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from typing import Literal, Optional, Union
import httpx
import litellm
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObject
from litellm.llms.custom_httpx.http_handler import (
AsyncHTTPHandler,
HTTPHandler,
_get_httpx_client,
get_async_httpx_client,
)
from litellm.llms.vertex_ai.vertex_ai_non_gemini import VertexAIError
from litellm.llms.vertex_ai.vertex_llm_base import VertexBase
from litellm.types.llms.vertex_ai import *
from litellm.types.utils import EmbeddingResponse
from .types import *
class VertexEmbedding(VertexBase):
def __init__(self) -> None:
super().__init__()
def embedding(
self,
model: str,
input: Union[list, str],
print_verbose,
model_response: EmbeddingResponse,
optional_params: dict,
logging_obj: LiteLLMLoggingObject,
custom_llm_provider: Literal[
"vertex_ai", "vertex_ai_beta", "gemini"
], # if it's vertex_ai or gemini (google ai studio)
timeout: Optional[Union[float, httpx.Timeout]],
api_key: Optional[str] = None,
encoding=None,
aembedding=False,
api_base: Optional[str] = None,
client: Optional[Union[AsyncHTTPHandler, HTTPHandler]] = None,
vertex_project: Optional[str] = None,
vertex_location: Optional[str] = None,
vertex_credentials: Optional[str] = None,
gemini_api_key: Optional[str] = None,
extra_headers: Optional[dict] = None,
) -> EmbeddingResponse:
if aembedding is True:
return self.async_embedding( # type: ignore
model=model,
input=input,
logging_obj=logging_obj,
model_response=model_response,
optional_params=optional_params,
encoding=encoding,
custom_llm_provider=custom_llm_provider,
timeout=timeout,
api_base=api_base,
vertex_project=vertex_project,
vertex_location=vertex_location,
vertex_credentials=vertex_credentials,
gemini_api_key=gemini_api_key,
extra_headers=extra_headers,
)
should_use_v1beta1_features = self.is_using_v1beta1_features(
optional_params=optional_params
)
_auth_header, vertex_project = self._ensure_access_token(
credentials=vertex_credentials,
project_id=vertex_project,
custom_llm_provider=custom_llm_provider,
)
auth_header, api_base = self._get_token_and_url(
model=model,
gemini_api_key=gemini_api_key,
auth_header=_auth_header,
vertex_project=vertex_project,
vertex_location=vertex_location,
vertex_credentials=vertex_credentials,
stream=False,
custom_llm_provider=custom_llm_provider,
api_base=api_base,
should_use_v1beta1_features=should_use_v1beta1_features,
mode="embedding",
)
headers = self.set_headers(auth_header=auth_header, extra_headers=extra_headers)
vertex_request: VertexEmbeddingRequest = (
litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request(
input=input, optional_params=optional_params, model=model
)
)
_client_params = {}
if timeout:
_client_params["timeout"] = timeout
if client is None or not isinstance(client, HTTPHandler):
client = _get_httpx_client(params=_client_params)
else:
client = client # type: ignore
## LOGGING
logging_obj.pre_call(
input=vertex_request,
api_key="",
additional_args={
"complete_input_dict": vertex_request,
"api_base": api_base,
"headers": headers,
},
)
try:
response = client.post(api_base, headers=headers, json=vertex_request) # type: ignore
response.raise_for_status()
except httpx.HTTPStatusError as err:
error_code = err.response.status_code
raise VertexAIError(status_code=error_code, message=err.response.text)
except httpx.TimeoutException:
raise VertexAIError(status_code=408, message="Timeout error occurred.")
_json_response = response.json()
## LOGGING POST-CALL
logging_obj.post_call(
input=input, api_key=None, original_response=_json_response
)
model_response = (
litellm.vertexAITextEmbeddingConfig.transform_vertex_response_to_openai(
response=_json_response, model=model, model_response=model_response
)
)
return model_response
async def async_embedding(
self,
model: str,
input: Union[list, str],
model_response: litellm.EmbeddingResponse,
logging_obj: LiteLLMLoggingObject,
optional_params: dict,
custom_llm_provider: Literal[
"vertex_ai", "vertex_ai_beta", "gemini"
], # if it's vertex_ai or gemini (google ai studio)
timeout: Optional[Union[float, httpx.Timeout]],
api_base: Optional[str] = None,
client: Optional[AsyncHTTPHandler] = None,
vertex_project: Optional[str] = None,
vertex_location: Optional[str] = None,
vertex_credentials: Optional[str] = None,
gemini_api_key: Optional[str] = None,
extra_headers: Optional[dict] = None,
encoding=None,
) -> litellm.EmbeddingResponse:
"""
Async embedding implementation
"""
should_use_v1beta1_features = self.is_using_v1beta1_features(
optional_params=optional_params
)
_auth_header, vertex_project = await self._ensure_access_token_async(
credentials=vertex_credentials,
project_id=vertex_project,
custom_llm_provider=custom_llm_provider,
)
auth_header, api_base = self._get_token_and_url(
model=model,
gemini_api_key=gemini_api_key,
auth_header=_auth_header,
vertex_project=vertex_project,
vertex_location=vertex_location,
vertex_credentials=vertex_credentials,
stream=False,
custom_llm_provider=custom_llm_provider,
api_base=api_base,
should_use_v1beta1_features=should_use_v1beta1_features,
mode="embedding",
)
headers = self.set_headers(auth_header=auth_header, extra_headers=extra_headers)
vertex_request: VertexEmbeddingRequest = (
litellm.vertexAITextEmbeddingConfig.transform_openai_request_to_vertex_embedding_request(
input=input, optional_params=optional_params, model=model
)
)
_async_client_params = {}
if timeout:
_async_client_params["timeout"] = timeout
if client is None or not isinstance(client, AsyncHTTPHandler):
client = get_async_httpx_client(
params=_async_client_params, llm_provider=litellm.LlmProviders.VERTEX_AI
)
else:
client = client # type: ignore
## LOGGING
logging_obj.pre_call(
input=vertex_request,
api_key="",
additional_args={
"complete_input_dict": vertex_request,
"api_base": api_base,
"headers": headers,
},
)
try:
response = await client.post(api_base, headers=headers, json=vertex_request) # type: ignore
response.raise_for_status()
except httpx.HTTPStatusError as err:
error_code = err.response.status_code
raise VertexAIError(status_code=error_code, message=err.response.text)
except httpx.TimeoutException:
raise VertexAIError(status_code=408, message="Timeout error occurred.")
_json_response = response.json()
## LOGGING POST-CALL
logging_obj.post_call(
input=input, api_key=None, original_response=_json_response
)
model_response = (
litellm.vertexAITextEmbeddingConfig.transform_vertex_response_to_openai(
response=_json_response, model=model, model_response=model_response
)
)
return model_response
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