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Code Quality Improvement - use vertex_ai/ as folder name for vertexAI (#7166)
* fix rename vertex ai * run ci/cd again
This commit is contained in:
Ishaan Jaff
GitHub
parent
26918487d6
commit
e09d3761d8
47 changed files with 58 additions and 58 deletions
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@ -0,0 +1,295 @@
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import json
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from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
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import httpx
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import litellm
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.llms.custom_httpx.http_handler import (
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AsyncHTTPHandler,
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HTTPHandler,
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get_async_httpx_client,
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)
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from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
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VertexAIError,
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VertexLLM,
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)
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from litellm.types.llms.vertex_ai import (
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Instance,
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InstanceImage,
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InstanceVideo,
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MultimodalPrediction,
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MultimodalPredictions,
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VertexMultimodalEmbeddingRequest,
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)
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from litellm.types.utils import Embedding
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from litellm.utils import is_base64_encoded
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class VertexMultimodalEmbedding(VertexLLM):
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def __init__(self) -> None:
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super().__init__()
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self.SUPPORTED_MULTIMODAL_EMBEDDING_MODELS = [
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"multimodalembedding",
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"multimodalembedding@001",
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]
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def multimodal_embedding(
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self,
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model: str,
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input: Union[list, str],
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print_verbose,
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model_response: litellm.EmbeddingResponse,
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custom_llm_provider: Literal["gemini", "vertex_ai"],
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optional_params: dict,
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logging_obj: LiteLLMLoggingObj,
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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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encoding=None,
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vertex_project=None,
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vertex_location=None,
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vertex_credentials=None,
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aembedding=False,
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timeout=300,
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client=None,
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) -> litellm.EmbeddingResponse:
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_auth_header, vertex_project = self._ensure_access_token(
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credentials=vertex_credentials,
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project_id=vertex_project,
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custom_llm_provider=custom_llm_provider,
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)
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auth_header, url = self._get_token_and_url(
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model=model,
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auth_header=_auth_header,
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gemini_api_key=api_key,
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vertex_project=vertex_project,
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vertex_location=vertex_location,
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vertex_credentials=vertex_credentials,
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stream=None,
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custom_llm_provider=custom_llm_provider,
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api_base=api_base,
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should_use_v1beta1_features=False,
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mode="embedding",
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)
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if client is None:
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_params = {}
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if timeout is not None:
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if isinstance(timeout, float) or isinstance(timeout, int):
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_httpx_timeout = httpx.Timeout(timeout)
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_params["timeout"] = _httpx_timeout
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else:
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_params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
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sync_handler: HTTPHandler = HTTPHandler(**_params) # type: ignore
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else:
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sync_handler = client # type: ignore
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optional_params = optional_params or {}
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request_data = VertexMultimodalEmbeddingRequest()
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if "instances" in optional_params:
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request_data["instances"] = optional_params["instances"]
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elif isinstance(input, list):
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vertex_instances: List[Instance] = self.process_openai_embedding_input(
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_input=input
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)
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request_data["instances"] = vertex_instances
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else:
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# construct instances
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vertex_request_instance = Instance(**optional_params)
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if isinstance(input, str):
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vertex_request_instance = self._process_input_element(input)
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request_data["instances"] = [vertex_request_instance]
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headers = {
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"Content-Type": "application/json; charset=utf-8",
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"Authorization": f"Bearer {auth_header}",
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}
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## LOGGING
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logging_obj.pre_call(
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input=input,
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api_key="",
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additional_args={
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"complete_input_dict": request_data,
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"api_base": url,
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"headers": headers,
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},
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)
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if aembedding is True:
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return self.async_multimodal_embedding( # type: ignore
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model=model,
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api_base=url,
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data=request_data,
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timeout=timeout,
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headers=headers,
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client=client,
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model_response=model_response,
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)
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response = sync_handler.post(
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url=url,
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headers=headers,
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data=json.dumps(request_data),
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)
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if response.status_code != 200:
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raise Exception(f"Error: {response.status_code} {response.text}")
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_json_response = response.json()
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if "predictions" not in _json_response:
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raise litellm.InternalServerError(
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message=f"embedding response does not contain 'predictions', got {_json_response}",
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llm_provider="vertex_ai",
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model=model,
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)
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_predictions = _json_response["predictions"]
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vertex_predictions = MultimodalPredictions(predictions=_predictions)
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model_response.data = self.transform_embedding_response_to_openai(
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predictions=vertex_predictions
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)
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model_response.model = model
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return model_response
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async def async_multimodal_embedding(
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self,
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model: str,
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api_base: str,
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data: VertexMultimodalEmbeddingRequest,
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model_response: litellm.EmbeddingResponse,
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timeout: Optional[Union[float, httpx.Timeout]],
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headers={},
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client: Optional[AsyncHTTPHandler] = None,
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) -> litellm.EmbeddingResponse:
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if client is None:
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_params = {}
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if timeout is not None:
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if isinstance(timeout, float) or isinstance(timeout, int):
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timeout = httpx.Timeout(timeout)
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_params["timeout"] = timeout
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client = get_async_httpx_client(
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llm_provider=litellm.LlmProviders.VERTEX_AI,
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params={"timeout": timeout},
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)
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else:
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client = client # type: ignore
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try:
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response = await client.post(api_base, headers=headers, json=data) # type: ignore
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response.raise_for_status()
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except httpx.HTTPStatusError as err:
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error_code = err.response.status_code
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raise VertexAIError(status_code=error_code, message=err.response.text)
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except httpx.TimeoutException:
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raise VertexAIError(status_code=408, message="Timeout error occurred.")
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_json_response = response.json()
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if "predictions" not in _json_response:
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raise litellm.InternalServerError(
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message=f"embedding response does not contain 'predictions', got {_json_response}",
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llm_provider="vertex_ai",
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model=model,
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)
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_predictions = _json_response["predictions"]
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vertex_predictions = MultimodalPredictions(predictions=_predictions)
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model_response.data = self.transform_embedding_response_to_openai(
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predictions=vertex_predictions
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)
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model_response.model = model
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return model_response
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def _process_input_element(self, input_element: str) -> Instance:
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"""
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Process the input element for multimodal embedding requests. checks if the if the input is gcs uri, base64 encoded image or plain text.
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Args:
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input_element (str): The input element to process.
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Returns:
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Dict[str, Any]: A dictionary representing the processed input element.
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"""
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if len(input_element) == 0:
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return Instance(text=input_element)
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elif "gs://" in input_element:
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if "mp4" in input_element:
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return Instance(video=InstanceVideo(gcsUri=input_element))
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else:
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return Instance(image=InstanceImage(gcsUri=input_element))
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elif is_base64_encoded(s=input_element):
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return Instance(image=InstanceImage(bytesBase64Encoded=input_element))
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else:
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return Instance(text=input_element)
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def process_openai_embedding_input(
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self, _input: Union[list, str]
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) -> List[Instance]:
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"""
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Process the input for multimodal embedding requests.
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Args:
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_input (Union[list, str]): The input data to process.
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Returns:
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List[Instance]: A list of processed VertexAI Instance objects.
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"""
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_input_list = None
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if not isinstance(_input, list):
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_input_list = [_input]
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else:
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_input_list = _input
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processed_instances = []
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for element in _input_list:
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if isinstance(element, str):
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instance = Instance(**self._process_input_element(element))
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elif isinstance(element, dict):
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instance = Instance(**element)
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else:
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raise ValueError(f"Unsupported input type: {type(element)}")
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processed_instances.append(instance)
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return processed_instances
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def transform_embedding_response_to_openai(
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self, predictions: MultimodalPredictions
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) -> List[Embedding]:
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openai_embeddings: List[Embedding] = []
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if "predictions" in predictions:
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for idx, _prediction in enumerate(predictions["predictions"]):
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if _prediction:
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if "textEmbedding" in _prediction:
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openai_embedding_object = Embedding(
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embedding=_prediction["textEmbedding"],
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index=idx,
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object="embedding",
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)
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openai_embeddings.append(openai_embedding_object)
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elif "imageEmbedding" in _prediction:
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openai_embedding_object = Embedding(
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embedding=_prediction["imageEmbedding"],
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index=idx,
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object="embedding",
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)
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openai_embeddings.append(openai_embedding_object)
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elif "videoEmbeddings" in _prediction:
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for video_embedding in _prediction["videoEmbeddings"]:
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openai_embedding_object = Embedding(
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embedding=video_embedding["embedding"],
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index=idx,
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object="embedding",
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)
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openai_embeddings.append(openai_embedding_object)
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return openai_embeddings
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