### Checked other resources

- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
- [X] I am sure that this is a bug in LangChain rather than my code.
- [X] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

### Example Code

```
from typing import Union

from typing import Optional

from langchain_core.pydantic_v1 import BaseModel, Field


class Joke(BaseModel):
    """Joke to tell user."""

    setup: str = Field(description="The setup of the joke")
    punchline: str = Field(description="The punchline to the joke")
    rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")


class ConversationalResponse(BaseModel):
    """Respond in a conversational manner. Be kind and helpful."""

    response: str = Field(description="A conversational response to the user's query")


class Response(BaseModel):
    output: Union[Joke, ConversationalResponse]

llm_modelgpt4 = ChatOpenAI(model="gpt-4o")
structured_llm = llm_modelgpt4.with_structured_output(Response)

structured_llm.invoke("tell me about the accounting")
```

### Error Message and Stack Trace (if applicable)

---------------------------------------------------------------------------
ValidationError                           Traceback (most recent call last)
Cell In[117], line 28
     23     output: Union[Joke, ConversationalResponse]
     26 structured_llm = llm_modelgpt4.with_structured_output(Response)
---> 28 structured_llm.invoke("tell me about the accounting")

File ~/.local/lib/python3.10/site-packages/langchain_core/runnables/base.py:2393, in RunnableSequence.invoke(self, input, config)
   2391 try:
   2392     for i, step in enumerate(self.steps):
-> 2393         input = step.invoke(
   2394             input,
   2395             # mark each step as a child run
   2396             patch_config(
   2397                 config, callbacks=run_manager.get_child(f"seq:step:{i+1}")
   2398             ),
   2399         )
   2400 # finish the root run
   2401 except BaseException as e:

File ~/.local/lib/python3.10/site-packages/langchain_core/output_parsers/base.py:169, in BaseOutputParser.invoke(self, input, config)
    165 def invoke(
    166     self, input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None
    167 ) -> T:
    168     if isinstance(input, BaseMessage):
--> 169         return self._call_with_config(
    170             lambda inner_input: self.parse_result(
    171                 [ChatGeneration(message=inner_input)]
    172             ),
    173             input,
    174             config,
    175             run_type="parser",
    176         )
    177     else:
    178         return self._call_with_config(
    179             lambda inner_input: self.parse_result([Generation(text=inner_input)]),
    180             input,
    181             config,
    182             run_type="parser",
    183         )

File ~/.local/lib/python3.10/site-packages/langchain_core/runnables/base.py:1503, in Runnable._call_with_config(self, func, input, config, run_type, **kwargs)
   1499     context = copy_context()
   1500     context.run(var_child_runnable_config.set, child_config)
   1501     output = cast(
   1502         Output,
-> 1503         context.run(
   1504             call_func_with_variable_args,  # type: ignore[arg-type]
   1505             func,  # type: ignore[arg-type]
   1506             input,  # type: ignore[arg-type]
   1507             config,
   1508             run_manager,
   1509             **kwargs,
   1510         ),
   1511     )
   1512 except BaseException as e:
   1513     run_manager.on_chain_error(e)

File ~/.local/lib/python3.10/site-packages/langchain_core/runnables/config.py:346, in call_func_with_variable_args(func, input, config, run_manager, **kwargs)
    344 if run_manager is not None and accepts_run_manager(func):
    345     kwargs["run_manager"] = run_manager
--> 346 return func(input, **kwargs)

File ~/.local/lib/python3.10/site-packages/langchain_core/output_parsers/base.py:170, in BaseOutputParser.invoke.<locals>.<lambda>(inner_input)
    165 def invoke(
    166     self, input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None
    167 ) -> T:
    168     if isinstance(input, BaseMessage):
    169         return self._call_with_config(
--> 170             lambda inner_input: self.parse_result(
    171                 [ChatGeneration(message=inner_input)]
    172             ),
    173             input,
    174             config,
    175             run_type="parser",
    176         )
    177     else:
    178         return self._call_with_config(
    179             lambda inner_input: self.parse_result([Generation(text=inner_input)]),
    180             input,
    181             config,
    182             run_type="parser",
    183         )

File ~/.local/lib/python3.10/site-packages/langchain_core/output_parsers/openai_tools.py:201, in PydanticToolsParser.parse_result(self, result, partial)
    199             continue
    200         else:
--> 201             raise e
    202 if self.first_tool_only:
    203     return pydantic_objects[0] if pydantic_objects else None

File ~/.local/lib/python3.10/site-packages/langchain_core/output_parsers/openai_tools.py:196, in PydanticToolsParser.parse_result(self, result, partial)
    191     if not isinstance(res["args"], dict):
    192         raise ValueError(
    193             f"Tool arguments must be specified as a dict, received: "
    194             f"{res['args']}"
    195         )
--> 196     pydantic_objects.append(name_dict[res["type"]](**res["args"]))
    197 except (ValidationError, ValueError) as e:
    198     if partial:

File ~/.local/lib/python3.10/site-packages/pydantic/v1/main.py:341, in BaseModel.__init__(__pydantic_self__, **data)
    339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
    340 if validation_error:
--> 341     raise validation_error
    342 try:
    343     object_setattr(__pydantic_self__, '__dict__', values)

ValidationError: 1 validation error for Response
output
  field required (type=value_error.missing)


### Description

Following the example in https://python.langchain.com/v0.2/docs/how_to/structured_output/#choosing-between-multiple-schemas , if we change the question to something else like "tell me about the accounting" rather than "Tell me a joke about cats", this error will show up. 

### System Info

System Information
------------------
> OS:  Linux
> OS Version:  #35~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue May  7 09:00:52 UTC 2
> Python Version:  3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]

Package Information
-------------------
> langchain_core: 0.2.1
> langchain: 0.2.1
> langchain_community: 0.2.1
> langsmith: 0.1.63
> langchain_chroma: 0.1.1
> langchain_cli: 0.0.23
> langchain_openai: 0.1.7
> langchain_text_splitters: 0.2.0
> langchainhub: 0.1.16
> langgraph: 0.0.55
> langserve: 0.2.1

Error when Choosing between multiple schemas
This is a GitHub Issue
repo:langchain
owner:langchain-ai
Title : Error when Choosing between multiple schemas
Issue date:

--- start body ---
### Checked other resources

- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
- [X] I am sure that this is a bug in LangChain rather than my code.
- [X] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

### Example Code

```
from typing import Union

from typing import Optional

from langchain_core.pydantic_v1 import BaseModel, Field


class Joke(BaseModel):
    """Joke to tell user."""

    setup: str = Field(description="The setup of the joke")
    punchline: str = Field(description="The punchline to the joke")
    rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")


class ConversationalResponse(BaseModel):
    """Respond in a conversational manner. Be kind and helpful."""

    response: str = Field(description="A conversational response to the user's query")


class Response(BaseModel):
    output: Union[Joke, ConversationalResponse]

llm_modelgpt4 = ChatOpenAI(model="gpt-4o")
structured_llm = llm_modelgpt4.with_structured_output(Response)

structured_llm.invoke("tell me about the accounting")
```

### Error Message and Stack Trace (if applicable)

---------------------------------------------------------------------------
ValidationError                           Traceback (most recent call last)
Cell In[117], line 28
     23     output: Union[Joke, ConversationalResponse]
     26 structured_llm = llm_modelgpt4.with_structured_output(Response)
---> 28 structured_llm.invoke("tell me about the accounting")

File ~/.local/lib/python3.10/site-packages/langchain_core/runnables/base.py:2393, in RunnableSequence.invoke(self, input, config)
   2391 try:
   2392     for i, step in enumerate(self.steps):
-> 2393         input = step.invoke(
   2394             input,
   2395             # mark each step as a child run
   2396             patch_config(
   2397                 config, callbacks=run_manager.get_child(f"seq:step:{i+1}")
   2398             ),
   2399         )
   2400 # finish the root run
   2401 except BaseException as e:

File ~/.local/lib/python3.10/site-packages/langchain_core/output_parsers/base.py:169, in BaseOutputParser.invoke(self, input, config)
    165 def invoke(
    166     self, input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None
    167 ) -> T:
    168     if isinstance(input, BaseMessage):
--> 169         return self._call_with_config(
    170             lambda inner_input: self.parse_result(
    171                 [ChatGeneration(message=inner_input)]
    172             ),
    173             input,
    174             config,
    175             run_type="parser",
    176         )
    177     else:
    178         return self._call_with_config(
    179             lambda inner_input: self.parse_result([Generation(text=inner_input)]),
    180             input,
    181             config,
    182             run_type="parser",
    183         )

File ~/.local/lib/python3.10/site-packages/langchain_core/runnables/base.py:1503, in Runnable._call_with_config(self, func, input, config, run_type, **kwargs)
   1499     context = copy_context()
   1500     context.run(var_child_runnable_config.set, child_config)
   1501     output = cast(
   1502         Output,
-> 1503         context.run(
   1504             call_func_with_variable_args,  # type: ignore[arg-type]
   1505             func,  # type: ignore[arg-type]
   1506             input,  # type: ignore[arg-type]
   1507             config,
   1508             run_manager,
   1509             **kwargs,
   1510         ),
   1511     )
   1512 except BaseException as e:
   1513     run_manager.on_chain_error(e)

File ~/.local/lib/python3.10/site-packages/langchain_core/runnables/config.py:346, in call_func_with_variable_args(func, input, config, run_manager, **kwargs)
    344 if run_manager is not None and accepts_run_manager(func):
    345     kwargs["run_manager"] = run_manager
--> 346 return func(input, **kwargs)

File ~/.local/lib/python3.10/site-packages/langchain_core/output_parsers/base.py:170, in BaseOutputParser.invoke.<locals>.<lambda>(inner_input)
    165 def invoke(
    166     self, input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None
    167 ) -> T:
    168     if isinstance(input, BaseMessage):
    169         return self._call_with_config(
--> 170             lambda inner_input: self.parse_result(
    171                 [ChatGeneration(message=inner_input)]
    172             ),
    173             input,
    174             config,
    175             run_type="parser",
    176         )
    177     else:
    178         return self._call_with_config(
    179             lambda inner_input: self.parse_result([Generation(text=inner_input)]),
    180             input,
    181             config,
    182             run_type="parser",
    183         )

File ~/.local/lib/python3.10/site-packages/langchain_core/output_parsers/openai_tools.py:201, in PydanticToolsParser.parse_result(self, result, partial)
    199             continue
    200         else:
--> 201             raise e
    202 if self.first_tool_only:
    203     return pydantic_objects[0] if pydantic_objects else None

File ~/.local/lib/python3.10/site-packages/langchain_core/output_parsers/openai_tools.py:196, in PydanticToolsParser.parse_result(self, result, partial)
    191     if not isinstance(res["args"], dict):
    192         raise ValueError(
    193             f"Tool arguments must be specified as a dict, received: "
    194             f"{res['args']}"
    195         )
--> 196     pydantic_objects.append(name_dict[res["type"]](**res["args"]))
    197 except (ValidationError, ValueError) as e:
    198     if partial:

File ~/.local/lib/python3.10/site-packages/pydantic/v1/main.py:341, in BaseModel.__init__(__pydantic_self__, **data)
    339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
    340 if validation_error:
--> 341     raise validation_error
    342 try:
    343     object_setattr(__pydantic_self__, '__dict__', values)

ValidationError: 1 validation error for Response
output
  field required (type=value_error.missing)


### Description

Following the example in https://python.langchain.com/v0.2/docs/how_to/structured_output/#choosing-between-multiple-schemas , if we change the question to something else like "tell me about the accounting" rather than "Tell me a joke about cats", this error will show up. 

### System Info

System Information
------------------
> OS:  Linux
> OS Version:  #35~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue May  7 09:00:52 UTC 2
> Python Version:  3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]

Package Information
-------------------
> langchain_core: 0.2.1
> langchain: 0.2.1
> langchain_community: 0.2.1
> langsmith: 0.1.63
> langchain_chroma: 0.1.1
> langchain_cli: 0.0.23
> langchain_openai: 0.1.7
> langchain_text_splitters: 0.2.0
> langchainhub: 0.1.16
> langgraph: 0.0.55
> langserve: 0.2.1

--- end body ---
