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Unverified Commit 748cb7d4 authored by Timothy Jaeryang Baek's avatar Timothy Jaeryang Baek Committed by GitHub
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Merge pull request #1654 from open-webui/dev 

0.1.121
parents b3da09f5 348186c4
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CHANGELOG.md
View file @ 748cb7d4
......@@ -5,6 +5,19 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [0.1.121] - 2024-04-24
### Fixed
- **🔧 Translation Issues**: Addressed various translation discrepancies.
- **🔒 LiteLLM Security Fix**: Updated LiteLLM version to resolve a security vulnerability.
- **🖥️ HTML Tag Display**: Rectified the issue where the '< br >' tag wasn't displaying correctly.
- **🔗 WebSocket Connection**: Resolved the failure of WebSocket connection under HTTPS security for ComfyUI server.
- **📜 FileReader Optimization**: Implemented FileReader initialization per image in multi-file drag & drop to ensure reusability.
- **🏷️ Tag Display**: Corrected tag display inconsistencies.
- **📦 Archived Chat Styling**: Fixed styling issues in archived chat.
- **🔖 Safari Copy Button Bug**: Addressed the bug where the copy button failed to copy links in Safari.
## [0.1.120] - 2024-04-20
### Added
......
Dockerfile
View file @ 748cb7d4
......@@ -8,8 +8,8 @@ ARG USE_CUDA_VER=cu121
# any sentence transformer model; models to use can be found at https://huggingface.co/models?library=sentence-transformers
# Leaderboard: https://huggingface.co/spaces/mteb/leaderboard
# for better performance and multilangauge support use "intfloat/multilingual-e5-large" (~2.5GB) or "intfloat/multilingual-e5-base" (~1.5GB)
# IMPORTANT: If you change the default model (all-MiniLM-L6-v2) and vice versa, you aren't able to use RAG Chat with your previous documents loaded in the WebUI! You need to re-embed them.
ARG USE_EMBEDDING_MODEL=all-MiniLM-L6-v2
# IMPORTANT: If you change the default model (sentence-transformers/all-MiniLM-L6-v2) and vice versa, you aren't able to use RAG Chat with your previous documents loaded in the WebUI! You need to re-embed them.
ARG USE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
######## WebUI frontend ########
FROM --platform=$BUILDPLATFORM node:21-alpine3.19 as build
......@@ -98,13 +98,13 @@ RUN pip3 install uv && \
# If you use CUDA the whisper and embedding model will be downloaded on first use
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir && \
uv pip install --system -r requirements.txt --no-cache-dir && \
python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])" && \
python -c "import os; from chromadb.utils import embedding_functions; sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=os.environ['RAG_EMBEDDING_MODEL'], device='cpu')"; \
python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"; \
else \
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \
uv pip install --system -r requirements.txt --no-cache-dir && \
python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])" && \
python -c "import os; from chromadb.utils import embedding_functions; sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=os.environ['RAG_EMBEDDING_MODEL'], device='cpu')"; \
python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"; \
fi
......
backend/apps/images/main.py
View file @ 748cb7d4
......@@ -35,8 +35,8 @@ from config import (
ENABLE_IMAGE_GENERATION,
AUTOMATIC1111_BASE_URL,
COMFYUI_BASE_URL,
OPENAI_API_BASE_URL,
OPENAI_API_KEY,
IMAGES_OPENAI_API_BASE_URL,
IMAGES_OPENAI_API_KEY,
)
......@@ -58,8 +58,8 @@ app.add_middleware(
app.state.ENGINE = ""
app.state.ENABLED = ENABLE_IMAGE_GENERATION
app.state.OPENAI_API_BASE_URL = OPENAI_API_BASE_URL
app.state.OPENAI_API_KEY = OPENAI_API_KEY
app.state.OPENAI_API_BASE_URL = IMAGES_OPENAI_API_BASE_URL
app.state.OPENAI_API_KEY = IMAGES_OPENAI_API_KEY
app.state.MODEL = ""
......@@ -135,27 +135,33 @@ async def update_engine_url(
}
class OpenAIKeyUpdateForm(BaseModel):
class OpenAIConfigUpdateForm(BaseModel):
url: str
key: str
@app.get("/key")
async def get_openai_key(user=Depends(get_admin_user)):
return {"OPENAI_API_KEY": app.state.OPENAI_API_KEY}
@app.get("/openai/config")
async def get_openai_config(user=Depends(get_admin_user)):
return {
"OPENAI_API_BASE_URL": app.state.OPENAI_API_BASE_URL,
"OPENAI_API_KEY": app.state.OPENAI_API_KEY,
}
@app.post("/key/update")
async def update_openai_key(
form_data: OpenAIKeyUpdateForm, user=Depends(get_admin_user)
@app.post("/openai/config/update")
async def update_openai_config(
form_data: OpenAIConfigUpdateForm, user=Depends(get_admin_user)
):
if form_data.key == "":
raise HTTPException(status_code=400, detail=ERROR_MESSAGES.API_KEY_NOT_FOUND)
app.state.OPENAI_API_BASE_URL = form_data.url
app.state.OPENAI_API_KEY = form_data.key
return {
"OPENAI_API_KEY": app.state.OPENAI_API_KEY,
"status": True,
"OPENAI_API_BASE_URL": app.state.OPENAI_API_BASE_URL,
"OPENAI_API_KEY": app.state.OPENAI_API_KEY,
}
......
backend/apps/litellm/main.py
View file @ 748cb7d4
import logging
import sys
from litellm.proxy.proxy_server import ProxyConfig, initialize
from litellm.proxy.proxy_server import app
from fastapi import FastAPI, Depends, HTTPException
from fastapi.routing import APIRoute
from fastapi.middleware.cors import CORSMiddleware
import logging
from fastapi import FastAPI, Request, Depends, status, Response
from fastapi.responses import JSONResponse
from starlette.middleware.base import BaseHTTPMiddleware, RequestResponseEndpoint
from starlette.responses import StreamingResponse
import json
import time
import requests
from utils.utils import get_http_authorization_cred, get_current_user
from pydantic import BaseModel, ConfigDict
from typing import Optional, List
from utils.utils import get_verified_user, get_current_user, get_admin_user
from config import SRC_LOG_LEVELS, ENV
from constants import MESSAGES
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["LITELLM"])
......@@ -20,81 +28,324 @@ log.setLevel(SRC_LOG_LEVELS["LITELLM"])
from config import (
MODEL_FILTER_ENABLED,
MODEL_FILTER_LIST,
DATA_DIR,
LITELLM_PROXY_PORT,
LITELLM_PROXY_HOST,
)
from litellm.utils import get_llm_provider
proxy_config = ProxyConfig()
import asyncio
import subprocess
import yaml
app = FastAPI()
async def config():
router, model_list, general_settings = await proxy_config.load_config(
router=None, config_file_path="./data/litellm/config.yaml"
)
origins = ["*"]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
LITELLM_CONFIG_DIR = f"{DATA_DIR}/litellm/config.yaml"
await initialize(config="./data/litellm/config.yaml", telemetry=False)
with open(LITELLM_CONFIG_DIR, "r") as file:
litellm_config = yaml.safe_load(file)
app.state.CONFIG = litellm_config
async def startup():
await config()
# Global variable to store the subprocess reference
background_process = None
async def run_background_process(command):
global background_process
log.info("run_background_process")
try:
# Log the command to be executed
log.info(f"Executing command: {command}")
# Execute the command and create a subprocess
process = await asyncio.create_subprocess_exec(
*command, stdout=subprocess.PIPE, stderr=subprocess.PIPE
)
background_process = process
log.info("Subprocess started successfully.")
# Capture STDERR for debugging purposes
stderr_output = await process.stderr.read()
stderr_text = stderr_output.decode().strip()
if stderr_text:
log.info(f"Subprocess STDERR: {stderr_text}")
# log.info output line by line
async for line in process.stdout:
log.info(line.decode().strip())
# Wait for the process to finish
returncode = await process.wait()
log.info(f"Subprocess exited with return code {returncode}")
except Exception as e:
log.error(f"Failed to start subprocess: {e}")
raise # Optionally re-raise the exception if you want it to propagate
async def start_litellm_background():
log.info("start_litellm_background")
# Command to run in the background
command = [
"litellm",
"--port",
str(LITELLM_PROXY_PORT),
"--host",
LITELLM_PROXY_HOST,
"--telemetry",
"False",
"--config",
LITELLM_CONFIG_DIR,
]
await run_background_process(command)
async def shutdown_litellm_background():
log.info("shutdown_litellm_background")
global background_process
if background_process:
background_process.terminate()
await background_process.wait() # Ensure the process has terminated
log.info("Subprocess terminated")
background_process = None
@app.on_event("startup")
async def on_startup():
await startup()
async def startup_event():
log.info("startup_event")
# TODO: Check config.yaml file and create one
asyncio.create_task(start_litellm_background())
app.state.MODEL_FILTER_ENABLED = MODEL_FILTER_ENABLED
app.state.MODEL_FILTER_LIST = MODEL_FILTER_LIST
@app.middleware("http")
async def auth_middleware(request: Request, call_next):
auth_header = request.headers.get("Authorization", "")
request.state.user = None
@app.get("/")
async def get_status():
return {"status": True}
async def restart_litellm():
"""
Endpoint to restart the litellm background service.
"""
log.info("Requested restart of litellm service.")
try:
user = get_current_user(get_http_authorization_cred(auth_header))
log.debug(f"user: {user}")
request.state.user = user
# Shut down the existing process if it is running
await shutdown_litellm_background()
log.info("litellm service shutdown complete.")
# Restart the background service
asyncio.create_task(start_litellm_background())
log.info("litellm service restart complete.")
return {
"status": "success",
"message": "litellm service restarted successfully.",
}
except Exception as e:
return JSONResponse(status_code=400, content={"detail": str(e)})
log.info(f"Error restarting litellm service: {e}")
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e)
)
@app.get("/restart")
async def restart_litellm_handler(user=Depends(get_admin_user)):
return await restart_litellm()
response = await call_next(request)
return response
@app.get("/config")
async def get_config(user=Depends(get_admin_user)):
return app.state.CONFIG
class ModifyModelsResponseMiddleware(BaseHTTPMiddleware):
async def dispatch(
self, request: Request, call_next: RequestResponseEndpoint
) -> Response:
class LiteLLMConfigForm(BaseModel):
general_settings: Optional[dict] = None
litellm_settings: Optional[dict] = None
model_list: Optional[List[dict]] = None
router_settings: Optional[dict] = None
response = await call_next(request)
user = request.state.user
model_config = ConfigDict(protected_namespaces=())
if "/models" in request.url.path:
if isinstance(response, StreamingResponse):
# Read the content of the streaming response
body = b""
async for chunk in response.body_iterator:
body += chunk
data = json.loads(body.decode("utf-8"))
@app.post("/config/update")
async def update_config(form_data: LiteLLMConfigForm, user=Depends(get_admin_user)):
app.state.CONFIG = form_data.model_dump(exclude_none=True)
with open(LITELLM_CONFIG_DIR, "w") as file:
yaml.dump(app.state.CONFIG, file)
await restart_litellm()
return app.state.CONFIG
@app.get("/models")
@app.get("/v1/models")
async def get_models(user=Depends(get_current_user)):
while not background_process:
await asyncio.sleep(0.1)
url = f"http://localhost:{LITELLM_PROXY_PORT}/v1"
r = None
try:
r = requests.request(method="GET", url=f"{url}/models")
r.raise_for_status()
data = r.json()
if app.state.MODEL_FILTER_ENABLED:
if user and user.role == "user":
data["data"] = list(
filter(
lambda model: model["id"]
in app.state.MODEL_FILTER_LIST,
lambda model: model["id"] in app.state.MODEL_FILTER_LIST,
data["data"],
)
)
# Modified Flag
data["modified"] = True
return JSONResponse(content=data)
return data
except Exception as e:
log.exception(e)
error_detail = "Open WebUI: Server Connection Error"
if r is not None:
try:
res = r.json()
if "error" in res:
error_detail = f"External: {res['error']}"
except:
error_detail = f"External: {e}"
return {
"data": [
{
"id": model["model_name"],
"object": "model",
"created": int(time.time()),
"owned_by": "openai",
}
for model in app.state.CONFIG["model_list"]
],
"object": "list",
}
@app.get("/model/info")
async def get_model_list(user=Depends(get_admin_user)):
return {"data": app.state.CONFIG["model_list"]}
class AddLiteLLMModelForm(BaseModel):
model_name: str
litellm_params: dict
model_config = ConfigDict(protected_namespaces=())
@app.post("/model/new")
async def add_model_to_config(
form_data: AddLiteLLMModelForm, user=Depends(get_admin_user)
):
try:
get_llm_provider(model=form_data.model_name)
app.state.CONFIG["model_list"].append(form_data.model_dump())
with open(LITELLM_CONFIG_DIR, "w") as file:
yaml.dump(app.state.CONFIG, file)
await restart_litellm()
return {"message": MESSAGES.MODEL_ADDED(form_data.model_name)}
except Exception as e:
print(e)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e)
)
class DeleteLiteLLMModelForm(BaseModel):
id: str
@app.post("/model/delete")
async def delete_model_from_config(
form_data: DeleteLiteLLMModelForm, user=Depends(get_admin_user)
):
app.state.CONFIG["model_list"] = [
model
for model in app.state.CONFIG["model_list"]
if model["model_name"] != form_data.id
]
with open(LITELLM_CONFIG_DIR, "w") as file:
yaml.dump(app.state.CONFIG, file)
return response
await restart_litellm()
return {"message": MESSAGES.MODEL_DELETED(form_data.id)}
app.add_middleware(ModifyModelsResponseMiddleware)
@app.api_route("/{path:path}", methods=["GET", "POST", "PUT", "DELETE"])
async def proxy(path: str, request: Request, user=Depends(get_verified_user)):
body = await request.body()
url = f"http://localhost:{LITELLM_PROXY_PORT}"
target_url = f"{url}/{path}"
headers = {}
# headers["Authorization"] = f"Bearer {key}"
headers["Content-Type"] = "application/json"
r = None
try:
r = requests.request(
method=request.method,
url=target_url,
data=body,
headers=headers,
stream=True,
)
r.raise_for_status()
# Check if response is SSE
if "text/event-stream" in r.headers.get("Content-Type", ""):
return StreamingResponse(
r.iter_content(chunk_size=8192),
status_code=r.status_code,
headers=dict(r.headers),
)
else:
response_data = r.json()
return response_data
except Exception as e:
log.exception(e)
error_detail = "Open WebUI: Server Connection Error"
if r is not None:
try:
res = r.json()
if "error" in res:
error_detail = f"External: {res['error']['message'] if 'message' in res['error'] else res['error']}"
except:
error_detail = f"External: {e}"
raise HTTPException(
status_code=r.status_code if r else 500, detail=error_detail
)
backend/apps/rag/main.py
View file @ 748cb7d4
......@@ -13,7 +13,6 @@ import os, shutil, logging, re
from pathlib import Path
from typing import List
from chromadb.utils import embedding_functions
from chromadb.utils.batch_utils import create_batches
from langchain_community.document_loaders import (
......@@ -38,6 +37,7 @@ import mimetypes
import uuid
import json
import sentence_transformers
from apps.ollama.main import generate_ollama_embeddings, GenerateEmbeddingsForm
......@@ -48,11 +48,8 @@ from apps.web.models.documents import (
)
from apps.rag.utils import (
query_doc,
query_embeddings_doc,
query_collection,
query_embeddings_collection,
get_embedding_model_path,
generate_openai_embeddings,
)
......@@ -69,7 +66,7 @@ from config import (
DOCS_DIR,
RAG_EMBEDDING_ENGINE,
RAG_EMBEDDING_MODEL,
RAG_EMBEDDING_MODEL_AUTO_UPDATE,
RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
RAG_OPENAI_API_BASE_URL,
RAG_OPENAI_API_KEY,
DEVICE_TYPE,
......@@ -101,15 +98,12 @@ app.state.OPENAI_API_KEY = RAG_OPENAI_API_KEY
app.state.PDF_EXTRACT_IMAGES = False
app.state.sentence_transformer_ef = (
embedding_functions.SentenceTransformerEmbeddingFunction(
model_name=get_embedding_model_path(
app.state.RAG_EMBEDDING_MODEL, RAG_EMBEDDING_MODEL_AUTO_UPDATE
),
if app.state.RAG_EMBEDDING_ENGINE == "":
app.state.sentence_transformer_ef = sentence_transformers.SentenceTransformer(
app.state.RAG_EMBEDDING_MODEL,
device=DEVICE_TYPE,
trust_remote_code=RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
)
)
origins = ["*"]
......@@ -185,13 +179,10 @@ async def update_embedding_config(
app.state.OPENAI_API_BASE_URL = form_data.openai_config.url
app.state.OPENAI_API_KEY = form_data.openai_config.key
else:
sentence_transformer_ef = (
embedding_functions.SentenceTransformerEmbeddingFunction(
model_name=get_embedding_model_path(
form_data.embedding_model, True
),
sentence_transformer_ef = sentence_transformers.SentenceTransformer(
app.state.RAG_EMBEDDING_MODEL,
device=DEVICE_TYPE,
)
trust_remote_code=RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
)
app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model
app.state.sentence_transformer_ef = sentence_transformer_ef
......@@ -294,17 +285,12 @@ def query_doc_handler(
form_data: QueryDocForm,
user=Depends(get_current_user),
):
try:
if app.state.RAG_EMBEDDING_ENGINE == "":
return query_doc(
collection_name=form_data.collection_name,
query=form_data.query,
k=form_data.k if form_data.k else app.state.TOP_K,
embedding_function=app.state.sentence_transformer_ef,
)
else:
if app.state.RAG_EMBEDDING_ENGINE == "ollama":
query_embeddings = app.state.sentence_transformer_ef.encode(
form_data.query
).tolist()
elif app.state.RAG_EMBEDDING_ENGINE == "ollama":
query_embeddings = generate_ollama_embeddings(
GenerateEmbeddingsForm(
**{
......@@ -323,6 +309,7 @@ def query_doc_handler(
return query_embeddings_doc(
collection_name=form_data.collection_name,
query=form_data.query,
query_embeddings=query_embeddings,
k=form_data.k if form_data.k else app.state.TOP_K,
)
......@@ -348,15 +335,10 @@ def query_collection_handler(
):
try:
if app.state.RAG_EMBEDDING_ENGINE == "":
return query_collection(
collection_names=form_data.collection_names,
query=form_data.query,
k=form_data.k if form_data.k else app.state.TOP_K,
embedding_function=app.state.sentence_transformer_ef,
)
else:
if app.state.RAG_EMBEDDING_ENGINE == "ollama":
query_embeddings = app.state.sentence_transformer_ef.encode(
form_data.query
).tolist()
elif app.state.RAG_EMBEDDING_ENGINE == "ollama":
query_embeddings = generate_ollama_embeddings(
GenerateEmbeddingsForm(
**{
......@@ -445,6 +427,8 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b
log.info(f"store_docs_in_vector_db {docs} {collection_name}")
texts = [doc.page_content for doc in docs]
texts = list(map(lambda x: x.replace("\n", " "), texts))
metadatas = [doc.metadata for doc in docs]
try:
......@@ -454,25 +438,11 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b
log.info(f"deleting existing collection {collection_name}")
CHROMA_CLIENT.delete_collection(name=collection_name)
if app.state.RAG_EMBEDDING_ENGINE == "":
collection = CHROMA_CLIENT.create_collection(
name=collection_name,
embedding_function=app.state.sentence_transformer_ef,
)
for batch in create_batches(
api=CHROMA_CLIENT,
ids=[str(uuid.uuid1()) for _ in texts],
metadatas=metadatas,
documents=texts,
):
collection.add(*batch)
else:
collection = CHROMA_CLIENT.create_collection(name=collection_name)
if app.state.RAG_EMBEDDING_ENGINE == "ollama":
if app.state.RAG_EMBEDDING_ENGINE == "":
embeddings = app.state.sentence_transformer_ef.encode(texts).tolist()
elif app.state.RAG_EMBEDDING_ENGINE == "ollama":
embeddings = [
generate_ollama_embeddings(
GenerateEmbeddingsForm(
......
backend/apps/rag/utils.py
View file @ 748cb7d4
import os
import re
import logging
from typing import List
import requests
from typing import List
from huggingface_hub import snapshot_download
from apps.ollama.main import generate_ollama_embeddings, GenerateEmbeddingsForm
from apps.ollama.main import (
generate_ollama_embeddings,
GenerateEmbeddingsForm,
)
from config import SRC_LOG_LEVELS, CHROMA_CLIENT
......@@ -16,29 +15,12 @@ log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["RAG"])
def query_doc(collection_name: str, query: str, k: int, embedding_function):
try:
# if you use docker use the model from the environment variable
collection = CHROMA_CLIENT.get_collection(
name=collection_name,
embedding_function=embedding_function,
)
result = collection.query(
query_texts=[query],
n_results=k,
)
return result
except Exception as e:
raise e
def query_embeddings_doc(collection_name: str, query_embeddings, k: int):
def query_embeddings_doc(collection_name: str, query: str, query_embeddings, k: int):
try:
# if you use docker use the model from the environment variable
log.info(f"query_embeddings_doc {query_embeddings}")
collection = CHROMA_CLIENT.get_collection(
name=collection_name,
)
collection = CHROMA_CLIENT.get_collection(name=collection_name)
result = collection.query(
query_embeddings=[query_embeddings],
n_results=k,
......@@ -95,43 +77,20 @@ def merge_and_sort_query_results(query_results, k):
return merged_query_results
def query_collection(
collection_names: List[str], query: str, k: int, embedding_function
def query_embeddings_collection(
collection_names: List[str], query: str, query_embeddings, k: int
):
results = []
for collection_name in collection_names:
try:
# if you use docker use the model from the environment variable
collection = CHROMA_CLIENT.get_collection(
name=collection_name,
embedding_function=embedding_function,
)
result = collection.query(
query_texts=[query],
n_results=k,
)
results.append(result)
except:
pass
return merge_and_sort_query_results(results, k)
def query_embeddings_collection(collection_names: List[str], query_embeddings, k: int):
results = []
log.info(f"query_embeddings_collection {query_embeddings}")
for collection_name in collection_names:
try:
collection = CHROMA_CLIENT.get_collection(name=collection_name)
result = collection.query(
query_embeddings=[query_embeddings],
n_results=k,
result = query_embeddings_doc(
collection_name=collection_name,
query=query,
query_embeddings=query_embeddings,
k=k,
)
results.append(result)
except:
......@@ -197,23 +156,8 @@ def rag_messages(
context = doc["content"]
else:
if embedding_engine == "":
if doc["type"] == "collection":
context = query_collection(
collection_names=doc["collection_names"],
query=query,
k=k,
embedding_function=embedding_function,
)
else:
context = query_doc(
collection_name=doc["collection_name"],
query=query,
k=k,
embedding_function=embedding_function,
)
else:
if embedding_engine == "ollama":
query_embeddings = embedding_function.encode(query).tolist()
elif embedding_engine == "ollama":
query_embeddings = generate_ollama_embeddings(
GenerateEmbeddingsForm(
**{
......@@ -233,12 +177,14 @@ def rag_messages(
if doc["type"] == "collection":
context = query_embeddings_collection(
collection_names=doc["collection_names"],
query=query,
query_embeddings=query_embeddings,
k=k,
)
else:
context = query_embeddings_doc(
collection_name=doc["collection_name"],
query=query,
query_embeddings=query_embeddings,
k=k,
)
......@@ -283,46 +229,6 @@ def rag_messages(
return messages
def get_embedding_model_path(
embedding_model: str, update_embedding_model: bool = False
):
# Construct huggingface_hub kwargs with local_files_only to return the snapshot path
cache_dir = os.getenv("SENTENCE_TRANSFORMERS_HOME")
local_files_only = not update_embedding_model
snapshot_kwargs = {
"cache_dir": cache_dir,
"local_files_only": local_files_only,
}
log.debug(f"embedding_model: {embedding_model}")
log.debug(f"snapshot_kwargs: {snapshot_kwargs}")
# Inspiration from upstream sentence_transformers
if (
os.path.exists(embedding_model)
or ("\\" in embedding_model or embedding_model.count("/") > 1)
and local_files_only
):
# If fully qualified path exists, return input, else set repo_id
return embedding_model
elif "/" not in embedding_model:
# Set valid repo_id for model short-name
embedding_model = "sentence-transformers" + "/" + embedding_model
snapshot_kwargs["repo_id"] = embedding_model
# Attempt to query the huggingface_hub library to determine the local path and/or to update
try:
embedding_model_repo_path = snapshot_download(**snapshot_kwargs)
log.debug(f"embedding_model_repo_path: {embedding_model_repo_path}")
return embedding_model_repo_path
except Exception as e:
log.exception(f"Cannot determine embedding model snapshot path: {e}")
return embedding_model
def generate_openai_embeddings(
model: str, text: str, key: str, url: str = "https://api.openai.com/v1"
):
......
backend/apps/web/routers/chats.py
View file @ 748cb7d4
......@@ -28,7 +28,7 @@ from apps.web.models.tags import (
from constants import ERROR_MESSAGES
from config import SRC_LOG_LEVELS
from config import SRC_LOG_LEVELS, ENABLE_ADMIN_EXPORT
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["MODELS"])
......@@ -79,6 +79,11 @@ async def get_all_user_chats(user=Depends(get_current_user)):
@router.get("/all/db", response_model=List[ChatResponse])
async def get_all_user_chats_in_db(user=Depends(get_admin_user)):
if not ENABLE_ADMIN_EXPORT:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail=ERROR_MESSAGES.ACCESS_PROHIBITED,
)
return [
ChatResponse(**{**chat.model_dump(), "chat": json.loads(chat.chat)})
for chat in Chats.get_all_chats()
......
backend/apps/web/routers/utils.py
View file @ 748cb7d4
......@@ -91,7 +91,11 @@ async def download_chat_as_pdf(
@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,
)
return FileResponse(
f"{DATA_DIR}/webui.db",
media_type="application/octet-stream",
......
backend/config.py
View file @ 748cb7d4
......@@ -382,6 +382,8 @@ MODEL_FILTER_LIST = [model.strip() for model in MODEL_FILTER_LIST.split(";")]
WEBHOOK_URL = os.environ.get("WEBHOOK_URL", "")
ENABLE_ADMIN_EXPORT = os.environ.get("ENABLE_ADMIN_EXPORT", "True").lower() == "true"
####################################
# WEBUI_VERSION
####################################
......@@ -416,18 +418,19 @@ if WEBUI_AUTH and WEBUI_SECRET_KEY == "":
####################################
CHROMA_DATA_PATH = f"{DATA_DIR}/vector_db"
# this uses the model defined in the Dockerfile ENV variable. If you dont use docker or docker based deployments such as k8s, the default embedding model will be used (all-MiniLM-L6-v2)
# this uses the model defined in the Dockerfile ENV variable. If you dont use docker or docker based deployments such as k8s, the default embedding model will be used (sentence-transformers/all-MiniLM-L6-v2)
RAG_EMBEDDING_ENGINE = os.environ.get("RAG_EMBEDDING_ENGINE", "")
RAG_EMBEDDING_MODEL = os.environ.get("RAG_EMBEDDING_MODEL", "all-MiniLM-L6-v2")
RAG_EMBEDDING_MODEL = os.environ.get(
"RAG_EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2"
)
log.info(f"Embedding model set: {RAG_EMBEDDING_MODEL}"),
RAG_EMBEDDING_MODEL_AUTO_UPDATE = (
os.environ.get("RAG_EMBEDDING_MODEL_AUTO_UPDATE", "").lower() == "true"
RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE = (
os.environ.get("RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE", "").lower() == "true"
)
# device type embedding models - "cpu" (default), "cuda" (nvidia gpu required) or "mps" (apple silicon) - choosing this right can lead to better performance
USE_CUDA = os.environ.get("USE_CUDA_DOCKER", "false")
......@@ -484,9 +487,24 @@ AUTOMATIC1111_BASE_URL = os.getenv("AUTOMATIC1111_BASE_URL", "")
COMFYUI_BASE_URL = os.getenv("COMFYUI_BASE_URL", "")
IMAGES_OPENAI_API_BASE_URL = os.getenv(
"IMAGES_OPENAI_API_BASE_URL", OPENAI_API_BASE_URL
)
IMAGES_OPENAI_API_KEY = os.getenv("IMAGES_OPENAI_API_KEY", OPENAI_API_KEY)
####################################
# Audio
####################################
AUDIO_OPENAI_API_BASE_URL = os.getenv("AUDIO_OPENAI_API_BASE_URL", OPENAI_API_BASE_URL)
AUDIO_OPENAI_API_KEY = os.getenv("AUDIO_OPENAI_API_KEY", OPENAI_API_KEY)
####################################
# LiteLLM
####################################
LITELLM_PROXY_PORT = int(os.getenv("LITELLM_PROXY_PORT", "14365"))
if LITELLM_PROXY_PORT < 0 or LITELLM_PROXY_PORT > 65535:
raise ValueError("Invalid port number for LITELLM_PROXY_PORT")
LITELLM_PROXY_HOST = os.getenv("LITELLM_PROXY_HOST", "127.0.0.1")
backend/constants.py
View file @ 748cb7d4
......@@ -3,6 +3,10 @@ from enum import Enum
class MESSAGES(str, Enum):
DEFAULT = lambda msg="": f"{msg if msg else ''}"
MODEL_ADDED = lambda model="": f"The model '{model}' has been added successfully."
MODEL_DELETED = (
lambda model="": f"The model '{model}' has been deleted successfully."
)
class WEBHOOK_MESSAGES(str, Enum):
......
backend/main.py
View file @ 748cb7d4
......@@ -20,12 +20,17 @@ from starlette.middleware.base import BaseHTTPMiddleware
from apps.ollama.main import app as ollama_app
from apps.openai.main import app as openai_app
from apps.litellm.main import app as litellm_app, startup as litellm_app_startup
from apps.litellm.main import (
app as litellm_app,
start_litellm_background,
shutdown_litellm_background,
)
from apps.audio.main import app as audio_app
from apps.images.main import app as images_app
from apps.rag.main import app as rag_app
from apps.web.main import app as webui_app
import asyncio
from pydantic import BaseModel
from typing import List
......@@ -47,6 +52,7 @@ from config import (
GLOBAL_LOG_LEVEL,
SRC_LOG_LEVELS,
WEBHOOK_URL,
ENABLE_ADMIN_EXPORT,
)
from constants import ERROR_MESSAGES
......@@ -170,7 +176,7 @@ async def check_url(request: Request, call_next):
@app.on_event("startup")
async def on_startup():
await litellm_app_startup()
asyncio.create_task(start_litellm_background())
app.mount("/api/v1", webui_app)
......@@ -202,6 +208,7 @@ async def get_app_config():
"default_models": webui_app.state.DEFAULT_MODELS,
"default_prompt_suggestions": webui_app.state.DEFAULT_PROMPT_SUGGESTIONS,
"trusted_header_auth": bool(webui_app.state.AUTH_TRUSTED_EMAIL_HEADER),
"admin_export_enabled": ENABLE_ADMIN_EXPORT,
}
......@@ -315,3 +322,8 @@ app.mount(
SPAStaticFiles(directory=FRONTEND_BUILD_DIR, html=True),
name="spa-static-files",
)
@app.on_event("shutdown")
async def shutdown_event():
await shutdown_litellm_background()
backend/requirements.txt
View file @ 748cb7d4
......@@ -17,7 +17,9 @@ peewee
peewee-migrate
bcrypt
litellm==1.30.7
litellm==1.35.17
litellm[proxy]==1.35.17
boto3
argon2-cffi
......@@ -25,6 +27,7 @@ apscheduler
google-generativeai
langchain
langchain-chroma
langchain-community
fake_useragent
chromadb
......@@ -43,6 +46,7 @@ opencv-python-headless
rapidocr-onnxruntime
fpdf2
rank_bm25
faster-whisper
......
package-lock.json
View file @ 748cb7d4
{
"name": "open-webui",
"version": "0.1.120",
"version": "0.1.121",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "open-webui",
"version": "0.1.120",
"version": "0.1.121",
"dependencies": {
"@sveltejs/adapter-node": "^1.3.1",
"async": "^3.2.5",
......
package.json
View file @ 748cb7d4
{
"name": "open-webui",
"version": "0.1.120",
"version": "0.1.121",
"private": true,
"scripts": {
"dev": "vite dev --host",
......
src/lib/apis/images/index.ts
View file @ 748cb7d4
......@@ -72,10 +72,10 @@ export const updateImageGenerationConfig = async (
return res;
};
export const getOpenAIKey = async (token: string = '') => {
export const getOpenAIConfig = async (token: string = '') => {
let error = null;
const res = await fetch(`${IMAGES_API_BASE_URL}/key`, {
const res = await fetch(`${IMAGES_API_BASE_URL}/openai/config`, {
method: 'GET',
headers: {
Accept: 'application/json',
......@@ -101,13 +101,13 @@ export const getOpenAIKey = async (token: string = '') => {
throw error;
}
return res.OPENAI_API_KEY;
return res;
};
export const updateOpenAIKey = async (token: string = '', key: string) => {
export const updateOpenAIConfig = async (token: string = '', url: string, key: string) => {
let error = null;
const res = await fetch(`${IMAGES_API_BASE_URL}/key/update`, {
const res = await fetch(`${IMAGES_API_BASE_URL}/openai/config/update`, {
method: 'POST',
headers: {
Accept: 'application/json',
......@@ -115,6 +115,7 @@ export const updateOpenAIKey = async (token: string = '', key: string) => {
...(token && { authorization: `Bearer ${token}` })
},
body: JSON.stringify({
url: url,
key: key
})
})
......@@ -136,7 +137,7 @@ export const updateOpenAIKey = async (token: string = '', key: string) => {
throw error;
}
return res.OPENAI_API_KEY;
return res;
};
export const getImageGenerationEngineUrls = async (token: string = '') => {
......
src/lib/apis/streaming/index.ts 0 → 100644
View file @ 748cb7d4
type TextStreamUpdate = {
done: boolean;
value: string;
};
// createOpenAITextStream takes a ReadableStreamDefaultReader from an SSE response,
// and returns an async generator that emits delta updates with large deltas chunked into random sized chunks
export async function createOpenAITextStream(
messageStream: ReadableStreamDefaultReader,
splitLargeDeltas: boolean
): Promise<AsyncGenerator<TextStreamUpdate>> {
let iterator = openAIStreamToIterator(messageStream);
if (splitLargeDeltas) {
iterator = streamLargeDeltasAsRandomChunks(iterator);
}
return iterator;
}
async function* openAIStreamToIterator(
reader: ReadableStreamDefaultReader
): AsyncGenerator<TextStreamUpdate> {
while (true) {
const { value, done } = await reader.read();
if (done) {
yield { done: true, value: '' };
break;
}
const lines = value.split('\n');
for (const line of lines) {
if (line !== '') {
console.log(line);
if (line === 'data: [DONE]') {
yield { done: true, value: '' };
} else {
const data = JSON.parse(line.replace(/^data: /, ''));
console.log(data);
yield { done: false, value: data.choices[0].delta.content ?? '' };
}
}
}
}
}
// streamLargeDeltasAsRandomChunks will chunk large deltas (length > 5) into random sized chunks between 1-3 characters
// This is to simulate a more fluid streaming, even though some providers may send large chunks of text at once
async function* streamLargeDeltasAsRandomChunks(
iterator: AsyncGenerator<TextStreamUpdate>
): AsyncGenerator<TextStreamUpdate> {
for await (const textStreamUpdate of iterator) {
if (textStreamUpdate.done) {
yield textStreamUpdate;
return;
}
let content = textStreamUpdate.value;
if (content.length < 5) {
yield { done: false, value: content };
continue;
}
while (content != '') {
const chunkSize = Math.min(Math.floor(Math.random() * 3) + 1, content.length);
const chunk = content.slice(0, chunkSize);
yield { done: false, value: chunk };
await sleep(5);
content = content.slice(chunkSize);
}
}
}
const sleep = (ms: number) => new Promise((resolve) => setTimeout(resolve, ms));
src/lib/components/admin/Settings/Database.svelte
View file @ 748cb7d4
<script lang="ts">
import { downloadDatabase } from '$lib/apis/utils';
import { onMount, getContext } from 'svelte';
import { config } from '$lib/stores';
const i18n = getContext('i18n');
......@@ -24,6 +25,7 @@
<div class=" flex w-full justify-between">
<!-- <div class=" self-center text-xs font-medium">{$i18n.t('Allow Chat Deletion')}</div> -->
{#if $config?.admin_export_enabled ?? true}
<button
class=" flex rounded-md py-1.5 px-3 w-full hover:bg-gray-200 dark:hover:bg-gray-800 transition"
type="button"
......@@ -50,6 +52,7 @@
</div>
<div class=" self-center text-sm font-medium">{$i18n.t('Download Database')}</div>
</button>
{/if}
</div>
</div>
</div>
......
src/lib/components/chat/Settings/Audio.svelte
View file @ 748cb7d4
......@@ -75,6 +75,7 @@
};
const updateConfigHandler = async () => {
if (TTSEngine === 'openai') {
const res = await updateAudioConfig(localStorage.token, {
url: OpenAIUrl,
key: OpenAIKey
......@@ -84,6 +85,7 @@
OpenAIUrl = res.OPENAI_API_BASE_URL;
OpenAIKey = res.OPENAI_API_KEY;
}
}
};
onMount(async () => {
......
src/lib/components/chat/Settings/Chats.svelte
View file @ 748cb7d4
......@@ -301,7 +301,7 @@
</button>
{/if}
{#if $user?.role === 'admin'}
{#if $user?.role === 'admin' && ($config?.admin_export_enabled ?? true)}
<hr class=" dark:border-gray-700" />
<button
......
src/lib/components/chat/Settings/Images.svelte
View file @ 748cb7d4
......@@ -15,8 +15,8 @@
updateImageSize,
getImageSteps,
updateImageSteps,
getOpenAIKey,
updateOpenAIKey
getOpenAIConfig,
updateOpenAIConfig
} from '$lib/apis/images';
import { getBackendConfig } from '$lib/apis';
const dispatch = createEventDispatcher();
......@@ -33,6 +33,7 @@
let AUTOMATIC1111_BASE_URL = '';
let COMFYUI_BASE_URL = '';
let OPENAI_API_BASE_URL = '';
let OPENAI_API_KEY = '';
let selectedModel = '';
......@@ -131,7 +132,10 @@
AUTOMATIC1111_BASE_URL = URLS.AUTOMATIC1111_BASE_URL;
COMFYUI_BASE_URL = URLS.COMFYUI_BASE_URL;
OPENAI_API_KEY = await getOpenAIKey(localStorage.token);
const config = await getOpenAIConfig(localStorage.token);
OPENAI_API_KEY = config.OPENAI_API_KEY;
OPENAI_API_BASE_URL = config.OPENAI_API_BASE_URL;
imageSize = await getImageSize(localStorage.token);
steps = await getImageSteps(localStorage.token);
......@@ -149,7 +153,7 @@
loading = true;
if (imageGenerationEngine === 'openai') {
await updateOpenAIKey(localStorage.token, OPENAI_API_KEY);
await updateOpenAIConfig(localStorage.token, OPENAI_API_BASE_URL, OPENAI_API_KEY);
}
await updateDefaultImageGenerationModel(localStorage.token, selectedModel);
......@@ -300,13 +304,22 @@
</button>
</div>
{:else if imageGenerationEngine === 'openai'}
<div class=" mb-2.5 text-sm font-medium">{$i18n.t('OpenAI API Key')}</div>
<div class="flex w-full">
<div class="flex-1 mr-2">
<div>
<div class=" mb-1.5 text-sm font-medium">{$i18n.t('OpenAI API Config')}</div>
<div class="flex gap-2 mb-1">
<input
class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
placeholder={$i18n.t('API Base URL')}
bind:value={OPENAI_API_BASE_URL}
required
/>
<input
class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
placeholder={$i18n.t('Enter API Key')}
placeholder={$i18n.t('API Key')}
bind:value={OPENAI_API_KEY}
required
/>
</div>
</div>
......@@ -319,6 +332,24 @@
<div class=" mb-2.5 text-sm font-medium">{$i18n.t('Set Default Model')}</div>
<div class="flex w-full">
<div class="flex-1 mr-2">
{#if imageGenerationEngine === 'openai' && !OPENAI_API_BASE_URL.includes('https://api.openai.com')}
<div class="flex w-full">
<div class="flex-1">
<input
list="model-list"
class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
bind:value={selectedModel}
placeholder="Select a model"
/>
<datalist id="model-list">
{#each models ?? [] as model}
<option value={model.id}>{model.name}</option>
{/each}
</datalist>
</div>
</div>
{:else}
<select
class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
bind:value={selectedModel}
......@@ -329,9 +360,11 @@
<option value="" disabled selected>{$i18n.t('Select a model')}</option>
{/if}
{#each models ?? [] as model}
<option value={model.id} class="bg-gray-100 dark:bg-gray-700">{model.name}</option>
<option value={model.id} class="bg-gray-100 dark:bg-gray-700">{model.name}</option
>
{/each}
</select>
{/if}
</div>
</div>
</div>
......
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