"""OpenAI model provider implementation."""
import logging
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from tools.models import ToolModelCategory
from .base import (
ModelCapabilities,
ModelResponse,
ProviderType,
create_temperature_constraint,
)
from .openai_compatible import OpenAICompatibleProvider
logger = logging.getLogger(__name__)
class OpenAIModelProvider(OpenAICompatibleProvider):
"""Official OpenAI API provider (api.openai.com)."""
# Model configurations using ModelCapabilities objects
SUPPORTED_MODELS = {
"gpt-5": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="gpt-5",
friendly_name="OpenAI (GPT-5)",
context_window=400_000, # 400K tokens
max_output_tokens=128_000, # 128K max output tokens
supports_extended_thinking=True, # Supports reasoning tokens
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=True,
supports_images=True, # GPT-5 supports vision
max_image_size_mb=20.0, # 20MB per OpenAI docs
supports_temperature=True, # Regular models accept temperature parameter
temperature_constraint=create_temperature_constraint("fixed"),
description="GPT-5 (400K context, 128K output) - Advanced model with reasoning support",
aliases=["gpt5", "gpt-5"],
),
"gpt-5-mini": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="gpt-5-mini",
friendly_name="OpenAI (GPT-5-mini)",
context_window=400_000, # 400K tokens
max_output_tokens=128_000, # 128K max output tokens
supports_extended_thinking=True, # Supports reasoning tokens
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=True,
supports_images=True, # GPT-5-mini supports vision
max_image_size_mb=20.0, # 20MB per OpenAI docs
supports_temperature=True,
temperature_constraint=create_temperature_constraint("fixed"),
description="GPT-5-mini (400K context, 128K output) - Efficient variant with reasoning support",
aliases=["gpt5-mini", "gpt5mini", "mini"],
),
"gpt-5-nano": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="gpt-5-nano",
friendly_name="OpenAI (GPT-5 nano)",
context_window=400_000,
max_output_tokens=128_000,
supports_extended_thinking=True,
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=True,
supports_images=True,
max_image_size_mb=20.0,
supports_temperature=True,
temperature_constraint=create_temperature_constraint("fixed"),
description="GPT-5 nano (400K context) - Fastest, cheapest version of GPT-5 for summarization and classification tasks",
aliases=["gpt5nano", "gpt5-nano", "nano"],
),
"o3": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="o3",
friendly_name="OpenAI (O3)",
context_window=200_000, # 200K tokens
max_output_tokens=65536, # 64K max output tokens
supports_extended_thinking=False,
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=True,
supports_images=True, # O3 models support vision
max_image_size_mb=20.0, # 20MB per OpenAI docs
supports_temperature=False, # O3 models don't accept temperature parameter
temperature_constraint=create_temperature_constraint("fixed"),
description="Strong reasoning (200K context) - Logical problems, code generation, systematic analysis",
aliases=[],
),
"o3-mini": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="o3-mini",
friendly_name="OpenAI (O3-mini)",
context_window=200_000, # 200K tokens
max_output_tokens=65536, # 64K max output tokens
supports_extended_thinking=False,
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=True,
supports_images=True, # O3 models support vision
max_image_size_mb=20.0, # 20MB per OpenAI docs
supports_temperature=False, # O3 models don't accept temperature parameter
temperature_constraint=create_temperature_constraint("fixed"),
description="Fast O3 variant (200K context) - Balanced performance/speed, moderate complexity",
aliases=["o3mini", "o3-mini"],
),
"o3-pro": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="o3-pro",
friendly_name="OpenAI (O3-Pro)",
context_window=200_000, # 200K tokens
max_output_tokens=65536, # 64K max output tokens
supports_extended_thinking=False,
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=True,
supports_images=True, # O3 models support vision
max_image_size_mb=20.0, # 20MB per OpenAI docs
supports_temperature=False, # O3 models don't accept temperature parameter
temperature_constraint=create_temperature_constraint("fixed"),
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.",
aliases=["o3-pro"],
),
"o4-mini": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="o4-mini",
friendly_name="OpenAI (O4-mini)",
context_window=200_000, # 200K tokens
max_output_tokens=65536, # 64K max output tokens
supports_extended_thinking=False,
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=True,
supports_images=True, # O4 models support vision
max_image_size_mb=20.0, # 20MB per OpenAI docs
supports_temperature=False, # O4 models don't accept temperature parameter
temperature_constraint=create_temperature_constraint("fixed"),
description="Latest reasoning model (200K context) - Optimized for shorter contexts, rapid reasoning",
aliases=["o4mini", "o4-mini"],
),
"gpt-4.1": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="gpt-4.1",
friendly_name="OpenAI (GPT 4.1)",
context_window=1_000_000, # 1M tokens
max_output_tokens=32_768,
supports_extended_thinking=False,
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=True,
supports_images=True, # GPT-4.1 supports vision
max_image_size_mb=20.0, # 20MB per OpenAI docs
supports_temperature=True, # Regular models accept temperature parameter
temperature_constraint=create_temperature_constraint("range"),
description="GPT-4.1 (1M context) - Advanced reasoning model with large context window",
aliases=["gpt4.1", "gpt-4.1"],
),
}
def __init__(self, api_key: str, **kwargs):
"""Initialize OpenAI provider with API key."""
# Set default OpenAI base URL, allow override for regions/custom endpoints
kwargs.setdefault("base_url", "https://api.openai.com/v1")
super().__init__(api_key, **kwargs)
def get_capabilities(self, model_name: str) -> ModelCapabilities:
"""Get capabilities for a specific OpenAI model."""
# First check if it's a key in SUPPORTED_MODELS
if model_name in self.SUPPORTED_MODELS:
# Check if model is allowed by restrictions
from utils.model_restrictions import get_restriction_service
restriction_service = get_restriction_service()
if not restriction_service.is_allowed(ProviderType.OPENAI, model_name, model_name):
raise ValueError(f"OpenAI model '{model_name}' is not allowed by restriction policy.")
return self.SUPPORTED_MODELS[model_name]
# Try resolving as alias
resolved_name = self._resolve_model_name(model_name)
# Check if resolved name is a key
if resolved_name in self.SUPPORTED_MODELS:
# Check if model is allowed by restrictions
from utils.model_restrictions import get_restriction_service
restriction_service = get_restriction_service()
if not restriction_service.is_allowed(ProviderType.OPENAI, resolved_name, model_name):
raise ValueError(f"OpenAI model '{model_name}' is not allowed by restriction policy.")
return self.SUPPORTED_MODELS[resolved_name]
# Finally check if resolved name matches any API model name
for key, capabilities in self.SUPPORTED_MODELS.items():
if resolved_name == capabilities.model_name:
# Check if model is allowed by restrictions
from utils.model_restrictions import get_restriction_service
restriction_service = get_restriction_service()
if not restriction_service.is_allowed(ProviderType.OPENAI, key, model_name):
raise ValueError(f"OpenAI model '{model_name}' is not allowed by restriction policy.")
return capabilities
raise ValueError(f"Unsupported OpenAI model: {model_name}")
def get_provider_type(self) -> ProviderType:
"""Get the provider type."""
return ProviderType.OPENAI
def validate_model_name(self, model_name: str) -> bool:
"""Validate if the model name is supported and allowed."""
resolved_name = self._resolve_model_name(model_name)
# First check if model is supported
if resolved_name not in self.SUPPORTED_MODELS:
return False
# Then check if model is allowed by restrictions
from utils.model_restrictions import get_restriction_service
restriction_service = get_restriction_service()
if not restriction_service.is_allowed(ProviderType.OPENAI, resolved_name, model_name):
logger.debug(f"OpenAI model '{model_name}' -> '{resolved_name}' blocked by restrictions")
return False
return True
def generate_content(
self,
prompt: str,
model_name: str,
system_prompt: Optional[str] = None,
temperature: float = 0.3,
max_output_tokens: Optional[int] = None,
**kwargs,
) -> ModelResponse:
"""Generate content using OpenAI API with proper model name resolution."""
# Resolve model alias before making API call
resolved_model_name = self._resolve_model_name(model_name)
# Call parent implementation with resolved model name
return super().generate_content(
prompt=prompt,
model_name=resolved_model_name,
system_prompt=system_prompt,
temperature=temperature,
max_output_tokens=max_output_tokens,
**kwargs,
)
def supports_thinking_mode(self, model_name: str) -> bool:
"""Check if the model supports extended thinking mode."""
# GPT-5 models support reasoning tokens (extended thinking)
resolved_name = self._resolve_model_name(model_name)
if resolved_name in ["gpt-5", "gpt-5-mini"]:
return True
# O3 models don't support extended thinking yet
return False
def get_preferred_model(self, category: "ToolModelCategory", allowed_models: list[str]) -> Optional[str]:
"""Get OpenAI's preferred model for a given category from allowed models.
Args:
category: The tool category requiring a model
allowed_models: Pre-filtered list of models allowed by restrictions
Returns:
Preferred model name or None
"""
from tools.models import ToolModelCategory
if not allowed_models:
return None
# Helper to find first available from preference list
def find_first(preferences: list[str]) -> Optional[str]:
"""Return first available model from preference list."""
for model in preferences:
if model in allowed_models:
return model
return None
if category == ToolModelCategory.EXTENDED_REASONING:
# Prefer models with extended thinking support
preferred = find_first(["o3", "o3-pro", "gpt-5"])
return preferred if preferred else allowed_models[0]
elif category == ToolModelCategory.FAST_RESPONSE:
# Prefer fast, cost-efficient models
preferred = find_first(["gpt-5", "gpt-5-mini", "o4-mini", "o3-mini"])
return preferred if preferred else allowed_models[0]
else: # BALANCED or default
# Prefer balanced performance/cost models
preferred = find_first(["gpt-5", "gpt-5-mini", "o4-mini", "o3-mini"])
return preferred if preferred else allowed_models[0]
