我已经编写了一个使用 OpenAI api 可以正常工作的脚本。我现在尝试将其切换到 AzureOpenAI，但似乎我遇到了 create_sql_agent() 的问题。您可以将 create_sql_agent 与 AzureOpenAI 模型 gpt-35-turbo-1106 一起使用吗？这可能是我在 AzureOpenAI() 中的 api_version 的问题吗？我收到的错误是“TypeError：Completions.create（）得到了意外的关键字参数'tools'”，我认为这也可能是使用'openai-tools'作为我的agent_type的选项？
import os
from langchain_openai import AzureOpenAI
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from dotenv import load_dotenv
from langchain.agents import AgentExecutor
from 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', # should it be 'gpt-35-turbo-1106'?
temperature = 0,
api_key = MY_KEY,
api_version = '2023-07-01-preview') #my api_version correct? Uncertain which one
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
prefix = """
You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.
You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table, only ask for the relevant columns given the question.
You have access to tools for interacting with the database.
Only use the below tools. Only use the information returned by the below tools to construct your final answer.
You MUST double-check your query before executing it. If you get an error while executing a query, rewrite the query and try again.
DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP, CASCADE, etc.) to the database.
If the question does not seem related to the database, just return "I don't know" as the answer.
If asked about a person do not return an 'ID' but return a first name and last name.
"""
suffix = """ I should look at the tables in the database to see what I can query. Then I should query the schema of the most relevant tables.
"""
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', #does this work with azure?
prompt=prompt,
verbose=False)
print(agent_executor.invoke("What are the names of the tables"))
---------------------------------------------------------------------------
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 = chunk
File ~\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 output
File ~\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: ignore
File ~\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'
您的型号名称和 API 版本应该没问题。但是，您应该使用聊天模型类型。
AzureChatOpenAI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')
创建 sql 代理时，使用
AgentTypefrom 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)