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import logging
import sys
import inspect
import json
from pydantic import BaseModel
from typing import AsyncGenerator, Generator, Iterator
from fastapi import (
Depends,
FastAPI,
File,
Form,
HTTPException,
Request,
UploadFile,
status,
)
from starlette.responses import Response, StreamingResponse
from open_webui.socket.main import (
get_event_call,
get_event_emitter,
)
from open_webui.models.functions import Functions
from open_webui.models.models import Models
from open_webui.utils.plugin import load_function_module_by_id
from open_webui.utils.tools import get_tools
from open_webui.utils.access_control import has_access
from open_webui.env import SRC_LOG_LEVELS, GLOBAL_LOG_LEVEL
from open_webui.utils.misc import (
add_or_update_system_message,
get_last_user_message,
prepend_to_first_user_message_content,
openai_chat_chunk_message_template,
openai_chat_completion_message_template,
)
from open_webui.utils.payload import (
apply_model_params_to_body_openai,
apply_model_system_prompt_to_body,
)
logging.basicConfig(stream=sys.stdout, level=GLOBAL_LOG_LEVEL)
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["MAIN"])
def get_function_module_by_id(request: Request, pipe_id: str):
# Check if function is already loaded
if pipe_id not in request.app.state.FUNCTIONS:
function_module, _, _ = load_function_module_by_id(pipe_id)
request.app.state.FUNCTIONS[pipe_id] = function_module
else:
function_module = request.app.state.FUNCTIONS[pipe_id]
if hasattr(function_module, "valves") and hasattr(function_module, "Valves"):
valves = Functions.get_function_valves_by_id(pipe_id)
function_module.valves = function_module.Valves(**(valves if valves else {}))
return function_module
async def get_function_models(request):
pipes = Functions.get_functions_by_type("pipe", active_only=True)
pipe_models = []
for pipe in pipes:
function_module = get_function_module_by_id(request, pipe.id)
# Check if function is a manifold
if hasattr(function_module, "pipes"):
sub_pipes = []
# Check if pipes is a function or a list
try:
if callable(function_module.pipes):
sub_pipes = function_module.pipes()
else:
sub_pipes = function_module.pipes
except Exception as e:
log.exception(e)
sub_pipes = []
log.debug(
f"get_function_models: function '{pipe.id}' is a manifold of {sub_pipes}"
)
for p in sub_pipes:
sub_pipe_id = f'{pipe.id}.{p["id"]}'
sub_pipe_name = p["name"]
if hasattr(function_module, "name"):
sub_pipe_name = f"{function_module.name}{sub_pipe_name}"
pipe_flag = {"type": pipe.type}
pipe_models.append(
{
"id": sub_pipe_id,
"name": sub_pipe_name,
"object": "model",
"created": pipe.created_at,
"owned_by": "openai",
"pipe": pipe_flag,
}
)
else:
pipe_flag = {"type": "pipe"}
log.debug(
f"get_function_models: function '{pipe.id}' is a single pipe {{ 'id': {pipe.id}, 'name': {pipe.name} }}"
)
pipe_models.append(
{
"id": pipe.id,
"name": pipe.name,
"object": "model",
"created": pipe.created_at,
"owned_by": "openai",
"pipe": pipe_flag,
}
)
return pipe_models
async def generate_function_chat_completion(
request, form_data, user, models: dict = {}
):
async def execute_pipe(pipe, params):
if inspect.iscoroutinefunction(pipe):
return await pipe(**params)
else:
return pipe(**params)
async def get_message_content(res: str | Generator | AsyncGenerator) -> str:
if isinstance(res, str):
return res
if isinstance(res, Generator):
return "".join(map(str, res))
if isinstance(res, AsyncGenerator):
return "".join([str(stream) async for stream in res])
def process_line(form_data: dict, line):
if isinstance(line, BaseModel):
line = line.model_dump_json()
line = f"data: {line}"
if isinstance(line, dict):
line = f"data: {json.dumps(line)}"
try:
line = line.decode("utf-8")
except Exception:
pass
if line.startswith("data:"):
return f"{line}\n\n"
else:
line = openai_chat_chunk_message_template(form_data["model"], line)
return f"data: {json.dumps(line)}\n\n"
def get_pipe_id(form_data: dict) -> str:
pipe_id = form_data["model"]
if "." in pipe_id:
pipe_id, _ = pipe_id.split(".", 1)
return pipe_id
def get_function_params(function_module, form_data, user, extra_params=None):
if extra_params is None:
extra_params = {}
pipe_id = get_pipe_id(form_data)
# Get the signature of the function
sig = inspect.signature(function_module.pipe)
params = {"body": form_data} | {
k: v for k, v in extra_params.items() if k in sig.parameters
}
if "__user__" in params and hasattr(function_module, "UserValves"):
user_valves = Functions.get_user_valves_by_id_and_user_id(pipe_id, user.id)
try:
params["__user__"]["valves"] = function_module.UserValves(**user_valves)
except Exception as e:
log.exception(e)
params["__user__"]["valves"] = function_module.UserValves()
return params
model_id = form_data.get("model")
model_info = Models.get_model_by_id(model_id)
metadata = form_data.pop("metadata", {})
files = metadata.get("files", [])
tool_ids = metadata.get("tool_ids", [])
# Check if tool_ids is None
if tool_ids is None:
tool_ids = []
__event_emitter__ = None
__event_call__ = None
__task__ = None
__task_body__ = None
if metadata:
if all(k in metadata for k in ("session_id", "chat_id", "message_id")):
__event_emitter__ = get_event_emitter(metadata)
__event_call__ = get_event_call(metadata)
__task__ = metadata.get("task", None)
__task_body__ = metadata.get("task_body", None)
extra_params = {
"__event_emitter__": __event_emitter__,
"__event_call__": __event_call__,
"__task__": __task__,
"__task_body__": __task_body__,
"__files__": files,
"__user__": {
"id": user.id,
"email": user.email,
"name": user.name,
"role": user.role,
},
"__metadata__": metadata,
"__request__": request,
}
extra_params["__tools__"] = get_tools(
request,
tool_ids,
user,
{
**extra_params,
"__model__": models.get(form_data["model"], None),
"__messages__": form_data["messages"],
"__files__": files,
},
)
if model_info:
if model_info.base_model_id:
form_data["model"] = model_info.base_model_id
params = model_info.params.model_dump()
form_data = apply_model_params_to_body_openai(params, form_data)
form_data = apply_model_system_prompt_to_body(params, form_data, user)
pipe_id = get_pipe_id(form_data)
function_module = get_function_module_by_id(request, pipe_id)
pipe = function_module.pipe
params = get_function_params(function_module, form_data, user, extra_params)
if form_data.get("stream", False):
async def stream_content():
try:
res = await execute_pipe(pipe, params)
# Directly return if the response is a StreamingResponse
if isinstance(res, StreamingResponse):
async for data in res.body_iterator:
yield data
return
if isinstance(res, dict):
yield f"data: {json.dumps(res)}\n\n"
return
except Exception as e:
log.error(f"Error: {e}")
yield f"data: {json.dumps({'error': {'detail':str(e)}})}\n\n"
return
if isinstance(res, str):
message = openai_chat_chunk_message_template(form_data["model"], res)
yield f"data: {json.dumps(message)}\n\n"
if isinstance(res, Iterator):
for line in res:
yield process_line(form_data, line)
if isinstance(res, AsyncGenerator):
async for line in res:
yield process_line(form_data, line)
if isinstance(res, str) or isinstance(res, Generator):
finish_message = openai_chat_chunk_message_template(
form_data["model"], ""
)
finish_message["choices"][0]["finish_reason"] = "stop"
yield f"data: {json.dumps(finish_message)}\n\n"
yield "data: [DONE]"
return StreamingResponse(stream_content(), media_type="text/event-stream")
else:
try:
res = await execute_pipe(pipe, params)
except Exception as e:
log.error(f"Error: {e}")
return {"error": {"detail": str(e)}}
if isinstance(res, StreamingResponse) or isinstance(res, dict):
return res
if isinstance(res, BaseModel):
return res.model_dump()
message = await get_message_content(res)
return openai_chat_completion_message_template(form_data["model"], message)
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