open-webui/backend/open_webui/utils/middleware.py
Timothy Jaeryang Baek 0d7d6899b9 refac
2024-12-24 23:45:21 -07:00

1002 lines
34 KiB
Python

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_user_id_from_session_pool,
)
from open_webui.routers.tasks import (
generate_queries,
generate_title,
generate_chat_tags,
)
from open_webui.routers.retrieval import process_web_search, SearchForm
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,
WEBUI_URL,
)
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_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(
{
"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"])]
sources = 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,
)
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 "top_p" in params:
form_data["top_p"] = params["top_p"]
if "frequency_penalty" in params:
form_data["frequency_penalty"] = params["frequency_penalty"]
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
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
)
try:
form_data, flags = await chat_completion_filter_functions_handler(
request, form_data, model, extra_params
)
except Exception as e:
return 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
):
if not isinstance(response, StreamingResponse):
return response
if not any(
content_type in response.headers["Content-Type"]
for content_type in ["text/event-stream", "application/x-ndjson"]
):
return response
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 event_emitter:
task_id = str(uuid4()) # Create a unique task ID.
# Handle as a background task
async def post_response_handler(response, events):
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,
},
)
assistant_message = get_last_assistant_message(form_data["messages"])
content = assistant_message if assistant_message else ""
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: ") :]
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}"
# Save message in the database
Chats.upsert_message_to_chat_by_id_and_message_id(
metadata["chat_id"],
metadata["message_id"],
{
"content": content,
},
)
except Exception as e:
done = "data: [DONE]" in line
title = Chats.get_chat_title_by_id(metadata["chat_id"])
if done:
data = {"done": True, "content": content, "title": title}
# Send a webhook notification if the user is not active
if (
get_user_id_from_session_pool(metadata["session_id"])
is None
):
webhook_url = Users.get_user_webhook_url_by_id(user.id)
if webhook_url:
post_webhook(
webhook_url,
f"{title} - {WEBUI_URL}/c/{metadata['chat_id']}\n\n{content}",
{
"action": "chat",
"message": content,
"title": title,
"url": f"{WEBUI_URL}/c/{metadata['chat_id']}",
},
)
else:
continue
await event_emitter(
{
"type": "chat:completion",
"data": data,
}
)
message_map = Chats.get_messages_by_chat_id(metadata["chat_id"])
message = message_map.get(metadata["message_id"])
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):
title = (
res.get("choices", [])[0]
.get("message", {})
.get(
"content",
message.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):
tags_string = (
res.get("choices", [])[0]
.get("message", {})
.get("content", "")
)
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:
print(f"Error: {e}")
except asyncio.CancelledError:
print("Task was cancelled!")
await event_emitter({"type": "task-cancelled"})
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),
)