chore: initial public snapshot for github upload
This commit is contained in:
Your Name
@@ -0,0 +1,414 @@
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#### Container Endpoints #####
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from typing import Any, Dict
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from fastapi import APIRouter, Depends, Request, Response
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from fastapi.responses import ORJSONResponse
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from litellm.proxy._types import *
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from litellm.proxy.auth.user_api_key_auth import UserAPIKeyAuth, user_api_key_auth
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from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
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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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get_custom_llm_provider_from_request_headers,
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get_custom_llm_provider_from_request_query,
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)
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router = APIRouter()
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@router.post(
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"/v1/containers",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["containers"],
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)
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@router.post(
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"/containers",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["containers"],
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)
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async def create_container(
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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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):
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"""
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Container creation endpoint for creating new containers.
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Follows the OpenAI Containers API spec:
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https://platform.openai.com/docs/api-reference/containers
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Example:
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```bash
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curl -X POST "http://localhost:4000/v1/containers" \
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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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"name": "My Container",
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"expires_after": {
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"anchor": "last_active_at",
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"minutes": 20
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}
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}'
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```
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Or specify provider via header:
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```bash
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curl -X POST "http://localhost:4000/v1/containers" \
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-H "Authorization: Bearer sk-1234" \
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-H "custom-llm-provider: azure" \
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-H "Content-Type: application/json" \
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-d '{
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"name": "My Container"
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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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general_settings,
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llm_router,
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proxy_config,
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proxy_logging_obj,
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select_data_generator,
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user_api_base,
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user_max_tokens,
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user_model,
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user_request_timeout,
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user_temperature,
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version,
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)
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# Read request body
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data = await _read_request_body(request=request)
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# Extract custom_llm_provider using priority chain
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# Priority: headers > query params > request body > default
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custom_llm_provider = (
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get_custom_llm_provider_from_request_headers(request=request)
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or get_custom_llm_provider_from_request_query(request=request)
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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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# Add custom_llm_provider to data
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data["custom_llm_provider"] = custom_llm_provider
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# Process request using ProxyBaseLLMRequestProcessing
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processor = ProxyBaseLLMRequestProcessing(data=data)
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try:
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return await processor.base_process_llm_request(
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request=request,
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fastapi_response=fastapi_response,
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user_api_key_dict=user_api_key_dict,
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route_type="acreate_container",
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proxy_logging_obj=proxy_logging_obj,
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llm_router=llm_router,
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general_settings=general_settings,
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proxy_config=proxy_config,
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select_data_generator=select_data_generator,
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model=None,
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user_model=user_model,
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user_temperature=user_temperature,
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user_request_timeout=user_request_timeout,
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user_max_tokens=user_max_tokens,
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user_api_base=user_api_base,
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version=version,
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)
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except Exception as e:
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raise await processor._handle_llm_api_exception(
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e=e,
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user_api_key_dict=user_api_key_dict,
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proxy_logging_obj=proxy_logging_obj,
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version=version,
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)
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@router.get(
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"/v1/containers",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["containers"],
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)
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@router.get(
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"/containers",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["containers"],
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)
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async def list_containers(
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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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):
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"""
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Container list endpoint for retrieving a list of containers.
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Follows the OpenAI Containers API spec:
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https://platform.openai.com/docs/api-reference/containers
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Example:
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```bash
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curl -X GET "http://localhost:4000/v1/containers?limit=20&order=desc" \
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-H "Authorization: Bearer sk-1234"
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```
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Or specify provider via header or query param:
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```bash
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curl -X GET "http://localhost:4000/v1/containers?custom_llm_provider=azure" \
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-H "Authorization: Bearer sk-1234"
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```
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"""
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from litellm.proxy.proxy_server import (
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general_settings,
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llm_router,
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proxy_config,
|
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proxy_logging_obj,
|
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select_data_generator,
|
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user_api_base,
|
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user_max_tokens,
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user_model,
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user_request_timeout,
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user_temperature,
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version,
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)
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# Read query parameters
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query_params = dict(request.query_params)
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data: Dict[str, Any] = {"query_params": query_params}
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# Extract custom_llm_provider using priority chain
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custom_llm_provider = (
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get_custom_llm_provider_from_request_headers(request=request)
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or get_custom_llm_provider_from_request_query(request=request)
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or "openai"
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)
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# Add custom_llm_provider to data
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data["custom_llm_provider"] = custom_llm_provider
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# Process request using ProxyBaseLLMRequestProcessing
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processor = ProxyBaseLLMRequestProcessing(data=data)
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try:
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return await processor.base_process_llm_request(
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request=request,
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fastapi_response=fastapi_response,
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user_api_key_dict=user_api_key_dict,
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route_type="alist_containers",
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proxy_logging_obj=proxy_logging_obj,
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llm_router=llm_router,
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general_settings=general_settings,
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proxy_config=proxy_config,
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select_data_generator=select_data_generator,
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model=None,
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user_model=user_model,
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user_temperature=user_temperature,
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user_request_timeout=user_request_timeout,
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user_max_tokens=user_max_tokens,
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user_api_base=user_api_base,
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version=version,
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)
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except Exception as e:
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raise await processor._handle_llm_api_exception(
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e=e,
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user_api_key_dict=user_api_key_dict,
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proxy_logging_obj=proxy_logging_obj,
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version=version,
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)
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@router.get(
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"/v1/containers/{container_id}",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["containers"],
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)
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@router.get(
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"/containers/{container_id}",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["containers"],
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)
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async def retrieve_container(
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request: Request,
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container_id: str,
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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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):
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"""
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Container retrieve endpoint for getting details of a specific container.
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Follows the OpenAI Containers API spec:
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https://platform.openai.com/docs/api-reference/containers
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|
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Example:
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```bash
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curl -X GET "http://localhost:4000/v1/containers/cntr_123" \
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-H "Authorization: Bearer sk-1234"
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```
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|
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Or specify provider via header:
|
||||
```bash
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curl -X GET "http://localhost:4000/v1/containers/cntr_123" \
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-H "Authorization: Bearer sk-1234" \
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-H "custom-llm-provider: azure"
|
||||
```
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||||
"""
|
||||
from litellm.proxy.proxy_server import (
|
||||
general_settings,
|
||||
llm_router,
|
||||
proxy_config,
|
||||
proxy_logging_obj,
|
||||
select_data_generator,
|
||||
user_api_base,
|
||||
user_max_tokens,
|
||||
user_model,
|
||||
user_request_timeout,
|
||||
user_temperature,
|
||||
version,
|
||||
)
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|
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# Include container_id in request data
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data: Dict[str, Any] = {"container_id": container_id}
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# Extract custom_llm_provider using priority chain
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||||
custom_llm_provider = (
|
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get_custom_llm_provider_from_request_headers(request=request)
|
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or get_custom_llm_provider_from_request_query(request=request)
|
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or "openai"
|
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)
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# Add custom_llm_provider to data
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data["custom_llm_provider"] = custom_llm_provider
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# Process request using ProxyBaseLLMRequestProcessing
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processor = ProxyBaseLLMRequestProcessing(data=data)
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try:
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return await processor.base_process_llm_request(
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request=request,
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fastapi_response=fastapi_response,
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user_api_key_dict=user_api_key_dict,
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route_type="aretrieve_container",
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proxy_logging_obj=proxy_logging_obj,
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llm_router=llm_router,
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general_settings=general_settings,
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proxy_config=proxy_config,
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select_data_generator=select_data_generator,
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model=None,
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user_model=user_model,
|
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user_temperature=user_temperature,
|
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user_request_timeout=user_request_timeout,
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user_max_tokens=user_max_tokens,
|
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user_api_base=user_api_base,
|
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version=version,
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)
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except Exception as e:
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raise await processor._handle_llm_api_exception(
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e=e,
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user_api_key_dict=user_api_key_dict,
|
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proxy_logging_obj=proxy_logging_obj,
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version=version,
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)
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@router.delete(
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"/v1/containers/{container_id}",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["containers"],
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)
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@router.delete(
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"/containers/{container_id}",
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dependencies=[Depends(user_api_key_auth)],
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response_class=ORJSONResponse,
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tags=["containers"],
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)
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async def delete_container(
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request: Request,
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container_id: str,
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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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):
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"""
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Container delete endpoint for deleting a specific container.
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||||
|
||||
Follows the OpenAI Containers API spec:
|
||||
https://platform.openai.com/docs/api-reference/containers
|
||||
|
||||
Example:
|
||||
```bash
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||||
curl -X DELETE "http://localhost:4000/v1/containers/cntr_123" \
|
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-H "Authorization: Bearer sk-1234"
|
||||
```
|
||||
|
||||
Or specify provider via header:
|
||||
```bash
|
||||
curl -X DELETE "http://localhost:4000/v1/containers/cntr_123" \
|
||||
-H "Authorization: Bearer sk-1234" \
|
||||
-H "custom-llm-provider: azure"
|
||||
```
|
||||
"""
|
||||
from litellm.proxy.proxy_server import (
|
||||
general_settings,
|
||||
llm_router,
|
||||
proxy_config,
|
||||
proxy_logging_obj,
|
||||
select_data_generator,
|
||||
user_api_base,
|
||||
user_max_tokens,
|
||||
user_model,
|
||||
user_request_timeout,
|
||||
user_temperature,
|
||||
version,
|
||||
)
|
||||
|
||||
# Include container_id in request data
|
||||
data: Dict[str, Any] = {"container_id": container_id}
|
||||
|
||||
# Extract custom_llm_provider using priority chain
|
||||
custom_llm_provider = (
|
||||
get_custom_llm_provider_from_request_headers(request=request)
|
||||
or get_custom_llm_provider_from_request_query(request=request)
|
||||
or "openai"
|
||||
)
|
||||
|
||||
# Add custom_llm_provider to data
|
||||
data["custom_llm_provider"] = custom_llm_provider
|
||||
|
||||
# Process request using ProxyBaseLLMRequestProcessing
|
||||
processor = ProxyBaseLLMRequestProcessing(data=data)
|
||||
try:
|
||||
return await processor.base_process_llm_request(
|
||||
request=request,
|
||||
fastapi_response=fastapi_response,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
route_type="adelete_container",
|
||||
proxy_logging_obj=proxy_logging_obj,
|
||||
llm_router=llm_router,
|
||||
general_settings=general_settings,
|
||||
proxy_config=proxy_config,
|
||||
select_data_generator=select_data_generator,
|
||||
model=None,
|
||||
user_model=user_model,
|
||||
user_temperature=user_temperature,
|
||||
user_request_timeout=user_request_timeout,
|
||||
user_max_tokens=user_max_tokens,
|
||||
user_api_base=user_api_base,
|
||||
version=version,
|
||||
)
|
||||
except Exception as e:
|
||||
raise await processor._handle_llm_api_exception(
|
||||
e=e,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
proxy_logging_obj=proxy_logging_obj,
|
||||
version=version,
|
||||
)
|
||||
|
||||
|
||||
# Register JSON-configured container file endpoints
|
||||
from litellm.proxy.container_endpoints.handler_factory import (
|
||||
register_container_file_endpoints,
|
||||
)
|
||||
|
||||
register_container_file_endpoints(router)
|
||||
@@ -0,0 +1,425 @@
|
||||
"""
|
||||
Factory for generating container proxy endpoints from JSON config.
|
||||
|
||||
This module reads the endpoints.json config and dynamically creates
|
||||
FastAPI route handlers for ALL container file endpoints.
|
||||
"""
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from fastapi import APIRouter, Depends, Request, Response
|
||||
from fastapi.responses import ORJSONResponse
|
||||
|
||||
from litellm.proxy._types import UserAPIKeyAuth
|
||||
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.openai_endpoint_utils import (
|
||||
get_custom_llm_provider_from_request_headers,
|
||||
get_custom_llm_provider_from_request_query,
|
||||
)
|
||||
|
||||
|
||||
def _load_endpoints_config() -> Dict:
|
||||
"""Load the endpoints configuration from JSON file."""
|
||||
config_path = Path(__file__).parent.parent.parent / "containers" / "endpoints.json"
|
||||
with open(config_path) as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def get_all_route_types() -> List[str]:
|
||||
"""Get all async route types for registration in route_llm_request.py"""
|
||||
config = _load_endpoints_config()
|
||||
return [endpoint["async_name"] for endpoint in config["endpoints"]]
|
||||
|
||||
|
||||
def _get_container_provider_config(custom_llm_provider: str):
|
||||
"""Get the container provider config for the given provider."""
|
||||
if custom_llm_provider == "openai":
|
||||
from litellm.llms.openai.containers.transformation import OpenAIContainerConfig
|
||||
|
||||
return OpenAIContainerConfig()
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Container API not supported for provider: {custom_llm_provider}"
|
||||
)
|
||||
|
||||
|
||||
def _create_handler_for_path_params(
|
||||
path_params: List[str],
|
||||
route_type: str,
|
||||
returns_binary: bool = False,
|
||||
is_multipart: bool = False,
|
||||
):
|
||||
"""
|
||||
Dynamically create a handler with the correct path parameter signature.
|
||||
"""
|
||||
# For binary content endpoints, use a different handler
|
||||
if returns_binary and path_params == ["container_id", "file_id"]:
|
||||
|
||||
async def handler_binary_content(
|
||||
request: Request,
|
||||
container_id: str,
|
||||
file_id: str,
|
||||
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
|
||||
):
|
||||
return await _process_binary_request(
|
||||
request=request,
|
||||
container_id=container_id,
|
||||
file_id=file_id,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
)
|
||||
|
||||
return handler_binary_content
|
||||
|
||||
# For multipart file upload endpoints
|
||||
if is_multipart:
|
||||
|
||||
async def handler_multipart_upload(
|
||||
request: Request,
|
||||
container_id: str,
|
||||
fastapi_response: Response,
|
||||
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
|
||||
):
|
||||
return await _process_multipart_upload_request(
|
||||
request=request,
|
||||
fastapi_response=fastapi_response,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
route_type=route_type,
|
||||
container_id=container_id,
|
||||
)
|
||||
|
||||
return handler_multipart_upload
|
||||
|
||||
# Create handlers for different path parameter combinations
|
||||
if path_params == ["container_id"]:
|
||||
|
||||
async def handler_container_id(
|
||||
request: Request,
|
||||
container_id: str,
|
||||
fastapi_response: Response,
|
||||
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
|
||||
):
|
||||
return await _process_request(
|
||||
request=request,
|
||||
fastapi_response=fastapi_response,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
route_type=route_type,
|
||||
path_params={"container_id": container_id},
|
||||
)
|
||||
|
||||
return handler_container_id
|
||||
|
||||
elif path_params == ["container_id", "file_id"]:
|
||||
|
||||
async def handler_container_file(
|
||||
request: Request,
|
||||
container_id: str,
|
||||
file_id: str,
|
||||
fastapi_response: Response,
|
||||
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
|
||||
):
|
||||
return await _process_request(
|
||||
request=request,
|
||||
fastapi_response=fastapi_response,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
route_type=route_type,
|
||||
path_params={"container_id": container_id, "file_id": file_id},
|
||||
)
|
||||
|
||||
return handler_container_file
|
||||
|
||||
else:
|
||||
# Fallback for no path params
|
||||
async def handler_no_params(
|
||||
request: Request,
|
||||
fastapi_response: Response,
|
||||
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
|
||||
):
|
||||
return await _process_request(
|
||||
request=request,
|
||||
fastapi_response=fastapi_response,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
route_type=route_type,
|
||||
path_params={},
|
||||
)
|
||||
|
||||
return handler_no_params
|
||||
|
||||
|
||||
async def _process_binary_request(
|
||||
request: Request,
|
||||
container_id: str,
|
||||
file_id: str,
|
||||
user_api_key_dict: UserAPIKeyAuth,
|
||||
):
|
||||
"""
|
||||
Process binary content requests using the proper transformation pattern.
|
||||
|
||||
This uses the provider config transformations and llm_http_handler
|
||||
to maintain consistency with the established pattern.
|
||||
"""
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging
|
||||
from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler
|
||||
from litellm.types.router import GenericLiteLLMParams
|
||||
|
||||
# Extract custom_llm_provider
|
||||
custom_llm_provider = (
|
||||
get_custom_llm_provider_from_request_headers(request=request)
|
||||
or get_custom_llm_provider_from_request_query(request=request)
|
||||
or "openai"
|
||||
)
|
||||
|
||||
# Get the provider config
|
||||
container_provider_config = _get_container_provider_config(custom_llm_provider)
|
||||
|
||||
# Build litellm_params - credentials are resolved by provider config from env
|
||||
litellm_params = GenericLiteLLMParams()
|
||||
|
||||
# Create logging object
|
||||
logging_obj = Logging(
|
||||
model="container-file-content",
|
||||
messages=[],
|
||||
stream=False,
|
||||
call_type="container_file_content",
|
||||
start_time=None,
|
||||
litellm_call_id="",
|
||||
function_id="",
|
||||
)
|
||||
|
||||
# Use the HTTP handler to make the request
|
||||
handler = BaseLLMHTTPHandler()
|
||||
|
||||
try:
|
||||
content = await handler.async_container_file_content_handler(
|
||||
container_id=container_id,
|
||||
file_id=file_id,
|
||||
container_provider_config=container_provider_config,
|
||||
litellm_params=litellm_params,
|
||||
logging_obj=logging_obj,
|
||||
)
|
||||
|
||||
# Determine content type based on common file extensions in the file_id
|
||||
content_type = "application/octet-stream"
|
||||
file_id_lower = file_id.lower()
|
||||
if ".png" in file_id_lower or file_id_lower.endswith("png"):
|
||||
content_type = "image/png"
|
||||
elif ".jpg" in file_id_lower or ".jpeg" in file_id_lower:
|
||||
content_type = "image/jpeg"
|
||||
elif ".gif" in file_id_lower:
|
||||
content_type = "image/gif"
|
||||
elif ".csv" in file_id_lower:
|
||||
content_type = "text/csv"
|
||||
elif ".json" in file_id_lower:
|
||||
content_type = "application/json"
|
||||
elif ".txt" in file_id_lower:
|
||||
content_type = "text/plain"
|
||||
elif ".pdf" in file_id_lower:
|
||||
content_type = "application/pdf"
|
||||
|
||||
return Response(
|
||||
content=content,
|
||||
media_type=content_type,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
|
||||
async def _process_multipart_upload_request(
|
||||
request: Request,
|
||||
fastapi_response: Response,
|
||||
user_api_key_dict: UserAPIKeyAuth,
|
||||
route_type: str,
|
||||
container_id: str,
|
||||
):
|
||||
"""Process multipart file upload requests."""
|
||||
from litellm.proxy.common_utils.http_parsing_utils import (
|
||||
convert_upload_files_to_file_data,
|
||||
get_form_data,
|
||||
)
|
||||
from litellm.proxy.proxy_server import (
|
||||
general_settings,
|
||||
llm_router,
|
||||
proxy_config,
|
||||
proxy_logging_obj,
|
||||
select_data_generator,
|
||||
user_api_base,
|
||||
user_max_tokens,
|
||||
user_model,
|
||||
user_request_timeout,
|
||||
user_temperature,
|
||||
version,
|
||||
)
|
||||
|
||||
# Parse multipart form data and convert files
|
||||
form_data = await get_form_data(request)
|
||||
data = await convert_upload_files_to_file_data(form_data)
|
||||
|
||||
if "file" not in data:
|
||||
from fastapi import HTTPException
|
||||
|
||||
raise HTTPException(status_code=400, detail="Missing required 'file' field")
|
||||
|
||||
# convert_upload_files_to_file_data returns list of tuples, extract single file
|
||||
file_list = data["file"]
|
||||
if isinstance(file_list, list) and len(file_list) > 0:
|
||||
data["file"] = file_list[0]
|
||||
|
||||
data["container_id"] = container_id
|
||||
|
||||
custom_llm_provider = (
|
||||
get_custom_llm_provider_from_request_headers(request=request)
|
||||
or get_custom_llm_provider_from_request_query(request=request)
|
||||
or "openai"
|
||||
)
|
||||
data["custom_llm_provider"] = custom_llm_provider
|
||||
|
||||
processor = ProxyBaseLLMRequestProcessing(data=data)
|
||||
try:
|
||||
return await processor.base_process_llm_request(
|
||||
request=request,
|
||||
fastapi_response=fastapi_response,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
route_type=route_type, # type: ignore[arg-type]
|
||||
proxy_logging_obj=proxy_logging_obj,
|
||||
llm_router=llm_router,
|
||||
general_settings=general_settings,
|
||||
proxy_config=proxy_config,
|
||||
select_data_generator=select_data_generator,
|
||||
model=None,
|
||||
user_model=user_model,
|
||||
user_temperature=user_temperature,
|
||||
user_request_timeout=user_request_timeout,
|
||||
user_max_tokens=user_max_tokens,
|
||||
user_api_base=user_api_base,
|
||||
version=version,
|
||||
)
|
||||
except Exception as e:
|
||||
raise await processor._handle_llm_api_exception(
|
||||
e=e,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
proxy_logging_obj=proxy_logging_obj,
|
||||
version=version,
|
||||
)
|
||||
|
||||
|
||||
async def _process_request(
|
||||
request: Request,
|
||||
fastapi_response: Response,
|
||||
user_api_key_dict: UserAPIKeyAuth,
|
||||
route_type: str,
|
||||
path_params: Dict[str, str],
|
||||
):
|
||||
"""Common request processing logic."""
|
||||
from litellm.proxy.proxy_server import (
|
||||
general_settings,
|
||||
llm_router,
|
||||
proxy_config,
|
||||
proxy_logging_obj,
|
||||
select_data_generator,
|
||||
user_api_base,
|
||||
user_max_tokens,
|
||||
user_model,
|
||||
user_request_timeout,
|
||||
user_temperature,
|
||||
version,
|
||||
)
|
||||
|
||||
query_params = dict(request.query_params)
|
||||
data: Dict[str, Any] = {
|
||||
"query_params": query_params,
|
||||
**path_params,
|
||||
}
|
||||
|
||||
custom_llm_provider = (
|
||||
get_custom_llm_provider_from_request_headers(request=request)
|
||||
or get_custom_llm_provider_from_request_query(request=request)
|
||||
or "openai"
|
||||
)
|
||||
data["custom_llm_provider"] = custom_llm_provider
|
||||
|
||||
processor = ProxyBaseLLMRequestProcessing(data=data)
|
||||
try:
|
||||
return await processor.base_process_llm_request(
|
||||
request=request,
|
||||
fastapi_response=fastapi_response,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
route_type=route_type, # type: ignore[arg-type]
|
||||
proxy_logging_obj=proxy_logging_obj,
|
||||
llm_router=llm_router,
|
||||
general_settings=general_settings,
|
||||
proxy_config=proxy_config,
|
||||
select_data_generator=select_data_generator,
|
||||
model=None,
|
||||
user_model=user_model,
|
||||
user_temperature=user_temperature,
|
||||
user_request_timeout=user_request_timeout,
|
||||
user_max_tokens=user_max_tokens,
|
||||
user_api_base=user_api_base,
|
||||
version=version,
|
||||
)
|
||||
except Exception as e:
|
||||
raise await processor._handle_llm_api_exception(
|
||||
e=e,
|
||||
user_api_key_dict=user_api_key_dict,
|
||||
proxy_logging_obj=proxy_logging_obj,
|
||||
version=version,
|
||||
)
|
||||
|
||||
|
||||
def register_container_file_endpoints(router: APIRouter) -> None:
|
||||
"""
|
||||
Register ALL container file endpoints from JSON config to the router.
|
||||
|
||||
This single function registers all endpoints defined in endpoints.json,
|
||||
eliminating the need for manual endpoint definitions.
|
||||
"""
|
||||
config = _load_endpoints_config()
|
||||
|
||||
for endpoint_config in config["endpoints"]:
|
||||
path = endpoint_config["path"]
|
||||
method = endpoint_config["method"].lower()
|
||||
path_params = endpoint_config.get("path_params", [])
|
||||
route_type = endpoint_config["async_name"]
|
||||
returns_binary = endpoint_config.get("returns_binary", False)
|
||||
is_multipart = endpoint_config.get("is_multipart", False)
|
||||
|
||||
# Create handler with correct signature for path params
|
||||
handler = _create_handler_for_path_params(
|
||||
path_params, route_type, returns_binary, is_multipart
|
||||
)
|
||||
|
||||
# Register routes
|
||||
route_method = getattr(router, method)
|
||||
|
||||
# For binary endpoints, don't use ORJSONResponse
|
||||
if returns_binary:
|
||||
# Register both /v1/... and /... paths without JSON response class
|
||||
route_method(
|
||||
f"/v1{path}",
|
||||
dependencies=[Depends(user_api_key_auth)],
|
||||
tags=["containers"],
|
||||
)(handler)
|
||||
|
||||
route_method(
|
||||
path,
|
||||
dependencies=[Depends(user_api_key_auth)],
|
||||
tags=["containers"],
|
||||
)(handler)
|
||||
else:
|
||||
# Register both /v1/... and /... paths with JSON response
|
||||
route_method(
|
||||
f"/v1{path}",
|
||||
dependencies=[Depends(user_api_key_auth)],
|
||||
response_class=ORJSONResponse,
|
||||
tags=["containers"],
|
||||
)(handler)
|
||||
|
||||
route_method(
|
||||
path,
|
||||
dependencies=[Depends(user_api_key_auth)],
|
||||
response_class=ORJSONResponse,
|
||||
tags=["containers"],
|
||||
)(handler)
|
||||
Reference in New Issue
Block a user