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feat: all native providers now read from catalog files like OpenRouter / Custom configs. Allows for greater control over the capabilities

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Fahad
2025-10-07 12:17:47 +04:00
parent 7d7c74b5a3
commit 2a706d5720
13 changed files with 704 additions and 397 deletions
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245
providers/openai_provider.py
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@@ -7,7 +7,8 @@ if TYPE_CHECKING:
from tools.models import ToolModelCategory
from .openai_compatible import OpenAICompatibleProvider
from .shared import ModelCapabilities, ProviderType, TemperatureConstraint
from .openai_registry import OpenAIModelRegistry
from .shared import ModelCapabilities, ProviderType
logger = logging.getLogger(__name__)
@@ -20,208 +21,53 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
OpenAI-compatible gateways) while still respecting restriction policies.
"""
# Model configurations using ModelCapabilities objects
MODEL_CAPABILITIES = {
"gpt-5": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="gpt-5",
friendly_name="OpenAI (GPT-5)",
intelligence_score=16,
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=False,
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=TemperatureConstraint.create("fixed"),
description="GPT-5 (400K context, 128K output) - Advanced model with reasoning support",
aliases=["gpt5", "gpt-5"],
),
"gpt-5-pro": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="gpt-5-pro",
friendly_name="OpenAI (GPT-5 Pro)",
intelligence_score=18,
use_openai_response_api=True,
context_window=400_000,
max_output_tokens=272_000,
supports_extended_thinking=True,
supports_system_prompts=True,
supports_streaming=False,
supports_function_calling=True,
supports_json_mode=True,
supports_images=True,
max_image_size_mb=20.0,
supports_temperature=True,
temperature_constraint=TemperatureConstraint.create("fixed"),
default_reasoning_effort="high",
description="GPT-5 Pro (400K context, 272K output) - Advanced model with reasoning support",
aliases=["gpt5pro", "gpt5-pro"],
),
"gpt-5-mini": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="gpt-5-mini",
friendly_name="OpenAI (GPT-5-mini)",
intelligence_score=15,
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=False,
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=TemperatureConstraint.create("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)",
intelligence_score=13,
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=TemperatureConstraint.create("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)",
intelligence_score=14,
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=TemperatureConstraint.create("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)",
intelligence_score=12,
context_window=200_000,
max_output_tokens=65536,
supports_extended_thinking=False,
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=False,
temperature_constraint=TemperatureConstraint.create("fixed"),
description="Fast O3 variant (200K context) - Balanced performance/speed, moderate complexity",
aliases=["o3mini"],
),
"o3-pro": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="o3-pro",
friendly_name="OpenAI (O3-Pro)",
intelligence_score=15,
context_window=200_000,
max_output_tokens=65536,
supports_extended_thinking=False,
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=False,
temperature_constraint=TemperatureConstraint.create("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=["o3pro"],
use_openai_response_api=True,
),
"o4-mini": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="o4-mini",
friendly_name="OpenAI (O4-mini)",
intelligence_score=11,
context_window=200_000,
supports_extended_thinking=False,
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=False,
temperature_constraint=TemperatureConstraint.create("fixed"),
description="Latest reasoning model (200K context) - Optimized for shorter contexts, rapid reasoning",
aliases=["o4mini"],
),
"gpt-4.1": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="gpt-4.1",
friendly_name="OpenAI (GPT 4.1)",
intelligence_score=13,
context_window=1_000_000,
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,
max_image_size_mb=20.0,
supports_temperature=True,
temperature_constraint=TemperatureConstraint.create("range"),
description="GPT-4.1 (1M context) - Advanced reasoning model with large context window",
aliases=["gpt4.1"],
),
"gpt-5-codex": ModelCapabilities(
provider=ProviderType.OPENAI,
model_name="gpt-5-codex",
friendly_name="OpenAI (GPT-5 Codex)",
intelligence_score=17,
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=TemperatureConstraint.create("range"),
description="GPT-5 Codex (400K context) Specialized for coding, refactoring, and software architecture.",
aliases=["gpt5-codex", "codex", "gpt-5-code", "gpt5-code"],
use_openai_response_api=True,
),
}
MODEL_CAPABILITIES: dict[str, ModelCapabilities] = {}
_registry: Optional[OpenAIModelRegistry] = None
def __init__(self, api_key: str, **kwargs):
"""Initialize OpenAI provider with API key."""
self._ensure_registry()
# 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)
self._invalidate_capability_cache()
# ------------------------------------------------------------------
# Registry access
# ------------------------------------------------------------------
@classmethod
def _ensure_registry(cls, *, force_reload: bool = False) -> None:
"""Load capability registry into MODEL_CAPABILITIES."""
if cls._registry is not None and not force_reload:
return
try:
registry = OpenAIModelRegistry()
except Exception as exc: # pragma: no cover - defensive logging
logger.warning("Unable to load OpenAI model registry: %s", exc)
cls._registry = None
cls.MODEL_CAPABILITIES = {}
return
cls._registry = registry
cls.MODEL_CAPABILITIES = dict(registry.model_map)
@classmethod
def reload_registry(cls) -> None:
"""Force registry reload (primarily for tests)."""
cls._ensure_registry(force_reload=True)
def get_all_model_capabilities(self) -> dict[str, ModelCapabilities]:
self._ensure_registry()
return super().get_all_model_capabilities()
def get_model_registry(self) -> Optional[dict[str, ModelCapabilities]]:
if self._registry is None:
return None
return dict(self._registry.model_map)
# ------------------------------------------------------------------
# Capability surface
@@ -234,6 +80,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
) -> Optional[ModelCapabilities]:
"""Look up OpenAI capabilities from built-ins or the custom registry."""
self._ensure_registry()
builtin = super()._lookup_capabilities(canonical_name, requested_name)
if builtin is not None:
return builtin
@@ -319,3 +166,7 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
# Include GPT-5-Codex for coding workflows
preferred = find_first(["gpt-5", "gpt-5-codex", "gpt-5-pro", "gpt-5-mini", "o4-mini", "o3-mini"])
return preferred if preferred else allowed_models[0]
# Load registry data at import time so dependent providers (Azure) can reuse it
OpenAIModelProvider._ensure_registry()