main.py 59.8 KB
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from contextlib import asynccontextmanager
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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 starlette.exceptions import HTTPException as StarletteHTTPException
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from starlette.middleware.base import BaseHTTPMiddleware
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from starlette.responses import StreamingResponse, Response
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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
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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.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

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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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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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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    AppConfig,
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)
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from constants import ERROR_MESSAGES

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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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@asynccontextmanager
async def lifespan(app: FastAPI):
    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:
        if model["owned_by"] == "ollama":
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            response = await generate_ollama_chat_completion(payload, user=user)
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        else:
            response = await generate_openai_chat_completion(payload, user=user)

        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:
                    toolkit_module = load_toolkit_module_by_id(tool_id)
                    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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                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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                        params = {
                            **params,
                            "__user__": {
                                "id": user.id,
                                "email": user.email,
                                "name": user.name,
                                "role": user.role,
                            },
                        }

                    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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            )
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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]

            # Check if the model has any filters
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            if "info" in model and "meta" in model["info"]:
                for filter_id in model["info"]["meta"].get("filterIds", []):
                    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:
                            function_module, function_type = load_function_module_by_id(
                                filter_id
                            )
                            webui_app.state.FUNCTIONS[filter_id] = function_module
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                        # Check if the function has a file_handler variable
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                        if hasattr(function_module, "file_handler"):
                            skip_files = function_module.file_handler
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                        try:
                            if hasattr(function_module, "inlet"):
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                                inlet = function_module.inlet

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

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

                                if "__id__" in sig.parameters:
                                    param = {
                                        **param,
                                        "__id__": filter_id,
                                    }

                                if inspect.iscoroutinefunction(inlet):
                                    data = await inlet(**param)
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                                else:
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                                    data = inlet(**param)
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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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            )
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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:
        await get_all_models()
    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():
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    pipe_models = []
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    openai_models = []
    ollama_models = []

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    pipe_models = await get_pipe_models()

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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()
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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")
async def get_models(user=Depends(get_verified_user)):
    models = await get_all_models()
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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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        form_data["user"] = {
            "id": user.id,
            "email": user.email,
            "name": user.name,
            "role": user.role,
        }
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        async def job():
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            pipe_id = form_data["model"]
            if "." in pipe_id:
                pipe_id, sub_pipe_id = pipe_id.split(".", 1)
            print(pipe_id)

            pipe = webui_app.state.FUNCTIONS[pipe_id].pipe
            if form_data["stream"]:

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                async def stream_content():
                    if inspect.iscoroutinefunction(pipe):
                        res = await pipe(body=form_data)
                    else:
                        res = pipe(body=form_data)
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                    if isinstance(res, str):
                        message = stream_message_template(form_data["model"], res)
                        yield f"data: {json.dumps(message)}\n\n"

                    if isinstance(res, Iterator):
                        for line in res:
                            if isinstance(line, BaseModel):
                                line = line.model_dump_json()
                                line = f"data: {line}"
                            try:
                                line = line.decode("utf-8")
                            except:
                                pass

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

                    if isinstance(res, str) or isinstance(res, Generator):
                        finish_message = {
                            "id": f"{form_data['model']}-{str(uuid.uuid4())}",
                            "object": "chat.completion.chunk",
                            "created": int(time.time()),
                            "model": form_data["model"],
                            "choices": [
                                {
                                    "index": 0,
                                    "delta": {},
                                    "logprobs": None,
                                    "finish_reason": "stop",
                                }
                            ],
                        }

                        yield f"data: {json.dumps(finish_message)}\n\n"
                        yield f"data: [DONE]"
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                return StreamingResponse(
                    stream_content(), media_type="text/event-stream"
                )
            else:
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                if inspect.iscoroutinefunction(pipe):
                    res = await pipe(body=form_data)
                else:
                    res = pipe(body=form_data)
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                if isinstance(res, dict):
                    return res
                elif isinstance(res, BaseModel):
                    return res.model_dump()
                else:
                    message = ""
                    if isinstance(res, str):
                        message = res
                    if isinstance(res, Generator):
                        for stream in res:
                            message = f"{message}{stream}"

                    return {
                        "id": f"{form_data['model']}-{str(uuid.uuid4())}",
                        "object": "chat.completion",
                        "created": int(time.time()),
                        "model": form_data["model"],
                        "choices": [
                            {
                                "index": 0,
                                "message": {
                                    "role": "assistant",
                                    "content": message,
                                },
                                "logprobs": None,
                                "finish_reason": "stop",
                            }
                        ],
                    }

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        return await job()
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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={
                        "user": {"id": user.id, "name": user.name, "role": user.role},
                        "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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    # Check if the model has any filters
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    if "info" in model and "meta" in model["info"]:
        for filter_id in model["info"]["meta"].get("filterIds", []):
            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:
                    function_module, function_type = load_function_module_by_id(
                        filter_id
                    )
                    webui_app.state.FUNCTIONS[filter_id] = function_module
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                try:
                    if hasattr(function_module, "outlet"):
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                        outlet = function_module.outlet
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                        # Get the signature of the function
                        sig = inspect.signature(outlet)
                        param = {"body": data}

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

                        if "__id__" in sig.parameters:
                            param = {
                                **param,
                                "__id__": filter_id,
                            }

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

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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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    if model["owned_by"] == "ollama":
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        return await generate_ollama_chat_completion(payload, user=user)
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    else:
        return await generate_openai_chat_completion(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]},
        )

    if model["owned_by"] == "ollama":
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        return await generate_ollama_chat_completion(payload, user=user)
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    else:
        return await generate_openai_chat_completion(payload, user=user)


@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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    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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    if model["owned_by"] == "ollama":
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        return await generate_ollama_chat_completion(payload, user=user)
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    else:
        return await generate_openai_chat_completion(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(
    urlIdx: Optional[int], pipeline_id: str, user=Depends(get_admin_user)
):
    models = await get_all_models()
    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(
    urlIdx: Optional[int], pipeline_id: str, user=Depends(get_admin_user)
):
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    models = await get_all_models()

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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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):
    models = await get_all_models()

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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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    }


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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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    return {"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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@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.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."
    )