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SQLDatabaseToolkit not working
SQLDatabaseToolkit is not currently working. Se errors attached.
This is the code that creates the errors:
llm = AzureChatOpenAI(deployment_name="gpt-4",temperature=0, max_tokens=500)
db = SQLDatabase.from_uri(db_url)
toolkit = SQLDatabaseToolkit(db=db)
agent_executor = create_sql_agent(llm=llm,toolkit=toolkit,verbose=True)
And if if I add the llm parameter to the toolkit:
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
agent_executor = create_sql_agent(llm=llm,toolkit=toolkit,verbose=True)
this is the error
---langchain-0.0.142
toolkit = SQLDatabaseToolkit(db=db)
llm = AzureOpenAI(deployment_name="text-davinci-003-gpt", model_name="text-davinci-003",temperature=0.0)
agent_executor = create_sql_agent(
llm=llm,
toolkit=toolkit,
verbose=True
)
Works for me
Checking again:
It did pass the original error if "deployment_name" and "model_name" are given to the llm declaration. Now there is another error surfacing: "InvalidRequestError: Must provide an 'engine' or 'deployment_id' parameter to create a <class 'openai.api_resources.completion.Completion'>" This error happens if I use gpt-4 as the model/deployment
llm = AzureChatOpenAI(deployment_name="gpt-4", model_name="gpt-4",temperature=0, max_tokens=500)
toolkit = SQLDatabaseToolkit(db=db)
agent = create_sql_agent(llm=llm,toolkit=toolkit,verbose=True)
response = agent.run(query_str)
see below full error dump
Thought:The covidtracking table has the information I need. I will query the total number of hospitalized patients in Texas and nationwide during July 2020.
Action: query_checker_sql_db
Action Input: SELECT state, SUM(hospitalizedIncrease) as total_hospitalized FROM covidtracking WHERE date >= '2020-07-01' AND date <= '2020-07-31' AND (state = 'TX' OR state = 'US') GROUP BY state
---------------------------------------------------------------------------
InvalidRequestError Traceback (most recent call last)
Input In [42], in <cell line: 1>()
----> 1 response = agent.run(query_str + suffix)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/chains/base.py:213, in Chain.run(self, *args, **kwargs)
211 if len(args) != 1:
212 raise ValueError("`run` supports only one positional argument.")
--> 213 return self(args[0])[self.output_keys[0]]
215 if kwargs and not args:
216 return self(kwargs)[self.output_keys[0]]
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/chains/base.py:116, in Chain.__call__(self, inputs, return_only_outputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
--> 116 raise e
117 self.callback_manager.on_chain_end(outputs, verbose=self.verbose)
118 return self.prep_outputs(inputs, outputs, return_only_outputs)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/chains/base.py:113, in Chain.__call__(self, inputs, return_only_outputs)
107 self.callback_manager.on_chain_start(
108 {"name": self.__class__.__name__},
109 inputs,
110 verbose=self.verbose,
111 )
112 try:
--> 113 outputs = self._call(inputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/agents/agent.py:792, in AgentExecutor._call(self, inputs)
790 # We now enter the agent loop (until it returns something).
791 while self._should_continue(iterations, time_elapsed):
--> 792 next_step_output = self._take_next_step(
793 name_to_tool_map, color_mapping, inputs, intermediate_steps
794 )
795 if isinstance(next_step_output, AgentFinish):
796 return self._return(next_step_output, intermediate_steps)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/agents/agent.py:695, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps)
693 tool_run_kwargs["llm_prefix"] = ""
694 # We then call the tool on the tool input to get an observation
--> 695 observation = tool.run(
696 agent_action.tool_input,
697 verbose=self.verbose,
698 color=color,
699 **tool_run_kwargs,
700 )
701 else:
702 tool_run_kwargs = self.agent.tool_run_logging_kwargs()
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/tools/base.py:73, in BaseTool.run(self, tool_input, verbose, start_color, color, **kwargs)
71 except (Exception, KeyboardInterrupt) as e:
72 self.callback_manager.on_tool_error(e, verbose=verbose_)
---> 73 raise e
74 self.callback_manager.on_tool_end(
75 observation, verbose=verbose_, color=color, name=self.name, **kwargs
76 )
77 return observation
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/tools/base.py:70, in BaseTool.run(self, tool_input, verbose, start_color, color, **kwargs)
62 self.callback_manager.on_tool_start(
63 {"name": self.name, "description": self.description},
64 tool_input,
(...)
67 **kwargs,
68 )
69 try:
---> 70 observation = self._run(tool_input)
71 except (Exception, KeyboardInterrupt) as e:
72 self.callback_manager.on_tool_error(e, verbose=verbose_)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/tools/sql_database/tool.py:111, in QueryCheckerTool._run(self, query)
109 def _run(self, query: str) -> str:
110 """Use the LLM to check the query."""
--> 111 return self.llm_chain.predict(query=query, dialect=self.db.dialect)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/chains/llm.py:151, in LLMChain.predict(self, **kwargs)
137 def predict(self, **kwargs: Any) -> str:
138 """Format prompt with kwargs and pass to LLM.
139
140 Args:
(...)
149 completion = llm.predict(adjective="funny")
150 """
--> 151 return self(kwargs)[self.output_key]
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/chains/base.py:116, in Chain.__call__(self, inputs, return_only_outputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
--> 116 raise e
117 self.callback_manager.on_chain_end(outputs, verbose=self.verbose)
118 return self.prep_outputs(inputs, outputs, return_only_outputs)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/chains/base.py:113, in Chain.__call__(self, inputs, return_only_outputs)
107 self.callback_manager.on_chain_start(
108 {"name": self.__class__.__name__},
109 inputs,
110 verbose=self.verbose,
111 )
112 try:
--> 113 outputs = self._call(inputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/chains/llm.py:57, in LLMChain._call(self, inputs)
56 def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
---> 57 return self.apply([inputs])[0]
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/chains/llm.py:118, in LLMChain.apply(self, input_list)
116 def apply(self, input_list: List[Dict[str, Any]]) -> List[Dict[str, str]]:
117 """Utilize the LLM generate method for speed gains."""
--> 118 response = self.generate(input_list)
119 return self.create_outputs(response)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/chains/llm.py:62, in LLMChain.generate(self, input_list)
60 """Generate LLM result from inputs."""
61 prompts, stop = self.prep_prompts(input_list)
---> 62 return self.llm.generate_prompt(prompts, stop)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/llms/base.py:107, in BaseLLM.generate_prompt(self, prompts, stop)
103 def generate_prompt(
104 self, prompts: List[PromptValue], stop: Optional[List[str]] = None
105 ) -> LLMResult:
106 prompt_strings = [p.to_string() for p in prompts]
--> 107 return self.generate(prompt_strings, stop=stop)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/llms/base.py:140, in BaseLLM.generate(self, prompts, stop)
138 except (KeyboardInterrupt, Exception) as e:
139 self.callback_manager.on_llm_error(e, verbose=self.verbose)
--> 140 raise e
141 self.callback_manager.on_llm_end(output, verbose=self.verbose)
142 return output
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/llms/base.py:137, in BaseLLM.generate(self, prompts, stop)
133 self.callback_manager.on_llm_start(
134 {"name": self.__class__.__name__}, prompts, verbose=self.verbose
135 )
136 try:
--> 137 output = self._generate(prompts, stop=stop)
138 except (KeyboardInterrupt, Exception) as e:
139 self.callback_manager.on_llm_error(e, verbose=self.verbose)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/llms/openai.py:290, in BaseOpenAI._generate(self, prompts, stop)
288 choices.extend(response["choices"])
289 else:
--> 290 response = completion_with_retry(self, prompt=_prompts, **params)
291 choices.extend(response["choices"])
292 if not self.streaming:
293 # Can't update token usage if streaming
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/llms/openai.py:99, in completion_with_retry(llm, **kwargs)
95 @retry_decorator
96 def _completion_with_retry(**kwargs: Any) -> Any:
97 return llm.client.create(**kwargs)
---> 99 return _completion_with_retry(**kwargs)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/tenacity/__init__.py:289, in BaseRetrying.wraps.<locals>.wrapped_f(*args, **kw)
287 @functools.wraps(f)
288 def wrapped_f(*args: t.Any, **kw: t.Any) -> t.Any:
--> 289 return self(f, *args, **kw)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/tenacity/__init__.py:379, in Retrying.__call__(self, fn, *args, **kwargs)
377 retry_state = RetryCallState(retry_object=self, fn=fn, args=args, kwargs=kwargs)
378 while True:
--> 379 do = self.iter(retry_state=retry_state)
380 if isinstance(do, DoAttempt):
381 try:
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/tenacity/__init__.py:314, in BaseRetrying.iter(self, retry_state)
312 is_explicit_retry = fut.failed and isinstance(fut.exception(), TryAgain)
313 if not (is_explicit_retry or self.retry(retry_state)):
--> 314 return fut.result()
316 if self.after is not None:
317 self.after(retry_state)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/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 /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/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 /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/tenacity/__init__.py:382, in Retrying.__call__(self, fn, *args, **kwargs)
380 if isinstance(do, DoAttempt):
381 try:
--> 382 result = fn(*args, **kwargs)
383 except BaseException: # noqa: B902
384 retry_state.set_exception(sys.exc_info()) # type: ignore[arg-type]
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/llms/openai.py:97, in completion_with_retry.<locals>._completion_with_retry(**kwargs)
95 @retry_decorator
96 def _completion_with_retry(**kwargs: Any) -> Any:
---> 97 return llm.client.create(**kwargs)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/openai/api_resources/completion.py:25, in Completion.create(cls, *args, **kwargs)
23 while True:
24 try:
---> 25 return super().create(*args, **kwargs)
26 except TryAgain as e:
27 if timeout is not None and time.time() > start + timeout:
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py:149, in EngineAPIResource.create(cls, api_key, api_base, api_type, request_id, api_version, organization, **params)
127 @classmethod
128 def create(
129 cls,
(...)
136 **params,
137 ):
138 (
139 deployment_id,
140 engine,
141 timeout,
142 stream,
143 headers,
144 request_timeout,
145 typed_api_type,
146 requestor,
147 url,
148 params,
--> 149 ) = cls.__prepare_create_request(
150 api_key, api_base, api_type, api_version, organization, **params
151 )
153 response, _, api_key = requestor.request(
154 "post",
155 url,
(...)
160 request_timeout=request_timeout,
161 )
163 if stream:
164 # must be an iterator
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py:83, in EngineAPIResource.__prepare_create_request(cls, api_key, api_base, api_type, api_version, organization, **params)
81 if typed_api_type in (util.ApiType.AZURE, util.ApiType.AZURE_AD):
82 if deployment_id is None and engine is None:
---> 83 raise error.InvalidRequestError(
84 "Must provide an 'engine' or 'deployment_id' parameter to create a %s"
85 % cls,
86 "engine",
87 )
88 else:
89 if model is None and engine is None:
InvalidRequestError: Must provide an 'engine' or 'deployment_id' parameter to create a <class 'openai.api_resources.completion.Completion'>
Yeah, now I get this error also. I posted it in https://github.com/hwchase17/langchain/issues/3031
Also if I use gpt-35-turbo I get another error:
llm = AzureChatOpenAI(deployment_name="gpt-35-turbo", model_name="gpt-35-turbo", temperature=0, max_tokens=500)
toolkit = SQLDatabaseToolkit(db=db)
agent = create_sql_agent(llm=llm,toolkit=toolkit,verbose=True)
response = agent.run(query_str)
Error:
> Entering new AgentExecutor chain...
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [88], in <cell line: 1>()
----> 1 response = agent.run(query_str)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/chains/base.py:213, in Chain.run(self, *args, **kwargs)
211 if len(args) != 1:
212 raise ValueError("`run` supports only one positional argument.")
--> 213 return self(args[0])[self.output_keys[0]]
215 if kwargs and not args:
216 return self(kwargs)[self.output_keys[0]]
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/chains/base.py:116, in Chain.__call__(self, inputs, return_only_outputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
--> 116 raise e
117 self.callback_manager.on_chain_end(outputs, verbose=self.verbose)
118 return self.prep_outputs(inputs, outputs, return_only_outputs)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/chains/base.py:113, in Chain.__call__(self, inputs, return_only_outputs)
107 self.callback_manager.on_chain_start(
108 {"name": self.__class__.__name__},
109 inputs,
110 verbose=self.verbose,
111 )
112 try:
--> 113 outputs = self._call(inputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/agents/agent.py:792, in AgentExecutor._call(self, inputs)
790 # We now enter the agent loop (until it returns something).
791 while self._should_continue(iterations, time_elapsed):
--> 792 next_step_output = self._take_next_step(
793 name_to_tool_map, color_mapping, inputs, intermediate_steps
794 )
795 if isinstance(next_step_output, AgentFinish):
796 return self._return(next_step_output, intermediate_steps)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/agents/agent.py:672, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps)
667 """Take a single step in the thought-action-observation loop.
668
669 Override this to take control of how the agent makes and acts on choices.
670 """
671 # Call the LLM to see what to do.
--> 672 output = self.agent.plan(intermediate_steps, **inputs)
673 # If the tool chosen is the finishing tool, then we end and return.
674 if isinstance(output, AgentFinish):
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/agents/agent.py:385, in Agent.plan(self, intermediate_steps, **kwargs)
383 full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
384 full_output = self.llm_chain.predict(**full_inputs)
--> 385 return self.output_parser.parse(full_output)
File /anaconda/envs/azureml_py310_sdkv2/lib/python3.10/site-packages/langchain/agents/mrkl/output_parser.py:20, in MRKLOutputParser.parse(self, text)
18 match = re.search(regex, text, re.DOTALL)
19 if not match:
---> 20 raise ValueError(f"Could not parse LLM output: `{text}`")
21 action = match.group(1).strip()
22 action_input = match.group(2)
ValueError: Could not parse LLM output: `Action: list_tables_sql_db, ""`
I am also facing the same error.Please Let me know incase you guys find a workaround
mckunkel fix worked for me as well. Try using AzureOpenAI instead of AzureChatOpenAI.
Hi, @pablomarin. I'm Dosu, and I'm helping the LangChain team manage their backlog. I wanted to let you know that we are marking this issue as stale.
Based on my understanding of the current state of the issue, it seems that you encountered an error related to the usage of the llm parameter in the SQLDatabaseToolkit. The original error was resolved by using the "deployment_name" and "model_name" parameters in the llm declaration. However, a new error has surfaced when using the gpt-4 model, and another user has reported a similar error with the gpt-35-turbo model. Unfortunately, no solution or workaround has been provided yet.
Before we close this issue, we wanted to check with you if it is still relevant to the latest version of the LangChain repository. If it is, please let us know by commenting on the issue. Otherwise, feel free to close the issue yourself, or it will be automatically closed in 7 days.
Thank you for your understanding and contribution to the LangChain community. Let us know if you have any further questions or concerns.
![dosubot[bot] avatar](https://avatars.githubusercontent.com/in/324583?v=4 "Published by a pub.dev verified publisher"){kind=small}
Latest versions solved the problem. Feel free to close it .
Pablo
On Thu, Sep 21, 2023 at 11:03 AM dosu-beta[bot] @.***> wrote:
Hi, @pablomarin https://github.com/pablomarin. I'm Dosu, and I'm helping the LangChain team manage their backlog. I wanted to let you know that we are marking this issue as stale.
Based on my understanding of the current state of the issue, it seems that you encountered an error related to the usage of the llm parameter in the SQLDatabaseToolkit. The original error was resolved by using the "deployment_name" and "model_name" parameters in the llm declaration. However, a new error has surfaced when using the gpt-4 model, and another user has reported a similar error with the gpt-35-turbo model. Unfortunately, no solution or workaround has been provided yet.
Before we close this issue, we wanted to check with you if it is still relevant to the latest version of the LangChain repository. If it is, please let us know by commenting on the issue. Otherwise, feel free to close the issue yourself, or it will be automatically closed in 7 days.
Thank you for your understanding and contribution to the LangChain community. Let us know if you have any further questions or concerns.
— Reply to this email directly, view it on GitHub https://github.com/langchain-ai/langchain/issues/2914#issuecomment-1729875199 or unsubscribe https://github.com/notifications/unsubscribe-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ou are receiving this email because you were mentioned.
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Thank you, @pablomarin, for confirming that the latest versions have resolved the problem. We appreciate your contribution to the LangChain community. We will proceed to close the issue.