open-webui/backend/open_webui/routers/retrieval.py

import json
import logging
import mimetypes
import os
import shutil
import asyncio

import uuid
from datetime import datetime
from pathlib import Path
from typing import Iterator, List, Optional, Sequence, Union

from fastapi import (
    Depends,
    FastAPI,
    File,
    Form,
    HTTPException,
    UploadFile,
    Request,
    status,
    APIRouter,
)
from fastapi.middleware.cors import CORSMiddleware
from fastapi.concurrency import run_in_threadpool
from pydantic import BaseModel
import tiktoken


from langchain.text_splitter import RecursiveCharacterTextSplitter, TokenTextSplitter
from langchain_text_splitters import MarkdownHeaderTextSplitter
from langchain_core.documents import Document

from open_webui.models.files import FileModel, Files
from open_webui.models.knowledge import Knowledges
from open_webui.storage.provider import Storage


from open_webui.retrieval.vector.factory import VECTOR_DB_CLIENT

# Document loaders
from open_webui.retrieval.loaders.main import Loader
from open_webui.retrieval.loaders.youtube import YoutubeLoader

# Web search engines
from open_webui.retrieval.web.main import SearchResult
from open_webui.retrieval.web.utils import get_web_loader
from open_webui.retrieval.web.ollama import search_ollama_cloud
from open_webui.retrieval.web.perplexity_search import search_perplexity_search
from open_webui.retrieval.web.brave import search_brave
from open_webui.retrieval.web.kagi import search_kagi
from open_webui.retrieval.web.mojeek import search_mojeek
from open_webui.retrieval.web.bocha import search_bocha
from open_webui.retrieval.web.duckduckgo import search_duckduckgo
from open_webui.retrieval.web.google_pse import search_google_pse
from open_webui.retrieval.web.jina_search import search_jina
from open_webui.retrieval.web.searchapi import search_searchapi
from open_webui.retrieval.web.serpapi import search_serpapi
from open_webui.retrieval.web.searxng import search_searxng
from open_webui.retrieval.web.yacy import search_yacy
from open_webui.retrieval.web.serper import search_serper
from open_webui.retrieval.web.serply import search_serply
from open_webui.retrieval.web.serpstack import search_serpstack
from open_webui.retrieval.web.tavily import search_tavily
from open_webui.retrieval.web.bing import search_bing
from open_webui.retrieval.web.exa import search_exa
from open_webui.retrieval.web.perplexity import search_perplexity
from open_webui.retrieval.web.sougou import search_sougou
from open_webui.retrieval.web.firecrawl import search_firecrawl
from open_webui.retrieval.web.external import search_external

from open_webui.retrieval.utils import (
    get_embedding_function,
    get_reranking_function,
    get_model_path,
    query_collection,
    query_collection_with_hybrid_search,
    query_doc,
    query_doc_with_hybrid_search,
)
from open_webui.retrieval.vector.utils import filter_metadata
from open_webui.utils.misc import (
    calculate_sha256_string,
)
from open_webui.utils.auth import get_admin_user, get_verified_user

from open_webui.config import (
    ENV,
    RAG_EMBEDDING_MODEL_AUTO_UPDATE,
    RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
    RAG_RERANKING_MODEL_AUTO_UPDATE,
    RAG_RERANKING_MODEL_TRUST_REMOTE_CODE,
    UPLOAD_DIR,
    DEFAULT_LOCALE,
    RAG_EMBEDDING_CONTENT_PREFIX,
    RAG_EMBEDDING_QUERY_PREFIX,
)
from open_webui.env import (
    SRC_LOG_LEVELS,
    DEVICE_TYPE,
    DOCKER,
    SENTENCE_TRANSFORMERS_BACKEND,
    SENTENCE_TRANSFORMERS_MODEL_KWARGS,
    SENTENCE_TRANSFORMERS_CROSS_ENCODER_BACKEND,
    SENTENCE_TRANSFORMERS_CROSS_ENCODER_MODEL_KWARGS,
)

from open_webui.constants import ERROR_MESSAGES

log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["RAG"])

##########################################
#
# Utility functions
#
##########################################


def get_ef(
    engine: str,
    embedding_model: str,
    auto_update: bool = False,
):
    ef = None
    if embedding_model and engine == "":
        from sentence_transformers import SentenceTransformer

        try:
            ef = SentenceTransformer(
                get_model_path(embedding_model, auto_update),
                device=DEVICE_TYPE,
                trust_remote_code=RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
                backend=SENTENCE_TRANSFORMERS_BACKEND,
                model_kwargs=SENTENCE_TRANSFORMERS_MODEL_KWARGS,
            )
        except Exception as e:
            log.debug(f"Error loading SentenceTransformer: {e}")

    return ef


def get_rf(
    engine: str = "",
    reranking_model: Optional[str] = None,
    external_reranker_url: str = "",
    external_reranker_api_key: str = "",
    auto_update: bool = False,
):
    rf = None
    if reranking_model:
        if any(model in reranking_model for model in ["jinaai/jina-colbert-v2"]):
            try:
                from open_webui.retrieval.models.colbert import ColBERT

                rf = ColBERT(
                    get_model_path(reranking_model, auto_update),
                    env="docker" if DOCKER else None,
                )

            except Exception as e:
                log.error(f"ColBERT: {e}")
                raise Exception(ERROR_MESSAGES.DEFAULT(e))
        else:
            if engine == "external":
                try:
                    from open_webui.retrieval.models.external import ExternalReranker

                    rf = ExternalReranker(
                        url=external_reranker_url,
                        api_key=external_reranker_api_key,
                        model=reranking_model,
                    )
                except Exception as e:
                    log.error(f"ExternalReranking: {e}")
                    raise Exception(ERROR_MESSAGES.DEFAULT(e))
            else:
                import sentence_transformers

                try:
                    rf = sentence_transformers.CrossEncoder(
                        get_model_path(reranking_model, auto_update),
                        device=DEVICE_TYPE,
                        trust_remote_code=RAG_RERANKING_MODEL_TRUST_REMOTE_CODE,
                        backend=SENTENCE_TRANSFORMERS_CROSS_ENCODER_BACKEND,
                        model_kwargs=SENTENCE_TRANSFORMERS_CROSS_ENCODER_MODEL_KWARGS,
                    )
                except Exception as e:
                    log.error(f"CrossEncoder: {e}")
                    raise Exception(ERROR_MESSAGES.DEFAULT("CrossEncoder error"))

    return rf


##########################################
#
# API routes
#
##########################################


router = APIRouter()


class CollectionNameForm(BaseModel):
    collection_name: Optional[str] = None


class ProcessUrlForm(CollectionNameForm):
    url: str


class SearchForm(BaseModel):
    queries: List[str]


@router.get("/")
async def get_status(request: Request):
    return {
        "status": True,
        "chunk_size": request.app.state.config.CHUNK_SIZE,
        "chunk_overlap": request.app.state.config.CHUNK_OVERLAP,
        "template": request.app.state.config.RAG_TEMPLATE,
        "embedding_engine": request.app.state.config.RAG_EMBEDDING_ENGINE,
        "embedding_model": request.app.state.config.RAG_EMBEDDING_MODEL,
        "reranking_model": request.app.state.config.RAG_RERANKING_MODEL,
        "embedding_batch_size": request.app.state.config.RAG_EMBEDDING_BATCH_SIZE,
    }


@router.get("/embedding")
async def get_embedding_config(request: Request, user=Depends(get_admin_user)):
    return {
        "status": True,
        "embedding_engine": request.app.state.config.RAG_EMBEDDING_ENGINE,
        "embedding_model": request.app.state.config.RAG_EMBEDDING_MODEL,
        "embedding_batch_size": request.app.state.config.RAG_EMBEDDING_BATCH_SIZE,
        "openai_config": {
            "url": request.app.state.config.RAG_OPENAI_API_BASE_URL,
            "key": request.app.state.config.RAG_OPENAI_API_KEY,
        },
        "ollama_config": {
            "url": request.app.state.config.RAG_OLLAMA_BASE_URL,
            "key": request.app.state.config.RAG_OLLAMA_API_KEY,
        },
        "azure_openai_config": {
            "url": request.app.state.config.RAG_AZURE_OPENAI_BASE_URL,
            "key": request.app.state.config.RAG_AZURE_OPENAI_API_KEY,
            "version": request.app.state.config.RAG_AZURE_OPENAI_API_VERSION,
        },
    }


class OpenAIConfigForm(BaseModel):
    url: str
    key: str


class OllamaConfigForm(BaseModel):
    url: str
    key: str


class AzureOpenAIConfigForm(BaseModel):
    url: str
    key: str
    version: str


class EmbeddingModelUpdateForm(BaseModel):
    openai_config: Optional[OpenAIConfigForm] = None
    ollama_config: Optional[OllamaConfigForm] = None
    azure_openai_config: Optional[AzureOpenAIConfigForm] = None
    embedding_engine: str
    embedding_model: str
    embedding_batch_size: Optional[int] = 1


@router.post("/embedding/update")
async def update_embedding_config(
    request: Request, form_data: EmbeddingModelUpdateForm, user=Depends(get_admin_user)
):
    log.info(
        f"Updating embedding model: {request.app.state.config.RAG_EMBEDDING_MODEL} to {form_data.embedding_model}"
    )
    if request.app.state.config.RAG_EMBEDDING_ENGINE == "":
        # unloads current internal embedding model and clears VRAM cache
        request.app.state.ef = None
        request.app.state.EMBEDDING_FUNCTION = None
        import gc

        gc.collect()
        if DEVICE_TYPE == "cuda":
            import torch

            if torch.cuda.is_available():
                torch.cuda.empty_cache()
    try:
        request.app.state.config.RAG_EMBEDDING_ENGINE = form_data.embedding_engine
        request.app.state.config.RAG_EMBEDDING_MODEL = form_data.embedding_model

        if request.app.state.config.RAG_EMBEDDING_ENGINE in [
            "ollama",
            "openai",
            "azure_openai",
        ]:
            if form_data.openai_config is not None:
                request.app.state.config.RAG_OPENAI_API_BASE_URL = (
                    form_data.openai_config.url
                )
                request.app.state.config.RAG_OPENAI_API_KEY = (
                    form_data.openai_config.key
                )

            if form_data.ollama_config is not None:
                request.app.state.config.RAG_OLLAMA_BASE_URL = (
                    form_data.ollama_config.url
                )
                request.app.state.config.RAG_OLLAMA_API_KEY = (
                    form_data.ollama_config.key
                )

            if form_data.azure_openai_config is not None:
                request.app.state.config.RAG_AZURE_OPENAI_BASE_URL = (
                    form_data.azure_openai_config.url
                )
                request.app.state.config.RAG_AZURE_OPENAI_API_KEY = (
                    form_data.azure_openai_config.key
                )
                request.app.state.config.RAG_AZURE_OPENAI_API_VERSION = (
                    form_data.azure_openai_config.version
                )

            request.app.state.config.RAG_EMBEDDING_BATCH_SIZE = (
                form_data.embedding_batch_size
            )

        request.app.state.ef = get_ef(
            request.app.state.config.RAG_EMBEDDING_ENGINE,
            request.app.state.config.RAG_EMBEDDING_MODEL,
        )

        request.app.state.EMBEDDING_FUNCTION = get_embedding_function(
            request.app.state.config.RAG_EMBEDDING_ENGINE,
            request.app.state.config.RAG_EMBEDDING_MODEL,
            request.app.state.ef,
            (
                request.app.state.config.RAG_OPENAI_API_BASE_URL
                if request.app.state.config.RAG_EMBEDDING_ENGINE == "openai"
                else (
                    request.app.state.config.RAG_OLLAMA_BASE_URL
                    if request.app.state.config.RAG_EMBEDDING_ENGINE == "ollama"
                    else request.app.state.config.RAG_AZURE_OPENAI_BASE_URL
                )
            ),
            (
                request.app.state.config.RAG_OPENAI_API_KEY
                if request.app.state.config.RAG_EMBEDDING_ENGINE == "openai"
                else (
                    request.app.state.config.RAG_OLLAMA_API_KEY
                    if request.app.state.config.RAG_EMBEDDING_ENGINE == "ollama"
                    else request.app.state.config.RAG_AZURE_OPENAI_API_KEY
                )
            ),
            request.app.state.config.RAG_EMBEDDING_BATCH_SIZE,
            azure_api_version=(
                request.app.state.config.RAG_AZURE_OPENAI_API_VERSION
                if request.app.state.config.RAG_EMBEDDING_ENGINE == "azure_openai"
                else None
            ),
        )

        return {
            "status": True,
            "embedding_engine": request.app.state.config.RAG_EMBEDDING_ENGINE,
            "embedding_model": request.app.state.config.RAG_EMBEDDING_MODEL,
            "embedding_batch_size": request.app.state.config.RAG_EMBEDDING_BATCH_SIZE,
            "openai_config": {
                "url": request.app.state.config.RAG_OPENAI_API_BASE_URL,
                "key": request.app.state.config.RAG_OPENAI_API_KEY,
            },
            "ollama_config": {
                "url": request.app.state.config.RAG_OLLAMA_BASE_URL,
                "key": request.app.state.config.RAG_OLLAMA_API_KEY,
            },
            "azure_openai_config": {
                "url": request.app.state.config.RAG_AZURE_OPENAI_BASE_URL,
                "key": request.app.state.config.RAG_AZURE_OPENAI_API_KEY,
                "version": request.app.state.config.RAG_AZURE_OPENAI_API_VERSION,
            },
        }
    except Exception as e:
        log.exception(f"Problem updating embedding model: {e}")
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=ERROR_MESSAGES.DEFAULT(e),
        )


@router.get("/config")
async def get_rag_config(request: Request, user=Depends(get_admin_user)):
    return {
        "status": True,
        # RAG settings
        "RAG_TEMPLATE": request.app.state.config.RAG_TEMPLATE,
        "TOP_K": request.app.state.config.TOP_K,
        "BYPASS_EMBEDDING_AND_RETRIEVAL": request.app.state.config.BYPASS_EMBEDDING_AND_RETRIEVAL,
        "RAG_FULL_CONTEXT": request.app.state.config.RAG_FULL_CONTEXT,
        # Hybrid search settings
        "ENABLE_RAG_HYBRID_SEARCH": request.app.state.config.ENABLE_RAG_HYBRID_SEARCH,
        "TOP_K_RERANKER": request.app.state.config.TOP_K_RERANKER,
        "RELEVANCE_THRESHOLD": request.app.state.config.RELEVANCE_THRESHOLD,
        "HYBRID_BM25_WEIGHT": request.app.state.config.HYBRID_BM25_WEIGHT,
        # Content extraction settings
        "CONTENT_EXTRACTION_ENGINE": request.app.state.config.CONTENT_EXTRACTION_ENGINE,
        "PDF_EXTRACT_IMAGES": request.app.state.config.PDF_EXTRACT_IMAGES,
        "DATALAB_MARKER_API_KEY": request.app.state.config.DATALAB_MARKER_API_KEY,
        "DATALAB_MARKER_API_BASE_URL": request.app.state.config.DATALAB_MARKER_API_BASE_URL,
        "DATALAB_MARKER_ADDITIONAL_CONFIG": request.app.state.config.DATALAB_MARKER_ADDITIONAL_CONFIG,
        "DATALAB_MARKER_SKIP_CACHE": request.app.state.config.DATALAB_MARKER_SKIP_CACHE,
        "DATALAB_MARKER_FORCE_OCR": request.app.state.config.DATALAB_MARKER_FORCE_OCR,
        "DATALAB_MARKER_PAGINATE": request.app.state.config.DATALAB_MARKER_PAGINATE,
        "DATALAB_MARKER_STRIP_EXISTING_OCR": request.app.state.config.DATALAB_MARKER_STRIP_EXISTING_OCR,
        "DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION": request.app.state.config.DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION,
        "DATALAB_MARKER_FORMAT_LINES": request.app.state.config.DATALAB_MARKER_FORMAT_LINES,
        "DATALAB_MARKER_USE_LLM": request.app.state.config.DATALAB_MARKER_USE_LLM,
        "DATALAB_MARKER_OUTPUT_FORMAT": request.app.state.config.DATALAB_MARKER_OUTPUT_FORMAT,
        "EXTERNAL_DOCUMENT_LOADER_URL": request.app.state.config.EXTERNAL_DOCUMENT_LOADER_URL,
        "EXTERNAL_DOCUMENT_LOADER_API_KEY": request.app.state.config.EXTERNAL_DOCUMENT_LOADER_API_KEY,
        "TIKA_SERVER_URL": request.app.state.config.TIKA_SERVER_URL,
        "DOCLING_SERVER_URL": request.app.state.config.DOCLING_SERVER_URL,
        "DOCLING_DO_OCR": request.app.state.config.DOCLING_DO_OCR,
        "DOCLING_FORCE_OCR": request.app.state.config.DOCLING_FORCE_OCR,
        "DOCLING_OCR_ENGINE": request.app.state.config.DOCLING_OCR_ENGINE,
        "DOCLING_OCR_LANG": request.app.state.config.DOCLING_OCR_LANG,
        "DOCLING_PDF_BACKEND": request.app.state.config.DOCLING_PDF_BACKEND,
        "DOCLING_TABLE_MODE": request.app.state.config.DOCLING_TABLE_MODE,
        "DOCLING_PIPELINE": request.app.state.config.DOCLING_PIPELINE,
        "DOCLING_DO_PICTURE_DESCRIPTION": request.app.state.config.DOCLING_DO_PICTURE_DESCRIPTION,
        "DOCLING_PICTURE_DESCRIPTION_MODE": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_MODE,
        "DOCLING_PICTURE_DESCRIPTION_LOCAL": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_LOCAL,
        "DOCLING_PICTURE_DESCRIPTION_API": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_API,
        "DOCUMENT_INTELLIGENCE_ENDPOINT": request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT,
        "DOCUMENT_INTELLIGENCE_KEY": request.app.state.config.DOCUMENT_INTELLIGENCE_KEY,
        "MISTRAL_OCR_API_KEY": request.app.state.config.MISTRAL_OCR_API_KEY,
        # Reranking settings
        "RAG_RERANKING_MODEL": request.app.state.config.RAG_RERANKING_MODEL,
        "RAG_RERANKING_ENGINE": request.app.state.config.RAG_RERANKING_ENGINE,
        "RAG_EXTERNAL_RERANKER_URL": request.app.state.config.RAG_EXTERNAL_RERANKER_URL,
        "RAG_EXTERNAL_RERANKER_API_KEY": request.app.state.config.RAG_EXTERNAL_RERANKER_API_KEY,
        # Chunking settings
        "TEXT_SPLITTER": request.app.state.config.TEXT_SPLITTER,
        "CHUNK_SIZE": request.app.state.config.CHUNK_SIZE,
        "CHUNK_OVERLAP": request.app.state.config.CHUNK_OVERLAP,
        # File upload settings
        "FILE_MAX_SIZE": request.app.state.config.FILE_MAX_SIZE,
        "FILE_MAX_COUNT": request.app.state.config.FILE_MAX_COUNT,
        "FILE_IMAGE_COMPRESSION_WIDTH": request.app.state.config.FILE_IMAGE_COMPRESSION_WIDTH,
        "FILE_IMAGE_COMPRESSION_HEIGHT": request.app.state.config.FILE_IMAGE_COMPRESSION_HEIGHT,
        "ALLOWED_FILE_EXTENSIONS": request.app.state.config.ALLOWED_FILE_EXTENSIONS,
        # Integration settings
        "ENABLE_GOOGLE_DRIVE_INTEGRATION": request.app.state.config.ENABLE_GOOGLE_DRIVE_INTEGRATION,
        "ENABLE_ONEDRIVE_INTEGRATION": request.app.state.config.ENABLE_ONEDRIVE_INTEGRATION,
        # Web search settings
        "web": {
            "ENABLE_WEB_SEARCH": request.app.state.config.ENABLE_WEB_SEARCH,
            "WEB_SEARCH_ENGINE": request.app.state.config.WEB_SEARCH_ENGINE,
            "WEB_SEARCH_TRUST_ENV": request.app.state.config.WEB_SEARCH_TRUST_ENV,
            "WEB_SEARCH_RESULT_COUNT": request.app.state.config.WEB_SEARCH_RESULT_COUNT,
            "WEB_SEARCH_CONCURRENT_REQUESTS": request.app.state.config.WEB_SEARCH_CONCURRENT_REQUESTS,
            "WEB_LOADER_CONCURRENT_REQUESTS": request.app.state.config.WEB_LOADER_CONCURRENT_REQUESTS,
            "WEB_SEARCH_DOMAIN_FILTER_LIST": request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            "BYPASS_WEB_SEARCH_EMBEDDING_AND_RETRIEVAL": request.app.state.config.BYPASS_WEB_SEARCH_EMBEDDING_AND_RETRIEVAL,
            "BYPASS_WEB_SEARCH_WEB_LOADER": request.app.state.config.BYPASS_WEB_SEARCH_WEB_LOADER,
            "OLLAMA_CLOUD_WEB_SEARCH_API_KEY": request.app.state.config.OLLAMA_CLOUD_WEB_SEARCH_API_KEY,
            "SEARXNG_QUERY_URL": request.app.state.config.SEARXNG_QUERY_URL,
            "YACY_QUERY_URL": request.app.state.config.YACY_QUERY_URL,
            "YACY_USERNAME": request.app.state.config.YACY_USERNAME,
            "YACY_PASSWORD": request.app.state.config.YACY_PASSWORD,
            "GOOGLE_PSE_API_KEY": request.app.state.config.GOOGLE_PSE_API_KEY,
            "GOOGLE_PSE_ENGINE_ID": request.app.state.config.GOOGLE_PSE_ENGINE_ID,
            "BRAVE_SEARCH_API_KEY": request.app.state.config.BRAVE_SEARCH_API_KEY,
            "KAGI_SEARCH_API_KEY": request.app.state.config.KAGI_SEARCH_API_KEY,
            "MOJEEK_SEARCH_API_KEY": request.app.state.config.MOJEEK_SEARCH_API_KEY,
            "BOCHA_SEARCH_API_KEY": request.app.state.config.BOCHA_SEARCH_API_KEY,
            "SERPSTACK_API_KEY": request.app.state.config.SERPSTACK_API_KEY,
            "SERPSTACK_HTTPS": request.app.state.config.SERPSTACK_HTTPS,
            "SERPER_API_KEY": request.app.state.config.SERPER_API_KEY,
            "SERPLY_API_KEY": request.app.state.config.SERPLY_API_KEY,
            "TAVILY_API_KEY": request.app.state.config.TAVILY_API_KEY,
            "SEARCHAPI_API_KEY": request.app.state.config.SEARCHAPI_API_KEY,
            "SEARCHAPI_ENGINE": request.app.state.config.SEARCHAPI_ENGINE,
            "SERPAPI_API_KEY": request.app.state.config.SERPAPI_API_KEY,
            "SERPAPI_ENGINE": request.app.state.config.SERPAPI_ENGINE,
            "JINA_API_KEY": request.app.state.config.JINA_API_KEY,
            "BING_SEARCH_V7_ENDPOINT": request.app.state.config.BING_SEARCH_V7_ENDPOINT,
            "BING_SEARCH_V7_SUBSCRIPTION_KEY": request.app.state.config.BING_SEARCH_V7_SUBSCRIPTION_KEY,
            "EXA_API_KEY": request.app.state.config.EXA_API_KEY,
            "PERPLEXITY_API_KEY": request.app.state.config.PERPLEXITY_API_KEY,
            "PERPLEXITY_MODEL": request.app.state.config.PERPLEXITY_MODEL,
            "PERPLEXITY_SEARCH_CONTEXT_USAGE": request.app.state.config.PERPLEXITY_SEARCH_CONTEXT_USAGE,
            "SOUGOU_API_SID": request.app.state.config.SOUGOU_API_SID,
            "SOUGOU_API_SK": request.app.state.config.SOUGOU_API_SK,
            "WEB_LOADER_ENGINE": request.app.state.config.WEB_LOADER_ENGINE,
            "ENABLE_WEB_LOADER_SSL_VERIFICATION": request.app.state.config.ENABLE_WEB_LOADER_SSL_VERIFICATION,
            "PLAYWRIGHT_WS_URL": request.app.state.config.PLAYWRIGHT_WS_URL,
            "PLAYWRIGHT_TIMEOUT": request.app.state.config.PLAYWRIGHT_TIMEOUT,
            "FIRECRAWL_API_KEY": request.app.state.config.FIRECRAWL_API_KEY,
            "FIRECRAWL_API_BASE_URL": request.app.state.config.FIRECRAWL_API_BASE_URL,
            "TAVILY_EXTRACT_DEPTH": request.app.state.config.TAVILY_EXTRACT_DEPTH,
            "EXTERNAL_WEB_SEARCH_URL": request.app.state.config.EXTERNAL_WEB_SEARCH_URL,
            "EXTERNAL_WEB_SEARCH_API_KEY": request.app.state.config.EXTERNAL_WEB_SEARCH_API_KEY,
            "EXTERNAL_WEB_LOADER_URL": request.app.state.config.EXTERNAL_WEB_LOADER_URL,
            "EXTERNAL_WEB_LOADER_API_KEY": request.app.state.config.EXTERNAL_WEB_LOADER_API_KEY,
            "YOUTUBE_LOADER_LANGUAGE": request.app.state.config.YOUTUBE_LOADER_LANGUAGE,
            "YOUTUBE_LOADER_PROXY_URL": request.app.state.config.YOUTUBE_LOADER_PROXY_URL,
            "YOUTUBE_LOADER_TRANSLATION": request.app.state.YOUTUBE_LOADER_TRANSLATION,
        },
    }


class WebConfig(BaseModel):
    ENABLE_WEB_SEARCH: Optional[bool] = None
    WEB_SEARCH_ENGINE: Optional[str] = None
    WEB_SEARCH_TRUST_ENV: Optional[bool] = None
    WEB_SEARCH_RESULT_COUNT: Optional[int] = None
    WEB_SEARCH_CONCURRENT_REQUESTS: Optional[int] = None
    WEB_LOADER_CONCURRENT_REQUESTS: Optional[int] = None
    WEB_SEARCH_DOMAIN_FILTER_LIST: Optional[List[str]] = []
    BYPASS_WEB_SEARCH_EMBEDDING_AND_RETRIEVAL: Optional[bool] = None
    BYPASS_WEB_SEARCH_WEB_LOADER: Optional[bool] = None
    OLLAMA_CLOUD_WEB_SEARCH_API_KEY: Optional[str] = None
    SEARXNG_QUERY_URL: Optional[str] = None
    YACY_QUERY_URL: Optional[str] = None
    YACY_USERNAME: Optional[str] = None
    YACY_PASSWORD: Optional[str] = None
    GOOGLE_PSE_API_KEY: Optional[str] = None
    GOOGLE_PSE_ENGINE_ID: Optional[str] = None
    BRAVE_SEARCH_API_KEY: Optional[str] = None
    KAGI_SEARCH_API_KEY: Optional[str] = None
    MOJEEK_SEARCH_API_KEY: Optional[str] = None
    BOCHA_SEARCH_API_KEY: Optional[str] = None
    SERPSTACK_API_KEY: Optional[str] = None
    SERPSTACK_HTTPS: Optional[bool] = None
    SERPER_API_KEY: Optional[str] = None
    SERPLY_API_KEY: Optional[str] = None
    TAVILY_API_KEY: Optional[str] = None
    SEARCHAPI_API_KEY: Optional[str] = None
    SEARCHAPI_ENGINE: Optional[str] = None
    SERPAPI_API_KEY: Optional[str] = None
    SERPAPI_ENGINE: Optional[str] = None
    JINA_API_KEY: Optional[str] = None
    BING_SEARCH_V7_ENDPOINT: Optional[str] = None
    BING_SEARCH_V7_SUBSCRIPTION_KEY: Optional[str] = None
    EXA_API_KEY: Optional[str] = None
    PERPLEXITY_API_KEY: Optional[str] = None
    PERPLEXITY_MODEL: Optional[str] = None
    PERPLEXITY_SEARCH_CONTEXT_USAGE: Optional[str] = None
    SOUGOU_API_SID: Optional[str] = None
    SOUGOU_API_SK: Optional[str] = None
    WEB_LOADER_ENGINE: Optional[str] = None
    ENABLE_WEB_LOADER_SSL_VERIFICATION: Optional[bool] = None
    PLAYWRIGHT_WS_URL: Optional[str] = None
    PLAYWRIGHT_TIMEOUT: Optional[int] = None
    FIRECRAWL_API_KEY: Optional[str] = None
    FIRECRAWL_API_BASE_URL: Optional[str] = None
    TAVILY_EXTRACT_DEPTH: Optional[str] = None
    EXTERNAL_WEB_SEARCH_URL: Optional[str] = None
    EXTERNAL_WEB_SEARCH_API_KEY: Optional[str] = None
    EXTERNAL_WEB_LOADER_URL: Optional[str] = None
    EXTERNAL_WEB_LOADER_API_KEY: Optional[str] = None
    YOUTUBE_LOADER_LANGUAGE: Optional[List[str]] = None
    YOUTUBE_LOADER_PROXY_URL: Optional[str] = None
    YOUTUBE_LOADER_TRANSLATION: Optional[str] = None


class ConfigForm(BaseModel):
    # RAG settings
    RAG_TEMPLATE: Optional[str] = None
    TOP_K: Optional[int] = None
    BYPASS_EMBEDDING_AND_RETRIEVAL: Optional[bool] = None
    RAG_FULL_CONTEXT: Optional[bool] = None

    # Hybrid search settings
    ENABLE_RAG_HYBRID_SEARCH: Optional[bool] = None
    TOP_K_RERANKER: Optional[int] = None
    RELEVANCE_THRESHOLD: Optional[float] = None
    HYBRID_BM25_WEIGHT: Optional[float] = None

    # Content extraction settings
    CONTENT_EXTRACTION_ENGINE: Optional[str] = None
    PDF_EXTRACT_IMAGES: Optional[bool] = None
    DATALAB_MARKER_API_KEY: Optional[str] = None
    DATALAB_MARKER_API_BASE_URL: Optional[str] = None
    DATALAB_MARKER_ADDITIONAL_CONFIG: Optional[str] = None
    DATALAB_MARKER_SKIP_CACHE: Optional[bool] = None
    DATALAB_MARKER_FORCE_OCR: Optional[bool] = None
    DATALAB_MARKER_PAGINATE: Optional[bool] = None
    DATALAB_MARKER_STRIP_EXISTING_OCR: Optional[bool] = None
    DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION: Optional[bool] = None
    DATALAB_MARKER_FORMAT_LINES: Optional[bool] = None
    DATALAB_MARKER_USE_LLM: Optional[bool] = None
    DATALAB_MARKER_OUTPUT_FORMAT: Optional[str] = None
    EXTERNAL_DOCUMENT_LOADER_URL: Optional[str] = None
    EXTERNAL_DOCUMENT_LOADER_API_KEY: Optional[str] = None

    TIKA_SERVER_URL: Optional[str] = None
    DOCLING_SERVER_URL: Optional[str] = None
    DOCLING_DO_OCR: Optional[bool] = None
    DOCLING_FORCE_OCR: Optional[bool] = None
    DOCLING_OCR_ENGINE: Optional[str] = None
    DOCLING_OCR_LANG: Optional[str] = None
    DOCLING_PDF_BACKEND: Optional[str] = None
    DOCLING_TABLE_MODE: Optional[str] = None
    DOCLING_PIPELINE: Optional[str] = None
    DOCLING_DO_PICTURE_DESCRIPTION: Optional[bool] = None
    DOCLING_PICTURE_DESCRIPTION_MODE: Optional[str] = None
    DOCLING_PICTURE_DESCRIPTION_LOCAL: Optional[dict] = None
    DOCLING_PICTURE_DESCRIPTION_API: Optional[dict] = None
    DOCUMENT_INTELLIGENCE_ENDPOINT: Optional[str] = None
    DOCUMENT_INTELLIGENCE_KEY: Optional[str] = None
    MISTRAL_OCR_API_KEY: Optional[str] = None

    # Reranking settings
    RAG_RERANKING_MODEL: Optional[str] = None
    RAG_RERANKING_ENGINE: Optional[str] = None
    RAG_EXTERNAL_RERANKER_URL: Optional[str] = None
    RAG_EXTERNAL_RERANKER_API_KEY: Optional[str] = None

    # Chunking settings
    TEXT_SPLITTER: Optional[str] = None
    CHUNK_SIZE: Optional[int] = None
    CHUNK_OVERLAP: Optional[int] = None

    # File upload settings
    FILE_MAX_SIZE: Optional[int] = None
    FILE_MAX_COUNT: Optional[int] = None
    FILE_IMAGE_COMPRESSION_WIDTH: Optional[int] = None
    FILE_IMAGE_COMPRESSION_HEIGHT: Optional[int] = None
    ALLOWED_FILE_EXTENSIONS: Optional[List[str]] = None

    # Integration settings
    ENABLE_GOOGLE_DRIVE_INTEGRATION: Optional[bool] = None
    ENABLE_ONEDRIVE_INTEGRATION: Optional[bool] = None

    # Web search settings
    web: Optional[WebConfig] = None


@router.post("/config/update")
async def update_rag_config(
    request: Request, form_data: ConfigForm, user=Depends(get_admin_user)
):
    # RAG settings
    request.app.state.config.RAG_TEMPLATE = (
        form_data.RAG_TEMPLATE
        if form_data.RAG_TEMPLATE is not None
        else request.app.state.config.RAG_TEMPLATE
    )
    request.app.state.config.TOP_K = (
        form_data.TOP_K
        if form_data.TOP_K is not None
        else request.app.state.config.TOP_K
    )
    request.app.state.config.BYPASS_EMBEDDING_AND_RETRIEVAL = (
        form_data.BYPASS_EMBEDDING_AND_RETRIEVAL
        if form_data.BYPASS_EMBEDDING_AND_RETRIEVAL is not None
        else request.app.state.config.BYPASS_EMBEDDING_AND_RETRIEVAL
    )
    request.app.state.config.RAG_FULL_CONTEXT = (
        form_data.RAG_FULL_CONTEXT
        if form_data.RAG_FULL_CONTEXT is not None
        else request.app.state.config.RAG_FULL_CONTEXT
    )

    # Hybrid search settings
    request.app.state.config.ENABLE_RAG_HYBRID_SEARCH = (
        form_data.ENABLE_RAG_HYBRID_SEARCH
        if form_data.ENABLE_RAG_HYBRID_SEARCH is not None
        else request.app.state.config.ENABLE_RAG_HYBRID_SEARCH
    )

    request.app.state.config.TOP_K_RERANKER = (
        form_data.TOP_K_RERANKER
        if form_data.TOP_K_RERANKER is not None
        else request.app.state.config.TOP_K_RERANKER
    )
    request.app.state.config.RELEVANCE_THRESHOLD = (
        form_data.RELEVANCE_THRESHOLD
        if form_data.RELEVANCE_THRESHOLD is not None
        else request.app.state.config.RELEVANCE_THRESHOLD
    )
    request.app.state.config.HYBRID_BM25_WEIGHT = (
        form_data.HYBRID_BM25_WEIGHT
        if form_data.HYBRID_BM25_WEIGHT is not None
        else request.app.state.config.HYBRID_BM25_WEIGHT
    )

    # Content extraction settings
    request.app.state.config.CONTENT_EXTRACTION_ENGINE = (
        form_data.CONTENT_EXTRACTION_ENGINE
        if form_data.CONTENT_EXTRACTION_ENGINE is not None
        else request.app.state.config.CONTENT_EXTRACTION_ENGINE
    )
    request.app.state.config.PDF_EXTRACT_IMAGES = (
        form_data.PDF_EXTRACT_IMAGES
        if form_data.PDF_EXTRACT_IMAGES is not None
        else request.app.state.config.PDF_EXTRACT_IMAGES
    )
    request.app.state.config.DATALAB_MARKER_API_KEY = (
        form_data.DATALAB_MARKER_API_KEY
        if form_data.DATALAB_MARKER_API_KEY is not None
        else request.app.state.config.DATALAB_MARKER_API_KEY
    )
    request.app.state.config.DATALAB_MARKER_API_BASE_URL = (
        form_data.DATALAB_MARKER_API_BASE_URL
        if form_data.DATALAB_MARKER_API_BASE_URL is not None
        else request.app.state.config.DATALAB_MARKER_API_BASE_URL
    )
    request.app.state.config.DATALAB_MARKER_ADDITIONAL_CONFIG = (
        form_data.DATALAB_MARKER_ADDITIONAL_CONFIG
        if form_data.DATALAB_MARKER_ADDITIONAL_CONFIG is not None
        else request.app.state.config.DATALAB_MARKER_ADDITIONAL_CONFIG
    )
    request.app.state.config.DATALAB_MARKER_SKIP_CACHE = (
        form_data.DATALAB_MARKER_SKIP_CACHE
        if form_data.DATALAB_MARKER_SKIP_CACHE is not None
        else request.app.state.config.DATALAB_MARKER_SKIP_CACHE
    )
    request.app.state.config.DATALAB_MARKER_FORCE_OCR = (
        form_data.DATALAB_MARKER_FORCE_OCR
        if form_data.DATALAB_MARKER_FORCE_OCR is not None
        else request.app.state.config.DATALAB_MARKER_FORCE_OCR
    )
    request.app.state.config.DATALAB_MARKER_PAGINATE = (
        form_data.DATALAB_MARKER_PAGINATE
        if form_data.DATALAB_MARKER_PAGINATE is not None
        else request.app.state.config.DATALAB_MARKER_PAGINATE
    )
    request.app.state.config.DATALAB_MARKER_STRIP_EXISTING_OCR = (
        form_data.DATALAB_MARKER_STRIP_EXISTING_OCR
        if form_data.DATALAB_MARKER_STRIP_EXISTING_OCR is not None
        else request.app.state.config.DATALAB_MARKER_STRIP_EXISTING_OCR
    )
    request.app.state.config.DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION = (
        form_data.DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION
        if form_data.DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION is not None
        else request.app.state.config.DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION
    )
    request.app.state.config.DATALAB_MARKER_FORMAT_LINES = (
        form_data.DATALAB_MARKER_FORMAT_LINES
        if form_data.DATALAB_MARKER_FORMAT_LINES is not None
        else request.app.state.config.DATALAB_MARKER_FORMAT_LINES
    )
    request.app.state.config.DATALAB_MARKER_OUTPUT_FORMAT = (
        form_data.DATALAB_MARKER_OUTPUT_FORMAT
        if form_data.DATALAB_MARKER_OUTPUT_FORMAT is not None
        else request.app.state.config.DATALAB_MARKER_OUTPUT_FORMAT
    )
    request.app.state.config.DATALAB_MARKER_USE_LLM = (
        form_data.DATALAB_MARKER_USE_LLM
        if form_data.DATALAB_MARKER_USE_LLM is not None
        else request.app.state.config.DATALAB_MARKER_USE_LLM
    )
    request.app.state.config.EXTERNAL_DOCUMENT_LOADER_URL = (
        form_data.EXTERNAL_DOCUMENT_LOADER_URL
        if form_data.EXTERNAL_DOCUMENT_LOADER_URL is not None
        else request.app.state.config.EXTERNAL_DOCUMENT_LOADER_URL
    )
    request.app.state.config.EXTERNAL_DOCUMENT_LOADER_API_KEY = (
        form_data.EXTERNAL_DOCUMENT_LOADER_API_KEY
        if form_data.EXTERNAL_DOCUMENT_LOADER_API_KEY is not None
        else request.app.state.config.EXTERNAL_DOCUMENT_LOADER_API_KEY
    )
    request.app.state.config.TIKA_SERVER_URL = (
        form_data.TIKA_SERVER_URL
        if form_data.TIKA_SERVER_URL is not None
        else request.app.state.config.TIKA_SERVER_URL
    )
    request.app.state.config.DOCLING_SERVER_URL = (
        form_data.DOCLING_SERVER_URL
        if form_data.DOCLING_SERVER_URL is not None
        else request.app.state.config.DOCLING_SERVER_URL
    )
    request.app.state.config.DOCLING_DO_OCR = (
        form_data.DOCLING_DO_OCR
        if form_data.DOCLING_DO_OCR is not None
        else request.app.state.config.DOCLING_DO_OCR
    )
    request.app.state.config.DOCLING_FORCE_OCR = (
        form_data.DOCLING_FORCE_OCR
        if form_data.DOCLING_FORCE_OCR is not None
        else request.app.state.config.DOCLING_FORCE_OCR
    )
    request.app.state.config.DOCLING_OCR_ENGINE = (
        form_data.DOCLING_OCR_ENGINE
        if form_data.DOCLING_OCR_ENGINE is not None
        else request.app.state.config.DOCLING_OCR_ENGINE
    )
    request.app.state.config.DOCLING_OCR_LANG = (
        form_data.DOCLING_OCR_LANG
        if form_data.DOCLING_OCR_LANG is not None
        else request.app.state.config.DOCLING_OCR_LANG
    )
    request.app.state.config.DOCLING_PDF_BACKEND = (
        form_data.DOCLING_PDF_BACKEND
        if form_data.DOCLING_PDF_BACKEND is not None
        else request.app.state.config.DOCLING_PDF_BACKEND
    )
    request.app.state.config.DOCLING_TABLE_MODE = (
        form_data.DOCLING_TABLE_MODE
        if form_data.DOCLING_TABLE_MODE is not None
        else request.app.state.config.DOCLING_TABLE_MODE
    )
    request.app.state.config.DOCLING_PIPELINE = (
        form_data.DOCLING_PIPELINE
        if form_data.DOCLING_PIPELINE is not None
        else request.app.state.config.DOCLING_PIPELINE
    )
    request.app.state.config.DOCLING_DO_PICTURE_DESCRIPTION = (
        form_data.DOCLING_DO_PICTURE_DESCRIPTION
        if form_data.DOCLING_DO_PICTURE_DESCRIPTION is not None
        else request.app.state.config.DOCLING_DO_PICTURE_DESCRIPTION
    )

    request.app.state.config.DOCLING_PICTURE_DESCRIPTION_MODE = (
        form_data.DOCLING_PICTURE_DESCRIPTION_MODE
        if form_data.DOCLING_PICTURE_DESCRIPTION_MODE is not None
        else request.app.state.config.DOCLING_PICTURE_DESCRIPTION_MODE
    )
    request.app.state.config.DOCLING_PICTURE_DESCRIPTION_LOCAL = (
        form_data.DOCLING_PICTURE_DESCRIPTION_LOCAL
        if form_data.DOCLING_PICTURE_DESCRIPTION_LOCAL is not None
        else request.app.state.config.DOCLING_PICTURE_DESCRIPTION_LOCAL
    )
    request.app.state.config.DOCLING_PICTURE_DESCRIPTION_API = (
        form_data.DOCLING_PICTURE_DESCRIPTION_API
        if form_data.DOCLING_PICTURE_DESCRIPTION_API is not None
        else request.app.state.config.DOCLING_PICTURE_DESCRIPTION_API
    )

    request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT = (
        form_data.DOCUMENT_INTELLIGENCE_ENDPOINT
        if form_data.DOCUMENT_INTELLIGENCE_ENDPOINT is not None
        else request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT
    )
    request.app.state.config.DOCUMENT_INTELLIGENCE_KEY = (
        form_data.DOCUMENT_INTELLIGENCE_KEY
        if form_data.DOCUMENT_INTELLIGENCE_KEY is not None
        else request.app.state.config.DOCUMENT_INTELLIGENCE_KEY
    )
    request.app.state.config.MISTRAL_OCR_API_KEY = (
        form_data.MISTRAL_OCR_API_KEY
        if form_data.MISTRAL_OCR_API_KEY is not None
        else request.app.state.config.MISTRAL_OCR_API_KEY
    )

    # Reranking settings
    if request.app.state.config.RAG_RERANKING_ENGINE == "":
        # Unloading the internal reranker and clear VRAM memory
        request.app.state.rf = None
        request.app.state.RERANKING_FUNCTION = None
        import gc

        gc.collect()
        if DEVICE_TYPE == "cuda":
            import torch

            if torch.cuda.is_available():
                torch.cuda.empty_cache()
    request.app.state.config.RAG_RERANKING_ENGINE = (
        form_data.RAG_RERANKING_ENGINE
        if form_data.RAG_RERANKING_ENGINE is not None
        else request.app.state.config.RAG_RERANKING_ENGINE
    )

    request.app.state.config.RAG_EXTERNAL_RERANKER_URL = (
        form_data.RAG_EXTERNAL_RERANKER_URL
        if form_data.RAG_EXTERNAL_RERANKER_URL is not None
        else request.app.state.config.RAG_EXTERNAL_RERANKER_URL
    )

    request.app.state.config.RAG_EXTERNAL_RERANKER_API_KEY = (
        form_data.RAG_EXTERNAL_RERANKER_API_KEY
        if form_data.RAG_EXTERNAL_RERANKER_API_KEY is not None
        else request.app.state.config.RAG_EXTERNAL_RERANKER_API_KEY
    )

    log.info(
        f"Updating reranking model: {request.app.state.config.RAG_RERANKING_MODEL} to {form_data.RAG_RERANKING_MODEL}"
    )
    try:
        request.app.state.config.RAG_RERANKING_MODEL = (
            form_data.RAG_RERANKING_MODEL
            if form_data.RAG_RERANKING_MODEL is not None
            else request.app.state.config.RAG_RERANKING_MODEL
        )

        try:
            if (
                request.app.state.config.ENABLE_RAG_HYBRID_SEARCH
                and not request.app.state.config.BYPASS_EMBEDDING_AND_RETRIEVAL
            ):
                request.app.state.rf = get_rf(
                    request.app.state.config.RAG_RERANKING_ENGINE,
                    request.app.state.config.RAG_RERANKING_MODEL,
                    request.app.state.config.RAG_EXTERNAL_RERANKER_URL,
                    request.app.state.config.RAG_EXTERNAL_RERANKER_API_KEY,
                    True,
                )

                request.app.state.RERANKING_FUNCTION = get_reranking_function(
                    request.app.state.config.RAG_RERANKING_ENGINE,
                    request.app.state.config.RAG_RERANKING_MODEL,
                    request.app.state.rf,
                )
        except Exception as e:
            log.error(f"Error loading reranking model: {e}")
            request.app.state.config.ENABLE_RAG_HYBRID_SEARCH = False
    except Exception as e:
        log.exception(f"Problem updating reranking model: {e}")
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=ERROR_MESSAGES.DEFAULT(e),
        )

    # Chunking settings
    request.app.state.config.TEXT_SPLITTER = (
        form_data.TEXT_SPLITTER
        if form_data.TEXT_SPLITTER is not None
        else request.app.state.config.TEXT_SPLITTER
    )
    request.app.state.config.CHUNK_SIZE = (
        form_data.CHUNK_SIZE
        if form_data.CHUNK_SIZE is not None
        else request.app.state.config.CHUNK_SIZE
    )
    request.app.state.config.CHUNK_OVERLAP = (
        form_data.CHUNK_OVERLAP
        if form_data.CHUNK_OVERLAP is not None
        else request.app.state.config.CHUNK_OVERLAP
    )

    # File upload settings
    request.app.state.config.FILE_MAX_SIZE = form_data.FILE_MAX_SIZE
    request.app.state.config.FILE_MAX_COUNT = form_data.FILE_MAX_COUNT
    request.app.state.config.FILE_IMAGE_COMPRESSION_WIDTH = (
        form_data.FILE_IMAGE_COMPRESSION_WIDTH
    )
    request.app.state.config.FILE_IMAGE_COMPRESSION_HEIGHT = (
        form_data.FILE_IMAGE_COMPRESSION_HEIGHT
    )
    request.app.state.config.ALLOWED_FILE_EXTENSIONS = (
        form_data.ALLOWED_FILE_EXTENSIONS
        if form_data.ALLOWED_FILE_EXTENSIONS is not None
        else request.app.state.config.ALLOWED_FILE_EXTENSIONS
    )

    # Integration settings
    request.app.state.config.ENABLE_GOOGLE_DRIVE_INTEGRATION = (
        form_data.ENABLE_GOOGLE_DRIVE_INTEGRATION
        if form_data.ENABLE_GOOGLE_DRIVE_INTEGRATION is not None
        else request.app.state.config.ENABLE_GOOGLE_DRIVE_INTEGRATION
    )
    request.app.state.config.ENABLE_ONEDRIVE_INTEGRATION = (
        form_data.ENABLE_ONEDRIVE_INTEGRATION
        if form_data.ENABLE_ONEDRIVE_INTEGRATION is not None
        else request.app.state.config.ENABLE_ONEDRIVE_INTEGRATION
    )

    if form_data.web is not None:
        # Web search settings
        request.app.state.config.ENABLE_WEB_SEARCH = form_data.web.ENABLE_WEB_SEARCH
        request.app.state.config.WEB_SEARCH_ENGINE = form_data.web.WEB_SEARCH_ENGINE
        request.app.state.config.WEB_SEARCH_TRUST_ENV = (
            form_data.web.WEB_SEARCH_TRUST_ENV
        )
        request.app.state.config.WEB_SEARCH_RESULT_COUNT = (
            form_data.web.WEB_SEARCH_RESULT_COUNT
        )
        request.app.state.config.WEB_SEARCH_CONCURRENT_REQUESTS = (
            form_data.web.WEB_SEARCH_CONCURRENT_REQUESTS
        )
        request.app.state.config.WEB_LOADER_CONCURRENT_REQUESTS = (
            form_data.web.WEB_LOADER_CONCURRENT_REQUESTS
        )
        request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST = (
            form_data.web.WEB_SEARCH_DOMAIN_FILTER_LIST
        )
        request.app.state.config.BYPASS_WEB_SEARCH_EMBEDDING_AND_RETRIEVAL = (
            form_data.web.BYPASS_WEB_SEARCH_EMBEDDING_AND_RETRIEVAL
        )
        request.app.state.config.BYPASS_WEB_SEARCH_WEB_LOADER = (
            form_data.web.BYPASS_WEB_SEARCH_WEB_LOADER
        )
        request.app.state.config.OLLAMA_CLOUD_WEB_SEARCH_API_KEY = (
            form_data.web.OLLAMA_CLOUD_WEB_SEARCH_API_KEY
        )
        request.app.state.config.SEARXNG_QUERY_URL = form_data.web.SEARXNG_QUERY_URL
        request.app.state.config.YACY_QUERY_URL = form_data.web.YACY_QUERY_URL
        request.app.state.config.YACY_USERNAME = form_data.web.YACY_USERNAME
        request.app.state.config.YACY_PASSWORD = form_data.web.YACY_PASSWORD
        request.app.state.config.GOOGLE_PSE_API_KEY = form_data.web.GOOGLE_PSE_API_KEY
        request.app.state.config.GOOGLE_PSE_ENGINE_ID = (
            form_data.web.GOOGLE_PSE_ENGINE_ID
        )
        request.app.state.config.BRAVE_SEARCH_API_KEY = (
            form_data.web.BRAVE_SEARCH_API_KEY
        )
        request.app.state.config.KAGI_SEARCH_API_KEY = form_data.web.KAGI_SEARCH_API_KEY
        request.app.state.config.MOJEEK_SEARCH_API_KEY = (
            form_data.web.MOJEEK_SEARCH_API_KEY
        )
        request.app.state.config.BOCHA_SEARCH_API_KEY = (
            form_data.web.BOCHA_SEARCH_API_KEY
        )
        request.app.state.config.SERPSTACK_API_KEY = form_data.web.SERPSTACK_API_KEY
        request.app.state.config.SERPSTACK_HTTPS = form_data.web.SERPSTACK_HTTPS
        request.app.state.config.SERPER_API_KEY = form_data.web.SERPER_API_KEY
        request.app.state.config.SERPLY_API_KEY = form_data.web.SERPLY_API_KEY
        request.app.state.config.TAVILY_API_KEY = form_data.web.TAVILY_API_KEY
        request.app.state.config.SEARCHAPI_API_KEY = form_data.web.SEARCHAPI_API_KEY
        request.app.state.config.SEARCHAPI_ENGINE = form_data.web.SEARCHAPI_ENGINE
        request.app.state.config.SERPAPI_API_KEY = form_data.web.SERPAPI_API_KEY
        request.app.state.config.SERPAPI_ENGINE = form_data.web.SERPAPI_ENGINE
        request.app.state.config.JINA_API_KEY = form_data.web.JINA_API_KEY
        request.app.state.config.BING_SEARCH_V7_ENDPOINT = (
            form_data.web.BING_SEARCH_V7_ENDPOINT
        )
        request.app.state.config.BING_SEARCH_V7_SUBSCRIPTION_KEY = (
            form_data.web.BING_SEARCH_V7_SUBSCRIPTION_KEY
        )
        request.app.state.config.EXA_API_KEY = form_data.web.EXA_API_KEY
        request.app.state.config.PERPLEXITY_API_KEY = form_data.web.PERPLEXITY_API_KEY
        request.app.state.config.PERPLEXITY_MODEL = form_data.web.PERPLEXITY_MODEL
        request.app.state.config.PERPLEXITY_SEARCH_CONTEXT_USAGE = (
            form_data.web.PERPLEXITY_SEARCH_CONTEXT_USAGE
        )
        request.app.state.config.SOUGOU_API_SID = form_data.web.SOUGOU_API_SID
        request.app.state.config.SOUGOU_API_SK = form_data.web.SOUGOU_API_SK

        # Web loader settings
        request.app.state.config.WEB_LOADER_ENGINE = form_data.web.WEB_LOADER_ENGINE
        request.app.state.config.ENABLE_WEB_LOADER_SSL_VERIFICATION = (
            form_data.web.ENABLE_WEB_LOADER_SSL_VERIFICATION
        )
        request.app.state.config.PLAYWRIGHT_WS_URL = form_data.web.PLAYWRIGHT_WS_URL
        request.app.state.config.PLAYWRIGHT_TIMEOUT = form_data.web.PLAYWRIGHT_TIMEOUT
        request.app.state.config.FIRECRAWL_API_KEY = form_data.web.FIRECRAWL_API_KEY
        request.app.state.config.FIRECRAWL_API_BASE_URL = (
            form_data.web.FIRECRAWL_API_BASE_URL
        )
        request.app.state.config.EXTERNAL_WEB_SEARCH_URL = (
            form_data.web.EXTERNAL_WEB_SEARCH_URL
        )
        request.app.state.config.EXTERNAL_WEB_SEARCH_API_KEY = (
            form_data.web.EXTERNAL_WEB_SEARCH_API_KEY
        )
        request.app.state.config.EXTERNAL_WEB_LOADER_URL = (
            form_data.web.EXTERNAL_WEB_LOADER_URL
        )
        request.app.state.config.EXTERNAL_WEB_LOADER_API_KEY = (
            form_data.web.EXTERNAL_WEB_LOADER_API_KEY
        )
        request.app.state.config.TAVILY_EXTRACT_DEPTH = (
            form_data.web.TAVILY_EXTRACT_DEPTH
        )
        request.app.state.config.YOUTUBE_LOADER_LANGUAGE = (
            form_data.web.YOUTUBE_LOADER_LANGUAGE
        )
        request.app.state.config.YOUTUBE_LOADER_PROXY_URL = (
            form_data.web.YOUTUBE_LOADER_PROXY_URL
        )
        request.app.state.YOUTUBE_LOADER_TRANSLATION = (
            form_data.web.YOUTUBE_LOADER_TRANSLATION
        )

    return {
        "status": True,
        # RAG settings
        "RAG_TEMPLATE": request.app.state.config.RAG_TEMPLATE,
        "TOP_K": request.app.state.config.TOP_K,
        "BYPASS_EMBEDDING_AND_RETRIEVAL": request.app.state.config.BYPASS_EMBEDDING_AND_RETRIEVAL,
        "RAG_FULL_CONTEXT": request.app.state.config.RAG_FULL_CONTEXT,
        # Hybrid search settings
        "ENABLE_RAG_HYBRID_SEARCH": request.app.state.config.ENABLE_RAG_HYBRID_SEARCH,
        "TOP_K_RERANKER": request.app.state.config.TOP_K_RERANKER,
        "RELEVANCE_THRESHOLD": request.app.state.config.RELEVANCE_THRESHOLD,
        "HYBRID_BM25_WEIGHT": request.app.state.config.HYBRID_BM25_WEIGHT,
        # Content extraction settings
        "CONTENT_EXTRACTION_ENGINE": request.app.state.config.CONTENT_EXTRACTION_ENGINE,
        "PDF_EXTRACT_IMAGES": request.app.state.config.PDF_EXTRACT_IMAGES,
        "DATALAB_MARKER_API_KEY": request.app.state.config.DATALAB_MARKER_API_KEY,
        "DATALAB_MARKER_API_BASE_URL": request.app.state.config.DATALAB_MARKER_API_BASE_URL,
        "DATALAB_MARKER_ADDITIONAL_CONFIG": request.app.state.config.DATALAB_MARKER_ADDITIONAL_CONFIG,
        "DATALAB_MARKER_SKIP_CACHE": request.app.state.config.DATALAB_MARKER_SKIP_CACHE,
        "DATALAB_MARKER_FORCE_OCR": request.app.state.config.DATALAB_MARKER_FORCE_OCR,
        "DATALAB_MARKER_PAGINATE": request.app.state.config.DATALAB_MARKER_PAGINATE,
        "DATALAB_MARKER_STRIP_EXISTING_OCR": request.app.state.config.DATALAB_MARKER_STRIP_EXISTING_OCR,
        "DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION": request.app.state.config.DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION,
        "DATALAB_MARKER_USE_LLM": request.app.state.config.DATALAB_MARKER_USE_LLM,
        "DATALAB_MARKER_OUTPUT_FORMAT": request.app.state.config.DATALAB_MARKER_OUTPUT_FORMAT,
        "EXTERNAL_DOCUMENT_LOADER_URL": request.app.state.config.EXTERNAL_DOCUMENT_LOADER_URL,
        "EXTERNAL_DOCUMENT_LOADER_API_KEY": request.app.state.config.EXTERNAL_DOCUMENT_LOADER_API_KEY,
        "TIKA_SERVER_URL": request.app.state.config.TIKA_SERVER_URL,
        "DOCLING_SERVER_URL": request.app.state.config.DOCLING_SERVER_URL,
        "DOCLING_DO_OCR": request.app.state.config.DOCLING_DO_OCR,
        "DOCLING_FORCE_OCR": request.app.state.config.DOCLING_FORCE_OCR,
        "DOCLING_OCR_ENGINE": request.app.state.config.DOCLING_OCR_ENGINE,
        "DOCLING_OCR_LANG": request.app.state.config.DOCLING_OCR_LANG,
        "DOCLING_PDF_BACKEND": request.app.state.config.DOCLING_PDF_BACKEND,
        "DOCLING_TABLE_MODE": request.app.state.config.DOCLING_TABLE_MODE,
        "DOCLING_PIPELINE": request.app.state.config.DOCLING_PIPELINE,
        "DOCLING_DO_PICTURE_DESCRIPTION": request.app.state.config.DOCLING_DO_PICTURE_DESCRIPTION,
        "DOCLING_PICTURE_DESCRIPTION_MODE": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_MODE,
        "DOCLING_PICTURE_DESCRIPTION_LOCAL": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_LOCAL,
        "DOCLING_PICTURE_DESCRIPTION_API": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_API,
        "DOCUMENT_INTELLIGENCE_ENDPOINT": request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT,
        "DOCUMENT_INTELLIGENCE_KEY": request.app.state.config.DOCUMENT_INTELLIGENCE_KEY,
        "MISTRAL_OCR_API_KEY": request.app.state.config.MISTRAL_OCR_API_KEY,
        # Reranking settings
        "RAG_RERANKING_MODEL": request.app.state.config.RAG_RERANKING_MODEL,
        "RAG_RERANKING_ENGINE": request.app.state.config.RAG_RERANKING_ENGINE,
        "RAG_EXTERNAL_RERANKER_URL": request.app.state.config.RAG_EXTERNAL_RERANKER_URL,
        "RAG_EXTERNAL_RERANKER_API_KEY": request.app.state.config.RAG_EXTERNAL_RERANKER_API_KEY,
        # Chunking settings
        "TEXT_SPLITTER": request.app.state.config.TEXT_SPLITTER,
        "CHUNK_SIZE": request.app.state.config.CHUNK_SIZE,
        "CHUNK_OVERLAP": request.app.state.config.CHUNK_OVERLAP,
        # File upload settings
        "FILE_MAX_SIZE": request.app.state.config.FILE_MAX_SIZE,
        "FILE_MAX_COUNT": request.app.state.config.FILE_MAX_COUNT,
        "FILE_IMAGE_COMPRESSION_WIDTH": request.app.state.config.FILE_IMAGE_COMPRESSION_WIDTH,
        "FILE_IMAGE_COMPRESSION_HEIGHT": request.app.state.config.FILE_IMAGE_COMPRESSION_HEIGHT,
        "ALLOWED_FILE_EXTENSIONS": request.app.state.config.ALLOWED_FILE_EXTENSIONS,
        # Integration settings
        "ENABLE_GOOGLE_DRIVE_INTEGRATION": request.app.state.config.ENABLE_GOOGLE_DRIVE_INTEGRATION,
        "ENABLE_ONEDRIVE_INTEGRATION": request.app.state.config.ENABLE_ONEDRIVE_INTEGRATION,
        # Web search settings
        "web": {
            "ENABLE_WEB_SEARCH": request.app.state.config.ENABLE_WEB_SEARCH,
            "WEB_SEARCH_ENGINE": request.app.state.config.WEB_SEARCH_ENGINE,
            "WEB_SEARCH_TRUST_ENV": request.app.state.config.WEB_SEARCH_TRUST_ENV,
            "WEB_SEARCH_RESULT_COUNT": request.app.state.config.WEB_SEARCH_RESULT_COUNT,
            "WEB_SEARCH_CONCURRENT_REQUESTS": request.app.state.config.WEB_SEARCH_CONCURRENT_REQUESTS,
            "WEB_LOADER_CONCURRENT_REQUESTS": request.app.state.config.WEB_LOADER_CONCURRENT_REQUESTS,
            "WEB_SEARCH_DOMAIN_FILTER_LIST": request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            "BYPASS_WEB_SEARCH_EMBEDDING_AND_RETRIEVAL": request.app.state.config.BYPASS_WEB_SEARCH_EMBEDDING_AND_RETRIEVAL,
            "BYPASS_WEB_SEARCH_WEB_LOADER": request.app.state.config.BYPASS_WEB_SEARCH_WEB_LOADER,
            "OLLAMA_CLOUD_WEB_SEARCH_API_KEY": request.app.state.config.OLLAMA_CLOUD_WEB_SEARCH_API_KEY,
            "SEARXNG_QUERY_URL": request.app.state.config.SEARXNG_QUERY_URL,
            "YACY_QUERY_URL": request.app.state.config.YACY_QUERY_URL,
            "YACY_USERNAME": request.app.state.config.YACY_USERNAME,
            "YACY_PASSWORD": request.app.state.config.YACY_PASSWORD,
            "GOOGLE_PSE_API_KEY": request.app.state.config.GOOGLE_PSE_API_KEY,
            "GOOGLE_PSE_ENGINE_ID": request.app.state.config.GOOGLE_PSE_ENGINE_ID,
            "BRAVE_SEARCH_API_KEY": request.app.state.config.BRAVE_SEARCH_API_KEY,
            "KAGI_SEARCH_API_KEY": request.app.state.config.KAGI_SEARCH_API_KEY,
            "MOJEEK_SEARCH_API_KEY": request.app.state.config.MOJEEK_SEARCH_API_KEY,
            "BOCHA_SEARCH_API_KEY": request.app.state.config.BOCHA_SEARCH_API_KEY,
            "SERPSTACK_API_KEY": request.app.state.config.SERPSTACK_API_KEY,
            "SERPSTACK_HTTPS": request.app.state.config.SERPSTACK_HTTPS,
            "SERPER_API_KEY": request.app.state.config.SERPER_API_KEY,
            "SERPLY_API_KEY": request.app.state.config.SERPLY_API_KEY,
            "TAVILY_API_KEY": request.app.state.config.TAVILY_API_KEY,
            "SEARCHAPI_API_KEY": request.app.state.config.SEARCHAPI_API_KEY,
            "SEARCHAPI_ENGINE": request.app.state.config.SEARCHAPI_ENGINE,
            "SERPAPI_API_KEY": request.app.state.config.SERPAPI_API_KEY,
            "SERPAPI_ENGINE": request.app.state.config.SERPAPI_ENGINE,
            "JINA_API_KEY": request.app.state.config.JINA_API_KEY,
            "BING_SEARCH_V7_ENDPOINT": request.app.state.config.BING_SEARCH_V7_ENDPOINT,
            "BING_SEARCH_V7_SUBSCRIPTION_KEY": request.app.state.config.BING_SEARCH_V7_SUBSCRIPTION_KEY,
            "EXA_API_KEY": request.app.state.config.EXA_API_KEY,
            "PERPLEXITY_API_KEY": request.app.state.config.PERPLEXITY_API_KEY,
            "PERPLEXITY_MODEL": request.app.state.config.PERPLEXITY_MODEL,
            "PERPLEXITY_SEARCH_CONTEXT_USAGE": request.app.state.config.PERPLEXITY_SEARCH_CONTEXT_USAGE,
            "SOUGOU_API_SID": request.app.state.config.SOUGOU_API_SID,
            "SOUGOU_API_SK": request.app.state.config.SOUGOU_API_SK,
            "WEB_LOADER_ENGINE": request.app.state.config.WEB_LOADER_ENGINE,
            "ENABLE_WEB_LOADER_SSL_VERIFICATION": request.app.state.config.ENABLE_WEB_LOADER_SSL_VERIFICATION,
            "PLAYWRIGHT_WS_URL": request.app.state.config.PLAYWRIGHT_WS_URL,
            "PLAYWRIGHT_TIMEOUT": request.app.state.config.PLAYWRIGHT_TIMEOUT,
            "FIRECRAWL_API_KEY": request.app.state.config.FIRECRAWL_API_KEY,
            "FIRECRAWL_API_BASE_URL": request.app.state.config.FIRECRAWL_API_BASE_URL,
            "TAVILY_EXTRACT_DEPTH": request.app.state.config.TAVILY_EXTRACT_DEPTH,
            "EXTERNAL_WEB_SEARCH_URL": request.app.state.config.EXTERNAL_WEB_SEARCH_URL,
            "EXTERNAL_WEB_SEARCH_API_KEY": request.app.state.config.EXTERNAL_WEB_SEARCH_API_KEY,
            "EXTERNAL_WEB_LOADER_URL": request.app.state.config.EXTERNAL_WEB_LOADER_URL,
            "EXTERNAL_WEB_LOADER_API_KEY": request.app.state.config.EXTERNAL_WEB_LOADER_API_KEY,
            "YOUTUBE_LOADER_LANGUAGE": request.app.state.config.YOUTUBE_LOADER_LANGUAGE,
            "YOUTUBE_LOADER_PROXY_URL": request.app.state.config.YOUTUBE_LOADER_PROXY_URL,
            "YOUTUBE_LOADER_TRANSLATION": request.app.state.YOUTUBE_LOADER_TRANSLATION,
        },
    }


####################################
#
# Document process and retrieval
#
####################################


def save_docs_to_vector_db(
    request: Request,
    docs,
    collection_name,
    metadata: Optional[dict] = None,
    overwrite: bool = False,
    split: bool = True,
    add: bool = False,
    user=None,
) -> bool:
    def _get_docs_info(docs: list[Document]) -> str:
        docs_info = set()

        # Trying to select relevant metadata identifying the document.
        for doc in docs:
            metadata = getattr(doc, "metadata", {})
            doc_name = metadata.get("name", "")
            if not doc_name:
                doc_name = metadata.get("title", "")
            if not doc_name:
                doc_name = metadata.get("source", "")
            if doc_name:
                docs_info.add(doc_name)

        return ", ".join(docs_info)

    log.info(
        f"save_docs_to_vector_db: document {_get_docs_info(docs)} {collection_name}"
    )

    # Check if entries with the same hash (metadata.hash) already exist
    if metadata and "hash" in metadata:
        result = VECTOR_DB_CLIENT.query(
            collection_name=collection_name,
            filter={"hash": metadata["hash"]},
        )

        if result is not None:
            existing_doc_ids = result.ids[0]
            if existing_doc_ids:
                log.info(f"Document with hash {metadata['hash']} already exists")
                raise ValueError(ERROR_MESSAGES.DUPLICATE_CONTENT)

    if split:
        if request.app.state.config.TEXT_SPLITTER in ["", "character"]:
            text_splitter = RecursiveCharacterTextSplitter(
                chunk_size=request.app.state.config.CHUNK_SIZE,
                chunk_overlap=request.app.state.config.CHUNK_OVERLAP,
                add_start_index=True,
            )
            docs = text_splitter.split_documents(docs)
        elif request.app.state.config.TEXT_SPLITTER == "token":
            log.info(
                f"Using token text splitter: {request.app.state.config.TIKTOKEN_ENCODING_NAME}"
            )

            tiktoken.get_encoding(str(request.app.state.config.TIKTOKEN_ENCODING_NAME))
            text_splitter = TokenTextSplitter(
                encoding_name=str(request.app.state.config.TIKTOKEN_ENCODING_NAME),
                chunk_size=request.app.state.config.CHUNK_SIZE,
                chunk_overlap=request.app.state.config.CHUNK_OVERLAP,
                add_start_index=True,
            )
            docs = text_splitter.split_documents(docs)
        elif request.app.state.config.TEXT_SPLITTER == "markdown_header":
            log.info("Using markdown header text splitter")

            # Define headers to split on - covering most common markdown header levels
            headers_to_split_on = [
                ("#", "Header 1"),
                ("##", "Header 2"),
                ("###", "Header 3"),
                ("####", "Header 4"),
                ("#####", "Header 5"),
                ("######", "Header 6"),
            ]

            markdown_splitter = MarkdownHeaderTextSplitter(
                headers_to_split_on=headers_to_split_on,
                strip_headers=False,  # Keep headers in content for context
            )

            md_split_docs = []
            for doc in docs:
                md_header_splits = markdown_splitter.split_text(doc.page_content)
                text_splitter = RecursiveCharacterTextSplitter(
                    chunk_size=request.app.state.config.CHUNK_SIZE,
                    chunk_overlap=request.app.state.config.CHUNK_OVERLAP,
                    add_start_index=True,
                )
                md_header_splits = text_splitter.split_documents(md_header_splits)

                # Convert back to Document objects, preserving original metadata
                for split_chunk in md_header_splits:
                    headings_list = []
                    # Extract header values in order based on headers_to_split_on
                    for _, header_meta_key_name in headers_to_split_on:
                        if header_meta_key_name in split_chunk.metadata:
                            headings_list.append(
                                split_chunk.metadata[header_meta_key_name]
                            )

                    md_split_docs.append(
                        Document(
                            page_content=split_chunk.page_content,
                            metadata={**doc.metadata, "headings": headings_list},
                        )
                    )

            docs = md_split_docs
        else:
            raise ValueError(ERROR_MESSAGES.DEFAULT("Invalid text splitter"))

    if len(docs) == 0:
        raise ValueError(ERROR_MESSAGES.EMPTY_CONTENT)

    texts = [doc.page_content for doc in docs]
    metadatas = [
        {
            **doc.metadata,
            **(metadata if metadata else {}),
            "embedding_config": {
                "engine": request.app.state.config.RAG_EMBEDDING_ENGINE,
                "model": request.app.state.config.RAG_EMBEDDING_MODEL,
            },
        }
        for doc in docs
    ]

    try:
        if VECTOR_DB_CLIENT.has_collection(collection_name=collection_name):
            log.info(f"collection {collection_name} already exists")

            if overwrite:
                VECTOR_DB_CLIENT.delete_collection(collection_name=collection_name)
                log.info(f"deleting existing collection {collection_name}")
            elif add is False:
                log.info(
                    f"collection {collection_name} already exists, overwrite is False and add is False"
                )
                return True

        log.info(f"generating embeddings for {collection_name}")
        embedding_function = get_embedding_function(
            request.app.state.config.RAG_EMBEDDING_ENGINE,
            request.app.state.config.RAG_EMBEDDING_MODEL,
            request.app.state.ef,
            (
                request.app.state.config.RAG_OPENAI_API_BASE_URL
                if request.app.state.config.RAG_EMBEDDING_ENGINE == "openai"
                else (
                    request.app.state.config.RAG_OLLAMA_BASE_URL
                    if request.app.state.config.RAG_EMBEDDING_ENGINE == "ollama"
                    else request.app.state.config.RAG_AZURE_OPENAI_BASE_URL
                )
            ),
            (
                request.app.state.config.RAG_OPENAI_API_KEY
                if request.app.state.config.RAG_EMBEDDING_ENGINE == "openai"
                else (
                    request.app.state.config.RAG_OLLAMA_API_KEY
                    if request.app.state.config.RAG_EMBEDDING_ENGINE == "ollama"
                    else request.app.state.config.RAG_AZURE_OPENAI_API_KEY
                )
            ),
            request.app.state.config.RAG_EMBEDDING_BATCH_SIZE,
            azure_api_version=(
                request.app.state.config.RAG_AZURE_OPENAI_API_VERSION
                if request.app.state.config.RAG_EMBEDDING_ENGINE == "azure_openai"
                else None
            ),
        )

        embeddings = embedding_function(
            list(map(lambda x: x.replace("\n", " "), texts)),
            prefix=RAG_EMBEDDING_CONTENT_PREFIX,
            user=user,
        )
        log.info(f"embeddings generated {len(embeddings)} for {len(texts)} items")

        items = [
            {
                "id": str(uuid.uuid4()),
                "text": text,
                "vector": embeddings[idx],
                "metadata": metadatas[idx],
            }
            for idx, text in enumerate(texts)
        ]

        log.info(f"adding to collection {collection_name}")
        VECTOR_DB_CLIENT.insert(
            collection_name=collection_name,
            items=items,
        )

        log.info(f"added {len(items)} items to collection {collection_name}")
        return True
    except Exception as e:
        log.exception(e)
        raise e


class ProcessFileForm(BaseModel):
    file_id: str
    content: Optional[str] = None
    collection_name: Optional[str] = None


@router.post("/process/file")
def process_file(
    request: Request,
    form_data: ProcessFileForm,
    user=Depends(get_verified_user),
):
    if user.role == "admin":
        file = Files.get_file_by_id(form_data.file_id)
    else:
        file = Files.get_file_by_id_and_user_id(form_data.file_id, user.id)

    if file:
        try:

            collection_name = form_data.collection_name

            if collection_name is None:
                collection_name = f"file-{file.id}"

            if form_data.content:
                # Update the content in the file
                # Usage: /files/{file_id}/data/content/update, /files/ (audio file upload pipeline)

                try:
                    # /files/{file_id}/data/content/update
                    VECTOR_DB_CLIENT.delete_collection(
                        collection_name=f"file-{file.id}"
                    )
                except:
                    # Audio file upload pipeline
                    pass

                docs = [
                    Document(
                        page_content=form_data.content.replace("<br/>", "\n"),
                        metadata={
                            **file.meta,
                            "name": file.filename,
                            "created_by": file.user_id,
                            "file_id": file.id,
                            "source": file.filename,
                        },
                    )
                ]

                text_content = form_data.content
            elif form_data.collection_name:
                # Check if the file has already been processed and save the content
                # Usage: /knowledge/{id}/file/add, /knowledge/{id}/file/update

                result = VECTOR_DB_CLIENT.query(
                    collection_name=f"file-{file.id}", filter={"file_id": file.id}
                )

                if result is not None and len(result.ids[0]) > 0:
                    docs = [
                        Document(
                            page_content=result.documents[0][idx],
                            metadata=result.metadatas[0][idx],
                        )
                        for idx, id in enumerate(result.ids[0])
                    ]
                else:
                    docs = [
                        Document(
                            page_content=file.data.get("content", ""),
                            metadata={
                                **file.meta,
                                "name": file.filename,
                                "created_by": file.user_id,
                                "file_id": file.id,
                                "source": file.filename,
                            },
                        )
                    ]

                text_content = file.data.get("content", "")
            else:
                # Process the file and save the content
                # Usage: /files/
                file_path = file.path
                if file_path:
                    file_path = Storage.get_file(file_path)
                    loader = Loader(
                        engine=request.app.state.config.CONTENT_EXTRACTION_ENGINE,
                        DATALAB_MARKER_API_KEY=request.app.state.config.DATALAB_MARKER_API_KEY,
                        DATALAB_MARKER_API_BASE_URL=request.app.state.config.DATALAB_MARKER_API_BASE_URL,
                        DATALAB_MARKER_ADDITIONAL_CONFIG=request.app.state.config.DATALAB_MARKER_ADDITIONAL_CONFIG,
                        DATALAB_MARKER_SKIP_CACHE=request.app.state.config.DATALAB_MARKER_SKIP_CACHE,
                        DATALAB_MARKER_FORCE_OCR=request.app.state.config.DATALAB_MARKER_FORCE_OCR,
                        DATALAB_MARKER_PAGINATE=request.app.state.config.DATALAB_MARKER_PAGINATE,
                        DATALAB_MARKER_STRIP_EXISTING_OCR=request.app.state.config.DATALAB_MARKER_STRIP_EXISTING_OCR,
                        DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION=request.app.state.config.DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION,
                        DATALAB_MARKER_FORMAT_LINES=request.app.state.config.DATALAB_MARKER_FORMAT_LINES,
                        DATALAB_MARKER_USE_LLM=request.app.state.config.DATALAB_MARKER_USE_LLM,
                        DATALAB_MARKER_OUTPUT_FORMAT=request.app.state.config.DATALAB_MARKER_OUTPUT_FORMAT,
                        EXTERNAL_DOCUMENT_LOADER_URL=request.app.state.config.EXTERNAL_DOCUMENT_LOADER_URL,
                        EXTERNAL_DOCUMENT_LOADER_API_KEY=request.app.state.config.EXTERNAL_DOCUMENT_LOADER_API_KEY,
                        TIKA_SERVER_URL=request.app.state.config.TIKA_SERVER_URL,
                        DOCLING_SERVER_URL=request.app.state.config.DOCLING_SERVER_URL,
                        DOCLING_PARAMS={
                            "do_ocr": request.app.state.config.DOCLING_DO_OCR,
                            "force_ocr": request.app.state.config.DOCLING_FORCE_OCR,
                            "ocr_engine": request.app.state.config.DOCLING_OCR_ENGINE,
                            "ocr_lang": request.app.state.config.DOCLING_OCR_LANG,
                            "pdf_backend": request.app.state.config.DOCLING_PDF_BACKEND,
                            "table_mode": request.app.state.config.DOCLING_TABLE_MODE,
                            "pipeline": request.app.state.config.DOCLING_PIPELINE,
                            "do_picture_description": request.app.state.config.DOCLING_DO_PICTURE_DESCRIPTION,
                            "picture_description_mode": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_MODE,
                            "picture_description_local": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_LOCAL,
                            "picture_description_api": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_API,
                        },
                        PDF_EXTRACT_IMAGES=request.app.state.config.PDF_EXTRACT_IMAGES,
                        DOCUMENT_INTELLIGENCE_ENDPOINT=request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT,
                        DOCUMENT_INTELLIGENCE_KEY=request.app.state.config.DOCUMENT_INTELLIGENCE_KEY,
                        MISTRAL_OCR_API_KEY=request.app.state.config.MISTRAL_OCR_API_KEY,
                    )
                    docs = loader.load(
                        file.filename, file.meta.get("content_type"), file_path
                    )

                    docs = [
                        Document(
                            page_content=doc.page_content,
                            metadata={
                                **filter_metadata(doc.metadata),
                                "name": file.filename,
                                "created_by": file.user_id,
                                "file_id": file.id,
                                "source": file.filename,
                            },
                        )
                        for doc in docs
                    ]
                else:
                    docs = [
                        Document(
                            page_content=file.data.get("content", ""),
                            metadata={
                                **file.meta,
                                "name": file.filename,
                                "created_by": file.user_id,
                                "file_id": file.id,
                                "source": file.filename,
                            },
                        )
                    ]
                text_content = " ".join([doc.page_content for doc in docs])

            log.debug(f"text_content: {text_content}")
            Files.update_file_data_by_id(
                file.id,
                {"content": text_content},
            )
            hash = calculate_sha256_string(text_content)
            Files.update_file_hash_by_id(file.id, hash)

            if request.app.state.config.BYPASS_EMBEDDING_AND_RETRIEVAL:
                Files.update_file_data_by_id(file.id, {"status": "completed"})
                return {
                    "status": True,
                    "collection_name": None,
                    "filename": file.filename,
                    "content": text_content,
                }
            else:
                try:
                    result = save_docs_to_vector_db(
                        request,
                        docs=docs,
                        collection_name=collection_name,
                        metadata={
                            "file_id": file.id,
                            "name": file.filename,
                            "hash": hash,
                        },
                        add=(True if form_data.collection_name else False),
                        user=user,
                    )
                    log.info(f"added {len(docs)} items to collection {collection_name}")

                    if result:
                        Files.update_file_metadata_by_id(
                            file.id,
                            {
                                "collection_name": collection_name,
                            },
                        )

                        Files.update_file_data_by_id(
                            file.id,
                            {"status": "completed"},
                        )

                        return {
                            "status": True,
                            "collection_name": collection_name,
                            "filename": file.filename,
                            "content": text_content,
                        }
                    else:
                        raise Exception("Error saving document to vector database")
                except Exception as e:
                    raise e

        except Exception as e:
            log.exception(e)
            Files.update_file_data_by_id(
                file.id,
                {"status": "failed"},
            )

            if "No pandoc was found" in str(e):
                raise HTTPException(
                    status_code=status.HTTP_400_BAD_REQUEST,
                    detail=ERROR_MESSAGES.PANDOC_NOT_INSTALLED,
                )
            else:
                raise HTTPException(
                    status_code=status.HTTP_400_BAD_REQUEST,
                    detail=str(e),
                )

    else:
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND, detail=ERROR_MESSAGES.NOT_FOUND
        )


class ProcessTextForm(BaseModel):
    name: str
    content: str
    collection_name: Optional[str] = None


@router.post("/process/text")
def process_text(
    request: Request,
    form_data: ProcessTextForm,
    user=Depends(get_verified_user),
):
    collection_name = form_data.collection_name
    if collection_name is None:
        collection_name = calculate_sha256_string(form_data.content)

    docs = [
        Document(
            page_content=form_data.content,
            metadata={"name": form_data.name, "created_by": user.id},
        )
    ]
    text_content = form_data.content
    log.debug(f"text_content: {text_content}")

    result = save_docs_to_vector_db(request, docs, collection_name, user=user)
    if result:
        return {
            "status": True,
            "collection_name": collection_name,
            "content": text_content,
        }
    else:
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=ERROR_MESSAGES.DEFAULT(),
        )


@router.post("/process/youtube")
def process_youtube_video(
    request: Request, form_data: ProcessUrlForm, user=Depends(get_verified_user)
):
    try:
        collection_name = form_data.collection_name
        if not collection_name:
            collection_name = calculate_sha256_string(form_data.url)[:63]

        loader = YoutubeLoader(
            form_data.url,
            language=request.app.state.config.YOUTUBE_LOADER_LANGUAGE,
            proxy_url=request.app.state.config.YOUTUBE_LOADER_PROXY_URL,
        )

        docs = loader.load()
        content = " ".join([doc.page_content for doc in docs])
        log.debug(f"text_content: {content}")

        save_docs_to_vector_db(
            request, docs, collection_name, overwrite=True, user=user
        )

        return {
            "status": True,
            "collection_name": collection_name,
            "filename": form_data.url,
            "file": {
                "data": {
                    "content": content,
                },
                "meta": {
                    "name": form_data.url,
                },
            },
        }
    except Exception as e:
        log.exception(e)
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=ERROR_MESSAGES.DEFAULT(e),
        )


@router.post("/process/web")
def process_web(
    request: Request, form_data: ProcessUrlForm, user=Depends(get_verified_user)
):
    try:
        collection_name = form_data.collection_name
        if not collection_name:
            collection_name = calculate_sha256_string(form_data.url)[:63]

        loader = get_web_loader(
            form_data.url,
            verify_ssl=request.app.state.config.ENABLE_WEB_LOADER_SSL_VERIFICATION,
            requests_per_second=request.app.state.config.WEB_LOADER_CONCURRENT_REQUESTS,
        )
        docs = loader.load()
        content = " ".join([doc.page_content for doc in docs])

        log.debug(f"text_content: {content}")

        if not request.app.state.config.BYPASS_WEB_SEARCH_EMBEDDING_AND_RETRIEVAL:
            save_docs_to_vector_db(
                request, docs, collection_name, overwrite=True, user=user
            )
        else:
            collection_name = None

        return {
            "status": True,
            "collection_name": collection_name,
            "filename": form_data.url,
            "file": {
                "data": {
                    "content": content,
                },
                "meta": {
                    "name": form_data.url,
                    "source": form_data.url,
                },
            },
        }
    except Exception as e:
        log.exception(e)
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=ERROR_MESSAGES.DEFAULT(e),
        )


def search_web(request: Request, engine: str, query: str) -> list[SearchResult]:
    """Search the web using a search engine and return the results as a list of SearchResult objects.
    Will look for a search engine API key in environment variables in the following order:
    - SEARXNG_QUERY_URL
    - YACY_QUERY_URL + YACY_USERNAME + YACY_PASSWORD
    - GOOGLE_PSE_API_KEY + GOOGLE_PSE_ENGINE_ID
    - BRAVE_SEARCH_API_KEY
    - KAGI_SEARCH_API_KEY
    - MOJEEK_SEARCH_API_KEY
    - BOCHA_SEARCH_API_KEY
    - SERPSTACK_API_KEY
    - SERPER_API_KEY
    - SERPLY_API_KEY
    - TAVILY_API_KEY
    - EXA_API_KEY
    - PERPLEXITY_API_KEY
    - SOUGOU_API_SID + SOUGOU_API_SK
    - SEARCHAPI_API_KEY + SEARCHAPI_ENGINE (by default `google`)
    - SERPAPI_API_KEY + SERPAPI_ENGINE (by default `google`)
    Args:
        query (str): The query to search for
    """

    # TODO: add playwright to search the web
    if engine == "ollama_cloud":
        return search_ollama_cloud(
            "https://ollama.com",
            request.app.state.config.OLLAMA_CLOUD_WEB_SEARCH_API_KEY,
            query,
            request.app.state.config.WEB_SEARCH_RESULT_COUNT,
            request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
        )
    elif engine == "perplexity_search":
        if request.app.state.config.PERPLEXITY_API_KEY:
            return search_perplexity_search(
                request.app.state.config.PERPLEXITY_API_KEY,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No PERPLEXITY_API_KEY found in environment variables")
    elif engine == "searxng":
        if request.app.state.config.SEARXNG_QUERY_URL:
            return search_searxng(
                request.app.state.config.SEARXNG_QUERY_URL,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No SEARXNG_QUERY_URL found in environment variables")
    elif engine == "yacy":
        if request.app.state.config.YACY_QUERY_URL:
            return search_yacy(
                request.app.state.config.YACY_QUERY_URL,
                request.app.state.config.YACY_USERNAME,
                request.app.state.config.YACY_PASSWORD,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No YACY_QUERY_URL found in environment variables")
    elif engine == "google_pse":
        if (
            request.app.state.config.GOOGLE_PSE_API_KEY
            and request.app.state.config.GOOGLE_PSE_ENGINE_ID
        ):
            return search_google_pse(
                request.app.state.config.GOOGLE_PSE_API_KEY,
                request.app.state.config.GOOGLE_PSE_ENGINE_ID,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception(
                "No GOOGLE_PSE_API_KEY or GOOGLE_PSE_ENGINE_ID found in environment variables"
            )
    elif engine == "brave":
        if request.app.state.config.BRAVE_SEARCH_API_KEY:
            return search_brave(
                request.app.state.config.BRAVE_SEARCH_API_KEY,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No BRAVE_SEARCH_API_KEY found in environment variables")
    elif engine == "kagi":
        if request.app.state.config.KAGI_SEARCH_API_KEY:
            return search_kagi(
                request.app.state.config.KAGI_SEARCH_API_KEY,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No KAGI_SEARCH_API_KEY found in environment variables")
    elif engine == "mojeek":
        if request.app.state.config.MOJEEK_SEARCH_API_KEY:
            return search_mojeek(
                request.app.state.config.MOJEEK_SEARCH_API_KEY,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No MOJEEK_SEARCH_API_KEY found in environment variables")
    elif engine == "bocha":
        if request.app.state.config.BOCHA_SEARCH_API_KEY:
            return search_bocha(
                request.app.state.config.BOCHA_SEARCH_API_KEY,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No BOCHA_SEARCH_API_KEY found in environment variables")
    elif engine == "serpstack":
        if request.app.state.config.SERPSTACK_API_KEY:
            return search_serpstack(
                request.app.state.config.SERPSTACK_API_KEY,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
                https_enabled=request.app.state.config.SERPSTACK_HTTPS,
            )
        else:
            raise Exception("No SERPSTACK_API_KEY found in environment variables")
    elif engine == "serper":
        if request.app.state.config.SERPER_API_KEY:
            return search_serper(
                request.app.state.config.SERPER_API_KEY,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No SERPER_API_KEY found in environment variables")
    elif engine == "serply":
        if request.app.state.config.SERPLY_API_KEY:
            return search_serply(
                request.app.state.config.SERPLY_API_KEY,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                filter_list=request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No SERPLY_API_KEY found in environment variables")
    elif engine == "duckduckgo":
        return search_duckduckgo(
            query,
            request.app.state.config.WEB_SEARCH_RESULT_COUNT,
            request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            concurrent_requests=request.app.state.config.WEB_SEARCH_CONCURRENT_REQUESTS,
        )
    elif engine == "tavily":
        if request.app.state.config.TAVILY_API_KEY:
            return search_tavily(
                request.app.state.config.TAVILY_API_KEY,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No TAVILY_API_KEY found in environment variables")
    elif engine == "exa":
        if request.app.state.config.EXA_API_KEY:
            return search_exa(
                request.app.state.config.EXA_API_KEY,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No EXA_API_KEY found in environment variables")
    elif engine == "searchapi":
        if request.app.state.config.SEARCHAPI_API_KEY:
            return search_searchapi(
                request.app.state.config.SEARCHAPI_API_KEY,
                request.app.state.config.SEARCHAPI_ENGINE,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No SEARCHAPI_API_KEY found in environment variables")
    elif engine == "serpapi":
        if request.app.state.config.SERPAPI_API_KEY:
            return search_serpapi(
                request.app.state.config.SERPAPI_API_KEY,
                request.app.state.config.SERPAPI_ENGINE,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No SERPAPI_API_KEY found in environment variables")
    elif engine == "jina":
        return search_jina(
            request.app.state.config.JINA_API_KEY,
            query,
            request.app.state.config.WEB_SEARCH_RESULT_COUNT,
        )
    elif engine == "bing":
        return search_bing(
            request.app.state.config.BING_SEARCH_V7_SUBSCRIPTION_KEY,
            request.app.state.config.BING_SEARCH_V7_ENDPOINT,
            str(DEFAULT_LOCALE),
            query,
            request.app.state.config.WEB_SEARCH_RESULT_COUNT,
            request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
        )
    elif engine == "exa":
        return search_exa(
            request.app.state.config.EXA_API_KEY,
            query,
            request.app.state.config.WEB_SEARCH_RESULT_COUNT,
            request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
        )
    elif engine == "perplexity":
        return search_perplexity(
            request.app.state.config.PERPLEXITY_API_KEY,
            query,
            request.app.state.config.WEB_SEARCH_RESULT_COUNT,
            request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            model=request.app.state.config.PERPLEXITY_MODEL,
            search_context_usage=request.app.state.config.PERPLEXITY_SEARCH_CONTEXT_USAGE,
        )
    elif engine == "sougou":
        if (
            request.app.state.config.SOUGOU_API_SID
            and request.app.state.config.SOUGOU_API_SK
        ):
            return search_sougou(
                request.app.state.config.SOUGOU_API_SID,
                request.app.state.config.SOUGOU_API_SK,
                query,
                request.app.state.config.WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception(
                "No SOUGOU_API_SID or SOUGOU_API_SK found in environment variables"
            )
    elif engine == "firecrawl":
        return search_firecrawl(
            request.app.state.config.FIRECRAWL_API_BASE_URL,
            request.app.state.config.FIRECRAWL_API_KEY,
            query,
            request.app.state.config.WEB_SEARCH_RESULT_COUNT,
            request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
        )
    elif engine == "external":
        return search_external(
            request.app.state.config.EXTERNAL_WEB_SEARCH_URL,
            request.app.state.config.EXTERNAL_WEB_SEARCH_API_KEY,
            query,
            request.app.state.config.WEB_SEARCH_RESULT_COUNT,
            request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
        )
    else:
        raise Exception("No search engine API key found in environment variables")


@router.post("/process/web/search")
async def process_web_search(
    request: Request, form_data: SearchForm, user=Depends(get_verified_user)
):

    urls = []
    result_items = []

    try:
        logging.debug(
            f"trying to web search with {request.app.state.config.WEB_SEARCH_ENGINE, form_data.queries}"
        )

        search_tasks = [
            run_in_threadpool(
                search_web,
                request,
                request.app.state.config.WEB_SEARCH_ENGINE,
                query,
            )
            for query in form_data.queries
        ]

        search_results = await asyncio.gather(*search_tasks)

        for result in search_results:
            if result:
                for item in result:
                    if item and item.link:
                        result_items.append(item)
                        urls.append(item.link)

        urls = list(dict.fromkeys(urls))
        log.debug(f"urls: {urls}")

    except Exception as e:
        log.exception(e)

        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=ERROR_MESSAGES.WEB_SEARCH_ERROR(e),
        )

    try:
        if request.app.state.config.BYPASS_WEB_SEARCH_WEB_LOADER:
            search_results = [
                item for result in search_results for item in result if result
            ]

            docs = [
                Document(
                    page_content=result.snippet,
                    metadata={
                        "source": result.link,
                        "title": result.title,
                        "snippet": result.snippet,
                        "link": result.link,
                    },
                )
                for result in search_results
                if hasattr(result, "snippet") and result.snippet is not None
            ]
        else:
            loader = get_web_loader(
                urls,
                verify_ssl=request.app.state.config.ENABLE_WEB_LOADER_SSL_VERIFICATION,
                requests_per_second=request.app.state.config.WEB_LOADER_CONCURRENT_REQUESTS,
                trust_env=request.app.state.config.WEB_SEARCH_TRUST_ENV,
            )
            docs = await loader.aload()

        urls = [
            doc.metadata.get("source") for doc in docs if doc.metadata.get("source")
        ]  # only keep the urls returned by the loader
        result_items = [
            dict(item) for item in result_items if item.link in urls
        ]  # only keep the search results that have been loaded

        if request.app.state.config.BYPASS_WEB_SEARCH_EMBEDDING_AND_RETRIEVAL:
            return {
                "status": True,
                "collection_name": None,
                "filenames": urls,
                "items": result_items,
                "docs": [
                    {
                        "content": doc.page_content,
                        "metadata": doc.metadata,
                    }
                    for doc in docs
                ],
                "loaded_count": len(docs),
            }
        else:
            # Create a single collection for all documents
            collection_name = (
                f"web-search-{calculate_sha256_string('-'.join(form_data.queries))}"[
                    :63
                ]
            )

            try:
                await run_in_threadpool(
                    save_docs_to_vector_db,
                    request,
                    docs,
                    collection_name,
                    overwrite=True,
                    user=user,
                )
            except Exception as e:
                log.debug(f"error saving docs: {e}")

            return {
                "status": True,
                "collection_names": [collection_name],
                "items": result_items,
                "filenames": urls,
                "loaded_count": len(docs),
            }
    except Exception as e:
        log.exception(e)
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=ERROR_MESSAGES.DEFAULT(e),
        )


class QueryDocForm(BaseModel):
    collection_name: str
    query: str
    k: Optional[int] = None
    k_reranker: Optional[int] = None
    r: Optional[float] = None
    hybrid: Optional[bool] = None


@router.post("/query/doc")
def query_doc_handler(
    request: Request,
    form_data: QueryDocForm,
    user=Depends(get_verified_user),
):
    try:
        if request.app.state.config.ENABLE_RAG_HYBRID_SEARCH and (
            form_data.hybrid is None or form_data.hybrid
        ):
            collection_results = {}
            collection_results[form_data.collection_name] = VECTOR_DB_CLIENT.get(
                collection_name=form_data.collection_name
            )
            return query_doc_with_hybrid_search(
                collection_name=form_data.collection_name,
                collection_result=collection_results[form_data.collection_name],
                query=form_data.query,
                embedding_function=lambda query, prefix: request.app.state.EMBEDDING_FUNCTION(
                    query, prefix=prefix, user=user
                ),
                k=form_data.k if form_data.k else request.app.state.config.TOP_K,
                reranking_function=(
                    (
                        lambda sentences: request.app.state.RERANKING_FUNCTION(
                            sentences, user=user
                        )
                    )
                    if request.app.state.RERANKING_FUNCTION
                    else None
                ),
                k_reranker=form_data.k_reranker
                or request.app.state.config.TOP_K_RERANKER,
                r=(
                    form_data.r
                    if form_data.r
                    else request.app.state.config.RELEVANCE_THRESHOLD
                ),
                hybrid_bm25_weight=(
                    form_data.hybrid_bm25_weight
                    if form_data.hybrid_bm25_weight
                    else request.app.state.config.HYBRID_BM25_WEIGHT
                ),
                user=user,
            )
        else:
            return query_doc(
                collection_name=form_data.collection_name,
                query_embedding=request.app.state.EMBEDDING_FUNCTION(
                    form_data.query, prefix=RAG_EMBEDDING_QUERY_PREFIX, user=user
                ),
                k=form_data.k if form_data.k else request.app.state.config.TOP_K,
                user=user,
            )
    except Exception as e:
        log.exception(e)
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=ERROR_MESSAGES.DEFAULT(e),
        )


class QueryCollectionsForm(BaseModel):
    collection_names: list[str]
    query: str
    k: Optional[int] = None
    k_reranker: Optional[int] = None
    r: Optional[float] = None
    hybrid: Optional[bool] = None
    hybrid_bm25_weight: Optional[float] = None


@router.post("/query/collection")
def query_collection_handler(
    request: Request,
    form_data: QueryCollectionsForm,
    user=Depends(get_verified_user),
):
    try:
        if request.app.state.config.ENABLE_RAG_HYBRID_SEARCH and (
            form_data.hybrid is None or form_data.hybrid
        ):
            return query_collection_with_hybrid_search(
                collection_names=form_data.collection_names,
                queries=[form_data.query],
                embedding_function=lambda query, prefix: request.app.state.EMBEDDING_FUNCTION(
                    query, prefix=prefix, user=user
                ),
                k=form_data.k if form_data.k else request.app.state.config.TOP_K,
                reranking_function=(
                    (
                        lambda sentences: request.app.state.RERANKING_FUNCTION(
                            sentences, user=user
                        )
                    )
                    if request.app.state.RERANKING_FUNCTION
                    else None
                ),
                k_reranker=form_data.k_reranker
                or request.app.state.config.TOP_K_RERANKER,
                r=(
                    form_data.r
                    if form_data.r
                    else request.app.state.config.RELEVANCE_THRESHOLD
                ),
                hybrid_bm25_weight=(
                    form_data.hybrid_bm25_weight
                    if form_data.hybrid_bm25_weight
                    else request.app.state.config.HYBRID_BM25_WEIGHT
                ),
            )
        else:
            return query_collection(
                collection_names=form_data.collection_names,
                queries=[form_data.query],
                embedding_function=lambda query, prefix: request.app.state.EMBEDDING_FUNCTION(
                    query, prefix=prefix, user=user
                ),
                k=form_data.k if form_data.k else request.app.state.config.TOP_K,
            )

    except Exception as e:
        log.exception(e)
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=ERROR_MESSAGES.DEFAULT(e),
        )


####################################
#
# Vector DB operations
#
####################################


class DeleteForm(BaseModel):
    collection_name: str
    file_id: str


@router.post("/delete")
def delete_entries_from_collection(form_data: DeleteForm, user=Depends(get_admin_user)):
    try:
        if VECTOR_DB_CLIENT.has_collection(collection_name=form_data.collection_name):
            file = Files.get_file_by_id(form_data.file_id)
            hash = file.hash

            VECTOR_DB_CLIENT.delete(
                collection_name=form_data.collection_name,
                metadata={"hash": hash},
            )
            return {"status": True}
        else:
            return {"status": False}
    except Exception as e:
        log.exception(e)
        return {"status": False}


@router.post("/reset/db")
def reset_vector_db(user=Depends(get_admin_user)):
    VECTOR_DB_CLIENT.reset()
    Knowledges.delete_all_knowledge()


@router.post("/reset/uploads")
def reset_upload_dir(user=Depends(get_admin_user)) -> bool:
    folder = f"{UPLOAD_DIR}"
    try:
        # Check if the directory exists
        if os.path.exists(folder):
            # Iterate over all the files and directories in the specified directory
            for filename in os.listdir(folder):
                file_path = os.path.join(folder, filename)
                try:
                    if os.path.isfile(file_path) or os.path.islink(file_path):
                        os.unlink(file_path)  # Remove the file or link
                    elif os.path.isdir(file_path):
                        shutil.rmtree(file_path)  # Remove the directory
                except Exception as e:
                    log.exception(f"Failed to delete {file_path}. Reason: {e}")
        else:
            log.warning(f"The directory {folder} does not exist")
    except Exception as e:
        log.exception(f"Failed to process the directory {folder}. Reason: {e}")
    return True


if ENV == "dev":

    @router.get("/ef/{text}")
    async def get_embeddings(request: Request, text: Optional[str] = "Hello World!"):
        return {
            "result": request.app.state.EMBEDDING_FUNCTION(
                text, prefix=RAG_EMBEDDING_QUERY_PREFIX
            )
        }


class BatchProcessFilesForm(BaseModel):
    files: List[FileModel]
    collection_name: str


class BatchProcessFilesResult(BaseModel):
    file_id: str
    status: str
    error: Optional[str] = None


class BatchProcessFilesResponse(BaseModel):
    results: List[BatchProcessFilesResult]
    errors: List[BatchProcessFilesResult]


@router.post("/process/files/batch")
def process_files_batch(
    request: Request,
    form_data: BatchProcessFilesForm,
    user=Depends(get_verified_user),
) -> BatchProcessFilesResponse:
    """
    Process a batch of files and save them to the vector database.
    """
    results: List[BatchProcessFilesResult] = []
    errors: List[BatchProcessFilesResult] = []
    collection_name = form_data.collection_name

    # Prepare all documents first
    all_docs: List[Document] = []
    for file in form_data.files:
        try:
            text_content = file.data.get("content", "")

            docs: List[Document] = [
                Document(
                    page_content=text_content.replace("<br/>", "\n"),
                    metadata={
                        **file.meta,
                        "name": file.filename,
                        "created_by": file.user_id,
                        "file_id": file.id,
                        "source": file.filename,
                    },
                )
            ]

            hash = calculate_sha256_string(text_content)
            Files.update_file_hash_by_id(file.id, hash)
            Files.update_file_data_by_id(file.id, {"content": text_content})

            all_docs.extend(docs)
            results.append(BatchProcessFilesResult(file_id=file.id, status="prepared"))

        except Exception as e:
            log.error(f"process_files_batch: Error processing file {file.id}: {str(e)}")
            errors.append(
                BatchProcessFilesResult(file_id=file.id, status="failed", error=str(e))
            )

    # Save all documents in one batch
    if all_docs:
        try:
            save_docs_to_vector_db(
                request=request,
                docs=all_docs,
                collection_name=collection_name,
                add=True,
                user=user,
            )

            # Update all files with collection name
            for result in results:
                Files.update_file_metadata_by_id(
                    result.file_id, {"collection_name": collection_name}
                )
                result.status = "completed"

        except Exception as e:
            log.error(
                f"process_files_batch: Error saving documents to vector DB: {str(e)}"
            )
            for result in results:
                result.status = "failed"
                errors.append(
                    BatchProcessFilesResult(file_id=result.file_id, error=str(e))
                )

    return BatchProcessFilesResponse(results=results, errors=errors)