Fixes O3-Pro connection https://github.com/BeehiveInnovations/zen-mcp-server/issues/56
New tests for O3-pro Improved prompts for shorthand input
This commit is contained in:
Fahad
@@ -32,12 +32,14 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
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"supports_images": True, # O3 models support vision
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"max_image_size_mb": 20.0, # 20MB per OpenAI docs
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},
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"o3-pro": {
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"o3-pro-2025-06-10": {
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"context_window": 200_000, # 200K tokens
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"supports_extended_thinking": False,
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"supports_images": True, # O3 models support vision
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"max_image_size_mb": 20.0, # 20MB per OpenAI docs
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},
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# Aliases
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"o3-pro": "o3-pro-2025-06-10",
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"o4-mini": {
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"context_window": 200_000, # 200K tokens
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"supports_extended_thinking": False,
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@@ -89,7 +91,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
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config = self.SUPPORTED_MODELS[resolved_name]
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# Define temperature constraints per model
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if resolved_name in ["o3", "o3-mini", "o3-pro", "o4-mini", "o4-mini-high"]:
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if resolved_name in ["o3", "o3-mini", "o3-pro", "o3-pro-2025-06-10", "o4-mini", "o4-mini-high"]:
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# O3 and O4 reasoning models only support temperature=1.0
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temp_constraint = FixedTemperatureConstraint(1.0)
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else:
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@@ -224,6 +224,138 @@ class OpenAICompatibleProvider(ModelProvider):
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return self._client
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def _generate_with_responses_endpoint(
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self,
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model_name: str,
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messages: list,
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temperature: float,
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max_output_tokens: Optional[int] = None,
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**kwargs,
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) -> ModelResponse:
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"""Generate content using the /v1/responses endpoint for o3-pro via OpenAI library."""
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# Convert messages to the correct format for responses endpoint
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input_messages = []
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for message in messages:
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role = message.get("role", "")
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content = message.get("content", "")
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if role == "system":
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# System messages can be treated as user messages for o3-pro
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input_messages.append(
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{"role": "user", "content": [{"type": "input_text", "text": f"System: {content}"}]}
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)
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elif role == "user":
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input_messages.append({"role": "user", "content": [{"type": "input_text", "text": content}]})
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elif role == "assistant":
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input_messages.append({"role": "assistant", "content": [{"type": "output_text", "text": content}]})
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# Prepare completion parameters for responses endpoint
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completion_params = {
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"model": model_name,
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"input": input_messages,
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"text": {"format": {"type": "text"}},
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"reasoning": {"effort": "medium", "summary": "auto"},
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"tools": [],
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"store": True,
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}
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# Temperature is not in the documented parameters for responses endpoint
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# but we'll try to add it in case it's supported
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# Add max tokens if specified
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if max_output_tokens:
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completion_params["max_tokens"] = max_output_tokens
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# Add any additional OpenAI-specific parameters
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for key, value in kwargs.items():
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if key in ["top_p", "frequency_penalty", "presence_penalty", "seed", "stop"]:
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completion_params[key] = value
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# Retry logic with progressive delays
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max_retries = 4
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retry_delays = [1, 3, 5, 8]
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last_exception = None
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for attempt in range(max_retries):
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try:
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# Use OpenAI client's responses endpoint
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response = self.client.responses.create(**completion_params)
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# Extract content and usage from responses endpoint format
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# The response format is different for responses endpoint
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content = ""
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if hasattr(response, "output") and response.output:
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if hasattr(response.output, "content") and response.output.content:
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# Look for output_text in content
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for content_item in response.output.content:
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if hasattr(content_item, "type") and content_item.type == "output_text":
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content = content_item.text
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break
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elif hasattr(response.output, "text"):
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content = response.output.text
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# Try to extract usage information
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usage = None
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if hasattr(response, "usage"):
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usage = self._extract_usage(response)
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elif hasattr(response, "input_tokens") and hasattr(response, "output_tokens"):
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usage = {
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"input_tokens": getattr(response, "input_tokens", 0),
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"output_tokens": getattr(response, "output_tokens", 0),
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"total_tokens": getattr(response, "input_tokens", 0) + getattr(response, "output_tokens", 0),
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}
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return ModelResponse(
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content=content,
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usage=usage,
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model_name=model_name,
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friendly_name=self.FRIENDLY_NAME,
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provider=self.get_provider_type(),
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metadata={
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"model": getattr(response, "model", model_name),
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"id": getattr(response, "id", ""),
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"created": getattr(response, "created_at", 0),
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"endpoint": "responses",
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},
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)
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except Exception as e:
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last_exception = e
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# Check if this is a retryable error
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error_str = str(e).lower()
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is_retryable = any(
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term in error_str
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for term in [
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"timeout",
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"connection",
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"network",
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"temporary",
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"unavailable",
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"retry",
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"429",
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"500",
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"502",
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"503",
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"504",
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]
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)
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if is_retryable and attempt < max_retries - 1:
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delay = retry_delays[attempt]
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logging.warning(
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f"Retryable error for o3-pro responses endpoint, attempt {attempt + 1}/{max_retries}: {str(e)}. Retrying in {delay}s..."
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)
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time.sleep(delay)
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else:
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break
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# If we get here, all retries failed
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error_msg = f"o3-pro responses endpoint error after {max_retries} attempts: {str(last_exception)}"
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logging.error(error_msg)
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raise RuntimeError(error_msg) from last_exception
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def generate_content(
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self,
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prompt: str,
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@@ -301,6 +433,22 @@ class OpenAICompatibleProvider(ModelProvider):
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if key in ["top_p", "frequency_penalty", "presence_penalty", "seed", "stop", "stream"]:
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completion_params[key] = value
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# Check if this is o3-pro and needs the responses endpoint
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resolved_model = model_name
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if hasattr(self, "_resolve_model_name"):
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resolved_model = self._resolve_model_name(model_name)
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if resolved_model == "o3-pro-2025-06-10":
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# This model requires the /v1/responses endpoint
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# If it fails, we should not fall back to chat/completions
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return self._generate_with_responses_endpoint(
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model_name=resolved_model,
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messages=messages,
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temperature=temperature,
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max_output_tokens=max_output_tokens,
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**kwargs,
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)
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# Retry logic with progressive delays
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max_retries = 4 # Total of 4 attempts
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retry_delays = [1, 3, 5, 8] # Progressive delays: 1s, 3s, 5s, 8s
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