import time
import logging
import sys
import asyncio
from aiocache import cached
from typing import Any, Optional
import random
import json
import inspect
from uuid import uuid4
from concurrent.futures import ThreadPoolExecutor
from fastapi import Request
from fastapi import BackgroundTasks
from starlette.responses import Response, StreamingResponse
from open_webui.models.chats import Chats
from open_webui.models.users import Users
from open_webui.socket.main import (
get_event_call,
get_event_emitter,
get_active_status_by_user_id,
)
from open_webui.routers.tasks import (
generate_queries,
generate_title,
generate_image_prompt,
generate_chat_tags,
)
from open_webui.routers.retrieval import process_web_search, SearchForm
from open_webui.routers.images import image_generations, GenerateImageForm
from open_webui.utils.webhook import post_webhook
from open_webui.models.users import UserModel
from open_webui.models.functions import Functions
from open_webui.models.models import Models
from open_webui.retrieval.utils import get_sources_from_files
from open_webui.utils.chat import generate_chat_completion
from open_webui.utils.task import (
get_task_model_id,
rag_template,
tools_function_calling_generation_template,
)
from open_webui.utils.misc import (
get_message_list,
add_or_update_system_message,
get_last_user_message,
get_last_assistant_message,
prepend_to_first_user_message_content,
)
from open_webui.utils.tools import get_tools
from open_webui.utils.plugin import load_function_module_by_id
from open_webui.tasks import create_task
from open_webui.config import DEFAULT_TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
from open_webui.env import (
SRC_LOG_LEVELS,
GLOBAL_LOG_LEVEL,
BYPASS_MODEL_ACCESS_CONTROL,
ENABLE_REALTIME_CHAT_SAVE,
)
from open_webui.constants import TASKS
logging.basicConfig(stream=sys.stdout, level=GLOBAL_LOG_LEVEL)
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["MAIN"])
async def chat_completion_filter_functions_handler(request, body, model, extra_params):
skip_files = None
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
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 request.app.state.FUNCTIONS:
function_module = request.app.state.FUNCTIONS[filter_id]
else:
function_module, _, _ = load_function_module_by_id(filter_id)
request.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
# Apply valves to the function
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 hasattr(function_module, "inlet"):
try:
inlet = function_module.inlet
# Create a dictionary of parameters to be passed to the function
params = {"body": body} | {
k: v
for k, v in {
**extra_params,
"__model__": model,
"__id__": filter_id,
}.items()
if k in inspect.signature(inlet).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, {}
async def chat_completion_tools_handler(
request: Request, body: dict, user: UserModel, models, extra_params: dict
) -> tuple[dict, dict]:
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_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)},
}
# 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
sources = []
task_model_id = get_task_model_id(
body["model"],
request.app.state.config.TASK_MODEL,
request.app.state.config.TASK_MODEL_EXTERNAL,
models,
)
tools = get_tools(
request,
tool_ids,
user,
{
**extra_params,
"__model__": 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 request.app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE != "":
template = request.app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
else:
template = DEFAULT_TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
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:
response = await generate_chat_completion(request, 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:
required_params = (
tools[tool_function_name]
.get("spec", {})
.get("parameters", {})
.get("required", [])
)
tool_function = tools[tool_function_name]["callable"]
tool_function_params = {
k: v
for k, v in tool_function_params.items()
if k in required_params
}
tool_output = await tool_function(**tool_function_params)
except Exception as e:
tool_output = str(e)
if isinstance(tool_output, str):
if tools[tool_function_name]["citation"]:
sources.append(
{
"source": {
"name": f"TOOL:{tools[tool_function_name]['toolkit_id']}/{tool_function_name}"
},
"document": [tool_output],
"metadata": [
{
"source": f"TOOL:{tools[tool_function_name]['toolkit_id']}/{tool_function_name}"
}
],
}
)
else:
sources.append(
{
"source": {},
"document": [tool_output],
"metadata": [
{
"source": f"TOOL:{tools[tool_function_name]['toolkit_id']}/{tool_function_name}"
}
],
}
)
if tools[tool_function_name]["file_handler"]:
skip_files = True
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: {sources}")
if skip_files and "files" in body.get("metadata", {}):
del body["metadata"]["files"]
return body, {"sources": sources}
async def chat_web_search_handler(
request: Request, form_data: dict, extra_params: dict, user
):
event_emitter = extra_params["__event_emitter__"]
await event_emitter(
{
"type": "status",
"data": {
"action": "web_search",
"description": "Generating search query",
"done": False,
},
}
)
messages = form_data["messages"]
user_message = get_last_user_message(messages)
queries = []
try:
res = await generate_queries(
request,
{
"model": form_data["model"],
"messages": messages,
"prompt": user_message,
"type": "web_search",
},
user,
)
response = res["choices"][0]["message"]["content"]
try:
bracket_start = response.find("{")
bracket_end = response.rfind("}") + 1
if bracket_start == -1 or bracket_end == -1:
raise Exception("No JSON object found in the response")
response = response[bracket_start:bracket_end]
queries = json.loads(response)
queries = queries.get("queries", [])
except Exception as e:
queries = [response]
except Exception as e:
log.exception(e)
queries = [user_message]
if len(queries) == 0:
await event_emitter(
{
"type": "status",
"data": {
"action": "web_search",
"description": "No search query generated",
"done": True,
},
}
)
return
searchQuery = queries[0]
await event_emitter(
{
"type": "status",
"data": {
"action": "web_search",
"description": 'Searching "{{searchQuery}}"',
"query": searchQuery,
"done": False,
},
}
)
try:
# Offload process_web_search to a separate thread
loop = asyncio.get_running_loop()
with ThreadPoolExecutor() as executor:
results = await loop.run_in_executor(
executor,
lambda: process_web_search(
request,
SearchForm(
**{
"query": searchQuery,
}
),
user,
),
)
if results:
await event_emitter(
{
"type": "status",
"data": {
"action": "web_search",
"description": "Searched {{count}} sites",
"query": searchQuery,
"urls": results["filenames"],
"done": True,
},
}
)
files = form_data.get("files", [])
files.append(
{
"collection_name": results["collection_name"],
"name": searchQuery,
"type": "web_search_results",
"urls": results["filenames"],
}
)
form_data["files"] = files
else:
await event_emitter(
{
"type": "status",
"data": {
"action": "web_search",
"description": "No search results found",
"query": searchQuery,
"done": True,
"error": True,
},
}
)
except Exception as e:
log.exception(e)
await event_emitter(
{
"type": "status",
"data": {
"action": "web_search",
"description": 'Error searching "{{searchQuery}}"',
"query": searchQuery,
"done": True,
"error": True,
},
}
)
return form_data
async def chat_image_generation_handler(
request: Request, form_data: dict, extra_params: dict, user
):
__event_emitter__ = extra_params["__event_emitter__"]
await __event_emitter__(
{
"type": "status",
"data": {"description": "Generating an image", "done": False},
}
)
messages = form_data["messages"]
user_message = get_last_user_message(messages)
prompt = user_message
negative_prompt = ""
if request.app.state.config.ENABLE_IMAGE_PROMPT_GENERATION:
try:
res = await generate_image_prompt(
request,
{
"model": form_data["model"],
"messages": messages,
},
user,
)
response = res["choices"][0]["message"]["content"]
try:
bracket_start = response.find("{")
bracket_end = response.rfind("}") + 1
if bracket_start == -1 or bracket_end == -1:
raise Exception("No JSON object found in the response")
response = response[bracket_start:bracket_end]
response = json.loads(response)
prompt = response.get("prompt", [])
except Exception as e:
prompt = user_message
except Exception as e:
log.exception(e)
prompt = user_message
system_message_content = ""
try:
images = await image_generations(
request=request,
form_data=GenerateImageForm(**{"prompt": prompt}),
user=user,
)
await __event_emitter__(
{
"type": "status",
"data": {"description": "Generated an image", "done": True},
}
)
for image in images:
await __event_emitter__(
{
"type": "message",
"data": {"content": f"\n"},
}
)
system_message_content = "<context>User is shown the generated image, tell the user that the image has been generated</context>"
except Exception as e:
log.exception(e)
await __event_emitter__(
{
"type": "status",
"data": {
"description": f"An error occured while generating an image",
"done": True,
},
}
)
system_message_content = "<context>Unable to generate an image, tell the user that an error occured</context>"
if system_message_content:
form_data["messages"] = add_or_update_system_message(
system_message_content, form_data["messages"]
)
return form_data
async def chat_completion_files_handler(
request: Request, body: dict, user: UserModel
) -> tuple[dict, dict[str, list]]:
sources = []
if files := body.get("metadata", {}).get("files", None):
try:
queries_response = await generate_queries(
request,
{
"model": body["model"],
"messages": body["messages"],
"type": "retrieval",
},
user,
)
queries_response = queries_response["choices"][0]["message"]["content"]
try:
bracket_start = queries_response.find("{")
bracket_end = queries_response.rfind("}") + 1
if bracket_start == -1 or bracket_end == -1:
raise Exception("No JSON object found in the response")
queries_response = queries_response[bracket_start:bracket_end]
queries_response = json.loads(queries_response)
except Exception as e:
queries_response = {"queries": [queries_response]}
queries = queries_response.get("queries", [])
except Exception as e:
queries = []
if len(queries) == 0:
queries = [get_last_user_message(body["messages"])]
try:
# Offload get_sources_from_files to a separate thread
loop = asyncio.get_running_loop()
with ThreadPoolExecutor() as executor:
sources = await loop.run_in_executor(
executor,
lambda: get_sources_from_files(
files=files,
queries=queries,
embedding_function=request.app.state.EMBEDDING_FUNCTION,
k=request.app.state.config.TOP_K,
reranking_function=request.app.state.rf,
r=request.app.state.config.RELEVANCE_THRESHOLD,
hybrid_search=request.app.state.config.ENABLE_RAG_HYBRID_SEARCH,
),
)
except Exception as e:
log.exception(e)
log.debug(f"rag_contexts:sources: {sources}")
return body, {"sources": sources}
def apply_params_to_form_data(form_data, model):
params = form_data.pop("params", {})
if model.get("ollama"):
form_data["options"] = params
if "format" in params:
form_data["format"] = params["format"]
if "keep_alive" in params:
form_data["keep_alive"] = params["keep_alive"]
else:
if "seed" in params:
form_data["seed"] = params["seed"]
if "stop" in params:
form_data["stop"] = params["stop"]
if "temperature" in params:
form_data["temperature"] = params["temperature"]
if "max_tokens" in params:
form_data["max_tokens"] = params["max_tokens"]
if "top_p" in params:
form_data["top_p"] = params["top_p"]
if "frequency_penalty" in params:
form_data["frequency_penalty"] = params["frequency_penalty"]
if "reasoning_effort" in params:
form_data["reasoning_effort"] = params["reasoning_effort"]
return form_data
async def process_chat_payload(request, form_data, metadata, user, model):
form_data = apply_params_to_form_data(form_data, model)
log.debug(f"form_data: {form_data}")
event_emitter = get_event_emitter(metadata)
event_call = get_event_call(metadata)
extra_params = {
"__event_emitter__": event_emitter,
"__event_call__": event_call,
"__user__": {
"id": user.id,
"email": user.email,
"name": user.name,
"role": user.role,
},
"__metadata__": metadata,
"__request__": request,
}
# Initialize events to store additional event to be sent to the client
# Initialize contexts and citation
models = request.app.state.MODELS
events = []
sources = []
user_message = get_last_user_message(form_data["messages"])
model_knowledge = model.get("info", {}).get("meta", {}).get("knowledge", False)
if model_knowledge:
await event_emitter(
{
"type": "status",
"data": {
"action": "knowledge_search",
"query": user_message,
"done": False,
},
}
)
knowledge_files = []
for item in model_knowledge:
if item.get("collection_name"):
knowledge_files.append(
{
"id": item.get("collection_name"),
"name": item.get("name"),
"legacy": True,
}
)
elif item.get("collection_names"):
knowledge_files.append(
{
"name": item.get("name"),
"type": "collection",
"collection_names": item.get("collection_names"),
"legacy": True,
}
)
else:
knowledge_files.append(item)
files = form_data.get("files", [])
files.extend(knowledge_files)
form_data["files"] = files
variables = form_data.pop("variables", None)
features = form_data.pop("features", None)
if features:
if "web_search" in features and features["web_search"]:
form_data = await chat_web_search_handler(
request, form_data, extra_params, user
)
if "image_generation" in features and features["image_generation"]:
form_data = await chat_image_generation_handler(
request, form_data, extra_params, user
)
try:
form_data, flags = await chat_completion_filter_functions_handler(
request, form_data, model, extra_params
)
except Exception as e:
raise Exception(f"Error: {e}")
tool_ids = form_data.pop("tool_ids", None)
files = form_data.pop("files", None)
# Remove files duplicates
if files:
files = list({json.dumps(f, sort_keys=True): f for f in files}.values())
metadata = {
**metadata,
"tool_ids": tool_ids,
"files": files,
}
form_data["metadata"] = metadata
try:
form_data, flags = await chat_completion_tools_handler(
request, form_data, user, models, extra_params
)
sources.extend(flags.get("sources", []))
except Exception as e:
log.exception(e)
try:
form_data, flags = await chat_completion_files_handler(request, form_data, user)
sources.extend(flags.get("sources", []))
except Exception as e:
log.exception(e)
# If context is not empty, insert it into the messages
if len(sources) > 0:
context_string = ""
for source_idx, source in enumerate(sources):
source_id = source.get("source", {}).get("name", "")
if "document" in source:
for doc_idx, doc_context in enumerate(source["document"]):
metadata = source.get("metadata")
doc_source_id = None
if metadata:
doc_source_id = metadata[doc_idx].get("source", source_id)
if source_id:
context_string += f"<source><source_id>{doc_source_id if doc_source_id is not None else source_id}</source_id><source_context>{doc_context}</source_context></source>\n"
else:
# If there is no source_id, then do not include the source_id tag
context_string += f"<source><source_context>{doc_context}</source_context></source>\n"
context_string = context_string.strip()
prompt = get_last_user_message(form_data["messages"])
if prompt is None:
raise Exception("No user message found")
if (
request.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":
form_data["messages"] = prepend_to_first_user_message_content(
rag_template(
request.app.state.config.RAG_TEMPLATE, context_string, prompt
),
form_data["messages"],
)
else:
form_data["messages"] = add_or_update_system_message(
rag_template(
request.app.state.config.RAG_TEMPLATE, context_string, prompt
),
form_data["messages"],
)
# If there are citations, add them to the data_items
sources = [source for source in sources if source.get("source", {}).get("name", "")]
if len(sources) > 0:
events.append({"sources": sources})
if model_knowledge:
await event_emitter(
{
"type": "status",
"data": {
"action": "knowledge_search",
"query": user_message,
"done": True,
"hidden": True,
},
}
)
return form_data, events
async def process_chat_response(
request, response, form_data, user, events, metadata, tasks
):
async def background_tasks_handler():
message_map = Chats.get_messages_by_chat_id(metadata["chat_id"])
message = message_map.get(metadata["message_id"]) if message_map else None
if message:
messages = get_message_list(message_map, message.get("id"))
if tasks:
if TASKS.TITLE_GENERATION in tasks:
if tasks[TASKS.TITLE_GENERATION]:
res = await generate_title(
request,
{
"model": message["model"],
"messages": messages,
"chat_id": metadata["chat_id"],
},
user,
)
if res and isinstance(res, dict):
if len(res.get("choices", [])) == 1:
title_string = (
res.get("choices", [])[0]
.get("message", {})
.get("content", message.get("content", "New Chat"))
)
else:
title_string = ""
title_string = title_string[
title_string.find("{") : title_string.rfind("}") + 1
]
try:
title = json.loads(title_string).get(
"title", "New Chat"
)
except Exception as e:
title = ""
if not title:
title = messages[0].get("content", "New Chat")
Chats.update_chat_title_by_id(metadata["chat_id"], title)
await event_emitter(
{
"type": "chat:title",
"data": title,
}
)
elif len(messages) == 2:
title = messages[0].get("content", "New Chat")
Chats.update_chat_title_by_id(metadata["chat_id"], title)
await event_emitter(
{
"type": "chat:title",
"data": message.get("content", "New Chat"),
}
)
if TASKS.TAGS_GENERATION in tasks and tasks[TASKS.TAGS_GENERATION]:
res = await generate_chat_tags(
request,
{
"model": message["model"],
"messages": messages,
"chat_id": metadata["chat_id"],
},
user,
)
if res and isinstance(res, dict):
if len(res.get("choices", [])) == 1:
tags_string = (
res.get("choices", [])[0]
.get("message", {})
.get("content", "")
)
else:
tags_string = ""
tags_string = tags_string[
tags_string.find("{") : tags_string.rfind("}") + 1
]
try:
tags = json.loads(tags_string).get("tags", [])
Chats.update_chat_tags_by_id(
metadata["chat_id"], tags, user
)
await event_emitter(
{
"type": "chat:tags",
"data": tags,
}
)
except Exception as e:
pass
event_emitter = None
if (
"session_id" in metadata
and metadata["session_id"]
and "chat_id" in metadata
and metadata["chat_id"]
and "message_id" in metadata
and metadata["message_id"]
):
event_emitter = get_event_emitter(metadata)
if not isinstance(response, StreamingResponse):
if event_emitter:
if "selected_model_id" in response:
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"selectedModelId": response["selected_model_id"],
},
)
if response.get("choices", [])[0].get("message", {}).get("content"):
content = response["choices"][0]["message"]["content"]
if content:
await event_emitter(
{
"type": "chat:completion",
"data": response,
}
)
title = Chats.get_chat_title_by_id(metadata["chat_id"])
await event_emitter(
{
"type": "chat:completion",
"data": {
"done": True,
"content": content,
"title": title,
},
}
)
# Save message in the database
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"content": content,
},
)
# Send a webhook notification if the user is not active
if get_active_status_by_user_id(user.id) is None:
webhook_url = Users.get_user_webhook_url_by_id(user.id)
if webhook_url:
post_webhook(
webhook_url,
f"{title} - {request.app.state.config.WEBUI_URL}/c/{metadata['chat_id']}\n\n{content}",
{
"action": "chat",
"message": content,
"title": title,
"url": f"{request.app.state.config.WEBUI_URL}/c/{metadata['chat_id']}",
},
)
await background_tasks_handler()
return response
else:
return response
if not any(
content_type in response.headers["Content-Type"]
for content_type in ["text/event-stream", "application/x-ndjson"]
):
return response
if event_emitter:
task_id = str(uuid4()) # Create a unique task ID.
# Handle as a background task
async def post_response_handler(response, events):
message = Chats.get_message_by_id_and_message_id(
metadata["chat_id"], metadata["message_id"]
)
content = message.get("content", "") if message else ""
try:
for event in events:
await event_emitter(
{
"type": "chat:completion",
"data": event,
}
)
# Save message in the database
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
**event,
},
)
# We might want to disable this by default
detect_reasoning = True
reasoning_tags = ["think", "reason", "reasoning", "thought", "Thought"]
current_tag = None
reasoning_start_time = None
reasoning_content = ""
ongoing_content = ""
async for line in response.body_iterator:
line = line.decode("utf-8") if isinstance(line, bytes) else line
data = line
# Skip empty lines
if not data.strip():
continue
# "data:" is the prefix for each event
if not data.startswith("data:"):
continue
# Remove the prefix
data = data[len("data:") :].strip()
try:
data = json.loads(data)
if "selected_model_id" in data:
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"selectedModelId": data["selected_model_id"],
},
)
else:
value = (
data.get("choices", [])[0]
.get("delta", {})
.get("content")
)
if value:
content = f"{content}{value}"
if detect_reasoning:
for tag in reasoning_tags:
start_tag = f"<{tag}>\n"
end_tag = f"</{tag}>\n"
if start_tag in content:
# Remove the start tag
content = content.replace(start_tag, "")
ongoing_content = content
reasoning_start_time = time.time()
reasoning_content = ""
current_tag = tag
break
if reasoning_start_time is not None:
# Remove the last value from the content
content = content[: -len(value)]
reasoning_content += value
end_tag = f"</{current_tag}>\n"
if end_tag in reasoning_content:
reasoning_end_time = time.time()
reasoning_duration = int(
reasoning_end_time
- reasoning_start_time
)
reasoning_content = (
reasoning_content.strip(
f"<{current_tag}>\n"
)
.strip(end_tag)
.strip()
)
if reasoning_content:
reasoning_display_content = "\n".join(
(
f"> {line}"
if not line.startswith(">")
else line
)
for line in reasoning_content.splitlines()
)
# Format reasoning with <details> tag
content = f'{ongoing_content}<details type="reasoning" done="true" duration="{reasoning_duration}">\n<summary>Thought for {reasoning_duration} seconds</summary>\n{reasoning_display_content}\n</details>\n'
else:
content = ""
reasoning_start_time = None
else:
reasoning_display_content = "\n".join(
(
f"> {line}"
if not line.startswith(">")
else line
)
for line in reasoning_content.splitlines()
)
# Show ongoing thought process
content = f'{ongoing_content}<details type="reasoning" done="false">\n<summary>Thinking…</summary>\n{reasoning_display_content}\n</details>\n'
if ENABLE_REALTIME_CHAT_SAVE:
# Save message in the database
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"content": content,
},
)
else:
data = {
"content": content,
}
await event_emitter(
{
"type": "chat:completion",
"data": data,
}
)
except Exception as e:
done = "data: [DONE]" in line
if done:
pass
else:
continue
title = Chats.get_chat_title_by_id(metadata["chat_id"])
data = {"done": True, "content": content, "title": title}
if not ENABLE_REALTIME_CHAT_SAVE:
# Save message in the database
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"content": content,
},
)
# Send a webhook notification if the user is not active
if get_active_status_by_user_id(user.id) is None:
webhook_url = Users.get_user_webhook_url_by_id(user.id)
if webhook_url:
post_webhook(
webhook_url,
f"{title} - {request.app.state.config.WEBUI_URL}/c/{metadata['chat_id']}\n\n{content}",
{
"action": "chat",
"message": content,
"title": title,
"url": f"{request.app.state.config.WEBUI_URL}/c/{metadata['chat_id']}",
},
)
await event_emitter(
{
"type": "chat:completion",
"data": data,
}
)
await background_tasks_handler()
except asyncio.CancelledError:
print("Task was cancelled!")
await event_emitter({"type": "task-cancelled"})
if not ENABLE_REALTIME_CHAT_SAVE:
# Save message in the database
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"content": content,
},
)
if response.background is not None:
await response.background()
# background_tasks.add_task(post_response_handler, response, events)
task_id, _ = create_task(post_response_handler(response, events))
return {"status": True, "task_id": task_id}
else:
# Fallback to the original response
async def stream_wrapper(original_generator, events):
def wrap_item(item):
return f"data: {item}\n\n"
for event in events:
yield wrap_item(json.dumps(event))
async for data in original_generator:
yield data
return StreamingResponse(
stream_wrapper(response.body_iterator, events),
headers=dict(response.headers),
background=response.background,
)