creating chat agent with langchain and openai getting no attribute error

题意：

使用 LangChain 和 OpenAI 创建聊天代理时遇到“没有属性错误”（Getting "no attribute" error when creating a chat agent with LangChain and OpenAI）

问题背景：

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?

我正在尝试使用下面的 Python 代码来测试一个聊天代理。我使用了 LangChain 的 agent 和 tool 功能。我为大型语言模型（LLM）定义了一些简单的函数，作为工具使用，当提示包含与工具相关的内容时，这些函数会被调用。我使用的是 OpenAI 的 gpt-3.5-turbo 模型作为 LLM。但是，当我尝试使用一个简单的提示来运行 conversational_agent 以返回一个随机数时，遇到了下面的错误消息。为工具定义的函数应该可以轻松完成这个任务。但是，我收到的错误消息提到 openai 没有该属性。请问有人知道问题可能出在哪里，以及如何修复吗？

code: 代码如下：

from config import api_key,new_personal_api_keyapikey=new_personal_api_key# apikey=api_keyimport osos.environ['OPENAI_API_KEY'] = apikeyfrom langchain.chains.conversation.memory import ConversationBufferWindowMemoryfrom langchain.agents import Tool
from langchain.tools import BaseTooldef 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 randomdef 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_agenttools = [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: 错误信息如下：

> 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 e311 run_manager.on_chain_end(outputs)312 final_outputs: Dict[str, Any] = self.prep_outputs(313     inputs, outputs, return_only_outputs314 )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_supported306         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_manager1156         )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 e311 run_manager.on_chain_end(outputs)312 final_outputs: Dict[str, Any] = self.prep_outputs(313     inputs, outputs, return_only_outputs314 )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_supported306         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 None119 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 e350 flattened_outputs = [351     LLMResult(generations=[res.generations], llm_output=res.llm_output)352     for res in results353 ]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, **kwargs494     )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, **params367 )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: Any295 ) -> Any:296     """Use tenacity to retry the completion call."""
--> 297     retry_decorator = _create_retry_decorator(self, run_manager=run_manager)299     @retry_decorator300     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 openai76     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_manager85     )AttributeError: module 'openai' has no attribute 'error'

问题解决：

this seems like version incompatibility issue. I checked the open api pypi:

“这看起来像是版本不兼容的问题。我检查了 openapi 的 PyPI 页面：”

looks like it was just updated yesterday. probably langchain has not been updated so both libraries are conflicting. if you downgrade your openai sdk it should work