"""X.AI (GROK) model provider implementation."""
import logging
from typing import Optional
from .base import (
ModelCapabilities,
ModelResponse,
ProviderType,
create_temperature_constraint,
)
from .openai_compatible import OpenAICompatibleProvider
logger = logging.getLogger(__name__)
class XAIModelProvider(OpenAICompatibleProvider):
"""X.AI GROK API provider (api.x.ai)."""
FRIENDLY_NAME = "X.AI"
# Model configurations using ModelCapabilities objects
SUPPORTED_MODELS = {
"grok-3": ModelCapabilities(
provider=ProviderType.XAI,
model_name="grok-3",
friendly_name="X.AI (Grok 3)",
context_window=131_072, # 131K tokens
max_output_tokens=131072,
supports_extended_thinking=False,
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=False, # Assuming GROK doesn't have JSON mode yet
supports_images=False, # Assuming GROK is text-only for now
max_image_size_mb=0.0,
supports_temperature=True,
temperature_constraint=create_temperature_constraint("range"),
description="GROK-3 (131K context) - Advanced reasoning model from X.AI, excellent for complex analysis",
aliases=["grok", "grok3"],
),
"grok-3-fast": ModelCapabilities(
provider=ProviderType.XAI,
model_name="grok-3-fast",
friendly_name="X.AI (Grok 3 Fast)",
context_window=131_072, # 131K tokens
max_output_tokens=131072,
supports_extended_thinking=False,
supports_system_prompts=True,
supports_streaming=True,
supports_function_calling=True,
supports_json_mode=False, # Assuming GROK doesn't have JSON mode yet
supports_images=False, # Assuming GROK is text-only for now
max_image_size_mb=0.0,
supports_temperature=True,
temperature_constraint=create_temperature_constraint("range"),
description="GROK-3 Fast (131K context) - Higher performance variant, faster processing but more expensive",
aliases=["grok3fast", "grokfast", "grok3-fast"],
),
}
def __init__(self, api_key: str, **kwargs):
"""Initialize X.AI provider with API key."""
# Set X.AI base URL
kwargs.setdefault("base_url", "https://api.x.ai/v1")
super().__init__(api_key, **kwargs)
def get_capabilities(self, model_name: str) -> ModelCapabilities:
"""Get capabilities for a specific X.AI model."""
# Resolve shorthand
resolved_name = self._resolve_model_name(model_name)
if resolved_name not in self.SUPPORTED_MODELS:
raise ValueError(f"Unsupported X.AI model: {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.XAI, resolved_name, model_name):
raise ValueError(f"X.AI model '{model_name}' is not allowed by restriction policy.")
# Return the ModelCapabilities object directly from SUPPORTED_MODELS
return self.SUPPORTED_MODELS[resolved_name]
def get_provider_type(self) -> ProviderType:
"""Get the provider type."""
return ProviderType.XAI
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.XAI, resolved_name, model_name):
logger.debug(f"X.AI 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 = 0.7,
max_output_tokens: Optional[int] = None,
**kwargs,
) -> ModelResponse:
"""Generate content using X.AI 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."""
# Currently GROK models do not support extended thinking
# This may change with future GROK model releases
return False
