chore: initial snapshot for gitea/github upload
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"""
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Translates from OpenAI's `/v1/chat/completions` to Moonshot AI's `/v1/chat/completions`
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"""
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from typing import Any, Coroutine, List, Literal, Optional, Tuple, Union, overload
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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handle_messages_with_content_list_to_str_conversion,
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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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class MoonshotChatConfig(OpenAIGPTConfig):
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@overload
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def _transform_messages(
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self, messages: List[AllMessageValues], model: str, is_async: Literal[True]
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) -> Coroutine[Any, Any, List[AllMessageValues]]:
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...
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@overload
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def _transform_messages(
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self,
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messages: List[AllMessageValues],
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model: str,
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is_async: Literal[False] = False,
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) -> List[AllMessageValues]:
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...
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def _transform_messages(
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self, messages: List[AllMessageValues], model: str, is_async: bool = False
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) -> Union[List[AllMessageValues], Coroutine[Any, Any, List[AllMessageValues]]]:
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"""
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Moonshot text-only models don't support content in list format.
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Multimodal models (kimi-k2.5, kimi-latest, etc.) accept the
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standard OpenAI content array with non-text blocks (image_url,
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input_audio, video_url, file, etc.).
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If any message contains a non-text content part, skip flattening
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so the multimodal payload is preserved.
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"""
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has_non_text = False
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for m in messages:
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_content = m.get("content")
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if _content and isinstance(_content, list):
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if any(c.get("type") != "text" for c in _content):
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has_non_text = True
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break
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if not has_non_text:
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messages = handle_messages_with_content_list_to_str_conversion(messages)
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if is_async:
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return super()._transform_messages(
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messages=messages, model=model, is_async=True
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)
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else:
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return super()._transform_messages(
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messages=messages, model=model, is_async=False
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)
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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 = (
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api_base
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or get_secret_str("MOONSHOT_API_BASE")
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or "https://api.moonshot.ai/v1"
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) # type: ignore
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dynamic_api_key = api_key or get_secret_str("MOONSHOT_API_KEY")
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return api_base, dynamic_api_key
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def get_complete_url(
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self,
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api_base: Optional[str],
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api_key: Optional[str],
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model: str,
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optional_params: dict,
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litellm_params: dict,
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stream: Optional[bool] = None,
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) -> str:
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"""
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If api_base is not provided, use the default Moonshot AI /chat/completions endpoint.
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"""
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if not api_base:
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api_base = "https://api.moonshot.ai/v1"
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if not api_base.endswith("/chat/completions"):
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api_base = f"{api_base}/chat/completions"
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return api_base
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def get_supported_openai_params(self, model: str) -> list:
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"""
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Get the supported OpenAI params for Moonshot AI models
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Moonshot AI limitations:
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- functions parameter is not supported (use tools instead)
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- tool_choice doesn't support "required" value
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- kimi-thinking-preview doesn't support tool calls at all
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"""
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excluded_params: List[str] = ["functions"]
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# kimi-thinking-preview has additional limitations
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if "kimi-thinking-preview" in model:
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excluded_params.extend(["tools", "tool_choice"])
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base_openai_params = super().get_supported_openai_params(model=model)
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final_params: List[str] = []
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for param in base_openai_params:
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if param not in excluded_params:
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final_params.append(param)
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return final_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,
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) -> dict:
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"""
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Map OpenAI parameters to Moonshot AI parameters
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Handles Moonshot AI specific limitations:
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- tool_choice doesn't support "required" value
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- Temperature <0.3 limitation for n>1
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"""
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supported_openai_params = self.get_supported_openai_params(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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optional_params[param] = value
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##########################################
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# temperature limitations
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# 1. `temperature` on KIMI API is [0, 1] but OpenAI is [0, 2]
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# 2. If temperature < 0.3 and n > 1, KIMI will raise an exception.
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# If we enter this condition, we set the temperature to 0.3 as suggested by Moonshot AI
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##########################################
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if "temperature" in optional_params:
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if optional_params["temperature"] > 1:
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optional_params["temperature"] = 1
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if optional_params["temperature"] < 0.3 and optional_params.get("n", 1) > 1:
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optional_params["temperature"] = 0.3
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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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Transform the overall request to be sent to the API.
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Returns:
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dict: The transformed request. Sent as the body of the API call.
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"""
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# Add tool_choice="required" message if needed
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if optional_params.get("tool_choice", None) == "required":
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messages = self._add_tool_choice_required_message(
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messages=messages,
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optional_params=optional_params,
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)
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# Call parent transform_request which handles _transform_messages
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return super().transform_request(
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model=model,
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messages=messages,
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optional_params=optional_params,
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litellm_params=litellm_params,
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headers=headers,
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)
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def _add_tool_choice_required_message(
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self, messages: List[AllMessageValues], optional_params: dict
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) -> List[AllMessageValues]:
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"""
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Add a message to the messages list to indicate that the tool choice is required.
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https://platform.moonshot.ai/docs/guide/migrating-from-openai-to-kimi#about-tool_choice
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"""
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messages.append(
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{
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"role": "user",
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"content": "Please select a tool to handle the current issue.", # Usually, the Kimi large language model understands the intention to invoke a tool and selects one for invocation
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}
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)
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optional_params.pop("tool_choice")
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return messages
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