main.py 66.7 KB
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import base64
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import uuid
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import subprocess
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from contextlib import asynccontextmanager
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from authlib.integrations.starlette_client import OAuth
from authlib.oidc.core import UserInfo
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from bs4 import BeautifulSoup
import json
import markdown
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import time
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import os
import sys
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import logging
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import aiohttp
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import requests
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import mimetypes
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import shutil
import os
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import uuid
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import inspect
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import asyncio
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from fastapi.concurrency import run_in_threadpool
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from fastapi import FastAPI, Request, Depends, status, UploadFile, File, Form
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from fastapi.staticfiles import StaticFiles
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from fastapi.responses import JSONResponse
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from fastapi import HTTPException
from fastapi.middleware.wsgi import WSGIMiddleware
from fastapi.middleware.cors import CORSMiddleware
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from sqlalchemy import text
from sqlalchemy.orm import Session
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from starlette.exceptions import HTTPException as StarletteHTTPException
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from starlette.middleware.base import BaseHTTPMiddleware
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from starlette.middleware.sessions import SessionMiddleware
from starlette.responses import StreamingResponse, Response, RedirectResponse
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from apps.socket.main import app as socket_app
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from apps.ollama.main import (
    app as ollama_app,
    OpenAIChatCompletionForm,
    get_all_models as get_ollama_models,
    generate_openai_chat_completion as generate_ollama_chat_completion,
)
from apps.openai.main import (
    app as openai_app,
    get_all_models as get_openai_models,
    generate_chat_completion as generate_openai_chat_completion,
)
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from apps.audio.main import app as audio_app
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from apps.images.main import app as images_app
from apps.rag.main import app as rag_app
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from apps.webui.main import (
    app as webui_app,
    get_pipe_models,
    generate_function_chat_completion,
)
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from apps.webui.internal.db import get_db, SessionLocal
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from pydantic import BaseModel
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from typing import List, Optional, Iterator, Generator, Union
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from apps.webui.models.auths import Auths
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from apps.webui.models.models import Models, ModelModel
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from apps.webui.models.tools import Tools
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from apps.webui.models.functions import Functions
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from apps.webui.models.users import Users
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from apps.webui.utils import load_toolkit_module_by_id, load_function_module_by_id
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from utils.utils import (
    get_admin_user,
    get_verified_user,
    get_current_user,
    get_http_authorization_cred,
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    get_password_hash,
    create_token,
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)
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from utils.task import (
    title_generation_template,
    search_query_generation_template,
    tools_function_calling_generation_template,
)
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from utils.misc import (
    get_last_user_message,
    add_or_update_system_message,
    stream_message_template,
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    parse_duration,
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)
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from apps.rag.utils import get_rag_context, rag_template
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from config import (
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    CONFIG_DATA,
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    WEBUI_NAME,
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    WEBUI_URL,
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    WEBUI_AUTH,
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    ENV,
    VERSION,
    CHANGELOG,
    FRONTEND_BUILD_DIR,
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    UPLOAD_DIR,
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    CACHE_DIR,
    STATIC_DIR,
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    ENABLE_OPENAI_API,
    ENABLE_OLLAMA_API,
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    ENABLE_MODEL_FILTER,
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    MODEL_FILTER_LIST,
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    GLOBAL_LOG_LEVEL,
    SRC_LOG_LEVELS,
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    WEBHOOK_URL,
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    ENABLE_ADMIN_EXPORT,
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    WEBUI_BUILD_HASH,
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    TASK_MODEL,
    TASK_MODEL_EXTERNAL,
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    TITLE_GENERATION_PROMPT_TEMPLATE,
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    SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE,
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    SEARCH_QUERY_PROMPT_LENGTH_THRESHOLD,
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    TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE,
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    SAFE_MODE,
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    OAUTH_PROVIDERS,
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    ENABLE_OAUTH_SIGNUP,
    OAUTH_MERGE_ACCOUNTS_BY_EMAIL,
    WEBUI_SECRET_KEY,
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    WEBUI_SESSION_COOKIE_SAME_SITE,
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    WEBUI_SESSION_COOKIE_SECURE,
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    AppConfig,
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    BACKEND_DIR,
    DATABASE_URL,
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)
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from constants import ERROR_MESSAGES, WEBHOOK_MESSAGES
from utils.webhook import post_webhook
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if SAFE_MODE:
    print("SAFE MODE ENABLED")
    Functions.deactivate_all_functions()


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


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

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

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def run_migrations():
    from alembic.config import Config
    from alembic import command

    alembic_cfg = Config(f"{BACKEND_DIR}/alembic.ini")
    alembic_cfg.set_main_option("sqlalchemy.url", DATABASE_URL)
    alembic_cfg.set_main_option("script_location", f"{BACKEND_DIR}/migrations")
    command.upgrade(alembic_cfg, "head")


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@asynccontextmanager
async def lifespan(app: FastAPI):
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    run_migrations()
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    yield


app = FastAPI(
    docs_url="/docs" if ENV == "dev" else None, redoc_url=None, lifespan=lifespan
)
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app.state.config = AppConfig()
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app.state.config.ENABLE_OPENAI_API = ENABLE_OPENAI_API
app.state.config.ENABLE_OLLAMA_API = ENABLE_OLLAMA_API

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app.state.config.ENABLE_MODEL_FILTER = ENABLE_MODEL_FILTER
app.state.config.MODEL_FILTER_LIST = MODEL_FILTER_LIST
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app.state.config.WEBHOOK_URL = WEBHOOK_URL
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app.state.config.TASK_MODEL = TASK_MODEL
app.state.config.TASK_MODEL_EXTERNAL = TASK_MODEL_EXTERNAL
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app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE = TITLE_GENERATION_PROMPT_TEMPLATE
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app.state.config.SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE = (
    SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE
)
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app.state.config.SEARCH_QUERY_PROMPT_LENGTH_THRESHOLD = (
    SEARCH_QUERY_PROMPT_LENGTH_THRESHOLD
)
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app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE = (
    TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
)
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app.state.MODELS = {}

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origins = ["*"]

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


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async def get_function_call_response(
    messages, files, tool_id, template, task_model_id, user
):
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    tool = Tools.get_tool_by_id(tool_id)
    tools_specs = json.dumps(tool.specs, indent=2)
    content = tools_function_calling_generation_template(template, tools_specs)
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    user_message = get_last_user_message(messages)
    prompt = (
        "History:\n"
        + "\n".join(
            [
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                f"{message['role'].upper()}: \"\"\"{message['content']}\"\"\""
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                for message in messages[::-1][:4]
            ]
        )
        + f"\nQuery: {user_message}"
    )

    print(prompt)

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    payload = {
        "model": task_model_id,
        "messages": [
            {"role": "system", "content": content},
            {"role": "user", "content": f"Query: {prompt}"},
        ],
        "stream": False,
    }

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    try:
        payload = filter_pipeline(payload, user)
    except Exception as e:
        raise e

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    model = app.state.MODELS[task_model_id]
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    response = None
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    try:
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        response = await generate_chat_completions(form_data=payload, user=user)
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        content = None

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        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"]
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        # Parse the function response
        if content is not None:
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            print(f"content: {content}")
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            result = json.loads(content)
            print(result)

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            citation = None
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            # Call the function
            if "name" in result:
                if tool_id in webui_app.state.TOOLS:
                    toolkit_module = webui_app.state.TOOLS[tool_id]
                else:
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                    toolkit_module, frontmatter = load_toolkit_module_by_id(tool_id)
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                    webui_app.state.TOOLS[tool_id] = toolkit_module

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                file_handler = False
                # check if toolkit_module has file_handler self variable
                if hasattr(toolkit_module, "file_handler"):
                    file_handler = True
                    print("file_handler: ", file_handler)

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                if hasattr(toolkit_module, "valves") and hasattr(
                    toolkit_module, "Valves"
                ):
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                    valves = Tools.get_tool_valves_by_id(tool_id)
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                    toolkit_module.valves = toolkit_module.Valves(
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                        **(valves if valves else {})
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                    )

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                function = getattr(toolkit_module, result["name"])
                function_result = None
                try:
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                    # Get the signature of the function
                    sig = inspect.signature(function)
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                    params = result["parameters"]

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                    if "__user__" in sig.parameters:
                        # Call the function with the '__user__' parameter included
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                        __user__ = {
                            "id": user.id,
                            "email": user.email,
                            "name": user.name,
                            "role": user.role,
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                        }

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

                        params = {**params, "__user__": __user__}
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                    if "__messages__" in sig.parameters:
                        # Call the function with the '__messages__' parameter included
                        params = {
                            **params,
                            "__messages__": messages,
                        }

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                    if "__files__" in sig.parameters:
                        # Call the function with the '__files__' parameter included
                        params = {
                            **params,
                            "__files__": files,
                        }

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                    if "__model__" in sig.parameters:
                        # Call the function with the '__model__' parameter included
                        params = {
                            **params,
                            "__model__": model,
                        }

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                    if "__id__" in sig.parameters:
                        # Call the function with the '__id__' parameter included
                        params = {
                            **params,
                            "__id__": tool_id,
                        }

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                    if inspect.iscoroutinefunction(function):
                        function_result = await function(**params)
                    else:
                        function_result = function(**params)
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                    if hasattr(toolkit_module, "citation") and toolkit_module.citation:
                        citation = {
                            "source": {"name": f"TOOL:{tool.name}/{result['name']}"},
                            "document": [function_result],
                            "metadata": [{"source": result["name"]}],
                        }
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                except Exception as e:
                    print(e)

                # Add the function result to the system prompt
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                if function_result is not None:
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                    return function_result, citation, file_handler
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    except Exception as e:
        print(f"Error: {e}")
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    return None, None, False
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class ChatCompletionMiddleware(BaseHTTPMiddleware):
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    async def dispatch(self, request: Request, call_next):
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        data_items = []
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        show_citations = False
        citations = []

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        if request.method == "POST" and any(
            endpoint in request.url.path
            for endpoint in ["/ollama/api/chat", "/chat/completions"]
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        ):
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            log.debug(f"request.url.path: {request.url.path}")
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            # Read the original request body
            body = await request.body()
            body_str = body.decode("utf-8")
            data = json.loads(body_str) if body_str else {}
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            user = get_current_user(
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                request,
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                get_http_authorization_cred(request.headers.get("Authorization")),
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                SessionLocal(),
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            )
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            # Flag to skip RAG completions if file_handler is present in tools/functions
            skip_files = False
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            if data.get("citations"):
                show_citations = True
                del data["citations"]
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            model_id = data["model"]
            if model_id not in app.state.MODELS:
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                raise HTTPException(
                    status_code=status.HTTP_404_NOT_FOUND,
                    detail="Model not found",
                )
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            model = app.state.MODELS[model_id]

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

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            filter_ids = [
                function.id
                for function in Functions.get_functions_by_type(
                    "filter", active_only=True
                )
            ]
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            # Check if the model has any filters
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            if "info" in model and "meta" in model["info"]:
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                filter_ids.extend(model["info"]["meta"].get("filterIds", []))
                filter_ids = list(set(filter_ids))

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            filter_ids.sort(key=get_priority)
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            for filter_id in filter_ids:
                filter = Functions.get_function_by_id(filter_id)
                if filter:
                    if filter_id in webui_app.state.FUNCTIONS:
                        function_module = webui_app.state.FUNCTIONS[filter_id]
                    else:
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                        function_module, function_type, frontmatter = (
                            load_function_module_by_id(filter_id)
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                        )
                        webui_app.state.FUNCTIONS[filter_id] = function_module
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                    # Check if the function has a file_handler variable
                    if hasattr(function_module, "file_handler"):
                        skip_files = function_module.file_handler
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                    if hasattr(function_module, "valves") and hasattr(
                        function_module, "Valves"
                    ):
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                        valves = Functions.get_function_valves_by_id(filter_id)
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                        function_module.valves = function_module.Valves(
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                            **(valves if valves else {})
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                        )

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

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

                            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
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                                            )
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                                        )
                                except Exception as e:
                                    print(e)
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                                params = {**params, "__user__": __user__}
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                            if "__id__" in sig.parameters:
                                params = {
                                    **params,
                                    "__id__": filter_id,
                                }
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                            if inspect.iscoroutinefunction(inlet):
                                data = await inlet(**params)
                            else:
                                data = inlet(**params)
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                    except Exception as e:
                        print(f"Error: {e}")
                        return JSONResponse(
                            status_code=status.HTTP_400_BAD_REQUEST,
                            content={"detail": str(e)},
                        )
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            # Set the task model
            task_model_id = data["model"]
            # Check if the user has a custom task model and use that model
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            if app.state.MODELS[task_model_id]["owned_by"] == "ollama":
                if (
                    app.state.config.TASK_MODEL
                    and app.state.config.TASK_MODEL in app.state.MODELS
                ):
                    task_model_id = app.state.config.TASK_MODEL
            else:
                if (
                    app.state.config.TASK_MODEL_EXTERNAL
                    and app.state.config.TASK_MODEL_EXTERNAL in app.state.MODELS
                ):
                    task_model_id = app.state.config.TASK_MODEL_EXTERNAL

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            prompt = get_last_user_message(data["messages"])
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            context = ""

            # If tool_ids field is present, call the functions
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            if "tool_ids" in data:
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                print(data["tool_ids"])
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                for tool_id in data["tool_ids"]:
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                    print(tool_id)
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                    try:
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                        response, citation, file_handler = (
                            await get_function_call_response(
                                messages=data["messages"],
                                files=data.get("files", []),
                                tool_id=tool_id,
                                template=app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE,
                                task_model_id=task_model_id,
                                user=user,
                            )
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                        )
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                        print(file_handler)
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                        if isinstance(response, str):
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                            context += ("\n" if context != "" else "") + response
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                        if citation:
                            citations.append(citation)
                            show_citations = True

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                        if file_handler:
                            skip_files = True

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                    except Exception as e:
                        print(f"Error: {e}")
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                del data["tool_ids"]

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                print(f"tool_context: {context}")
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            # If files field is present, generate RAG completions
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            # If skip_files is True, skip the RAG completions
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            if "files" in data:
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                if not skip_files:
                    data = {**data}
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                    rag_context, rag_citations = get_rag_context(
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                        files=data["files"],
                        messages=data["messages"],
                        embedding_function=rag_app.state.EMBEDDING_FUNCTION,
                        k=rag_app.state.config.TOP_K,
                        reranking_function=rag_app.state.sentence_transformer_rf,
                        r=rag_app.state.config.RELEVANCE_THRESHOLD,
                        hybrid_search=rag_app.state.config.ENABLE_RAG_HYBRID_SEARCH,
                    )
                    if rag_context:
                        context += ("\n" if context != "" else "") + rag_context
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                    log.debug(f"rag_context: {rag_context}, citations: {citations}")
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                    if rag_citations:
                        citations.extend(rag_citations)
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                del data["files"]
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            if show_citations and len(citations) > 0:
                data_items.append({"citations": citations})
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            if context != "":
                system_prompt = rag_template(
                    rag_app.state.config.RAG_TEMPLATE, context, prompt
                )
                print(system_prompt)
                data["messages"] = add_or_update_system_message(
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                    system_prompt, data["messages"]
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                )
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            modified_body_bytes = json.dumps(data).encode("utf-8")
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            # 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"
                ],
            ]
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            response = await call_next(request)
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            if isinstance(response, StreamingResponse):
                # If it's a streaming response, inject it as SSE event or NDJSON line
                content_type = response.headers.get("Content-Type")
                if "text/event-stream" in content_type:
                    return StreamingResponse(
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                        self.openai_stream_wrapper(response.body_iterator, data_items),
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                    )
                if "application/x-ndjson" in content_type:
                    return StreamingResponse(
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                        self.ollama_stream_wrapper(response.body_iterator, data_items),
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                    )
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            else:
                return response
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        # If it's not a chat completion request, just pass it through
        response = await call_next(request)
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        return response

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

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    async def openai_stream_wrapper(self, original_generator, data_items):
        for item in data_items:
            yield f"data: {json.dumps(item)}\n\n"

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        async for data in original_generator:
            yield data

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    async def ollama_stream_wrapper(self, original_generator, data_items):
        for item in data_items:
            yield f"{json.dumps(item)}\n"

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        async for data in original_generator:
            yield data

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app.add_middleware(ChatCompletionMiddleware)
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##################################
#
# Pipeline Middleware
#
##################################

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def filter_pipeline(payload, user):
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    user = {"id": user.id, "email": user.email, "name": user.name, "role": user.role}
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    model_id = payload["model"]
    filters = [
        model
        for model in app.state.MODELS.values()
        if "pipeline" in model
        and "type" in model["pipeline"]
        and model["pipeline"]["type"] == "filter"
        and (
            model["pipeline"]["pipelines"] == ["*"]
            or any(
                model_id == target_model_id
                for target_model_id in model["pipeline"]["pipelines"]
            )
        )
    ]
    sorted_filters = sorted(filters, key=lambda x: x["pipeline"]["priority"])

    model = app.state.MODELS[model_id]

    if "pipeline" in model:
        sorted_filters.append(model)

    for filter in sorted_filters:
        r = None
        try:
            urlIdx = filter["urlIdx"]

            url = openai_app.state.config.OPENAI_API_BASE_URLS[urlIdx]
            key = openai_app.state.config.OPENAI_API_KEYS[urlIdx]

            if key != "":
                headers = {"Authorization": f"Bearer {key}"}
                r = requests.post(
                    f"{url}/{filter['id']}/filter/inlet",
                    headers=headers,
                    json={
                        "user": user,
                        "body": payload,
                    },
                )

                r.raise_for_status()
                payload = r.json()
        except Exception as e:
            # Handle connection error here
            print(f"Connection error: {e}")

            if r is not None:
                try:
                    res = r.json()
                except:
                    pass
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                if "detail" in res:
                    raise Exception(r.status_code, res["detail"])
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            else:
                pass

    if "pipeline" not in app.state.MODELS[model_id]:
        if "chat_id" in payload:
            del payload["chat_id"]

        if "title" in payload:
            del payload["title"]
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        if "task" in payload:
            del payload["task"]

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    return payload


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class PipelineMiddleware(BaseHTTPMiddleware):
    async def dispatch(self, request: Request, call_next):
        if request.method == "POST" and (
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            "/ollama/api/chat" in request.url.path
            or "/chat/completions" in request.url.path
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        ):
            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 {}

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            user = get_current_user(
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                request,
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                get_http_authorization_cred(request.headers.get("Authorization")),
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                SessionLocal(),
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            )
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            try:
                data = filter_pipeline(data, user)
            except Exception as e:
                return JSONResponse(
                    status_code=e.args[0],
                    content={"detail": e.args[1]},
                )
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            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)


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


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@app.middleware("http")
async def check_url(request: Request, call_next):
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    if len(app.state.MODELS) == 0:
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        db = SessionLocal()
        await get_all_models(db)
        db.commit()
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    else:
        pass

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    start_time = int(time.time())
    response = await call_next(request)
    process_time = int(time.time()) - start_time
    response.headers["X-Process-Time"] = str(process_time)

    return response


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@app.middleware("http")
async def update_embedding_function(request: Request, call_next):
    response = await call_next(request)
    if "/embedding/update" in request.url.path:
        webui_app.state.EMBEDDING_FUNCTION = rag_app.state.EMBEDDING_FUNCTION
    return response
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app.mount("/ws", socket_app)

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app.mount("/ollama", ollama_app)
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app.mount("/openai", openai_app)
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app.mount("/images/api/v1", images_app)
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app.mount("/audio/api/v1", audio_app)
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app.mount("/rag/api/v1", rag_app)

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app.mount("/api/v1", webui_app)

webui_app.state.EMBEDDING_FUNCTION = rag_app.state.EMBEDDING_FUNCTION

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async def get_all_models(db: Session):
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    pipe_models = []
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    openai_models = []
    ollama_models = []

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    pipe_models = await get_pipe_models(db)
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    if app.state.config.ENABLE_OPENAI_API:
        openai_models = await get_openai_models()
        openai_models = openai_models["data"]

    if app.state.config.ENABLE_OLLAMA_API:
        ollama_models = await get_ollama_models()
        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"]
        ]

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    models = pipe_models + openai_models + ollama_models
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    custom_models = Models.get_all_models(db)
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    for custom_model in custom_models:
        if custom_model.base_model_id == None:
            for model in models:
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                if (
                    custom_model.id == model["id"]
                    or custom_model.id == model["id"].split(":")[0]
                ):
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                    model["name"] = custom_model.name
                    model["info"] = custom_model.model_dump()
        else:
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            owned_by = "openai"
            for model in models:
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                if (
                    custom_model.base_model_id == model["id"]
                    or custom_model.base_model_id == model["id"].split(":")[0]
                ):
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                    owned_by = model["owned_by"]
                    break

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            models.append(
                {
                    "id": custom_model.id,
                    "name": custom_model.name,
                    "object": "model",
                    "created": custom_model.created_at,
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                    "owned_by": owned_by,
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                    "info": custom_model.model_dump(),
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                    "preset": True,
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                }
            )

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

    webui_app.state.MODELS = app.state.MODELS

    return models


@app.get("/api/models")
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async def get_models(user=Depends(get_verified_user), db=Depends(get_db)):
    models = await get_all_models(db)
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    # Filter out filter pipelines
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    models = [
        model
        for model in models
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        if "pipeline" not in model or model["pipeline"].get("type", None) != "filter"
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    ]

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    if app.state.config.ENABLE_MODEL_FILTER:
        if user.role == "user":
            models = list(
                filter(
                    lambda model: model["id"] in app.state.config.MODEL_FILTER_LIST,
                    models,
                )
            )
            return {"data": models}

    return {"data": models}


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@app.post("/api/chat/completions")
async def generate_chat_completions(form_data: dict, user=Depends(get_verified_user)):
    model_id = form_data["model"]
    if model_id not in app.state.MODELS:
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND,
            detail="Model not found",
        )

    model = app.state.MODELS[model_id]
    print(model)

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    pipe = model.get("pipe")
    if pipe:
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        return await generate_function_chat_completion(form_data, user=user)
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    if model["owned_by"] == "ollama":
        return await generate_ollama_chat_completion(form_data, user=user)
    else:
        return await generate_openai_chat_completion(form_data, user=user)


@app.post("/api/chat/completed")
async def chat_completed(form_data: dict, user=Depends(get_verified_user)):
    data = form_data
    model_id = data["model"]
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    if model_id not in app.state.MODELS:
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND,
            detail="Model not found",
        )
    model = app.state.MODELS[model_id]
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    filters = [
        model
        for model in app.state.MODELS.values()
        if "pipeline" in model
        and "type" in model["pipeline"]
        and model["pipeline"]["type"] == "filter"
        and (
            model["pipeline"]["pipelines"] == ["*"]
            or any(
                model_id == target_model_id
                for target_model_id in model["pipeline"]["pipelines"]
            )
        )
    ]

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    sorted_filters = sorted(filters, key=lambda x: x["pipeline"]["priority"])
    if "pipeline" in model:
        sorted_filters = [model] + sorted_filters
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    for filter in sorted_filters:
        r = None
        try:
            urlIdx = filter["urlIdx"]

            url = openai_app.state.config.OPENAI_API_BASE_URLS[urlIdx]
            key = openai_app.state.config.OPENAI_API_KEYS[urlIdx]

            if key != "":
                headers = {"Authorization": f"Bearer {key}"}
                r = requests.post(
                    f"{url}/{filter['id']}/filter/outlet",
                    headers=headers,
                    json={
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                        "user": {
                            "id": user.id,
                            "name": user.name,
                            "email": user.email,
                            "role": user.role,
                        },
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                        "body": data,
                    },
                )

                r.raise_for_status()
                data = r.json()
        except Exception as e:
            # Handle connection error here
            print(f"Connection error: {e}")

            if r is not None:
                try:
                    res = r.json()
                    if "detail" in res:
                        return JSONResponse(
                            status_code=r.status_code,
                            content=res,
                        )
                except:
                    pass

            else:
                pass

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

    filter_ids = [
        function.id
        for function in Functions.get_functions_by_type("filter", active_only=True)
    ]
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    # Check if the model has any filters
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    if "info" in model and "meta" in model["info"]:
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        filter_ids.extend(model["info"]["meta"].get("filterIds", []))
        filter_ids = list(set(filter_ids))
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    # Sort filter_ids by priority, using the get_priority function
    filter_ids.sort(key=get_priority)
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    for filter_id in filter_ids:
        filter = Functions.get_function_by_id(filter_id)
        if filter:
            if filter_id in webui_app.state.FUNCTIONS:
                function_module = webui_app.state.FUNCTIONS[filter_id]
            else:
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                function_module, function_type, frontmatter = (
                    load_function_module_by_id(filter_id)
                )
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                webui_app.state.FUNCTIONS[filter_id] = function_module
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            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 {})
                )

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

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

                    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
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                                    )
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                                )
                        except Exception as e:
                            print(e)
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                        params = {**params, "__user__": __user__}
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                    if "__id__" in sig.parameters:
                        params = {
                            **params,
                            "__id__": filter_id,
                        }
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                    if inspect.iscoroutinefunction(outlet):
                        data = await outlet(**params)
                    else:
                        data = outlet(**params)
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            except Exception as e:
                print(f"Error: {e}")
                return JSONResponse(
                    status_code=status.HTTP_400_BAD_REQUEST,
                    content={"detail": str(e)},
                )
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    return data


##################################
#
# Task Endpoints
#
##################################


# TODO: Refactor task API endpoints below into a separate file


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@app.get("/api/task/config")
async def get_task_config(user=Depends(get_verified_user)):
    return {
        "TASK_MODEL": app.state.config.TASK_MODEL,
        "TASK_MODEL_EXTERNAL": app.state.config.TASK_MODEL_EXTERNAL,
        "TITLE_GENERATION_PROMPT_TEMPLATE": app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE,
        "SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE": app.state.config.SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE,
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        "SEARCH_QUERY_PROMPT_LENGTH_THRESHOLD": app.state.config.SEARCH_QUERY_PROMPT_LENGTH_THRESHOLD,
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        "TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE": app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE,
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    }


class TaskConfigForm(BaseModel):
    TASK_MODEL: Optional[str]
    TASK_MODEL_EXTERNAL: Optional[str]
    TITLE_GENERATION_PROMPT_TEMPLATE: str
    SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE: str
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    SEARCH_QUERY_PROMPT_LENGTH_THRESHOLD: int
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    TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE: str
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@app.post("/api/task/config/update")
async def update_task_config(form_data: TaskConfigForm, user=Depends(get_admin_user)):
    app.state.config.TASK_MODEL = form_data.TASK_MODEL
    app.state.config.TASK_MODEL_EXTERNAL = form_data.TASK_MODEL_EXTERNAL
    app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE = (
        form_data.TITLE_GENERATION_PROMPT_TEMPLATE
    )
    app.state.config.SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE = (
        form_data.SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE
    )
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    app.state.config.SEARCH_QUERY_PROMPT_LENGTH_THRESHOLD = (
        form_data.SEARCH_QUERY_PROMPT_LENGTH_THRESHOLD
    )
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    app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE = (
        form_data.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
    )
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    return {
        "TASK_MODEL": app.state.config.TASK_MODEL,
        "TASK_MODEL_EXTERNAL": app.state.config.TASK_MODEL_EXTERNAL,
        "TITLE_GENERATION_PROMPT_TEMPLATE": app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE,
        "SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE": app.state.config.SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE,
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        "SEARCH_QUERY_PROMPT_LENGTH_THRESHOLD": app.state.config.SEARCH_QUERY_PROMPT_LENGTH_THRESHOLD,
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        "TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE": app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE,
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    }


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@app.post("/api/task/title/completions")
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async def generate_title(form_data: dict, user=Depends(get_verified_user)):
    print("generate_title")
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    model_id = form_data["model"]
    if model_id not in app.state.MODELS:
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND,
            detail="Model not found",
        )

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    # Check if the user has a custom task model
    # If the user has a custom task model, use that model
    if app.state.MODELS[model_id]["owned_by"] == "ollama":
        if app.state.config.TASK_MODEL:
            task_model_id = app.state.config.TASK_MODEL
            if task_model_id in app.state.MODELS:
                model_id = task_model_id
    else:
        if app.state.config.TASK_MODEL_EXTERNAL:
            task_model_id = app.state.config.TASK_MODEL_EXTERNAL
            if task_model_id in app.state.MODELS:
                model_id = task_model_id

    print(model_id)
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    model = app.state.MODELS[model_id]

    template = app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE

    content = title_generation_template(
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        template,
        form_data["prompt"],
        {
            "name": user.name,
            "location": user.info.get("location") if user.info else None,
        },
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    )

    payload = {
        "model": model_id,
        "messages": [{"role": "user", "content": content}],
        "stream": False,
        "max_tokens": 50,
        "chat_id": form_data.get("chat_id", None),
        "title": True,
    }

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    log.debug(payload)
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    try:
        payload = filter_pipeline(payload, user)
    except Exception as e:
        return JSONResponse(
            status_code=e.args[0],
            content={"detail": e.args[1]},
        )
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    return await generate_chat_completions(form_data=payload, user=user)
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@app.post("/api/task/query/completions")
async def generate_search_query(form_data: dict, user=Depends(get_verified_user)):
    print("generate_search_query")

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    if len(form_data["prompt"]) < app.state.config.SEARCH_QUERY_PROMPT_LENGTH_THRESHOLD:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=f"Skip search query generation for short prompts (< {app.state.config.SEARCH_QUERY_PROMPT_LENGTH_THRESHOLD} characters)",
        )

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

    # Check if the user has a custom task model
    # If the user has a custom task model, use that model
    if app.state.MODELS[model_id]["owned_by"] == "ollama":
        if app.state.config.TASK_MODEL:
            task_model_id = app.state.config.TASK_MODEL
            if task_model_id in app.state.MODELS:
                model_id = task_model_id
    else:
        if app.state.config.TASK_MODEL_EXTERNAL:
            task_model_id = app.state.config.TASK_MODEL_EXTERNAL
            if task_model_id in app.state.MODELS:
                model_id = task_model_id

    print(model_id)
    model = app.state.MODELS[model_id]

    template = app.state.config.SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE

    content = search_query_generation_template(
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        template, form_data["prompt"], {"name": user.name}
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    )

    payload = {
        "model": model_id,
        "messages": [{"role": "user", "content": content}],
        "stream": False,
        "max_tokens": 30,
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        "task": True,
    }

    print(payload)

    try:
        payload = filter_pipeline(payload, user)
    except Exception as e:
        return JSONResponse(
            status_code=e.args[0],
            content={"detail": e.args[1]},
        )

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    return await generate_chat_completions(form_data=payload, user=user)
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@app.post("/api/task/emoji/completions")
async def generate_emoji(form_data: dict, user=Depends(get_verified_user)):
    print("generate_emoji")

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

    # Check if the user has a custom task model
    # If the user has a custom task model, use that model
    if app.state.MODELS[model_id]["owned_by"] == "ollama":
        if app.state.config.TASK_MODEL:
            task_model_id = app.state.config.TASK_MODEL
            if task_model_id in app.state.MODELS:
                model_id = task_model_id
    else:
        if app.state.config.TASK_MODEL_EXTERNAL:
            task_model_id = app.state.config.TASK_MODEL_EXTERNAL
            if task_model_id in app.state.MODELS:
                model_id = task_model_id

    print(model_id)
    model = app.state.MODELS[model_id]

    template = '''
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Your task is to reflect the speaker's likely facial expression through a fitting emoji. Interpret emotions from the message and reflect their facial expression using fitting, diverse emojis (e.g., 😊, 😢, 😡, 😱).
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Message: """{{prompt}}"""
'''

    content = title_generation_template(
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        template,
        form_data["prompt"],
        {
            "name": user.name,
            "location": user.info.get("location") if user.info else None,
        },
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    )

    payload = {
        "model": model_id,
        "messages": [{"role": "user", "content": content}],
        "stream": False,
        "max_tokens": 4,
        "chat_id": form_data.get("chat_id", None),
        "task": True,
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    }

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    try:
        payload = filter_pipeline(payload, user)
    except Exception as e:
        return JSONResponse(
            status_code=e.args[0],
            content={"detail": e.args[1]},
        )
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    return await generate_chat_completions(form_data=payload, user=user)
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@app.post("/api/task/tools/completions")
async def get_tools_function_calling(form_data: dict, user=Depends(get_verified_user)):
    print("get_tools_function_calling")

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

    # Check if the user has a custom task model
    # If the user has a custom task model, use that model
    if app.state.MODELS[model_id]["owned_by"] == "ollama":
        if app.state.config.TASK_MODEL:
            task_model_id = app.state.config.TASK_MODEL
            if task_model_id in app.state.MODELS:
                model_id = task_model_id
    else:
        if app.state.config.TASK_MODEL_EXTERNAL:
            task_model_id = app.state.config.TASK_MODEL_EXTERNAL
            if task_model_id in app.state.MODELS:
                model_id = task_model_id

    print(model_id)
    template = app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE

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    try:
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        context, citation, file_handler = await get_function_call_response(
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            form_data["messages"],
            form_data.get("files", []),
            form_data["tool_id"],
            template,
            model_id,
            user,
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        )
        return context
    except Exception as e:
        return JSONResponse(
            status_code=e.args[0],
            content={"detail": e.args[1]},
        )
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##################################
#
# Pipelines Endpoints
#
##################################
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# TODO: Refactor pipelines API endpoints below into a separate file
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@app.get("/api/pipelines/list")
async def get_pipelines_list(user=Depends(get_admin_user)):
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    responses = await get_openai_models(raw=True)

    print(responses)
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    urlIdxs = [
        idx
        for idx, response in enumerate(responses)
        if response != None and "pipelines" in response
    ]
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    return {
        "data": [
            {
                "url": openai_app.state.config.OPENAI_API_BASE_URLS[urlIdx],
                "idx": urlIdx,
            }
            for urlIdx in urlIdxs
        ]
    }


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@app.post("/api/pipelines/upload")
async def upload_pipeline(
    urlIdx: int = Form(...), file: UploadFile = File(...), user=Depends(get_admin_user)
):
    print("upload_pipeline", urlIdx, file.filename)
    # Check if the uploaded file is a python file
    if not file.filename.endswith(".py"):
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail="Only Python (.py) files are allowed.",
        )

    upload_folder = f"{CACHE_DIR}/pipelines"
    os.makedirs(upload_folder, exist_ok=True)
    file_path = os.path.join(upload_folder, file.filename)

    try:
        # Save the uploaded file
        with open(file_path, "wb") as buffer:
            shutil.copyfileobj(file.file, buffer)

        url = openai_app.state.config.OPENAI_API_BASE_URLS[urlIdx]
        key = openai_app.state.config.OPENAI_API_KEYS[urlIdx]

        headers = {"Authorization": f"Bearer {key}"}

        with open(file_path, "rb") as f:
            files = {"file": f}
            r = requests.post(f"{url}/pipelines/upload", headers=headers, files=files)

        r.raise_for_status()
        data = r.json()

        return {**data}
    except Exception as e:
        # Handle connection error here
        print(f"Connection error: {e}")

        detail = "Pipeline not found"
        if r is not None:
            try:
                res = r.json()
                if "detail" in res:
                    detail = res["detail"]
            except:
                pass

        raise HTTPException(
            status_code=(r.status_code if r is not None else status.HTTP_404_NOT_FOUND),
            detail=detail,
        )
    finally:
        # Ensure the file is deleted after the upload is completed or on failure
        if os.path.exists(file_path):
            os.remove(file_path)


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class AddPipelineForm(BaseModel):
    url: str
    urlIdx: int


@app.post("/api/pipelines/add")
async def add_pipeline(form_data: AddPipelineForm, user=Depends(get_admin_user)):

    r = None
    try:
        urlIdx = form_data.urlIdx

        url = openai_app.state.config.OPENAI_API_BASE_URLS[urlIdx]
        key = openai_app.state.config.OPENAI_API_KEYS[urlIdx]

        headers = {"Authorization": f"Bearer {key}"}
        r = requests.post(
            f"{url}/pipelines/add", headers=headers, json={"url": form_data.url}
        )

        r.raise_for_status()
        data = r.json()

        return {**data}
    except Exception as e:
        # Handle connection error here
        print(f"Connection error: {e}")

        detail = "Pipeline not found"
        if r is not None:
            try:
                res = r.json()
                if "detail" in res:
                    detail = res["detail"]
            except:
                pass

        raise HTTPException(
            status_code=(r.status_code if r is not None else status.HTTP_404_NOT_FOUND),
            detail=detail,
        )


class DeletePipelineForm(BaseModel):
    id: str
    urlIdx: int


@app.delete("/api/pipelines/delete")
async def delete_pipeline(form_data: DeletePipelineForm, user=Depends(get_admin_user)):

    r = None
    try:
        urlIdx = form_data.urlIdx

        url = openai_app.state.config.OPENAI_API_BASE_URLS[urlIdx]
        key = openai_app.state.config.OPENAI_API_KEYS[urlIdx]

        headers = {"Authorization": f"Bearer {key}"}
        r = requests.delete(
            f"{url}/pipelines/delete", headers=headers, json={"id": form_data.id}
        )

        r.raise_for_status()
        data = r.json()

        return {**data}
    except Exception as e:
        # Handle connection error here
        print(f"Connection error: {e}")

        detail = "Pipeline not found"
        if r is not None:
            try:
                res = r.json()
                if "detail" in res:
                    detail = res["detail"]
            except:
                pass

        raise HTTPException(
            status_code=(r.status_code if r is not None else status.HTTP_404_NOT_FOUND),
            detail=detail,
        )


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@app.get("/api/pipelines")
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async def get_pipelines(urlIdx: Optional[int] = None, user=Depends(get_admin_user)):
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    r = None
    try:
        urlIdx
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        url = openai_app.state.config.OPENAI_API_BASE_URLS[urlIdx]
        key = openai_app.state.config.OPENAI_API_KEYS[urlIdx]
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        headers = {"Authorization": f"Bearer {key}"}
        r = requests.get(f"{url}/pipelines", headers=headers)
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        r.raise_for_status()
        data = r.json()
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        return {**data}
    except Exception as e:
        # Handle connection error here
        print(f"Connection error: {e}")
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        detail = "Pipeline not found"
        if r is not None:
            try:
                res = r.json()
                if "detail" in res:
                    detail = res["detail"]
            except:
                pass

        raise HTTPException(
            status_code=(r.status_code if r is not None else status.HTTP_404_NOT_FOUND),
            detail=detail,
        )
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@app.get("/api/pipelines/{pipeline_id}/valves")
async def get_pipeline_valves(
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    urlIdx: Optional[int],
    pipeline_id: str,
    user=Depends(get_admin_user),
    db=Depends(get_db),
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):
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    models = await get_all_models(db)
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    r = None
    try:
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        url = openai_app.state.config.OPENAI_API_BASE_URLS[urlIdx]
        key = openai_app.state.config.OPENAI_API_KEYS[urlIdx]
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        headers = {"Authorization": f"Bearer {key}"}
        r = requests.get(f"{url}/{pipeline_id}/valves", headers=headers)
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        r.raise_for_status()
        data = r.json()
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        return {**data}
    except Exception as e:
        # Handle connection error here
        print(f"Connection error: {e}")
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        detail = "Pipeline not found"
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        if r is not None:
            try:
                res = r.json()
                if "detail" in res:
                    detail = res["detail"]
            except:
                pass
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        raise HTTPException(
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            status_code=(r.status_code if r is not None else status.HTTP_404_NOT_FOUND),
            detail=detail,
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        )


@app.get("/api/pipelines/{pipeline_id}/valves/spec")
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async def get_pipeline_valves_spec(
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    urlIdx: Optional[int],
    pipeline_id: str,
    user=Depends(get_admin_user),
    db=Depends(get_db),
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):
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    models = await get_all_models(db)
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    r = None
    try:
        url = openai_app.state.config.OPENAI_API_BASE_URLS[urlIdx]
        key = openai_app.state.config.OPENAI_API_KEYS[urlIdx]
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        headers = {"Authorization": f"Bearer {key}"}
        r = requests.get(f"{url}/{pipeline_id}/valves/spec", headers=headers)
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        r.raise_for_status()
        data = r.json()
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        return {**data}
    except Exception as e:
        # Handle connection error here
        print(f"Connection error: {e}")
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        detail = "Pipeline not found"
        if r is not None:
            try:
                res = r.json()
                if "detail" in res:
                    detail = res["detail"]
            except:
                pass
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        raise HTTPException(
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            status_code=(r.status_code if r is not None else status.HTTP_404_NOT_FOUND),
            detail=detail,
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        )


@app.post("/api/pipelines/{pipeline_id}/valves/update")
async def update_pipeline_valves(
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    urlIdx: Optional[int],
    pipeline_id: str,
    form_data: dict,
    user=Depends(get_admin_user),
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    db=Depends(get_db),
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):
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    models = await get_all_models(db)
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    r = None
    try:
        url = openai_app.state.config.OPENAI_API_BASE_URLS[urlIdx]
        key = openai_app.state.config.OPENAI_API_KEYS[urlIdx]
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        headers = {"Authorization": f"Bearer {key}"}
        r = requests.post(
            f"{url}/{pipeline_id}/valves/update",
            headers=headers,
            json={**form_data},
        )
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        r.raise_for_status()
        data = r.json()
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        return {**data}
    except Exception as e:
        # Handle connection error here
        print(f"Connection error: {e}")
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        detail = "Pipeline not found"
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        if r is not None:
            try:
                res = r.json()
                if "detail" in res:
                    detail = res["detail"]
            except:
                pass
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        raise HTTPException(
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            status_code=(r.status_code if r is not None else status.HTTP_404_NOT_FOUND),
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            detail=detail,
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        )


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


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@app.get("/api/config")
async def get_app_config():
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    # Checking and Handling the Absence of 'ui' in CONFIG_DATA
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    default_locale = "en-US"
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    if "ui" in CONFIG_DATA:
        default_locale = CONFIG_DATA["ui"].get("default_locale", "en-US")

    # The Rest of the Function Now Uses the Variables Defined Above
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    return {
        "status": True,
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        "name": WEBUI_NAME,
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        "version": VERSION,
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        "default_locale": default_locale,
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        "default_models": webui_app.state.config.DEFAULT_MODELS,
        "default_prompt_suggestions": webui_app.state.config.DEFAULT_PROMPT_SUGGESTIONS,
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        "features": {
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            "auth": WEBUI_AUTH,
            "auth_trusted_header": bool(webui_app.state.AUTH_TRUSTED_EMAIL_HEADER),
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            "enable_signup": webui_app.state.config.ENABLE_SIGNUP,
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            "enable_web_search": rag_app.state.config.ENABLE_RAG_WEB_SEARCH,
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            "enable_admin_export": ENABLE_ADMIN_EXPORT,
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        },
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        "audio": {
            "tts": {
                "engine": audio_app.state.config.TTS_ENGINE,
                "voice": audio_app.state.config.TTS_VOICE,
            },
            "stt": {
                "engine": audio_app.state.config.STT_ENGINE,
            },
        },
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        "oauth": {
            "providers": {
                name: config.get("name", name)
                for name, config in OAUTH_PROVIDERS.items()
            }
        },
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    }


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@app.get("/api/config/model/filter")
async def get_model_filter_config(user=Depends(get_admin_user)):
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    return {
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        "enabled": app.state.config.ENABLE_MODEL_FILTER,
        "models": app.state.config.MODEL_FILTER_LIST,
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    }
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class ModelFilterConfigForm(BaseModel):
    enabled: bool
    models: List[str]


@app.post("/api/config/model/filter")
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async def update_model_filter_config(
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    form_data: ModelFilterConfigForm, user=Depends(get_admin_user)
):
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    app.state.config.ENABLE_MODEL_FILTER = form_data.enabled
    app.state.config.MODEL_FILTER_LIST = form_data.models
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    return {
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        "enabled": app.state.config.ENABLE_MODEL_FILTER,
        "models": app.state.config.MODEL_FILTER_LIST,
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    }
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# TODO: webhook endpoint should be under config endpoints


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


class UrlForm(BaseModel):
    url: str


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


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@app.get("/api/changelog")
async def get_app_changelog():
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    return {key: CHANGELOG[key] for idx, key in enumerate(CHANGELOG) if idx < 5}
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@app.get("/api/version/updates")
async def get_app_latest_release_version():
    try:
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        async with aiohttp.ClientSession(trust_env=True) as session:
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            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 aiohttp.ClientError as e:
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        raise HTTPException(
            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
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            detail=ERROR_MESSAGES.RATE_LIMIT_EXCEEDED,
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        )

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

oauth = OAuth()

for provider_name, provider_config in OAUTH_PROVIDERS.items():
    oauth.register(
        name=provider_name,
        client_id=provider_config["client_id"],
        client_secret=provider_config["client_secret"],
        server_metadata_url=provider_config["server_metadata_url"],
        client_kwargs={
            "scope": provider_config["scope"],
        },
    )

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


@app.get("/oauth/{provider}/login")
async def oauth_login(provider: str, request: Request):
    if provider not in OAUTH_PROVIDERS:
        raise HTTPException(404)
    redirect_uri = request.url_for("oauth_callback", provider=provider)
    return await oauth.create_client(provider).authorize_redirect(request, redirect_uri)


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# 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 alreayd taken
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@app.get("/oauth/{provider}/callback")
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async def oauth_callback(provider: str, request: Request, response: Response):
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    if provider not in OAUTH_PROVIDERS:
        raise HTTPException(404)
    client = oauth.create_client(provider)
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    try:
        token = await client.authorize_access_token(request)
    except Exception as e:
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        log.warning(f"OAuth callback error: {e}")
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        raise HTTPException(400, detail=ERROR_MESSAGES.INVALID_CRED)
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    user_data: UserInfo = token["userinfo"]

    sub = user_data.get("sub")
    if not sub:
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        log.warning(f"OAuth callback failed, sub is missing: {user_data}")
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        raise HTTPException(400, detail=ERROR_MESSAGES.INVALID_CRED)
    provider_sub = f"{provider}@{sub}"
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    email = user_data.get("email", "").lower()
    # We currently mandate that email addresses are provided
    if not email:
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        log.warning(f"OAuth callback failed, email is missing: {user_data}")
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        raise HTTPException(400, detail=ERROR_MESSAGES.INVALID_CRED)
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    # Check if the user exists
    user = Users.get_user_by_oauth_sub(provider_sub)

    if not user:
        # If the user does not exist, check if merging is enabled
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        if OAUTH_MERGE_ACCOUNTS_BY_EMAIL.value:
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            # Check if the user exists by email
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            user = Users.get_user_by_email(email)
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            if user:
                # Update the user with the new oauth sub
                Users.update_user_oauth_sub_by_id(user.id, provider_sub)

    if not user:
        # If the user does not exist, check if signups are enabled
        if ENABLE_OAUTH_SIGNUP.value:
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            # Check if an existing user with the same email already exists
            existing_user = Users.get_user_by_email(user_data.get("email", "").lower())
            if existing_user:
                raise HTTPException(400, detail=ERROR_MESSAGES.EMAIL_TAKEN)

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            picture_url = user_data.get("picture", "")
            if picture_url:
                # Download the profile image into a base64 string
                try:
                    async with aiohttp.ClientSession() as session:
                        async with session.get(picture_url) as resp:
                            picture = await resp.read()
                            base64_encoded_picture = base64.b64encode(picture).decode(
                                "utf-8"
                            )
                            guessed_mime_type = mimetypes.guess_type(picture_url)[0]
                            if guessed_mime_type is None:
                                # assume JPG, browsers are tolerant enough of image formats
                                guessed_mime_type = "image/jpeg"
                            picture_url = f"data:{guessed_mime_type};base64,{base64_encoded_picture}"
                except Exception as e:
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                    log.error(f"Error downloading profile image '{picture_url}': {e}")
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                    picture_url = ""
            if not picture_url:
                picture_url = "/user.png"
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            user = Auths.insert_new_auth(
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                email=email,
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                password=get_password_hash(
                    str(uuid.uuid4())
                ),  # Random password, not used
                name=user_data.get("name", "User"),
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                profile_image_url=picture_url,
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                role=webui_app.state.config.DEFAULT_USER_ROLE,
                oauth_sub=provider_sub,
            )

            if webui_app.state.config.WEBHOOK_URL:
                post_webhook(
                    webui_app.state.config.WEBHOOK_URL,
                    WEBHOOK_MESSAGES.USER_SIGNUP(user.name),
                    {
                        "action": "signup",
                        "message": WEBHOOK_MESSAGES.USER_SIGNUP(user.name),
                        "user": user.model_dump_json(exclude_none=True),
                    },
                )
        else:
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            raise HTTPException(
                status.HTTP_403_FORBIDDEN, detail=ERROR_MESSAGES.ACCESS_PROHIBITED
            )
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    jwt_token = create_token(
        data={"id": user.id},
        expires_delta=parse_duration(webui_app.state.config.JWT_EXPIRES_IN),
    )

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    # Set the cookie token
    response.set_cookie(
        key="token",
        value=token,
        httponly=True,  # Ensures the cookie is not accessible via JavaScript
    )

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    # Redirect back to the frontend with the JWT token
    redirect_url = f"{request.base_url}auth#token={jwt_token}"
    return RedirectResponse(url=redirect_url)


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@app.get("/manifest.json")
async def get_manifest_json():
    return {
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        "name": WEBUI_NAME,
        "short_name": WEBUI_NAME,
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        "start_url": "/",
        "display": "standalone",
        "background_color": "#343541",
        "orientation": "portrait-primary",
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        "icons": [{"src": "/static/logo.png", "type": "image/png", "sizes": "500x500"}],
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    }

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@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}/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")


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


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


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app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
app.mount("/cache", StaticFiles(directory=CACHE_DIR), name="cache")
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if os.path.exists(FRONTEND_BUILD_DIR):
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    mimetypes.add_type("text/javascript", ".js")
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    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."
    )