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

import json
import logging
import mimetypes
import os
import shutil

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 pydantic import BaseModel
import tiktoken


from langchain.text_splitter import RecursiveCharacterTextSplitter, TokenTextSplitter
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.connector 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.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.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.searxng import search_searxng
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.utils import (
    get_embedding_function,
    get_model_path,
    query_collection,
    query_collection_with_hybrid_search,
    query_doc,
    query_doc_with_hybrid_search,
)
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,
)
from open_webui.env import (
    SRC_LOG_LEVELS,
    DEVICE_TYPE,
    DOCKER,
)
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,
            )
        except Exception as e:
            log.debug(f"Error loading SentenceTransformer: {e}")

    return ef


def get_rf(
    reranking_model: 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:
            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,
                )
            except:
                log.error("CrossEncoder error")
                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(CollectionNameForm):
    query: 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,
        },
    }


@router.get("/reranking")
async def get_reraanking_config(request: Request, user=Depends(get_admin_user)):
    return {
        "status": True,
        "reranking_model": request.app.state.config.RAG_RERANKING_MODEL,
    }


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


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


class EmbeddingModelUpdateForm(BaseModel):
    openai_config: Optional[OpenAIConfigForm] = None
    ollama_config: Optional[OllamaConfigForm] = 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}"
    )
    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"]:
            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
                )

            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
            ),
            (
                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
            ),
            request.app.state.config.RAG_EMBEDDING_BATCH_SIZE,
        )

        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,
            },
        }
    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),
        )


class RerankingModelUpdateForm(BaseModel):
    reranking_model: str


@router.post("/reranking/update")
async def update_reranking_config(
    request: Request, form_data: RerankingModelUpdateForm, user=Depends(get_admin_user)
):
    log.info(
        f"Updating reranking model: {request.app.state.config.RAG_RERANKING_MODEL} to {form_data.reranking_model}"
    )
    try:
        request.app.state.config.RAG_RERANKING_MODEL = form_data.reranking_model

        try:
            request.app.state.rf = get_rf(
                request.app.state.config.RAG_RERANKING_MODEL,
                True,
            )
        except Exception as e:
            log.error(f"Error loading reranking model: {e}")
            request.app.state.config.ENABLE_RAG_HYBRID_SEARCH = False

        return {
            "status": True,
            "reranking_model": request.app.state.config.RAG_RERANKING_MODEL,
        }
    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),
        )


@router.get("/config")
async def get_rag_config(request: Request, user=Depends(get_admin_user)):
    return {
        "status": True,
        "pdf_extract_images": request.app.state.config.PDF_EXTRACT_IMAGES,
        "enable_google_drive_integration": request.app.state.config.ENABLE_GOOGLE_DRIVE_INTEGRATION,
        "content_extraction": {
            "engine": request.app.state.config.CONTENT_EXTRACTION_ENGINE,
            "tika_server_url": request.app.state.config.TIKA_SERVER_URL,
            "document_intelligence_config": {
                "endpoint": request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT,
                "key": request.app.state.config.DOCUMENT_INTELLIGENCE_KEY,
            },
        },
        "chunk": {
            "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": {
            "max_size": request.app.state.config.FILE_MAX_SIZE,
            "max_count": request.app.state.config.FILE_MAX_COUNT,
        },
        "youtube": {
            "language": request.app.state.config.YOUTUBE_LOADER_LANGUAGE,
            "translation": request.app.state.YOUTUBE_LOADER_TRANSLATION,
            "proxy_url": request.app.state.config.YOUTUBE_LOADER_PROXY_URL,
        },
        "web": {
            "web_loader_ssl_verification": request.app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
            "search": {
                "enabled": request.app.state.config.ENABLE_RAG_WEB_SEARCH,
                "drive": request.app.state.config.ENABLE_GOOGLE_DRIVE_INTEGRATION,
                "engine": request.app.state.config.RAG_WEB_SEARCH_ENGINE,
                "searxng_query_url": request.app.state.config.SEARXNG_QUERY_URL,
                "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,
                "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,
                "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,
                "result_count": request.app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
                "concurrent_requests": request.app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
            },
        },
    }


class FileConfig(BaseModel):
    max_size: Optional[int] = None
    max_count: Optional[int] = None


class DocumentIntelligenceConfigForm(BaseModel):
    endpoint: str
    key: str


class ContentExtractionConfig(BaseModel):
    engine: str = ""
    tika_server_url: Optional[str] = None
    document_intelligence_config: Optional[DocumentIntelligenceConfigForm] = None


class ChunkParamUpdateForm(BaseModel):
    text_splitter: Optional[str] = None
    chunk_size: int
    chunk_overlap: int


class YoutubeLoaderConfig(BaseModel):
    language: list[str]
    translation: Optional[str] = None
    proxy_url: str = ""


class WebSearchConfig(BaseModel):
    enabled: bool
    engine: Optional[str] = None
    searxng_query_url: 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
    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
    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
    result_count: Optional[int] = None
    concurrent_requests: Optional[int] = None


class WebConfig(BaseModel):
    search: WebSearchConfig
    web_loader_ssl_verification: Optional[bool] = None


class ConfigUpdateForm(BaseModel):
    pdf_extract_images: Optional[bool] = None
    enable_google_drive_integration: Optional[bool] = None
    file: Optional[FileConfig] = None
    content_extraction: Optional[ContentExtractionConfig] = None
    chunk: Optional[ChunkParamUpdateForm] = None
    youtube: Optional[YoutubeLoaderConfig] = None
    web: Optional[WebConfig] = None


@router.post("/config/update")
async def update_rag_config(
    request: Request, form_data: ConfigUpdateForm, user=Depends(get_admin_user)
):
    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.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
    )

    if form_data.file is not None:
        request.app.state.config.FILE_MAX_SIZE = form_data.file.max_size
        request.app.state.config.FILE_MAX_COUNT = form_data.file.max_count

    if form_data.content_extraction is not None:
        log.info(
            f"Updating content extraction: {request.app.state.config.CONTENT_EXTRACTION_ENGINE} to {form_data.content_extraction.engine}"
        )
        request.app.state.config.CONTENT_EXTRACTION_ENGINE = (
            form_data.content_extraction.engine
        )
        request.app.state.config.TIKA_SERVER_URL = (
            form_data.content_extraction.tika_server_url
        )
        if form_data.content_extraction.document_intelligence_config is not None:
            request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT = (
                form_data.content_extraction.document_intelligence_config.endpoint
            )
            request.app.state.config.DOCUMENT_INTELLIGENCE_KEY = (
                form_data.content_extraction.document_intelligence_config.key
            )

    if form_data.chunk is not None:
        request.app.state.config.TEXT_SPLITTER = form_data.chunk.text_splitter
        request.app.state.config.CHUNK_SIZE = form_data.chunk.chunk_size
        request.app.state.config.CHUNK_OVERLAP = form_data.chunk.chunk_overlap

    if form_data.youtube is not None:
        request.app.state.config.YOUTUBE_LOADER_LANGUAGE = form_data.youtube.language
        request.app.state.config.YOUTUBE_LOADER_PROXY_URL = form_data.youtube.proxy_url
        request.app.state.YOUTUBE_LOADER_TRANSLATION = form_data.youtube.translation

    if form_data.web is not None:
        request.app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION = (
            # Note: When UI "Bypass SSL verification for Websites"=True then ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION=False
            form_data.web.web_loader_ssl_verification
        )

        request.app.state.config.ENABLE_RAG_WEB_SEARCH = form_data.web.search.enabled
        request.app.state.config.RAG_WEB_SEARCH_ENGINE = form_data.web.search.engine
        request.app.state.config.SEARXNG_QUERY_URL = (
            form_data.web.search.searxng_query_url
        )
        request.app.state.config.GOOGLE_PSE_API_KEY = (
            form_data.web.search.google_pse_api_key
        )
        request.app.state.config.GOOGLE_PSE_ENGINE_ID = (
            form_data.web.search.google_pse_engine_id
        )
        request.app.state.config.BRAVE_SEARCH_API_KEY = (
            form_data.web.search.brave_search_api_key
        )
        request.app.state.config.KAGI_SEARCH_API_KEY = (
            form_data.web.search.kagi_search_api_key
        )
        request.app.state.config.MOJEEK_SEARCH_API_KEY = (
            form_data.web.search.mojeek_search_api_key
        )
        request.app.state.config.SERPSTACK_API_KEY = (
            form_data.web.search.serpstack_api_key
        )
        request.app.state.config.SERPSTACK_HTTPS = form_data.web.search.serpstack_https
        request.app.state.config.SERPER_API_KEY = form_data.web.search.serper_api_key
        request.app.state.config.SERPLY_API_KEY = form_data.web.search.serply_api_key
        request.app.state.config.TAVILY_API_KEY = form_data.web.search.tavily_api_key
        request.app.state.config.SEARCHAPI_API_KEY = (
            form_data.web.search.searchapi_api_key
        )
        request.app.state.config.SEARCHAPI_ENGINE = (
            form_data.web.search.searchapi_engine
        )

        request.app.state.config.JINA_API_KEY = form_data.web.search.jina_api_key
        request.app.state.config.BING_SEARCH_V7_ENDPOINT = (
            form_data.web.search.bing_search_v7_endpoint
        )
        request.app.state.config.BING_SEARCH_V7_SUBSCRIPTION_KEY = (
            form_data.web.search.bing_search_v7_subscription_key
        )

        request.app.state.config.EXA_API_KEY = form_data.web.search.exa_api_key

        request.app.state.config.RAG_WEB_SEARCH_RESULT_COUNT = (
            form_data.web.search.result_count
        )
        request.app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS = (
            form_data.web.search.concurrent_requests
        )

    return {
        "status": True,
        "pdf_extract_images": request.app.state.config.PDF_EXTRACT_IMAGES,
        "file": {
            "max_size": request.app.state.config.FILE_MAX_SIZE,
            "max_count": request.app.state.config.FILE_MAX_COUNT,
        },
        "content_extraction": {
            "engine": request.app.state.config.CONTENT_EXTRACTION_ENGINE,
            "tika_server_url": request.app.state.config.TIKA_SERVER_URL,
            "document_intelligence_config": {
                "endpoint": request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT,
                "key": request.app.state.config.DOCUMENT_INTELLIGENCE_KEY,
            },
        },
        "chunk": {
            "text_splitter": request.app.state.config.TEXT_SPLITTER,
            "chunk_size": request.app.state.config.CHUNK_SIZE,
            "chunk_overlap": request.app.state.config.CHUNK_OVERLAP,
        },
        "youtube": {
            "language": request.app.state.config.YOUTUBE_LOADER_LANGUAGE,
            "proxy_url": request.app.state.config.YOUTUBE_LOADER_PROXY_URL,
            "translation": request.app.state.YOUTUBE_LOADER_TRANSLATION,
        },
        "web": {
            "web_loader_ssl_verification": request.app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
            "search": {
                "enabled": request.app.state.config.ENABLE_RAG_WEB_SEARCH,
                "engine": request.app.state.config.RAG_WEB_SEARCH_ENGINE,
                "searxng_query_url": request.app.state.config.SEARXNG_QUERY_URL,
                "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,
                "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,
                "serachapi_api_key": request.app.state.config.SEARCHAPI_API_KEY,
                "searchapi_engine": request.app.state.config.SEARCHAPI_ENGINE,
                "tavily_api_key": request.app.state.config.TAVILY_API_KEY,
                "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,
                "result_count": request.app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
                "concurrent_requests": request.app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
            },
        },
    }


@router.get("/template")
async def get_rag_template(request: Request, user=Depends(get_verified_user)):
    return {
        "status": True,
        "template": request.app.state.config.RAG_TEMPLATE,
    }


@router.get("/query/settings")
async def get_query_settings(request: Request, user=Depends(get_admin_user)):
    return {
        "status": True,
        "template": request.app.state.config.RAG_TEMPLATE,
        "k": request.app.state.config.TOP_K,
        "r": request.app.state.config.RELEVANCE_THRESHOLD,
        "hybrid": request.app.state.config.ENABLE_RAG_HYBRID_SEARCH,
    }


class QuerySettingsForm(BaseModel):
    k: Optional[int] = None
    r: Optional[float] = None
    template: Optional[str] = None
    hybrid: Optional[bool] = None


@router.post("/query/settings/update")
async def update_query_settings(
    request: Request, form_data: QuerySettingsForm, user=Depends(get_admin_user)
):
    request.app.state.config.RAG_TEMPLATE = form_data.template
    request.app.state.config.TOP_K = form_data.k if form_data.k else 4
    request.app.state.config.RELEVANCE_THRESHOLD = form_data.r if form_data.r else 0.0

    request.app.state.config.ENABLE_RAG_HYBRID_SEARCH = (
        form_data.hybrid if form_data.hybrid else False
    )

    return {
        "status": True,
        "template": request.app.state.config.RAG_TEMPLATE,
        "k": request.app.state.config.TOP_K,
        "r": request.app.state.config.RELEVANCE_THRESHOLD,
        "hybrid": request.app.state.config.ENABLE_RAG_HYBRID_SEARCH,
    }


####################################
#
# 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,
            )
        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,
            )
        else:
            raise ValueError(ERROR_MESSAGES.DEFAULT("Invalid text splitter"))

        docs = text_splitter.split_documents(docs)

    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": json.dumps(
                {
                    "engine": request.app.state.config.RAG_EMBEDDING_ENGINE,
                    "model": request.app.state.config.RAG_EMBEDDING_MODEL,
                }
            ),
        }
        for doc in docs
    ]

    # ChromaDB does not like datetime formats
    # for meta-data so convert them to string.
    for metadata in metadatas:
        for key, value in metadata.items():
            if isinstance(value, datetime):
                metadata[key] = str(value)

    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"adding to collection {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
            ),
            (
                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
            ),
            request.app.state.config.RAG_EMBEDDING_BATCH_SIZE,
        )

        embeddings = embedding_function(
            list(map(lambda x: x.replace("\n", " "), texts)), user=user
        )

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

        VECTOR_DB_CLIENT.insert(
            collection_name=collection_name,
            items=items,
        )

        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),
):
    try:
        file = Files.get_file_by_id(form_data.file_id)

        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

            VECTOR_DB_CLIENT.delete_collection(collection_name=f"file-{file.id}")

            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,
                    TIKA_SERVER_URL=request.app.state.config.TIKA_SERVER_URL,
                    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,
                )
                docs = loader.load(
                    file.filename, file.meta.get("content_type"), file_path
                )

                docs = [
                    Document(
                        page_content=doc.page_content,
                        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)

        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,
            )

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

                return {
                    "status": True,
                    "collection_name": collection_name,
                    "filename": file.filename,
                    "content": text_content,
                }
        except Exception as e:
            raise e
    except Exception as e:
        log.exception(e)
        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),
            )


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_RAG_WEB_LOADER_SSL_VERIFICATION,
            requests_per_second=request.app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
        )
        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),
        )


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
    - GOOGLE_PSE_API_KEY + GOOGLE_PSE_ENGINE_ID
    - BRAVE_SEARCH_API_KEY
    - KAGI_SEARCH_API_KEY
    - MOJEEK_SEARCH_API_KEY
    - SERPSTACK_API_KEY
    - SERPER_API_KEY
    - SERPLY_API_KEY
    - TAVILY_API_KEY
    - EXA_API_KEY
    - SEARCHAPI_API_KEY + SEARCHAPI_ENGINE (by default `google`)
    Args:
        query (str): The query to search for
    """

    # TODO: add playwright to search the web
    if 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.RAG_WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No SEARXNG_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.RAG_WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.RAG_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.RAG_WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.RAG_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.RAG_WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.RAG_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.RAG_WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No MOJEEK_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.RAG_WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.RAG_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.RAG_WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.RAG_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.RAG_WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.RAG_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.RAG_WEB_SEARCH_RESULT_COUNT,
            request.app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
        )
    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.RAG_WEB_SEARCH_RESULT_COUNT,
            )
        else:
            raise Exception("No TAVILY_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.RAG_WEB_SEARCH_RESULT_COUNT,
                request.app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
            )
        else:
            raise Exception("No SEARCHAPI_API_KEY found in environment variables")
    elif engine == "jina":
        return search_jina(
            request.app.state.config.JINA_API_KEY,
            query,
            request.app.state.config.RAG_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.RAG_WEB_SEARCH_RESULT_COUNT,
            request.app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
        )
    elif engine == "exa":
        return search_exa(
            request.app.state.config.EXA_API_KEY,
            query,
            request.app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
            request.app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
        )
    else:
        raise Exception("No search engine API key found in environment variables")


@router.post("/process/web/search")
def process_web_search(
    request: Request, form_data: SearchForm, user=Depends(get_verified_user)
):
    try:
        logging.info(
            f"trying to web search with {request.app.state.config.RAG_WEB_SEARCH_ENGINE, form_data.query}"
        )
        web_results = search_web(
            request, request.app.state.config.RAG_WEB_SEARCH_ENGINE, form_data.query
        )
    except Exception as e:
        log.exception(e)

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

    log.debug(f"web_results: {web_results}")

    try:
        collection_name = form_data.collection_name
        if collection_name == "" or collection_name is None:
            collection_name = f"web-search-{calculate_sha256_string(form_data.query)}"[
                :63
            ]

        urls = [result.link for result in web_results]
        loader = get_web_loader(
            urls,
            verify_ssl=request.app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
            requests_per_second=request.app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
        )
        docs = loader.load()
        save_docs_to_vector_db(
            request, docs, collection_name, overwrite=True, user=user
        )

        return {
            "status": True,
            "collection_name": collection_name,
            "filenames": urls,
        }
    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
    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:
            return query_doc_with_hybrid_search(
                collection_name=form_data.collection_name,
                query=form_data.query,
                embedding_function=lambda query: request.app.state.EMBEDDING_FUNCTION(
                    query, user=user
                ),
                k=form_data.k if form_data.k else request.app.state.config.TOP_K,
                reranking_function=request.app.state.rf,
                r=(
                    form_data.r
                    if form_data.r
                    else request.app.state.config.RELEVANCE_THRESHOLD
                ),
                user=user,
            )
        else:
            return query_doc(
                collection_name=form_data.collection_name,
                query_embedding=request.app.state.EMBEDDING_FUNCTION(
                    form_data.query, 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
    r: Optional[float] = None
    hybrid: Optional[bool] = 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:
            return query_collection_with_hybrid_search(
                collection_names=form_data.collection_names,
                queries=[form_data.query],
                embedding_function=lambda query: request.app.state.EMBEDDING_FUNCTION(
                    query, user=user
                ),
                k=form_data.k if form_data.k else request.app.state.config.TOP_K,
                reranking_function=request.app.state.rf,
                r=(
                    form_data.r
                    if form_data.r
                    else request.app.state.config.RELEVANCE_THRESHOLD
                ),
            )
        else:
            return query_collection(
                collection_names=form_data.collection_names,
                queries=[form_data.query],
                embedding_function=lambda query: request.app.state.EMBEDDING_FUNCTION(
                    query, 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:
                    print(f"Failed to delete {file_path}. Reason: {e}")
        else:
            print(f"The directory {folder} does not exist")
    except Exception as e:
        print(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)}


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)