Langchain SQL agent with Azure OpenAI fails on invoke method returning Internal server error 500 #24488
Replies: 3 comments 5 replies
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Hey there, @abhinabsarkar! I'm here to help you with your coding issues and questions. Let's work together to tackle this bug and find a solution. To resolve the Internal Server Error (500) when invoking the LangChain SQL agent with Azure OpenAI, ensure that you have correctly configured the environment variables and the deployment name. Here is a checklist and example code to help you verify your setup:
If the error persists, double-check the Azure portal to ensure that your deployment is active and that the API key is valid. Additionally, verify that the endpoint URL is correct and that there are no network issues preventing access to the Azure OpenAI service. Also, ensure that the |
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@dosu as I confirmed in the original post that the environment variables are set & I can interact with DB & LLM using Langchain. The code fails for the create_sql_agent when trying to use the executor. It doesn't give me any specific error message on why it fails other than error code 500. |
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To resolve the Internal Server Error (500) when using
Here is an example of how to correctly set up the from langchain_openai import ChatOpenAI
from langchain_community.agent_toolkits import create_sql_agent
from langchain_community.utilities import SQLDatabase
# Initialize the database connection
db = SQLDatabase.from_uri("your_azure_sql_connection_string")
# Initialize the language model
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
# Create the SQL agent executor
agent_executor = create_sql_agent(
llm,
db=db,
agent_type="openai-tools",
verbose=True
)Ensure that the Additionally, consider the following security best practices when building Q&A systems over SQL databases:
For more detailed guidance on setting up and using SQL agents in LangChain, refer to the LangChain SQL Quickstart Guide [1][2][3]. |
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@dosu I am getting the following error with my code. Entering new SQL Agent Executor chain... |
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Can anyone advise what is that I am missing here? |
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I am having the exact same problem. I have tested my chat LLM and my DB LLM individually and they work. Once I put it together in an agent chain, we run into the error openai.InternalServerError: Error code: 500 - {'statusCode': 500, 'message': 'Internal server error', 'activityId': 'xxx-xxx-xxx-xxx'}We have followed all the API documentation on how to build a SQL Agent, this was all working perfectly until Thursday last week (25/07/2024). def init_langchain_client(
chat: AzureChatOpenAI,
memory: ConversationSummaryBufferMemory,
suffix: str,
tools: List[ToolType],
system_prefix: str,
) -> AgentExecutor:
"""
Initialise the Langchain client with necessary configurations.
Returns
-------
AgentExecutor
The agent executor configured with SQL database tools and Azure OpenAI models.
"""
prompt = PromptTemplate.from_template(system_prefix + suffix)
agent = create_react_agent(
llm=chat,
tools=tools,
prompt=prompt,
)
agent_chain = AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
verbose=True,
memory=memory,
handle_parsing_errors=True,
return_intermediate_steps=True,
)
return agent_chainlangchain_agent_chain = init_langchain_client(
chat=chat,
memory=memory,
suffix=SUFFIX_REACT,
tools=tools,
system_prefix=system_prefix,
)langchain_agent_chain.invoke({"input":"Who are you?"})---------------------------------------------------------------------------
InternalServerError Traceback (most recent call last)
Cell In[4], line 1
----> 1 langchain_agent_chain.invoke({"input":"Who are you?"})
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain/chains/base.py:163, in Chain.invoke(self, input, config, **kwargs)
161 except BaseException as e:
162 run_manager.on_chain_error(e)
--> 163 raise e
164 run_manager.on_chain_end(outputs)
166 if include_run_info:
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain/chains/base.py:153, in Chain.invoke(self, input, config, **kwargs)
150 try:
151 self._validate_inputs(inputs)
152 outputs = (
--> 153 self._call(inputs, run_manager=run_manager)
154 if new_arg_supported
155 else self._call(inputs)
156 )
158 final_outputs: Dict[str, Any] = self.prep_outputs(
159 inputs, outputs, return_only_outputs
160 )
161 except BaseException as e:
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain/agents/agent.py:1432, in AgentExecutor._call(self, inputs, run_manager)
1430 # We now enter the agent loop (until it returns something).
1431 while self._should_continue(iterations, time_elapsed):
-> 1432 next_step_output = self._take_next_step(
1433 name_to_tool_map,
1434 color_mapping,
1435 inputs,
1436 intermediate_steps,
1437 run_manager=run_manager,
1438 )
1439 if isinstance(next_step_output, AgentFinish):
1440 return self._return(
1441 next_step_output, intermediate_steps, run_manager=run_manager
1442 )
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain/agents/agent.py:1138, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
1129 def _take_next_step(
1130 self,
1131 name_to_tool_map: Dict[str, BaseTool],
(...)
1135 run_manager: Optional[CallbackManagerForChainRun] = None,
1136 ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
1137 return self._consume_next_step(
-> 1138 [
1139 a
1140 for a in self._iter_next_step(
1141 name_to_tool_map,
1142 color_mapping,
1143 inputs,
1144 intermediate_steps,
1145 run_manager,
1146 )
1147 ]
1148 )
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain/agents/agent.py:1166, in AgentExecutor._iter_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
1163 intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
1165 # Call the LLM to see what to do.
-> 1166 output = self.agent.plan(
1167 intermediate_steps,
1168 callbacks=run_manager.get_child() if run_manager else None,
1169 **inputs,
1170 )
1171 except OutputParserException as e:
1172 if isinstance(self.handle_parsing_errors, bool):
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain/agents/agent.py:397, in RunnableAgent.plan(self, intermediate_steps, callbacks, **kwargs)
389 final_output: Any = None
390 if self.stream_runnable:
391 # Use streaming to make sure that the underlying LLM is invoked in a
392 # streaming
(...)
395 # Because the response from the plan is not a generator, we need to
396 # accumulate the output into final output and return that.
--> 397 for chunk in self.runnable.stream(inputs, config={"callbacks": callbacks}):
398 if final_output is None:
399 final_output = chunk
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain_core/runnables/base.py:2875, in RunnableSequence.stream(self, input, config, **kwargs)
2869 def stream(
2870 self,
2871 input: Input,
2872 config: Optional[RunnableConfig] = None,
2873 **kwargs: Optional[Any],
2874 ) -> Iterator[Output]:
-> 2875 yield from self.transform(iter([input]), config, **kwargs)
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain_core/runnables/base.py:2862, in RunnableSequence.transform(self, input, config, **kwargs)
2856 def transform(
2857 self,
2858 input: Iterator[Input],
2859 config: Optional[RunnableConfig] = None,
2860 **kwargs: Optional[Any],
2861 ) -> Iterator[Output]:
-> 2862 yield from self._transform_stream_with_config(
2863 input,
2864 self._transform,
2865 patch_config(config, run_name=(config or {}).get("run_name") or self.name),
2866 **kwargs,
2867 )
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain_core/runnables/base.py:1881, in Runnable._transform_stream_with_config(self, input, transformer, config, run_type, **kwargs)
1879 try:
1880 while True:
-> 1881 chunk: Output = context.run(next, iterator) # type: ignore
1882 yield chunk
1883 if final_output_supported:
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain_core/runnables/base.py:2826, in RunnableSequence._transform(self, input, run_manager, config)
2817 for step in steps:
2818 final_pipeline = step.transform(
2819 final_pipeline,
2820 patch_config(
(...)
2823 ),
2824 )
-> 2826 for output in final_pipeline:
2827 yield output
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain_core/runnables/base.py:1282, in Runnable.transform(self, input, config, **kwargs)
1279 final: Input
1280 got_first_val = False
-> 1282 for ichunk in input:
1283 # The default implementation of transform is to buffer input and
1284 # then call stream.
1285 # It'll attempt to gather all input into a single chunk using
1286 # the `+` operator.
1287 # If the input is not addable, then we'll assume that we can
1288 # only operate on the last chunk,
1289 # and we'll iterate until we get to the last chunk.
1290 if not got_first_val:
1291 final = ichunk
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain_core/runnables/base.py:4736, in RunnableBindingBase.transform(self, input, config, **kwargs)
4730 def transform(
4731 self,
4732 input: Iterator[Input],
4733 config: Optional[RunnableConfig] = None,
4734 **kwargs: Any,
4735 ) -> Iterator[Output]:
-> 4736 yield from self.bound.transform(
4737 input,
4738 self._merge_configs(config),
4739 **{**self.kwargs, **kwargs},
4740 )
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain_core/runnables/base.py:1300, in Runnable.transform(self, input, config, **kwargs)
1297 final = ichunk
1299 if got_first_val:
-> 1300 yield from self.stream(final, config, **kwargs)
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py:249, in BaseChatModel.stream(self, input, config, stop, **kwargs)
242 except BaseException as e:
243 run_manager.on_llm_error(
244 e,
245 response=LLMResult(
246 generations=[[generation]] if generation else []
247 ),
248 )
--> 249 raise e
250 else:
251 run_manager.on_llm_end(LLMResult(generations=[[generation]]))
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py:229, in BaseChatModel.stream(self, input, config, stop, **kwargs)
227 generation: Optional[ChatGenerationChunk] = None
228 try:
--> 229 for chunk in self._stream(messages, stop=stop, **kwargs):
230 if chunk.message.id is None:
231 chunk.message.id = f"run-{run_manager.run_id}"
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/langchain_openai/chat_models/base.py:480, in BaseChatOpenAI._stream(self, messages, stop, run_manager, **kwargs)
477 params = {**params, **kwargs, "stream": True}
479 default_chunk_class = AIMessageChunk
--> 480 with self.client.create(messages=message_dicts, **params) as response:
481 for chunk in response:
482 if not isinstance(chunk, dict):
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/openai/_utils/_utils.py:277, in required_args.<locals>.inner.<locals>.wrapper(*args, **kwargs)
275 msg = f"Missing required argument: {quote(missing[0])}"
276 raise TypeError(msg)
--> 277 return func(*args, **kwargs)
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/openai/resources/chat/completions.py:643, in Completions.create(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, parallel_tool_calls, presence_penalty, response_format, seed, service_tier, stop, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)
609 @required_args(["messages", "model"], ["messages", "model", "stream"])
610 def create(
611 self,
(...)
641 timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
642 ) -> ChatCompletion | Stream[ChatCompletionChunk]:
--> 643 return self._post(
644 "/chat/completions",
645 body=maybe_transform(
646 {
647 "messages": messages,
648 "model": model,
649 "frequency_penalty": frequency_penalty,
650 "function_call": function_call,
651 "functions": functions,
652 "logit_bias": logit_bias,
653 "logprobs": logprobs,
654 "max_tokens": max_tokens,
655 "n": n,
656 "parallel_tool_calls": parallel_tool_calls,
657 "presence_penalty": presence_penalty,
658 "response_format": response_format,
659 "seed": seed,
660 "service_tier": service_tier,
661 "stop": stop,
662 "stream": stream,
663 "stream_options": stream_options,
664 "temperature": temperature,
665 "tool_choice": tool_choice,
666 "tools": tools,
667 "top_logprobs": top_logprobs,
668 "top_p": top_p,
669 "user": user,
670 },
671 completion_create_params.CompletionCreateParams,
672 ),
673 options=make_request_options(
674 extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
675 ),
676 cast_to=ChatCompletion,
677 stream=stream or False,
678 stream_cls=Stream[ChatCompletionChunk],
679 )
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/openai/_base_client.py:1250, in SyncAPIClient.post(self, path, cast_to, body, options, files, stream, stream_cls)
1236 def post(
1237 self,
1238 path: str,
(...)
1245 stream_cls: type[_StreamT] | None = None,
1246 ) -> ResponseT | _StreamT:
1247 opts = FinalRequestOptions.construct(
1248 method="post", url=path, json_data=body, files=to_httpx_files(files), **options
1249 )
-> 1250 return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/openai/_base_client.py:931, in SyncAPIClient.request(self, cast_to, options, remaining_retries, stream, stream_cls)
922 def request(
923 self,
924 cast_to: Type[ResponseT],
(...)
929 stream_cls: type[_StreamT] | None = None,
930 ) -> ResponseT | _StreamT:
--> 931 return self._request(
932 cast_to=cast_to,
933 options=options,
934 stream=stream,
935 stream_cls=stream_cls,
936 remaining_retries=remaining_retries,
937 )
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/openai/_base_client.py:1015, in SyncAPIClient._request(self, cast_to, options, remaining_retries, stream, stream_cls)
1013 if retries > 0 and self._should_retry(err.response):
1014 err.response.close()
-> 1015 return self._retry_request(
1016 options,
1017 cast_to,
1018 retries,
1019 err.response.headers,
1020 stream=stream,
1021 stream_cls=stream_cls,
1022 )
1024 # If the response is streamed then we need to explicitly read the response
1025 # to completion before attempting to access the response text.
1026 if not err.response.is_closed:
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/openai/_base_client.py:1063, in SyncAPIClient._retry_request(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)
1059 # In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a
1060 # different thread if necessary.
1061 time.sleep(timeout)
-> 1063 return self._request(
1064 options=options,
1065 cast_to=cast_to,
1066 remaining_retries=remaining,
1067 stream=stream,
1068 stream_cls=stream_cls,
1069 )
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/openai/_base_client.py:1015, in SyncAPIClient._request(self, cast_to, options, remaining_retries, stream, stream_cls)
1013 if retries > 0 and self._should_retry(err.response):
1014 err.response.close()
-> 1015 return self._retry_request(
1016 options,
1017 cast_to,
1018 retries,
1019 err.response.headers,
1020 stream=stream,
1021 stream_cls=stream_cls,
1022 )
1024 # If the response is streamed then we need to explicitly read the response
1025 # to completion before attempting to access the response text.
1026 if not err.response.is_closed:
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/openai/_base_client.py:1063, in SyncAPIClient._retry_request(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)
1059 # In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a
1060 # different thread if necessary.
1061 time.sleep(timeout)
-> 1063 return self._request(
1064 options=options,
1065 cast_to=cast_to,
1066 remaining_retries=remaining,
1067 stream=stream,
1068 stream_cls=stream_cls,
1069 )
File ~/xxx/xxx/virtualenvs/xxx/lib/python3.12/site-packages/openai/_base_client.py:1030, in SyncAPIClient._request(self, cast_to, options, remaining_retries, stream, stream_cls)
1027 err.response.read()
1029 log.debug("Re-raising status error")
-> 1030 raise self._make_status_error_from_response(err.response) from None
1032 return self._process_response(
1033 cast_to=cast_to,
1034 options=options,
(...)
1037 stream_cls=stream_cls,
1038 )
openai.InternalServerError: Error code: 500 - {'statusCode': 500, 'message': 'Internal server error', 'activityId': 'xxx-xxx-xxx-xxx'} |
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Hey did you find any solution for this?? |
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I contacted our cognitive services team - this happened just after the massive OpenAI outage and then they were running maintenance. So I created my own subscription and it worked again. (sorry - not the answer you were hoping for... but ours was an infra issue and maintenance) |
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Checked other resources
Commit to Help
Example Code
Description
System Info
Langchain version
langchain==0.2.10
langchain-community==0.2.9
langchain-core==0.2.22
langchain-openai==0.1.16
langchain-text-splitters==0.2.2
Platform
WSL 2 running on Ubuntu in Windows 11
Python version
Python 3.8.10
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