mirror of
https://github.com/open-webui/open-webui
synced 2024-11-06 16:59:42 +00:00
2388 lines
76 KiB
Python
2388 lines
76 KiB
Python
import asyncio
|
|
import inspect
|
|
import json
|
|
import logging
|
|
import mimetypes
|
|
import os
|
|
import shutil
|
|
import sys
|
|
import time
|
|
from contextlib import asynccontextmanager
|
|
from typing import Optional
|
|
|
|
import aiohttp
|
|
import requests
|
|
from fastapi import (
|
|
Depends,
|
|
FastAPI,
|
|
File,
|
|
Form,
|
|
HTTPException,
|
|
Request,
|
|
UploadFile,
|
|
status,
|
|
)
|
|
from fastapi.middleware.cors import CORSMiddleware
|
|
from fastapi.responses import JSONResponse
|
|
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 Response, StreamingResponse
|
|
|
|
from open_webui.apps.audio.main import app as audio_app
|
|
from open_webui.apps.images.main import app as images_app
|
|
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,
|
|
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.internal.db import Session
|
|
from open_webui.apps.webui.main import (
|
|
app as webui_app,
|
|
generate_function_chat_completion,
|
|
get_all_models as get_open_webui_models,
|
|
)
|
|
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.config import (
|
|
CACHE_DIR,
|
|
CORS_ALLOW_ORIGIN,
|
|
DEFAULT_LOCALE,
|
|
ENABLE_ADMIN_CHAT_ACCESS,
|
|
ENABLE_ADMIN_EXPORT,
|
|
ENABLE_MODEL_FILTER,
|
|
ENABLE_OLLAMA_API,
|
|
ENABLE_OPENAI_API,
|
|
ENV,
|
|
FRONTEND_BUILD_DIR,
|
|
MODEL_FILTER_LIST,
|
|
OAUTH_PROVIDERS,
|
|
ENABLE_SEARCH_QUERY,
|
|
SEARCH_QUERY_GENERATION_PROMPT_TEMPLATE,
|
|
STATIC_DIR,
|
|
TASK_MODEL,
|
|
TASK_MODEL_EXTERNAL,
|
|
TITLE_GENERATION_PROMPT_TEMPLATE,
|
|
TAGS_GENERATION_PROMPT_TEMPLATE,
|
|
TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE,
|
|
WEBHOOK_URL,
|
|
WEBUI_AUTH,
|
|
WEBUI_NAME,
|
|
AppConfig,
|
|
reset_config,
|
|
)
|
|
from open_webui.constants import TASKS
|
|
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 open_webui.utils.misc import (
|
|
add_or_update_system_message,
|
|
get_last_user_message,
|
|
prepend_to_first_user_message_content,
|
|
)
|
|
from open_webui.utils.oauth import oauth_manager
|
|
from open_webui.utils.payload import convert_payload_openai_to_ollama
|
|
from open_webui.utils.response import (
|
|
convert_response_ollama_to_openai,
|
|
convert_streaming_response_ollama_to_openai,
|
|
)
|
|
from open_webui.utils.security_headers import SecurityHeadersMiddleware
|
|
from open_webui.utils.task import (
|
|
moa_response_generation_template,
|
|
tags_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 (
|
|
decode_token,
|
|
get_admin_user,
|
|
get_current_user,
|
|
get_http_authorization_cred,
|
|
get_verified_user,
|
|
)
|
|
|
|
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.TAGS_GENERATION_PROMPT_TEMPLATE = TAGS_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
|
|
|
|
|
|
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
|
|
# Initialize 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
|
|
|
|
try:
|
|
body, flags = await chat_completion_tools_handler(body, user, extra_params)
|
|
contexts.extend(flags.get("contexts", []))
|
|
citations.extend(flags.get("citations", []))
|
|
except Exception as e:
|
|
log.exception(e)
|
|
|
|
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})
|
|
|
|
modified_body_bytes = json.dumps(body).encode("utf-8")
|
|
# Replace the request body with the modified one
|
|
request._body = modified_body_bytes
|
|
# Set custom header to ensure content-length matches new body length
|
|
request.headers.__dict__["_list"] = [
|
|
(b"content-length", str(len(modified_body_bytes)).encode("utf-8")),
|
|
*[(k, v) for k, v in request.headers.raw if k.lower() != b"content-length"],
|
|
]
|
|
|
|
response = await call_next(request)
|
|
if not isinstance(response, StreamingResponse):
|
|
return response
|
|
|
|
content_type = response.headers["Content-Type"]
|
|
is_openai = "text/event-stream" in content_type
|
|
is_ollama = "application/x-ndjson" in content_type
|
|
if not is_openai and not is_ollama:
|
|
return response
|
|
|
|
def wrap_item(item):
|
|
return f"data: {item}\n\n" if is_openai else f"{item}\n"
|
|
|
|
async def stream_wrapper(original_generator, data_items):
|
|
for item in data_items:
|
|
yield wrap_item(json.dumps(item))
|
|
|
|
async for data in original_generator:
|
|
yield data
|
|
|
|
return StreamingResponse(
|
|
stream_wrapper(response.body_iterator, data_items),
|
|
headers=dict(response.headers),
|
|
)
|
|
|
|
async def _receive(self, body: bytes):
|
|
return {"type": "http.request", "body": body, "more_body": False}
|
|
|
|
|
|
app.add_middleware(ChatCompletionMiddleware)
|
|
|
|
|
|
##################################
|
|
#
|
|
# 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
|
|
open_webui_models = []
|
|
openai_models = []
|
|
ollama_models = []
|
|
|
|
open_webui_models = await get_open_webui_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 = open_webui_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"]
|
|
break
|
|
|
|
if custom_model.meta:
|
|
meta = custom_model.meta.model_dump()
|
|
if "actionIds" in meta:
|
|
action_ids.extend(meta["actionIds"])
|
|
|
|
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)):
|
|
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)
|
|
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),
|
|
headers=dict(response.headers),
|
|
)
|
|
else:
|
|
return convert_response_ollama_to_openai(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,
|
|
"TAGS_GENERATION_PROMPT_TEMPLATE": app.state.config.TAGS_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
|
|
TAGS_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.TAGS_GENERATION_PROMPT_TEMPLATE = (
|
|
form_data.TAGS_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,
|
|
"TAGS_GENERATION_PROMPT_TEMPLATE": app.state.config.TAGS_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 chat history, 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
|
|
|
|
<chat_history>
|
|
{{MESSAGES:END:2}}
|
|
</chat_history>"""
|
|
|
|
content = title_generation_template(
|
|
template,
|
|
form_data["messages"],
|
|
{
|
|
"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/tags/completions")
|
|
async def generate_chat_tags(form_data: dict, user=Depends(get_verified_user)):
|
|
print("generate_chat_tags")
|
|
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)
|
|
|
|
if app.state.config.TAGS_GENERATION_PROMPT_TEMPLATE != "":
|
|
template = app.state.config.TAGS_GENERATION_PROMPT_TEMPLATE
|
|
else:
|
|
template = """### Task:
|
|
Generate 1-3 broad tags categorizing the main themes of the chat history, along with 1-3 more specific subtopic tags.
|
|
|
|
### Guidelines:
|
|
- Start with high-level domains (e.g. Science, Technology, Philosophy, Arts, Politics, Business, Health, Sports, Entertainment, Education)
|
|
- Consider including relevant subfields/subdomains if they are strongly represented throughout the conversation
|
|
- If content is too short (less than 3 messages) or too diverse, use only ["General"]
|
|
- Use the chat's primary language; default to English if multilingual
|
|
- Prioritize accuracy over specificity
|
|
|
|
### Output:
|
|
JSON format: { "tags": ["tag1", "tag2", "tag3"] }
|
|
|
|
### Chat History:
|
|
<chat_history>
|
|
{{MESSAGES:END:6}}
|
|
</chat_history>"""
|
|
|
|
content = tags_generation_template(
|
|
template, form_data["messages"], {"name": user.name}
|
|
)
|
|
|
|
print("content", content)
|
|
payload = {
|
|
"model": task_model_id,
|
|
"messages": [{"role": "user", "content": content}],
|
|
"stream": False,
|
|
"metadata": {"task": str(TASKS.TAGS_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
|
|
############################
|
|
|
|
# SessionMiddleware is used by authlib for oauth
|
|
if len(OAUTH_PROVIDERS) > 0:
|
|
app.add_middleware(
|
|
SessionMiddleware,
|
|
secret_key=WEBUI_SECRET_KEY,
|
|
session_cookie="oui-session",
|
|
same_site=WEBUI_SESSION_COOKIE_SAME_SITE,
|
|
https_only=WEBUI_SESSION_COOKIE_SECURE,
|
|
)
|
|
|
|
|
|
@app.get("/oauth/{provider}/login")
|
|
async def oauth_login(provider: str, request: Request):
|
|
return await oauth_manager.handle_login(provider, request)
|
|
|
|
|
|
# OAuth login logic is as follows:
|
|
# 1. Attempt to find a user with matching subject ID, tied to the provider
|
|
# 2. If OAUTH_MERGE_ACCOUNTS_BY_EMAIL is true, find a user with the email address provided via OAuth
|
|
# - This is considered insecure in general, as OAuth providers do not always verify email addresses
|
|
# 3. If there is no user, and ENABLE_OAUTH_SIGNUP is true, create a user
|
|
# - Email addresses are considered unique, so we fail registration if the email address is already taken
|
|
@app.get("/oauth/{provider}/callback")
|
|
async def oauth_callback(provider: str, request: Request, response: Response):
|
|
return await oauth_manager.handle_callback(provider, request, response)
|
|
|
|
|
|
@app.get("/manifest.json")
|
|
async def get_manifest_json():
|
|
return {
|
|
"name": WEBUI_NAME,
|
|
"short_name": WEBUI_NAME,
|
|
"description": "Open WebUI is an open, extensible, user-friendly interface for AI that adapts to your workflow.",
|
|
"start_url": "/",
|
|
"display": "standalone",
|
|
"background_color": "#343541",
|
|
"orientation": "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."
|
|
)
|