chore: initial public snapshot for github upload
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"""
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Volcengine Embedding Transformation
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Transforms OpenAI embedding requests to Volcengine format
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"""
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from typing import List, Optional, Union, Dict, Any
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import httpx
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from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
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from litellm.types.utils import EmbeddingResponse
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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from ..common_utils import get_volcengine_base_url, get_volcengine_headers
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class VolcEngineEmbeddingConfig(BaseEmbeddingConfig):
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"""
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Configuration class for Volcengine embedding models.
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Reference: https://ark.cn-beijing.volces.com/api/v3/embeddings
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"""
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def __init__(
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self,
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encoding_format: Optional[str] = None,
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) -> None:
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locals_ = locals().copy()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return super().get_config()
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def get_supported_openai_params(self, model: str) -> List[str]:
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"""
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Get the list of OpenAI parameters supported by Volcengine embedding models.
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Args:
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model: The model name
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Returns:
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List of supported parameter names
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"""
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return [
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"encoding_format",
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"user",
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"extra_headers",
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]
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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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Get the complete URL for volcengine embedding API calls.
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Args:
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api_base: Optional custom API base URL
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api_key: API key (not used for URL construction)
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model: Model name (not used for URL construction)
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optional_params: Optional parameters (not used for URL construction)
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litellm_params: LiteLLM parameters (not used for URL construction)
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stream: Stream parameter (not used for URL construction)
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Returns:
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Complete URL for the embedding API endpoint
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"""
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base_url = get_volcengine_base_url(api_base)
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# Construct the complete URL with /embeddings endpoint
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if base_url.endswith("/api/v3"):
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return f"{base_url}/embeddings"
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else:
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return f"{base_url}/api/v3/embeddings"
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def map_openai_params(
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self,
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non_default_params: Dict[str, Any],
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optional_params: Dict[str, Any],
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model: str,
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drop_params: bool,
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) -> Dict[str, Any]:
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"""
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Map OpenAI embedding parameters to Volcengine format.
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Args:
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non_default_params: Parameters that are not default values
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optional_params: Optional parameters dict to update
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model: The model name
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drop_params: Whether to drop unsupported parameters
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Returns:
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Updated optional_params dict
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"""
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for param, value in non_default_params.items():
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if param == "encoding_format":
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# Volcengine supports: float, base64, null
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if value in ["float", "base64", None]:
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optional_params["encoding_format"] = value
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else:
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if not drop_params:
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raise ValueError(
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f"Unsupported encoding_format: {value}. Volcengine supports: float, base64, null"
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)
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elif param == "user":
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# Keep user parameter as-is
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optional_params["user"] = value
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elif param in self.get_supported_openai_params(model):
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optional_params[param] = value
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elif not drop_params:
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raise ValueError(f"Unsupported parameter for Volcengine: {param}")
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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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"""Transform embedding request to Volcengine format"""
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# Prepare request data (only the JSON body, not the full request)
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data = {
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"model": model,
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"input": input if isinstance(input, list) else [input],
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}
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# Add optional parameters from optional_params
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if "encoding_format" in optional_params:
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encoding_format = optional_params["encoding_format"]
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if encoding_format is not None:
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data["encoding_format"] = encoding_format
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if "user" in optional_params:
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user = optional_params["user"]
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if user is not None:
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data["user"] = user
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return data
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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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"""Transform Volcengine response to EmbeddingResponse"""
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try:
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response_json = raw_response.json()
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except Exception as e:
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raise ValueError(f"Failed to parse Volcengine response as JSON: {str(e)}")
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# Volcengine response format matches OpenAI format closely
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# Just need to ensure all required fields are present
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transformed_response = {
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"object": "list",
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"data": response_json.get("data", []),
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"model": response_json.get("model", model),
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"usage": response_json.get("usage", {}),
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}
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# Add id if present
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if "id" in response_json:
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transformed_response["id"] = response_json["id"]
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# Create EmbeddingResponse from transformed data
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return EmbeddingResponse(**transformed_response)
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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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"""Validate environment and return headers"""
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# Get Volcengine headers
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if api_key is None:
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raise ValueError("api_key is required for Volcengine authentication")
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volcengine_headers = get_volcengine_headers(api_key)
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return {**headers, **volcengine_headers}
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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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"""Get error class for Volcengine errors"""
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from ..common_utils import VolcEngineError
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# Convert dict to httpx.Headers if needed
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if isinstance(headers, dict):
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headers = httpx.Headers(headers)
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return VolcEngineError(
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status_code=status_code,
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message=error_message,
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headers=headers,
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)
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