chore: initial public snapshot for github upload
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"""
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Support for OpenAI's `/v1/chat/completions` endpoint.
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Calls done in OpenAI/openai.py as Vercel AI Gateway is openai-compatible.
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Docs: https://vercel.com/docs/ai-gateway
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"""
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from typing import List, Optional, Tuple, Union
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import httpx
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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from litellm.types.llms.openai import AllMessageValues
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from litellm.secret_managers.main import get_secret_str
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import litellm
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from ...openai.chat.gpt_transformation import OpenAIGPTConfig
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from ..common_utils import VercelAIGatewayException
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class VercelAIGatewayConfig(OpenAIGPTConfig):
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@property
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def custom_llm_provider(self) -> Optional[str]:
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return "vercel_ai_gateway"
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def get_supported_openai_params(self, model: str) -> list:
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base_params = super().get_supported_openai_params(model)
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if "extra_body" not in base_params:
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base_params.append("extra_body")
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return base_params
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def _get_openai_compatible_provider_info(
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self, api_base: Optional[str], api_key: Optional[str]
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) -> Tuple[Optional[str], Optional[str]]:
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api_base = (
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api_base
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or get_secret_str("VERCEL_AI_GATEWAY_API_BASE")
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or "https://ai-gateway.vercel.sh/v1"
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)
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user_api_key = (
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api_key
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or get_secret_str("VERCEL_AI_GATEWAY_API_KEY")
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or get_secret_str("VERCEL_OIDC_TOKEN")
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)
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return api_base, user_api_key
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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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mapped_openai_params = super().map_openai_params(
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non_default_params, optional_params, model, drop_params
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)
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# Vercel AI Gateway-only parameters
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extra_body = {}
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provider_options = non_default_params.pop("providerOptions", None)
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if provider_options is not None:
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extra_body["providerOptions"] = provider_options
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mapped_openai_params[
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"extra_body"
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] = extra_body # openai client supports `extra_body` param
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return mapped_openai_params
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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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"""
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Transform the overall request to be sent to the API.
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Returns:
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dict: The transformed request. Sent as the body of the API call.
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"""
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return super().transform_request(
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model, messages, optional_params, litellm_params, headers
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)
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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 VercelAIGatewayException(
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message=error_message,
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status_code=status_code,
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headers=headers,
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)
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def get_models(
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self, api_key: Optional[str] = None, api_base: Optional[str] = None
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) -> List[str]:
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api_base, _ = self._get_openai_compatible_provider_info(api_base, api_key)
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if api_base is None:
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api_base = "https://ai-gateway.vercel.sh/v1"
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models_url = f"{api_base}/models"
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response = litellm.module_level_client.get(url=models_url)
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if response.status_code != 200:
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raise Exception(f"Failed to get models: {response.text}")
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models = response.json()["data"]
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return [model["id"] for model in models]
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@@ -0,0 +1,5 @@
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from litellm.llms.base_llm.chat.transformation import BaseLLMException
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class VercelAIGatewayException(BaseLLMException):
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pass
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@@ -0,0 +1,176 @@
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"""
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Vercel AI Gateway Embedding API Configuration.
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This module provides the configuration for Vercel AI Gateway's Embedding API.
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Vercel AI Gateway is OpenAI-compatible and supports embeddings via the /v1/embeddings endpoint.
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Docs: https://vercel.com/docs/ai-gateway/openai-compat/embeddings
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"""
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from typing import TYPE_CHECKING, Any, Optional
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import httpx
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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
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from litellm.types.utils import EmbeddingResponse
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from litellm.utils import convert_to_model_response_object
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from ..common_utils import VercelAIGatewayException
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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 VercelAIGatewayEmbeddingConfig(BaseEmbeddingConfig):
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"""
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Configuration for Vercel AI Gateway's Embedding API.
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Reference: https://vercel.com/docs/ai-gateway/openai-compat/embeddings
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"""
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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,
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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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"""
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Validate environment and set up headers for Vercel AI Gateway API.
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Vercel AI Gateway requires:
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- Authorization header with Bearer token (API key or OIDC token)
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"""
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vercel_headers = {
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"Content-Type": "application/json",
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}
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# Add Authorization header if api_key is provided
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if api_key:
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vercel_headers["Authorization"] = f"Bearer {api_key}"
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# Merge with existing headers (user's extra_headers take priority)
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merged_headers = {**vercel_headers, **headers}
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return merged_headers
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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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"""
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Get the complete URL for Vercel AI Gateway Embedding API endpoint.
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"""
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if api_base:
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api_base = api_base.rstrip("/")
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else:
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api_base = (
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get_secret_str("VERCEL_AI_GATEWAY_API_BASE")
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or "https://ai-gateway.vercel.sh/v1"
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)
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return f"{api_base}/embeddings"
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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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"""
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Transform embedding request to Vercel AI Gateway format (OpenAI-compatible).
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"""
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# Ensure input is a list
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if isinstance(input, str):
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input = [input]
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# Strip 'vercel_ai_gateway/' prefix if present
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if model.startswith("vercel_ai_gateway/"):
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model = model.replace("vercel_ai_gateway/", "", 1)
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return {
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"model": model,
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"input": input,
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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],
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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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"""
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Transform embedding response from Vercel AI Gateway format (OpenAI-compatible).
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"""
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logging_obj.post_call(original_response=raw_response.text)
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# Vercel AI Gateway returns standard OpenAI-compatible embedding response
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response_json = raw_response.json()
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return convert_to_model_response_object(
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response_object=response_json,
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model_response_object=model_response,
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response_type="embedding",
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)
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def get_supported_openai_params(self, model: str) -> list:
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"""
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Get list of supported OpenAI parameters for Vercel AI Gateway embeddings.
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Vercel AI Gateway supports the standard OpenAI embeddings parameters
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and auto-maps 'dimensions' to each provider's expected field.
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"""
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return [
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"timeout",
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"dimensions",
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"encoding_format",
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"user",
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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 parameters to Vercel AI Gateway format.
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"""
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for param, value in non_default_params.items():
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if param in self.get_supported_openai_params(model):
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optional_params[param] = value
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return optional_params
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def get_error_class(
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self, error_message: str, status_code: int, headers: Any
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) -> Any:
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"""
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Get the error class for Vercel AI Gateway errors.
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"""
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return VercelAIGatewayException(
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message=error_message,
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status_code=status_code,
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headers=headers,
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)
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