mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 11:14:04 +00:00
c7b288ce30
* VoyageEmbeddingConfig * fix voyage logic to get params * add voyage embedding transformation * add get_provider_embedding_config * use BaseEmbeddingConfig * voyage clean up * use llm http handler for embedding transformations * test_voyage_ai_embedding_extra_params * add voyage async * test_voyage_ai_embedding_extra_params * add async for llm http handler * update BaseLLMEmbeddingTest * test_voyage_ai_embedding_extra_params * fix linting * fix get_provider_embedding_config * fix anthropic text test * update location of base/chat/transformation * fix import path * fix IBMWatsonXAIConfig
94 lines
2.4 KiB
Python
94 lines
2.4 KiB
Python
import types
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from abc import ABC, abstractmethod
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from typing import (
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TYPE_CHECKING,
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Any,
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AsyncIterator,
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Callable,
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Dict,
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Iterator,
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List,
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Optional,
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TypedDict,
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Union,
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)
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import httpx
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from litellm.llms.base_llm.chat.transformation import BaseConfig
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from litellm.types.llms.openai import AllMessageValues
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from litellm.types.utils import EmbeddingResponse, ModelResponse
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
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LiteLLMLoggingObj = _LiteLLMLoggingObj
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else:
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LiteLLMLoggingObj = Any
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class BaseEmbeddingConfig(BaseConfig, ABC):
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@abstractmethod
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def transform_embedding_request(
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self,
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model: str,
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input: Union[str, List[str], List[float], List[List[float]]],
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optional_params: dict,
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headers: dict,
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) -> dict:
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return {}
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@abstractmethod
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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] = None,
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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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return model_response
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def get_complete_url(self, api_base: Optional[str], model: str) -> str:
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"""
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OPTIONAL
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Get the complete url for the request
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Some providers need `model` in `api_base`
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"""
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return api_base or ""
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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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raise NotImplementedError(
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"EmbeddingConfig does not need a request transformation for chat models"
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)
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def transform_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: ModelResponse,
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logging_obj: LiteLLMLoggingObj,
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request_data: dict,
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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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encoding: Any,
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api_key: Optional[str] = None,
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json_mode: Optional[bool] = None,
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) -> ModelResponse:
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raise NotImplementedError(
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"EmbeddingConfig does not need a response transformation for chat models"
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)
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