refactor: removed subclass override when the base class should be resolving the model name
refactor: always disable "stream"
This commit is contained in:
Fahad
@@ -60,16 +60,19 @@ class ModelProvider(ABC):
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customise. Subclasses usually only override ``_lookup_capabilities`` to
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integrate a registry or dynamic source, or ``_finalise_capabilities`` to
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tweak the returned object.
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Args:
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model_name: Canonical model name or its alias
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"""
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resolved_name = self._resolve_model_name(model_name)
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capabilities = self._lookup_capabilities(resolved_name, model_name)
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resolved_model_name = self._resolve_model_name(model_name)
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capabilities = self._lookup_capabilities(resolved_model_name, model_name)
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if capabilities is None:
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self._raise_unsupported_model(model_name)
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self._ensure_model_allowed(capabilities, resolved_name, model_name)
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return self._finalise_capabilities(capabilities, resolved_name, model_name)
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self._ensure_model_allowed(capabilities, resolved_model_name, model_name)
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return self._finalise_capabilities(capabilities, resolved_model_name, model_name)
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def get_all_model_capabilities(self) -> dict[str, ModelCapabilities]:
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"""Return statically declared capabilities when available."""
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@@ -150,7 +153,38 @@ class ModelProvider(ABC):
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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 model."""
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"""Generate content using the model.
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This is the core method that all providers must implement to generate responses
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from their models. Providers should handle model-specific capabilities and
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constraints appropriately.
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Args:
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prompt: The main user prompt/query to send to the model
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model_name: Canonical model name or its alias that the provider supports
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system_prompt: Optional system instructions to prepend to the prompt for
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establishing context, behavior, or role
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temperature: Controls randomness in generation (0.0=deterministic, 1.0=creative),
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default 0.3. Some models may not support temperature control
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max_output_tokens: Optional maximum number of tokens to generate in the response.
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If not specified, uses the model's default limit
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**kwargs: Additional provider-specific parameters that vary by implementation
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(e.g., thinking_mode for Gemini, top_p for OpenAI, images for vision models)
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Returns:
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ModelResponse: Standardized response object containing:
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- content: The generated text response
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- usage: Token usage statistics (input/output/total)
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- model_name: The model that was actually used
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- friendly_name: Human-readable provider/model identifier
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- provider: The ProviderType enum value
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- metadata: Provider-specific metadata (finish_reason, safety info, etc.)
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Raises:
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ValueError: If the model is not supported, parameters are invalid,
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or the model is restricted by policy
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RuntimeError: If the API call fails after retries
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"""
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def count_tokens(self, text: str, model_name: str) -> int:
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"""Estimate token usage for a piece of text."""
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@@ -276,7 +310,12 @@ class ModelProvider(ABC):
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# Validation hooks
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# ------------------------------------------------------------------
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def validate_model_name(self, model_name: str) -> bool:
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"""Return ``True`` when the model resolves to an allowed capability."""
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"""
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Return ``True`` when the model resolves to an allowed capability.
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Args:
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model_name: Canonical model name or its alias
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"""
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try:
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self.get_capabilities(model_name)
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@@ -285,7 +324,12 @@ class ModelProvider(ABC):
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return True
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def validate_parameters(self, model_name: str, temperature: float, **kwargs) -> None:
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"""Validate model parameters against capabilities."""
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"""
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Validate model parameters against capabilities.
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Args:
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model_name: Canonical model name or its alias
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"""
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capabilities = self.get_capabilities(model_name)
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@@ -364,7 +408,7 @@ class ModelProvider(ABC):
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model configuration sources.
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Args:
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model_name: Model name that may be an alias
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model_name: Canonical model name or its alias
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Returns:
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Resolved model name
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@@ -6,7 +6,7 @@ from typing import Optional
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from .openai_compatible import OpenAICompatibleProvider
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from .openrouter_registry import OpenRouterModelRegistry
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from .shared import ModelCapabilities, ModelResponse, ProviderType
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from .shared import ModelCapabilities, ProviderType
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class CustomProvider(OpenAICompatibleProvider):
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@@ -113,49 +113,6 @@ class CustomProvider(OpenAICompatibleProvider):
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return ProviderType.CUSTOM
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# ------------------------------------------------------------------
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# Validation
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# ------------------------------------------------------------------
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# ------------------------------------------------------------------
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# Request execution
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# ------------------------------------------------------------------
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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 the custom API.
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Args:
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prompt: User prompt to send to the model
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model_name: Name of the model to use
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system_prompt: Optional system prompt for model behavior
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temperature: Sampling temperature
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max_output_tokens: Maximum tokens to generate
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**kwargs: Additional provider-specific parameters
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Returns:
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ModelResponse with generated content and metadata
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"""
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# Resolve model alias to actual model name
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resolved_model = self._resolve_model_name(model_name)
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# Call parent method 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,
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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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# ------------------------------------------------------------------
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# Registry helpers
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# ------------------------------------------------------------------
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@@ -333,15 +333,17 @@ class DIALModelProvider(OpenAICompatibleProvider):
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/openai/deployments/{deployment}/chat/completions
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Args:
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prompt: User prompt
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model_name: Model name or alias
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system_prompt: Optional system prompt
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temperature: Sampling temperature
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max_output_tokens: Maximum tokens to generate
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**kwargs: Additional provider-specific parameters
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prompt: The main user prompt/query to send to the model
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model_name: Model name or alias (e.g., "o3", "sonnet-4.1", "gemini-2.5-pro")
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system_prompt: Optional system instructions to prepend to the prompt for context/behavior
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temperature: Sampling temperature for randomness (0.0=deterministic, 1.0=creative), default 0.3
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Note: O3/O4 models don't support temperature and will ignore this parameter
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max_output_tokens: Optional maximum number of tokens to generate in the response
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images: Optional list of image paths or data URLs to include with the prompt (for vision-capable models)
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**kwargs: Additional OpenAI-compatible parameters (top_p, frequency_penalty, presence_penalty, seed, stop)
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Returns:
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ModelResponse with generated content and metadata
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ModelResponse: Contains the generated content, token usage stats, model metadata, and finish reason
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"""
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# Validate model name against allow-list
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if not self.validate_model_name(model_name):
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@@ -381,6 +383,7 @@ class DIALModelProvider(OpenAICompatibleProvider):
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completion_params = {
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"model": resolved_model,
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"messages": messages,
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"stream": False,
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}
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# Determine temperature support from capabilities
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@@ -397,7 +400,7 @@ class DIALModelProvider(OpenAICompatibleProvider):
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# Add additional 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", "stream"]:
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if not supports_temperature and key in ["top_p", "frequency_penalty", "presence_penalty"]:
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if not supports_temperature and key in ["top_p", "frequency_penalty", "presence_penalty", "stream"]:
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continue
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completion_params[key] = value
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@@ -437,9 +440,9 @@ class DIALModelProvider(OpenAICompatibleProvider):
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except Exception as exc:
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attempts = max(attempt_counter["value"], 1)
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if attempts == 1:
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raise ValueError(f"DIAL API error for model {model_name}: {exc}") from exc
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raise ValueError(f"DIAL API error for model {resolved_model}: {exc}") from exc
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raise ValueError(f"DIAL API error for model {model_name} after {attempts} attempts: {exc}") from exc
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raise ValueError(f"DIAL API error for model {resolved_model} after {attempts} attempts: {exc}") from exc
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def close(self) -> None:
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"""Clean up HTTP clients when provider is closed."""
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@@ -172,12 +172,28 @@ class GeminiModelProvider(ModelProvider):
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images: Optional[list[str]] = None,
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**kwargs,
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) -> ModelResponse:
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"""Generate content using Gemini model."""
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"""
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Generate content using Gemini model.
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Args:
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prompt: The main user prompt/query to send to the model
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model_name: Canonical model name or its alias (e.g., "gemini-2.5-pro", "flash", "pro")
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system_prompt: Optional system instructions to prepend to the prompt for context/behavior
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temperature: Controls randomness in generation (0.0=deterministic, 1.0=creative), default 0.3
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max_output_tokens: Optional maximum number of tokens to generate in the response
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thinking_mode: Thinking budget level for models that support it ("minimal", "low", "medium", "high", "max"), default "medium"
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images: Optional list of image paths or data URLs to include with the prompt (for vision models)
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**kwargs: Additional keyword arguments (reserved for future use)
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Returns:
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ModelResponse: Contains the generated content, token usage stats, model metadata, and safety information
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"""
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# Validate parameters and fetch capabilities
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resolved_name = self._resolve_model_name(model_name)
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self.validate_parameters(model_name, temperature)
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capabilities = self.get_capabilities(model_name)
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resolved_model_name = self._resolve_model_name(model_name)
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# Prepare content parts (text and potentially images)
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parts = []
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@@ -201,7 +217,7 @@ class GeminiModelProvider(ModelProvider):
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# Continue with other images and text
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continue
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elif images and not capabilities.supports_images:
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logger.warning(f"Model {resolved_name} does not support images, ignoring {len(images)} image(s)")
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logger.warning(f"Model {resolved_model_name} does not support images, ignoring {len(images)} image(s)")
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# Create contents structure
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contents = [{"parts": parts}]
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@@ -219,7 +235,7 @@ class GeminiModelProvider(ModelProvider):
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# Add thinking configuration for models that support it
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if capabilities.supports_extended_thinking and thinking_mode in self.THINKING_BUDGETS:
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# Get model's max thinking tokens and calculate actual budget
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model_config = self.MODEL_CAPABILITIES.get(resolved_name)
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model_config = self.MODEL_CAPABILITIES.get(resolved_model_name)
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if model_config and model_config.max_thinking_tokens > 0:
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max_thinking_tokens = model_config.max_thinking_tokens
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actual_thinking_budget = int(max_thinking_tokens * self.THINKING_BUDGETS[thinking_mode])
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@@ -233,7 +249,7 @@ class GeminiModelProvider(ModelProvider):
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def _attempt() -> ModelResponse:
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attempt_counter["value"] += 1
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response = self.client.models.generate_content(
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model=resolved_name,
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model=resolved_model_name,
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contents=contents,
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config=generation_config,
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)
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@@ -308,7 +324,7 @@ class GeminiModelProvider(ModelProvider):
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return ModelResponse(
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content=response.text,
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usage=usage,
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model_name=resolved_name,
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model_name=resolved_model_name,
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friendly_name="Gemini",
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provider=ProviderType.GOOGLE,
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metadata={
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@@ -324,12 +340,12 @@ class GeminiModelProvider(ModelProvider):
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operation=_attempt,
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max_attempts=max_retries,
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delays=retry_delays,
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log_prefix=f"Gemini API ({resolved_name})",
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log_prefix=f"Gemini API ({resolved_model_name})",
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)
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except Exception as exc:
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attempts = max(attempt_counter["value"], 1)
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error_msg = (
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f"Gemini API error for model {resolved_name} after {attempts} attempt"
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f"Gemini API error for model {resolved_model_name} after {attempts} attempt"
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f"{'s' if attempts > 1 else ''}: {exc}"
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)
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raise RuntimeError(error_msg) from exc
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@@ -462,10 +462,11 @@ class OpenAICompatibleProvider(ModelProvider):
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Args:
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prompt: User prompt to send to the model
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model_name: Name of the model to use
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model_name: Canonical model name or its alias
|
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system_prompt: Optional system prompt for model behavior
|
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temperature: Sampling temperature
|
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max_output_tokens: Maximum tokens to generate
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images: Optional list of image paths or data URLs to include with the prompt (for vision models)
|
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**kwargs: Additional provider-specific parameters
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Returns:
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@@ -497,6 +498,9 @@ class OpenAICompatibleProvider(ModelProvider):
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# Validate parameters with the effective temperature
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self.validate_parameters(model_name, effective_temperature)
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# Resolve to canonical model name
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resolved_model = self._resolve_model_name(model_name)
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# Prepare messages
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messages = []
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if system_prompt:
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@@ -518,7 +522,7 @@ class OpenAICompatibleProvider(ModelProvider):
|
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# Continue with other images and text
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continue
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elif images and (not capabilities or not capabilities.supports_images):
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logging.warning(f"Model {model_name} does not support images, ignoring {len(images)} image(s)")
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logging.warning(f"Model {resolved_model} does not support images, ignoring {len(images)} image(s)")
|
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# Add user message
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if len(user_content) == 1:
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@@ -529,14 +533,14 @@ class OpenAICompatibleProvider(ModelProvider):
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messages.append({"role": "user", "content": user_content})
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# Prepare completion parameters
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# Always disable streaming for OpenRouter
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# MCP doesn't use streaming, and this avoids issues with O3 model access
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completion_params = {
|
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"model": model_name,
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"model": resolved_model,
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"messages": messages,
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"stream": False,
|
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}
|
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|
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# Check model capabilities once to determine parameter support
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resolved_model = self._resolve_model_name(model_name)
|
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# Use the effective temperature we calculated earlier
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supports_sampling = effective_temperature is not None
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|
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@@ -553,7 +557,7 @@ class OpenAICompatibleProvider(ModelProvider):
|
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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", "stream"]:
|
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# Reasoning models (those that don't support temperature) also don't support these parameters
|
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if not supports_sampling and key in ["top_p", "frequency_penalty", "presence_penalty"]:
|
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if not supports_sampling and key in ["top_p", "frequency_penalty", "presence_penalty", "stream"]:
|
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continue # Skip unsupported parameters for reasoning models
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completion_params[key] = value
|
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|
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@@ -585,7 +589,7 @@ class OpenAICompatibleProvider(ModelProvider):
|
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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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model_name=resolved_model,
|
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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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@@ -601,12 +605,12 @@ class OpenAICompatibleProvider(ModelProvider):
|
||||
operation=_attempt,
|
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max_attempts=max_retries,
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delays=retry_delays,
|
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log_prefix=f"{self.FRIENDLY_NAME} API ({model_name})",
|
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log_prefix=f"{self.FRIENDLY_NAME} API ({resolved_model})",
|
||||
)
|
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except Exception as exc:
|
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attempts = max(attempt_counter["value"], 1)
|
||||
error_msg = (
|
||||
f"{self.FRIENDLY_NAME} API error for model {model_name} after {attempts} attempt"
|
||||
f"{self.FRIENDLY_NAME} API error for model {resolved_model} after {attempts} attempt"
|
||||
f"{'s' if attempts > 1 else ''}: {exc}"
|
||||
)
|
||||
logging.error(error_msg)
|
||||
@@ -618,7 +622,7 @@ class OpenAICompatibleProvider(ModelProvider):
|
||||
For proxy providers, this may use generic capabilities.
|
||||
|
||||
Args:
|
||||
model_name: Model to validate for
|
||||
model_name: Canonical model name or its alias
|
||||
temperature: Temperature to validate
|
||||
**kwargs: Additional parameters to validate
|
||||
"""
|
||||
|
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@@ -7,7 +7,7 @@ if TYPE_CHECKING:
|
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from tools.models import ToolModelCategory
|
||||
|
||||
from .openai_compatible import OpenAICompatibleProvider
|
||||
from .shared import ModelCapabilities, ModelResponse, ProviderType, TemperatureConstraint
|
||||
from .shared import ModelCapabilities, ProviderType, TemperatureConstraint
|
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|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -253,33 +253,6 @@ class OpenAIModelProvider(OpenAICompatibleProvider):
|
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"""Get the provider type."""
|
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return ProviderType.OPENAI
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Request execution
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
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,
|
||||
)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Provider preferences
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
@@ -8,7 +8,6 @@ from .openai_compatible import OpenAICompatibleProvider
|
||||
from .openrouter_registry import OpenRouterModelRegistry
|
||||
from .shared import (
|
||||
ModelCapabilities,
|
||||
ModelResponse,
|
||||
ProviderType,
|
||||
RangeTemperatureConstraint,
|
||||
)
|
||||
@@ -111,50 +110,6 @@ class OpenRouterProvider(OpenAICompatibleProvider):
|
||||
"""Identify this provider for restrictions and logging."""
|
||||
return ProviderType.OPENROUTER
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Request execution
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
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 the OpenRouter API.
|
||||
|
||||
Args:
|
||||
prompt: User prompt to send to the model
|
||||
model_name: Name of the model (or alias) to use
|
||||
system_prompt: Optional system prompt for model behavior
|
||||
temperature: Sampling temperature
|
||||
max_output_tokens: Maximum tokens to generate
|
||||
**kwargs: Additional provider-specific parameters
|
||||
|
||||
Returns:
|
||||
ModelResponse with generated content and metadata
|
||||
"""
|
||||
# Resolve model alias to actual OpenRouter model name
|
||||
resolved_model = self._resolve_model_name(model_name)
|
||||
|
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# Always disable streaming for OpenRouter
|
||||
# MCP doesn't use streaming, and this avoids issues with O3 model access
|
||||
if "stream" not in kwargs:
|
||||
kwargs["stream"] = False
|
||||
|
||||
# Call parent method with resolved model name
|
||||
return super().generate_content(
|
||||
prompt=prompt,
|
||||
model_name=resolved_model,
|
||||
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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|
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# ------------------------------------------------------------------
|
||||
# Registry helpers
|
||||
# ------------------------------------------------------------------
|
||||
|
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@@ -7,7 +7,7 @@ if TYPE_CHECKING:
|
||||
from tools.models import ToolModelCategory
|
||||
|
||||
from .openai_compatible import OpenAICompatibleProvider
|
||||
from .shared import ModelCapabilities, ModelResponse, ProviderType, TemperatureConstraint
|
||||
from .shared import ModelCapabilities, ProviderType, TemperatureConstraint
|
||||
|
||||
logger = logging.getLogger(__name__)
|
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|
||||
@@ -92,29 +92,6 @@ class XAIModelProvider(OpenAICompatibleProvider):
|
||||
"""Get the provider type."""
|
||||
return ProviderType.XAI
|
||||
|
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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 X.AI 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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|
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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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|
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def get_preferred_model(self, category: "ToolModelCategory", allowed_models: list[str]) -> Optional[str]:
|
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"""Get XAI's preferred model for a given category from allowed models.
|
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|
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|
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@@ -38,12 +38,12 @@
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@@ -112,8 +119,15 @@
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File diff suppressed because one or more lines are too long
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"encoding": "base64",
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"size": 1944
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"size": 2013
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},
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"headers": {
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"access-control-expose-headers": "X-Request-ID",
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"alt-svc": "h3=\":443\"; ma=86400",
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"cf-cache-status": "DYNAMIC",
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"cf-ray": "988eb697ed875633-DXB",
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"cf-ray": "98929783adac1ddc-DXB",
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"connection": "keep-alive",
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"content-encoding": "gzip",
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"content-type": "application/json",
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"date": "Fri, 03 Oct 2025 19:06:16 GMT",
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"date": "Sat, 04 Oct 2025 06:24:03 GMT",
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"openai-organization": "beehive-innovations-fze",
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"openai-processing-ms": "25291",
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"openai-processing-ms": "22586",
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"openai-project": "proj_QP57xBVPOlWpp0vuJEPGwXK3",
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"openai-version": "2020-10-01",
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"server": "cloudflare",
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"strict-transport-security": "max-age=31536000; includeSubDomains; preload",
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"transfer-encoding": "chunked",
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"x-content-type-options": "nosniff",
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"x-envoy-upstream-service-time": "25458",
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"x-envoy-upstream-service-time": "22850",
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"x-openai-proxy-wasm": "v0.1",
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"x-ratelimit-limit-requests": "500",
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"x-ratelimit-limit-tokens": "500000",
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@@ -71,7 +72,7 @@
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"x-ratelimit-remaining-tokens": "498616",
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"x-ratelimit-reset-requests": "120ms",
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"x-ratelimit-reset-tokens": "166ms",
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"x-request-id": "req_abf338b3f2ca4fe59bb7e90d8283467e"
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"x-request-id": "req_ff455537c7304182a59d16581f9aca63"
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},
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"reason_phrase": "OK",
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"status_code": 200
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|
||||
File diff suppressed because one or more lines are too long
@@ -330,10 +330,10 @@ class TestAutoModeProviderSelection:
|
||||
assert provider.get_provider_type() == expected_provider_type, f"Wrong provider for '{alias}'"
|
||||
|
||||
# Test alias resolution
|
||||
resolved_name = provider._resolve_model_name(alias)
|
||||
resolved_model_name = provider._resolve_model_name(alias)
|
||||
assert (
|
||||
resolved_name == expected_resolved_name
|
||||
), f"Alias '{alias}' should resolve to '{expected_resolved_name}', got '{resolved_name}'"
|
||||
resolved_model_name == expected_resolved_name
|
||||
), f"Alias '{alias}' should resolve to '{expected_resolved_name}', got '{resolved_model_name}'"
|
||||
|
||||
finally:
|
||||
# Restore original environment
|
||||
|
||||
@@ -27,8 +27,10 @@ class TestModelResolutionBug:
|
||||
provider = OpenRouterProvider("test_key")
|
||||
|
||||
# Test alias resolution
|
||||
resolved = provider._resolve_model_name("gemini")
|
||||
assert resolved == "google/gemini-2.5-pro", f"Expected 'google/gemini-2.5-pro', got '{resolved}'"
|
||||
resolved_model_name = provider._resolve_model_name("gemini")
|
||||
assert (
|
||||
resolved_model_name == "google/gemini-2.5-pro"
|
||||
), f"Expected 'google/gemini-2.5-pro', got '{resolved_model_name}'"
|
||||
|
||||
# Test that it also works with 'pro' alias
|
||||
resolved_pro = provider._resolve_model_name("pro")
|
||||
|
||||
@@ -353,10 +353,10 @@ class TestLocaleModelIntegration(unittest.TestCase):
|
||||
provider = OpenAIModelProvider(api_key="test")
|
||||
model_names = ["gpt-4", "gemini-2.5-flash", "anthropic/claude-opus-4.1", "o3-pro"]
|
||||
for model_name in model_names:
|
||||
resolved = provider._resolve_model_name(model_name)
|
||||
self.assertIsInstance(resolved, str)
|
||||
resolved_model_name = provider._resolve_model_name(model_name)
|
||||
self.assertIsInstance(resolved_model_name, str)
|
||||
model_data = {
|
||||
"model": resolved,
|
||||
"model": resolved_model_name,
|
||||
"description": f"Model {model_name} - advanced development 🚀",
|
||||
"capabilities": ["generation", "review", "creation"],
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user