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0de6419a29
open-webui/backend/open_webui/main.py
Samuel 0de6419a29 fixhandle arguments in ollama
2024-11-02 13:50:14 +00:00

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import base64
import inspect
import json
import logging
import mimetypes
import os
import shutil
import sys
import time
import uuid
import asyncio
from contextlib import asynccontextmanager
from typing import Optional, Callable
import aiohttp
import requests
from open_webui.apps.ollama.main import (
app as ollama_app,
get_all_models as get_ollama_models,
generate_chat_completion as generate_ollama_chat_completion,
generate_openai_chat_completion as generate_ollama_openai_chat_completion,
GenerateChatCompletionForm,
)
from open_webui.apps.openai.main import (
app as openai_app,
generate_chat_completion as generate_openai_chat_completion,
get_all_models as get_openai_models,
)
from open_webui.apps.retrieval.main import app as retrieval_app
from open_webui.apps.retrieval.utils import get_rag_context, rag_template
from open_webui.apps.socket.main import (
app as socket_app,
periodic_usage_pool_cleanup,
get_event_call,
get_event_emitter,
)
from open_webui.apps.webui.main import (
app as webui_app,
generate_function_chat_completion,
get_pipe_models,
)
from open_webui.apps.webui.internal.db import Session
from open_webui.apps.webui.models.auths import Auths
from open_webui.apps.webui.models.functions import Functions
from open_webui.apps.webui.models.models import Models
from open_webui.apps.webui.models.users import UserModel, Users
from open_webui.apps.webui.utils import load_function_module_by_id
from open_webui.apps.audio.main import app as audio_app
from open_webui.apps.images.main import app as images_app
from authlib.integrations.starlette_client import OAuth
from authlib.oidc.core import UserInfo
from open_webui.config import (
CACHE_DIR,
CORS_ALLOW_ORIGIN,
DEFAULT_LOCALE,
ENABLE_ADMIN_CHAT_ACCESS,
ENABLE_ADMIN_EXPORT,
ENABLE_MODEL_FILTER,
ENABLE_OAUTH_SIGNUP,
ENABLE_OLLAMA_API,
ENABLE_OPENAI_API,
ENV,
FRONTEND_BUILD_DIR,
MODEL_FILTER_LIST,
OAUTH_MERGE_ACCOUNTS_BY_EMAIL,
OAUTH_PROVIDERS,
ENABLE_SEARCH_QUERY,
SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE,
STATIC_DIR,
TASK_MODEL,
TASK_MODEL_EXTERNAL,
TITLE_GENERATION_PROMPT_TEMPLATE,
TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE,
WEBHOOK_URL,
WEBUI_AUTH,
WEBUI_NAME,
AppConfig,
run_migrations,
reset_config,
)
from open_webui.constants import ERROR_MESSAGES, TASKS, WEBHOOK_MESSAGES
from open_webui.env import (
CHANGELOG,
GLOBAL_LOG_LEVEL,
SAFE_MODE,
SRC_LOG_LEVELS,
VERSION,
WEBUI_BUILD_HASH,
WEBUI_SECRET_KEY,
WEBUI_SESSION_COOKIE_SAME_SITE,
WEBUI_SESSION_COOKIE_SECURE,
WEBUI_URL,
RESET_CONFIG_ON_START,
OFFLINE_MODE,
)
from fastapi import (
Depends,
FastAPI,
File,
Form,
HTTPException,
Request,
UploadFile,
status,
)
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
from sqlalchemy import text
from starlette.exceptions import HTTPException as StarletteHTTPException
from starlette.middleware.base import BaseHTTPMiddleware
from starlette.middleware.sessions import SessionMiddleware
from starlette.responses import RedirectResponse, Response, StreamingResponse
from open_webui.utils.security_headers import SecurityHeadersMiddleware
from open_webui.utils.misc import (
add_or_update_system_message,
get_last_user_message,
parse_duration,
prepend_to_first_user_message_content,
)
from open_webui.utils.task import (
moa_response_generation_template,
search_query_generation_template,
title_generation_template,
tools_function_calling_generation_template,
)
from open_webui.utils.tools import get_tools
from open_webui.utils.utils import (
create_token,
decode_token,
get_admin_user,
get_current_user,
get_http_authorization_cred,
get_password_hash,
get_verified_user,
)
from open_webui.utils.webhook import post_webhook
from open_webui.utils.payload import convert_payload_openai_to_ollama
from open_webui.utils.response import (
convert_response_ollama_to_openai,
convert_streaming_response_ollama_to_openai,
)
if SAFE_MODE:
print("SAFE MODE ENABLED")
Functions.deactivate_all_functions()
logging.basicConfig(stream=sys.stdout, level=GLOBAL_LOG_LEVEL)
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["MAIN"])
class SPAStaticFiles(StaticFiles):
async def get_response(self, path: str, scope):
try:
return await super().get_response(path, scope)
except (HTTPException, StarletteHTTPException) as ex:
if ex.status_code == 404:
return await super().get_response("index.html", scope)
else:
raise ex
print(
rf"""
___ __ __ _ _ _ ___
/ _ \ _ __ ___ _ __ \ \ / /__| |__ | | | |_ _|
| | | | '_ \ / _ \ '_ \ \ \ /\ / / _ \ '_ \| | | || |
| |_| | |_) | __/ | | | \ V V / __/ |_) | |_| || |
\___/| .__/ \___|_| |_| \_/\_/ \___|_.__/ \___/|___|
|_|
v{VERSION} - building the best open-source AI user interface.
{f"Commit: {WEBUI_BUILD_HASH}" if WEBUI_BUILD_HASH != "dev-build" else ""}
https://github.com/open-webui/open-webui
"""
)
@asynccontextmanager
async def lifespan(app: FastAPI):
if RESET_CONFIG_ON_START:
reset_config()
asyncio.create_task(periodic_usage_pool_cleanup())
yield
app = FastAPI(
docs_url="/docs" if ENV == "dev" else None, redoc_url=None, lifespan=lifespan
)
app.state.config = AppConfig()
app.state.config.ENABLE_OPENAI_API = ENABLE_OPENAI_API
app.state.config.ENABLE_OLLAMA_API = ENABLE_OLLAMA_API
app.state.config.ENABLE_MODEL_FILTER = ENABLE_MODEL_FILTER
app.state.config.MODEL_FILTER_LIST = MODEL_FILTER_LIST
app.state.config.WEBHOOK_URL = WEBHOOK_URL
app.state.config.TASK_MODEL = TASK_MODEL
app.state.config.TASK_MODEL_EXTERNAL = TASK_MODEL_EXTERNAL
app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE = TITLE_GENERATION_PROMPT_TEMPLATE
app.state.config.SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE = (
SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE
)
app.state.config.ENABLE_SEARCH_QUERY = ENABLE_SEARCH_QUERY
app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE = (
TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
)
app.state.MODELS = {}
##################################
#
# ChatCompletion Middleware
#
##################################
def get_task_model_id(default_model_id):
# Set the task model
task_model_id = default_model_id
# Check if the user has a custom task model and use that model
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
return task_model_id
def get_filter_function_ids(model):
def get_priority(function_id):
function = Functions.get_function_by_id(function_id)
if function is not None and hasattr(function, "valves"):
# TODO: Fix FunctionModel
return (function.valves if function.valves else {}).get("priority", 0)
return 0
filter_ids = [function.id for function in Functions.get_global_filter_functions()]
if "info" in model and "meta" in model["info"]:
filter_ids.extend(model["info"]["meta"].get("filterIds", []))
filter_ids = list(set(filter_ids))
enabled_filter_ids = [
function.id
for function in Functions.get_functions_by_type("filter", active_only=True)
]
filter_ids = [
filter_id for filter_id in filter_ids if filter_id in enabled_filter_ids
]
filter_ids.sort(key=get_priority)
return filter_ids
async def chat_completion_filter_functions_handler(body, model, extra_params):
skip_files = None
filter_ids = get_filter_function_ids(model)
for filter_id in filter_ids:
filter = Functions.get_function_by_id(filter_id)
if not filter:
continue
if filter_id in webui_app.state.FUNCTIONS:
function_module = webui_app.state.FUNCTIONS[filter_id]
else:
function_module, _, _ = load_function_module_by_id(filter_id)
webui_app.state.FUNCTIONS[filter_id] = function_module
# Check if the function has a file_handler variable
if hasattr(function_module, "file_handler"):
skip_files = function_module.file_handler
if hasattr(function_module, "valves") and hasattr(function_module, "Valves"):
valves = Functions.get_function_valves_by_id(filter_id)
function_module.valves = function_module.Valves(
**(valves if valves else {})
)
if not hasattr(function_module, "inlet"):
continue
try:
inlet = function_module.inlet
# Get the signature of the function
sig = inspect.signature(inlet)
params = {"body": body} | {
k: v
for k, v in {
**extra_params,
"__model__": model,
"__id__": filter_id,
}.items()
if k in sig.parameters
}
if "__user__" in params and hasattr(function_module, "UserValves"):
try:
params["__user__"]["valves"] = function_module.UserValves(
**Functions.get_user_valves_by_id_and_user_id(
filter_id, params["__user__"]["id"]
)
)
except Exception as e:
print(e)
if inspect.iscoroutinefunction(inlet):
body = await inlet(**params)
else:
body = inlet(**params)
except Exception as e:
print(f"Error: {e}")
raise e
if skip_files and "files" in body.get("metadata", {}):
del body["metadata"]["files"]
return body, {}
def get_tools_function_calling_payload(messages, task_model_id, content):
user_message = get_last_user_message(messages)
history = "\n".join(
f"{message['role'].upper()}: \"\"\"{message['content']}\"\"\""
for message in messages[::-1][:4]
)
prompt = f"History:\n{history}\nQuery: {user_message}"
return {
"model": task_model_id,
"messages": [
{"role": "system", "content": content},
{"role": "user", "content": f"Query: {prompt}"},
],
"stream": False,
"metadata": {"task": str(TASKS.FUNCTION_CALLING)},
}
async def get_content_from_response(response) -> Optional[str]:
content = None
if hasattr(response, "body_iterator"):
async for chunk in response.body_iterator:
data = json.loads(chunk.decode("utf-8"))
content = data["choices"][0]["message"]["content"]
# Cleanup any remaining background tasks if necessary
if response.background is not None:
await response.background()
else:
content = response["choices"][0]["message"]["content"]
return content
def get_tools_body(
body: dict, user: UserModel, extra_params: dict
) -> tuple[dict, dict]:
metadata = body.get("metadata", {})
tool_ids = metadata.get("tool_ids", None)
log.debug(f"{tool_ids=}")
if not tool_ids:
return body, {}
task_model_id = get_task_model_id(body["model"])
tools = get_tools(
webui_app,
tool_ids,
user,
{
**extra_params,
"__model__": app.state.MODELS[task_model_id],
"__messages__": body["messages"],
"__files__": metadata.get("files", []),
},
)
log.info(f"{tools=}")
specs = [tool["spec"] for tool in tools.values()]
tools_args = [ { "type" : "function", "function" : spec } \
for spec in specs ]
body["tools"] = tools_args
return body, tools
def update_body_request(request: Request,
body: dict) -> None:
modified_body_bytes = json.dumps(body).encode("utf-8")
# Replace the request body with the modified one
request._body = modified_body_bytes
# Set custom header to ensure content-length matches new body length
request.headers.__dict__["_list"] = [
(b"content-length", str(len(modified_body_bytes)).encode("utf-8")),
*[(k, v) for k, v in request.headers.raw if k.lower() != b"content-length"],
]
return None
def extract_json(binary:bytes)->dict:
try:
s = binary.decode("utf-8")
return json.loads(s[ s.find("{") : s.rfind("}") + 1 ])
except Exception as e:
return None
def fill_with_delta(fcall_dict:dict, delta:dict) -> None:
if not 'delta' in delta['choices'][0]:
return
j = delta['choices'][0]['delta']['tool_calls'][0]
if 'id' in j:
fcall_dict["id"] += j["id"] or ''
if 'function' in j:
if 'name' in j['function']:
fcall_dict['function']['name'] += j['function']['name'] or ''
if 'arguments' in j['function']:
fcall_dict['function']['arguments'] += j['function']['arguments'] or ''
async def handle_streaming_response(request: Request, response: Response,
tools: dict,
data_items: list,
call_next: Callable,
user : UserModel) -> StreamingResponse:
content_type = response.headers["Content-Type"]
is_openai = "text/event-stream" in content_type
is_ollama = "application/x-ndjson" in content_type
def wrap_item(item):
return f"data: {item}\n\n" if is_openai else f"{item}\n"
async def stream_wrapper(original_generator, data_items):
for item in data_items:
yield wrap_item(json.dumps(item))
citations = []
body = json.loads(request._body)
generator = original_generator
try:
while True:
peek = await anext(generator)
peek_json = extract_json(peek)
if peek == b'data: [DONE]\n' and len(citations) > 0 :
yield wrap_item(json.dumps({ "citations" : citations}))
if peek_json is None or \
not 'choices' in peek_json or \
not 'tool_calls' in peek_json['choices'][0]['delta']:
yield peek
continue
# We reached a tool call we consume all the messages to assemble it
log.debug("async tool call detected")
tool_calls = [] # id, name, arguments
tool_calls.append({'id':'', 'type': 'function', 'function' : {'name':'', 'arguments':''}})
current_index = peek_json['choices'][0]['delta']['tool_calls'][0]['index']
fill_with_delta(tool_calls[current_index], peek_json)
async for data in generator:
delta = extract_json(data)
if delta is None or \
not 'choices' in delta or \
not 'tool_calls' in delta['choices'][0]['delta'] or \
delta['choices'][0].get('finish_reason', None) is not None:
continue
i = delta['choices'][0]['delta']['tool_calls'][0]['index']
if i != current_index:
tool_calls.append({'id':'', 'type': 'function', 'function' : {'name':'', 'arguments':''}})
current_index = i
fill_with_delta(tool_calls[i], delta)
log.debug(f"tools to call { [ t['function']['name'] for t in tool_calls] }")
body["messages"].append( {'role': 'assistant',
'content': None,
'refusal': None,
'tool_calls': tool_calls })
for tool_call in tool_calls:
tool_function_name = tool_call["function"]["name"]
tool_function_params = extract_json(tool_call["function"]["arguments"])
if not tool_call["function"]["arguments"]:
tool_function_params = {}
else:
tool_function_params = json.loads(tool_call["function"]["arguments"])
log.debug(f"calling {tool_function_name} with params {tool_function_params}")
try:
tool_output = await tools[tool_function_name]["callable"](**tool_function_params)
except Exception as e:
tool_output = str(e)
# With several tools potentially called together may be this doesn't make sense
if tools[tool_function_name]["file_handler"] and "files" in body.get("metadata", {}):
del body["metadata"]["files"]
if tools[tool_function_name]["citation"]:
citations.append( {
"source": {
"name": f"TOOL:{tools[tool_function_name]['toolkit_id']}/{tool_function_name}"
},
"document": [tool_output],
"metadata": [{"source": tool_function_name}],
})
# Append the tool output to the messages
body["messages"].append({
"role": "tool",
"tool_call_id" : tool_call["id"],
"name": tool_function_name,
"content": tool_output
})
# Make another request to the model with the updated context
log.debug("calling the model again with tool output included")
update_body_request(request, body)
response = await generate_chat_completions(form_data = body, user = user )
# body_iterator here does not have __anext_() so it has to be done this way
generator = response.body_iterator.__aiter__()
except StopAsyncIteration:
pass
except Exception as e:
log.exception(f"Error: {e}")
return StreamingResponse(
stream_wrapper(response.body_iterator, data_items),
headers=dict(response.headers),
)
async def handle_nonstreaming_response(request: Request, response: Response,
tools: dict, user: UserModel, data_items: list) -> JSONResponse:
# It only should be one response since we are in the non streaming scenario
content = ''
async for data in response.body_iterator:
content += data.decode()
citations = []
response_dict = json.loads(content)
body = json.loads(request._body)
is_ollama = False
if app.state.MODELS[body["model"]]["owned_by"] == "ollama":
is_ollama = True
is_openai = not is_ollama
while (is_ollama and "tool_calls" in response_dict.get("message", {})) or \
(is_openai and response_dict["choices"][0]["finish_reason"] == "tool_calls"):
if is_ollama:
message = response_dict.get("message", {})
tool_calls = message.get("tool_calls", [])
if message:
body["messages"].append(message)
else:
body["messages"].append(response_dict["choices"][0]["message"])
tool_calls = response_dict["choices"][0]["message"].get("tool_calls", [])
for tool_call in tool_calls:
tool_function_name = tool_call["function"]["name"]
if not tool_call["function"]["arguments"]:
tool_function_params = {}
else:
if is_openai:
tool_function_params = json.loads(tool_call["function"]["arguments"])
if is_ollama:
tool_function_params = tool_call["function"]["arguments"]
try:
tool_output = await tools[tool_function_name]["callable"](**tool_function_params)
except Exception as e:
tool_output = str(e)
if tools[tool_function_name]["citation"]:
citations.append( {
"source": {
"name": f"TOOL:{tools[tool_function_name]['toolkit_id']}/{tool_function_name}"
},
"document": [tool_output],
"metadata": [{"source": tool_function_name}],
})
# Append the tool output to the messages
body["messages"].append({
"role": "tool",
"tool_call_id" : tool_call.get("id",""),
"name": tool_function_name,
"content": tool_output
})
# Make another request to the model with the updated context
update_body_request(request, body)
response_dict = await generate_chat_completions(form_data = body, user = user, as_openai = is_openai)
#FIXME: is it possible to handle citations?
return JSONResponse(content = response_dict)
async def chat_completion_tools_handler(
body: dict, user: UserModel, extra_params: dict
) -> tuple[dict, dict]:
# If tool_ids field is present, call the functions
metadata = body.get("metadata", {})
tool_ids = metadata.get("tool_ids", None)
log.debug(f"{tool_ids=}")
if not tool_ids:
return body, {}
skip_files = False
contexts = []
citations = []
task_model_id = get_task_model_id(body["model"])
tools = get_tools(
webui_app,
tool_ids,
user,
{
**extra_params,
"__model__": app.state.MODELS[task_model_id],
"__messages__": body["messages"],
"__files__": metadata.get("files", []),
},
)
log.info(f"{tools=}")
specs = [tool["spec"] for tool in tools.values()]
tools_specs = json.dumps(specs)
if app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE != "":
template = app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
else:
template = """Available Tools: {{TOOLS}}\nReturn an empty string if no tools match the query. If a function tool matches, construct and return a JSON object in the format {\"name\": \"functionName\", \"parameters\": {\"requiredFunctionParamKey\": \"requiredFunctionParamValue\"}} using the appropriate tool and its parameters. Only return the object and limit the response to the JSON object without additional text."""
tools_function_calling_prompt = tools_function_calling_generation_template(
template, tools_specs
)
log.info(f"{tools_function_calling_prompt=}")
payload = get_tools_function_calling_payload(
body["messages"], task_model_id, tools_function_calling_prompt
)
try:
payload = filter_pipeline(payload, user)
except Exception as e:
raise e
try:
response = await generate_chat_completions(form_data=payload, user=user)
log.debug(f"{response=}")
content = await get_content_from_response(response)
log.debug(f"{content=}")
if not content:
return body, {}
try:
content = content[content.find("{") : content.rfind("}") + 1]
if not content:
raise Exception("No JSON object found in the response")
result = json.loads(content)
tool_function_name = result.get("name", None)
if tool_function_name not in tools:
return body, {}
tool_function_params = result.get("parameters", {})
try:
tool_output = await tools[tool_function_name]["callable"](
**tool_function_params
)
except Exception as e:
tool_output = str(e)
if tools[tool_function_name]["citation"]:
citations.append(
{
"source": {
"name": f"TOOL:{tools[tool_function_name]['toolkit_id']}/{tool_function_name}"
},
"document": [tool_output],
"metadata": [{"source": tool_function_name}],
}
)
if tools[tool_function_name]["file_handler"]:
skip_files = True
if isinstance(tool_output, str):
contexts.append(tool_output)
except Exception as e:
log.exception(f"Error: {e}")
content = None
except Exception as e:
log.exception(f"Error: {e}")
content = None
log.debug(f"tool_contexts: {contexts}")
if skip_files and "files" in body.get("metadata", {}):
del body["metadata"]["files"]
return body, {"contexts": contexts, "citations": citations}
async def chat_completion_files_handler(body) -> tuple[dict, dict[str, list]]:
contexts = []
citations = []
if files := body.get("metadata", {}).get("files", None):
contexts, citations = get_rag_context(
files=files,
messages=body["messages"],
embedding_function=retrieval_app.state.EMBEDDING_FUNCTION,
k=retrieval_app.state.config.TOP_K,
reranking_function=retrieval_app.state.sentence_transformer_rf,
r=retrieval_app.state.config.RELEVANCE_THRESHOLD,
hybrid_search=retrieval_app.state.config.ENABLE_RAG_HYBRID_SEARCH,
)
log.debug(f"rag_contexts: {contexts}, citations: {citations}")
return body, {"contexts": contexts, "citations": citations}
def is_chat_completion_request(request):
return request.method == "POST" and any(
endpoint in request.url.path
for endpoint in ["/ollama/api/chat", "/chat/completions"]
)
async def get_body_and_model_and_user(request):
# Read the original request body
body = await request.body()
body_str = body.decode("utf-8")
body = json.loads(body_str) if body_str else {}
model_id = body["model"]
if model_id not in app.state.MODELS:
raise Exception("Model not found")
model = app.state.MODELS[model_id]
user = get_current_user(
request,
get_http_authorization_cred(request.headers.get("Authorization")),
)
return body, model, user
class ChatCompletionMiddleware(BaseHTTPMiddleware):
async def dispatch(self, request: Request, call_next):
if not is_chat_completion_request(request):
return await call_next(request)
log.debug(f"request.url.path: {request.url.path}")
try:
body, model, user = await get_body_and_model_and_user(request)
except Exception as e:
return JSONResponse(
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": str(e)},
)
metadata = {
"chat_id": body.pop("chat_id", None),
"message_id": body.pop("id", None),
"session_id": body.pop("session_id", None),
"tool_ids": body.get("tool_ids", None),
"files": body.get("files", None),
}
body["metadata"] = metadata
extra_params = {
"__event_emitter__": get_event_emitter(metadata),
"__event_call__": get_event_call(metadata),
"__user__": {
"id": user.id,
"email": user.email,
"name": user.name,
"role": user.role,
},
}
# Initialize data_items to store additional data to be sent to the client
# Initalize contexts and citation
data_items = []
contexts = []
citations = []
try:
body, flags = await chat_completion_filter_functions_handler(
body, model, extra_params
)
except Exception as e:
return JSONResponse(
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": str(e)},
)
metadata = {
**metadata,
"tool_ids": body.pop("tool_ids", None),
"files": body.pop("files", None),
}
body["metadata"] = metadata
body, tools = get_tools_body(body, user, extra_params)
try:
body, flags = await chat_completion_files_handler(body)
contexts.extend(flags.get("contexts", []))
citations.extend(flags.get("citations", []))
except Exception as e:
log.exception(e)
# If context is not empty, insert it into the messages
if len(contexts) > 0:
context_string = "/n".join(contexts).strip()
prompt = get_last_user_message(body["messages"])
if prompt is None:
raise Exception("No user message found")
if (
retrieval_app.state.config.RELEVANCE_THRESHOLD == 0
and context_string.strip() == ""
):
log.debug(
f"With a 0 relevancy threshold for RAG, the context cannot be empty"
)
# Workaround for Ollama 2.0+ system prompt issue
# TODO: replace with add_or_update_system_message
if model["owned_by"] == "ollama":
body["messages"] = prepend_to_first_user_message_content(
rag_template(
retrieval_app.state.config.RAG_TEMPLATE, context_string, prompt
),
body["messages"],
)
else:
body["messages"] = add_or_update_system_message(
rag_template(
retrieval_app.state.config.RAG_TEMPLATE, context_string, prompt
),
body["messages"],
)
# If there are citations, add them to the data_items
if len(citations) > 0:
data_items.append({"citations": citations})
update_body_request(request, body)
first_response = await call_next(request)
if body.get("stream", False) is False:
return await handle_nonstreaming_response(request, first_response, tools, user, data_items)
return await handle_streaming_response(request, first_response, tools, data_items, call_next, user)
async def _receive(self, body: bytes):
return {"type": "http.request", "body": body, "more_body": False}
app.add_middleware(ChatCompletionMiddleware)
##################################
#
# Pipeline Middleware
#
##################################
def get_sorted_filters(model_id):
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"])
return sorted_filters
def filter_pipeline(payload, user):
user = {"id": user.id, "email": user.email, "name": user.name, "role": user.role}
model_id = payload["model"]
sorted_filters = get_sorted_filters(model_id)
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 == "":
continue
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:
res = r.json()
if "detail" in res:
raise Exception(r.status_code, res["detail"])
return payload
class PipelineMiddleware(BaseHTTPMiddleware):
async def dispatch(self, request: Request, call_next):
if not is_chat_completion_request(request):
return await call_next(request)
log.debug(f"request.url.path: {request.url.path}")
# Read the original request body
body = await request.body()
# Decode body to string
body_str = body.decode("utf-8")
# Parse string to JSON
data = json.loads(body_str) if body_str else {}
try:
user = get_current_user(
request,
get_http_authorization_cred(request.headers["Authorization"]),
)
except KeyError as e:
if len(e.args) > 1:
return JSONResponse(
status_code=e.args[0],
content={"detail": e.args[1]},
)
else:
return JSONResponse(
status_code=status.HTTP_401_UNAUTHORIZED,
content={"detail": "Not authenticated"},
)
try:
data = filter_pipeline(data, user)
except Exception as e:
if len(e.args) > 1:
return JSONResponse(
status_code=e.args[0],
content={"detail": e.args[1]},
)
else:
return JSONResponse(
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": str(e)},
)
modified_body_bytes = json.dumps(data).encode("utf-8")
# Replace the request body with the modified one
request._body = modified_body_bytes
# Set custom header to ensure content-length matches new body length
request.headers.__dict__["_list"] = [
(b"content-length", str(len(modified_body_bytes)).encode("utf-8")),
*[(k, v) for k, v in request.headers.raw if k.lower() != b"content-length"],
]
response = await call_next(request)
return response
async def _receive(self, body: bytes):
return {"type": "http.request", "body": body, "more_body": False}
app.add_middleware(PipelineMiddleware)
from urllib.parse import urlencode, parse_qs, urlparse
class RedirectMiddleware(BaseHTTPMiddleware):
async def dispatch(self, request: Request, call_next):
# Check if the request is a GET request
if request.method == "GET":
path = request.url.path
query_params = dict(parse_qs(urlparse(str(request.url)).query))
# Check for the specific watch path and the presence of 'v' parameter
if path.endswith("/watch") and "v" in query_params:
video_id = query_params["v"][0] # Extract the first 'v' parameter
encoded_video_id = urlencode({"youtube": video_id})
redirect_url = f"/?{encoded_video_id}"
return RedirectResponse(url=redirect_url)
# Proceed with the normal flow of other requests
response = await call_next(request)
return response
# Add the middleware to the app
app.add_middleware(RedirectMiddleware)
app.add_middleware(
CORSMiddleware,
allow_origins=CORS_ALLOW_ORIGIN,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.add_middleware(SecurityHeadersMiddleware)
@app.middleware("http")
async def commit_session_after_request(request: Request, call_next):
response = await call_next(request)
log.debug("Commit session after request")
Session.commit()
return response
@app.middleware("http")
async def check_url(request: Request, call_next):
if len(app.state.MODELS) == 0:
await get_all_models()
else:
pass
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
@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 = retrieval_app.state.EMBEDDING_FUNCTION
return response
@app.middleware("http")
async def inspect_websocket(request: Request, call_next):
if (
"/ws/socket.io" in request.url.path
and request.query_params.get("transport") == "websocket"
):
upgrade = (request.headers.get("Upgrade") or "").lower()
connection = (request.headers.get("Connection") or "").lower().split(",")
# Check that there's the correct headers for an upgrade, else reject the connection
# This is to work around this upstream issue: https://github.com/miguelgrinberg/python-engineio/issues/367
if upgrade != "websocket" or "upgrade" not in connection:
return JSONResponse(
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": "Invalid WebSocket upgrade request"},
)
return await call_next(request)
app.mount("/ws", socket_app)
app.mount("/ollama", ollama_app)
app.mount("/openai", openai_app)
app.mount("/images/api/v1", images_app)
app.mount("/audio/api/v1", audio_app)
app.mount("/retrieval/api/v1", retrieval_app)
app.mount("/api/v1", webui_app)
webui_app.state.EMBEDDING_FUNCTION = retrieval_app.state.EMBEDDING_FUNCTION
async def get_all_models():
# TODO: Optimize this function
pipe_models = []
openai_models = []
ollama_models = []
pipe_models = await get_pipe_models()
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"]
]
models = pipe_models + openai_models + ollama_models
global_action_ids = [
function.id for function in Functions.get_global_action_functions()
]
enabled_action_ids = [
function.id
for function in Functions.get_functions_by_type("action", active_only=True)
]
custom_models = Models.get_all_models()
for custom_model in custom_models:
if custom_model.base_model_id is None:
for model in models:
if (
custom_model.id == model["id"]
or custom_model.id == model["id"].split(":")[0]
):
model["name"] = custom_model.name
model["info"] = custom_model.model_dump()
action_ids = []
if "info" in model and "meta" in model["info"]:
action_ids.extend(model["info"]["meta"].get("actionIds", []))
model["action_ids"] = action_ids
else:
owned_by = "openai"
pipe = None
action_ids = []
for model in models:
if (
custom_model.base_model_id == model["id"]
or custom_model.base_model_id == model["id"].split(":")[0]
):
owned_by = model["owned_by"]
if "pipe" in model:
pipe = model["pipe"]
if "info" in model and "meta" in model["info"]:
action_ids.extend(model["info"]["meta"].get("actionIds", []))
break
models.append(
{
"id": custom_model.id,
"name": custom_model.name,
"object": "model",
"created": custom_model.created_at,
"owned_by": owned_by,
"info": custom_model.model_dump(),
"preset": True,
**({"pipe": pipe} if pipe is not None else {}),
"action_ids": action_ids,
}
)
for model in models:
action_ids = []
if "action_ids" in model:
action_ids = model["action_ids"]
del model["action_ids"]
action_ids = action_ids + global_action_ids
action_ids = list(set(action_ids))
action_ids = [
action_id for action_id in action_ids if action_id in enabled_action_ids
]
model["actions"] = []
for action_id in action_ids:
action = Functions.get_function_by_id(action_id)
if action is None:
raise Exception(f"Action not found: {action_id}")
if action_id in webui_app.state.FUNCTIONS:
function_module = webui_app.state.FUNCTIONS[action_id]
else:
function_module, _, _ = load_function_module_by_id(action_id)
webui_app.state.FUNCTIONS[action_id] = function_module
__webui__ = False
if hasattr(function_module, "__webui__"):
__webui__ = function_module.__webui__
if hasattr(function_module, "actions"):
actions = function_module.actions
model["actions"].extend(
[
{
"id": f"{action_id}.{_action['id']}",
"name": _action.get(
"name", f"{action.name} ({_action['id']})"
),
"description": action.meta.description,
"icon_url": _action.get(
"icon_url", action.meta.manifest.get("icon_url", None)
),
**({"__webui__": __webui__} if __webui__ else {}),
}
for _action in actions
]
)
else:
model["actions"].append(
{
"id": action_id,
"name": action.name,
"description": action.meta.description,
"icon_url": action.meta.manifest.get("icon_url", None),
**({"__webui__": __webui__} if __webui__ else {}),
}
)
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()
# Filter out filter pipelines
models = [
model
for model in models
if "pipeline" not in model or model["pipeline"].get("type", None) != "filter"
]
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}
@app.post("/api/chat/completions")
async def generate_chat_completions(form_data: dict, user=Depends(get_verified_user), as_openai:bool = True):
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",
)
if app.state.config.ENABLE_MODEL_FILTER:
if user.role == "user" and model_id not in app.state.config.MODEL_FILTER_LIST:
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Model not found",
)
model = app.state.MODELS[model_id]
if model.get("pipe"):
return await generate_function_chat_completion(form_data, user=user)
if model["owned_by"] == "ollama":
# Using /ollama/api/chat endpoint
form_data = convert_payload_openai_to_ollama(form_data)
log.debug(f"{form_data=}")
form_data = GenerateChatCompletionForm(**form_data)
response = await generate_ollama_chat_completion(form_data=form_data, user=user)
if form_data.stream:
response.headers["content-type"] = "text/event-stream"
return StreamingResponse(
convert_streaming_response_ollama_to_openai(response) if as_openai else response,
headers=dict(response.headers),
)
else:
return convert_response_ollama_to_openai(response) if as_openai else response
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"]
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]
sorted_filters = get_sorted_filters(model_id)
if "pipeline" in model:
sorted_filters = [model] + sorted_filters
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,
"email": user.email,
"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 Exception:
pass
else:
pass
__event_emitter__ = get_event_emitter(
{
"chat_id": data["chat_id"],
"message_id": data["id"],
"session_id": data["session_id"],
}
)
__event_call__ = get_event_call(
{
"chat_id": data["chat_id"],
"message_id": data["id"],
"session_id": data["session_id"],
}
)
def get_priority(function_id):
function = Functions.get_function_by_id(function_id)
if function is not None and hasattr(function, "valves"):
# TODO: Fix FunctionModel to include vavles
return (function.valves if function.valves else {}).get("priority", 0)
return 0
filter_ids = [function.id for function in Functions.get_global_filter_functions()]
if "info" in model and "meta" in model["info"]:
filter_ids.extend(model["info"]["meta"].get("filterIds", []))
filter_ids = list(set(filter_ids))
enabled_filter_ids = [
function.id
for function in Functions.get_functions_by_type("filter", active_only=True)
]
filter_ids = [
filter_id for filter_id in filter_ids if filter_id in enabled_filter_ids
]
# Sort filter_ids by priority, using the get_priority function
filter_ids.sort(key=get_priority)
for filter_id in filter_ids:
filter = Functions.get_function_by_id(filter_id)
if not filter:
continue
if filter_id in webui_app.state.FUNCTIONS:
function_module = webui_app.state.FUNCTIONS[filter_id]
else:
function_module, _, _ = load_function_module_by_id(filter_id)
webui_app.state.FUNCTIONS[filter_id] = function_module
if hasattr(function_module, "valves") and hasattr(function_module, "Valves"):
valves = Functions.get_function_valves_by_id(filter_id)
function_module.valves = function_module.Valves(
**(valves if valves else {})
)
if not hasattr(function_module, "outlet"):
continue
try:
outlet = function_module.outlet
# Get the signature of the function
sig = inspect.signature(outlet)
params = {"body": data}
# Extra parameters to be passed to the function
extra_params = {
"__model__": model,
"__id__": filter_id,
"__event_emitter__": __event_emitter__,
"__event_call__": __event_call__,
}
# Add extra params in contained in function signature
for key, value in extra_params.items():
if key in sig.parameters:
params[key] = value
if "__user__" in sig.parameters:
__user__ = {
"id": user.id,
"email": user.email,
"name": user.name,
"role": user.role,
}
try:
if hasattr(function_module, "UserValves"):
__user__["valves"] = function_module.UserValves(
**Functions.get_user_valves_by_id_and_user_id(
filter_id, user.id
)
)
except Exception as e:
print(e)
params = {**params, "__user__": __user__}
if inspect.iscoroutinefunction(outlet):
data = await outlet(**params)
else:
data = outlet(**params)
except Exception as e:
print(f"Error: {e}")
return JSONResponse(
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": str(e)},
)
return data
@app.post("/api/chat/actions/{action_id}")
async def chat_action(action_id: str, form_data: dict, user=Depends(get_verified_user)):
if "." in action_id:
action_id, sub_action_id = action_id.split(".")
else:
sub_action_id = None
action = Functions.get_function_by_id(action_id)
if not action:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="Action not found",
)
data = form_data
model_id = 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]
__event_emitter__ = get_event_emitter(
{
"chat_id": data["chat_id"],
"message_id": data["id"],
"session_id": data["session_id"],
}
)
__event_call__ = get_event_call(
{
"chat_id": data["chat_id"],
"message_id": data["id"],
"session_id": data["session_id"],
}
)
if action_id in webui_app.state.FUNCTIONS:
function_module = webui_app.state.FUNCTIONS[action_id]
else:
function_module, _, _ = load_function_module_by_id(action_id)
webui_app.state.FUNCTIONS[action_id] = function_module
if hasattr(function_module, "valves") and hasattr(function_module, "Valves"):
valves = Functions.get_function_valves_by_id(action_id)
function_module.valves = function_module.Valves(**(valves if valves else {}))
if hasattr(function_module, "action"):
try:
action = function_module.action
# Get the signature of the function
sig = inspect.signature(action)
params = {"body": data}
# Extra parameters to be passed to the function
extra_params = {
"__model__": model,
"__id__": sub_action_id if sub_action_id is not None else action_id,
"__event_emitter__": __event_emitter__,
"__event_call__": __event_call__,
}
# Add extra params in contained in function signature
for key, value in extra_params.items():
if key in sig.parameters:
params[key] = value
if "__user__" in sig.parameters:
__user__ = {
"id": user.id,
"email": user.email,
"name": user.name,
"role": user.role,
}
try:
if hasattr(function_module, "UserValves"):
__user__["valves"] = function_module.UserValves(
**Functions.get_user_valves_by_id_and_user_id(
action_id, user.id
)
)
except Exception as e:
print(e)
params = {**params, "__user__": __user__}
if inspect.iscoroutinefunction(action):
data = await action(**params)
else:
data = action(**params)
except Exception as e:
print(f"Error: {e}")
return JSONResponse(
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": str(e)},
)
return data
##################################
#
# Task Endpoints
#
##################################
# TODO: Refactor task API endpoints below into a separate file
@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,
"ENABLE_SEARCH_QUERY": app.state.config.ENABLE_SEARCH_QUERY,
"SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE": app.state.config.SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE,
"TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE": app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE,
}
class TaskConfigForm(BaseModel):
TASK_MODEL: Optional[str]
TASK_MODEL_EXTERNAL: Optional[str]
TITLE_GENERATION_PROMPT_TEMPLATE: str
SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE: str
ENABLE_SEARCH_QUERY: bool
TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE: str
@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
)
app.state.config.ENABLE_SEARCH_QUERY = form_data.ENABLE_SEARCH_QUERY
app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE = (
form_data.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
)
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,
"ENABLE_SEARCH_QUERY": app.state.config.ENABLE_SEARCH_QUERY,
"TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE": app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE,
}
@app.post("/api/task/title/completions")
async def generate_title(form_data: dict, user=Depends(get_verified_user)):
print("generate_title")
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
task_model_id = get_task_model_id(model_id)
print(task_model_id)
model = app.state.MODELS[task_model_id]
if app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE != "":
template = app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE
else:
template = """Create a concise, 3-5 word title with an emoji as a title for the prompt in the given language. Suitable Emojis for the summary can be used to enhance understanding but avoid quotation marks or special formatting. RESPOND ONLY WITH THE TITLE TEXT.
Examples of titles:
📉 Stock Market Trends
🍪 Perfect Chocolate Chip Recipe
Evolution of Music Streaming
Remote Work Productivity Tips
Artificial Intelligence in Healthcare
🎮 Video Game Development Insights
Prompt: {{prompt:middletruncate:8000}}"""
content = title_generation_template(
template,
form_data["prompt"],
{
"name": user.name,
"location": user.info.get("location") if user.info else None,
},
)
payload = {
"model": task_model_id,
"messages": [{"role": "user", "content": content}],
"stream": False,
**(
{"max_tokens": 50}
if app.state.MODELS[task_model_id]["owned_by"] == "ollama"
else {
"max_completion_tokens": 50,
}
),
"chat_id": form_data.get("chat_id", None),
"metadata": {"task": str(TASKS.TITLE_GENERATION), "task_body": form_data},
}
log.debug(payload)
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user)
except Exception as e:
if len(e.args) > 1:
return JSONResponse(
status_code=e.args[0],
content={"detail": e.args[1]},
)
else:
return JSONResponse(
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": str(e)},
)
if "chat_id" in payload:
del payload["chat_id"]
return await generate_chat_completions(form_data=payload, user=user)
@app.post("/api/task/query/completions")
async def generate_search_query(form_data: dict, user=Depends(get_verified_user)):
print("generate_search_query")
if not app.state.config.ENABLE_SEARCH_QUERY:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"Search query generation is disabled",
)
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
task_model_id = get_task_model_id(model_id)
print(task_model_id)
model = app.state.MODELS[task_model_id]
if app.state.config.SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE != "":
template = app.state.config.SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE
else:
template = """Given the user's message and interaction history, decide if a web search is necessary. You must be concise and exclusively provide a search query if one is necessary. Refrain from verbose responses or any additional commentary. Prefer suggesting a search if uncertain to provide comprehensive or updated information. If a search isn't needed at all, respond with an empty string. Default to a search query when in doubt. Today's date is {{CURRENT_DATE}}.
User Message:
{{prompt:end:4000}}
Interaction History:
{{MESSAGES:END:6}}
Search Query:"""
content = search_query_generation_template(
template, form_data["messages"], {"name": user.name}
)
print("content", content)
payload = {
"model": task_model_id,
"messages": [{"role": "user", "content": content}],
"stream": False,
**(
{"max_tokens": 30}
if app.state.MODELS[task_model_id]["owned_by"] == "ollama"
else {
"max_completion_tokens": 30,
}
),
"metadata": {"task": str(TASKS.QUERY_GENERATION), "task_body": form_data},
}
log.debug(payload)
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user)
except Exception as e:
if len(e.args) > 1:
return JSONResponse(
status_code=e.args[0],
content={"detail": e.args[1]},
)
else:
return JSONResponse(
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": str(e)},
)
if "chat_id" in payload:
del payload["chat_id"]
return await generate_chat_completions(form_data=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
task_model_id = get_task_model_id(model_id)
print(task_model_id)
model = app.state.MODELS[task_model_id]
template = '''
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., 😊, 😢, 😡, 😱).
Message: """{{prompt}}"""
'''
content = title_generation_template(
template,
form_data["prompt"],
{
"name": user.name,
"location": user.info.get("location") if user.info else None,
},
)
payload = {
"model": task_model_id,
"messages": [{"role": "user", "content": content}],
"stream": False,
**(
{"max_tokens": 4}
if app.state.MODELS[task_model_id]["owned_by"] == "ollama"
else {
"max_completion_tokens": 4,
}
),
"chat_id": form_data.get("chat_id", None),
"metadata": {"task": str(TASKS.EMOJI_GENERATION), "task_body": form_data},
}
log.debug(payload)
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user)
except Exception as e:
if len(e.args) > 1:
return JSONResponse(
status_code=e.args[0],
content={"detail": e.args[1]},
)
else:
return JSONResponse(
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": str(e)},
)
if "chat_id" in payload:
del payload["chat_id"]
return await generate_chat_completions(form_data=payload, user=user)
@app.post("/api/task/moa/completions")
async def generate_moa_response(form_data: dict, user=Depends(get_verified_user)):
print("generate_moa_response")
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
task_model_id = get_task_model_id(model_id)
print(task_model_id)
model = app.state.MODELS[task_model_id]
template = """You have been provided with a set of responses from various models to the latest user query: "{{prompt}}"
Your task is to synthesize these responses into a single, high-quality response. It is crucial to critically evaluate the information provided in these responses, recognizing that some of it may be biased or incorrect. Your response should not simply replicate the given answers but should offer a refined, accurate, and comprehensive reply to the instruction. Ensure your response is well-structured, coherent, and adheres to the highest standards of accuracy and reliability.
Responses from models: {{responses}}"""
content = moa_response_generation_template(
template,
form_data["prompt"],
form_data["responses"],
)
payload = {
"model": task_model_id,
"messages": [{"role": "user", "content": content}],
"stream": form_data.get("stream", False),
"chat_id": form_data.get("chat_id", None),
"metadata": {
"task": str(TASKS.MOA_RESPONSE_GENERATION),
"task_body": form_data,
},
}
log.debug(payload)
try:
payload = filter_pipeline(payload, user)
except Exception as e:
if len(e.args) > 1:
return JSONResponse(
status_code=e.args[0],
content={"detail": e.args[1]},
)
else:
return JSONResponse(
status_code=status.HTTP_400_BAD_REQUEST,
content={"detail": str(e)},
)
if "chat_id" in payload:
del payload["chat_id"]
return await generate_chat_completions(form_data=payload, user=user)
##################################
#
# Pipelines Endpoints
#
##################################
# TODO: Refactor pipelines API endpoints below into a separate file
@app.get("/api/pipelines/list")
async def get_pipelines_list(user=Depends(get_admin_user)):
responses = await get_openai_models(raw=True)
print(responses)
urlIdxs = [
idx
for idx, response in enumerate(responses)
if response is not None and "pipelines" in response
]
return {
"data": [
{
"url": openai_app.state.config.OPENAI_API_BASE_URLS[urlIdx],
"idx": urlIdx,
}
for urlIdx in urlIdxs
]
}
@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 and 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)
r = None
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"
status_code = status.HTTP_404_NOT_FOUND
if r is not None:
status_code = r.status_code
try:
res = r.json()
if "detail" in res:
detail = res["detail"]
except Exception:
pass
raise HTTPException(
status_code=status_code,
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)
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 Exception:
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 Exception:
pass
raise HTTPException(
status_code=(r.status_code if r is not None else status.HTTP_404_NOT_FOUND),
detail=detail,
)
@app.get("/api/pipelines")
async def get_pipelines(urlIdx: Optional[int] = None, user=Depends(get_admin_user)):
r = None
try:
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.get(f"{url}/pipelines", headers=headers)
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 Exception:
pass
raise HTTPException(
status_code=(r.status_code if r is not None else status.HTTP_404_NOT_FOUND),
detail=detail,
)
@app.get("/api/pipelines/{pipeline_id}/valves")
async def get_pipeline_valves(
urlIdx: Optional[int],
pipeline_id: str,
user=Depends(get_admin_user),
):
r = None
try:
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.get(f"{url}/{pipeline_id}/valves", headers=headers)
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 Exception:
pass
raise HTTPException(
status_code=(r.status_code if r is not None else status.HTTP_404_NOT_FOUND),
detail=detail,
)
@app.get("/api/pipelines/{pipeline_id}/valves/spec")
async def get_pipeline_valves_spec(
urlIdx: Optional[int],
pipeline_id: str,
user=Depends(get_admin_user),
):
r = None
try:
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.get(f"{url}/{pipeline_id}/valves/spec", headers=headers)
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 Exception:
pass
raise HTTPException(
status_code=(r.status_code if r is not None else status.HTTP_404_NOT_FOUND),
detail=detail,
)
@app.post("/api/pipelines/{pipeline_id}/valves/update")
async def update_pipeline_valves(
urlIdx: Optional[int],
pipeline_id: str,
form_data: dict,
user=Depends(get_admin_user),
):
r = None
try:
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}/{pipeline_id}/valves/update",
headers=headers,
json={**form_data},
)
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 Exception:
pass
raise HTTPException(
status_code=(r.status_code if r is not None else status.HTTP_404_NOT_FOUND),
detail=detail,
)
##################################
#
# Config Endpoints
#
##################################
@app.get("/api/config")
async def get_app_config(request: Request):
user = None
if "token" in request.cookies:
token = request.cookies.get("token")
data = decode_token(token)
if data is not None and "id" in data:
user = Users.get_user_by_id(data["id"])
return {
"status": True,
"name": WEBUI_NAME,
"version": VERSION,
"default_locale": str(DEFAULT_LOCALE),
"oauth": {
"providers": {
name: config.get("name", name)
for name, config in OAUTH_PROVIDERS.items()
}
},
"features": {
"auth": WEBUI_AUTH,
"auth_trusted_header": bool(webui_app.state.AUTH_TRUSTED_EMAIL_HEADER),
"enable_signup": webui_app.state.config.ENABLE_SIGNUP,
"enable_login_form": webui_app.state.config.ENABLE_LOGIN_FORM,
**(
{
"enable_web_search": retrieval_app.state.config.ENABLE_RAG_WEB_SEARCH,
"enable_image_generation": images_app.state.config.ENABLED,
"enable_community_sharing": webui_app.state.config.ENABLE_COMMUNITY_SHARING,
"enable_message_rating": webui_app.state.config.ENABLE_MESSAGE_RATING,
"enable_admin_export": ENABLE_ADMIN_EXPORT,
"enable_admin_chat_access": ENABLE_ADMIN_CHAT_ACCESS,
}
if user is not None
else {}
),
},
**(
{
"default_models": webui_app.state.config.DEFAULT_MODELS,
"default_prompt_suggestions": webui_app.state.config.DEFAULT_PROMPT_SUGGESTIONS,
"audio": {
"tts": {
"engine": audio_app.state.config.TTS_ENGINE,
"voice": audio_app.state.config.TTS_VOICE,
"split_on": audio_app.state.config.TTS_SPLIT_ON,
},
"stt": {
"engine": audio_app.state.config.STT_ENGINE,
},
},
"file": {
"max_size": retrieval_app.state.config.FILE_MAX_SIZE,
"max_count": retrieval_app.state.config.FILE_MAX_COUNT,
},
"permissions": {**webui_app.state.config.USER_PERMISSIONS},
}
if user is not None
else {}
),
}
@app.get("/api/config/model/filter")
async def get_model_filter_config(user=Depends(get_admin_user)):
return {
"enabled": app.state.config.ENABLE_MODEL_FILTER,
"models": app.state.config.MODEL_FILTER_LIST,
}
class ModelFilterConfigForm(BaseModel):
enabled: bool
models: list[str]
@app.post("/api/config/model/filter")
async def update_model_filter_config(
form_data: ModelFilterConfigForm, user=Depends(get_admin_user)
):
app.state.config.ENABLE_MODEL_FILTER = form_data.enabled
app.state.config.MODEL_FILTER_LIST = form_data.models
return {
"enabled": app.state.config.ENABLE_MODEL_FILTER,
"models": app.state.config.MODEL_FILTER_LIST,
}
# TODO: webhook endpoint should be under config endpoints
@app.get("/api/webhook")
async def get_webhook_url(user=Depends(get_admin_user)):
return {
"url": app.state.config.WEBHOOK_URL,
}
class UrlForm(BaseModel):
url: str
@app.post("/api/webhook")
async def update_webhook_url(form_data: UrlForm, user=Depends(get_admin_user)):
app.state.config.WEBHOOK_URL = form_data.url
webui_app.state.WEBHOOK_URL = app.state.config.WEBHOOK_URL
return {"url": app.state.config.WEBHOOK_URL}
@app.get("/api/version")
async def get_app_version():
return {
"version": VERSION,
}
@app.get("/api/changelog")
async def get_app_changelog():
return {key: CHANGELOG[key] for idx, key in enumerate(CHANGELOG) if idx < 5}
@app.get("/api/version/updates")
async def get_app_latest_release_version():
if OFFLINE_MODE:
log.debug(
f"Offline mode is enabled, returning current version as latest version"
)
return {"current": VERSION, "latest": VERSION}
try:
timeout = aiohttp.ClientTimeout(total=1)
async with aiohttp.ClientSession(timeout=timeout, trust_env=True) as session:
async with session.get(
"https://api.github.com/repos/open-webui/open-webui/releases/latest"
) as response:
response.raise_for_status()
data = await response.json()
latest_version = data["tag_name"]
return {"current": VERSION, "latest": latest_version[1:]}
except Exception as e:
log.debug(e)
return {"current": VERSION, "latest": VERSION}
############################
# 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"],
},
redirect_uri=provider_config["redirect_uri"],
)
# SessionMiddleware is used by authlib for oauth
if len(OAUTH_PROVIDERS) > 0:
app.add_middleware(
SessionMiddleware,
secret_key=WEBUI_SECRET_KEY,
session_cookie="oui-session",
same_site=WEBUI_SESSION_COOKIE_SAME_SITE,
https_only=WEBUI_SESSION_COOKIE_SECURE,
)
@app.get("/oauth/{provider}/login")
async def oauth_login(provider: str, request: Request):
if provider not in OAUTH_PROVIDERS:
raise HTTPException(404)
# If the provider has a custom redirect URL, use that, otherwise automatically generate one
redirect_uri = OAUTH_PROVIDERS[provider].get("redirect_uri") or request.url_for(
"oauth_callback", provider=provider
)
client = oauth.create_client(provider)
if client is None:
raise HTTPException(404)
return await client.authorize_redirect(request, redirect_uri)
# 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
@app.get("/oauth/{provider}/callback")
async def oauth_callback(provider: str, request: Request, response: Response):
if provider not in OAUTH_PROVIDERS:
raise HTTPException(404)
client = oauth.create_client(provider)
try:
token = await client.authorize_access_token(request)
except Exception as e:
log.warning(f"OAuth callback error: {e}")
raise HTTPException(400, detail=ERROR_MESSAGES.INVALID_CRED)
user_data: UserInfo = token["userinfo"]
sub = user_data.get("sub")
if not sub:
log.warning(f"OAuth callback failed, sub is missing: {user_data}")
raise HTTPException(400, detail=ERROR_MESSAGES.INVALID_CRED)
provider_sub = f"{provider}@{sub}"
email_claim = webui_app.state.config.OAUTH_EMAIL_CLAIM
email = user_data.get(email_claim, "").lower()
# We currently mandate that email addresses are provided
if not email:
log.warning(f"OAuth callback failed, email is missing: {user_data}")
raise HTTPException(400, detail=ERROR_MESSAGES.INVALID_CRED)
# 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
if OAUTH_MERGE_ACCOUNTS_BY_EMAIL.value:
# Check if the user exists by email
user = Users.get_user_by_email(email)
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:
# 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)
picture_claim = webui_app.state.config.OAUTH_PICTURE_CLAIM
picture_url = user_data.get(picture_claim, "")
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:
log.error(f"Error downloading profile image '{picture_url}': {e}")
picture_url = ""
if not picture_url:
picture_url = "/user.png"
username_claim = webui_app.state.config.OAUTH_USERNAME_CLAIM
role = (
"admin"
if Users.get_num_users() == 0
else webui_app.state.config.DEFAULT_USER_ROLE
)
user = Auths.insert_new_auth(
email=email,
password=get_password_hash(
str(uuid.uuid4())
), # Random password, not used
name=user_data.get(username_claim, "User"),
profile_image_url=picture_url,
role=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:
raise HTTPException(
status.HTTP_403_FORBIDDEN, detail=ERROR_MESSAGES.ACCESS_PROHIBITED
)
jwt_token = create_token(
data={"id": user.id},
expires_delta=parse_duration(webui_app.state.config.JWT_EXPIRES_IN),
)
# Set the cookie token
response.set_cookie(
key="token",
value=jwt_token,
httponly=True, # Ensures the cookie is not accessible via JavaScript
samesite=WEBUI_SESSION_COOKIE_SAME_SITE,
secure=WEBUI_SESSION_COOKIE_SECURE,
)
# Redirect back to the frontend with the JWT token
redirect_url = f"{request.base_url}auth#token={jwt_token}"
return RedirectResponse(url=redirect_url)
@app.get("/manifest.json")
async def get_manifest_json():
return {
"name": WEBUI_NAME,
"short_name": WEBUI_NAME,
"description": "Open WebUI is an open, extensible, user-friendly interface for AI that adapts to your workflow.",
"start_url": "/",
"display": "standalone",
"background_color": "#343541",
"orientation": "any",
"icons": [
{
"src": "/static/logo.png",
"type": "image/png",
"sizes": "500x500",
"purpose": "any",
},
{
"src": "/static/logo.png",
"type": "image/png",
"sizes": "500x500",
"purpose": "maskable",
},
],
}
@app.get("/opensearch.xml")
async def get_opensearch_xml():
xml_content = rf"""
<OpenSearchDescription xmlns="http://a9.com/-/spec/opensearch/1.1/" xmlns:moz="http://www.mozilla.org/2006/browser/search/">
<ShortName>{WEBUI_NAME}</ShortName>
<Description>Search {WEBUI_NAME}</Description>
<InputEncoding>UTF-8</InputEncoding>
<Image width="16" height="16" type="image/x-icon">{WEBUI_URL}/static/favicon.png</Image>
<Url type="text/html" method="get" template="{WEBUI_URL}/?q={"{searchTerms}"}"/>
<moz:SearchForm>{WEBUI_URL}</moz:SearchForm>
</OpenSearchDescription>
"""
return Response(content=xml_content, media_type="application/xml")
@app.get("/health")
async def healthcheck():
return {"status": True}
@app.get("/health/db")
async def healthcheck_with_db():
Session.execute(text("SELECT 1;")).all()
return {"status": True}
app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
app.mount("/cache", StaticFiles(directory=CACHE_DIR), name="cache")
if os.path.exists(FRONTEND_BUILD_DIR):
mimetypes.add_type("text/javascript", ".js")
app.mount(
"/",
SPAStaticFiles(directory=FRONTEND_BUILD_DIR, html=True),
name="spa-static-files",
)
else:
log.warning(
f"Frontend build directory not found at '{FRONTEND_BUILD_DIR}'. Serving API only."
)
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