chore: initial public snapshot for github upload
This commit is contained in:
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@@ -0,0 +1,160 @@
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"""
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Support for OVHCloud AI Endpoints `/v1/audio/transcriptions` endpoint.
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Our unified API follows the OpenAI standard.
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More information on our website: https://endpoints.ai.cloud.ovh.net
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"""
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from typing import List, Optional, Union
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import httpx
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from litellm.litellm_core_utils.audio_utils.utils import process_audio_file
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from litellm.llms.base_llm.audio_transcription.transformation import (
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AudioTranscriptionRequestData,
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BaseAudioTranscriptionConfig,
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)
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import (
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AllMessageValues,
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OpenAIAudioTranscriptionOptionalParams,
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)
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from litellm.types.utils import FileTypes, TranscriptionResponse
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from ..utils import OVHCloudException
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class OVHCloudAudioTranscriptionConfig(BaseAudioTranscriptionConfig):
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def get_supported_openai_params(
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self, model: str
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) -> List[OpenAIAudioTranscriptionOptionalParams]:
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# OVHCloud implements the OpenAI-compatible Whisper interface.
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# We pass through the same optional params as the OpenAI Whisper API.
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return [
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"language",
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"prompt",
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"response_format",
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"timestamp_granularities",
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"temperature",
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]
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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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supported_params = self.get_supported_openai_params(model)
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for k, v in non_default_params.items():
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if k in supported_params:
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optional_params[k] = v
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return optional_params
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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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api_base = (
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"https://oai.endpoints.kepler.ai.cloud.ovh.net/v1"
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if api_base is None
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else api_base.rstrip("/")
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)
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complete_url = f"{api_base}/audio/transcriptions"
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return complete_url
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def get_error_class(
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self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
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) -> BaseLLMException:
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return OVHCloudException(
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message=error_message,
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status_code=status_code,
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headers=headers,
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)
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def validate_environment(
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self,
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headers: dict,
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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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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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) -> dict:
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if api_key is None:
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api_key = get_secret_str("OVHCLOUD_API_KEY")
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default_headers = {
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"Authorization": f"Bearer {api_key}",
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"accept": "application/json",
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}
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# Caller can override / extend headers if needed
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default_headers.update(headers or {})
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return default_headers
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def transform_audio_transcription_request(
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self,
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model: str,
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audio_file: FileTypes,
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optional_params: dict,
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litellm_params: dict,
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) -> AudioTranscriptionRequestData:
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"""
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Transform the audio transcription request into OpenAI-compatible form-data.
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OVHCloud follows OpenAI's `/audio/transcriptions` format, so we:
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- Build a multipart form-data body with `file`, `model`, and optional params
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- Let the shared HTTP handler set the proper content-type boundary
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"""
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processed_audio = process_audio_file(audio_file)
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# Base form fields: model + OpenAI-compatible optional params
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form_fields: dict = {
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"model": model,
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}
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# Include OpenAI-compatible optional params
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for key in self.get_supported_openai_params(model):
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value = optional_params.get(key)
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if value is not None:
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form_fields[key] = value
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files = {
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"file": (
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processed_audio.filename,
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processed_audio.file_content,
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processed_audio.content_type,
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)
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}
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return AudioTranscriptionRequestData(data=form_fields, files=files)
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def transform_audio_transcription_response(
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self,
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raw_response: httpx.Response,
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) -> TranscriptionResponse:
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"""
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Transform OVHCloud audio transcription response to OpenAI-compatible TranscriptionResponse.
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"""
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try:
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response_json = raw_response.json()
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except Exception:
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raise OVHCloudException(
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message=raw_response.text,
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status_code=raw_response.status_code,
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headers=raw_response.headers,
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)
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text = response_json.get("text") or response_json.get("transcript") or ""
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response = TranscriptionResponse(text=text)
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response._hidden_params = response_json
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return response
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@@ -0,0 +1,148 @@
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"""
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Support for OVHCloud AI Endpoints `/v1/chat/completions` endpoint.
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Our unified API follows the OpenAI standard.
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More information on our website: https://endpoints.ai.cloud.ovh.net
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"""
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from typing import Optional, Union, List
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import httpx
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from litellm.utils import ModelResponseStream, get_model_info
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from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
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from litellm._logging import verbose_logger
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from litellm.llms.ovhcloud.utils import OVHCloudException
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from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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from litellm.types.llms.openai import AllMessageValues
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class OVHCloudChatConfig(OpenAIGPTConfig):
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@property
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def custom_llm_provider(self) -> Optional[str]:
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return "ovhcloud"
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def get_supported_openai_params(self, model: str) -> list:
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"""
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Details about function calling support can be found here:
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https://help.ovhcloud.com/csm/en-gb-public-cloud-ai-endpoints-function-calling?id=kb_article_view&sysparm_article=KB0071907
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"""
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supports_function_calling: Optional[bool] = None
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try:
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model_info = get_model_info(model, custom_llm_provider="ovhcloud")
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supports_function_calling = model_info.get(
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"supports_function_calling", False
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)
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except Exception as e:
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verbose_logger.debug(f"Error getting supported OpenAI params: {e}")
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pass
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optional_params = super().get_supported_openai_params(model)
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if supports_function_calling is not True:
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verbose_logger.debug(
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"You can see our models supporting function_calling in our catalog: https://endpoints.ai.cloud.ovh.net/catalog "
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)
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optional_params.remove("tools")
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optional_params.remove("tool_choice")
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optional_params.remove("function_call")
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optional_params.remove("response_format")
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return optional_params
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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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api_base = (
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"https://oai.endpoints.kepler.ai.cloud.ovh.net/v1"
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if api_base is None
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else api_base.rstrip("/")
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)
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complete_url = f"{api_base}/chat/completions"
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return complete_url
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def get_error_class(
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self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
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) -> BaseLLMException:
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return OVHCloudException(
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message=error_message,
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status_code=status_code,
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headers=headers,
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)
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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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mapped_openai_params = super().map_openai_params(
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non_default_params, optional_params, model, drop_params
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)
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return mapped_openai_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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extra_body = optional_params.pop("extra_body", {})
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response = super().transform_request(
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model, messages, optional_params, litellm_params, headers
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)
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response.update(extra_body)
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return response
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class OVHCloudChatCompletionStreamingHandler(BaseModelResponseIterator):
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"""
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Handler for OVHCloud AI Endpoints streaming chat completion responses
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"""
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def chunk_parser(self, chunk: dict) -> ModelResponseStream:
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"""
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Parse individual chunks from streaming response
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"""
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try:
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if "error" in chunk:
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error_chunk = chunk["error"]
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error_message = "OVHCloud Error: {}".format(
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error_chunk.get("message", "Unknown error")
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)
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raise OVHCloudException(
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message=error_message,
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status_code=error_chunk.get("code", 400),
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headers={"Content-Type": "application/json"},
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)
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new_choices = []
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for choice in chunk["choices"]:
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if "delta" in choice and "reasoning" in choice["delta"]:
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choice["delta"]["reasoning_content"] = choice["delta"].get(
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"reasoning"
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)
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new_choices.append(choice)
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return ModelResponseStream(
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id=chunk["id"],
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object="chat.completion.chunk",
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created=chunk["created"],
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usage=chunk.get("usage"),
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model=chunk["model"],
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choices=new_choices,
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)
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except KeyError as e:
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raise OVHCloudException(
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message=f"KeyError: {e}, Got unexpected response from CometAPI: {chunk}",
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status_code=400,
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headers={"Content-Type": "application/json"},
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)
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except Exception as e:
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raise e
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@@ -0,0 +1,126 @@
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"""
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This is OpenAI compatible - no transformation is applied
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"""
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from typing import List, Optional, Union
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import httpx
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
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from litellm.types.utils import EmbeddingResponse, Usage
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from ..utils import OVHCloudException
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class OVHCloudEmbeddingConfig(BaseEmbeddingConfig):
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def __init__(self) -> None:
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pass
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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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api_base = (
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"https://oai.endpoints.kepler.ai.cloud.ovh.net/v1"
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if api_base is None
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else api_base.rstrip("/")
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)
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complete_url = f"{api_base}/embeddings"
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return complete_url
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def validate_environment(
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self,
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headers: dict,
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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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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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) -> dict:
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if api_key is None:
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api_key = get_secret_str("OVHCLOUD_API_KEY")
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default_headers = {
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"Authorization": f"Bearer {api_key}",
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"accept": "application/json",
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"Content-Type": "application/json",
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}
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if "Authorization" in headers:
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default_headers["Authorization"] = headers["Authorization"]
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return {**default_headers, **headers}
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def get_supported_openai_params(self, model: str):
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return []
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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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):
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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 in supported_openai_params:
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optional_params[param] = value
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return optional_params
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def transform_embedding_request(
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self,
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model: str,
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input: AllEmbeddingInputValues,
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optional_params: dict,
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headers: dict,
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) -> dict:
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return {"input": input, "model": model, **optional_params}
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def transform_embedding_response(
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self,
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model: str,
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raw_response: httpx.Response,
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model_response: EmbeddingResponse,
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logging_obj: LiteLLMLoggingObj,
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api_key: Optional[str],
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request_data: dict,
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optional_params: dict,
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litellm_params: dict,
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) -> EmbeddingResponse:
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try:
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raw_response_json = raw_response.json()
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except Exception:
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raise OVHCloudException(
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message=raw_response.text,
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status_code=raw_response.status_code,
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headers=raw_response.headers,
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)
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model_response.model = raw_response_json.get("model")
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model_response.data = raw_response_json.get("data")
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model_response.object = raw_response_json.get("object")
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usage = Usage(
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prompt_tokens=raw_response_json.get("usage", {}).get("prompt_tokens", 0),
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total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0),
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)
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model_response.usage = usage
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return model_response
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def get_error_class(
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self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
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) -> BaseLLMException:
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return OVHCloudException(
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message=error_message, status_code=status_code, headers=headers
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)
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@@ -0,0 +1,7 @@
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
|
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|
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|
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class OVHCloudException(BaseLLMException):
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"""OVHCloud AI Endpoints exception handling class"""
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pass
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