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refactor: remove unused function
This commit is contained in:
Krrish Dholakia
parent
0a60390521
commit
c051ab5b5a
3 changed files with 2 additions and 212 deletions
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@ -4,12 +4,12 @@ Support for Volcengine (ByteDance) chat and embedding models
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"""
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from .chat.transformation import VolcEngineChatConfig
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from .embedding import VolcEngineEmbeddingHandler, VolcEngineEmbeddingConfig
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from .common_utils import (
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VolcEngineError,
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get_volcengine_base_url,
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get_volcengine_headers,
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)
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from .embedding import VolcEngineEmbeddingConfig
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# For backward compatibility, keep the old class name
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VolcEngineConfig = VolcEngineChatConfig
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@ -17,7 +17,6 @@ VolcEngineConfig = VolcEngineChatConfig
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__all__ = [
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"VolcEngineChatConfig",
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"VolcEngineConfig", # backward compatibility
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"VolcEngineEmbeddingHandler",
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"VolcEngineEmbeddingConfig",
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"VolcEngineError",
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"get_volcengine_base_url",
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@ -2,7 +2,6 @@
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Volcengine Embedding Module
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"""
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from .handler import VolcEngineEmbeddingHandler
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from .transformation import VolcEngineEmbeddingConfig
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__all__ = ["VolcEngineEmbeddingHandler", "VolcEngineEmbeddingConfig"]
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__all__ = ["VolcEngineEmbeddingConfig"]
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@ -1,208 +0,0 @@
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"""
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Volcengine Embedding Handler
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Handles embedding requests to Volcengine's embedding API
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"""
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from typing import Dict, 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.custom_httpx.http_handler import HTTPHandler, AsyncHTTPHandler
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from litellm.types.utils import EmbeddingResponse
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import litellm
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from .transformation import VolcEngineEmbeddingConfig
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from ..common_utils import VolcEngineError
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class VolcEngineEmbeddingHandler:
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"""Handler for Volcengine embedding API calls"""
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def __init__(self):
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self.config = VolcEngineEmbeddingConfig()
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def _convert_to_litellm_response(self, transformed_response: Dict, model: str, input: Union[str, List[str]]) -> EmbeddingResponse:
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"""Convert transformed response to LiteLLM EmbeddingResponse"""
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model_response = EmbeddingResponse()
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model_response.object = transformed_response.get("object", "list")
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model_response.data = transformed_response.get("data", [])
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model_response.model = transformed_response.get("model", model)
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# Set usage information
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usage_data = transformed_response.get("usage", {})
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if usage_data:
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model_response.usage = litellm.Usage(
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prompt_tokens=usage_data.get("prompt_tokens", 0),
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completion_tokens=0,
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total_tokens=usage_data.get("total_tokens", usage_data.get("prompt_tokens", 0)),
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prompt_tokens_details=None,
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completion_tokens_details=None,
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)
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return model_response
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def embedding(
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self,
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model: str,
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input: Union[str, List[str]],
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api_key: str,
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api_base: Optional[str] = None,
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encoding_format: Optional[str] = "float",
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user: Optional[str] = None,
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timeout: Optional[Union[float, httpx.Timeout]] = None,
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extra_headers: Optional[Dict[str, str]] = None,
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litellm_logging_obj: Optional[LiteLLMLoggingObj] = None,
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**kwargs,
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) -> EmbeddingResponse:
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"""
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Synchronous embedding call to Volcengine API.
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Args:
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model: Volcengine model ID (e.g., "doubao-embedding-text-240715")
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input: Text or list of texts to embed
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api_key: Volcengine API key
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api_base: Optional custom API base URL
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encoding_format: Response format (float, base64, null)
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user: Optional user identifier
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timeout: Request timeout
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extra_headers: Optional additional headers
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litellm_logging_obj: Optional logging object
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**kwargs: Additional parameters
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Returns:
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EmbeddingResponse object
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"""
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# Transform request to Volcengine format
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request_data = self.config.transform_request(
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model=model,
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input=input,
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api_key=api_key,
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api_base=api_base,
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encoding_format=encoding_format,
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user=user,
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extra_headers=extra_headers,
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**kwargs,
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)
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# Make HTTP request
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try:
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client = HTTPHandler(timeout=timeout)
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response = client.post(
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url=request_data["url"],
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headers=request_data["headers"],
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json=request_data["data"],
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)
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except Exception as e:
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raise VolcEngineError(
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status_code=500,
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message=f"Network error during embedding request: {str(e)}",
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)
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# Handle HTTP errors
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if response.status_code != 200:
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error_message = f"Volcengine embedding request failed with status {response.status_code}"
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try:
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error_details = response.json()
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if "error" in error_details:
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error_message += f": {error_details['error']}"
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elif "message" in error_details:
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error_message += f": {error_details['message']}"
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except Exception:
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error_message += f": {response.text}"
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raise VolcEngineError(
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status_code=response.status_code,
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message=error_message,
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headers=response.headers,
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)
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# Transform response to OpenAI format
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transformed_response = self.config.transform_response(
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response=response, model=model, input=input, encoding=encoding_format
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)
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# Convert to LiteLLM EmbeddingResponse
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return self._convert_to_litellm_response(transformed_response, model, input)
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async def async_embedding(
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self,
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model: str,
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input: Union[str, List[str]],
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api_key: str,
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api_base: Optional[str] = None,
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encoding_format: Optional[str] = "float",
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user: Optional[str] = None,
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timeout: Optional[Union[float, httpx.Timeout]] = None,
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extra_headers: Optional[Dict[str, str]] = None,
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litellm_logging_obj: Optional[LiteLLMLoggingObj] = None,
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**kwargs,
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) -> EmbeddingResponse:
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"""
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Asynchronous embedding call to Volcengine API.
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Args:
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model: Volcengine model ID (e.g., "doubao-embedding-text-240715")
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input: Text or list of texts to embed
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api_key: Volcengine API key
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api_base: Optional custom API base URL
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encoding_format: Response format (float, base64, null)
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user: Optional user identifier
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timeout: Request timeout
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extra_headers: Optional additional headers
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litellm_logging_obj: Optional logging object
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**kwargs: Additional parameters
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Returns:
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EmbeddingResponse object
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"""
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# Transform request to Volcengine format
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request_data = self.config.transform_request(
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model=model,
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input=input,
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api_key=api_key,
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api_base=api_base,
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encoding_format=encoding_format,
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user=user,
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extra_headers=extra_headers,
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**kwargs,
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)
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# Make async HTTP request
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try:
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client = AsyncHTTPHandler(timeout=timeout)
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response = await client.post(
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url=request_data["url"],
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headers=request_data["headers"],
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json=request_data["data"],
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)
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except Exception as e:
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raise VolcEngineError(
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status_code=500,
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message=f"Network error during embedding request: {str(e)}",
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)
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# Handle HTTP errors
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if response.status_code != 200:
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error_message = f"Volcengine embedding request failed with status {response.status_code}"
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try:
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error_details = response.json()
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if "error" in error_details:
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error_message += f": {error_details['error']}"
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elif "message" in error_details:
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error_message += f": {error_details['message']}"
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except Exception:
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error_message += f": {response.text}"
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raise VolcEngineError(
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status_code=response.status_code,
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message=error_message,
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headers=response.headers,
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)
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# Transform response to OpenAI format
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transformed_response = self.config.transform_response(
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response=response, model=model, input=input, encoding=encoding_format
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)
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# Convert to LiteLLM EmbeddingResponse
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return self._convert_to_litellm_response(transformed_response, model, input)
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