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无效的工具结果

您向模型传递了太多、太少或不匹配的ToolMessages。

当使用模型调用工具时，模型响应的AIMessage将包含一个tool_calls数组。为了继续流程，您传递回模型的下一条消息必须为数组中的每个项目包含一个ToolMessage，其中包含该工具调用的结果。每个ToolMessage必须有一个tool_call_id字段，该字段与AIMessage上的tool_calls之一匹配。

例如，给定模型以下响应：

from typing import List

from langchain_core.messages import BaseMessage, HumanMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-4o-mini")


@tool
def foo_tool() -> str:
    """
    A dummy tool that returns 'action complete!'
    """
    return "action complete!"


model_with_tools = model.bind_tools([foo_tool])

chat_history: List[BaseMessage] = [
    HumanMessage(content='Call tool "foo" twice with no arguments')
]

response_message = model_with_tools.invoke(chat_history)

print(response_message.tool_calls)
[{'name': 'foo_tool', 'args': {}, 'id': 'call_dq9O0eGHrryBwDRCnk0deHK4', 'type': 'tool_call'}, {'name': 'foo_tool', 'args': {}, 'id': 'call_mjLuNyXNHoUIXHiBtXhaWdxN', 'type': 'tool_call'}]

仅使用一个工具响应调用模型将导致错误：

from langchain_core.messages import AIMessage, ToolMessage

tool_call = response_message.tool_calls[0]
tool_response = foo_tool.invoke(tool_call)

chat_history.append(
    AIMessage(
        content=response_message.content,
        additional_kwargs=response_message.additional_kwargs,
    )
)
chat_history.append(
    ToolMessage(content=str(tool_response), tool_call_id=tool_call.get("id"))
)

final_response = model_with_tools.invoke(chat_history)
print(final_response)
API Reference:AIMessage | ToolMessage
---------------------------------------------------------------------------
``````output
BadRequestError                           Traceback (most recent call last)
``````output
Cell In[3], line 9
      6 chat_history.append(AIMessage(content=response_message.content, additional_kwargs=response_message.additional_kwargs))
      7 chat_history.append(ToolMessage(content=str(tool_response), tool_call_id=tool_call.get('id')))
----> 9 final_response = model_with_tools.invoke(chat_history)
     10 print(final_response)
``````output
File ~/langchain/oss-py/libs/core/langchain_core/runnables/base.py:5354, in RunnableBindingBase.invoke(self, input, config, **kwargs)
   5348 def invoke(
   5349     self,
   5350     input: Input,
   5351     config: Optional[RunnableConfig] = None,
   5352     **kwargs: Optional[Any],
   5353 ) -> Output:
-> 5354     return self.bound.invoke(
   5355         input,
   5356         self._merge_configs(config),
   5357         **{**self.kwargs, **kwargs},
   5358     )
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:286, in BaseChatModel.invoke(self, input, config, stop, **kwargs)
    275 def invoke(
    276     self,
    277     input: LanguageModelInput,
   (...)
    281     **kwargs: Any,
    282 ) -> BaseMessage:
    283     config = ensure_config(config)
    284     return cast(
    285         ChatGeneration,
--> 286         self.generate_prompt(
    287             [self._convert_input(input)],
    288             stop=stop,
    289             callbacks=config.get("callbacks"),
    290             tags=config.get("tags"),
    291             metadata=config.get("metadata"),
    292             run_name=config.get("run_name"),
    293             run_id=config.pop("run_id", None),
    294             **kwargs,
    295         ).generations[0][0],
    296     ).message
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:786, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
    778 def generate_prompt(
    779     self,
    780     prompts: list[PromptValue],
   (...)
    783     **kwargs: Any,
    784 ) -> LLMResult:
    785     prompt_messages = [p.to_messages() for p in prompts]
--> 786     return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:643, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)
    641         if run_managers:
    642             run_managers[i].on_llm_error(e, response=LLMResult(generations=[]))
--> 643         raise e
    644 flattened_outputs = [
    645     LLMResult(generations=[res.generations], llm_output=res.llm_output)  # type: ignore[list-item]
    646     for res in results
    647 ]
    648 llm_output = self._combine_llm_outputs([res.llm_output for res in results])
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:633, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)
    630 for i, m in enumerate(messages):
    631     try:
    632         results.append(
--> 633             self._generate_with_cache(
    634                 m,
    635                 stop=stop,
    636                 run_manager=run_managers[i] if run_managers else None,
    637                 **kwargs,
    638             )
    639         )
    640     except BaseException as e:
    641         if run_managers:
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:851, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
    849 else:
    850     if inspect.signature(self._generate).parameters.get("run_manager"):
--> 851         result = self._generate(
    852             messages, stop=stop, run_manager=run_manager, **kwargs
    853         )
    854     else:
    855         result = self._generate(messages, stop=stop, **kwargs)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/langchain_openai/chat_models/base.py:686, in BaseChatOpenAI._generate(self, messages, stop, run_manager, **kwargs)
    684     generation_info = {"headers": dict(raw_response.headers)}
    685 else:
--> 686     response = self.client.create(**payload)
    687 return self._create_chat_result(response, generation_info)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_utils/_utils.py:274, in required_args.<locals>.inner.<locals>.wrapper(*args, **kwargs)
    272             msg = f"Missing required argument: {quote(missing[0])}"
    273     raise TypeError(msg)
--> 274 return func(*args, **kwargs)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/resources/chat/completions.py:742, in Completions.create(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, n, parallel_tool_calls, presence_penalty, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)
    704 @required_args(["messages", "model"], ["messages", "model", "stream"])
    705 def create(
    706     self,
   (...)
    739     timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
    740 ) -> ChatCompletion | Stream[ChatCompletionChunk]:
    741     validate_response_format(response_format)
--> 742     return self._post(
    743         "/chat/completions",
    744         body=maybe_transform(
    745             {
    746                 "messages": messages,
    747                 "model": model,
    748                 "frequency_penalty": frequency_penalty,
    749                 "function_call": function_call,
    750                 "functions": functions,
    751                 "logit_bias": logit_bias,
    752                 "logprobs": logprobs,
    753                 "max_completion_tokens": max_completion_tokens,
    754                 "max_tokens": max_tokens,
    755                 "metadata": metadata,
    756                 "n": n,
    757                 "parallel_tool_calls": parallel_tool_calls,
    758                 "presence_penalty": presence_penalty,
    759                 "response_format": response_format,
    760                 "seed": seed,
    761                 "service_tier": service_tier,
    762                 "stop": stop,
    763                 "store": store,
    764                 "stream": stream,
    765                 "stream_options": stream_options,
    766                 "temperature": temperature,
    767                 "tool_choice": tool_choice,
    768                 "tools": tools,
    769                 "top_logprobs": top_logprobs,
    770                 "top_p": top_p,
    771                 "user": user,
    772             },
    773             completion_create_params.CompletionCreateParams,
    774         ),
    775         options=make_request_options(
    776             extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
    777         ),
    778         cast_to=ChatCompletion,
    779         stream=stream or False,
    780         stream_cls=Stream[ChatCompletionChunk],
    781     )
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1270, in SyncAPIClient.post(self, path, cast_to, body, options, files, stream, stream_cls)
   1256 def post(
   1257     self,
   1258     path: str,
   (...)
   1265     stream_cls: type[_StreamT] | None = None,
   1266 ) -> ResponseT | _StreamT:
   1267     opts = FinalRequestOptions.construct(
   1268         method="post", url=path, json_data=body, files=to_httpx_files(files), **options
   1269     )
-> 1270     return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:947, in SyncAPIClient.request(self, cast_to, options, remaining_retries, stream, stream_cls)
    944 else:
    945     retries_taken = 0
--> 947 return self._request(
    948     cast_to=cast_to,
    949     options=options,
    950     stream=stream,
    951     stream_cls=stream_cls,
    952     retries_taken=retries_taken,
    953 )
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1051, in SyncAPIClient._request(self, cast_to, options, retries_taken, stream, stream_cls)
   1048         err.response.read()
   1050     log.debug("Re-raising status error")
-> 1051     raise self._make_status_error_from_response(err.response) from None
   1053 return self._process_response(
   1054     cast_to=cast_to,
   1055     options=options,
   (...)
   1059     retries_taken=retries_taken,
   1060 )
``````output
BadRequestError: Error code: 400 - {'error': {'message': "An assistant message with 'tool_calls' must be followed by tool messages responding to each 'tool_call_id'. The following tool_call_ids did not have response messages: call_mjLuNyXNHoUIXHiBtXhaWdxN", 'type': 'invalid_request_error', 'param': 'messages', 'code': None}}

如果我们添加第二个响应，调用将按预期成功，因为我们现在每个工具调用都有一个工具响应：

tool_response_2 = foo_tool.invoke(response_message.tool_calls[1])

chat_history.append(tool_response_2)

model_with_tools.invoke(chat_history)
AIMessage(content='Both calls to the tool "foo" have been completed successfully. The output for each call is "action complete!".', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 24, 'prompt_tokens': 137, 'total_tokens': 161, 'completion_tokens_details': {'audio_tokens': None, 'reasoning_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_e2bde53e6e', 'finish_reason': 'stop', 'logprobs': None}, id='run-b5ac3c54-4e26-4da4-853a-d0ab1cba90e0-0', usage_metadata={'input_tokens': 137, 'output_tokens': 24, 'total_tokens': 161, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 0}})

但是如果我们添加一个重复的、额外的工具响应，调用将再次失败：

duplicate_tool_response_2 = foo_tool.invoke(response_message.tool_calls[1])

chat_history.append(duplicate_tool_response_2)

await model_with_tools.invoke(chat_history)
---------------------------------------------------------------------------
``````output
BadRequestError                           Traceback (most recent call last)
``````output
Cell In[7], line 5
      1 duplicate_tool_response_2 = foo_tool.invoke(response_message.tool_calls[1])
      3 chat_history.append(duplicate_tool_response_2)
----> 5 await model_with_tools.invoke(chat_history)
``````output
File ~/langchain/oss-py/libs/core/langchain_core/runnables/base.py:5354, in RunnableBindingBase.invoke(self, input, config, **kwargs)
   5348 def invoke(
   5349     self,
   5350     input: Input,
   5351     config: Optional[RunnableConfig] = None,
   5352     **kwargs: Optional[Any],
   5353 ) -> Output:
-> 5354     return self.bound.invoke(
   5355         input,
   5356         self._merge_configs(config),
   5357         **{**self.kwargs, **kwargs},
   5358     )
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:286, in BaseChatModel.invoke(self, input, config, stop, **kwargs)
    275 def invoke(
    276     self,
    277     input: LanguageModelInput,
   (...)
    281     **kwargs: Any,
    282 ) -> BaseMessage:
    283     config = ensure_config(config)
    284     return cast(
    285         ChatGeneration,
--> 286         self.generate_prompt(
    287             [self._convert_input(input)],
    288             stop=stop,
    289             callbacks=config.get("callbacks"),
    290             tags=config.get("tags"),
    291             metadata=config.get("metadata"),
    292             run_name=config.get("run_name"),
    293             run_id=config.pop("run_id", None),
    294             **kwargs,
    295         ).generations[0][0],
    296     ).message
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:786, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
    778 def generate_prompt(
    779     self,
    780     prompts: list[PromptValue],
   (...)
    783     **kwargs: Any,
    784 ) -> LLMResult:
    785     prompt_messages = [p.to_messages() for p in prompts]
--> 786     return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:643, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)
    641         if run_managers:
    642             run_managers[i].on_llm_error(e, response=LLMResult(generations=[]))
--> 643         raise e
    644 flattened_outputs = [
    645     LLMResult(generations=[res.generations], llm_output=res.llm_output)  # type: ignore[list-item]
    646     for res in results
    647 ]
    648 llm_output = self._combine_llm_outputs([res.llm_output for res in results])
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:633, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)
    630 for i, m in enumerate(messages):
    631     try:
    632         results.append(
--> 633             self._generate_with_cache(
    634                 m,
    635                 stop=stop,
    636                 run_manager=run_managers[i] if run_managers else None,
    637                 **kwargs,
    638             )
    639         )
    640     except BaseException as e:
    641         if run_managers:
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:851, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
    849 else:
    850     if inspect.signature(self._generate).parameters.get("run_manager"):
--> 851         result = self._generate(
    852             messages, stop=stop, run_manager=run_manager, **kwargs
    853         )
    854     else:
    855         result = self._generate(messages, stop=stop, **kwargs)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/langchain_openai/chat_models/base.py:686, in BaseChatOpenAI._generate(self, messages, stop, run_manager, **kwargs)
    684     generation_info = {"headers": dict(raw_response.headers)}
    685 else:
--> 686     response = self.client.create(**payload)
    687 return self._create_chat_result(response, generation_info)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_utils/_utils.py:274, in required_args.<locals>.inner.<locals>.wrapper(*args, **kwargs)
    272             msg = f"Missing required argument: {quote(missing[0])}"
    273     raise TypeError(msg)
--> 274 return func(*args, **kwargs)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/resources/chat/completions.py:742, in Completions.create(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, n, parallel_tool_calls, presence_penalty, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)
    704 @required_args(["messages", "model"], ["messages", "model", "stream"])
    705 def create(
    706     self,
   (...)
    739     timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
    740 ) -> ChatCompletion | Stream[ChatCompletionChunk]:
    741     validate_response_format(response_format)
--> 742     return self._post(
    743         "/chat/completions",
    744         body=maybe_transform(
    745             {
    746                 "messages": messages,
    747                 "model": model,
    748                 "frequency_penalty": frequency_penalty,
    749                 "function_call": function_call,
    750                 "functions": functions,
    751                 "logit_bias": logit_bias,
    752                 "logprobs": logprobs,
    753                 "max_completion_tokens": max_completion_tokens,
    754                 "max_tokens": max_tokens,
    755                 "metadata": metadata,
    756                 "n": n,
    757                 "parallel_tool_calls": parallel_tool_calls,
    758                 "presence_penalty": presence_penalty,
    759                 "response_format": response_format,
    760                 "seed": seed,
    761                 "service_tier": service_tier,
    762                 "stop": stop,
    763                 "store": store,
    764                 "stream": stream,
    765                 "stream_options": stream_options,
    766                 "temperature": temperature,
    767                 "tool_choice": tool_choice,
    768                 "tools": tools,
    769                 "top_logprobs": top_logprobs,
    770                 "top_p": top_p,
    771                 "user": user,
    772             },
    773             completion_create_params.CompletionCreateParams,
    774         ),
    775         options=make_request_options(
    776             extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
    777         ),
    778         cast_to=ChatCompletion,
    779         stream=stream or False,
    780         stream_cls=Stream[ChatCompletionChunk],
    781     )
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1270, in SyncAPIClient.post(self, path, cast_to, body, options, files, stream, stream_cls)
   1256 def post(
   1257     self,
   1258     path: str,
   (...)
   1265     stream_cls: type[_StreamT] | None = None,
   1266 ) -> ResponseT | _StreamT:
   1267     opts = FinalRequestOptions.construct(
   1268         method="post", url=path, json_data=body, files=to_httpx_files(files), **options
   1269     )
-> 1270     return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:947, in SyncAPIClient.request(self, cast_to, options, remaining_retries, stream, stream_cls)
    944 else:
    945     retries_taken = 0
--> 947 return self._request(
    948     cast_to=cast_to,
    949     options=options,
    950     stream=stream,
    951     stream_cls=stream_cls,
    952     retries_taken=retries_taken,
    953 )
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1051, in SyncAPIClient._request(self, cast_to, options, retries_taken, stream, stream_cls)
   1048         err.response.read()
   1050     log.debug("Re-raising status error")
-> 1051     raise self._make_status_error_from_response(err.response) from None
   1053 return self._process_response(
   1054     cast_to=cast_to,
   1055     options=options,
   (...)
   1059     retries_taken=retries_taken,
   1060 )
``````output
BadRequestError: Error code: 400 - {'error': {'message': "Invalid parameter: messages with role 'tool' must be a response to a preceeding message with 'tool_calls'.", 'type': 'invalid_request_error', 'param': 'messages.[4].role', 'code': None}}

如果ToolMessages前面没有带有工具调用的AIMessage，则不应将其传递回模型。例如，这将失败：

model_with_tools.invoke(
    [ToolMessage(content="action completed!", tool_call_id="dummy")]
)
---------------------------------------------------------------------------
``````output
BadRequestError                           Traceback (most recent call last)
``````output
Cell In[8], line 1
----> 1 model_with_tools.invoke([ToolMessage(content="action completed!", tool_call_id="dummy")])
``````output
File ~/langchain/oss-py/libs/core/langchain_core/runnables/base.py:5354, in RunnableBindingBase.invoke(self, input, config, **kwargs)
   5348 def invoke(
   5349     self,
   5350     input: Input,
   5351     config: Optional[RunnableConfig] = None,
   5352     **kwargs: Optional[Any],
   5353 ) -> Output:
-> 5354     return self.bound.invoke(
   5355         input,
   5356         self._merge_configs(config),
   5357         **{**self.kwargs, **kwargs},
   5358     )
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:286, in BaseChatModel.invoke(self, input, config, stop, **kwargs)
    275 def invoke(
    276     self,
    277     input: LanguageModelInput,
   (...)
    281     **kwargs: Any,
    282 ) -> BaseMessage:
    283     config = ensure_config(config)
    284     return cast(
    285         ChatGeneration,
--> 286         self.generate_prompt(
    287             [self._convert_input(input)],
    288             stop=stop,
    289             callbacks=config.get("callbacks"),
    290             tags=config.get("tags"),
    291             metadata=config.get("metadata"),
    292             run_name=config.get("run_name"),
    293             run_id=config.pop("run_id", None),
    294             **kwargs,
    295         ).generations[0][0],
    296     ).message
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:786, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
    778 def generate_prompt(
    779     self,
    780     prompts: list[PromptValue],
   (...)
    783     **kwargs: Any,
    784 ) -> LLMResult:
    785     prompt_messages = [p.to_messages() for p in prompts]
--> 786     return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:643, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)
    641         if run_managers:
    642             run_managers[i].on_llm_error(e, response=LLMResult(generations=[]))
--> 643         raise e
    644 flattened_outputs = [
    645     LLMResult(generations=[res.generations], llm_output=res.llm_output)  # type: ignore[list-item]
    646     for res in results
    647 ]
    648 llm_output = self._combine_llm_outputs([res.llm_output for res in results])
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:633, in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)
    630 for i, m in enumerate(messages):
    631     try:
    632         results.append(
--> 633             self._generate_with_cache(
    634                 m,
    635                 stop=stop,
    636                 run_manager=run_managers[i] if run_managers else None,
    637                 **kwargs,
    638             )
    639         )
    640     except BaseException as e:
    641         if run_managers:
``````output
File ~/langchain/oss-py/libs/core/langchain_core/language_models/chat_models.py:851, in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
    849 else:
    850     if inspect.signature(self._generate).parameters.get("run_manager"):
--> 851         result = self._generate(
    852             messages, stop=stop, run_manager=run_manager, **kwargs
    853         )
    854     else:
    855         result = self._generate(messages, stop=stop, **kwargs)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/langchain_openai/chat_models/base.py:686, in BaseChatOpenAI._generate(self, messages, stop, run_manager, **kwargs)
    684     generation_info = {"headers": dict(raw_response.headers)}
    685 else:
--> 686     response = self.client.create(**payload)
    687 return self._create_chat_result(response, generation_info)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_utils/_utils.py:274, in required_args.<locals>.inner.<locals>.wrapper(*args, **kwargs)
    272             msg = f"Missing required argument: {quote(missing[0])}"
    273     raise TypeError(msg)
--> 274 return func(*args, **kwargs)
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/resources/chat/completions.py:742, in Completions.create(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, n, parallel_tool_calls, presence_penalty, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)
    704 @required_args(["messages", "model"], ["messages", "model", "stream"])
    705 def create(
    706     self,
   (...)
    739     timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
    740 ) -> ChatCompletion | Stream[ChatCompletionChunk]:
    741     validate_response_format(response_format)
--> 742     return self._post(
    743         "/chat/completions",
    744         body=maybe_transform(
    745             {
    746                 "messages": messages,
    747                 "model": model,
    748                 "frequency_penalty": frequency_penalty,
    749                 "function_call": function_call,
    750                 "functions": functions,
    751                 "logit_bias": logit_bias,
    752                 "logprobs": logprobs,
    753                 "max_completion_tokens": max_completion_tokens,
    754                 "max_tokens": max_tokens,
    755                 "metadata": metadata,
    756                 "n": n,
    757                 "parallel_tool_calls": parallel_tool_calls,
    758                 "presence_penalty": presence_penalty,
    759                 "response_format": response_format,
    760                 "seed": seed,
    761                 "service_tier": service_tier,
    762                 "stop": stop,
    763                 "store": store,
    764                 "stream": stream,
    765                 "stream_options": stream_options,
    766                 "temperature": temperature,
    767                 "tool_choice": tool_choice,
    768                 "tools": tools,
    769                 "top_logprobs": top_logprobs,
    770                 "top_p": top_p,
    771                 "user": user,
    772             },
    773             completion_create_params.CompletionCreateParams,
    774         ),
    775         options=make_request_options(
    776             extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
    777         ),
    778         cast_to=ChatCompletion,
    779         stream=stream or False,
    780         stream_cls=Stream[ChatCompletionChunk],
    781     )
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1270, in SyncAPIClient.post(self, path, cast_to, body, options, files, stream, stream_cls)
   1256 def post(
   1257     self,
   1258     path: str,
   (...)
   1265     stream_cls: type[_StreamT] | None = None,
   1266 ) -> ResponseT | _StreamT:
   1267     opts = FinalRequestOptions.construct(
   1268         method="post", url=path, json_data=body, files=to_httpx_files(files), **options
   1269     )
-> 1270     return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:947, in SyncAPIClient.request(self, cast_to, options, remaining_retries, stream, stream_cls)
    944 else:
    945     retries_taken = 0
--> 947 return self._request(
    948     cast_to=cast_to,
    949     options=options,
    950     stream=stream,
    951     stream_cls=stream_cls,
    952     retries_taken=retries_taken,
    953 )
``````output
File ~/langchain/oss-py/docs/.venv/lib/python3.11/site-packages/openai/_base_client.py:1051, in SyncAPIClient._request(self, cast_to, options, retries_taken, stream, stream_cls)
   1048         err.response.read()
   1050     log.debug("Re-raising status error")
-> 1051     raise self._make_status_error_from_response(err.response) from None
   1053 return self._process_response(
   1054     cast_to=cast_to,
   1055     options=options,
   (...)
   1059     retries_taken=retries_taken,
   1060 )
``````output
BadRequestError: Error code: 400 - {'error': {'message': "Invalid parameter: messages with role 'tool' must be a response to a preceeding message with 'tool_calls'.", 'type': 'invalid_request_error', 'param': 'messages.[0].role', 'code': None}}

查看本指南以获取有关工具调用的更多详细信息。

故障排除​

以下内容可能有助于解决此错误：

  • 如果您使用的是自定义执行器，而不是像LangGraph的ToolNode或旧版LangChain的AgentExecutor这样的预构建执行器，请确保您为每个工具调用调用并返回一个工具的结果。
  • 如果您正在使用few-shot tool call examples与您手动创建的消息，并且您想要模拟失败， 您仍然需要传递一个ToolMessage，其内容指示失败。

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