chore: initial public snapshot for github upload
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@@ -0,0 +1,378 @@
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import json
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from typing import Any, Coroutine, Dict, Optional, Union
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import httpx
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import litellm
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from litellm.llms.custom_httpx.http_handler import (
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_get_httpx_client,
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get_async_httpx_client,
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)
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from litellm.llms.vertex_ai.common_utils import get_vertex_base_url
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from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
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from litellm.types.llms.openai import CreateBatchRequest
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from litellm.types.llms.vertex_ai import (
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VERTEX_CREDENTIALS_TYPES,
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VertexAIBatchPredictionJob,
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)
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from litellm.types.utils import LiteLLMBatch
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from .transformation import VertexAIBatchTransformation
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class VertexAIBatchPrediction(VertexLLM):
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def __init__(self, gcs_bucket_name: str, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.gcs_bucket_name = gcs_bucket_name
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def create_batch(
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self,
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_is_async: bool,
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create_batch_data: CreateBatchRequest,
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api_base: Optional[str],
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vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
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vertex_project: Optional[str],
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vertex_location: Optional[str],
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timeout: Union[float, httpx.Timeout],
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max_retries: Optional[int],
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) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
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sync_handler = _get_httpx_client()
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access_token, project_id = 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="vertex_ai",
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)
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default_api_base = self.create_vertex_batch_url(
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vertex_location=vertex_location or "us-central1",
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vertex_project=vertex_project or project_id,
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)
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if len(default_api_base.split(":")) > 1:
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endpoint = default_api_base.split(":")[-1]
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else:
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endpoint = ""
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_, api_base = self._check_custom_proxy(
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api_base=api_base,
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custom_llm_provider="vertex_ai",
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gemini_api_key=None,
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endpoint=endpoint,
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stream=None,
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auth_header=None,
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url=default_api_base,
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model=None,
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vertex_project=vertex_project or project_id,
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vertex_location=vertex_location or "us-central1",
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vertex_api_version="v1",
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)
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headers = {
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"Content-Type": "application/json; charset=utf-8",
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"Authorization": f"Bearer {access_token}",
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}
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vertex_batch_request: VertexAIBatchPredictionJob = VertexAIBatchTransformation.transform_openai_batch_request_to_vertex_ai_batch_request(
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request=create_batch_data
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)
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if _is_async is True:
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return self._async_create_batch(
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vertex_batch_request=vertex_batch_request,
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api_base=api_base,
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headers=headers,
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)
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response = sync_handler.post(
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url=api_base,
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headers=headers,
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data=json.dumps(vertex_batch_request),
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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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vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
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response=_json_response
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)
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return vertex_batch_response
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async def _async_create_batch(
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self,
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vertex_batch_request: VertexAIBatchPredictionJob,
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api_base: str,
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headers: Dict[str, str],
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) -> LiteLLMBatch:
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client = get_async_httpx_client(
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llm_provider=litellm.LlmProviders.VERTEX_AI,
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)
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try:
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response = await client.post(
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url=api_base,
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headers=headers,
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data=json.dumps(vertex_batch_request),
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)
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except httpx.HTTPStatusError as e:
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error_body = e.response.text
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litellm.verbose_logger.error(
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"Vertex AI batch create failed: status=%s, body=%s",
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e.response.status_code,
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error_body[:1000],
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)
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raise
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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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vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
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response=_json_response
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)
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return vertex_batch_response
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def create_vertex_batch_url(
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self,
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vertex_location: str,
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vertex_project: str,
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) -> str:
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"""Return the base url for the vertex garden models"""
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# POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/batchPredictionJobs
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base_url = get_vertex_base_url(vertex_location)
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return f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/batchPredictionJobs"
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def retrieve_batch(
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self,
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_is_async: bool,
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batch_id: str,
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api_base: Optional[str],
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vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
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vertex_project: Optional[str],
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vertex_location: Optional[str],
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timeout: Union[float, httpx.Timeout],
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max_retries: Optional[int],
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logging_obj: Optional[Any] = None,
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) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
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sync_handler = _get_httpx_client()
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access_token, project_id = 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="vertex_ai",
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)
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default_api_base = self.create_vertex_batch_url(
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vertex_location=vertex_location or "us-central1",
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vertex_project=vertex_project or project_id,
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)
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# Append batch_id to the URL
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default_api_base = f"{default_api_base}/{batch_id}"
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if len(default_api_base.split(":")) > 1:
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endpoint = default_api_base.split(":")[-1]
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else:
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endpoint = ""
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_, api_base = self._check_custom_proxy(
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api_base=api_base,
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custom_llm_provider="vertex_ai",
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gemini_api_key=None,
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endpoint=endpoint,
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stream=None,
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auth_header=None,
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url=default_api_base,
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model=None,
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vertex_project=vertex_project or project_id,
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vertex_location=vertex_location or "us-central1",
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vertex_api_version="v1",
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)
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headers = {
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"Content-Type": "application/json; charset=utf-8",
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"Authorization": f"Bearer {access_token}",
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}
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if _is_async is True:
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return self._async_retrieve_batch(
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api_base=api_base,
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headers=headers,
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logging_obj=logging_obj,
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)
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# Log the request using logging_obj if available
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if logging_obj is not None:
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from litellm.litellm_core_utils.litellm_logging import Logging
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if isinstance(logging_obj, Logging):
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logging_obj.pre_call(
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input="",
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api_key="",
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additional_args={
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"complete_input_dict": {},
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"api_base": api_base,
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"headers": headers,
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"request_str": (
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f"\nGET Request Sent from LiteLLM:\n"
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f"curl -X GET \\\n"
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f"{api_base} \\\n"
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f"-H 'Authorization: Bearer ***REDACTED***' \\\n"
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f"-H 'Content-Type: application/json; charset=utf-8'\n"
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),
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},
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)
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response = sync_handler.get(
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url=api_base,
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headers=headers,
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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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vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
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response=_json_response
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)
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return vertex_batch_response
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async def _async_retrieve_batch(
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self,
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api_base: str,
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headers: Dict[str, str],
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logging_obj: Optional[Any] = None,
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) -> LiteLLMBatch:
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client = get_async_httpx_client(
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llm_provider=litellm.LlmProviders.VERTEX_AI,
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)
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# Log the request using logging_obj if available
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if logging_obj is not None:
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from litellm.litellm_core_utils.litellm_logging import Logging
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if isinstance(logging_obj, Logging):
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logging_obj.pre_call(
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input="",
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api_key="",
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additional_args={
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"complete_input_dict": {},
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"api_base": api_base,
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"headers": headers,
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"request_str": (
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f"\nGET Request Sent from LiteLLM:\n"
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f"curl -X GET \\\n"
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f"{api_base} \\\n"
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f"-H 'Authorization: Bearer ***REDACTED***' \\\n"
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f"-H 'Content-Type: application/json; charset=utf-8'\n"
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),
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},
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)
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response = await client.get(
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url=api_base,
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headers=headers,
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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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vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response(
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response=_json_response
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)
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return vertex_batch_response
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def list_batches(
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self,
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_is_async: bool,
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after: Optional[str],
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limit: Optional[int],
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api_base: Optional[str],
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vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
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vertex_project: Optional[str],
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vertex_location: Optional[str],
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timeout: Union[float, httpx.Timeout],
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max_retries: Optional[int],
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):
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sync_handler = _get_httpx_client()
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access_token, project_id = 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="vertex_ai",
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)
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default_api_base = self.create_vertex_batch_url(
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vertex_location=vertex_location or "us-central1",
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vertex_project=vertex_project or project_id,
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)
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if len(default_api_base.split(":")) > 1:
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endpoint = default_api_base.split(":")[-1]
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else:
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endpoint = ""
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_, api_base = self._check_custom_proxy(
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api_base=api_base,
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custom_llm_provider="vertex_ai",
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gemini_api_key=None,
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endpoint=endpoint,
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stream=None,
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auth_header=None,
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url=default_api_base,
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)
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headers = {
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"Content-Type": "application/json; charset=utf-8",
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"Authorization": f"Bearer {access_token}",
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}
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params: Dict[str, Any] = {}
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if limit is not None:
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params["pageSize"] = str(limit)
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if after is not None:
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params["pageToken"] = after
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if _is_async is True:
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return self._async_list_batches(
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api_base=api_base,
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headers=headers,
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params=params,
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)
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response = sync_handler.get(
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url=api_base,
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headers=headers,
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params=params,
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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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vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_list_response_to_openai_list_response(
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response=_json_response
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)
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return vertex_batch_response
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async def _async_list_batches(
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self,
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api_base: str,
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headers: Dict[str, str],
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params: Dict[str, Any],
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):
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client = get_async_httpx_client(
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llm_provider=litellm.LlmProviders.VERTEX_AI,
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)
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response = await client.get(
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url=api_base,
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headers=headers,
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params=params,
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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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vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_list_response_to_openai_list_response(
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response=_json_response
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)
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return vertex_batch_response
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