open-webui/backend/open_webui/main.py

import asyncio
import inspect
import json
import logging
import mimetypes
import os
import shutil
import sys
import time
import random
from typing import AsyncGenerator, Generator, Iterator

from contextlib import asynccontextmanager
from urllib.parse import urlencode, parse_qs, urlparse
from pydantic import BaseModel
from sqlalchemy import text

from typing import Optional
from aiocache import cached
import aiohttp
import requests
from fastapi import (
    Depends,
    FastAPI,
    File,
    Form,
    HTTPException,
    Request,
    UploadFile,
    status,
)
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, RedirectResponse
from fastapi.staticfiles import StaticFiles

from starlette.exceptions import HTTPException as StarletteHTTPException
from starlette.middleware.base import BaseHTTPMiddleware
from starlette.middleware.sessions import SessionMiddleware
from starlette.responses import Response, StreamingResponse


from open_webui.socket.main import (
    app as socket_app,
    periodic_usage_pool_cleanup,
    get_event_call,
    get_event_emitter,
)

from open_webui.routers import (
    audio,
    images,
    ollama,
    openai,
    retrieval,
    pipelines,
    tasks,
    auths,
    chats,
    folders,
    configs,
    groups,
    files,
    functions,
    memories,
    models,
    knowledge,
    prompts,
    evaluations,
    tools,
    users,
    utils,
)
from open_webui.routers.retrieval import (
    get_embedding_function,
    get_ef,
    get_rf,
)
from open_webui.routers.pipelines import (
    process_pipeline_inlet_filter,
    process_pipeline_outlet_filter,
)

from open_webui.retrieval.utils import get_sources_from_files


from open_webui.internal.db import Session

from open_webui.models.functions import Functions
from open_webui.models.models import Models
from open_webui.models.users import UserModel, Users


from open_webui.constants import TASKS
from open_webui.config import (
    # Ollama
    ENABLE_OLLAMA_API,
    OLLAMA_BASE_URLS,
    OLLAMA_API_CONFIGS,
    # OpenAI
    ENABLE_OPENAI_API,
    OPENAI_API_BASE_URLS,
    OPENAI_API_KEYS,
    OPENAI_API_CONFIGS,
    # Image
    AUTOMATIC1111_API_AUTH,
    AUTOMATIC1111_BASE_URL,
    AUTOMATIC1111_CFG_SCALE,
    AUTOMATIC1111_SAMPLER,
    AUTOMATIC1111_SCHEDULER,
    COMFYUI_BASE_URL,
    COMFYUI_WORKFLOW,
    COMFYUI_WORKFLOW_NODES,
    ENABLE_IMAGE_GENERATION,
    IMAGE_GENERATION_ENGINE,
    IMAGE_GENERATION_MODEL,
    IMAGE_SIZE,
    IMAGE_STEPS,
    IMAGES_OPENAI_API_BASE_URL,
    IMAGES_OPENAI_API_KEY,
    # Audio
    AUDIO_STT_ENGINE,
    AUDIO_STT_MODEL,
    AUDIO_STT_OPENAI_API_BASE_URL,
    AUDIO_STT_OPENAI_API_KEY,
    AUDIO_TTS_API_KEY,
    AUDIO_TTS_ENGINE,
    AUDIO_TTS_MODEL,
    AUDIO_TTS_OPENAI_API_BASE_URL,
    AUDIO_TTS_OPENAI_API_KEY,
    AUDIO_TTS_SPLIT_ON,
    AUDIO_TTS_VOICE,
    AUDIO_TTS_AZURE_SPEECH_REGION,
    AUDIO_TTS_AZURE_SPEECH_OUTPUT_FORMAT,
    WHISPER_MODEL,
    WHISPER_MODEL_AUTO_UPDATE,
    WHISPER_MODEL_DIR,
    # Retrieval
    RAG_TEMPLATE,
    DEFAULT_RAG_TEMPLATE,
    RAG_EMBEDDING_MODEL,
    RAG_EMBEDDING_MODEL_AUTO_UPDATE,
    RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
    RAG_RERANKING_MODEL,
    RAG_RERANKING_MODEL_AUTO_UPDATE,
    RAG_RERANKING_MODEL_TRUST_REMOTE_CODE,
    RAG_EMBEDDING_ENGINE,
    RAG_EMBEDDING_BATCH_SIZE,
    RAG_RELEVANCE_THRESHOLD,
    RAG_FILE_MAX_COUNT,
    RAG_FILE_MAX_SIZE,
    RAG_OPENAI_API_BASE_URL,
    RAG_OPENAI_API_KEY,
    RAG_OLLAMA_BASE_URL,
    RAG_OLLAMA_API_KEY,
    CHUNK_OVERLAP,
    CHUNK_SIZE,
    CONTENT_EXTRACTION_ENGINE,
    TIKA_SERVER_URL,
    RAG_TOP_K,
    RAG_TEXT_SPLITTER,
    TIKTOKEN_ENCODING_NAME,
    PDF_EXTRACT_IMAGES,
    YOUTUBE_LOADER_LANGUAGE,
    YOUTUBE_LOADER_PROXY_URL,
    # Retrieval (Web Search)
    RAG_WEB_SEARCH_ENGINE,
    RAG_WEB_SEARCH_RESULT_COUNT,
    RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
    RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
    JINA_API_KEY,
    SEARCHAPI_API_KEY,
    SEARCHAPI_ENGINE,
    SEARXNG_QUERY_URL,
    SERPER_API_KEY,
    SERPLY_API_KEY,
    SERPSTACK_API_KEY,
    SERPSTACK_HTTPS,
    TAVILY_API_KEY,
    BING_SEARCH_V7_ENDPOINT,
    BING_SEARCH_V7_SUBSCRIPTION_KEY,
    BRAVE_SEARCH_API_KEY,
    KAGI_SEARCH_API_KEY,
    MOJEEK_SEARCH_API_KEY,
    GOOGLE_PSE_API_KEY,
    GOOGLE_PSE_ENGINE_ID,
    ENABLE_RAG_HYBRID_SEARCH,
    ENABLE_RAG_LOCAL_WEB_FETCH,
    ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
    ENABLE_RAG_WEB_SEARCH,
    UPLOAD_DIR,
    # WebUI
    WEBUI_AUTH,
    WEBUI_NAME,
    WEBUI_BANNERS,
    WEBHOOK_URL,
    ADMIN_EMAIL,
    SHOW_ADMIN_DETAILS,
    JWT_EXPIRES_IN,
    ENABLE_SIGNUP,
    ENABLE_LOGIN_FORM,
    ENABLE_API_KEY,
    ENABLE_COMMUNITY_SHARING,
    ENABLE_MESSAGE_RATING,
    ENABLE_EVALUATION_ARENA_MODELS,
    USER_PERMISSIONS,
    DEFAULT_USER_ROLE,
    DEFAULT_PROMPT_SUGGESTIONS,
    DEFAULT_MODELS,
    DEFAULT_ARENA_MODEL,
    MODEL_ORDER_LIST,
    EVALUATION_ARENA_MODELS,
    # WebUI (OAuth)
    ENABLE_OAUTH_ROLE_MANAGEMENT,
    OAUTH_ROLES_CLAIM,
    OAUTH_EMAIL_CLAIM,
    OAUTH_PICTURE_CLAIM,
    OAUTH_USERNAME_CLAIM,
    OAUTH_ALLOWED_ROLES,
    OAUTH_ADMIN_ROLES,
    # WebUI (LDAP)
    ENABLE_LDAP,
    LDAP_SERVER_LABEL,
    LDAP_SERVER_HOST,
    LDAP_SERVER_PORT,
    LDAP_ATTRIBUTE_FOR_USERNAME,
    LDAP_SEARCH_FILTERS,
    LDAP_SEARCH_BASE,
    LDAP_APP_DN,
    LDAP_APP_PASSWORD,
    LDAP_USE_TLS,
    LDAP_CA_CERT_FILE,
    LDAP_CIPHERS,
    # Misc
    ENV,
    CACHE_DIR,
    STATIC_DIR,
    FRONTEND_BUILD_DIR,
    CORS_ALLOW_ORIGIN,
    DEFAULT_LOCALE,
    OAUTH_PROVIDERS,
    # Admin
    ENABLE_ADMIN_CHAT_ACCESS,
    ENABLE_ADMIN_EXPORT,
    # Tasks
    TASK_MODEL,
    TASK_MODEL_EXTERNAL,
    ENABLE_TAGS_GENERATION,
    ENABLE_SEARCH_QUERY_GENERATION,
    ENABLE_RETRIEVAL_QUERY_GENERATION,
    ENABLE_AUTOCOMPLETE_GENERATION,
    TITLE_GENERATION_PROMPT_TEMPLATE,
    TAGS_GENERATION_PROMPT_TEMPLATE,
    TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE,
    QUERY_GENERATION_PROMPT_TEMPLATE,
    AUTOCOMPLETE_GENERATION_PROMPT_TEMPLATE,
    AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH,
    AppConfig,
    reset_config,
)
from open_webui.env import (
    CHANGELOG,
    GLOBAL_LOG_LEVEL,
    SAFE_MODE,
    SRC_LOG_LEVELS,
    VERSION,
    WEBUI_URL,
    WEBUI_BUILD_HASH,
    WEBUI_SECRET_KEY,
    WEBUI_SESSION_COOKIE_SAME_SITE,
    WEBUI_SESSION_COOKIE_SECURE,
    WEBUI_AUTH_TRUSTED_EMAIL_HEADER,
    WEBUI_AUTH_TRUSTED_NAME_HEADER,
    BYPASS_MODEL_ACCESS_CONTROL,
    RESET_CONFIG_ON_START,
    OFFLINE_MODE,
)

from open_webui.utils.plugin import load_function_module_by_id
from open_webui.utils.misc import (
    add_or_update_system_message,
    get_last_user_message,
    prepend_to_first_user_message_content,
    openai_chat_chunk_message_template,
    openai_chat_completion_message_template,
)
from open_webui.utils.payload import (
    apply_model_params_to_body_openai,
    apply_model_system_prompt_to_body,
)

from open_webui.utils.payload import convert_payload_openai_to_ollama
from open_webui.utils.response import (
    convert_response_ollama_to_openai,
    convert_streaming_response_ollama_to_openai,
)

from open_webui.utils.task import (
    get_task_model_id,
    rag_template,
    tools_function_calling_generation_template,
)
from open_webui.utils.tools import get_tools
from open_webui.utils.access_control import has_access

from open_webui.utils.auth import (
    decode_token,
    get_admin_user,
    get_current_user,
    get_http_authorization_cred,
    get_verified_user,
)
from open_webui.utils.oauth import oauth_manager
from open_webui.utils.security_headers import SecurityHeadersMiddleware


if SAFE_MODE:
    print("SAFE MODE ENABLED")
    Functions.deactivate_all_functions()

logging.basicConfig(stream=sys.stdout, level=GLOBAL_LOG_LEVEL)
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["MAIN"])


class SPAStaticFiles(StaticFiles):
    async def get_response(self, path: str, scope):
        try:
            return await super().get_response(path, scope)
        except (HTTPException, StarletteHTTPException) as ex:
            if ex.status_code == 404:
                return await super().get_response("index.html", scope)
            else:
                raise ex


print(
    rf"""
  ___                    __        __   _     _   _ ___
 / _ \ _ __   ___ _ __   \ \      / /__| |__ | | | |_ _|
| | | | '_ \ / _ \ '_ \   \ \ /\ / / _ \ '_ \| | | || |
| |_| | |_) |  __/ | | |   \ V  V /  __/ |_) | |_| || |
 \___/| .__/ \___|_| |_|    \_/\_/ \___|_.__/ \___/|___|
      |_|


v{VERSION} - building the best open-source AI user interface.
{f"Commit: {WEBUI_BUILD_HASH}" if WEBUI_BUILD_HASH != "dev-build" else ""}
https://github.com/open-webui/open-webui
"""
)


@asynccontextmanager
async def lifespan(app: FastAPI):
    if RESET_CONFIG_ON_START:
        reset_config()

    asyncio.create_task(periodic_usage_pool_cleanup())
    yield


app = FastAPI(
    docs_url="/docs" if ENV == "dev" else None,
    openapi_url="/openapi.json" if ENV == "dev" else None,
    redoc_url=None,
    lifespan=lifespan,
)

app.state.config = AppConfig()


########################################
#
# OLLAMA
#
########################################


app.state.config.ENABLE_OLLAMA_API = ENABLE_OLLAMA_API
app.state.config.OLLAMA_BASE_URLS = OLLAMA_BASE_URLS
app.state.config.OLLAMA_API_CONFIGS = OLLAMA_API_CONFIGS

app.state.OLLAMA_MODELS = {}

########################################
#
# OPENAI
#
########################################

app.state.config.ENABLE_OPENAI_API = ENABLE_OPENAI_API
app.state.config.OPENAI_API_BASE_URLS = OPENAI_API_BASE_URLS
app.state.config.OPENAI_API_KEYS = OPENAI_API_KEYS
app.state.config.OPENAI_API_CONFIGS = OPENAI_API_CONFIGS

app.state.OPENAI_MODELS = {}

########################################
#
# WEBUI
#
########################################

app.state.config.ENABLE_SIGNUP = ENABLE_SIGNUP
app.state.config.ENABLE_LOGIN_FORM = ENABLE_LOGIN_FORM
app.state.config.ENABLE_API_KEY = ENABLE_API_KEY

app.state.config.JWT_EXPIRES_IN = JWT_EXPIRES_IN

app.state.config.SHOW_ADMIN_DETAILS = SHOW_ADMIN_DETAILS
app.state.config.ADMIN_EMAIL = ADMIN_EMAIL


app.state.config.DEFAULT_MODELS = DEFAULT_MODELS
app.state.config.DEFAULT_PROMPT_SUGGESTIONS = DEFAULT_PROMPT_SUGGESTIONS
app.state.config.DEFAULT_USER_ROLE = DEFAULT_USER_ROLE

app.state.config.USER_PERMISSIONS = USER_PERMISSIONS
app.state.config.WEBHOOK_URL = WEBHOOK_URL
app.state.config.BANNERS = WEBUI_BANNERS
app.state.config.MODEL_ORDER_LIST = MODEL_ORDER_LIST

app.state.config.ENABLE_COMMUNITY_SHARING = ENABLE_COMMUNITY_SHARING
app.state.config.ENABLE_MESSAGE_RATING = ENABLE_MESSAGE_RATING

app.state.config.ENABLE_EVALUATION_ARENA_MODELS = ENABLE_EVALUATION_ARENA_MODELS
app.state.config.EVALUATION_ARENA_MODELS = EVALUATION_ARENA_MODELS

app.state.config.OAUTH_USERNAME_CLAIM = OAUTH_USERNAME_CLAIM
app.state.config.OAUTH_PICTURE_CLAIM = OAUTH_PICTURE_CLAIM
app.state.config.OAUTH_EMAIL_CLAIM = OAUTH_EMAIL_CLAIM

app.state.config.ENABLE_OAUTH_ROLE_MANAGEMENT = ENABLE_OAUTH_ROLE_MANAGEMENT
app.state.config.OAUTH_ROLES_CLAIM = OAUTH_ROLES_CLAIM
app.state.config.OAUTH_ALLOWED_ROLES = OAUTH_ALLOWED_ROLES
app.state.config.OAUTH_ADMIN_ROLES = OAUTH_ADMIN_ROLES

app.state.config.ENABLE_LDAP = ENABLE_LDAP
app.state.config.LDAP_SERVER_LABEL = LDAP_SERVER_LABEL
app.state.config.LDAP_SERVER_HOST = LDAP_SERVER_HOST
app.state.config.LDAP_SERVER_PORT = LDAP_SERVER_PORT
app.state.config.LDAP_ATTRIBUTE_FOR_USERNAME = LDAP_ATTRIBUTE_FOR_USERNAME
app.state.config.LDAP_APP_DN = LDAP_APP_DN
app.state.config.LDAP_APP_PASSWORD = LDAP_APP_PASSWORD
app.state.config.LDAP_SEARCH_BASE = LDAP_SEARCH_BASE
app.state.config.LDAP_SEARCH_FILTERS = LDAP_SEARCH_FILTERS
app.state.config.LDAP_USE_TLS = LDAP_USE_TLS
app.state.config.LDAP_CA_CERT_FILE = LDAP_CA_CERT_FILE
app.state.config.LDAP_CIPHERS = LDAP_CIPHERS


app.state.AUTH_TRUSTED_EMAIL_HEADER = WEBUI_AUTH_TRUSTED_EMAIL_HEADER
app.state.AUTH_TRUSTED_NAME_HEADER = WEBUI_AUTH_TRUSTED_NAME_HEADER

app.state.TOOLS = {}
app.state.FUNCTIONS = {}


########################################
#
# RETRIEVAL
#
########################################


app.state.config.TOP_K = RAG_TOP_K
app.state.config.RELEVANCE_THRESHOLD = RAG_RELEVANCE_THRESHOLD
app.state.config.FILE_MAX_SIZE = RAG_FILE_MAX_SIZE
app.state.config.FILE_MAX_COUNT = RAG_FILE_MAX_COUNT

app.state.config.ENABLE_RAG_HYBRID_SEARCH = ENABLE_RAG_HYBRID_SEARCH
app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION = (
    ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION
)

app.state.config.CONTENT_EXTRACTION_ENGINE = CONTENT_EXTRACTION_ENGINE
app.state.config.TIKA_SERVER_URL = TIKA_SERVER_URL

app.state.config.TEXT_SPLITTER = RAG_TEXT_SPLITTER
app.state.config.TIKTOKEN_ENCODING_NAME = TIKTOKEN_ENCODING_NAME

app.state.config.CHUNK_SIZE = CHUNK_SIZE
app.state.config.CHUNK_OVERLAP = CHUNK_OVERLAP

app.state.config.RAG_EMBEDDING_ENGINE = RAG_EMBEDDING_ENGINE
app.state.config.RAG_EMBEDDING_MODEL = RAG_EMBEDDING_MODEL
app.state.config.RAG_EMBEDDING_BATCH_SIZE = RAG_EMBEDDING_BATCH_SIZE
app.state.config.RAG_RERANKING_MODEL = RAG_RERANKING_MODEL
app.state.config.RAG_TEMPLATE = RAG_TEMPLATE

app.state.config.RAG_OPENAI_API_BASE_URL = RAG_OPENAI_API_BASE_URL
app.state.config.RAG_OPENAI_API_KEY = RAG_OPENAI_API_KEY

app.state.config.RAG_OLLAMA_BASE_URL = RAG_OLLAMA_BASE_URL
app.state.config.RAG_OLLAMA_API_KEY = RAG_OLLAMA_API_KEY

app.state.config.PDF_EXTRACT_IMAGES = PDF_EXTRACT_IMAGES

app.state.config.YOUTUBE_LOADER_LANGUAGE = YOUTUBE_LOADER_LANGUAGE
app.state.config.YOUTUBE_LOADER_PROXY_URL = YOUTUBE_LOADER_PROXY_URL


app.state.config.ENABLE_RAG_WEB_SEARCH = ENABLE_RAG_WEB_SEARCH
app.state.config.RAG_WEB_SEARCH_ENGINE = RAG_WEB_SEARCH_ENGINE
app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST = RAG_WEB_SEARCH_DOMAIN_FILTER_LIST

app.state.config.SEARXNG_QUERY_URL = SEARXNG_QUERY_URL
app.state.config.GOOGLE_PSE_API_KEY = GOOGLE_PSE_API_KEY
app.state.config.GOOGLE_PSE_ENGINE_ID = GOOGLE_PSE_ENGINE_ID
app.state.config.BRAVE_SEARCH_API_KEY = BRAVE_SEARCH_API_KEY
app.state.config.KAGI_SEARCH_API_KEY = KAGI_SEARCH_API_KEY
app.state.config.MOJEEK_SEARCH_API_KEY = MOJEEK_SEARCH_API_KEY
app.state.config.SERPSTACK_API_KEY = SERPSTACK_API_KEY
app.state.config.SERPSTACK_HTTPS = SERPSTACK_HTTPS
app.state.config.SERPER_API_KEY = SERPER_API_KEY
app.state.config.SERPLY_API_KEY = SERPLY_API_KEY
app.state.config.TAVILY_API_KEY = TAVILY_API_KEY
app.state.config.SEARCHAPI_API_KEY = SEARCHAPI_API_KEY
app.state.config.SEARCHAPI_ENGINE = SEARCHAPI_ENGINE
app.state.config.JINA_API_KEY = JINA_API_KEY
app.state.config.BING_SEARCH_V7_ENDPOINT = BING_SEARCH_V7_ENDPOINT
app.state.config.BING_SEARCH_V7_SUBSCRIPTION_KEY = BING_SEARCH_V7_SUBSCRIPTION_KEY

app.state.config.RAG_WEB_SEARCH_RESULT_COUNT = RAG_WEB_SEARCH_RESULT_COUNT
app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS = RAG_WEB_SEARCH_CONCURRENT_REQUESTS

app.state.EMBEDDING_FUNCTION = None
app.state.ef = None
app.state.rf = None

app.state.YOUTUBE_LOADER_TRANSLATION = None


app.state.EMBEDDING_FUNCTION = get_embedding_function(
    app.state.config.RAG_EMBEDDING_ENGINE,
    app.state.config.RAG_EMBEDDING_MODEL,
    app.state.ef,
    (
        app.state.config.RAG_OPENAI_API_BASE_URL
        if app.state.config.RAG_EMBEDDING_ENGINE == "openai"
        else app.state.config.RAG_OLLAMA_BASE_URL
    ),
    (
        app.state.config.RAG_OPENAI_API_KEY
        if app.state.config.RAG_EMBEDDING_ENGINE == "openai"
        else app.state.config.RAG_OLLAMA_API_KEY
    ),
    app.state.config.RAG_EMBEDDING_BATCH_SIZE,
)

try:
    app.state.ef = get_ef(
        app.state.config.RAG_EMBEDDING_ENGINE,
        app.state.config.RAG_EMBEDDING_MODEL,
        RAG_EMBEDDING_MODEL_AUTO_UPDATE,
    )

    app.state.rf = get_rf(
        app.state.config.RAG_RERANKING_MODEL,
        RAG_RERANKING_MODEL_AUTO_UPDATE,
    )
except Exception as e:
    log.error(f"Error updating models: {e}")
    pass


########################################
#
# IMAGES
#
########################################

app.state.config.IMAGE_GENERATION_ENGINE = IMAGE_GENERATION_ENGINE
app.state.config.ENABLE_IMAGE_GENERATION = ENABLE_IMAGE_GENERATION

app.state.config.IMAGES_OPENAI_API_BASE_URL = IMAGES_OPENAI_API_BASE_URL
app.state.config.IMAGES_OPENAI_API_KEY = IMAGES_OPENAI_API_KEY

app.state.config.IMAGE_GENERATION_MODEL = IMAGE_GENERATION_MODEL

app.state.config.AUTOMATIC1111_BASE_URL = AUTOMATIC1111_BASE_URL
app.state.config.AUTOMATIC1111_API_AUTH = AUTOMATIC1111_API_AUTH
app.state.config.AUTOMATIC1111_CFG_SCALE = AUTOMATIC1111_CFG_SCALE
app.state.config.AUTOMATIC1111_SAMPLER = AUTOMATIC1111_SAMPLER
app.state.config.AUTOMATIC1111_SCHEDULER = AUTOMATIC1111_SCHEDULER
app.state.config.COMFYUI_BASE_URL = COMFYUI_BASE_URL
app.state.config.COMFYUI_WORKFLOW = COMFYUI_WORKFLOW
app.state.config.COMFYUI_WORKFLOW_NODES = COMFYUI_WORKFLOW_NODES

app.state.config.IMAGE_SIZE = IMAGE_SIZE
app.state.config.IMAGE_STEPS = IMAGE_STEPS


########################################
#
# AUDIO
#
########################################

app.state.config.STT_OPENAI_API_BASE_URL = AUDIO_STT_OPENAI_API_BASE_URL
app.state.config.STT_OPENAI_API_KEY = AUDIO_STT_OPENAI_API_KEY
app.state.config.STT_ENGINE = AUDIO_STT_ENGINE
app.state.config.STT_MODEL = AUDIO_STT_MODEL

app.state.config.WHISPER_MODEL = WHISPER_MODEL

app.state.config.TTS_OPENAI_API_BASE_URL = AUDIO_TTS_OPENAI_API_BASE_URL
app.state.config.TTS_OPENAI_API_KEY = AUDIO_TTS_OPENAI_API_KEY
app.state.config.TTS_ENGINE = AUDIO_TTS_ENGINE
app.state.config.TTS_MODEL = AUDIO_TTS_MODEL
app.state.config.TTS_VOICE = AUDIO_TTS_VOICE
app.state.config.TTS_API_KEY = AUDIO_TTS_API_KEY
app.state.config.TTS_SPLIT_ON = AUDIO_TTS_SPLIT_ON


app.state.config.TTS_AZURE_SPEECH_REGION = AUDIO_TTS_AZURE_SPEECH_REGION
app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT = AUDIO_TTS_AZURE_SPEECH_OUTPUT_FORMAT


app.state.faster_whisper_model = None
app.state.speech_synthesiser = None
app.state.speech_speaker_embeddings_dataset = None


########################################
#
# TASKS
#
########################################


app.state.config.TASK_MODEL = TASK_MODEL
app.state.config.TASK_MODEL_EXTERNAL = TASK_MODEL_EXTERNAL


app.state.config.ENABLE_SEARCH_QUERY_GENERATION = ENABLE_SEARCH_QUERY_GENERATION
app.state.config.ENABLE_RETRIEVAL_QUERY_GENERATION = ENABLE_RETRIEVAL_QUERY_GENERATION
app.state.config.ENABLE_AUTOCOMPLETE_GENERATION = ENABLE_AUTOCOMPLETE_GENERATION
app.state.config.ENABLE_TAGS_GENERATION = ENABLE_TAGS_GENERATION


app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE = TITLE_GENERATION_PROMPT_TEMPLATE
app.state.config.TAGS_GENERATION_PROMPT_TEMPLATE = TAGS_GENERATION_PROMPT_TEMPLATE
app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE = (
    TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
)
app.state.config.QUERY_GENERATION_PROMPT_TEMPLATE = QUERY_GENERATION_PROMPT_TEMPLATE
app.state.config.AUTOCOMPLETE_GENERATION_PROMPT_TEMPLATE = (
    AUTOCOMPLETE_GENERATION_PROMPT_TEMPLATE
)
app.state.config.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH = (
    AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH
)


########################################
#
# WEBUI
#
########################################

app.state.MODELS = {}

##################################
#
# ChatCompletion Middleware
#
##################################


async def chat_completion_filter_functions_handler(body, model, extra_params):
    skip_files = None

    def get_filter_function_ids(model):
        def get_priority(function_id):
            function = Functions.get_function_by_id(function_id)
            if function is not None and hasattr(function, "valves"):
                # TODO: Fix FunctionModel
                return (function.valves if function.valves else {}).get("priority", 0)
            return 0

        filter_ids = [
            function.id for function in Functions.get_global_filter_functions()
        ]
        if "info" in model and "meta" in model["info"]:
            filter_ids.extend(model["info"]["meta"].get("filterIds", []))
            filter_ids = list(set(filter_ids))

        enabled_filter_ids = [
            function.id
            for function in Functions.get_functions_by_type("filter", active_only=True)
        ]

        filter_ids = [
            filter_id for filter_id in filter_ids if filter_id in enabled_filter_ids
        ]

        filter_ids.sort(key=get_priority)
        return filter_ids

    filter_ids = get_filter_function_ids(model)
    for filter_id in filter_ids:
        filter = Functions.get_function_by_id(filter_id)
        if not filter:
            continue

        if filter_id in app.state.FUNCTIONS:
            function_module = app.state.FUNCTIONS[filter_id]
        else:
            function_module, _, _ = load_function_module_by_id(filter_id)
            app.state.FUNCTIONS[filter_id] = function_module

        # Check if the function has a file_handler variable
        if hasattr(function_module, "file_handler"):
            skip_files = function_module.file_handler

        if hasattr(function_module, "valves") and hasattr(function_module, "Valves"):
            valves = Functions.get_function_valves_by_id(filter_id)
            function_module.valves = function_module.Valves(
                **(valves if valves else {})
            )

        if not hasattr(function_module, "inlet"):
            continue

        try:
            inlet = function_module.inlet

            # Get the signature of the function
            sig = inspect.signature(inlet)
            params = {"body": body} | {
                k: v
                for k, v in {
                    **extra_params,
                    "__model__": model,
                    "__id__": filter_id,
                }.items()
                if k in sig.parameters
            }

            if "__user__" in params and hasattr(function_module, "UserValves"):
                try:
                    params["__user__"]["valves"] = function_module.UserValves(
                        **Functions.get_user_valves_by_id_and_user_id(
                            filter_id, params["__user__"]["id"]
                        )
                    )
                except Exception as e:
                    print(e)

            if inspect.iscoroutinefunction(inlet):
                body = await inlet(**params)
            else:
                body = inlet(**params)

        except Exception as e:
            print(f"Error: {e}")
            raise e

    if skip_files and "files" in body.get("metadata", {}):
        del body["metadata"]["files"]

    return body, {}


def get_tools_function_calling_payload(messages, task_model_id, content):
    user_message = get_last_user_message(messages)
    history = "\n".join(
        f"{message['role'].upper()}: \"\"\"{message['content']}\"\"\""
        for message in messages[::-1][:4]
    )

    prompt = f"History:\n{history}\nQuery: {user_message}"

    return {
        "model": task_model_id,
        "messages": [
            {"role": "system", "content": content},
            {"role": "user", "content": f"Query: {prompt}"},
        ],
        "stream": False,
        "metadata": {"task": str(TASKS.FUNCTION_CALLING)},
    }


async def get_content_from_response(response) -> Optional[str]:
    content = None
    if hasattr(response, "body_iterator"):
        async for chunk in response.body_iterator:
            data = json.loads(chunk.decode("utf-8"))
            content = data["choices"][0]["message"]["content"]

        # Cleanup any remaining background tasks if necessary
        if response.background is not None:
            await response.background()
    else:
        content = response["choices"][0]["message"]["content"]
    return content


async def chat_completion_tools_handler(
    body: dict, user: UserModel, models, extra_params: dict
) -> tuple[dict, dict]:
    # If tool_ids field is present, call the functions
    metadata = body.get("metadata", {})

    tool_ids = metadata.get("tool_ids", None)
    log.debug(f"{tool_ids=}")
    if not tool_ids:
        return body, {}

    skip_files = False
    sources = []

    task_model_id = get_task_model_id(
        body["model"],
        app.state.config.TASK_MODEL,
        app.state.config.TASK_MODEL_EXTERNAL,
        models,
    )
    tools = get_tools(
        app,
        tool_ids,
        user,
        {
            **extra_params,
            "__model__": models[task_model_id],
            "__messages__": body["messages"],
            "__files__": metadata.get("files", []),
        },
    )
    log.info(f"{tools=}")

    specs = [tool["spec"] for tool in tools.values()]
    tools_specs = json.dumps(specs)

    if app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE != "":
        template = app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
    else:
        template = """Available Tools: {{TOOLS}}\nReturn an empty string if no tools match the query. If a function tool matches, construct and return a JSON object in the format {\"name\": \"functionName\", \"parameters\": {\"requiredFunctionParamKey\": \"requiredFunctionParamValue\"}} using the appropriate tool and its parameters. Only return the object and limit the response to the JSON object without additional text."""

    tools_function_calling_prompt = tools_function_calling_generation_template(
        template, tools_specs
    )
    log.info(f"{tools_function_calling_prompt=}")
    payload = get_tools_function_calling_payload(
        body["messages"], task_model_id, tools_function_calling_prompt
    )

    try:
        payload = process_pipeline_inlet_filter(request, payload, user, models)
    except Exception as e:
        raise e

    try:
        response = await generate_chat_completions(form_data=payload, user=user)
        log.debug(f"{response=}")
        content = await get_content_from_response(response)
        log.debug(f"{content=}")

        if not content:
            return body, {}

        try:
            content = content[content.find("{") : content.rfind("}") + 1]
            if not content:
                raise Exception("No JSON object found in the response")

            result = json.loads(content)

            tool_function_name = result.get("name", None)
            if tool_function_name not in tools:
                return body, {}

            tool_function_params = result.get("parameters", {})

            try:
                required_params = (
                    tools[tool_function_name]
                    .get("spec", {})
                    .get("parameters", {})
                    .get("required", [])
                )
                tool_function = tools[tool_function_name]["callable"]
                tool_function_params = {
                    k: v
                    for k, v in tool_function_params.items()
                    if k in required_params
                }
                tool_output = await tool_function(**tool_function_params)

            except Exception as e:
                tool_output = str(e)

            if isinstance(tool_output, str):
                if tools[tool_function_name]["citation"]:
                    sources.append(
                        {
                            "source": {
                                "name": f"TOOL:{tools[tool_function_name]['toolkit_id']}/{tool_function_name}"
                            },
                            "document": [tool_output],
                            "metadata": [
                                {
                                    "source": f"TOOL:{tools[tool_function_name]['toolkit_id']}/{tool_function_name}"
                                }
                            ],
                        }
                    )
                else:
                    sources.append(
                        {
                            "source": {},
                            "document": [tool_output],
                            "metadata": [
                                {
                                    "source": f"TOOL:{tools[tool_function_name]['toolkit_id']}/{tool_function_name}"
                                }
                            ],
                        }
                    )

                if tools[tool_function_name]["file_handler"]:
                    skip_files = True

        except Exception as e:
            log.exception(f"Error: {e}")
            content = None
    except Exception as e:
        log.exception(f"Error: {e}")
        content = None

    log.debug(f"tool_contexts: {sources}")

    if skip_files and "files" in body.get("metadata", {}):
        del body["metadata"]["files"]

    return body, {"sources": sources}


async def chat_completion_files_handler(
    body: dict, user: UserModel
) -> tuple[dict, dict[str, list]]:
    sources = []

    if files := body.get("metadata", {}).get("files", None):
        try:
            queries_response = await generate_queries(
                {
                    "model": body["model"],
                    "messages": body["messages"],
                    "type": "retrieval",
                },
                user,
            )
            queries_response = queries_response["choices"][0]["message"]["content"]

            try:
                bracket_start = queries_response.find("{")
                bracket_end = queries_response.rfind("}") + 1

                if bracket_start == -1 or bracket_end == -1:
                    raise Exception("No JSON object found in the response")

                queries_response = queries_response[bracket_start:bracket_end]
                queries_response = json.loads(queries_response)
            except Exception as e:
                queries_response = {"queries": [queries_response]}

            queries = queries_response.get("queries", [])
        except Exception as e:
            queries = []

        if len(queries) == 0:
            queries = [get_last_user_message(body["messages"])]

        sources = get_sources_from_files(
            files=files,
            queries=queries,
            embedding_function=app.state.EMBEDDING_FUNCTION,
            k=app.state.config.TOP_K,
            reranking_function=app.state.rf,
            r=app.state.config.RELEVANCE_THRESHOLD,
            hybrid_search=app.state.config.ENABLE_RAG_HYBRID_SEARCH,
        )

        log.debug(f"rag_contexts:sources: {sources}")
    return body, {"sources": sources}


async def get_body_and_model_and_user(request, models):
    # Read the original request body
    body = await request.body()
    body_str = body.decode("utf-8")
    body = json.loads(body_str) if body_str else {}

    model_id = body["model"]
    if model_id not in models:
        raise Exception("Model not found")
    model = models[model_id]

    user = get_current_user(
        request,
        get_http_authorization_cred(request.headers.get("Authorization")),
    )

    return body, model, user


class ChatCompletionMiddleware(BaseHTTPMiddleware):
    async def dispatch(self, request: Request, call_next):
        if not (
            request.method == "POST"
            and any(
                endpoint in request.url.path
                for endpoint in ["/ollama/api/chat", "/chat/completions"]
            )
        ):
            return await call_next(request)
        log.debug(f"request.url.path: {request.url.path}")

        model_list = await get_all_models()
        models = {model["id"]: model for model in model_list}

        try:
            body, model, user = await get_body_and_model_and_user(request, models)
        except Exception as e:
            return JSONResponse(
                status_code=status.HTTP_400_BAD_REQUEST,
                content={"detail": str(e)},
            )

        model_info = Models.get_model_by_id(model["id"])
        if user.role == "user" and not BYPASS_MODEL_ACCESS_CONTROL:
            if model.get("arena"):
                if not has_access(
                    user.id,
                    type="read",
                    access_control=model.get("info", {})
                    .get("meta", {})
                    .get("access_control", {}),
                ):
                    raise HTTPException(
                        status_code=403,
                        detail="Model not found",
                    )
            else:
                if not model_info:
                    return JSONResponse(
                        status_code=status.HTTP_404_NOT_FOUND,
                        content={"detail": "Model not found"},
                    )
                elif not (
                    user.id == model_info.user_id
                    or has_access(
                        user.id, type="read", access_control=model_info.access_control
                    )
                ):
                    return JSONResponse(
                        status_code=status.HTTP_403_FORBIDDEN,
                        content={"detail": "User does not have access to the model"},
                    )

        metadata = {
            "chat_id": body.pop("chat_id", None),
            "message_id": body.pop("id", None),
            "session_id": body.pop("session_id", None),
            "tool_ids": body.get("tool_ids", None),
            "files": body.get("files", None),
        }
        body["metadata"] = metadata

        extra_params = {
            "__event_emitter__": get_event_emitter(metadata),
            "__event_call__": get_event_call(metadata),
            "__user__": {
                "id": user.id,
                "email": user.email,
                "name": user.name,
                "role": user.role,
            },
            "__metadata__": metadata,
        }

        # Initialize data_items to store additional data to be sent to the client
        # Initialize contexts and citation
        data_items = []
        sources = []

        try:
            body, flags = await chat_completion_filter_functions_handler(
                body, model, extra_params
            )
        except Exception as e:
            return JSONResponse(
                status_code=status.HTTP_400_BAD_REQUEST,
                content={"detail": str(e)},
            )

        tool_ids = body.pop("tool_ids", None)
        files = body.pop("files", None)

        metadata = {
            **metadata,
            "tool_ids": tool_ids,
            "files": files,
        }
        body["metadata"] = metadata

        try:
            body, flags = await chat_completion_tools_handler(
                body, user, models, extra_params
            )
            sources.extend(flags.get("sources", []))
        except Exception as e:
            log.exception(e)

        try:
            body, flags = await chat_completion_files_handler(body, user)
            sources.extend(flags.get("sources", []))
        except Exception as e:
            log.exception(e)

        # If context is not empty, insert it into the messages
        if len(sources) > 0:
            context_string = ""
            for source_idx, source in enumerate(sources):
                source_id = source.get("source", {}).get("name", "")

                if "document" in source:
                    for doc_idx, doc_context in enumerate(source["document"]):
                        metadata = source.get("metadata")
                        doc_source_id = None

                        if metadata:
                            doc_source_id = metadata[doc_idx].get("source", source_id)

                        if source_id:
                            context_string += f"<source><source_id>{doc_source_id if doc_source_id is not None else source_id}</source_id><source_context>{doc_context}</source_context></source>\n"
                        else:
                            # If there is no source_id, then do not include the source_id tag
                            context_string += f"<source><source_context>{doc_context}</source_context></source>\n"

            context_string = context_string.strip()
            prompt = get_last_user_message(body["messages"])

            if prompt is None:
                raise Exception("No user message found")
            if (
                app.state.config.RELEVANCE_THRESHOLD == 0
                and context_string.strip() == ""
            ):
                log.debug(
                    f"With a 0 relevancy threshold for RAG, the context cannot be empty"
                )

            # Workaround for Ollama 2.0+ system prompt issue
            # TODO: replace with add_or_update_system_message
            if model["owned_by"] == "ollama":
                body["messages"] = prepend_to_first_user_message_content(
                    rag_template(app.state.config.RAG_TEMPLATE, context_string, prompt),
                    body["messages"],
                )
            else:
                body["messages"] = add_or_update_system_message(
                    rag_template(app.state.config.RAG_TEMPLATE, context_string, prompt),
                    body["messages"],
                )

        # If there are citations, add them to the data_items
        sources = [
            source for source in sources if source.get("source", {}).get("name", "")
        ]
        if len(sources) > 0:
            data_items.append({"sources": sources})

        modified_body_bytes = json.dumps(body).encode("utf-8")
        # Replace the request body with the modified one
        request._body = modified_body_bytes
        # Set custom header to ensure content-length matches new body length
        request.headers.__dict__["_list"] = [
            (b"content-length", str(len(modified_body_bytes)).encode("utf-8")),
            *[(k, v) for k, v in request.headers.raw if k.lower() != b"content-length"],
        ]

        response = await call_next(request)
        if not isinstance(response, StreamingResponse):
            return response

        content_type = response.headers["Content-Type"]
        is_openai = "text/event-stream" in content_type
        is_ollama = "application/x-ndjson" in content_type
        if not is_openai and not is_ollama:
            return response

        def wrap_item(item):
            return f"data: {item}\n\n" if is_openai else f"{item}\n"

        async def stream_wrapper(original_generator, data_items):
            for item in data_items:
                yield wrap_item(json.dumps(item))

            async for data in original_generator:
                yield data

        return StreamingResponse(
            stream_wrapper(response.body_iterator, data_items),
            headers=dict(response.headers),
        )

    async def _receive(self, body: bytes):
        return {"type": "http.request", "body": body, "more_body": False}


app.add_middleware(ChatCompletionMiddleware)


class PipelineMiddleware(BaseHTTPMiddleware):
    async def dispatch(self, request: Request, call_next):
        if not (
            request.method == "POST"
            and any(
                endpoint in request.url.path
                for endpoint in ["/ollama/api/chat", "/chat/completions"]
            )
        ):
            return await call_next(request)

        log.debug(f"request.url.path: {request.url.path}")

        # Read the original request body
        body = await request.body()
        # Decode body to string
        body_str = body.decode("utf-8")
        # Parse string to JSON
        data = json.loads(body_str) if body_str else {}

        try:
            user = get_current_user(
                request,
                get_http_authorization_cred(request.headers["Authorization"]),
            )
        except KeyError as e:
            if len(e.args) > 1:
                return JSONResponse(
                    status_code=e.args[0],
                    content={"detail": e.args[1]},
                )
            else:
                return JSONResponse(
                    status_code=status.HTTP_401_UNAUTHORIZED,
                    content={"detail": "Not authenticated"},
                )
        except HTTPException as e:
            return JSONResponse(
                status_code=e.status_code,
                content={"detail": e.detail},
            )

        await get_all_models()
        models = app.state.MODELS

        try:
            data = process_pipeline_inlet_filter(request, data, user, models)
        except Exception as e:
            if len(e.args) > 1:
                return JSONResponse(
                    status_code=e.args[0],
                    content={"detail": e.args[1]},
                )
            else:
                return JSONResponse(
                    status_code=status.HTTP_400_BAD_REQUEST,
                    content={"detail": str(e)},
                )

        modified_body_bytes = json.dumps(data).encode("utf-8")
        # Replace the request body with the modified one
        request._body = modified_body_bytes
        # Set custom header to ensure content-length matches new body length
        request.headers.__dict__["_list"] = [
            (b"content-length", str(len(modified_body_bytes)).encode("utf-8")),
            *[(k, v) for k, v in request.headers.raw if k.lower() != b"content-length"],
        ]

        response = await call_next(request)
        return response

    async def _receive(self, body: bytes):
        return {"type": "http.request", "body": body, "more_body": False}


app.add_middleware(PipelineMiddleware)


class RedirectMiddleware(BaseHTTPMiddleware):
    async def dispatch(self, request: Request, call_next):
        # Check if the request is a GET request
        if request.method == "GET":
            path = request.url.path
            query_params = dict(parse_qs(urlparse(str(request.url)).query))

            # Check for the specific watch path and the presence of 'v' parameter
            if path.endswith("/watch") and "v" in query_params:
                video_id = query_params["v"][0]  # Extract the first 'v' parameter
                encoded_video_id = urlencode({"youtube": video_id})
                redirect_url = f"/?{encoded_video_id}"
                return RedirectResponse(url=redirect_url)

        # Proceed with the normal flow of other requests
        response = await call_next(request)
        return response


# Add the middleware to the app
app.add_middleware(RedirectMiddleware)
app.add_middleware(SecurityHeadersMiddleware)


@app.middleware("http")
async def commit_session_after_request(request: Request, call_next):
    response = await call_next(request)
    # log.debug("Commit session after request")
    Session.commit()
    return response


@app.middleware("http")
async def check_url(request: Request, call_next):
    start_time = int(time.time())
    request.state.enable_api_key = app.state.config.ENABLE_API_KEY
    response = await call_next(request)
    process_time = int(time.time()) - start_time
    response.headers["X-Process-Time"] = str(process_time)
    return response


@app.middleware("http")
async def inspect_websocket(request: Request, call_next):
    if (
        "/ws/socket.io" in request.url.path
        and request.query_params.get("transport") == "websocket"
    ):
        upgrade = (request.headers.get("Upgrade") or "").lower()
        connection = (request.headers.get("Connection") or "").lower().split(",")
        # Check that there's the correct headers for an upgrade, else reject the connection
        # This is to work around this upstream issue: https://github.com/miguelgrinberg/python-engineio/issues/367
        if upgrade != "websocket" or "upgrade" not in connection:
            return JSONResponse(
                status_code=status.HTTP_400_BAD_REQUEST,
                content={"detail": "Invalid WebSocket upgrade request"},
            )
    return await call_next(request)


app.add_middleware(
    CORSMiddleware,
    allow_origins=CORS_ALLOW_ORIGIN,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


app.mount("/ws", socket_app)


app.include_router(ollama.router, prefix="/ollama", tags=["ollama"])
app.include_router(openai.router, prefix="/openai", tags=["openai"])


app.include_router(pipelines.router, prefix="/api/pipelines", tags=["pipelines"])
app.include_router(tasks.router, prefix="/api/tasks", tags=["tasks"])


app.include_router(images.router, prefix="/api/v1/images", tags=["images"])
app.include_router(audio.router, prefix="/api/v1/audio", tags=["audio"])
app.include_router(retrieval.router, prefix="/api/v1/retrieval", tags=["retrieval"])


app.include_router(configs.router, prefix="/api/v1/configs", tags=["configs"])

app.include_router(auths.router, prefix="/api/v1/auths", tags=["auths"])
app.include_router(users.router, prefix="/api/v1/users", tags=["users"])

app.include_router(chats.router, prefix="/api/v1/chats", tags=["chats"])

app.include_router(models.router, prefix="/api/v1/models", tags=["models"])
app.include_router(knowledge.router, prefix="/api/v1/knowledge", tags=["knowledge"])
app.include_router(prompts.router, prefix="/api/v1/prompts", tags=["prompts"])
app.include_router(tools.router, prefix="/api/v1/tools", tags=["tools"])

app.include_router(memories.router, prefix="/api/v1/memories", tags=["memories"])
app.include_router(folders.router, prefix="/api/v1/folders", tags=["folders"])
app.include_router(groups.router, prefix="/api/v1/groups", tags=["groups"])
app.include_router(files.router, prefix="/api/v1/files", tags=["files"])
app.include_router(functions.router, prefix="/api/v1/functions", tags=["functions"])
app.include_router(
    evaluations.router, prefix="/api/v1/evaluations", tags=["evaluations"]
)
app.include_router(utils.router, prefix="/api/v1/utils", tags=["utils"])


##################################
#
# Chat Endpoints
#
##################################


def get_function_module(pipe_id: str):
    # Check if function is already loaded
    if pipe_id not in app.state.FUNCTIONS:
        function_module, _, _ = load_function_module_by_id(pipe_id)
        app.state.FUNCTIONS[pipe_id] = function_module
    else:
        function_module = app.state.FUNCTIONS[pipe_id]

    if hasattr(function_module, "valves") and hasattr(function_module, "Valves"):
        valves = Functions.get_function_valves_by_id(pipe_id)
        function_module.valves = function_module.Valves(**(valves if valves else {}))
    return function_module


async def get_function_models():
    pipes = Functions.get_functions_by_type("pipe", active_only=True)
    pipe_models = []

    for pipe in pipes:
        function_module = get_function_module(pipe.id)

        # Check if function is a manifold
        if hasattr(function_module, "pipes"):
            sub_pipes = []

            # Check if pipes is a function or a list

            try:
                if callable(function_module.pipes):
                    sub_pipes = function_module.pipes()
                else:
                    sub_pipes = function_module.pipes
            except Exception as e:
                log.exception(e)
                sub_pipes = []

            log.debug(
                f"get_function_models: function '{pipe.id}' is a manifold of {sub_pipes}"
            )

            for p in sub_pipes:
                sub_pipe_id = f'{pipe.id}.{p["id"]}'
                sub_pipe_name = p["name"]

                if hasattr(function_module, "name"):
                    sub_pipe_name = f"{function_module.name}{sub_pipe_name}"

                pipe_flag = {"type": pipe.type}

                pipe_models.append(
                    {
                        "id": sub_pipe_id,
                        "name": sub_pipe_name,
                        "object": "model",
                        "created": pipe.created_at,
                        "owned_by": "openai",
                        "pipe": pipe_flag,
                    }
                )
        else:
            pipe_flag = {"type": "pipe"}

            log.debug(
                f"get_function_models: function '{pipe.id}' is a single pipe {{ 'id': {pipe.id}, 'name': {pipe.name} }}"
            )

            pipe_models.append(
                {
                    "id": pipe.id,
                    "name": pipe.name,
                    "object": "model",
                    "created": pipe.created_at,
                    "owned_by": "openai",
                    "pipe": pipe_flag,
                }
            )

    return pipe_models


async def generate_function_chat_completion(form_data, user, models: dict = {}):
    async def execute_pipe(pipe, params):
        if inspect.iscoroutinefunction(pipe):
            return await pipe(**params)
        else:
            return pipe(**params)

    async def get_message_content(res: str | Generator | AsyncGenerator) -> str:
        if isinstance(res, str):
            return res
        if isinstance(res, Generator):
            return "".join(map(str, res))
        if isinstance(res, AsyncGenerator):
            return "".join([str(stream) async for stream in res])

    def process_line(form_data: dict, line):
        if isinstance(line, BaseModel):
            line = line.model_dump_json()
            line = f"data: {line}"
        if isinstance(line, dict):
            line = f"data: {json.dumps(line)}"

        try:
            line = line.decode("utf-8")
        except Exception:
            pass

        if line.startswith("data:"):
            return f"{line}\n\n"
        else:
            line = openai_chat_chunk_message_template(form_data["model"], line)
            return f"data: {json.dumps(line)}\n\n"

    def get_pipe_id(form_data: dict) -> str:
        pipe_id = form_data["model"]
        if "." in pipe_id:
            pipe_id, _ = pipe_id.split(".", 1)
        return pipe_id

    def get_function_params(function_module, form_data, user, extra_params=None):
        if extra_params is None:
            extra_params = {}

        pipe_id = get_pipe_id(form_data)

        # Get the signature of the function
        sig = inspect.signature(function_module.pipe)
        params = {"body": form_data} | {
            k: v for k, v in extra_params.items() if k in sig.parameters
        }

        if "__user__" in params and hasattr(function_module, "UserValves"):
            user_valves = Functions.get_user_valves_by_id_and_user_id(pipe_id, user.id)
            try:
                params["__user__"]["valves"] = function_module.UserValves(**user_valves)
            except Exception as e:
                log.exception(e)
                params["__user__"]["valves"] = function_module.UserValves()

        return params

    model_id = form_data.get("model")
    model_info = Models.get_model_by_id(model_id)

    metadata = form_data.pop("metadata", {})

    files = metadata.get("files", [])
    tool_ids = metadata.get("tool_ids", [])
    # Check if tool_ids is None
    if tool_ids is None:
        tool_ids = []

    __event_emitter__ = None
    __event_call__ = None
    __task__ = None
    __task_body__ = None

    if metadata:
        if all(k in metadata for k in ("session_id", "chat_id", "message_id")):
            __event_emitter__ = get_event_emitter(metadata)
            __event_call__ = get_event_call(metadata)
        __task__ = metadata.get("task", None)
        __task_body__ = metadata.get("task_body", None)

    extra_params = {
        "__event_emitter__": __event_emitter__,
        "__event_call__": __event_call__,
        "__task__": __task__,
        "__task_body__": __task_body__,
        "__files__": files,
        "__user__": {
            "id": user.id,
            "email": user.email,
            "name": user.name,
            "role": user.role,
        },
        "__metadata__": metadata,
    }
    extra_params["__tools__"] = get_tools(
        app,
        tool_ids,
        user,
        {
            **extra_params,
            "__model__": models.get(form_data["model"], None),
            "__messages__": form_data["messages"],
            "__files__": files,
        },
    )

    if model_info:
        if model_info.base_model_id:
            form_data["model"] = model_info.base_model_id

        params = model_info.params.model_dump()
        form_data = apply_model_params_to_body_openai(params, form_data)
        form_data = apply_model_system_prompt_to_body(params, form_data, user)

    pipe_id = get_pipe_id(form_data)
    function_module = get_function_module(pipe_id)

    pipe = function_module.pipe
    params = get_function_params(function_module, form_data, user, extra_params)

    if form_data.get("stream", False):

        async def stream_content():
            try:
                res = await execute_pipe(pipe, params)

                # Directly return if the response is a StreamingResponse
                if isinstance(res, StreamingResponse):
                    async for data in res.body_iterator:
                        yield data
                    return
                if isinstance(res, dict):
                    yield f"data: {json.dumps(res)}\n\n"
                    return

            except Exception as e:
                log.error(f"Error: {e}")
                yield f"data: {json.dumps({'error': {'detail':str(e)}})}\n\n"
                return

            if isinstance(res, str):
                message = openai_chat_chunk_message_template(form_data["model"], res)
                yield f"data: {json.dumps(message)}\n\n"

            if isinstance(res, Iterator):
                for line in res:
                    yield process_line(form_data, line)

            if isinstance(res, AsyncGenerator):
                async for line in res:
                    yield process_line(form_data, line)

            if isinstance(res, str) or isinstance(res, Generator):
                finish_message = openai_chat_chunk_message_template(
                    form_data["model"], ""
                )
                finish_message["choices"][0]["finish_reason"] = "stop"
                yield f"data: {json.dumps(finish_message)}\n\n"
                yield "data: [DONE]"

        return StreamingResponse(stream_content(), media_type="text/event-stream")
    else:
        try:
            res = await execute_pipe(pipe, params)

        except Exception as e:
            log.error(f"Error: {e}")
            return {"error": {"detail": str(e)}}

        if isinstance(res, StreamingResponse) or isinstance(res, dict):
            return res
        if isinstance(res, BaseModel):
            return res.model_dump()

        message = await get_message_content(res)
        return openai_chat_completion_message_template(form_data["model"], message)


async def get_all_base_models(request):
    function_models = []
    openai_models = []
    ollama_models = []

    if app.state.config.ENABLE_OPENAI_API:
        openai_models = await openai.get_all_models(request)
        openai_models = openai_models["data"]

    if app.state.config.ENABLE_OLLAMA_API:
        ollama_models = await ollama.get_all_models(request)
        ollama_models = [
            {
                "id": model["model"],
                "name": model["name"],
                "object": "model",
                "created": int(time.time()),
                "owned_by": "ollama",
                "ollama": model,
            }
            for model in ollama_models["models"]
        ]

    function_models = await get_function_models()
    models = function_models + openai_models + ollama_models

    # Add arena models
    if app.state.config.ENABLE_EVALUATION_ARENA_MODELS:
        arena_models = []
        if len(app.state.config.EVALUATION_ARENA_MODELS) > 0:
            arena_models = [
                {
                    "id": model["id"],
                    "name": model["name"],
                    "info": {
                        "meta": model["meta"],
                    },
                    "object": "model",
                    "created": int(time.time()),
                    "owned_by": "arena",
                    "arena": True,
                }
                for model in app.state.config.EVALUATION_ARENA_MODELS
            ]
        else:
            # Add default arena model
            arena_models = [
                {
                    "id": DEFAULT_ARENA_MODEL["id"],
                    "name": DEFAULT_ARENA_MODEL["name"],
                    "info": {
                        "meta": DEFAULT_ARENA_MODEL["meta"],
                    },
                    "object": "model",
                    "created": int(time.time()),
                    "owned_by": "arena",
                    "arena": True,
                }
            ]
        models = models + arena_models

    return models


@cached(ttl=3)
async def get_all_models(request):
    models = await get_all_base_models(request)

    # If there are no models, return an empty list
    if len([model for model in models if not model.get("arena", False)]) == 0:
        return []

    global_action_ids = [
        function.id for function in Functions.get_global_action_functions()
    ]
    enabled_action_ids = [
        function.id
        for function in Functions.get_functions_by_type("action", active_only=True)
    ]

    custom_models = Models.get_all_models()
    for custom_model in custom_models:
        if custom_model.base_model_id is None:
            for model in models:
                if (
                    custom_model.id == model["id"]
                    or custom_model.id == model["id"].split(":")[0]
                ):
                    if custom_model.is_active:
                        model["name"] = custom_model.name
                        model["info"] = custom_model.model_dump()

                        action_ids = []
                        if "info" in model and "meta" in model["info"]:
                            action_ids.extend(
                                model["info"]["meta"].get("actionIds", [])
                            )

                        model["action_ids"] = action_ids
                    else:
                        models.remove(model)

        elif custom_model.is_active and (
            custom_model.id not in [model["id"] for model in models]
        ):
            owned_by = "openai"
            pipe = None
            action_ids = []

            for model in models:
                if (
                    custom_model.base_model_id == model["id"]
                    or custom_model.base_model_id == model["id"].split(":")[0]
                ):
                    owned_by = model["owned_by"]
                    if "pipe" in model:
                        pipe = model["pipe"]
                    break

            if custom_model.meta:
                meta = custom_model.meta.model_dump()
                if "actionIds" in meta:
                    action_ids.extend(meta["actionIds"])

            models.append(
                {
                    "id": f"{custom_model.id}",
                    "name": custom_model.name,
                    "object": "model",
                    "created": custom_model.created_at,
                    "owned_by": owned_by,
                    "info": custom_model.model_dump(),
                    "preset": True,
                    **({"pipe": pipe} if pipe is not None else {}),
                    "action_ids": action_ids,
                }
            )

    # Process action_ids to get the actions
    def get_action_items_from_module(function, module):
        actions = []
        if hasattr(module, "actions"):
            actions = module.actions
            return [
                {
                    "id": f"{function.id}.{action['id']}",
                    "name": action.get("name", f"{function.name} ({action['id']})"),
                    "description": function.meta.description,
                    "icon_url": action.get(
                        "icon_url", function.meta.manifest.get("icon_url", None)
                    ),
                }
                for action in actions
            ]
        else:
            return [
                {
                    "id": function.id,
                    "name": function.name,
                    "description": function.meta.description,
                    "icon_url": function.meta.manifest.get("icon_url", None),
                }
            ]

    def get_function_module_by_id(function_id):
        if function_id in app.state.FUNCTIONS:
            function_module = app.state.FUNCTIONS[function_id]
        else:
            function_module, _, _ = load_function_module_by_id(function_id)
            app.state.FUNCTIONS[function_id] = function_module

    for model in models:
        action_ids = [
            action_id
            for action_id in list(set(model.pop("action_ids", []) + global_action_ids))
            if action_id in enabled_action_ids
        ]

        model["actions"] = []
        for action_id in action_ids:
            action_function = Functions.get_function_by_id(action_id)
            if action_function is None:
                raise Exception(f"Action not found: {action_id}")

            function_module = get_function_module_by_id(action_id)
            model["actions"].extend(
                get_action_items_from_module(action_function, function_module)
            )
    log.debug(f"get_all_models() returned {len(models)} models")

    app.state.MODELS = {model["id"]: model for model in models}
    return models


@app.get("/api/models")
async def get_models(request: Request, user=Depends(get_verified_user)):
    models = await get_all_models(request)

    # Filter out filter pipelines
    models = [
        model
        for model in models
        if "pipeline" not in model or model["pipeline"].get("type", None) != "filter"
    ]

    model_order_list = app.state.config.MODEL_ORDER_LIST
    if model_order_list:
        model_order_dict = {model_id: i for i, model_id in enumerate(model_order_list)}
        # Sort models by order list priority, with fallback for those not in the list
        models.sort(
            key=lambda x: (model_order_dict.get(x["id"], float("inf")), x["name"])
        )

    # Filter out models that the user does not have access to
    if user.role == "user" and not BYPASS_MODEL_ACCESS_CONTROL:
        filtered_models = []
        for model in models:
            if model.get("arena"):
                if has_access(
                    user.id,
                    type="read",
                    access_control=model.get("info", {})
                    .get("meta", {})
                    .get("access_control", {}),
                ):
                    filtered_models.append(model)
                continue

            model_info = Models.get_model_by_id(model["id"])
            if model_info:
                if user.id == model_info.user_id or has_access(
                    user.id, type="read", access_control=model_info.access_control
                ):
                    filtered_models.append(model)
        models = filtered_models

    log.debug(
        f"/api/models returned filtered models accessible to the user: {json.dumps([model['id'] for model in models])}"
    )

    return {"data": models}


@app.get("/api/models/base")
async def get_base_models(user=Depends(get_admin_user)):
    models = await get_all_base_models()

    # Filter out arena models
    models = [model for model in models if not model.get("arena", False)]
    return {"data": models}


@app.post("/api/chat/completions")
async def generate_chat_completions(
    form_data: dict,
    user=Depends(get_verified_user),
    bypass_filter: bool = False,
):
    if BYPASS_MODEL_ACCESS_CONTROL:
        bypass_filter = True

    model_list = app.state.MODELS
    models = {model["id"]: model for model in model_list}

    model_id = form_data["model"]
    if model_id not in models:
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND,
            detail="Model not found",
        )

    model = models[model_id]

    # Check if user has access to the model
    if not bypass_filter and user.role == "user":
        if model.get("arena"):
            if not has_access(
                user.id,
                type="read",
                access_control=model.get("info", {})
                .get("meta", {})
                .get("access_control", {}),
            ):
                raise HTTPException(
                    status_code=403,
                    detail="Model not found",
                )
        else:
            model_info = Models.get_model_by_id(model_id)
            if not model_info:
                raise HTTPException(
                    status_code=404,
                    detail="Model not found",
                )
            elif not (
                user.id == model_info.user_id
                or has_access(
                    user.id, type="read", access_control=model_info.access_control
                )
            ):
                raise HTTPException(
                    status_code=403,
                    detail="Model not found",
                )

    if model["owned_by"] == "arena":
        model_ids = model.get("info", {}).get("meta", {}).get("model_ids")
        filter_mode = model.get("info", {}).get("meta", {}).get("filter_mode")
        if model_ids and filter_mode == "exclude":
            model_ids = [
                model["id"]
                for model in await get_all_models()
                if model.get("owned_by") != "arena" and model["id"] not in model_ids
            ]

        selected_model_id = None
        if isinstance(model_ids, list) and model_ids:
            selected_model_id = random.choice(model_ids)
        else:
            model_ids = [
                model["id"]
                for model in await get_all_models()
                if model.get("owned_by") != "arena"
            ]
            selected_model_id = random.choice(model_ids)

        form_data["model"] = selected_model_id

        if form_data.get("stream") == True:

            async def stream_wrapper(stream):
                yield f"data: {json.dumps({'selected_model_id': selected_model_id})}\n\n"
                async for chunk in stream:
                    yield chunk

            response = await generate_chat_completions(
                form_data, user, bypass_filter=True
            )
            return StreamingResponse(
                stream_wrapper(response.body_iterator), media_type="text/event-stream"
            )
        else:
            return {
                **(
                    await generate_chat_completions(form_data, user, bypass_filter=True)
                ),
                "selected_model_id": selected_model_id,
            }

    if model.get("pipe"):
        # Below does not require bypass_filter because this is the only route the uses this function and it is already bypassing the filter
        return await generate_function_chat_completion(
            form_data, user=user, models=models
        )
    if model["owned_by"] == "ollama":
        # Using /ollama/api/chat endpoint
        form_data = convert_payload_openai_to_ollama(form_data)
        response = await generate_ollama_chat_completion(
            form_data=form_data, user=user, bypass_filter=bypass_filter
        )
        if form_data.stream:
            response.headers["content-type"] = "text/event-stream"
            return StreamingResponse(
                convert_streaming_response_ollama_to_openai(response),
                headers=dict(response.headers),
            )
        else:
            return convert_response_ollama_to_openai(response)
    else:
        return await generate_openai_chat_completion(
            form_data, user=user, bypass_filter=bypass_filter
        )


@app.post("/api/chat/completed")
async def chat_completed(
    request: Request, form_data: dict, user=Depends(get_verified_user)
):
    model_list = await get_all_models(request)
    models = {model["id"]: model for model in model_list}

    data = form_data
    model_id = data["model"]
    if model_id not in models:
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND,
            detail="Model not found",
        )

    model = models[model_id]

    try:
        data = process_pipeline_outlet_filter(request, data, user, models)
    except Exception as e:
        return HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=str(e),
        )

    __event_emitter__ = get_event_emitter(
        {
            "chat_id": data["chat_id"],
            "message_id": data["id"],
            "session_id": data["session_id"],
        }
    )

    __event_call__ = get_event_call(
        {
            "chat_id": data["chat_id"],
            "message_id": data["id"],
            "session_id": data["session_id"],
        }
    )

    def get_priority(function_id):
        function = Functions.get_function_by_id(function_id)
        if function is not None and hasattr(function, "valves"):
            # TODO: Fix FunctionModel to include vavles
            return (function.valves if function.valves else {}).get("priority", 0)
        return 0

    filter_ids = [function.id for function in Functions.get_global_filter_functions()]
    if "info" in model and "meta" in model["info"]:
        filter_ids.extend(model["info"]["meta"].get("filterIds", []))
        filter_ids = list(set(filter_ids))

    enabled_filter_ids = [
        function.id
        for function in Functions.get_functions_by_type("filter", active_only=True)
    ]
    filter_ids = [
        filter_id for filter_id in filter_ids if filter_id in enabled_filter_ids
    ]

    # Sort filter_ids by priority, using the get_priority function
    filter_ids.sort(key=get_priority)

    for filter_id in filter_ids:
        filter = Functions.get_function_by_id(filter_id)
        if not filter:
            continue

        if filter_id in app.state.FUNCTIONS:
            function_module = app.state.FUNCTIONS[filter_id]
        else:
            function_module, _, _ = load_function_module_by_id(filter_id)
            app.state.FUNCTIONS[filter_id] = function_module

        if hasattr(function_module, "valves") and hasattr(function_module, "Valves"):
            valves = Functions.get_function_valves_by_id(filter_id)
            function_module.valves = function_module.Valves(
                **(valves if valves else {})
            )

        if not hasattr(function_module, "outlet"):
            continue
        try:
            outlet = function_module.outlet

            # Get the signature of the function
            sig = inspect.signature(outlet)
            params = {"body": data}

            # Extra parameters to be passed to the function
            extra_params = {
                "__model__": model,
                "__id__": filter_id,
                "__event_emitter__": __event_emitter__,
                "__event_call__": __event_call__,
            }

            # Add extra params in contained in function signature
            for key, value in extra_params.items():
                if key in sig.parameters:
                    params[key] = value

            if "__user__" in sig.parameters:
                __user__ = {
                    "id": user.id,
                    "email": user.email,
                    "name": user.name,
                    "role": user.role,
                }

                try:
                    if hasattr(function_module, "UserValves"):
                        __user__["valves"] = function_module.UserValves(
                            **Functions.get_user_valves_by_id_and_user_id(
                                filter_id, user.id
                            )
                        )
                except Exception as e:
                    print(e)

                params = {**params, "__user__": __user__}

            if inspect.iscoroutinefunction(outlet):
                data = await outlet(**params)
            else:
                data = outlet(**params)

        except Exception as e:
            print(f"Error: {e}")
            return JSONResponse(
                status_code=status.HTTP_400_BAD_REQUEST,
                content={"detail": str(e)},
            )

    return data


@app.post("/api/chat/actions/{action_id}")
async def chat_action(action_id: str, form_data: dict, user=Depends(get_verified_user)):
    if "." in action_id:
        action_id, sub_action_id = action_id.split(".")
    else:
        sub_action_id = None

    action = Functions.get_function_by_id(action_id)
    if not action:
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND,
            detail="Action not found",
        )

    model_list = await get_all_models()
    models = {model["id"]: model for model in model_list}

    data = form_data
    model_id = data["model"]

    if model_id not in models:
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND,
            detail="Model not found",
        )
    model = models[model_id]

    __event_emitter__ = get_event_emitter(
        {
            "chat_id": data["chat_id"],
            "message_id": data["id"],
            "session_id": data["session_id"],
        }
    )
    __event_call__ = get_event_call(
        {
            "chat_id": data["chat_id"],
            "message_id": data["id"],
            "session_id": data["session_id"],
        }
    )

    if action_id in app.state.FUNCTIONS:
        function_module = app.state.FUNCTIONS[action_id]
    else:
        function_module, _, _ = load_function_module_by_id(action_id)
        app.state.FUNCTIONS[action_id] = function_module

    if hasattr(function_module, "valves") and hasattr(function_module, "Valves"):
        valves = Functions.get_function_valves_by_id(action_id)
        function_module.valves = function_module.Valves(**(valves if valves else {}))

    if hasattr(function_module, "action"):
        try:
            action = function_module.action

            # Get the signature of the function
            sig = inspect.signature(action)
            params = {"body": data}

            # Extra parameters to be passed to the function
            extra_params = {
                "__model__": model,
                "__id__": sub_action_id if sub_action_id is not None else action_id,
                "__event_emitter__": __event_emitter__,
                "__event_call__": __event_call__,
            }

            # Add extra params in contained in function signature
            for key, value in extra_params.items():
                if key in sig.parameters:
                    params[key] = value

            if "__user__" in sig.parameters:
                __user__ = {
                    "id": user.id,
                    "email": user.email,
                    "name": user.name,
                    "role": user.role,
                }

                try:
                    if hasattr(function_module, "UserValves"):
                        __user__["valves"] = function_module.UserValves(
                            **Functions.get_user_valves_by_id_and_user_id(
                                action_id, user.id
                            )
                        )
                except Exception as e:
                    print(e)

                params = {**params, "__user__": __user__}

            if inspect.iscoroutinefunction(action):
                data = await action(**params)
            else:
                data = action(**params)

        except Exception as e:
            print(f"Error: {e}")
            return JSONResponse(
                status_code=status.HTTP_400_BAD_REQUEST,
                content={"detail": str(e)},
            )

    return data


##################################
#
# Config Endpoints
#
##################################


@app.get("/api/config")
async def get_app_config(request: Request):
    user = None
    if "token" in request.cookies:
        token = request.cookies.get("token")
        try:
            data = decode_token(token)
        except Exception as e:
            log.debug(e)
            raise HTTPException(
                status_code=status.HTTP_401_UNAUTHORIZED,
                detail="Invalid token",
            )
        if data is not None and "id" in data:
            user = Users.get_user_by_id(data["id"])

    onboarding = False
    if user is None:
        user_count = Users.get_num_users()
        onboarding = user_count == 0

    return {
        **({"onboarding": True} if onboarding else {}),
        "status": True,
        "name": WEBUI_NAME,
        "version": VERSION,
        "default_locale": str(DEFAULT_LOCALE),
        "oauth": {
            "providers": {
                name: config.get("name", name)
                for name, config in OAUTH_PROVIDERS.items()
            }
        },
        "features": {
            "auth": WEBUI_AUTH,
            "auth_trusted_header": bool(app.state.AUTH_TRUSTED_EMAIL_HEADER),
            "enable_ldap": app.state.config.ENABLE_LDAP,
            "enable_api_key": app.state.config.ENABLE_API_KEY,
            "enable_signup": app.state.config.ENABLE_SIGNUP,
            "enable_login_form": app.state.config.ENABLE_LOGIN_FORM,
            **(
                {
                    "enable_web_search": app.state.config.ENABLE_RAG_WEB_SEARCH,
                    "enable_image_generation": app.state.config.ENABLE_IMAGE_GENERATION,
                    "enable_community_sharing": app.state.config.ENABLE_COMMUNITY_SHARING,
                    "enable_message_rating": app.state.config.ENABLE_MESSAGE_RATING,
                    "enable_admin_export": ENABLE_ADMIN_EXPORT,
                    "enable_admin_chat_access": ENABLE_ADMIN_CHAT_ACCESS,
                }
                if user is not None
                else {}
            ),
        },
        **(
            {
                "default_models": app.state.config.DEFAULT_MODELS,
                "default_prompt_suggestions": app.state.config.DEFAULT_PROMPT_SUGGESTIONS,
                "audio": {
                    "tts": {
                        "engine": app.state.config.TTS_ENGINE,
                        "voice": app.state.config.TTS_VOICE,
                        "split_on": app.state.config.TTS_SPLIT_ON,
                    },
                    "stt": {
                        "engine": app.state.config.STT_ENGINE,
                    },
                },
                "file": {
                    "max_size": app.state.config.FILE_MAX_SIZE,
                    "max_count": app.state.config.FILE_MAX_COUNT,
                },
                "permissions": {**app.state.config.USER_PERMISSIONS},
            }
            if user is not None
            else {}
        ),
    }


class UrlForm(BaseModel):
    url: str


@app.get("/api/webhook")
async def get_webhook_url(user=Depends(get_admin_user)):
    return {
        "url": app.state.config.WEBHOOK_URL,
    }


@app.post("/api/webhook")
async def update_webhook_url(form_data: UrlForm, user=Depends(get_admin_user)):
    app.state.config.WEBHOOK_URL = form_data.url
    app.state.WEBHOOK_URL = app.state.config.WEBHOOK_URL
    return {"url": app.state.config.WEBHOOK_URL}


@app.get("/api/version")
async def get_app_version():
    return {
        "version": VERSION,
    }


@app.get("/api/version/updates")
async def get_app_latest_release_version():
    if OFFLINE_MODE:
        log.debug(
            f"Offline mode is enabled, returning current version as latest version"
        )
        return {"current": VERSION, "latest": VERSION}
    try:
        timeout = aiohttp.ClientTimeout(total=1)
        async with aiohttp.ClientSession(timeout=timeout, trust_env=True) as session:
            async with session.get(
                "https://api.github.com/repos/open-webui/open-webui/releases/latest"
            ) as response:
                response.raise_for_status()
                data = await response.json()
                latest_version = data["tag_name"]

                return {"current": VERSION, "latest": latest_version[1:]}
    except Exception as e:
        log.debug(e)
        return {"current": VERSION, "latest": VERSION}


@app.get("/api/changelog")
async def get_app_changelog():
    return {key: CHANGELOG[key] for idx, key in enumerate(CHANGELOG) if idx < 5}


############################
# OAuth Login & Callback
############################

# SessionMiddleware is used by authlib for oauth
if len(OAUTH_PROVIDERS) > 0:
    app.add_middleware(
        SessionMiddleware,
        secret_key=WEBUI_SECRET_KEY,
        session_cookie="oui-session",
        same_site=WEBUI_SESSION_COOKIE_SAME_SITE,
        https_only=WEBUI_SESSION_COOKIE_SECURE,
    )


@app.get("/oauth/{provider}/login")
async def oauth_login(provider: str, request: Request):
    return await oauth_manager.handle_login(provider, request)


# OAuth login logic is as follows:
# 1. Attempt to find a user with matching subject ID, tied to the provider
# 2. If OAUTH_MERGE_ACCOUNTS_BY_EMAIL is true, find a user with the email address provided via OAuth
#    - This is considered insecure in general, as OAuth providers do not always verify email addresses
# 3. If there is no user, and ENABLE_OAUTH_SIGNUP is true, create a user
#    - Email addresses are considered unique, so we fail registration if the email address is already taken
@app.get("/oauth/{provider}/callback")
async def oauth_callback(provider: str, request: Request, response: Response):
    return await oauth_manager.handle_callback(provider, request, response)


@app.get("/manifest.json")
async def get_manifest_json():
    return {
        "name": WEBUI_NAME,
        "short_name": WEBUI_NAME,
        "description": "Open WebUI is an open, extensible, user-friendly interface for AI that adapts to your workflow.",
        "start_url": "/",
        "display": "standalone",
        "background_color": "#343541",
        "orientation": "natural",
        "icons": [
            {
                "src": "/static/logo.png",
                "type": "image/png",
                "sizes": "500x500",
                "purpose": "any",
            },
            {
                "src": "/static/logo.png",
                "type": "image/png",
                "sizes": "500x500",
                "purpose": "maskable",
            },
        ],
    }


@app.get("/opensearch.xml")
async def get_opensearch_xml():
    xml_content = rf"""
    <OpenSearchDescription xmlns="http://a9.com/-/spec/opensearch/1.1/" xmlns:moz="http://www.mozilla.org/2006/browser/search/">
    <ShortName>{WEBUI_NAME}</ShortName>
    <Description>Search {WEBUI_NAME}</Description>
    <InputEncoding>UTF-8</InputEncoding>
    <Image width="16" height="16" type="image/x-icon">{WEBUI_URL}/static/favicon.png</Image>
    <Url type="text/html" method="get" template="{WEBUI_URL}/?q={"{searchTerms}"}"/>
    <moz:SearchForm>{WEBUI_URL}</moz:SearchForm>
    </OpenSearchDescription>
    """
    return Response(content=xml_content, media_type="application/xml")


@app.get("/health")
async def healthcheck():
    return {"status": True}


@app.get("/health/db")
async def healthcheck_with_db():
    Session.execute(text("SELECT 1;")).all()
    return {"status": True}


app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
app.mount("/cache", StaticFiles(directory=CACHE_DIR), name="cache")

if os.path.exists(FRONTEND_BUILD_DIR):
    mimetypes.add_type("text/javascript", ".js")
    app.mount(
        "/",
        SPAStaticFiles(directory=FRONTEND_BUILD_DIR, html=True),
        name="spa-static-files",
    )
else:
    log.warning(
        f"Frontend build directory not found at '{FRONTEND_BUILD_DIR}'. Serving API only."
    )