fix(core): Fix tool name check in name_dict for PydanticToolsParser (#33479)

- **Description:** The root cause of this issue is that when a user
defines `model_config` in a `BaseModel`, the `{"type": <tool_name>}`
value is derived from the title specified in `model_config` when the
results are parsed
[here](https://vscode.dev/github/keenborder786/langchain/blob/fix/tool_name_dict/libs/core/langchain_core/output_parsers/openai_tools.py#L199).
However,
[tool.__name__](https://vscode.dev/github/keenborder786/langchain/blob/fix/tool_name_dict/libs/core/langchain_core/output_parsers/openai_tools.py#L331)
uses the class name (in uppercase) of the `BaseModel`, resulting in a
`KeyError` when a custom title is provided in `model_config`.
 

The Best Solution will be to use the title provided in `model_config`
attribute if provided one since that is what `type` will be parsed to,
if not then use `tool.__name__`. But need to make sure that this works
only for Pydantic V2.

  - **Issue:** #27260

---------

Co-authored-by: Mason Daugherty <mason@langchain.dev>

Author: keenborder786

libs/core/langchain_core/output_parsers/openai_tools.py

@@ -15,7 +15,11 @@
 from langchain_core.output_parsers.transform import BaseCumulativeTransformOutputParser
 from langchain_core.outputs import ChatGeneration, Generation
 from langchain_core.utils.json import parse_partial_json
-from langchain_core.utils.pydantic import TypeBaseModel
+from langchain_core.utils.pydantic import (
+    TypeBaseModel,
+    is_pydantic_v1_subclass,
+    is_pydantic_v2_subclass,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -323,7 +327,15 @@ def parse_result(self, result: list[Generation], *, partial: bool = False) -> An
             return None if self.first_tool_only else []
 
         json_results = [json_results] if self.first_tool_only else json_results
-        name_dict = {tool.__name__: tool for tool in self.tools}
+        name_dict_v2: dict[str, TypeBaseModel] = {
+            tool.model_config.get("title") or tool.__name__: tool
+            for tool in self.tools
+            if is_pydantic_v2_subclass(tool)
+        }
+        name_dict_v1: dict[str, TypeBaseModel] = {
+            tool.__name__: tool for tool in self.tools if is_pydantic_v1_subclass(tool)
+        }
+        name_dict: dict[str, TypeBaseModel] = {**name_dict_v2, **name_dict_v1}
         pydantic_objects = []
         for res in json_results:
             if not isinstance(res["args"], dict):