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57bc03b30b
* add BaseReasoningEffortTests * BaseReasoningLLMTests * fix test rename * docs update thinking / reasoning content docs
90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
from typing import List, Optional, Tuple
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import litellm
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from litellm._logging import verbose_logger
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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strip_name_from_messages,
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)
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import AllMessageValues
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from ...openai.chat.gpt_transformation import OpenAIGPTConfig
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XAI_API_BASE = "https://api.x.ai/v1"
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class XAIChatConfig(OpenAIGPTConfig):
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@property
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def custom_llm_provider(self) -> Optional[str]:
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return "xai"
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def _get_openai_compatible_provider_info(
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self, api_base: Optional[str], api_key: Optional[str]
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) -> Tuple[Optional[str], Optional[str]]:
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api_base = api_base or get_secret_str("XAI_API_BASE") or XAI_API_BASE # type: ignore
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dynamic_api_key = api_key or get_secret_str("XAI_API_KEY")
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return api_base, dynamic_api_key
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def get_supported_openai_params(self, model: str) -> list:
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base_openai_params = [
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"frequency_penalty",
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"logit_bias",
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"logprobs",
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"max_tokens",
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"n",
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"presence_penalty",
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"response_format",
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"seed",
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"stop",
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"stream",
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"stream_options",
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"temperature",
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"tool_choice",
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"tools",
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"top_logprobs",
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"top_p",
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"user",
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]
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try:
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if litellm.supports_reasoning(
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model=model, custom_llm_provider=self.custom_llm_provider
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):
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base_openai_params.append("reasoning_effort")
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except Exception as e:
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verbose_logger.debug(f"Error checking if model supports reasoning: {e}")
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return base_openai_params
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def map_openai_params(
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self,
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non_default_params: dict,
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optional_params: dict,
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model: str,
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drop_params: bool = False,
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) -> dict:
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supported_openai_params = self.get_supported_openai_params(model=model)
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for param, value in non_default_params.items():
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if param == "max_completion_tokens":
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optional_params["max_tokens"] = value
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elif param in supported_openai_params:
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if value is not None:
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optional_params[param] = value
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return optional_params
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def transform_request(
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self,
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model: str,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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headers: dict,
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) -> dict:
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"""
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Handle https://github.com/BerriAI/litellm/issues/9720
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Filter out 'name' from messages
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"""
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messages = strip_name_from_messages(messages)
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return super().transform_request(
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model, messages, optional_params, litellm_params, headers
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)
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