######################################################################
# /v1/batches Endpoints
######################################################################
import asyncio
from typing import Dict, Optional, cast
from fastapi import APIRouter, Depends, HTTPException, Path, Request, Response
import litellm
from litellm._logging import verbose_proxy_logger
from litellm.batches.main import (
CancelBatchRequest,
CreateBatchRequest,
RetrieveBatchRequest,
)
from litellm.proxy._types import *
from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
from litellm.proxy.common_utils.http_parsing_utils import _read_request_body
from litellm.proxy.common_utils.openai_endpoint_utils import (
get_custom_llm_provider_from_request_body,
)
from litellm.proxy.openai_files_endpoints.files_endpoints import is_known_model
from litellm.proxy.utils import handle_exception_on_proxy
router = APIRouter()
@router.post(
"/{provider}/v1/batches",
dependencies=[Depends(user_api_key_auth)],
tags=["batch"],
)
@router.post(
"/v1/batches",
dependencies=[Depends(user_api_key_auth)],
tags=["batch"],
)
@router.post(
"/batches",
dependencies=[Depends(user_api_key_auth)],
tags=["batch"],
)
async def create_batch(
request: Request,
fastapi_response: Response,
provider: Optional[str] = None,
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
):
"""
Create large batches of API requests for asynchronous processing.
This is the equivalent of POST https://api.openai.com/v1/batch
Supports Identical Params as: https://platform.openai.com/docs/api-reference/batch
Example Curl
```
curl http://localhost:4000/v1/batches \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
-d '{
"input_file_id": "file-abc123",
"endpoint": "/v1/chat/completions",
"completion_window": "24h"
}'
```
"""
from litellm.proxy.proxy_server import (
add_litellm_data_to_request,
general_settings,
llm_router,
proxy_config,
proxy_logging_obj,
version,
)
data: Dict = {}
try:
data = await _read_request_body(request=request)
verbose_proxy_logger.debug(
"Request received by LiteLLM:\n{}".format(json.dumps(data, indent=4)),
)
# Include original request and headers in the data
data = await add_litellm_data_to_request(
data=data,
request=request,
general_settings=general_settings,
user_api_key_dict=user_api_key_dict,
version=version,
proxy_config=proxy_config,
)
## check if model is a loadbalanced model
router_model: Optional[str] = None
is_router_model = False
if litellm.enable_loadbalancing_on_batch_endpoints is True:
router_model = data.get("model", None)
is_router_model = is_known_model(model=router_model, llm_router=llm_router)
custom_llm_provider = (
provider or data.pop("custom_llm_provider", None) or "openai"
)
_create_batch_data = CreateBatchRequest(**data)
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_batch(**_create_batch_data) # type: ignore
else:
response = await litellm.acreate_batch(
custom_llm_provider=custom_llm_provider, **_create_batch_data # type: ignore
)
### ALERTING ###
asyncio.create_task(
proxy_logging_obj.update_request_status(
litellm_call_id=data.get("litellm_call_id", ""), status="success"
)
)
### RESPONSE HEADERS ###
hidden_params = getattr(response, "_hidden_params", {}) or {}
model_id = hidden_params.get("model_id", None) or ""
cache_key = hidden_params.get("cache_key", None) or ""
api_base = hidden_params.get("api_base", None) or ""
fastapi_response.headers.update(
ProxyBaseLLMRequestProcessing.get_custom_headers(
user_api_key_dict=user_api_key_dict,
model_id=model_id,
cache_key=cache_key,
api_base=api_base,
version=version,
model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
request_data=data,
)
)
return response
except Exception as e:
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.exception(
"litellm.proxy.proxy_server.create_batch(): Exception occured - {}".format(
str(e)
)
)
raise handle_exception_on_proxy(e)
@router.get(
"/{provider}/v1/batches/{batch_id:path}",
dependencies=[Depends(user_api_key_auth)],
tags=["batch"],
)
@router.get(
"/v1/batches/{batch_id:path}",
dependencies=[Depends(user_api_key_auth)],
tags=["batch"],
)
@router.get(
"/batches/{batch_id:path}",
dependencies=[Depends(user_api_key_auth)],
tags=["batch"],
)
async def retrieve_batch(
request: Request,
fastapi_response: Response,
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
provider: Optional[str] = None,
batch_id: str = Path(
title="Batch ID to retrieve", description="The ID of the batch to retrieve"
),
):
"""
Retrieves a batch.
This is the equivalent of GET https://api.openai.com/v1/batches/{batch_id}
Supports Identical Params as: https://platform.openai.com/docs/api-reference/batch/retrieve
Example Curl
```
curl http://localhost:4000/v1/batches/batch_abc123 \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
```
"""
from litellm.proxy.proxy_server import (
add_litellm_data_to_request,
general_settings,
llm_router,
proxy_config,
proxy_logging_obj,
version,
)
data: Dict = {}
try:
## check if model is a loadbalanced model
_retrieve_batch_request = RetrieveBatchRequest(
batch_id=batch_id,
)
data = cast(dict, _retrieve_batch_request)
# setup logging
data["litellm_call_id"] = request.headers.get(
"x-litellm-call-id", str(uuid.uuid4())
)
# Include original request and headers in the data
data = await add_litellm_data_to_request(
data=data,
request=request,
general_settings=general_settings,
user_api_key_dict=user_api_key_dict,
version=version,
proxy_config=proxy_config,
)
if litellm.enable_loadbalancing_on_batch_endpoints is True:
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.aretrieve_batch(**data) # type: ignore
else:
custom_llm_provider = (
provider
or await get_custom_llm_provider_from_request_body(request=request)
or "openai"
)
response = await litellm.aretrieve_batch(
custom_llm_provider=custom_llm_provider, **data # type: ignore
)
### ALERTING ###
asyncio.create_task(
proxy_logging_obj.update_request_status(
litellm_call_id=data.get("litellm_call_id", ""), status="success"
)
)
### RESPONSE HEADERS ###
hidden_params = getattr(response, "_hidden_params", {}) or {}
model_id = hidden_params.get("model_id", None) or ""
cache_key = hidden_params.get("cache_key", None) or ""
api_base = hidden_params.get("api_base", None) or ""
fastapi_response.headers.update(
ProxyBaseLLMRequestProcessing.get_custom_headers(
user_api_key_dict=user_api_key_dict,
model_id=model_id,
cache_key=cache_key,
api_base=api_base,
version=version,
model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
request_data=data,
)
)
return response
except Exception as e:
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.exception(
"litellm.proxy.proxy_server.retrieve_batch(): Exception occured - {}".format(
str(e)
)
)
raise handle_exception_on_proxy(e)
@router.get(
"/{provider}/v1/batches",
dependencies=[Depends(user_api_key_auth)],
tags=["batch"],
)
@router.get(
"/v1/batches",
dependencies=[Depends(user_api_key_auth)],
tags=["batch"],
)
@router.get(
"/batches",
dependencies=[Depends(user_api_key_auth)],
tags=["batch"],
)
async def list_batches(
request: Request,
fastapi_response: Response,
provider: Optional[str] = None,
limit: Optional[int] = None,
after: Optional[str] = None,
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
):
"""
Lists
This is the equivalent of GET https://api.openai.com/v1/batches/
Supports Identical Params as: https://platform.openai.com/docs/api-reference/batch/list
Example Curl
```
curl http://localhost:4000/v1/batches?limit=2 \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
```
"""
from litellm.proxy.proxy_server import proxy_logging_obj, version
verbose_proxy_logger.debug("GET /v1/batches after={} limit={}".format(after, limit))
try:
custom_llm_provider = (
provider
or await get_custom_llm_provider_from_request_body(request=request)
or "openai"
)
response = await litellm.alist_batches(
custom_llm_provider=custom_llm_provider, # type: ignore
after=after,
limit=limit,
)
### RESPONSE HEADERS ###
hidden_params = getattr(response, "_hidden_params", {}) or {}
model_id = hidden_params.get("model_id", None) or ""
cache_key = hidden_params.get("cache_key", None) or ""
api_base = hidden_params.get("api_base", None) or ""
fastapi_response.headers.update(
ProxyBaseLLMRequestProcessing.get_custom_headers(
user_api_key_dict=user_api_key_dict,
model_id=model_id,
cache_key=cache_key,
api_base=api_base,
version=version,
model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
)
)
return response
except Exception as e:
await proxy_logging_obj.post_call_failure_hook(
user_api_key_dict=user_api_key_dict,
original_exception=e,
request_data={"after": after, "limit": limit},
)
verbose_proxy_logger.error(
"litellm.proxy.proxy_server.retrieve_batch(): Exception occured - {}".format(
str(e)
)
)
raise handle_exception_on_proxy(e)
@router.post(
"/{provider}/v1/batches/{batch_id:path}/cancel",
dependencies=[Depends(user_api_key_auth)],
tags=["batch"],
)
@router.post(
"/v1/batches/{batch_id:path}/cancel",
dependencies=[Depends(user_api_key_auth)],
tags=["batch"],
)
@router.post(
"/batches/{batch_id:path}/cancel",
dependencies=[Depends(user_api_key_auth)],
tags=["batch"],
)
async def cancel_batch(
request: Request,
batch_id: str,
fastapi_response: Response,
provider: Optional[str] = None,
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
):
"""
Cancel a batch.
This is the equivalent of POST https://api.openai.com/v1/batches/{batch_id}/cancel
Supports Identical Params as: https://platform.openai.com/docs/api-reference/batch/cancel
Example Curl
```
curl http://localhost:4000/v1/batches/batch_abc123/cancel \
-H "Authorization: Bearer sk-1234" \
-H "Content-Type: application/json" \
-X POST
```
"""
from litellm.proxy.proxy_server import (
add_litellm_data_to_request,
general_settings,
proxy_config,
proxy_logging_obj,
version,
)
data: Dict = {}
try:
data = await _read_request_body(request=request)
verbose_proxy_logger.debug(
"Request received by LiteLLM:\n{}".format(json.dumps(data, indent=4)),
)
# Include original request and headers in the data
data = await add_litellm_data_to_request(
data=data,
request=request,
general_settings=general_settings,
user_api_key_dict=user_api_key_dict,
version=version,
proxy_config=proxy_config,
)
custom_llm_provider = (
provider or data.pop("custom_llm_provider", None) or "openai"
)
_cancel_batch_data = CancelBatchRequest(batch_id=batch_id, **data)
response = await litellm.acancel_batch(
custom_llm_provider=custom_llm_provider, # type: ignore
**_cancel_batch_data
)
### ALERTING ###
asyncio.create_task(
proxy_logging_obj.update_request_status(
litellm_call_id=data.get("litellm_call_id", ""), status="success"
)
)
### RESPONSE HEADERS ###
hidden_params = getattr(response, "_hidden_params", {}) or {}
model_id = hidden_params.get("model_id", None) or ""
cache_key = hidden_params.get("cache_key", None) or ""
api_base = hidden_params.get("api_base", None) or ""
fastapi_response.headers.update(
ProxyBaseLLMRequestProcessing.get_custom_headers(
user_api_key_dict=user_api_key_dict,
model_id=model_id,
cache_key=cache_key,
api_base=api_base,
version=version,
model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
request_data=data,
)
)
return response
except Exception as e:
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.exception(
"litellm.proxy.proxy_server.create_batch(): Exception occured - {}".format(
str(e)
)
)
raise handle_exception_on_proxy(e)
######################################################################
# END OF /v1/batches Endpoints Implementation
######################################################################
