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LiteLLM Minor Fixes & Improvements (04/02/2025) (#9725)

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* Add date picker to usage tab + Add reasoning_content token tracking across all providers on streaming (#9722)

* feat(new_usage.tsx): add date picker for new usage tab

allow user to look back on their usage data

* feat(anthropic/chat/transformation.py): report reasoning tokens in completion token details

allows usage tracking on how many reasoning tokens are actually being used

* feat(streaming_chunk_builder.py): return reasoning_tokens in anthropic/openai streaming response

allows tracking reasoning_token usage across providers

* Fix update team metadata + fix bulk adding models on Ui  (#9721)

* fix(handle_add_model_submit.tsx): fix bulk adding models

* fix(team_info.tsx): fix team metadata update

Fixes https://github.com/BerriAI/litellm/issues/9689

* (v0) Unified file id - allow calling multiple providers with same file id (#9718)

* feat(files_endpoints.py): initial commit adding 'target_model_names' support

allow developer to specify all the models they want to call with the file

* feat(files_endpoints.py): return unified files endpoint

* test(test_files_endpoints.py): add validation test - if invalid purpose submitted

* feat: more updates

* feat: initial working commit of unified file id translation

* fix: additional fixes

* fix(router.py): remove model replace logic in jsonl on acreate_file

enables file upload to work for chat completion requests as well

* fix(files_endpoints.py): remove whitespace around model name

* fix(azure/handler.py): return acreate_file with correct response type

* fix: fix linting errors

* test: fix mock test to run on github actions

* fix: fix ruff errors

* fix: fix file too large error

* fix(utils.py): remove redundant var

* test: modify test to work on github actions

* test: update tests

* test: more debug logs to understand ci/cd issue

* test: fix test for respx

* test: skip mock respx test

fails on ci/cd - not clear why

* fix: fix ruff check

* fix: fix test

* fix(model_connection_test.tsx): fix linting error

* test: update unit tests
This commit is contained in:
Krish Dholakia 2025-04-03 11:48:52 -07:00 • committed by GitHub
parent ad57b7b331
commit 0ce878e804
27 changed files with 889 additions and 96 deletions
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102
litellm/proxy/openai_files_endpoints/files_endpoints.py
View file

@ -7,7 +7,7 @@
import asyncio
import traceback
from typing import Optional
from typing import Optional, cast, get_args
import httpx
from fastapi import (
@ -31,7 +31,10 @@ from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessin
from litellm.proxy.common_utils.openai_endpoint_utils import (
get_custom_llm_provider_from_request_body,
)
from litellm.proxy.hooks.managed_files import _PROXY_LiteLLMManagedFiles
from litellm.proxy.utils import ProxyLogging
from litellm.router import Router
from litellm.types.llms.openai import OpenAIFileObject, OpenAIFilesPurpose
router = APIRouter()
@ -104,6 +107,53 @@ def is_known_model(model: Optional[str], llm_router: Optional[Router]) -> bool:
return is_in_list
async def _deprecated_loadbalanced_create_file(
llm_router: Optional[Router],
router_model: str,
_create_file_request: CreateFileRequest,
) -> OpenAIFileObject:
if llm_router is None:
raise HTTPException(
status_code=500,
detail={
"error": "LLM Router not initialized. Ensure models added to proxy."
},
)
response = await llm_router.acreate_file(model=router_model, **_create_file_request)
return response
async def create_file_for_each_model(
llm_router: Optional[Router],
_create_file_request: CreateFileRequest,
target_model_names_list: List[str],
purpose: OpenAIFilesPurpose,
proxy_logging_obj: ProxyLogging,
user_api_key_dict: UserAPIKeyAuth,
) -> OpenAIFileObject:
if llm_router is None:
raise HTTPException(
status_code=500,
detail={
"error": "LLM Router not initialized. Ensure models added to proxy."
},
)
responses = []
for model in target_model_names_list:
individual_response = await llm_router.acreate_file(
model=model, **_create_file_request
)
responses.append(individual_response)
response = await _PROXY_LiteLLMManagedFiles.return_unified_file_id(
file_objects=responses,
purpose=purpose,
internal_usage_cache=proxy_logging_obj.internal_usage_cache,
litellm_parent_otel_span=user_api_key_dict.parent_otel_span,
)
return response
@router.post(
"/{provider}/v1/files",
dependencies=[Depends(user_api_key_auth)],
@ -123,6 +173,7 @@ async def create_file(
request: Request,
fastapi_response: Response,
purpose: str = Form(...),
target_model_names: str = Form(default=""),
provider: Optional[str] = None,
custom_llm_provider: str = Form(default="openai"),
file: UploadFile = File(...),
@ -162,8 +213,25 @@ async def create_file(
or await get_custom_llm_provider_from_request_body(request=request)
or "openai"
)
target_model_names_list = (
target_model_names.split(",") if target_model_names else []
)
target_model_names_list = [model.strip() for model in target_model_names_list]
# Prepare the data for forwarding
# Replace with:
valid_purposes = get_args(OpenAIFilesPurpose)
if purpose not in valid_purposes:
raise HTTPException(
status_code=400,
detail={
"error": f"Invalid purpose: {purpose}. Must be one of: {valid_purposes}",
},
)
# Cast purpose to OpenAIFilesPurpose type
purpose = cast(OpenAIFilesPurpose, purpose)
data = {"purpose": purpose}
# Include original request and headers in the data
@ -192,21 +260,25 @@ async def create_file(
_create_file_request = CreateFileRequest(file=file_data, **data)
response: Optional[OpenAIFileObject] = None
if (
litellm.enable_loadbalancing_on_batch_endpoints is True
and is_router_model
and router_model is not None
):
if llm_router is None:
raise HTTPException(
status_code=500,
detail={
"error": "LLM Router not initialized. Ensure models added to proxy."
},
)
response = await llm_router.acreate_file(
model=router_model, **_create_file_request
response = await _deprecated_loadbalanced_create_file(
llm_router=llm_router,
router_model=router_model,
_create_file_request=_create_file_request,
)
elif target_model_names_list:
response = await create_file_for_each_model(
llm_router=llm_router,
_create_file_request=_create_file_request,
target_model_names_list=target_model_names_list,
purpose=purpose,
proxy_logging_obj=proxy_logging_obj,
user_api_key_dict=user_api_key_dict,
)
else:
# get configs for custom_llm_provider
@ -220,6 +292,11 @@ async def create_file(
# for now use custom_llm_provider=="openai" -> this will change as LiteLLM adds more providers for acreate_batch
response = await litellm.acreate_file(**_create_file_request, custom_llm_provider=custom_llm_provider) # type: ignore
if response is None:
raise HTTPException(
status_code=500,
detail={"error": "Failed to create file. Please try again."},
)
### ALERTING ###
asyncio.create_task(
proxy_logging_obj.update_request_status(
@ -248,12 +325,11 @@ async def create_file(
await proxy_logging_obj.post_call_failure_hook(
user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data
)
verbose_proxy_logger.error(
verbose_proxy_logger.exception(
"litellm.proxy.proxy_server.create_file(): Exception occured - {}".format(
str(e)
)
)
verbose_proxy_logger.debug(traceback.format_exc())
if isinstance(e, HTTPException):
raise ProxyException(
message=getattr(e, "message", str(e.detail)),