python-3.xopenai-apilangchain
creating chat agent with langchain and openai getting no attribute error
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'
Solution
this seems like version incompatibility issue. I checked the open api pypi:
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