Use ModelCapabilities consistently instead of dictionaries
Moved aliases as part of SUPPORTED_MODELS instead of shorthand, more in line with how custom_models are declared Further refactoring to cleanup some code
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Fahad
@@ -17,71 +17,110 @@ logger = logging.getLogger(__name__)
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class OpenAIModelProvider(OpenAICompatibleProvider):
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"""Official OpenAI API provider (api.openai.com)."""
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# Model configurations
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# Model configurations using ModelCapabilities objects
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SUPPORTED_MODELS = {
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"o3": {
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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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"supports_temperature": False, # O3 models don't accept temperature parameter
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"temperature_constraint": "fixed", # Fixed at 1.0
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"description": "Strong reasoning (200K context) - Logical problems, code generation, systematic analysis",
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},
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"o3-mini": {
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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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"supports_temperature": False, # O3 models don't accept temperature parameter
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"temperature_constraint": "fixed", # Fixed at 1.0
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"description": "Fast O3 variant (200K context) - Balanced performance/speed, moderate complexity",
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},
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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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"supports_temperature": False, # O3 models don't accept temperature parameter
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"temperature_constraint": "fixed", # Fixed at 1.0
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"description": "Professional-grade reasoning (200K context) - EXTREMELY EXPENSIVE: Only for the most complex problems requiring universe-scale complexity analysis OR when the user explicitly asks for this model. Use sparingly for critical architectural decisions or exceptionally complex debugging that other models cannot handle.",
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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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"supports_images": True, # O4 models support vision
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"max_image_size_mb": 20.0, # 20MB per OpenAI docs
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"supports_temperature": False, # O4 models don't accept temperature parameter
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"temperature_constraint": "fixed", # Fixed at 1.0
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"description": "Latest reasoning model (200K context) - Optimized for shorter contexts, rapid reasoning",
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},
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"o4-mini-high": {
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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, # O4 models support vision
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"max_image_size_mb": 20.0, # 20MB per OpenAI docs
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"supports_temperature": False, # O4 models don't accept temperature parameter
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"temperature_constraint": "fixed", # Fixed at 1.0
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"description": "Enhanced O4 mini (200K context) - Higher reasoning effort for complex tasks",
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},
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"gpt-4.1-2025-04-14": {
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"context_window": 1_000_000, # 1M tokens
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"supports_extended_thinking": False,
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"supports_images": True, # GPT-4.1 supports vision
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"max_image_size_mb": 20.0, # 20MB per OpenAI docs
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"supports_temperature": True, # Regular models accept temperature parameter
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"temperature_constraint": "range", # 0.0-2.0 range
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"description": "GPT-4.1 (1M context) - Advanced reasoning model with large context window",
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},
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# Shorthands
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"mini": "o4-mini", # Default 'mini' to latest mini model
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"o3mini": "o3-mini",
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"o4mini": "o4-mini",
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"o4minihigh": "o4-mini-high",
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"o4minihi": "o4-mini-high",
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"gpt4.1": "gpt-4.1-2025-04-14",
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"o3": ModelCapabilities(
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provider=ProviderType.OPENAI,
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model_name="o3",
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friendly_name="OpenAI (O3)",
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context_window=200_000, # 200K tokens
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supports_extended_thinking=False,
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supports_system_prompts=True,
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supports_streaming=True,
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supports_function_calling=True,
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supports_json_mode=True,
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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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supports_temperature=False, # O3 models don't accept temperature parameter
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temperature_constraint=create_temperature_constraint("fixed"),
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description="Strong reasoning (200K context) - Logical problems, code generation, systematic analysis",
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aliases=[],
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),
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"o3-mini": ModelCapabilities(
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provider=ProviderType.OPENAI,
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model_name="o3-mini",
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friendly_name="OpenAI (O3-mini)",
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context_window=200_000, # 200K tokens
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supports_extended_thinking=False,
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supports_system_prompts=True,
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supports_streaming=True,
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supports_function_calling=True,
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supports_json_mode=True,
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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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supports_temperature=False, # O3 models don't accept temperature parameter
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temperature_constraint=create_temperature_constraint("fixed"),
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description="Fast O3 variant (200K context) - Balanced performance/speed, moderate complexity",
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aliases=["o3mini", "o3-mini"],
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),
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"o3-pro-2025-06-10": ModelCapabilities(
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provider=ProviderType.OPENAI,
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model_name="o3-pro-2025-06-10",
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friendly_name="OpenAI (O3-Pro)",
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context_window=200_000, # 200K tokens
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supports_extended_thinking=False,
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supports_system_prompts=True,
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supports_streaming=True,
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supports_function_calling=True,
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supports_json_mode=True,
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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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supports_temperature=False, # O3 models don't accept temperature parameter
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temperature_constraint=create_temperature_constraint("fixed"),
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description="Professional-grade reasoning (200K context) - EXTREMELY EXPENSIVE: Only for the most complex problems requiring universe-scale complexity analysis OR when the user explicitly asks for this model. Use sparingly for critical architectural decisions or exceptionally complex debugging that other models cannot handle.",
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aliases=["o3-pro"],
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),
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"o4-mini": ModelCapabilities(
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provider=ProviderType.OPENAI,
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model_name="o4-mini",
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friendly_name="OpenAI (O4-mini)",
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context_window=200_000, # 200K tokens
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supports_extended_thinking=False,
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supports_system_prompts=True,
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supports_streaming=True,
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supports_function_calling=True,
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supports_json_mode=True,
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supports_images=True, # O4 models support vision
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max_image_size_mb=20.0, # 20MB per OpenAI docs
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supports_temperature=False, # O4 models don't accept temperature parameter
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temperature_constraint=create_temperature_constraint("fixed"),
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description="Latest reasoning model (200K context) - Optimized for shorter contexts, rapid reasoning",
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aliases=["mini", "o4mini"],
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),
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"o4-mini-high": ModelCapabilities(
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provider=ProviderType.OPENAI,
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model_name="o4-mini-high",
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friendly_name="OpenAI (O4-mini-high)",
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context_window=200_000, # 200K tokens
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supports_extended_thinking=False,
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supports_system_prompts=True,
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supports_streaming=True,
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supports_function_calling=True,
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supports_json_mode=True,
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supports_images=True, # O4 models support vision
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max_image_size_mb=20.0, # 20MB per OpenAI docs
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supports_temperature=False, # O4 models don't accept temperature parameter
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temperature_constraint=create_temperature_constraint("fixed"),
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description="Enhanced O4 mini (200K context) - Higher reasoning effort for complex tasks",
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aliases=["o4minihigh", "o4minihi", "mini-high"],
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),
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"gpt-4.1-2025-04-14": ModelCapabilities(
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provider=ProviderType.OPENAI,
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model_name="gpt-4.1-2025-04-14",
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friendly_name="OpenAI (GPT 4.1)",
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context_window=1_000_000, # 1M tokens
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supports_extended_thinking=False,
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supports_system_prompts=True,
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supports_streaming=True,
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supports_function_calling=True,
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supports_json_mode=True,
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supports_images=True, # GPT-4.1 supports vision
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max_image_size_mb=20.0, # 20MB per OpenAI docs
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supports_temperature=True, # Regular models accept temperature parameter
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temperature_constraint=create_temperature_constraint("range"),
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description="GPT-4.1 (1M context) - Advanced reasoning model with large context window",
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aliases=["gpt4.1"],
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),
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}
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def __init__(self, api_key: str, **kwargs):
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@@ -95,7 +134,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
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# Resolve shorthand
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resolved_name = self._resolve_model_name(model_name)
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if resolved_name not in self.SUPPORTED_MODELS or isinstance(self.SUPPORTED_MODELS[resolved_name], str):
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if resolved_name not in self.SUPPORTED_MODELS:
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raise ValueError(f"Unsupported OpenAI model: {model_name}")
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# Check if model is allowed by restrictions
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@@ -105,27 +144,8 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
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if not restriction_service.is_allowed(ProviderType.OPENAI, resolved_name, model_name):
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raise ValueError(f"OpenAI model '{model_name}' is not allowed by restriction policy.")
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config = self.SUPPORTED_MODELS[resolved_name]
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# Get temperature constraints and support from configuration
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supports_temperature = config.get("supports_temperature", True) # Default to True for backward compatibility
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temp_constraint_type = config.get("temperature_constraint", "range") # Default to range
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temp_constraint = create_temperature_constraint(temp_constraint_type)
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return ModelCapabilities(
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provider=ProviderType.OPENAI,
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model_name=model_name,
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friendly_name="OpenAI",
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context_window=config["context_window"],
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supports_extended_thinking=config["supports_extended_thinking"],
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supports_system_prompts=True,
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supports_streaming=True,
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supports_function_calling=True,
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supports_images=config.get("supports_images", False),
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max_image_size_mb=config.get("max_image_size_mb", 0.0),
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supports_temperature=supports_temperature,
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temperature_constraint=temp_constraint,
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)
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# Return the ModelCapabilities object directly from SUPPORTED_MODELS
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return self.SUPPORTED_MODELS[resolved_name]
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def get_provider_type(self) -> ProviderType:
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"""Get the provider type."""
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@@ -136,7 +156,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
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resolved_name = self._resolve_model_name(model_name)
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# First check if model is supported
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if resolved_name not in self.SUPPORTED_MODELS or not isinstance(self.SUPPORTED_MODELS[resolved_name], dict):
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if resolved_name not in self.SUPPORTED_MODELS:
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return False
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# Then check if model is allowed by restrictions
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@@ -177,61 +197,3 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
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# Currently no OpenAI models support extended thinking
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# This may change with future O3 models
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return False
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def list_models(self, respect_restrictions: bool = True) -> list[str]:
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"""Return a list of model names supported by this provider.
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Args:
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respect_restrictions: Whether to apply provider-specific restriction logic.
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Returns:
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List of model names available from this provider
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"""
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from utils.model_restrictions import get_restriction_service
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restriction_service = get_restriction_service() if respect_restrictions else None
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models = []
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for model_name, config in self.SUPPORTED_MODELS.items():
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# Handle both base models (dict configs) and aliases (string values)
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if isinstance(config, str):
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# This is an alias - check if the target model would be allowed
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target_model = config
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if restriction_service and not restriction_service.is_allowed(self.get_provider_type(), target_model):
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continue
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# Allow the alias
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models.append(model_name)
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else:
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# This is a base model with config dict
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# Check restrictions if enabled
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if restriction_service and not restriction_service.is_allowed(self.get_provider_type(), model_name):
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continue
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models.append(model_name)
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return models
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def list_all_known_models(self) -> list[str]:
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"""Return all model names known by this provider, including alias targets.
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Returns:
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List of all model names and alias targets known by this provider
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"""
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all_models = set()
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for model_name, config in self.SUPPORTED_MODELS.items():
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# Add the model name itself
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all_models.add(model_name.lower())
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# If it's an alias (string value), add the target model too
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if isinstance(config, str):
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all_models.add(config.lower())
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return list(all_models)
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def _resolve_model_name(self, model_name: str) -> str:
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"""Resolve model shorthand to full name."""
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# Check if it's a shorthand
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shorthand_value = self.SUPPORTED_MODELS.get(model_name)
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if isinstance(shorthand_value, str):
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return shorthand_value
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return model_name
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