I'm trying to test a chat agent using the python code below.  I'm using langchain agent and tool from langchain.  I'm defining a couple of simple functions for the LLM to use as tools when a prompt mentions something relevant to the tool.  I'm using the openai gpt-3.5-turbo model for the LLM.  I'm getting the error message below when trying to run conversational_agent with a simple prompt to return a random number.  The function defined for the tool should do this easily.  I'm getting the error message below mentioning that openai doesn't have the attribute.  Does anyone see what the issue might be and can you suggest how to fix it?
code:
from config import api_key,new_personal_api_key

apikey=new_personal_api_key

# apikey=api_key

import os

os.environ['OPENAI_API_KEY'] = apikey


from langchain.chains.conversation.memory import ConversationBufferWindowMemory


from langchain.agents import Tool
from langchain.tools import BaseTool

def meaning_of_life(input=""):
    return 'The meaning of life is 42 if rounded but is actually 42.17658'
    
life_tool = Tool(
    name='Meaning of Life',
    func= meaning_of_life,
    description="Useful for when you need to answer questions about the meaning of life. input should be MOL "
)


import random

def random_num(input=""):
    return random.randint(0,5)
    
    
random_tool = Tool(
    name='Random number',
    func= random_num,
    description="Useful for when you need to get a random number. input should be 'random'"
)

from langchain import OpenAI 
from langchain.chat_models import ChatOpenAI

# Set up the turbo LLM
turbo_llm = ChatOpenAI(
    temperature=0,
    model_name='gpt-3.5-turbo'
)



from langchain.agents import initialize_agent

tools = [random_tool, life_tool]

# conversational agent memory
memory = ConversationBufferWindowMemory(
    memory_key='chat_history',
    k=3,
    return_messages=True
)


# create our agent
conversational_agent = initialize_agent(
    agent='chat-conversational-react-description',
    tools=tools,
    llm=turbo_llm,
#     llm=local_llm,
    verbose=True,
    max_iterations=3,
    early_stopping_method='generate',
    memory=memory,
    handle_parsing_errors=True
)


conversational_agent('Can you give me a random number?')

error:
> Entering new AgentExecutor chain...

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[12], line 1
----> 1 conversational_agent('Can you give me a random number?')

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chains/base.py:310, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
    308 except BaseException as e:
    309     run_manager.on_chain_error(e)
--> 310     raise e
    311 run_manager.on_chain_end(outputs)
    312 final_outputs: Dict[str, Any] = self.prep_outputs(
    313     inputs, outputs, return_only_outputs
    314 )

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chains/base.py:304, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
    297 run_manager = callback_manager.on_chain_start(
    298     dumpd(self),
    299     inputs,
    300     name=run_name,
    301 )
    302 try:
    303     outputs = (
--> 304         self._call(inputs, run_manager=run_manager)
    305         if new_arg_supported
    306         else self._call(inputs)
    307     )
    308 except BaseException as e:
    309     run_manager.on_chain_error(e)

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/agents/agent.py:1146, in AgentExecutor._call(self, inputs, run_manager)
   1144 # We now enter the agent loop (until it returns something).
   1145 while self._should_continue(iterations, time_elapsed):
-> 1146     next_step_output = self._take_next_step(
   1147         name_to_tool_map,
   1148         color_mapping,
   1149         inputs,
   1150         intermediate_steps,
   1151         run_manager=run_manager,
   1152     )
   1153     if isinstance(next_step_output, AgentFinish):
   1154         return self._return(
   1155             next_step_output, intermediate_steps, run_manager=run_manager
   1156         )

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/agents/agent.py:933, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
    930     intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
    932     # Call the LLM to see what to do.
--> 933     output = self.agent.plan(
    934         intermediate_steps,
    935         callbacks=run_manager.get_child() if run_manager else None,
    936         **inputs,
    937     )
    938 except OutputParserException as e:
    939     if isinstance(self.handle_parsing_errors, bool):

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/agents/agent.py:546, in Agent.plan(self, intermediate_steps, callbacks, **kwargs)
    534 """Given input, decided what to do.
    535 
    536 Args:
   (...)
    543     Action specifying what tool to use.
    544 """
    545 full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
--> 546 full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
    547 return self.output_parser.parse(full_output)

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chains/llm.py:298, in LLMChain.predict(self, callbacks, **kwargs)
    283 def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
    284     """Format prompt with kwargs and pass to LLM.
    285 
    286     Args:
   (...)
    296             completion = llm.predict(adjective="funny")
    297     """
--> 298     return self(kwargs, callbacks=callbacks)[self.output_key]

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chains/base.py:310, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
    308 except BaseException as e:
    309     run_manager.on_chain_error(e)
--> 310     raise e
    311 run_manager.on_chain_end(outputs)
    312 final_outputs: Dict[str, Any] = self.prep_outputs(
    313     inputs, outputs, return_only_outputs
    314 )

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chains/base.py:304, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
    297 run_manager = callback_manager.on_chain_start(
    298     dumpd(self),
    299     inputs,
    300     name=run_name,
    301 )
    302 try:
    303     outputs = (
--> 304         self._call(inputs, run_manager=run_manager)
    305         if new_arg_supported
    306         else self._call(inputs)
    307     )
    308 except BaseException as e:
    309     run_manager.on_chain_error(e)

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chains/llm.py:108, in LLMChain._call(self, inputs, run_manager)
    103 def _call(
    104     self,
    105     inputs: Dict[str, Any],
    106     run_manager: Optional[CallbackManagerForChainRun] = None,
    107 ) -> Dict[str, str]:
--> 108     response = self.generate([inputs], run_manager=run_manager)
    109     return self.create_outputs(response)[0]

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chains/llm.py:120, in LLMChain.generate(self, input_list, run_manager)
    118 callbacks = run_manager.get_child() if run_manager else None
    119 if isinstance(self.llm, BaseLanguageModel):
--> 120     return self.llm.generate_prompt(
    121         prompts,
    122         stop,
    123         callbacks=callbacks,
    124         **self.llm_kwargs,
    125     )
    126 else:
    127     results = self.llm.bind(stop=stop, **self.llm_kwargs).batch(
    128         cast(List, prompts), {"callbacks": callbacks}
    129     )

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chat_models/base.py:459, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
    451 def generate_prompt(
    452     self,
    453     prompts: List[PromptValue],
   (...)
    456     **kwargs: Any,
    457 ) -> LLMResult:
    458     prompt_messages = [p.to_messages() for p in prompts]
--> 459     return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chat_models/base.py:349, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
    347         if run_managers:
    348             run_managers[i].on_llm_error(e)
--> 349         raise e
    350 flattened_outputs = [
    351     LLMResult(generations=[res.generations], llm_output=res.llm_output)
    352     for res in results
    353 ]
    354 llm_output = self._combine_llm_outputs([res.llm_output for res in results])

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chat_models/base.py:339, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
    336 for i, m in enumerate(messages):
    337     try:
    338         results.append(
--> 339             self._generate_with_cache(
    340                 m,
    341                 stop=stop,
    342                 run_manager=run_managers[i] if run_managers else None,
    343                 **kwargs,
    344             )
    345         )
    346     except BaseException as e:
    347         if run_managers:

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chat_models/base.py:492, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
    488     raise ValueError(
    489         "Asked to cache, but no cache found at `langchain.cache`."
    490     )
    491 if new_arg_supported:
--> 492     return self._generate(
    493         messages, stop=stop, run_manager=run_manager, **kwargs
    494     )
    495 else:
    496     return self._generate(messages, stop=stop, **kwargs)

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chat_models/openai.py:365, in ChatOpenAI._generate(self, messages, stop, run_manager, stream, **kwargs)
    363 message_dicts, params = self._create_message_dicts(messages, stop)
    364 params = {**params, **kwargs}
--> 365 response = self.completion_with_retry(
    366     messages=message_dicts, run_manager=run_manager, **params
    367 )
    368 return self._create_chat_result(response)

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chat_models/openai.py:297, in ChatOpenAI.completion_with_retry(self, run_manager, **kwargs)
    293 def completion_with_retry(
    294     self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
    295 ) -> Any:
    296     """Use tenacity to retry the completion call."""
--> 297     retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
    299     @retry_decorator
    300     def _completion_with_retry(**kwargs: Any) -> Any:
    301         return self.client.create(**kwargs)

File ~/anaconda3/envs/llm_110623/lib/python3.10/site-packages/langchain/chat_models/openai.py:77, in _create_retry_decorator(llm, run_manager)
     68 def _create_retry_decorator(
     69     llm: ChatOpenAI,
     70     run_manager: Optional[
     71         Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
     72     ] = None,
     73 ) -> Callable[[Any], Any]:
     74     import openai
     76     errors = [
---> 77         openai.error.Timeout,
     78         openai.error.APIError,
     79         openai.error.APIConnectionError,
     80         openai.error.RateLimitError,
     81         openai.error.ServiceUnavailableError,
     82     ]
     83     return create_base_retry_decorator(
     84         error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
     85     )

AttributeError: module 'openai' has no attribute 'error'