[module-4]with_structured_output not work

[waics]

my llm

from langchain_openai import ChatOpenAI
import os

os.environ[var] = getpass.getpass("ZHIPUAI_API_KEY")

llm = ChatOpenAI(
    model="glm-4-plus", 
    temperature=0.5,
    openai_api_key = os.environ["ZHIPU_API_KEY"],
    openai_api_base = "https://open.bigmodel.cn/api/paas/v4/"

my run(recommend)
click here -> langsmith run

code like
structured_llm = llm.with_structured_output(Perspectives)
don't work well oftenly.

It should return structed data, according to the schema defined in Perspectives like below,

class Perspectives(BaseModel):
    analysts: List[Analyst] = Field(
        description="Comprehensive list of analysts with their roles and affiliations.",
    )

But when invoke structured_llm, it always return Null randomly. I guess it's due to the message returned by llm originally.
How to make this process more stable.

Here is the traceback

{
	"name": "AttributeError",
	"message": "'NoneType' object has no attribute 'search_query'",
	"stack": "---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[125], line 4
      2 messages = [HumanMessage(f\"So you said you were writing an article on {topic}?\")]
      3 thread = {\"configurable\": {\"thread_id\": \"1\"}}
----> 4 interview = interview_graph.invoke({\"analyst\": analysts[0], \"messages\": messages, \"max_num_turns\": 2}, thread)
      5 Markdown(interview['sections'][0])

File d:\\software\\miniconda3\\envs\\lc-academy-env\\lib\\site-packages\\langgraph\\pregel\\__init__.py:1844, in Pregel.invoke(self, input, config, stream_mode, output_keys, interrupt_before, interrupt_after, debug, **kwargs)
   1842 else:
   1843     chunks = []
-> 1844 for chunk in self.stream(
   1845     input,
   1846     config,
   1847     stream_mode=stream_mode,
   1848     output_keys=output_keys,
   1849     interrupt_before=interrupt_before,
   1850     interrupt_after=interrupt_after,
   1851     debug=debug,
   1852     **kwargs,
   1853 ):
   1854     if stream_mode == \"values\":
   1855         latest = chunk

File d:\\software\\miniconda3\\envs\\lc-academy-env\\lib\\site-packages\\langgraph\\pregel\\__init__.py:1573, in Pregel.stream(self, input, config, stream_mode, output_keys, interrupt_before, interrupt_after, debug, subgraphs)
   1567     # Similarly to Bulk Synchronous Parallel / Pregel model
   1568     # computation proceeds in steps, while there are channel updates
   1569     # channel updates from step N are only visible in step N+1
   1570     # channels are guaranteed to be immutable for the duration of the step,
   1571     # with channel updates applied only at the transition between steps
   1572     while loop.tick(input_keys=self.input_channels):
-> 1573         for _ in runner.tick(
   1574             loop.tasks.values(),
   1575             timeout=self.step_timeout,
   1576             retry_policy=self.retry_policy,
   1577             get_waiter=get_waiter,
   1578         ):
   1579             # emit output
   1580             yield from output()
   1581 # emit output

File d:\\software\\miniconda3\\envs\\lc-academy-env\\lib\\site-packages\\langgraph\\pregel\\runner.py:159, in PregelRunner.tick(self, tasks, reraise, timeout, retry_policy, get_waiter)
    157     yield
    158 # panic on failure or timeout
--> 159 _panic_or_proceed(
    160     done_futures.union(f for f, t in futures.items() if t is not None),
    161     panic=reraise,
    162 )

File d:\\software\\miniconda3\\envs\\lc-academy-env\\lib\\site-packages\\langgraph\\pregel\\runner.py:367, in _panic_or_proceed(futs, timeout_exc_cls, panic)
    365 # raise the exception
    366 if panic:
--> 367     raise exc
    368 else:
    369     return

File d:\\software\\miniconda3\\envs\\lc-academy-env\\lib\\site-packages\\langgraph\\pregel\\executor.py:70, in BackgroundExecutor.done(self, task)
     68 def done(self, task: concurrent.futures.Future) -> None:
     69     try:
---> 70         task.result()
     71     except GraphInterrupt:
     72         # This exception is an interruption signal, not an error
     73         # so we don't want to re-raise it on exit
     74         self.tasks.pop(task)

File d:\\software\\miniconda3\\envs\\lc-academy-env\\lib\\concurrent\\futures\\_base.py:451, in Future.result(self, timeout)
    449     raise CancelledError()
    450 elif self._state == FINISHED:
--> 451     return self.__get_result()
    453 self._condition.wait(timeout)
    455 if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:

File d:\\software\\miniconda3\\envs\\lc-academy-env\\lib\\concurrent\\futures\\_base.py:403, in Future.__get_result(self)
    401 if self._exception:
    402     try:
--> 403         raise self._exception
    404     finally:
    405         # Break a reference cycle with the exception in self._exception
    406         self = None

File d:\\software\\miniconda3\\envs\\lc-academy-env\\lib\\concurrent\\futures\\thread.py:58, in _WorkItem.run(self)
     55     return
     57 try:
---> 58     result = self.fn(*self.args, **self.kwargs)
     59 except BaseException as exc:
     60     self.future.set_exception(exc)

File d:\\software\\miniconda3\\envs\\lc-academy-env\\lib\\site-packages\\langgraph\\pregel\\retry.py:40, in run_with_retry(task, retry_policy, writer)
     38 task.writes.clear()
     39 # run the task
---> 40 task.proc.invoke(task.input, config)
     41 # if successful, end
     42 break

File d:\\software\\miniconda3\\envs\\lc-academy-env\\lib\\site-packages\\langgraph\\utils\\runnable.py:410, in RunnableSeq.invoke(self, input, config, **kwargs)
    408 context.run(_set_config_context, config)
    409 if i == 0:
--> 410     input = context.run(step.invoke, input, config, **kwargs)
    411 else:
    412     input = context.run(step.invoke, input, config)

File d:\\software\\miniconda3\\envs\\lc-academy-env\\lib\\site-packages\\langgraph\\utils\\runnable.py:184, in RunnableCallable.invoke(self, input, config, **kwargs)
    182 else:
    183     context.run(_set_config_context, config)
--> 184     ret = context.run(self.func, input, **kwargs)
    185 if isinstance(ret, Runnable) and self.recurse:
    186     return ret.invoke(input, config)

Cell In[122], line 23, in search_web(state)
     20 search_query = structured_llm.invoke([search_instructions]+state['messages']) #   append the final question
     22 # Search
---> 23 search_docs = tavily_search.invoke(search_query.search_query)
     25  # Format
     26 formatted_search_docs = \"\
\
---\
\
\".join(
     27     [
     28         f'<Document href=\"{doc[\"url\"]}\"/>\
{doc[\"content\"]}\
</Document>'
     29         for doc in search_docs
     30     ]
     31 )

AttributeError: 'NoneType' object has no attribute 'search_query'"}

my run(recommend)
click here -> langsmith run

[hinthornw]

what llm are you using. Please provide a reproducible example

[waics]

what llm are you using. Please provide a reproducible example

Updated.
Thank you for your attention