from typing import Dict, Optional

from fastapi import APIRouter, Depends, Request, Response

import litellm
from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook import (
    LiteLLM_ManagedVectorStore,
)
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

router = APIRouter()
########################################################
# OpenAI Compatible Endpoints
########################################################

def _update_request_data_with_litellm_managed_vector_store_registry(
    data: Dict,
    vector_store_id: str,
) -> Dict:
    """
    Update the request data with the litellm managed vector store registry.

    """
    if litellm.vector_store_registry is not None:
        vector_store_to_run: Optional[LiteLLM_ManagedVectorStore] = litellm.vector_store_registry.get_litellm_managed_vector_store_from_registry(
            vector_store_id=vector_store_id
        )
        if vector_store_to_run is not None:
            if "custom_llm_provider" in vector_store_to_run:
                data["custom_llm_provider"] = vector_store_to_run.get("custom_llm_provider")
            
            if "litellm_credential_name" in vector_store_to_run:
                data["litellm_credential_name"] = vector_store_to_run.get("litellm_credential_name")

            if "litellm_params" in vector_store_to_run:
                litellm_params = vector_store_to_run.get("litellm_params", {}) or {}
                data.update(litellm_params)
    return data

@router.post("/v1/vector_stores/{vector_store_id}/search", dependencies=[Depends(user_api_key_auth)])
@router.post("/vector_stores/{vector_store_id}/search", dependencies=[Depends(user_api_key_auth)])
async def vector_store_search(
    request: Request,
    vector_store_id: str,
    fastapi_response: Response,
    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
):
    """
    Search a vector store.

    API Reference:
    https://platform.openai.com/docs/api-reference/vector-stores/search
    """
    from litellm.proxy.proxy_server import (
        _read_request_body,
        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,
    )

    data = await _read_request_body(request=request)
    if "vector_store_id" not in data:
        data["vector_store_id"] = vector_store_id
    
    data = _update_request_data_with_litellm_managed_vector_store_registry(
        data=data,
        vector_store_id=vector_store_id
    )
    
    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="avector_store_search",
            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,
        )



@router.post("/v1/vector_stores", dependencies=[Depends(user_api_key_auth)])
@router.post("/vector_stores", dependencies=[Depends(user_api_key_auth)])
async def vector_store_create(
    request: Request,
    fastapi_response: Response,
    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
):
    """
    Create a vector store.

    API Reference:
    https://platform.openai.com/docs/api-reference/vector-stores/create
    """
    from litellm.proxy.proxy_server import (
        _read_request_body,
        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,
    )

    data = await _read_request_body(request=request)
    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="avector_store_create",
            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,
        )