[Bug]: tools.0.function.description is too long

[fengyunzaidushi]

Bug Description

{
	"name": "BadRequestError",
	"message": "Error code: 400 - {'error': {'message': '\\'\\\
        Use this tool to load data from the following function. It must then be read from\\\
        the corresponding read_search_and_retrieve_documents function.\\\
\\\
        search_and_retrieve_documents(query: str, num_results: Union[int, NoneType] = 10, include_domains: Union[List[str], NoneType] = None, exclude_domains: Union[List[str], NoneType] = None, start_published_date: Union[str, NoneType] = None, end_published_date: Union[str, NoneType] = None) -> List[llama_index.schema.Document]\\\
\\\
        Combines the functionality of `search` and `retrieve_documents`\\\
\\\
        Args:\\\
            query (str): the natural language query\\\
            num_results (Optional[int]): Number of results. Defaults to 10.\\\
            include_domains (Optional[List(str)]): A list of top level domains to search, like [\"wsj.com\"]\\\
            exclude_domains (Optional[List(str)]): Top level domains to exclude.\\\
            start_published_date (Optional[str]): A date string like \"2020-06-15\".\\\
            end_published_date (Optional[str]): End date string\\\
        \\\
    \\' is too long - \\'tools.0.function.description\\'', 'type': 'invalid_request_error', 'param': None, 'code': None}}",
	"stack": "---------------------------------------------------------------------------
BadRequestError                           Traceback (most recent call last)
/mnt/sda/github/01yue/llama-hub/llama_hub/tools/notebooks/exa.ipynb 单元格 26 line 2
      <a href='vscode-notebook-cell:/mnt/sda/github/01yue/llama-hub/llama_hub/tools/notebooks/exa.ipynb#Y113sZmlsZQ%3D%3D?line=0'>1</a> print(
----> <a href='vscode-notebook-cell:/mnt/sda/github/01yue/llama-hub/llama_hub/tools/notebooks/exa.ipynb#Y113sZmlsZQ%3D%3D?line=1'>2</a>     agent.chat(
      <a href='vscode-notebook-cell:/mnt/sda/github/01yue/llama-hub/llama_hub/tools/notebooks/exa.ipynb#Y113sZmlsZQ%3D%3D?line=2'>3</a>         \"Can you summarize everything published in the last month regarding news on\"
      <a href='vscode-notebook-cell:/mnt/sda/github/01yue/llama-hub/llama_hub/tools/notebooks/exa.ipynb#Y113sZmlsZQ%3D%3D?line=3'>4</a>         \" research augment generation\"
      <a href='vscode-notebook-cell:/mnt/sda/github/01yue/llama-hub/llama_hub/tools/notebooks/exa.ipynb#Y113sZmlsZQ%3D%3D?line=4'>5</a>     )
      <a href='vscode-notebook-cell:/mnt/sda/github/01yue/llama-hub/llama_hub/tools/notebooks/exa.ipynb#Y113sZmlsZQ%3D%3D?line=5'>6</a> )

File /mnt/sda/github/12yue/llama_index/llama_index/callbacks/utils.py:41, in trace_method.<locals>.decorator.<locals>.wrapper(self, *args, **kwargs)
     39 callback_manager = cast(CallbackManager, callback_manager)
     40 with callback_manager.as_trace(trace_id):
---> 41     return func(self, *args, **kwargs)

File /mnt/sda/github/12yue/llama_index/llama_index/agent/runner/base.py:497, in AgentRunner.chat(self, message, chat_history, tool_choice)
    492     tool_choice = self.default_tool_choice
    493 with self.callback_manager.event(
    494     CBEventType.AGENT_STEP,
    495     payload={EventPayload.MESSAGES: [message]},
    496 ) as e:
--> 497     chat_response = self._chat(
    498         message, chat_history, tool_choice, mode=ChatResponseMode.WAIT
    499     )
    500     assert isinstance(chat_response, AgentChatResponse)
    501     e.on_end(payload={EventPayload.RESPONSE: chat_response})

File /mnt/sda/github/12yue/llama_index/llama_index/agent/runner/base.py:442, in AgentRunner._chat(self, message, chat_history, tool_choice, mode)
    439 result_output = None
    440 while True:
    441     # pass step queue in as argument, assume step executor is stateless
--> 442     cur_step_output = self._run_step(
    443         task.task_id, mode=mode, tool_choice=tool_choice
    444     )
    446     if cur_step_output.is_last:
    447         result_output = cur_step_output

File /mnt/sda/github/12yue/llama_index/llama_index/agent/runner/base.py:304, in AgentRunner._run_step(self, task_id, step, mode, **kwargs)
    300 # TODO: figure out if you can dynamically swap in different step executors
    301 # not clear when you would do that by theoretically possible
    303 if mode == ChatResponseMode.WAIT:
--> 304     cur_step_output = self.agent_worker.run_step(step, task, **kwargs)
    305 elif mode == ChatResponseMode.STREAM:
    306     cur_step_output = self.agent_worker.stream_step(step, task, **kwargs)

File /mnt/sda/github/12yue/llama_index/llama_index/callbacks/utils.py:41, in trace_method.<locals>.decorator.<locals>.wrapper(self, *args, **kwargs)
     39 callback_manager = cast(CallbackManager, callback_manager)
     40 with callback_manager.as_trace(trace_id):
---> 41     return func(self, *args, **kwargs)

File /mnt/sda/github/12yue/llama_index/llama_index/agent/openai/step.py:573, in OpenAIAgentWorker.run_step(self, step, task, **kwargs)
    571 \"\"\"Run step.\"\"\"
    572 tool_choice = kwargs.get(\"tool_choice\", \"auto\")
--> 573 return self._run_step(
    574     step, task, mode=ChatResponseMode.WAIT, tool_choice=tool_choice
    575 )

File /mnt/sda/github/12yue/llama_index/llama_index/agent/openai/step.py:448, in OpenAIAgentWorker._run_step(self, step, task, mode, tool_choice)
    444 openai_tools = [tool.metadata.to_openai_tool() for tool in tools]
    446 llm_chat_kwargs = self._get_llm_chat_kwargs(task, openai_tools, tool_choice)
--> 448 agent_chat_response = self._get_agent_response(
    449     task, mode=mode, **llm_chat_kwargs
    450 )
    452 # TODO: implement _should_continue
    453 latest_tool_calls = self.get_latest_tool_calls(task) or []

File /mnt/sda/github/12yue/llama_index/llama_index/agent/openai/step.py:322, in OpenAIAgentWorker._get_agent_response(self, task, mode, **llm_chat_kwargs)
    318 def _get_agent_response(
    319     self, task: Task, mode: ChatResponseMode, **llm_chat_kwargs: Any
    320 ) -> AGENT_CHAT_RESPONSE_TYPE:
    321     if mode == ChatResponseMode.WAIT:
--> 322         chat_response: ChatResponse = self._llm.chat(**llm_chat_kwargs)
    323         return self._process_message(task, chat_response)
    324     elif mode == ChatResponseMode.STREAM:

File /mnt/sda/github/12yue/llama_index/llama_index/llms/base.py:100, in llm_chat_callback.<locals>.wrap.<locals>.wrapped_llm_chat(_self, messages, **kwargs)
     91 with wrapper_logic(_self) as callback_manager:
     92     event_id = callback_manager.on_event_start(
     93         CBEventType.LLM,
     94         payload={
   (...)
     98         },
     99     )
--> 100     f_return_val = f(_self, messages, **kwargs)
    102     if isinstance(f_return_val, Generator):
    103         # intercept the generator and add a callback to the end
    104         def wrapped_gen() -> ChatResponseGen:

File /mnt/sda/github/12yue/llama_index/llama_index/llms/openai.py:237, in OpenAI.chat(self, messages, **kwargs)
    235 else:
    236     chat_fn = completion_to_chat_decorator(self._complete)
--> 237 return chat_fn(messages, **kwargs)

File /mnt/sda/github/12yue/llama_index/llama_index/llms/openai.py:296, in OpenAI._chat(self, messages, **kwargs)
    294 client = self._get_client()
    295 message_dicts = to_openai_message_dicts(messages)
--> 296 response = client.chat.completions.create(
    297     messages=message_dicts,
    298     stream=False,
    299     **self._get_model_kwargs(**kwargs),
    300 )
    301 openai_message = response.choices[0].message
    302 message = from_openai_message(openai_message)

File /opt/miniconda3/envs/llamapy38/lib/python3.8/site-packages/openai/_utils/_utils.py:271, in required_args.<locals>.inner.<locals>.wrapper(*args, **kwargs)
    269             msg = f\"Missing required argument: {quote(missing[0])}\"
    270     raise TypeError(msg)
--> 271 return func(*args, **kwargs)

File /opt/miniconda3/envs/llamapy38/lib/python3.8/site-packages/openai/resources/chat/completions.py:648, in Completions.create(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, presence_penalty, response_format, seed, stop, stream, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)
    599 @required_args([\"messages\", \"model\"], [\"messages\", \"model\", \"stream\"])
    600 def create(
    601     self,
   (...)
    646     timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
    647 ) -> ChatCompletion | Stream[ChatCompletionChunk]:
--> 648     return self._post(
    649         \"/chat/completions\",
    650         body=maybe_transform(
    651             {
    652                 \"messages\": messages,
    653                 \"model\": model,
    654                 \"frequency_penalty\": frequency_penalty,
    655                 \"function_call\": function_call,
    656                 \"functions\": functions,
    657                 \"logit_bias\": logit_bias,
    658                 \"logprobs\": logprobs,
    659                 \"max_tokens\": max_tokens,
    660                 \"n\": n,
    661                 \"presence_penalty\": presence_penalty,
    662                 \"response_format\": response_format,
    663                 \"seed\": seed,
    664                 \"stop\": stop,
    665                 \"stream\": stream,
    666                 \"temperature\": temperature,
    667                 \"tool_choice\": tool_choice,
    668                 \"tools\": tools,
    669                 \"top_logprobs\": top_logprobs,
    670                 \"top_p\": top_p,
    671                 \"user\": user,
    672             },
    673             completion_create_params.CompletionCreateParams,
    674         ),
    675         options=make_request_options(
    676             extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
    677         ),
    678         cast_to=ChatCompletion,
    679         stream=stream or False,
    680         stream_cls=Stream[ChatCompletionChunk],
    681     )

File /opt/miniconda3/envs/llamapy38/lib/python3.8/site-packages/openai/_base_client.py:1179, in SyncAPIClient.post(self, path, cast_to, body, options, files, stream, stream_cls)
   1165 def post(
   1166     self,
   1167     path: str,
   (...)
   1174     stream_cls: type[_StreamT] | None = None,
   1175 ) -> ResponseT | _StreamT:
   1176     opts = FinalRequestOptions.construct(
   1177         method=\"post\", url=path, json_data=body, files=to_httpx_files(files), **options
   1178     )
-> 1179     return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))

File /opt/miniconda3/envs/llamapy38/lib/python3.8/site-packages/openai/_base_client.py:868, in SyncAPIClient.request(self, cast_to, options, remaining_retries, stream, stream_cls)
    859 def request(
    860     self,
    861     cast_to: Type[ResponseT],
   (...)
    866     stream_cls: type[_StreamT] | None = None,
    867 ) -> ResponseT | _StreamT:
--> 868     return self._request(
    869         cast_to=cast_to,
    870         options=options,
    871         stream=stream,
    872         stream_cls=stream_cls,
    873         remaining_retries=remaining_retries,
    874     )

File /opt/miniconda3/envs/llamapy38/lib/python3.8/site-packages/openai/_base_client.py:959, in SyncAPIClient._request(self, cast_to, options, remaining_retries, stream, stream_cls)
    956         err.response.read()
    958     log.debug(\"Re-raising status error\")
--> 959     raise self._make_status_error_from_response(err.response) from None
    961 return self._process_response(
    962     cast_to=cast_to,
    963     options=options,
   (...)
    966     stream_cls=stream_cls,
    967 )

BadRequestError: Error code: 400 - {'error': {'message': '\\'\\\
        Use this tool to load data from the following function. It must then be read from\\\
        the corresponding read_search_and_retrieve_documents function.\\\
\\\
        search_and_retrieve_documents(query: str, num_results: Union[int, NoneType] = 10, include_domains: Union[List[str], NoneType] = None, exclude_domains: Union[List[str], NoneType] = None, start_published_date: Union[str, NoneType] = None, end_published_date: Union[str, NoneType] = None) -> List[llama_index.schema.Document]\\\
\\\
        Combines the functionality of `search` and `retrieve_documents`\\\
\\\
        Args:\\\
            query (str): the natural language query\\\
            num_results (Optional[int]): Number of results. Defaults to 10.\\\
            include_domains (Optional[List(str)]): A list of top level domains to search, like [\"wsj.com\"]\\\
            exclude_domains (Optional[List(str)]): Top level domains to exclude.\\\
            start_published_date (Optional[str]): A date string like \"2020-06-15\".\\\
            end_published_date (Optional[str]): End date string\\\
        \\\
    \\' is too long - \\'tools.0.function.description\\'', 'type': 'invalid_request_error', 'param': None, 'code': None}}"
}

Version

Version: 0.9.39

Steps to Reproduce

https://github.com/run-llama/llama-hub/blob/main/llama_hub/tools/notebooks/exa.ipynb
when i run the last line code:
print( agent.chat( "Can you summarize everything published in the last month regarding news on" " superconductors" ) )

Relevant Logs/Tracbacks

No response