"""
RAG Endpoints for LiteLLM Proxy.

Provides:
- /rag/ingest: All-in-one document ingestion pipeline (Upload -> Chunk -> Embed -> Vector Store)
- /rag/query: RAG query pipeline (Search -> Rerank -> LLM Completion)
"""

import base64
from typing import Any, Dict, Optional, Tuple

import orjson
from fastapi import APIRouter, Depends, HTTPException, Request, Response, status
from fastapi.responses import ORJSONResponse

import litellm
from litellm._logging import verbose_proxy_logger
from litellm.proxy._types import *
from litellm.proxy.auth.user_api_key_auth import UserAPIKeyAuth, user_api_key_auth
from litellm.proxy.common_utils.http_parsing_utils import (
    _read_request_body,
    _safe_get_request_headers,
    get_form_data,
)

router = APIRouter()


def _build_file_metadata_entry(
    response: Any,
    file_data: Optional[Tuple[str, bytes, str]] = None,
    file_url: Optional[str] = None,
) -> Dict[str, Any]:
    """
    Build a file metadata entry for storing in vector_store_metadata.
    
    Args:
        response: The response from litellm.aingest containing file_id
        file_data: Optional tuple of (filename, content, content_type)
        file_url: Optional URL if file was ingested from URL
    
    Returns:
        Dictionary with file metadata (file_id, filename, file_url, ingested_at, etc.)
    """
    from datetime import datetime, timezone

    # Extract file_id from response
    file_id = None
    if hasattr(response, "get"):
        file_id = response.get("file_id")
    elif hasattr(response, "file_id"):
        file_id = response.file_id
    
    # Extract file information from file_data tuple
    filename = None
    file_size = None
    content_type = None
    
    if file_data:
        filename = file_data[0]
        file_size = len(file_data[1]) if len(file_data) > 1 else None
        content_type = file_data[2] if len(file_data) > 2 else None
    
    # Build file metadata entry
    file_entry = {
        "file_id": file_id,
        "filename": filename,
        "file_url": file_url,
        "ingested_at": datetime.now(timezone.utc).isoformat(),
    }
    
    # Add optional fields if available
    if file_size is not None:
        file_entry["file_size"] = file_size
    if content_type is not None:
        file_entry["content_type"] = content_type
    
    return file_entry


async def _save_vector_store_to_db_from_rag_ingest(
    response: Any,
    ingest_options: Dict[str, Any],
    prisma_client,
    user_api_key_dict: UserAPIKeyAuth,
    file_data: Optional[Tuple[str, bytes, str]] = None,
    file_url: Optional[str] = None,
) -> None:
    """
    Helper function to save a newly created vector store from RAG ingest to the database.
    
    This function:
    - Extracts vector store ID and config from the ingest response
    - Checks if the vector store already exists in the database
    - Creates a new database entry if it doesn't exist
    - Adds the vector store to the registry
    - Tracks team_id and user_id for access control
    
    Args:
        response: The response from litellm.aingest()
        ingest_options: The ingest options containing vector store config
        prisma_client: The Prisma database client
        user_api_key_dict: User API key authentication info
    """
    from litellm.proxy.vector_store_endpoints.management_endpoints import (
        create_vector_store_in_db,
    )

    # Handle both dict and object responses
    if hasattr(response, "get"):
        vector_store_id = response.get("vector_store_id")
    elif hasattr(response, "vector_store_id"):
        vector_store_id = response.vector_store_id
    else:
        verbose_proxy_logger.warning(
            f"Unable to extract vector_store_id from response type: {type(response)}"
        )
        return

    if vector_store_id is None or not isinstance(vector_store_id, str):
        verbose_proxy_logger.warning(
            "Vector store ID is None or not a string, skipping database save"
        )
        return

    vector_store_config = ingest_options.get("vector_store", {})
    custom_llm_provider = vector_store_config.get("custom_llm_provider")
    
    # Extract litellm_vector_store_params for custom name and description
    litellm_vector_store_params = ingest_options.get("litellm_vector_store_params", {})
    custom_vector_store_name = litellm_vector_store_params.get("vector_store_name")
    custom_vector_store_description = litellm_vector_store_params.get("vector_store_description")
    
    # Extract provider-specific params from vector_store_config to save as litellm_params
    # This ensures params like aws_region_name, embedding_model, etc. are available for search
    provider_specific_params = {}
    excluded_keys = {"custom_llm_provider", "vector_store_id"}
    for key, value in vector_store_config.items():
        if key not in excluded_keys and value is not None:
            provider_specific_params[key] = value

    # Build file metadata entry using helper
    file_entry = _build_file_metadata_entry(
        response=response,
        file_data=file_data,
        file_url=file_url,
    )

    try:
        # Check if vector store already exists in database
        existing_vector_store = (
            await prisma_client.db.litellm_managedvectorstorestable.find_unique(
                where={"vector_store_id": vector_store_id}
            )
        )

        # Only create if it doesn't exist
        if existing_vector_store is None:
            verbose_proxy_logger.info(
                f"Saving newly created vector store {vector_store_id} to database"
            )

            # Initialize metadata with first file
            initial_metadata = {
                "ingested_files": [file_entry]
            }
            
            # Use custom name if provided, otherwise default
            vector_store_name = custom_vector_store_name or f"RAG Vector Store - {vector_store_id[:8]}"
            vector_store_description = custom_vector_store_description or "Created via RAG ingest endpoint"

            await create_vector_store_in_db(
                vector_store_id=vector_store_id,
                custom_llm_provider=custom_llm_provider or "openai",
                prisma_client=prisma_client,
                vector_store_name=vector_store_name,
                vector_store_description=vector_store_description,
                vector_store_metadata=initial_metadata,
                litellm_params=provider_specific_params if provider_specific_params else None,
                team_id=user_api_key_dict.team_id,
                user_id=user_api_key_dict.user_id,
            )

            verbose_proxy_logger.info(
                f"Vector store {vector_store_id} saved to database successfully"
            )
        else:
            verbose_proxy_logger.info(
                f"Vector store {vector_store_id} already exists, appending file to metadata"
            )
            
            # Update existing vector store with new file
            existing_metadata = existing_vector_store.vector_store_metadata or {}
            if isinstance(existing_metadata, str):
                import json
                existing_metadata = json.loads(existing_metadata)
            
            ingested_files = existing_metadata.get("ingested_files", [])
            ingested_files.append(file_entry)
            existing_metadata["ingested_files"] = ingested_files
            
            # Update the vector store
            from litellm.proxy.utils import safe_dumps
            await prisma_client.db.litellm_managedvectorstorestable.update(
                where={"vector_store_id": vector_store_id},
                data={"vector_store_metadata": safe_dumps(existing_metadata)}
            )
            
            verbose_proxy_logger.info(
                f"Added file {file_entry.get('filename') or file_entry.get('file_url', 'Unknown')} to vector store {vector_store_id} metadata"
            )
    except Exception as db_error:
        # Log the error but don't fail the request since ingestion succeeded
        verbose_proxy_logger.exception(
            f"Failed to save vector store {vector_store_id} to database: {db_error}"
        )


async def parse_rag_ingest_request(
    request: Request,
) -> Tuple[Dict[str, Any], Optional[Tuple[str, bytes, str]], Optional[str], Optional[str]]:
    """
    Parse RAG ingest request.

    Supports:
    - Form: file + request JSON in form field
    - JSON body for URL-based ingestion

    Returns:
        Tuple of (ingest_options, file_data, file_url, file_id)
    """
    headers = _safe_get_request_headers(request)
    content_type = headers.get("content-type", "")

    file_data = None
    file_url = None
    file_id = None
    ingest_options: Dict[str, Any] = {}

    if "multipart/form-data" in content_type:
        # Form upload
        form_data = await get_form_data(request)

        # Get file
        file_obj = form_data.get("file")
        if file_obj is not None and hasattr(file_obj, "read"):
            file_content = await file_obj.read()
            file_data = (file_obj.filename, file_content, file_obj.content_type)

        # Parse JSON from 'request' form field (contains full request body as JSON)
        request_json_str = form_data.get("request")
        if request_json_str:
            request_data = orjson.loads(request_json_str)
            ingest_options = request_data.get("ingest_options", {})
            file_url = request_data.get("file_url")
            file_id = request_data.get("file_id")

    else:
        # JSON body
        data = await _read_request_body(request)
        ingest_options = data.get("ingest_options", {})
        file_url = data.get("file_url")
        file_id = data.get("file_id")

        # Handle base64-encoded file in JSON body
        file_obj = data.get("file")
        if file_obj and isinstance(file_obj, dict):
            filename = file_obj.get("filename")
            content_b64 = file_obj.get("content")
            content_type = file_obj.get("content_type", "application/octet-stream")

            if filename and content_b64:
                try:
                    file_content = base64.b64decode(content_b64)
                    file_data = (filename, file_content, content_type)
                except Exception as e:
                    raise HTTPException(
                        status_code=400,
                        detail={"error": f"Invalid base64 content: {e}"},
                    )

    # Validate
    if file_data is None and file_url is None and file_id is None:
        raise HTTPException(
            status_code=400,
            detail={"error": "Must provide file, file_url, or file_id"},
        )

    if "vector_store" not in ingest_options:
        raise HTTPException(
            status_code=400,
            detail={"error": "ingest_options must contain 'vector_store' configuration"},
        )

    return ingest_options, file_data, file_url, file_id


@router.post(
    "/v1/rag/ingest",
    dependencies=[Depends(user_api_key_auth)],
    response_class=ORJSONResponse,
    tags=["rag"],
)
@router.post(
    "/rag/ingest",
    dependencies=[Depends(user_api_key_auth)],
    response_class=ORJSONResponse,
    tags=["rag"],
)
async def rag_ingest(
    request: Request,
    fastapi_response: Response,
    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
):
    """
    RAG Ingest endpoint - all-in-one document ingestion pipeline.

    Supports form upload (for files) or JSON body (for URLs).

    ## Form upload (for files):
    ```bash
    curl -X POST "http://localhost:4000/v1/rag/ingest" \\
        -H "Authorization: Bearer sk-1234" \\
        -F file="@document.pdf" \\
        -F 'ingest_options={"vector_store": {"custom_llm_provider": "openai"}}'
    ```

    ## JSON body (for URLs):
    ```bash
    curl -X POST "http://localhost:4000/v1/rag/ingest" \\
        -H "Authorization: Bearer sk-1234" \\
        -H "Content-Type: application/json" \\
        -d '{
            "file_url": "https://example.com/document.pdf",
            "ingest_options": {"vector_store": {"custom_llm_provider": "openai"}}
        }'
    ```

    ## Bedrock:
    ```bash
    curl -X POST "http://localhost:4000/v1/rag/ingest" \\
        -H "Authorization: Bearer sk-1234" \\
        -F file="@document.pdf" \\
        -F 'ingest_options={"vector_store": {"custom_llm_provider": "bedrock"}}'
    ```
    """
    from litellm.proxy.proxy_server import (
        add_litellm_data_to_request,
        general_settings,
        llm_router,
        prisma_client,
        proxy_config,
        version,
    )

    try:
        # Parse request
        ingest_options, file_data, file_url, file_id = await parse_rag_ingest_request(request)

        # INTERNAL_USER_VIEW_ONLY can ingest to existing vector stores only
        if (
            user_api_key_dict.user_role == LitellmUserRoles.INTERNAL_USER_VIEW_ONLY.value
            and not ingest_options.get("vector_store", {}).get("vector_store_id")
        ):
            raise HTTPException(
                status_code=status.HTTP_403_FORBIDDEN,
                detail={
                    "error": "internal_user_viewer role can only ingest files to an existing vector store. "
                    "Provide 'vector_store_id' in ingest_options.vector_store."
                },
            )

        # Add litellm data
        request_data: Dict[str, Any] = {}
        request_data = await add_litellm_data_to_request(
            data=request_data,
            request=request,
            general_settings=general_settings,
            user_api_key_dict=user_api_key_dict,
            version=version,
            proxy_config=proxy_config,
        )

        verbose_proxy_logger.debug(f"RAG Ingest - options: {ingest_options}")

        # Call ingest
        response = await litellm.aingest(
            ingest_options=ingest_options,
            file_data=file_data,
            file_url=file_url,
            file_id=file_id,
            router=llm_router,
            **request_data,
        )

        # Save vector store to database if it was newly created and prisma_client is available
        verbose_proxy_logger.debug(
            f"RAG Ingest - Checking database save conditions: prisma_client={prisma_client is not None}, response={response is not None}, response_type={type(response)}"
        )
        
        if prisma_client is not None and response is not None:
            await _save_vector_store_to_db_from_rag_ingest(
                response=response,
                ingest_options=ingest_options,
                prisma_client=prisma_client,
                user_api_key_dict=user_api_key_dict,
                file_data=file_data,
                file_url=file_url,
            )
        else:
            verbose_proxy_logger.warning(
                f"Skipping database save: prisma_client={prisma_client is not None}, response={response is not None}"
            )

        return response

    except HTTPException:
        raise
    except Exception as e:
        verbose_proxy_logger.exception(f"RAG Ingest failed: {e}")
        raise HTTPException(
            status_code=500,
            detail={"error": str(e)},
        )


@router.post(
    "/v1/rag/query",
    dependencies=[Depends(user_api_key_auth)],
    response_class=ORJSONResponse,
    tags=["rag"],
)
@router.post(
    "/rag/query",
    dependencies=[Depends(user_api_key_auth)],
    response_class=ORJSONResponse,
    tags=["rag"],
)
async def rag_query(
    request: Request,
    fastapi_response: Response,
    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
):
    """
    RAG Query endpoint - search vector store, optionally rerank, and generate LLM response.

    This endpoint:
    1. Extracts the query from the last user message
    2. Searches the vector store for relevant context
    3. Optionally reranks the results
    4. Generates an LLM response with the retrieved context

    ## Example Request:
    ```bash
    curl -X POST "http://localhost:4000/v1/rag/query" \\
        -H "Authorization: Bearer sk-1234" \\
        -H "Content-Type: application/json" \\
        -d '{
            "model": "gpt-4o-mini",
            "messages": [{"role": "user", "content": "What is LiteLLM?"}],
            "retrieval_config": {
                "vector_store_id": "vs_abc123",
                "custom_llm_provider": "openai",
                "top_k": 5
            }
        }'
    ```

    ## With Reranking:
    ```bash
    curl -X POST "http://localhost:4000/v1/rag/query" \\
        -H "Authorization: Bearer sk-1234" \\
        -H "Content-Type: application/json" \\
        -d '{
            "model": "gpt-4o-mini",
            "messages": [{"role": "user", "content": "What is LiteLLM?"}],
            "retrieval_config": {
                "vector_store_id": "vs_abc123",
                "custom_llm_provider": "openai",
                "top_k": 10
            },
            "rerank": {
                "enabled": true,
                "model": "cohere/rerank-english-v3.0",
                "top_n": 3
            }
        }'
    ```
    """
    from litellm.proxy.proxy_server import (
        add_litellm_data_to_request,
        general_settings,
        llm_router,
        proxy_config,
        version,
    )

    try:
        # Parse request body
        data = await _read_request_body(request)

        # Extract required fields
        model = data.get("model")
        messages = data.get("messages")
        retrieval_config = data.get("retrieval_config")
        rerank = data.get("rerank")
        stream = data.get("stream", False)

        # Validate required fields
        if not model:
            raise HTTPException(
                status_code=400,
                detail={"error": "model is required"},
            )
        if not messages:
            raise HTTPException(
                status_code=400,
                detail={"error": "messages is required"},
            )
        if not retrieval_config:
            raise HTTPException(
                status_code=400,
                detail={"error": "retrieval_config is required"},
            )
        if "vector_store_id" not in retrieval_config:
            raise HTTPException(
                status_code=400,
                detail={"error": "retrieval_config must contain 'vector_store_id'"},
            )

        # Add litellm data
        request_data: Dict[str, Any] = {}
        request_data = await add_litellm_data_to_request(
            data=request_data,
            request=request,
            general_settings=general_settings,
            user_api_key_dict=user_api_key_dict,
            version=version,
            proxy_config=proxy_config,
        )

        verbose_proxy_logger.debug(
            f"RAG Query - model: {model}, retrieval_config: {retrieval_config}"
        )

        # Call query
        response = await litellm.aquery(
            model=model,
            messages=messages,
            retrieval_config=retrieval_config,
            rerank=rerank,
            stream=stream,
            router=llm_router,
            **request_data,
        )

        return response

    except HTTPException:
        raise
    except Exception as e:
        verbose_proxy_logger.exception(f"RAG Query failed: {e}")
        raise HTTPException(
            status_code=500,
            detail={"error": str(e)},
        )
