chore: initial public snapshot for github upload
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@@ -0,0 +1,148 @@
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from typing import 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.base_llm.chat.transformation import BaseLLMException
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from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
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from litellm.types.utils import EmbeddingResponse, Usage
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class VoyageError(BaseLLMException):
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def __init__(
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self,
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status_code: int,
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message: str,
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headers: Union[dict, httpx.Headers] = {},
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):
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self.status_code = status_code
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self.message = message
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self.request = httpx.Request(
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method="POST", url="https://api.voyageai.com/v1/embeddings"
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)
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self.response = httpx.Response(status_code=status_code, request=self.request)
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super().__init__(
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status_code=status_code,
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message=message,
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headers=headers,
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)
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class VoyageEmbeddingConfig(BaseEmbeddingConfig):
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"""
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Reference: https://docs.voyageai.com/reference/embeddings-api
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"""
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def __init__(self) -> None:
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pass
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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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if api_base:
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if not api_base.endswith("/embeddings"):
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api_base = f"{api_base}/embeddings"
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return api_base
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return "https://api.voyageai.com/v1/embeddings"
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def get_supported_openai_params(self, model: str) -> list:
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return [
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"encoding_format",
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"dimensions",
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]
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def map_openai_params(
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self,
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non_default_params: dict,
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optional_params: dict,
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model: str,
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drop_params: bool,
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) -> dict:
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"""
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Map OpenAI params to Voyage params
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Reference: https://docs.voyageai.com/reference/embeddings-api
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"""
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if "encoding_format" in non_default_params:
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optional_params["encoding_format"] = non_default_params["encoding_format"]
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if "dimensions" in non_default_params:
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optional_params["output_dimension"] = non_default_params["dimensions"]
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return optional_params
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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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if api_key is None:
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api_key = (
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get_secret_str("VOYAGE_API_KEY")
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or get_secret_str("VOYAGE_AI_API_KEY")
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or get_secret_str("VOYAGE_AI_TOKEN")
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)
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return {
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"Authorization": f"Bearer {api_key}",
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}
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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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"input": input,
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"model": model,
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**optional_params,
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}
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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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try:
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raw_response_json = raw_response.json()
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except Exception:
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raise VoyageError(
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message=raw_response.text, status_code=raw_response.status_code
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)
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# model_response.usage
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model_response.model = raw_response_json.get("model")
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model_response.data = raw_response_json.get("data")
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model_response.object = raw_response_json.get("object")
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usage = Usage(
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prompt_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0),
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total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0),
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)
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model_response.usage = usage
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return model_response
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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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return VoyageError(
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message=error_message, status_code=status_code, headers=headers
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)
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@@ -0,0 +1,153 @@
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"""
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This module is used to transform the request and response for the Voyage contextualized embeddings API.
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This would be used for all the contextualized embeddings models in Voyage.
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"""
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from typing import 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.base_llm.chat.transformation import BaseLLMException
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from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
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from litellm.types.utils import EmbeddingResponse, Usage
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class VoyageError(BaseLLMException):
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def __init__(
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self,
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status_code: int,
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message: str,
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headers: Union[dict, httpx.Headers] = {},
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):
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self.status_code = status_code
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self.message = message
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self.request = httpx.Request(
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method="POST", url="https://api.voyageai.com/v1/contextualizedembeddings"
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)
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self.response = httpx.Response(status_code=status_code, request=self.request)
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super().__init__(
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status_code=status_code,
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message=message,
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headers=headers,
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)
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class VoyageContextualEmbeddingConfig(BaseEmbeddingConfig):
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"""
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Reference: https://docs.voyageai.com/reference/embeddings-api
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"""
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def __init__(self) -> None:
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pass
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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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if api_base:
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if not api_base.endswith("/contextualizedembeddings"):
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api_base = f"{api_base}/contextualizedembeddings"
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return api_base
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return "https://api.voyageai.com/v1/contextualizedembeddings"
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def get_supported_openai_params(self, model: str) -> list:
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return ["encoding_format", "dimensions"]
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def map_openai_params(
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self,
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non_default_params: dict,
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optional_params: dict,
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model: str,
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drop_params: bool,
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) -> dict:
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"""
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Map OpenAI params to Voyage params
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Reference: https://docs.voyageai.com/reference/contextualized-embeddings-api
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"""
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if "encoding_format" in non_default_params:
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optional_params["encoding_format"] = non_default_params["encoding_format"]
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if "dimensions" in non_default_params:
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optional_params["output_dimension"] = non_default_params["dimensions"]
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return optional_params
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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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if api_key is None:
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api_key = (
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get_secret_str("VOYAGE_API_KEY")
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or get_secret_str("VOYAGE_AI_API_KEY")
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or get_secret_str("VOYAGE_AI_TOKEN")
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)
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return {
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"Authorization": f"Bearer {api_key}",
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}
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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[AllEmbeddingInputValues, List[List[str]]],
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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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"inputs": input,
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"model": model,
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**optional_params,
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}
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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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try:
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raw_response_json = raw_response.json()
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except Exception:
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raise VoyageError(
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message=raw_response.text, status_code=raw_response.status_code
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)
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# model_response.usage
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model_response.model = raw_response_json.get("model")
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model_response.data = raw_response_json.get("data")
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model_response.object = raw_response_json.get("object")
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usage = Usage(
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prompt_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0),
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total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0),
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)
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model_response.usage = usage
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return model_response
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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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return VoyageError(
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message=error_message, status_code=status_code, headers=headers
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)
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@staticmethod
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def is_contextualized_embeddings(model: str) -> bool:
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return "context" in model.lower()
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