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* refactor: initial commit moving watsonx_text to base_llm_http_handler + clarifying new provider directory structure * refactor(watsonx/completion/handler.py): move to using base llm http handler removes 'requests' library usage * fix(watsonx_text/transformation.py): fix result transformation migrates to transformation.py, for usage with base llm http handler * fix(streaming_handler.py): migrate watsonx streaming to transformation.py ensures streaming works with base llm http handler * fix(streaming_handler.py): fix streaming linting errors and remove watsonx conditional logic * fix(watsonx/): fix chat route post completion route refactor * refactor(watsonx/embed): refactor watsonx to use base llm http handler for embedding calls as well * refactor(base.py): remove requests library usage from litellm * build(pyproject.toml): remove requests library usage * fix: fix linting errors * fix: fix linting errors * fix(types/utils.py): fix validation errors for modelresponsestream * fix(replicate/handler.py): fix linting errors * fix(litellm_logging.py): handle modelresponsestream object * fix(streaming_handler.py): fix modelresponsestream args * fix: remove unused imports * test: fix test * fix: fix test * test: fix test * test: fix tests * test: fix test * test: fix patch target * test: fix test
87 lines
2.4 KiB
Python
87 lines
2.4 KiB
Python
from abc import ABC, abstractmethod
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from typing import TYPE_CHECKING, Any, List, Optional
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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 AllEmbeddingInputValues, 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: AllEmbeddingInputValues,
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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],
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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(
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self,
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api_base: Optional[str],
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model: str,
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optional_params: dict,
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stream: Optional[bool] = None,
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) -> 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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