UniqAI/open-webui
1
0
Fork 0
mirror of https://github.com/open-webui/open-webui synced 2024-12-29 07:12:07 +00:00
Code Issues Actions 5 Packages Projects Releases Wiki Activity

general refac

Browse Source
This commit is contained in:
Timothy Jaeryang Baek 2024-12-10 00:00:01 -08:00
parent 1349c6049e
commit f6bec8d9f3
5 changed files with 700 additions and 576 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

581
backend/open_webui/main.py
View File

@ -130,12 +130,6 @@ from open_webui.utils.response import (
from open_webui.utils.security_headers import SecurityHeadersMiddleware from open_webui.utils.security_headers import SecurityHeadersMiddleware
from open_webui.utils.task import ( from open_webui.utils.task import (
rag_template, rag_template,
title_generation_template,
query_generation_template,
autocomplete_generation_template,
tags_generation_template,
emoji_generation_template,
moa_response_generation_template,
tools_function_calling_generation_template, tools_function_calling_generation_template,
) )
from open_webui.utils.tools import get_tools from open_webui.utils.tools import get_tools
@ -1263,12 +1257,15 @@ async def get_base_models(user=Depends(get_admin_user)):
@app.post("/api/chat/completions") @app.post("/api/chat/completions")
async def generate_chat_completions( async def generate_chat_completions(
form_data: dict, user=Depends(get_verified_user), bypass_filter: bool = False request: Request,
form_data: dict,
user=Depends(get_verified_user),
bypass_filter: bool = False,
): ):
if BYPASS_MODEL_ACCESS_CONTROL: if BYPASS_MODEL_ACCESS_CONTROL:
bypass_filter = True bypass_filter = True
model_list = await get_all_models() model_list = request.state.models
models = {model["id"]: model for model in model_list} models = {model["id"]: model for model in model_list}
model_id = form_data["model"] model_id = form_data["model"]
@ -1665,574 +1662,6 @@ async def chat_action(action_id: str, form_data: dict, user=Depends(get_verified
return data return data
##################################
#
# Task Endpoints
#
##################################
# TODO: Refactor task API endpoints below into a separate file
@app.get("/api/task/config")
async def get_task_config(user=Depends(get_verified_user)):
return {
"TASK_MODEL": app.state.config.TASK_MODEL,
"TASK_MODEL_EXTERNAL": app.state.config.TASK_MODEL_EXTERNAL,
"TITLE_GENERATION_PROMPT_TEMPLATE": app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE,
"ENABLE_AUTOCOMPLETE_GENERATION": app.state.config.ENABLE_AUTOCOMPLETE_GENERATION,
"AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH": app.state.config.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH,
"TAGS_GENERATION_PROMPT_TEMPLATE": app.state.config.TAGS_GENERATION_PROMPT_TEMPLATE,
"ENABLE_TAGS_GENERATION": app.state.config.ENABLE_TAGS_GENERATION,
"ENABLE_SEARCH_QUERY_GENERATION": app.state.config.ENABLE_SEARCH_QUERY_GENERATION,
"ENABLE_RETRIEVAL_QUERY_GENERATION": app.state.config.ENABLE_RETRIEVAL_QUERY_GENERATION,
"QUERY_GENERATION_PROMPT_TEMPLATE": app.state.config.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
ENABLE_AUTOCOMPLETE_GENERATION: bool
AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH: int
TAGS_GENERATION_PROMPT_TEMPLATE: str
ENABLE_TAGS_GENERATION: bool
ENABLE_SEARCH_QUERY_GENERATION: bool
ENABLE_RETRIEVAL_QUERY_GENERATION: bool
QUERY_GENERATION_PROMPT_TEMPLATE: str
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.ENABLE_AUTOCOMPLETE_GENERATION = (
form_data.ENABLE_AUTOCOMPLETE_GENERATION
)
app.state.config.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH = (
form_data.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH
)
app.state.config.TAGS_GENERATION_PROMPT_TEMPLATE = (
form_data.TAGS_GENERATION_PROMPT_TEMPLATE
)
app.state.config.ENABLE_TAGS_GENERATION = form_data.ENABLE_TAGS_GENERATION
app.state.config.ENABLE_SEARCH_QUERY_GENERATION = (
form_data.ENABLE_SEARCH_QUERY_GENERATION
)
app.state.config.ENABLE_RETRIEVAL_QUERY_GENERATION = (
form_data.ENABLE_RETRIEVAL_QUERY_GENERATION
)
app.state.config.QUERY_GENERATION_PROMPT_TEMPLATE = (
form_data.QUERY_GENERATION_PROMPT_TEMPLATE
)
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,
"ENABLE_AUTOCOMPLETE_GENERATION": app.state.config.ENABLE_AUTOCOMPLETE_GENERATION,
"AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH": app.state.config.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH,
"TAGS_GENERATION_PROMPT_TEMPLATE": app.state.config.TAGS_GENERATION_PROMPT_TEMPLATE,
"ENABLE_TAGS_GENERATION": app.state.config.ENABLE_TAGS_GENERATION,
"ENABLE_SEARCH_QUERY_GENERATION": app.state.config.ENABLE_SEARCH_QUERY_GENERATION,
"ENABLE_RETRIEVAL_QUERY_GENERATION": app.state.config.ENABLE_RETRIEVAL_QUERY_GENERATION,
"QUERY_GENERATION_PROMPT_TEMPLATE": app.state.config.QUERY_GENERATION_PROMPT_TEMPLATE,
"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)):
model_list = await get_all_models()
models = {model["id"]: model for model in model_list}
model_id = form_data["model"]
if model_id not in 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,
app.state.config.TASK_MODEL,
app.state.config.TASK_MODEL_EXTERNAL,
models,
)
log.debug(
f"generating chat title using model {task_model_id} for user {user.email} "
)
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 models[task_model_id]["owned_by"] == "ollama"
else {
"max_completion_tokens": 50,
}
),
"metadata": {
"task": str(TASKS.TITLE_GENERATION),
"task_body": form_data,
"chat_id": form_data.get("chat_id", None),
},
}
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user, models)
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)):
if not app.state.config.ENABLE_TAGS_GENERATION:
return JSONResponse(
status_code=status.HTTP_200_OK,
content={"detail": "Tags generation is disabled"},
)
model_list = await get_all_models()
models = {model["id"]: model for model in model_list}
model_id = form_data["model"]
if model_id not in 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,
app.state.config.TASK_MODEL,
app.state.config.TASK_MODEL_EXTERNAL,
models,
)
log.debug(
f"generating chat tags using model {task_model_id} for user {user.email} "
)
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}
)
payload = {
"model": task_model_id,
"messages": [{"role": "user", "content": content}],
"stream": False,
"metadata": {
"task": str(TASKS.TAGS_GENERATION),
"task_body": form_data,
"chat_id": form_data.get("chat_id", None),
},
}
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user, models)
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/queries/completions")
async def generate_queries(form_data: dict, user=Depends(get_verified_user)):
type = form_data.get("type")
if type == "web_search":
if not app.state.config.ENABLE_SEARCH_QUERY_GENERATION:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"Search query generation is disabled",
)
elif type == "retrieval":
if not app.state.config.ENABLE_RETRIEVAL_QUERY_GENERATION:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"Query generation is disabled",
)
model_list = await get_all_models()
models = {model["id"]: model for model in model_list}
model_id = form_data["model"]
if model_id not in 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,
app.state.config.TASK_MODEL,
app.state.config.TASK_MODEL_EXTERNAL,
models,
)
log.debug(
f"generating {type} queries using model {task_model_id} for user {user.email}"
)
if (app.state.config.QUERY_GENERATION_PROMPT_TEMPLATE).strip() != "":
template = app.state.config.QUERY_GENERATION_PROMPT_TEMPLATE
else:
template = DEFAULT_QUERY_GENERATION_PROMPT_TEMPLATE
content = query_generation_template(
template, form_data["messages"], {"name": user.name}
)
payload = {
"model": task_model_id,
"messages": [{"role": "user", "content": content}],
"stream": False,
"metadata": {
"task": str(TASKS.QUERY_GENERATION),
"task_body": form_data,
"chat_id": form_data.get("chat_id", None),
},
}
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user, models)
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/auto/completions")
async def generate_autocompletion(form_data: dict, user=Depends(get_verified_user)):
if not app.state.config.ENABLE_AUTOCOMPLETE_GENERATION:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"Autocompletion generation is disabled",
)
type = form_data.get("type")
prompt = form_data.get("prompt")
messages = form_data.get("messages")
if app.state.config.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH > 0:
if len(prompt) > app.state.config.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"Input prompt exceeds maximum length of {app.state.config.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH}",
)
model_list = await get_all_models()
models = {model["id"]: model for model in model_list}
model_id = form_data["model"]
if model_id not in 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,
app.state.config.TASK_MODEL,
app.state.config.TASK_MODEL_EXTERNAL,
models,
)
log.debug(
f"generating autocompletion using model {task_model_id} for user {user.email}"
)
if (app.state.config.AUTOCOMPLETE_GENERATION_PROMPT_TEMPLATE).strip() != "":
template = app.state.config.AUTOCOMPLETE_GENERATION_PROMPT_TEMPLATE
else:
template = DEFAULT_AUTOCOMPLETE_GENERATION_PROMPT_TEMPLATE
content = autocomplete_generation_template(
template, prompt, messages, type, {"name": user.name}
)
payload = {
"model": task_model_id,
"messages": [{"role": "user", "content": content}],
"stream": False,
"metadata": {
"task": str(TASKS.AUTOCOMPLETE_GENERATION),
"task_body": form_data,
"chat_id": form_data.get("chat_id", None),
},
}
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user, models)
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)):
model_list = await get_all_models()
models = {model["id"]: model for model in model_list}
model_id = form_data["model"]
if model_id not in 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,
app.state.config.TASK_MODEL,
app.state.config.TASK_MODEL_EXTERNAL,
models,
)
log.debug(f"generating emoji using model {task_model_id} for user {user.email} ")
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 = emoji_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 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},
}
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user, models)
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)):
model_list = await get_all_models()
models = {model["id"]: model for model in model_list}
model_id = form_data["model"]
if model_id not in 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,
app.state.config.TASK_MODEL,
app.state.config.TASK_MODEL_EXTERNAL,
models,
)
log.debug(f"generating MOA model {task_model_id} for user {user.email} ")
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,
},
}
try:
payload = filter_pipeline(payload, user, models)
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 # Pipelines Endpoints

0
backend/open_webui/routers/chat.py Normal file
View File

99
backend/open_webui/routers/pipelines.py Normal file
View File

@ -0,0 +1,99 @@
from fastapi import APIRouter, Depends, HTTPException, Response, status
from pydantic import BaseModel
from starlette.responses import FileResponse
from open_webui.apps.webui.models.chats import ChatTitleMessagesForm
from open_webui.config import DATA_DIR, ENABLE_ADMIN_EXPORT
from open_webui.constants import ERROR_MESSAGES
from open_webui.utils.misc import get_gravatar_url
from open_webui.utils.pdf_generator import PDFGenerator
from open_webui.utils.auth import get_admin_user
router = APIRouter()
@router.get("/gravatar")
async def get_gravatar(
email: str,
):
return get_gravatar_url(email)
class CodeFormatRequest(BaseModel):
code: str
@router.post("/code/format")
async def format_code(request: CodeFormatRequest):
try:
formatted_code = black.format_str(request.code, mode=black.Mode())
return {"code": formatted_code}
except black.NothingChanged:
return {"code": request.code}
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
class MarkdownForm(BaseModel):
md: str
@router.post("/markdown")
async def get_html_from_markdown(
form_data: MarkdownForm,
):
return {"html": markdown.markdown(form_data.md)}
class ChatForm(BaseModel):
title: str
messages: list[dict]
@router.post("/pdf")
async def download_chat_as_pdf(
form_data: ChatTitleMessagesForm,
):
try:
pdf_bytes = PDFGenerator(form_data).generate_chat_pdf()
return Response(
content=pdf_bytes,
media_type="application/pdf",
headers={"Content-Disposition": "attachment;filename=chat.pdf"},
)
except Exception as e:
print(e)
raise HTTPException(status_code=400, detail=str(e))
@router.get("/db/download")
async def download_db(user=Depends(get_admin_user)):
if not ENABLE_ADMIN_EXPORT:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail=ERROR_MESSAGES.ACCESS_PROHIBITED,
)
from open_webui.apps.webui.internal.db import engine
if engine.name != "sqlite":
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ERROR_MESSAGES.DB_NOT_SQLITE,
)
return FileResponse(
engine.url.database,
media_type="application/octet-stream",
filename="webui.db",
)
@router.get("/litellm/config")
async def download_litellm_config_yaml(user=Depends(get_admin_user)):
return FileResponse(
f"{DATA_DIR}/litellm/config.yaml",
media_type="application/octet-stream",
filename="config.yaml",
)

596
backend/open_webui/routers/tasks.py Normal file
View File

@ -0,0 +1,596 @@
from fastapi import APIRouter, Depends, HTTPException, Response, status, Request
from pydantic import BaseModel
from starlette.responses import FileResponse
from typing import Optional
from open_webui.utils.task import (
title_generation_template,
query_generation_template,
autocomplete_generation_template,
tags_generation_template,
emoji_generation_template,
moa_response_generation_template,
)
from open_webui.utils.auth import get_admin_user, get_verified_user
router = APIRouter()
##################################
#
# Task Endpoints
#
##################################
@router.get("/config")
async def get_task_config(request: Request, user=Depends(get_verified_user)):
return {
"TASK_MODEL": request.app.state.config.TASK_MODEL,
"TASK_MODEL_EXTERNAL": request.app.state.config.TASK_MODEL_EXTERNAL,
"TITLE_GENERATION_PROMPT_TEMPLATE": request.app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE,
"ENABLE_AUTOCOMPLETE_GENERATION": request.app.state.config.ENABLE_AUTOCOMPLETE_GENERATION,
"AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH": request.app.state.config.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH,
"TAGS_GENERATION_PROMPT_TEMPLATE": request.app.state.config.TAGS_GENERATION_PROMPT_TEMPLATE,
"ENABLE_TAGS_GENERATION": request.app.state.config.ENABLE_TAGS_GENERATION,
"ENABLE_SEARCH_QUERY_GENERATION": request.app.state.config.ENABLE_SEARCH_QUERY_GENERATION,
"ENABLE_RETRIEVAL_QUERY_GENERATION": request.app.state.config.ENABLE_RETRIEVAL_QUERY_GENERATION,
"QUERY_GENERATION_PROMPT_TEMPLATE": request.app.state.config.QUERY_GENERATION_PROMPT_TEMPLATE,
"TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE": request.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
ENABLE_AUTOCOMPLETE_GENERATION: bool
AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH: int
TAGS_GENERATION_PROMPT_TEMPLATE: str
ENABLE_TAGS_GENERATION: bool
ENABLE_SEARCH_QUERY_GENERATION: bool
ENABLE_RETRIEVAL_QUERY_GENERATION: bool
QUERY_GENERATION_PROMPT_TEMPLATE: str
TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE: str
@router.post("/config/update")
async def update_task_config(
request: Request, form_data: TaskConfigForm, user=Depends(get_admin_user)
):
request.app.state.config.TASK_MODEL = form_data.TASK_MODEL
request.app.state.config.TASK_MODEL_EXTERNAL = form_data.TASK_MODEL_EXTERNAL
request.app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE = (
form_data.TITLE_GENERATION_PROMPT_TEMPLATE
)
request.app.state.config.ENABLE_AUTOCOMPLETE_GENERATION = (
form_data.ENABLE_AUTOCOMPLETE_GENERATION
)
request.app.state.config.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH = (
form_data.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH
)
request.app.state.config.TAGS_GENERATION_PROMPT_TEMPLATE = (
form_data.TAGS_GENERATION_PROMPT_TEMPLATE
)
request.app.state.config.ENABLE_TAGS_GENERATION = form_data.ENABLE_TAGS_GENERATION
request.app.state.config.ENABLE_SEARCH_QUERY_GENERATION = (
form_data.ENABLE_SEARCH_QUERY_GENERATION
)
request.app.state.config.ENABLE_RETRIEVAL_QUERY_GENERATION = (
form_data.ENABLE_RETRIEVAL_QUERY_GENERATION
)
request.app.state.config.QUERY_GENERATION_PROMPT_TEMPLATE = (
form_data.QUERY_GENERATION_PROMPT_TEMPLATE
)
request.app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE = (
form_data.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
)
return {
"TASK_MODEL": request.app.state.config.TASK_MODEL,
"TASK_MODEL_EXTERNAL": request.app.state.config.TASK_MODEL_EXTERNAL,
"TITLE_GENERATION_PROMPT_TEMPLATE": request.app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE,
"ENABLE_AUTOCOMPLETE_GENERATION": request.app.state.config.ENABLE_AUTOCOMPLETE_GENERATION,
"AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH": request.app.state.config.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH,
"TAGS_GENERATION_PROMPT_TEMPLATE": request.app.state.config.TAGS_GENERATION_PROMPT_TEMPLATE,
"ENABLE_TAGS_GENERATION": request.app.state.config.ENABLE_TAGS_GENERATION,
"ENABLE_SEARCH_QUERY_GENERATION": request.app.state.config.ENABLE_SEARCH_QUERY_GENERATION,
"ENABLE_RETRIEVAL_QUERY_GENERATION": request.app.state.config.ENABLE_RETRIEVAL_QUERY_GENERATION,
"QUERY_GENERATION_PROMPT_TEMPLATE": request.app.state.config.QUERY_GENERATION_PROMPT_TEMPLATE,
"TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE": request.app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE,
}
@router.post("/title/completions")
async def generate_title(
request: Request, form_data: dict, user=Depends(get_verified_user)
):
model_list = await get_all_models()
models = {model["id"]: model for model in model_list}
model_id = form_data["model"]
if model_id not in 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,
request.app.state.config.TASK_MODEL,
request.app.state.config.TASK_MODEL_EXTERNAL,
models,
)
log.debug(
f"generating chat title using model {task_model_id} for user {user.email} "
)
if request.app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE != "":
template = request.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 models[task_model_id]["owned_by"] == "ollama"
else {
"max_completion_tokens": 50,
}
),
"metadata": {
"task": str(TASKS.TITLE_GENERATION),
"task_body": form_data,
"chat_id": form_data.get("chat_id", None),
},
}
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user, models)
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)
@router.post("/tags/completions")
async def generate_chat_tags(form_data: dict, user=Depends(get_verified_user)):
if not request.app.state.config.ENABLE_TAGS_GENERATION:
return JSONResponse(
status_code=status.HTTP_200_OK,
content={"detail": "Tags generation is disabled"},
)
model_list = await get_all_models()
models = {model["id"]: model for model in model_list}
model_id = form_data["model"]
if model_id not in 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,
request.app.state.config.TASK_MODEL,
request.app.state.config.TASK_MODEL_EXTERNAL,
models,
)
log.debug(
f"generating chat tags using model {task_model_id} for user {user.email} "
)
if request.app.state.config.TAGS_GENERATION_PROMPT_TEMPLATE != "":
template = request.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}
)
payload = {
"model": task_model_id,
"messages": [{"role": "user", "content": content}],
"stream": False,
"metadata": {
"task": str(TASKS.TAGS_GENERATION),
"task_body": form_data,
"chat_id": form_data.get("chat_id", None),
},
}
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user, models)
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)
@router.post("/queries/completions")
async def generate_queries(
request: Request, form_data: dict, user=Depends(get_verified_user)
):
type = form_data.get("type")
if type == "web_search":
if not request.app.state.config.ENABLE_SEARCH_QUERY_GENERATION:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"Search query generation is disabled",
)
elif type == "retrieval":
if not request.app.state.config.ENABLE_RETRIEVAL_QUERY_GENERATION:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"Query generation is disabled",
)
model_list = await get_all_models()
models = {model["id"]: model for model in model_list}
model_id = form_data["model"]
if model_id not in 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,
request.app.state.config.TASK_MODEL,
request.app.state.config.TASK_MODEL_EXTERNAL,
models,
)
log.debug(
f"generating {type} queries using model {task_model_id} for user {user.email}"
)
if (request.app.state.config.QUERY_GENERATION_PROMPT_TEMPLATE).strip() != "":
template = request.app.state.config.QUERY_GENERATION_PROMPT_TEMPLATE
else:
template = DEFAULT_QUERY_GENERATION_PROMPT_TEMPLATE
content = query_generation_template(
template, form_data["messages"], {"name": user.name}
)
payload = {
"model": task_model_id,
"messages": [{"role": "user", "content": content}],
"stream": False,
"metadata": {
"task": str(TASKS.QUERY_GENERATION),
"task_body": form_data,
"chat_id": form_data.get("chat_id", None),
},
}
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user, models)
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)
@router.post("/auto/completions")
async def generate_autocompletion(
request: Request, form_data: dict, user=Depends(get_verified_user)
):
if not request.app.state.config.ENABLE_AUTOCOMPLETE_GENERATION:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"Autocompletion generation is disabled",
)
type = form_data.get("type")
prompt = form_data.get("prompt")
messages = form_data.get("messages")
if request.app.state.config.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH > 0:
if (
len(prompt)
> request.app.state.config.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH
):
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"Input prompt exceeds maximum length of {request.app.state.config.AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH}",
)
model_list = await get_all_models()
models = {model["id"]: model for model in model_list}
model_id = form_data["model"]
if model_id not in 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,
request.app.state.config.TASK_MODEL,
request.app.state.config.TASK_MODEL_EXTERNAL,
models,
)
log.debug(
f"generating autocompletion using model {task_model_id} for user {user.email}"
)
if (request.app.state.config.AUTOCOMPLETE_GENERATION_PROMPT_TEMPLATE).strip() != "":
template = request.app.state.config.AUTOCOMPLETE_GENERATION_PROMPT_TEMPLATE
else:
template = DEFAULT_AUTOCOMPLETE_GENERATION_PROMPT_TEMPLATE
content = autocomplete_generation_template(
template, prompt, messages, type, {"name": user.name}
)
payload = {
"model": task_model_id,
"messages": [{"role": "user", "content": content}],
"stream": False,
"metadata": {
"task": str(TASKS.AUTOCOMPLETE_GENERATION),
"task_body": form_data,
"chat_id": form_data.get("chat_id", None),
},
}
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user, models)
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)
@router.post("/emoji/completions")
async def generate_emoji(
request: Request, form_data: dict, user=Depends(get_verified_user)
):
model_list = await get_all_models()
models = {model["id"]: model for model in model_list}
model_id = form_data["model"]
if model_id not in 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,
request.app.state.config.TASK_MODEL,
request.app.state.config.TASK_MODEL_EXTERNAL,
models,
)
log.debug(f"generating emoji using model {task_model_id} for user {user.email} ")
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 = emoji_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 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},
}
# Handle pipeline filters
try:
payload = filter_pipeline(payload, user, models)
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)
@router.post("/moa/completions")
async def generate_moa_response(
request: Request, form_data: dict, user=Depends(get_verified_user)
):
model_list = await get_all_models()
models = {model["id"]: model for model in model_list}
model_id = form_data["model"]
if model_id not in 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,
request.app.state.config.TASK_MODEL,
request.app.state.config.TASK_MODEL_EXTERNAL,
models,
)
log.debug(f"generating MOA model {task_model_id} for user {user.email} ")
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,
},
}
try:
payload = filter_pipeline(payload, user, models)
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)

BIN
backend/open_webui/utils/logo.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 6.0 KiB