mirror of
https://github.com/BerriAI/litellm.git
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0519c0c507
* test: fix import for test
* fix: fix bad error string
* docs: cleanup files docs
* fix(files/main.py): cleanup error string
* style: initial commit with a provider/config pattern for files api
google ai studio files api onboarding
* fix: test
* feat(gemini/files/transformation.py): support gemini files api response transformation
* fix(gemini/files/transformation.py): return file id as gemini uri
allows id to be passed in to chat completion request, just like openai
* feat(llm_http_handler.py): support async route for files api on llm_http_handler
* fix: fix linting errors
* fix: fix model info check
* fix: fix ruff errors
* fix: fix linting errors
* Revert "fix: fix linting errors"
This reverts commit 926a5a527f.
* fix: fix linting errors
* test: fix test
* test: fix tests
96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
from typing import Any, Coroutine, Optional, Union
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import httpx
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from litellm import LlmProviders
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from litellm.integrations.gcs_bucket.gcs_bucket_base import (
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GCSBucketBase,
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GCSLoggingConfig,
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)
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from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
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from litellm.types.llms.openai import CreateFileRequest, OpenAIFileObject
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from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES
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from .transformation import VertexAIFilesTransformation
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vertex_ai_files_transformation = VertexAIFilesTransformation()
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class VertexAIFilesHandler(GCSBucketBase):
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"""
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Handles Calling VertexAI in OpenAI Files API format v1/files/*
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This implementation uploads files on GCS Buckets
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"""
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def __init__(self):
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super().__init__()
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self.async_httpx_client = get_async_httpx_client(
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llm_provider=LlmProviders.VERTEX_AI,
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)
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async def async_create_file(
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self,
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create_file_data: CreateFileRequest,
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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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) -> OpenAIFileObject:
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gcs_logging_config: GCSLoggingConfig = await self.get_gcs_logging_config(
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kwargs={}
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)
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headers = await self.construct_request_headers(
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vertex_instance=gcs_logging_config["vertex_instance"],
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service_account_json=gcs_logging_config["path_service_account"],
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)
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bucket_name = gcs_logging_config["bucket_name"]
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(
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logging_payload,
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object_name,
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) = vertex_ai_files_transformation.transform_openai_file_content_to_vertex_ai_file_content(
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openai_file_content=create_file_data.get("file")
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)
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gcs_upload_response = await self._log_json_data_on_gcs(
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headers=headers,
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bucket_name=bucket_name,
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object_name=object_name,
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logging_payload=logging_payload,
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)
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return vertex_ai_files_transformation.transform_gcs_bucket_response_to_openai_file_object(
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create_file_data=create_file_data,
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gcs_upload_response=gcs_upload_response,
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)
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def create_file(
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self,
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_is_async: bool,
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create_file_data: CreateFileRequest,
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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[OpenAIFileObject, Coroutine[Any, Any, OpenAIFileObject]]:
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"""
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Creates a file on VertexAI GCS Bucket
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Only supported for Async litellm.acreate_file
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"""
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if _is_async:
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return self.async_create_file(
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create_file_data=create_file_data,
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api_base=api_base,
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vertex_credentials=vertex_credentials,
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vertex_project=vertex_project,
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vertex_location=vertex_location,
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timeout=timeout,
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max_retries=max_retries,
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)
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return None # type: ignore
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