mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 02:34:29 +00:00
47e12802df
* run azure testing on ci/cd * update docs on azure batches endpoints * add input azure.jsonl * refactor - use separate file for batches endpoints * fixes for passing custom llm provider to /batch endpoints * pass custom llm provider to files endpoints * update azure batches doc * add info for azure batches api * update batches endpoints * use simple helper for raising proxy exception * update config.yml * fix imports * update tests * use existing settings * update env var used * update configs * update config.yml * update ft testing
360 lines
11 KiB
Python
360 lines
11 KiB
Python
######################################################################
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# /v1/batches Endpoints
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import asyncio
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######################################################################
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from typing import Dict, Optional
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from fastapi import APIRouter, Depends, HTTPException, Path, Request, Response
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import litellm
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from litellm._logging import verbose_proxy_logger
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from litellm.batches.main import CreateBatchRequest, RetrieveBatchRequest
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from litellm.proxy._types import *
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from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
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from litellm.proxy.common_utils.http_parsing_utils import _read_request_body
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from litellm.proxy.common_utils.openai_endpoint_utils import (
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get_custom_llm_provider_from_request_body,
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)
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from litellm.proxy.openai_files_endpoints.files_endpoints import is_known_model
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from litellm.proxy.utils import handle_exception_on_proxy
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router = APIRouter()
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@router.post(
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"/{provider}/v1/batches",
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dependencies=[Depends(user_api_key_auth)],
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tags=["batch"],
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)
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@router.post(
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"/v1/batches",
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dependencies=[Depends(user_api_key_auth)],
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tags=["batch"],
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)
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@router.post(
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"/batches",
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dependencies=[Depends(user_api_key_auth)],
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tags=["batch"],
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)
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async def create_batch(
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request: Request,
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fastapi_response: Response,
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provider: Optional[str] = None,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Create large batches of API requests for asynchronous processing.
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This is the equivalent of POST https://api.openai.com/v1/batch
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Supports Identical Params as: https://platform.openai.com/docs/api-reference/batch
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Example Curl
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```
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curl http://localhost:4000/v1/batches \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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-d '{
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"input_file_id": "file-abc123",
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"endpoint": "/v1/chat/completions",
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"completion_window": "24h"
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}'
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```
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"""
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from litellm.proxy.proxy_server import (
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add_litellm_data_to_request,
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general_settings,
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get_custom_headers,
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llm_router,
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proxy_config,
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proxy_logging_obj,
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version,
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)
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data: Dict = {}
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try:
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data = await _read_request_body(request=request)
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verbose_proxy_logger.debug(
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"Request received by LiteLLM:\n{}".format(json.dumps(data, indent=4)),
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)
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# Include original request and headers in the data
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data = await add_litellm_data_to_request(
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data=data,
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request=request,
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general_settings=general_settings,
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user_api_key_dict=user_api_key_dict,
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version=version,
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proxy_config=proxy_config,
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)
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## check if model is a loadbalanced model
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router_model: Optional[str] = None
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is_router_model = False
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if litellm.enable_loadbalancing_on_batch_endpoints is True:
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router_model = data.get("model", None)
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is_router_model = is_known_model(model=router_model, llm_router=llm_router)
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custom_llm_provider = (
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provider or data.pop("custom_llm_provider", None) or "openai"
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)
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_create_batch_data = CreateBatchRequest(**data)
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if (
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litellm.enable_loadbalancing_on_batch_endpoints is True
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and is_router_model
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and router_model is not None
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):
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if llm_router is None:
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raise HTTPException(
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status_code=500,
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detail={
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"error": "LLM Router not initialized. Ensure models added to proxy."
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},
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)
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response = await llm_router.acreate_batch(**_create_batch_data) # type: ignore
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else:
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response = await litellm.acreate_batch(
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custom_llm_provider=custom_llm_provider, **_create_batch_data # type: ignore
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)
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### ALERTING ###
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asyncio.create_task(
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proxy_logging_obj.update_request_status(
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litellm_call_id=data.get("litellm_call_id", ""), status="success"
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)
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)
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### RESPONSE HEADERS ###
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hidden_params = getattr(response, "_hidden_params", {}) or {}
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model_id = hidden_params.get("model_id", None) or ""
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cache_key = hidden_params.get("cache_key", None) or ""
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api_base = hidden_params.get("api_base", None) or ""
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fastapi_response.headers.update(
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get_custom_headers(
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user_api_key_dict=user_api_key_dict,
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model_id=model_id,
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cache_key=cache_key,
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api_base=api_base,
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version=version,
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model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
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request_data=data,
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)
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)
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return response
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except Exception as e:
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await proxy_logging_obj.post_call_failure_hook(
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user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data
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)
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verbose_proxy_logger.exception(
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"litellm.proxy.proxy_server.create_batch(): Exception occured - {}".format(
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str(e)
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)
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)
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raise handle_exception_on_proxy(e)
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@router.get(
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"/{provider}/v1/batches/{batch_id:path}",
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dependencies=[Depends(user_api_key_auth)],
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tags=["batch"],
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)
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@router.get(
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"/v1/batches/{batch_id:path}",
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dependencies=[Depends(user_api_key_auth)],
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tags=["batch"],
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)
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@router.get(
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"/batches/{batch_id:path}",
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dependencies=[Depends(user_api_key_auth)],
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tags=["batch"],
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)
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async def retrieve_batch(
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request: Request,
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fastapi_response: Response,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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provider: Optional[str] = None,
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batch_id: str = Path(
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title="Batch ID to retrieve", description="The ID of the batch to retrieve"
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),
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):
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"""
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Retrieves a batch.
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This is the equivalent of GET https://api.openai.com/v1/batches/{batch_id}
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Supports Identical Params as: https://platform.openai.com/docs/api-reference/batch/retrieve
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Example Curl
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```
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curl http://localhost:4000/v1/batches/batch_abc123 \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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```
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"""
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from litellm.proxy.proxy_server import (
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get_custom_headers,
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llm_router,
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proxy_logging_obj,
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version,
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)
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data: Dict = {}
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try:
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## check if model is a loadbalanced model
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_retrieve_batch_request = RetrieveBatchRequest(
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batch_id=batch_id,
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)
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if litellm.enable_loadbalancing_on_batch_endpoints is True:
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if llm_router is None:
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raise HTTPException(
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status_code=500,
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detail={
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"error": "LLM Router not initialized. Ensure models added to proxy."
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},
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)
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response = await llm_router.aretrieve_batch(**_retrieve_batch_request) # type: ignore
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else:
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custom_llm_provider = (
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provider
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or await get_custom_llm_provider_from_request_body(request=request)
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or "openai"
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)
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response = await litellm.aretrieve_batch(
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custom_llm_provider=custom_llm_provider, **_retrieve_batch_request # type: ignore
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)
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### ALERTING ###
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asyncio.create_task(
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proxy_logging_obj.update_request_status(
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litellm_call_id=data.get("litellm_call_id", ""), status="success"
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)
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)
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### RESPONSE HEADERS ###
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hidden_params = getattr(response, "_hidden_params", {}) or {}
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model_id = hidden_params.get("model_id", None) or ""
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cache_key = hidden_params.get("cache_key", None) or ""
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api_base = hidden_params.get("api_base", None) or ""
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fastapi_response.headers.update(
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get_custom_headers(
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user_api_key_dict=user_api_key_dict,
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model_id=model_id,
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cache_key=cache_key,
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api_base=api_base,
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version=version,
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model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
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request_data=data,
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)
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)
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return response
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except Exception as e:
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await proxy_logging_obj.post_call_failure_hook(
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user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data
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)
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verbose_proxy_logger.exception(
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"litellm.proxy.proxy_server.retrieve_batch(): Exception occured - {}".format(
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str(e)
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)
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)
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raise handle_exception_on_proxy(e)
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@router.get(
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"/{provider}/v1/batches",
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dependencies=[Depends(user_api_key_auth)],
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tags=["batch"],
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)
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@router.get(
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"/v1/batches",
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dependencies=[Depends(user_api_key_auth)],
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tags=["batch"],
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)
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@router.get(
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"/batches",
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dependencies=[Depends(user_api_key_auth)],
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tags=["batch"],
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)
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async def list_batches(
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request: Request,
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fastapi_response: Response,
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provider: Optional[str] = None,
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limit: Optional[int] = None,
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after: Optional[str] = None,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Lists
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This is the equivalent of GET https://api.openai.com/v1/batches/
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Supports Identical Params as: https://platform.openai.com/docs/api-reference/batch/list
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Example Curl
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```
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curl http://localhost:4000/v1/batches?limit=2 \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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```
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"""
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from litellm.proxy.proxy_server import (
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get_custom_headers,
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proxy_logging_obj,
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version,
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)
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verbose_proxy_logger.debug("GET /v1/batches after={} limit={}".format(after, limit))
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try:
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custom_llm_provider = (
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provider
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or await get_custom_llm_provider_from_request_body(request=request)
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or "openai"
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)
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response = await litellm.alist_batches(
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custom_llm_provider=custom_llm_provider, # type: ignore
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after=after,
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limit=limit,
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)
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### RESPONSE HEADERS ###
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hidden_params = getattr(response, "_hidden_params", {}) or {}
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model_id = hidden_params.get("model_id", None) or ""
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cache_key = hidden_params.get("cache_key", None) or ""
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api_base = hidden_params.get("api_base", None) or ""
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fastapi_response.headers.update(
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get_custom_headers(
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user_api_key_dict=user_api_key_dict,
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model_id=model_id,
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cache_key=cache_key,
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api_base=api_base,
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version=version,
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model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
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)
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)
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return response
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except Exception as e:
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await proxy_logging_obj.post_call_failure_hook(
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user_api_key_dict=user_api_key_dict,
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original_exception=e,
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request_data={"after": after, "limit": limit},
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)
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verbose_proxy_logger.error(
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"litellm.proxy.proxy_server.retrieve_batch(): Exception occured - {}".format(
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str(e)
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)
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)
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raise handle_exception_on_proxy(e)
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######################################################################
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# END OF /v1/batches Endpoints Implementation
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######################################################################
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