350 lines
15 KiB
Python
350 lines
15 KiB
Python
"""OpenAI model provider implementation."""
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import logging
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from typing import TYPE_CHECKING, Optional
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if TYPE_CHECKING:
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from tools.models import ToolModelCategory
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from .openai_compatible import OpenAICompatibleProvider
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from .shared import (
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ModelCapabilities,
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ModelResponse,
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ProviderType,
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create_temperature_constraint,
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)
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logger = logging.getLogger(__name__)
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class OpenAIModelProvider(OpenAICompatibleProvider):
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"""Implementation that talks to api.openai.com using rich model metadata.
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In addition to the built-in catalogue, the provider can surface models
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defined in ``conf/custom_models.json`` (for organisations running their own
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OpenAI-compatible gateways) while still respecting restriction policies.
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"""
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# Model configurations using ModelCapabilities objects
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SUPPORTED_MODELS = {
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"gpt-5": ModelCapabilities(
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provider=ProviderType.OPENAI,
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model_name="gpt-5",
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friendly_name="OpenAI (GPT-5)",
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context_window=400_000, # 400K tokens
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max_output_tokens=128_000, # 128K max output tokens
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supports_extended_thinking=True, # Supports reasoning tokens
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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-5 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("fixed"),
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description="GPT-5 (400K context, 128K output) - Advanced model with reasoning support",
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aliases=["gpt5"],
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),
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"gpt-5-mini": ModelCapabilities(
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provider=ProviderType.OPENAI,
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model_name="gpt-5-mini",
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friendly_name="OpenAI (GPT-5-mini)",
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context_window=400_000, # 400K tokens
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max_output_tokens=128_000, # 128K max output tokens
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supports_extended_thinking=True, # Supports reasoning tokens
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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-5-mini supports vision
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max_image_size_mb=20.0, # 20MB per OpenAI docs
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supports_temperature=True,
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temperature_constraint=create_temperature_constraint("fixed"),
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description="GPT-5-mini (400K context, 128K output) - Efficient variant with reasoning support",
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aliases=["gpt5-mini", "gpt5mini", "mini"],
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),
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"gpt-5-nano": ModelCapabilities(
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provider=ProviderType.OPENAI,
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model_name="gpt-5-nano",
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friendly_name="OpenAI (GPT-5 nano)",
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context_window=400_000,
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max_output_tokens=128_000,
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supports_extended_thinking=True,
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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,
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max_image_size_mb=20.0,
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supports_temperature=True,
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temperature_constraint=create_temperature_constraint("fixed"),
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description="GPT-5 nano (400K context) - Fastest, cheapest version of GPT-5 for summarization and classification tasks",
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aliases=["gpt5nano", "gpt5-nano", "nano"],
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),
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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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max_output_tokens=65536, # 64K max output 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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max_output_tokens=65536, # 64K max output 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"],
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),
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"o3-pro": ModelCapabilities(
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provider=ProviderType.OPENAI,
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model_name="o3-pro",
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friendly_name="OpenAI (O3-Pro)",
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context_window=200_000, # 200K tokens
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max_output_tokens=65536, # 64K max output 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=["o3pro"],
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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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max_output_tokens=65536, # 64K max output 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=["o4mini"],
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),
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"gpt-4.1": ModelCapabilities(
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provider=ProviderType.OPENAI,
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model_name="gpt-4.1",
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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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max_output_tokens=32_768,
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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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"""Initialize OpenAI provider with API key."""
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# Set default OpenAI base URL, allow override for regions/custom endpoints
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kwargs.setdefault("base_url", "https://api.openai.com/v1")
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super().__init__(api_key, **kwargs)
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def get_capabilities(self, model_name: str) -> ModelCapabilities:
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"""Get capabilities for a specific OpenAI model."""
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# First check if it's a key in SUPPORTED_MODELS
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if model_name in self.SUPPORTED_MODELS:
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self._check_model_restrictions(model_name, model_name)
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return self.SUPPORTED_MODELS[model_name]
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# Try resolving as alias
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resolved_name = self._resolve_model_name(model_name)
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# Check if resolved name is a key
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if resolved_name in self.SUPPORTED_MODELS:
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self._check_model_restrictions(resolved_name, model_name)
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return self.SUPPORTED_MODELS[resolved_name]
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# Finally check if resolved name matches any API model name
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for key, capabilities in self.SUPPORTED_MODELS.items():
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if resolved_name == capabilities.model_name:
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self._check_model_restrictions(key, model_name)
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return capabilities
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# Check custom models registry for user-configured OpenAI models
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try:
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from .openrouter_registry import OpenRouterModelRegistry
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registry = OpenRouterModelRegistry()
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config = registry.get_model_config(resolved_name)
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if config and config.provider == ProviderType.OPENAI:
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self._check_model_restrictions(config.model_name, model_name)
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# Update provider type to ensure consistency
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config.provider = ProviderType.OPENAI
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return config
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except Exception as e:
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# Log but don't fail - registry might not be available
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logger.debug(f"Could not check custom models registry for '{resolved_name}': {e}")
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raise ValueError(f"Unsupported OpenAI model: {model_name}")
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def _check_model_restrictions(self, provider_model_name: str, user_model_name: str) -> None:
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"""Check if a model is allowed by restriction policy.
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Args:
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provider_model_name: The model name used by the provider
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user_model_name: The model name requested by the user
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Raises:
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ValueError: If the model is not allowed by restriction policy
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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()
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if not restriction_service.is_allowed(ProviderType.OPENAI, provider_model_name, user_model_name):
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raise ValueError(f"OpenAI model '{user_model_name}' is not allowed by restriction policy.")
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def get_provider_type(self) -> ProviderType:
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"""Get the provider type."""
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return ProviderType.OPENAI
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def validate_model_name(self, model_name: str) -> bool:
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"""Validate if the model name is supported and allowed."""
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resolved_name = self._resolve_model_name(model_name)
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# First, determine which model name to check against restrictions.
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model_to_check = None
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is_custom_model = False
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if resolved_name in self.SUPPORTED_MODELS:
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model_to_check = resolved_name
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else:
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# If not a built-in model, check the custom models registry.
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try:
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from .openrouter_registry import OpenRouterModelRegistry
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registry = OpenRouterModelRegistry()
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config = registry.get_model_config(resolved_name)
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if config and config.provider == ProviderType.OPENAI:
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model_to_check = config.model_name
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is_custom_model = True
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except Exception as e:
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# Log but don't fail - registry might not be available.
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logger.debug(f"Could not check custom models registry for '{resolved_name}': {e}")
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# If no model was found (neither built-in nor custom), it's invalid.
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if not model_to_check:
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return False
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# Now, perform the restriction check once.
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from utils.model_restrictions import get_restriction_service
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restriction_service = get_restriction_service()
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if not restriction_service.is_allowed(ProviderType.OPENAI, model_to_check, model_name):
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model_type = "custom " if is_custom_model else ""
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logger.debug(f"OpenAI {model_type}model '{model_name}' -> '{resolved_name}' blocked by restrictions")
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return False
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return True
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def generate_content(
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self,
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prompt: str,
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model_name: str,
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system_prompt: Optional[str] = None,
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temperature: float = 0.3,
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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 OpenAI API with proper model name resolution."""
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# Resolve model alias before making API call
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resolved_model_name = self._resolve_model_name(model_name)
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# Call parent implementation with resolved model name
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return super().generate_content(
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prompt=prompt,
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model_name=resolved_model_name,
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system_prompt=system_prompt,
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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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def supports_thinking_mode(self, model_name: str) -> bool:
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"""Check if the model supports extended thinking mode."""
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try:
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return self.get_capabilities(model_name).supports_extended_thinking
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except ValueError:
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return False
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def get_preferred_model(self, category: "ToolModelCategory", allowed_models: list[str]) -> Optional[str]:
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"""Get OpenAI's preferred model for a given category from allowed models.
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Args:
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category: The tool category requiring a model
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allowed_models: Pre-filtered list of models allowed by restrictions
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Returns:
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Preferred model name or None
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"""
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from tools.models import ToolModelCategory
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if not allowed_models:
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return None
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# Helper to find first available from preference list
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def find_first(preferences: list[str]) -> Optional[str]:
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"""Return first available model from preference list."""
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for model in preferences:
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if model in allowed_models:
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return model
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return None
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if category == ToolModelCategory.EXTENDED_REASONING:
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# Prefer models with extended thinking support
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preferred = find_first(["o3", "o3-pro", "gpt-5"])
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return preferred if preferred else allowed_models[0]
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elif category == ToolModelCategory.FAST_RESPONSE:
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# Prefer fast, cost-efficient models
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preferred = find_first(["gpt-5", "gpt-5-mini", "o4-mini", "o3-mini"])
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return preferred if preferred else allowed_models[0]
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else: # BALANCED or default
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# Prefer balanced performance/cost models
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preferred = find_first(["gpt-5", "gpt-5-mini", "o4-mini", "o3-mini"])
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return preferred if preferred else allowed_models[0]
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