litellm-mirror/litellm/llms/gemini/files/transformation.py
Krish Dholakia 6ba3c4a4f8
VertexAI non-jsonl file storage support (#9781)
* test: add initial e2e test

* fix(vertex_ai/files): initial commit adding sync file create support

* refactor: initial commit of vertex ai non-jsonl files reaching gcp endpoint

* fix(vertex_ai/files/transformation.py): initial working commit of non-jsonl file call reaching backend endpoint

* fix(vertex_ai/files/transformation.py): working e2e non-jsonl file upload

* test: working e2e jsonl call

* test: unit testing for jsonl file creation

* fix(vertex_ai/transformation.py): reset file pointer after read

allow multiple reads on same file object

* fix: fix linting errors

* fix: fix ruff linting errors

* fix: fix import

* fix: fix linting error

* fix: fix linting error

* fix(vertex_ai/files/transformation.py): fix linting error

* test: update test

* test: update tests

* fix: fix linting errors

* fix: fix test

* fix: fix linting error
2025-04-09 14:01:48 -07:00

173 lines
5.3 KiB
Python

"""
Supports writing files to Google AI Studio Files API.
For vertex ai, check out the vertex_ai/files/handler.py file.
"""
import time
from typing import List, Optional
import httpx
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
from litellm.llms.base_llm.files.transformation import (
BaseFilesConfig,
LiteLLMLoggingObj,
)
from litellm.types.llms.gemini import GeminiCreateFilesResponseObject
from litellm.types.llms.openai import (
CreateFileRequest,
OpenAICreateFileRequestOptionalParams,
OpenAIFileObject,
)
from litellm.types.utils import LlmProviders
from ..common_utils import GeminiModelInfo
class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig):
def __init__(self):
pass
@property
def custom_llm_provider(self) -> LlmProviders:
return LlmProviders.GEMINI
def get_complete_url(
self,
api_base: Optional[str],
api_key: Optional[str],
model: str,
optional_params: dict,
litellm_params: dict,
stream: Optional[bool] = None,
) -> str:
"""
OPTIONAL
Get the complete url for the request
Some providers need `model` in `api_base`
"""
endpoint = "upload/v1beta/files"
api_base = self.get_api_base(api_base)
if not api_base:
raise ValueError("api_base is required")
if not api_key:
raise ValueError("api_key is required")
url = "{}/{}?key={}".format(api_base, endpoint, api_key)
return url
def get_supported_openai_params(
self, model: str
) -> List[OpenAICreateFileRequestOptionalParams]:
return []
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool,
) -> dict:
return optional_params
def transform_create_file_request(
self,
model: str,
create_file_data: CreateFileRequest,
optional_params: dict,
litellm_params: dict,
) -> dict:
"""
Transform the OpenAI-style file creation request into Gemini's format
Returns:
dict: Contains both request data and headers for the two-step upload
"""
# Extract the file information
file_data = create_file_data.get("file")
if file_data is None:
raise ValueError("File data is required")
# Use the common utility function to extract file data
extracted_data = extract_file_data(file_data)
# Get file size
file_size = len(extracted_data["content"])
# Step 1: Initial resumable upload request
headers = {
"X-Goog-Upload-Protocol": "resumable",
"X-Goog-Upload-Command": "start",
"X-Goog-Upload-Header-Content-Length": str(file_size),
"X-Goog-Upload-Header-Content-Type": extracted_data["content_type"],
"Content-Type": "application/json",
}
headers.update(extracted_data["headers"]) # Add any custom headers
# Initial metadata request body
initial_data = {
"file": {
"display_name": extracted_data["filename"] or str(int(time.time()))
}
}
# Step 2: Actual file upload data
upload_headers = {
"Content-Length": str(file_size),
"X-Goog-Upload-Offset": "0",
"X-Goog-Upload-Command": "upload, finalize",
}
return {
"initial_request": {"headers": headers, "data": initial_data},
"upload_request": {
"headers": upload_headers,
"data": extracted_data["content"],
},
}
def transform_create_file_response(
self,
model: Optional[str],
raw_response: httpx.Response,
logging_obj: LiteLLMLoggingObj,
litellm_params: dict,
) -> OpenAIFileObject:
"""
Transform Gemini's file upload response into OpenAI-style FileObject
"""
try:
response_json = raw_response.json()
response_object = GeminiCreateFilesResponseObject(
**response_json.get("file", {}) # type: ignore
)
# Extract file information from Gemini response
return OpenAIFileObject(
id=response_object["uri"], # Gemini uses URI as identifier
bytes=int(
response_object["sizeBytes"]
), # Gemini doesn't return file size
created_at=int(
time.mktime(
time.strptime(
response_object["createTime"].replace("Z", "+00:00"),
"%Y-%m-%dT%H:%M:%S.%f%z",
)
)
),
filename=response_object["displayName"],
object="file",
purpose="user_data", # Default to assistants as that's the main use case
status="uploaded",
status_details=None,
)
except Exception as e:
verbose_logger.exception(f"Error parsing file upload response: {str(e)}")
raise ValueError(f"Error parsing file upload response: {str(e)}")