我已经编写了一个使用 OpenAI API 的脚本，运行得很好。现在我尝试将其切换到 AzureOpenAI，但似乎在使用 create_sql_agent() 时遇到了问题。您能使用 AzureOpenAI 模型 gpt-35-turbo-1106 创建 SQL 代理吗？这是否与我在 AzureOpenAI() 中的 api_version 设置有关？我收到的错误是“TypeError: Completions.create() got an unexpected keyword argument ‘tools’”，我认为这也可能是由于我使用了 ‘openai-tools’ 作为 agent_type 的选项导致的？
代码
import osfrom langchain_openai import AzureOpenAIfrom langchain.agents import create_sql_agentfrom langchain.agents.agent_toolkits import SQLDatabaseToolkitfrom langchain.sql_database import SQLDatabasefrom dotenv import load_dotenvfrom langchain.agents import AgentExecutorfrom langchain_core.prompts.chat import ( ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate, AIMessagePromptTemplate, MessagesPlaceholder,)path = (os.getcwd()+'\creds.env')load_dotenv(path) db = SQLDatabase.from_uri( f"postgresql://{os.environ.get('user')}:{os.environ.get('password')}@{os.environ.get('host')}:{os.environ.get('port')}/{os.environ.get('database')}")llm = AzureOpenAI(azure_endpoint=MY_ENDPOINT, deployment_name=MY_DEPLOYMENT_NAME, model_name='gpt-35-turbo', # 应该使用 'gpt-35-turbo-1106' 吗？ temperature = 0, api_key = MY_KEY, api_version = '2023-07-01-preview') # 我的 api_version 是否正确？不确定使用哪个版本toolkit = SQLDatabaseToolkit(db=db, llm=llm)prefix = """您是一个设计用于与 SQL 数据库交互的代理。给定一个输入问题，创建一个语法正确的 {dialect} 查询来运行，然后查看查询结果并返回答案。除非用户指定希望获得的特定数量的示例，否则始终将查询限制在最多 {top_k} 个结果。您可以按相关列对结果进行排序，以返回数据库中最有趣的示例。永远不要查询特定表的所有列，只询问与问题相关的列。您可以访问与数据库交互的工具。仅使用以下工具。仅使用以下工具返回的信息来构建您的最终答案。您必须在执行查询之前仔细检查您的查询。如果在执行查询时遇到错误，请重写查询并再次尝试。不要对数据库执行任何 DML 语句（INSERT, UPDATE, DELETE, DROP, CASCADE 等）。如果问题似乎与数据库无关，只需返回“我不知道”作为答案。如果被问及某个人，不要返回“ID”，而是返回名字和姓氏。"""suffix = """我应该查看数据库中的表，看看我可以查询什么。然后我应该查询最相关表的架构。"""messages = [ SystemMessagePromptTemplate.from_template(prefix), HumanMessagePromptTemplate.from_template("{input}"), AIMessagePromptTemplate.from_template(suffix), MessagesPlaceholder(variable_name="agent_scratchpad"), ]agent_executor = create_sql_agent(llm, toolkit=toolkit, agent_type='openai-tools', # 这是否与 Azure 兼容？ prompt=prompt, verbose=False)print(agent_executor.invoke("表的名称是什么"))错误
---------------------------------------------------------------------------TypeError Traceback (most recent call last)Cell In[69], line 1----> 1 print(agent_executor.invoke("What are the names of the tables"))File ~\AppData\Local\Programs\Python\Python311\Lib\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 ~\AppData\Local\Programs\Python\Python311\Lib\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 ~\AppData\Local\Programs\Python\Python311\Lib\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 ~\AppData\Local\Programs\Python\Python311\Lib\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 ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\agents\agent.py:1138, in <listcomp>(.0) 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 ~\AppData\Local\Programs\Python\Python311\Lib\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 ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\agents\agent.py:514, in RunnableMultiActionAgent.plan(self, intermediate_steps, callbacks, **kwargs) 506 final_output: Any = None 507 if self.stream_runnable: 508 # Use streaming to make sure that the underlying LLM is invoked in a 509 # streaming (...) 512 # Because the response from the plan is not a generator, we need to 513 # accumulate the output into final output and return that.--> 514 for chunk in self.runnable.stream(inputs, config={"callbacks": callbacks}): 515 if final_output is None: 516 final_output = chunkFile ~\AppData\Local\Programs\Python\Python311\Lib\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 ~\AppData\Local\Programs\Python\Python311\Lib\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 ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain_core\runnables\base.py:1880, in Runnable._transform_stream_with_config(self, input, transformer, config, run_type, **kwargs) 1878 try: 1879 while True:-> 1880 chunk: Output = context.run(next, iterator) # type: ignore 1881 yield chunk 1882 if final_output_supported:File ~\AppData\Local\Programs\Python\Python311\Lib\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 outputFile ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain_core\runnables\base.py:1283, in Runnable.transform(self, input, config, **kwargs) 1280 final: Input 1281 got_first_val = False-> 1283 for chunk in input: 1284 if not got_first_val: 1285 final = adapt_first_streaming_chunk(chunk) # type: ignoreFile ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain_core\runnables\base.py:4728, in RunnableBindingBase.transform(self, input, config, **kwargs) 4722 def transform( 4723 self, 4724 input: Iterator[Input], 4725 config: Optional[RunnableConfig] = None, 4726 **kwargs: Any, 4727 ) -> Iterator[Output]:-> 4728 yield from self.bound.transform( 4729 input, 4730 self._merge_configs(config), 4731 **{**self.kwargs, **kwargs}, 4732 )File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain_core\runnables\base.py:1300, in Runnable.transform(self, input, config, **kwargs) 1293 raise TypeError( 1294 f"Failed while trying to add together " 1295 f"type {type(final)} and {type(chunk)}." 1296 f"These types should be addable for transform to work." 1297 ) 1299 if got_first_val:-> 1300 yield from self.stream(final, config, **kwargs)File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain_core\language_models\llms.py:458, in BaseLLM.stream(self, input, config, stop, **kwargs) 451 except BaseException as e: 452 run_manager.on_llm_error( 453 e, 454 response=LLMResult( 455 generations=[[generation]] if generation else [] 456 ), 457 )--> 458 raise e 459 else: 460 run_manager.on_llm_end(LLMResult(generations=[[generation]]))File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain_core\language_models\llms.py:442, in BaseLLM.stream(self, input, config, stop, **kwargs) 440 generation: Optional[GenerationChunk] = None 441 try:--> 442 for chunk in self._stream( 443 prompt, stop=stop, run_manager=run_manager, **kwargs 444 ): 445 yield chunk.text 446 if generation is None:File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain_openai\llms\base.py:262, in BaseOpenAI._stream(self, prompt, stop, run_manager, **kwargs) 260 params = {**self._invocation_params, **kwargs, "stream": True} 261 self.get_sub_prompts(params, [prompt], stop) # this mutates params--> 262 for stream_resp in self.client.create(prompt=prompt, **params): 263 if not isinstance(stream_resp, dict): 264 stream_resp = stream_resp.model_dump()File ~\AppData\Local\Programs\Python\Python311\Lib\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)TypeError: Completions.create() got an unexpected keyword argument 'tools'回答：
您的模型名称和 API 版本应该没问题。但是，您应该使用聊天模型类型。为此使用 AzureChatOpenAI 类。
更新您的代码：
from langchain.chat_models import AzureChatOpenAI# ...llm = AzureChatOpenAI(azure_endpoint=MY_ENDPOINT, deployment_name=MY_DEPLOYMENT_NAME, model_name='gpt-35-turbo', temperature = 0, api_key = MY_KEY, api_version = '2023-07-01-preview')在创建 SQL 代理时，使用 AgentType 枚举器，并使用零镜头来告诉代理不使用记忆。
from langchain.agents import AgentType, create_sql_agent# ...agent_executor = create_sql_agent(llm=llm, toolkit=toolkit, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, prompt=prompt, verbose=False)