chains/openai_functions/qa_with_structure.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Optional, Type, Union
+from typing import Any, List, Optional, Type, Union, cast
 
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.messages import HumanMessage, SystemMessage
@@ -10,6 +10,7 @@ from langchain_core.output_parsers.openai_functions import (
 from langchain_core.prompts import PromptTemplate
 from langchain_core.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
 from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_core.utils.pydantic import is_basemodel_subclass
 
 from langchain.chains.llm import LLMChain
 from langchain.chains.openai_functions.utils import get_llm_kwargs
@@ -45,7 +46,7 @@ def create_qa_with_structure_chain(
 
     """
     if output_parser == "pydantic":
-        if not (isinstance(schema, type) and issubclass(schema, BaseModel)):
+        if not (isinstance(schema, type) and is_basemodel_subclass(schema)):
             raise ValueError(
                 "Must provide a pydantic class for schema when output_parser is "
                 "'pydantic'."
@@ -60,10 +61,10 @@ def create_qa_with_structure_chain(
             f"Got unexpected output_parser: {output_parser}. "
             f"Should be one of `pydantic` or `base`."
         )
-    if isinstance(schema, type) and issubclass(schema, BaseModel):
-        schema_dict = schema.schema()
+    if isinstance(schema, type) and is_basemodel_subclass(schema):
+        schema_dict = cast(dict, schema.schema())
     else:
-        schema_dict = schema
+        schema_dict = cast(dict, schema)
     function = {
         "name": schema_dict["title"],
         "description": schema_dict["description"],

libs/langchain/langchain/chains/structured_output/base.py

@@ -24,6 +24,7 @@ from langchain_core.utils.function_calling import (
     convert_to_openai_function,
     convert_to_openai_tool,
 )
+from langchain_core.utils.pydantic import is_basemodel_subclass
 
 
 @deprecated(
@@ -465,7 +466,7 @@ def _get_openai_tool_output_parser(
     *,
     first_tool_only: bool = False,
 ) -> Union[BaseOutputParser, BaseGenerationOutputParser]:
-    if isinstance(tool, type) and issubclass(tool, BaseModel):
+    if isinstance(tool, type) and is_basemodel_subclass(tool):
         output_parser: Union[BaseOutputParser, BaseGenerationOutputParser] = (
             PydanticToolsParser(tools=[tool], first_tool_only=first_tool_only)
         )
@@ -493,7 +494,7 @@ def get_openai_output_parser(
             not a Pydantic class, then the output parser will automatically extract
             only the function arguments and not the function name.
     """
-    if isinstance(functions[0], type) and issubclass(functions[0], BaseModel):
+    if isinstance(functions[0], type) and is_basemodel_subclass(functions[0]):
         if len(functions) > 1:
             pydantic_schema: Union[Dict, Type[BaseModel]] = {
                 convert_to_openai_function(fn)["name"]: fn for fn in functions
@@ -516,7 +517,7 @@ def _create_openai_json_runnable(
     output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None,
 ) -> Runnable:
     """"""
-    if isinstance(output_schema, type) and issubclass(output_schema, BaseModel):
+    if isinstance(output_schema, type) and is_basemodel_subclass(output_schema):
         output_parser = output_parser or PydanticOutputParser(
             pydantic_object=output_schema,  # type: ignore
         )