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https://github.com/BerriAI/litellm.git
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d82fa10f93
* Add cohere v2/rerank support (#8421) * Support v2 endpoint cohere rerank * Add tests and docs * Make v1 default if old params used * Update docs * Update docs pt 2 * Update tests * Add e2e test * Clean up code * Use inheritence for new config * Fix linting issues (#8608) * Fix cohere v2 failing test + linting (#8672) * Fix test and unused imports * Fix tests * fix: fix linting errors * test: handle tgai instability * fix: skip service unavailable err * test: print logs for unstable test * test: skip unreliable tests --------- Co-authored-by: vibhavbhat <vibhavb00@gmail.com>
128 lines
3.7 KiB
Python
128 lines
3.7 KiB
Python
from abc import ABC, abstractmethod
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
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import httpx
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from litellm.types.rerank import OptionalRerankParams, RerankBilledUnits, RerankResponse
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from litellm.types.utils import ModelInfo
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from ..chat.transformation import BaseLLMException
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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 BaseRerankConfig(ABC):
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@abstractmethod
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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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api_key: Optional[str] = None,
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) -> dict:
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pass
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@abstractmethod
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def transform_rerank_request(
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self,
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model: str,
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optional_rerank_params: OptionalRerankParams,
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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_rerank_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: RerankResponse,
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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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) -> RerankResponse:
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return model_response
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@abstractmethod
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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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@abstractmethod
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def get_supported_cohere_rerank_params(self, model: str) -> list:
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pass
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@abstractmethod
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def map_cohere_rerank_params(
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self,
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non_default_params: dict,
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model: str,
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drop_params: bool,
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query: str,
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documents: List[Union[str, Dict[str, Any]]],
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custom_llm_provider: Optional[str] = None,
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top_n: Optional[int] = None,
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rank_fields: Optional[List[str]] = None,
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return_documents: Optional[bool] = True,
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max_chunks_per_doc: Optional[int] = None,
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max_tokens_per_doc: Optional[int] = None,
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) -> OptionalRerankParams:
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pass
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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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raise BaseLLMException(
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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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def calculate_rerank_cost(
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self,
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model: str,
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custom_llm_provider: Optional[str] = None,
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billed_units: Optional[RerankBilledUnits] = None,
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model_info: Optional[ModelInfo] = None,
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) -> Tuple[float, float]:
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"""
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Calculates the cost per query for a given rerank model.
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Input:
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- model: str, the model name without provider prefix
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- custom_llm_provider: str, the provider used for the model. If provided, used to check if the litellm model info is for that provider.
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- num_queries: int, the number of queries to calculate the cost for
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- model_info: ModelInfo, the model info for the given model
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Returns:
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Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
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"""
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if (
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model_info is None
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or "input_cost_per_query" not in model_info
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or model_info["input_cost_per_query"] is None
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or billed_units is None
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):
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return 0.0, 0.0
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search_units = billed_units.get("search_units")
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if search_units is None:
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return 0.0, 0.0
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prompt_cost = model_info["input_cost_per_query"] * search_units
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return prompt_cost, 0.0
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