trying to replicate the math tutor example using Azure OpenAI API, but response_format is flagged as “Invalid parameter”.
Full error message:
"name": "BadRequestError",
"message": "Error code: 400 - {'error': {'message': \"Invalid parameter: 'response_format' must be one of 'json_object', 'text'.\", 'type': 'invalid_request_error', 'param': 'response_format', 'code': None}}",
"stack": "---------------------------------------------------------------------------
BadRequestError Traceback (most recent call last)
Cell In[10], line 55
25 response_format ={
26 \"type\": \"json_schema\",
27 \"json_schema\": {
(...)
50 }
51 }
54 # Ask the model to use the function
---> 55 response = client.chat.completions.create(
56 model=deployment_name,
57 messages=messages,
58 response_format=response_format,
59 )
61 json.loads(response.choices[0].message.tool_calls[0].function.arguments)
File ~\\AppData\\Local\\Programs\\Python\\Python312\\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)
File ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\openai\\resources\\chat\\completions.py:590, in Completions.create(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, presence_penalty, response_format, seed, stop, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)
558 @required_args([\"messages\", \"model\"], [\"messages\", \"model\", \"stream\"])
559 def create(
560 self,
(...)
588 timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
589 ) -> ChatCompletion | Stream[ChatCompletionChunk]:
--> 590 return self._post(
591 \"/chat/completions\",
592 body=maybe_transform(
593 {
594 \"messages\": messages,
595 \"model\": model,
596 \"frequency_penalty\": frequency_penalty,
597 \"function_call\": function_call,
598 \"functions\": functions,
599 \"logit_bias\": logit_bias,
600 \"logprobs\": logprobs,
601 \"max_tokens\": max_tokens,
602 \"n\": n,
603 \"presence_penalty\": presence_penalty,
604 \"response_format\": response_format,
605 \"seed\": seed,
606 \"stop\": stop,
607 \"stream\": stream,
608 \"stream_options\": stream_options,
609 \"temperature\": temperature,
610 \"tool_choice\": tool_choice,
611 \"tools\": tools,
612 \"top_logprobs\": top_logprobs,
613 \"top_p\": top_p,
614 \"user\": user,
615 },
616 completion_create_params.CompletionCreateParams,
617 ),
618 options=make_request_options(
619 extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
620 ),
621 cast_to=ChatCompletion,
622 stream=stream or False,
623 stream_cls=Stream[ChatCompletionChunk],
624 )
File ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\openai\\_base_client.py:1240, in SyncAPIClient.post(self, path, cast_to, body, options, files, stream, stream_cls)
1226 def post(
1227 self,
1228 path: str,
(...)
1235 stream_cls: type[_StreamT] | None = None,
1236 ) -> ResponseT | _StreamT:
1237 opts = FinalRequestOptions.construct(
1238 method=\"post\", url=path, json_data=body, files=to_httpx_files(files), **options
1239 )
-> 1240 return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
File ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\openai\\_base_client.py:921, in SyncAPIClient.request(self, cast_to, options, remaining_retries, stream, stream_cls)
912 def request(
913 self,
914 cast_to: Type[ResponseT],
(...)
919 stream_cls: type[_StreamT] | None = None,
920 ) -> ResponseT | _StreamT:
--> 921 return self._request(
922 cast_to=cast_to,
923 options=options,
924 stream=stream,
925 stream_cls=stream_cls,
926 remaining_retries=remaining_retries,
927 )
File ~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\openai\\_base_client.py:1020, in SyncAPIClient._request(self, cast_to, options, remaining_retries, stream, stream_cls)
1017 err.response.read()
1019 log.debug(\"Re-raising status error\")
-> 1020 raise self._make_status_error_from_response(err.response) from None
1022 return self._process_response(
1023 cast_to=cast_to,
1024 options=options,
(...)
1027 stream_cls=stream_cls,
1028 )
BadRequestError: Error code: 400 - {'error': {'message': \"Invalid parameter: 'response_format' must be one of 'json_object', 'text'.\", 'type': 'invalid_request_error', 'param': 'response_format', 'code': None}}"
}
code:
import os
import json
from openai import AzureOpenAI
from textwrap import dedent
# Initialize the Azure OpenAI client
client = AzureOpenAI(
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
api_version=os.getenv("OpenAI_API_VERSION")
)
deployment_name = os.getenv("AZURE_OPENAI_DEPLOYMENT_ID")
# Initial user message
messages = [
{"role": "system", "content": dedent("""
You are a helpful math tutor. You will be provided with a math problem,
and your goal will be to output a step by step solution, along with a final answer.
For each step, just provide the output as an equation use the explanation field to detail the reasoning.
""")},
{"role": "user", "content": "solve 8x + 31 = 2"}
]
# Define the JSON schema for structured output
response_format ={
"type": "json_schema",
"json_schema": {
"name": "math_reasoning",
"schema": {
"type": "object",
"properties": {
"steps": {
"type": "array",
"items": {
"type": "object",
"properties": {
"explanation": {"type": "string"},
"output": {"type": "string"}
},
"required": ["explanation", "output"],
"additionalProperties": False
}
},
"final_answer": {"type": "string"}
},
"required": ["steps", "final_answer"],
"additionalProperties": False
},
"strict": True
}
}
# Ask the model to use the function
response = client.chat.completions.create(
model=deployment_name,
messages=messages,
response_format=response_format,
)
json.loads(response.choices[0].message.tool_calls[0].function.arguments)