Unable to run sample notebook using Azure OpenAI.

[satfl]

Hello. I am willing to try running the agentchat_auto_feedback_from_code_execution.ipynb . I have a gpt-4 model with a custom name deployed on Azure. Locally, I have created OAI_CONFIG_LIST file, where I specify the key, the model name, and an API base.

 [
    {
        "model": "custom_model_name",
        "api_key": "",
        "api_base": "https://custom-azure-open-ai-name.openai.azure.com",
        "api_version": "2023-07-01-preview"
    }
]

The file seems to be read properly. I have also adjusted the filter_dict accordingly. However, when I try running the first cell in example task from the notebook i get InvalidRequestError: Resource not found.
I can also confirm that the same model with the same key i perfectly responsive through regular openai python library

Here is the full error stack trace:

{
	"name": "InvalidRequestError",
	"message": "Resource not found",
	"stack": "---------------------------------------------------------------------------
InvalidRequestError                       Traceback (most recent call last)
\\AutoGen\\autogen-main\
otebook\\agentchat_auto_feedback_from_code_execution.ipynb Cell 11 line 2
     <a href='vscode-notebook-cell:/AutoGen/autogen-main/notebook/agentchat_auto_feedback_from_code_execution.ipynb#X13sZmlsZQ%3D%3D?line=10'>11</a> user_proxy = autogen.UserProxyAgent(
     <a href='vscode-notebook-cell:/AutoGen/autogen-main/notebook/agentchat_auto_feedback_from_code_execution.ipynb#X13sZmlsZQ%3D%3D?line=11'>12</a>     name=\"user_proxy\",
     <a href='vscode-notebook-cell:/AutoGen/autogen-main/notebook/agentchat_auto_feedback_from_code_execution.ipynb#X13sZmlsZQ%3D%3D?line=12'>13</a>     human_input_mode=\"NEVER\",
   (...)
     <a href='vscode-notebook-cell:/AutoGen/autogen-main/notebook/agentchat_auto_feedback_from_code_execution.ipynb#X13sZmlsZQ%3D%3D?line=18'>19</a>     },
     <a href='vscode-notebook-cell:/AutoGen/autogen-main/notebook/agentchat_auto_feedback_from_code_execution.ipynb#X13sZmlsZQ%3D%3D?line=19'>20</a> )
     <a href='vscode-notebook-cell:/AutoGen/autogen-main/notebook/agentchat_auto_feedback_from_code_execution.ipynb#X13sZmlsZQ%3D%3D?line=20'>21</a> # the assistant receives a message from the user_proxy, which contains the task description
---> <a href='vscode-notebook-cell:/AutoGen/autogen-main/notebook/agentchat_auto_feedback_from_code_execution.ipynb#X13sZmlsZQ%3D%3D?line=21'>22</a> user_proxy.initiate_chat(
     <a href='vscode-notebook-cell:/AutoGen/autogen-main/notebook/agentchat_auto_feedback_from_code_execution.ipynb#X13sZmlsZQ%3D%3D?line=22'>23</a>     assistant,
     <a href='vscode-notebook-cell:/AutoGen/autogen-main/notebook/agentchat_auto_feedback_from_code_execution.ipynb#X13sZmlsZQ%3D%3D?line=23'>24</a>     message=\"\"\"What date is today? Compare the year-to-date gain for META and TESLA.\"\"\",
     <a href='vscode-notebook-cell:/AutoGen/autogen-main/notebook/agentchat_auto_feedback_from_code_execution.ipynb#X13sZmlsZQ%3D%3D?line=24'>25</a> )

File \\AutoGen\\.venv\\Lib\\site-packages\\autogen\\agentchat\\conversable_agent.py:531, in ConversableAgent.initiate_chat(self, recipient, clear_history, silent, **context)
    517 \"\"\"Initiate a chat with the recipient agent.
    518 
    519 Reset the consecutive auto reply counter.
   (...)
    528         \"message\" needs to be provided if the `generate_init_message` method is not overridden.
    529 \"\"\"
    530 self._prepare_chat(recipient, clear_history)
--> 531 self.send(self.generate_init_message(**context), recipient, silent=silent)

File \\AutoGen\\.venv\\Lib\\site-packages\\autogen\\agentchat\\conversable_agent.py:334, in ConversableAgent.send(self, message, recipient, request_reply, silent)
    332 valid = self._append_oai_message(message, \"assistant\", recipient)
    333 if valid:
--> 334     recipient.receive(message, self, request_reply, silent)
    335 else:
    336     raise ValueError(
    337         \"Message can't be converted into a valid ChatCompletion message. Either content or function_call must be provided.\"
    338     )

File \\AutoGen\\.venv\\Lib\\site-packages\\autogen\\agentchat\\conversable_agent.py:462, in ConversableAgent.receive(self, message, sender, request_reply, silent)
    460 if request_reply is False or request_reply is None and self.reply_at_receive[sender] is False:
    461     return
--> 462 reply = self.generate_reply(messages=self.chat_messages[sender], sender=sender)
    463 if reply is not None:
    464     self.send(reply, sender, silent=silent)

File \\AutoGen\\.venv\\Lib\\site-packages\\autogen\\agentchat\\conversable_agent.py:779, in ConversableAgent.generate_reply(self, messages, sender, exclude)
    777     continue
    778 if self._match_trigger(reply_func_tuple[\"trigger\"], sender):
--> 779     final, reply = reply_func(self, messages=messages, sender=sender, config=reply_func_tuple[\"config\"])
    780     if final:
    781         return reply

File \\AutoGen\\.venv\\Lib\\site-packages\\autogen\\agentchat\\conversable_agent.py:606, in ConversableAgent.generate_oai_reply(self, messages, sender, config)
    603     messages = self._oai_messages[sender]
    605 # TODO: #1143 handle token limit exceeded error
--> 606 response = oai.ChatCompletion.create(
    607     context=messages[-1].pop(\"context\", None), messages=self._oai_system_message + messages, **llm_config
    608 )
    609 return True, oai.ChatCompletion.extract_text_or_function_call(response)[0]

File \\AutoGen\\.venv\\Lib\\site-packages\\autogen\\oai\\completion.py:789, in Completion.create(cls, context, use_cache, config_list, filter_func, raise_on_ratelimit_or_timeout, allow_format_str_template, **config)
    787     base_config[\"max_retry_period\"] = 0
    788 try:
--> 789     response = cls.create(
    790         context,
    791         use_cache,
    792         raise_on_ratelimit_or_timeout=i < last or raise_on_ratelimit_or_timeout,
    793         **base_config,
    794     )
    795     if response == -1:
    796         return response

File \\AutoGen\\.venv\\Lib\\site-packages\\autogen\\oai\\completion.py:820, in Completion.create(cls, context, use_cache, config_list, filter_func, raise_on_ratelimit_or_timeout, allow_format_str_template, **config)
    818 with diskcache.Cache(cls.cache_path) as cls._cache:
    819     cls.set_cache(seed)
--> 820     return cls._get_response(params, raise_on_ratelimit_or_timeout=raise_on_ratelimit_or_timeout)

File \\AutoGen\\.venv\\Lib\\site-packages\\autogen\\oai\\completion.py:210, in Completion._get_response(cls, config, raise_on_ratelimit_or_timeout, use_cache)
    208         response = openai_completion.create(**config)
    209     else:
--> 210         response = openai_completion.create(request_timeout=request_timeout, **config)
    211 except (
    212     ServiceUnavailableError,
    213     APIConnectionError,
    214 ):
    215     # transient error
    216     logger.info(f\"retrying in {retry_wait_time} seconds...\", exc_info=1)

File \\AutoGen\\.venv\\Lib\\site-packages\\openai\\api_resources\\chat_completion.py:25, in ChatCompletion.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 \\AutoGen\\.venv\\Lib\\site-packages\\openai\\api_resources\\abstract\\engine_api_resource.py:155, in EngineAPIResource.create(cls, api_key, api_base, api_type, request_id, api_version, organization, **params)
    129 @classmethod
    130 def create(
    131     cls,
   (...)
    138     **params,
    139 ):
    140     (
    141         deployment_id,
    142         engine,
   (...)
    152         api_key, api_base, api_type, api_version, organization, **params
    153     )
--> 155     response, _, api_key = requestor.request(
    156         \"post\",
    157         url,
    158         params=params,
    159         headers=headers,
    160         stream=stream,
    161         request_id=request_id,
    162         request_timeout=request_timeout,
    163     )
    165     if stream:
    166         # must be an iterator
    167         assert not isinstance(response, OpenAIResponse)

File \\AutoGen\\.venv\\Lib\\site-packages\\openai\\api_requestor.py:299, in APIRequestor.request(self, method, url, params, headers, files, stream, request_id, request_timeout)
    278 def request(
    279     self,
    280     method,
   (...)
    287     request_timeout: Optional[Union[float, Tuple[float, float]]] = None,
    288 ) -> Tuple[Union[OpenAIResponse, Iterator[OpenAIResponse]], bool, str]:
    289     result = self.request_raw(
    290         method.lower(),
    291         url,
   (...)
    297         request_timeout=request_timeout,
    298     )
--> 299     resp, got_stream = self._interpret_response(result, stream)
    300     return resp, got_stream, self.api_key

File \\AutoGen\\.venv\\Lib\\site-packages\\openai\\api_requestor.py:710, in APIRequestor._interpret_response(self, result, stream)
    702     return (
    703         self._interpret_response_line(
    704             line, result.status_code, result.headers, stream=True
    705         )
    706         for line in parse_stream(result.iter_lines())
    707     ), True
    708 else:
    709     return (
--> 710         self._interpret_response_line(
    711             result.content.decode(\"utf-8\"),
    712             result.status_code,
    713             result.headers,
    714             stream=False,
    715         ),
    716         False,
    717     )

File \\AutoGen\\.venv\\Lib\\site-packages\\openai\\api_requestor.py:775, in APIRequestor._interpret_response_line(self, rbody, rcode, rheaders, stream)
    773 stream_error = stream and \"error\" in resp.data
    774 if stream_error or not 200 <= rcode < 300:
--> 775     raise self.handle_error_response(
    776         rbody, rcode, resp.data, rheaders, stream_error=stream_error
    777     )
    778 return resp

InvalidRequestError: Resource not found"
}

[gagb]

apt_type is missing?

[satfl]

apt_type is missing?

Thank you, it was indeed the api_type that was missing.
So the correct list looks the following way:

 [
    {
        "model": "custom_model_name",
        "api_key": "",
        "api_type": "azure",
        "api_base": "https://custom-azure-open-ai-name.openai.azure.com",
        "api_version": "2023-07-01-preview"
    }
]