Merge branch 'open-webui:main' into next

backend/open_webui/config.py

@@ -347,6 +347,24 @@ MICROSOFT_CLIENT_TENANT_ID = PersistentConfig(
     os.environ.get("MICROSOFT_CLIENT_TENANT_ID", ""),
 )
 
+MICROSOFT_CLIENT_LOGIN_BASE_URL = PersistentConfig(
+    "MICROSOFT_CLIENT_LOGIN_BASE_URL",
+    "oauth.microsoft.login_base_url",
+    os.environ.get(
+        "MICROSOFT_CLIENT_LOGIN_BASE_URL", "https://login.microsoftonline.com"
+    ),
+)
+
+MICROSOFT_CLIENT_PICTURE_URL = PersistentConfig(
+    "MICROSOFT_CLIENT_PICTURE_URL",
+    "oauth.microsoft.picture_url",
+    os.environ.get(
+        "MICROSOFT_CLIENT_PICTURE_URL",
+        "https://graph.microsoft.com/v1.0/me/photo/$value",
+    ),
+)
+
+
 MICROSOFT_OAUTH_SCOPE = PersistentConfig(
     "MICROSOFT_OAUTH_SCOPE",
     "oauth.microsoft.scope",
@@ -542,7 +560,7 @@ def load_oauth_providers():
                 name="microsoft",
                 client_id=MICROSOFT_CLIENT_ID.value,
                 client_secret=MICROSOFT_CLIENT_SECRET.value,
-                server_metadata_url=f"https://login.microsoftonline.com/{MICROSOFT_CLIENT_TENANT_ID.value}/v2.0/.well-known/openid-configuration?appid={MICROSOFT_CLIENT_ID.value}",
+                server_metadata_url=f"{MICROSOFT_CLIENT_LOGIN_BASE_URL.value}/{MICROSOFT_CLIENT_TENANT_ID.value}/v2.0/.well-known/openid-configuration?appid={MICROSOFT_CLIENT_ID.value}",
                 client_kwargs={
                     "scope": MICROSOFT_OAUTH_SCOPE.value,
                 },
@@ -551,7 +569,7 @@ def load_oauth_providers():
 
         OAUTH_PROVIDERS["microsoft"] = {
             "redirect_uri": MICROSOFT_REDIRECT_URI.value,
-            "picture_url": "https://graph.microsoft.com/v1.0/me/photo/$value",
+            "picture_url": MICROSOFT_CLIENT_PICTURE_URL.value,
             "register": microsoft_oauth_register,
         }
 
@@ -901,9 +919,7 @@ TOOL_SERVER_CONNECTIONS = PersistentConfig(
 ####################################
 
 
-WEBUI_URL = PersistentConfig(
-    "WEBUI_URL", "webui.url", os.environ.get("WEBUI_URL", "http://localhost:3000")
-)
+WEBUI_URL = PersistentConfig("WEBUI_URL", "webui.url", os.environ.get("WEBUI_URL", ""))
 
 
 ENABLE_SIGNUP = PersistentConfig(
@@ -1247,12 +1263,6 @@ if THREAD_POOL_SIZE is not None and isinstance(THREAD_POOL_SIZE, str):
         THREAD_POOL_SIZE = None
 
 
-def validate_cors_origins(origins):
-    for origin in origins:
-        if origin != "*":
-            validate_cors_origin(origin)
-
-
 def validate_cors_origin(origin):
     parsed_url = urlparse(origin)
 
@@ -1272,16 +1282,17 @@ def validate_cors_origin(origin):
 # To test CORS_ALLOW_ORIGIN locally, you can set something like
 # CORS_ALLOW_ORIGIN=http://localhost:5173;http://localhost:8080
 # in your .env file depending on your frontend port, 5173 in this case.
-CORS_ALLOW_ORIGIN = os.environ.get(
-    "CORS_ALLOW_ORIGIN", "*;http://localhost:5173;http://localhost:8080"
-).split(";")
+CORS_ALLOW_ORIGIN = os.environ.get("CORS_ALLOW_ORIGIN", "*").split(";")
 
-if "*" in CORS_ALLOW_ORIGIN:
+if CORS_ALLOW_ORIGIN == ["*"]:
     log.warning(
         "\n\nWARNING: CORS_ALLOW_ORIGIN IS SET TO '*' - NOT RECOMMENDED FOR PRODUCTION DEPLOYMENTS.\n"
     )
-
-validate_cors_origins(CORS_ALLOW_ORIGIN)
+else:
+    # You have to pick between a single wildcard or a list of origins.
+    # Doing both will result in CORS errors in the browser.
+    for origin in CORS_ALLOW_ORIGIN:
+        validate_cors_origin(origin)
 
 
 class BannerModel(BaseModel):
@@ -1413,6 +1424,35 @@ Strictly return in JSON format:
 {{MESSAGES:END:6}}
 </chat_history>"""
 
+
+FOLLOW_UP_GENERATION_PROMPT_TEMPLATE = PersistentConfig(
+    "FOLLOW_UP_GENERATION_PROMPT_TEMPLATE",
+    "task.follow_up.prompt_template",
+    os.environ.get("FOLLOW_UP_GENERATION_PROMPT_TEMPLATE", ""),
+)
+
+DEFAULT_FOLLOW_UP_GENERATION_PROMPT_TEMPLATE = """### Task:
+Suggest 3-5 relevant follow-up questions or prompts that the user might naturally ask next in this conversation as a **user**, based on the chat history, to help continue or deepen the discussion.
+### Guidelines:
+- Write all follow-up questions from the user’s point of view, directed to the assistant.
+- Make questions concise, clear, and directly related to the discussed topic(s).
+- Only suggest follow-ups that make sense given the chat content and do not repeat what was already covered.
+- If the conversation is very short or not specific, suggest more general (but relevant) follow-ups the user might ask.
+- Use the conversation's primary language; default to English if multilingual.
+- Response must be a JSON array of strings, no extra text or formatting.
+### Output:
+JSON format: { "follow_ups": ["Question 1?", "Question 2?", "Question 3?"] }
+### Chat History:
+<chat_history>
+{{MESSAGES:END:6}}
+</chat_history>"""
+
+ENABLE_FOLLOW_UP_GENERATION = PersistentConfig(
+    "ENABLE_FOLLOW_UP_GENERATION",
+    "task.follow_up.enable",
+    os.environ.get("ENABLE_FOLLOW_UP_GENERATION", "True").lower() == "true",
+)
+
 ENABLE_TAGS_GENERATION = PersistentConfig(
     "ENABLE_TAGS_GENERATION",
     "task.tags.enable",
@@ -1786,6 +1826,13 @@ PGVECTOR_INITIALIZE_MAX_VECTOR_LENGTH = int(
     os.environ.get("PGVECTOR_INITIALIZE_MAX_VECTOR_LENGTH", "1536")
 )
 
+PGVECTOR_PGCRYPTO = os.getenv("PGVECTOR_PGCRYPTO", "false").lower() == "true"
+PGVECTOR_PGCRYPTO_KEY = os.getenv("PGVECTOR_PGCRYPTO_KEY", None)
+if PGVECTOR_PGCRYPTO and not PGVECTOR_PGCRYPTO_KEY:
+    raise ValueError(
+        "PGVECTOR_PGCRYPTO is enabled but PGVECTOR_PGCRYPTO_KEY is not set. Please provide a valid key."
+    )
+
 # Pinecone
 PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY", None)
 PINECONE_ENVIRONMENT = os.environ.get("PINECONE_ENVIRONMENT", None)
@@ -1946,6 +1993,40 @@ DOCLING_DO_PICTURE_DESCRIPTION = PersistentConfig(
     os.getenv("DOCLING_DO_PICTURE_DESCRIPTION", "False").lower() == "true",
 )
 
+DOCLING_PICTURE_DESCRIPTION_MODE = PersistentConfig(
+    "DOCLING_PICTURE_DESCRIPTION_MODE",
+    "rag.docling_picture_description_mode",
+    os.getenv("DOCLING_PICTURE_DESCRIPTION_MODE", ""),
+)
+
+
+docling_picture_description_local = os.getenv("DOCLING_PICTURE_DESCRIPTION_LOCAL", "")
+try:
+    docling_picture_description_local = json.loads(docling_picture_description_local)
+except json.JSONDecodeError:
+    docling_picture_description_local = {}
+
+
+DOCLING_PICTURE_DESCRIPTION_LOCAL = PersistentConfig(
+    "DOCLING_PICTURE_DESCRIPTION_LOCAL",
+    "rag.docling_picture_description_local",
+    docling_picture_description_local,
+)
+
+docling_picture_description_api = os.getenv("DOCLING_PICTURE_DESCRIPTION_API", "")
+try:
+    docling_picture_description_api = json.loads(docling_picture_description_api)
+except json.JSONDecodeError:
+    docling_picture_description_api = {}
+
+
+DOCLING_PICTURE_DESCRIPTION_API = PersistentConfig(
+    "DOCLING_PICTURE_DESCRIPTION_API",
+    "rag.docling_picture_description_api",
+    docling_picture_description_api,
+)
+
+
 DOCUMENT_INTELLIGENCE_ENDPOINT = PersistentConfig(
     "DOCUMENT_INTELLIGENCE_ENDPOINT",
     "rag.document_intelligence_endpoint",
@@ -2445,6 +2526,18 @@ PERPLEXITY_API_KEY = PersistentConfig(
     os.getenv("PERPLEXITY_API_KEY", ""),
 )
 
+PERPLEXITY_MODEL = PersistentConfig(
+    "PERPLEXITY_MODEL",
+    "rag.web.search.perplexity_model",
+    os.getenv("PERPLEXITY_MODEL", "sonar"),
+)
+
+PERPLEXITY_SEARCH_CONTEXT_USAGE = PersistentConfig(
+    "PERPLEXITY_SEARCH_CONTEXT_USAGE",
+    "rag.web.search.perplexity_search_context_usage",
+    os.getenv("PERPLEXITY_SEARCH_CONTEXT_USAGE", "medium"),
+)
+
 SOUGOU_API_SID = PersistentConfig(
     "SOUGOU_API_SID",
     "rag.web.search.sougou_api_sid",

backend/open_webui/constants.py

@@ -111,6 +111,7 @@ class TASKS(str, Enum):
 
     DEFAULT = lambda task="": f"{task if task else 'generation'}"
     TITLE_GENERATION = "title_generation"
+    FOLLOW_UP_GENERATION = "follow_up_generation"
     TAGS_GENERATION = "tags_generation"
     EMOJI_GENERATION = "emoji_generation"
     QUERY_GENERATION = "query_generation"

backend/open_webui/env.py

@@ -5,6 +5,7 @@ import os
 import pkgutil
 import sys
 import shutil
+from uuid import uuid4
 from pathlib import Path
 
 import markdown
@@ -130,6 +131,7 @@ else:
         PACKAGE_DATA = {"version": "0.0.0"}
 
 VERSION = PACKAGE_DATA["version"]
+INSTANCE_ID = os.environ.get("INSTANCE_ID", str(uuid4()))
 
 
 # Function to parse each section

backend/open_webui/functions.py

@@ -25,6 +25,7 @@ from open_webui.socket.main import (
 )
 
 
+from open_webui.models.users import UserModel
 from open_webui.models.functions import Functions
 from open_webui.models.models import Models
 
@@ -227,12 +228,7 @@ async def generate_function_chat_completion(
         "__task__": __task__,
         "__task_body__": __task_body__,
         "__files__": files,
-        "__user__": {
-            "id": user.id,
-            "email": user.email,
-            "name": user.name,
-            "role": user.role,
-        },
+        "__user__": user.model_dump() if isinstance(user, UserModel) else {},
         "__metadata__": metadata,
         "__request__": request,
     }

backend/open_webui/main.py

@@ -8,6 +8,8 @@ import shutil
 import sys
 import time
 import random
+from uuid import uuid4
+
 
 from contextlib import asynccontextmanager
 from urllib.parse import urlencode, parse_qs, urlparse
@@ -19,6 +21,7 @@ from aiocache import cached
 import aiohttp
 import anyio.to_thread
 import requests
+from redis import Redis
 
 
 from fastapi import (
@@ -37,7 +40,7 @@ from fastapi import (
 from fastapi.openapi.docs import get_swagger_ui_html
 
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse, RedirectResponse
+from fastapi.responses import FileResponse, JSONResponse, RedirectResponse
 from fastapi.staticfiles import StaticFiles
 
 from starlette_compress import CompressMiddleware
@@ -231,6 +234,9 @@ from open_webui.config import (
     DOCLING_OCR_ENGINE,
     DOCLING_OCR_LANG,
     DOCLING_DO_PICTURE_DESCRIPTION,
+    DOCLING_PICTURE_DESCRIPTION_MODE,
+    DOCLING_PICTURE_DESCRIPTION_LOCAL,
+    DOCLING_PICTURE_DESCRIPTION_API,
     DOCUMENT_INTELLIGENCE_ENDPOINT,
     DOCUMENT_INTELLIGENCE_KEY,
     MISTRAL_OCR_API_KEY,
@@ -268,6 +274,8 @@ from open_webui.config import (
     BRAVE_SEARCH_API_KEY,
     EXA_API_KEY,
     PERPLEXITY_API_KEY,
+    PERPLEXITY_MODEL,
+    PERPLEXITY_SEARCH_CONTEXT_USAGE,
     SOUGOU_API_SID,
     SOUGOU_API_SK,
     KAGI_SEARCH_API_KEY,
@@ -359,10 +367,12 @@ from open_webui.config import (
     TASK_MODEL_EXTERNAL,
     ENABLE_TAGS_GENERATION,
     ENABLE_TITLE_GENERATION,
+    ENABLE_FOLLOW_UP_GENERATION,
     ENABLE_SEARCH_QUERY_GENERATION,
     ENABLE_RETRIEVAL_QUERY_GENERATION,
     ENABLE_AUTOCOMPLETE_GENERATION,
     TITLE_GENERATION_PROMPT_TEMPLATE,
+    FOLLOW_UP_GENERATION_PROMPT_TEMPLATE,
     TAGS_GENERATION_PROMPT_TEMPLATE,
     IMAGE_PROMPT_GENERATION_PROMPT_TEMPLATE,
     TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE,
@@ -384,6 +394,7 @@ from open_webui.env import (
     SAFE_MODE,
     SRC_LOG_LEVELS,
     VERSION,
+    INSTANCE_ID,
     WEBUI_BUILD_HASH,
     WEBUI_SECRET_KEY,
     WEBUI_SESSION_COOKIE_SAME_SITE,
@@ -411,6 +422,7 @@ from open_webui.utils.chat import (
     chat_completed as chat_completed_handler,
     chat_action as chat_action_handler,
 )
+from open_webui.utils.embeddings import generate_embeddings
 from open_webui.utils.middleware import process_chat_payload, process_chat_response
 from open_webui.utils.access_control import has_access
 
@@ -424,8 +436,10 @@ from open_webui.utils.auth import (
 from open_webui.utils.plugin import install_tool_and_function_dependencies
 from open_webui.utils.oauth import OAuthManager
 from open_webui.utils.security_headers import SecurityHeadersMiddleware
+from open_webui.utils.redis import get_redis_connection
 
 from open_webui.tasks import (
+    redis_task_command_listener,
     list_task_ids_by_chat_id,
     stop_task,
     list_tasks,
@@ -477,7 +491,9 @@ https://github.com/open-webui/open-webui
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
+    app.state.instance_id = INSTANCE_ID
     start_logger()
+
     if RESET_CONFIG_ON_START:
         reset_config()
 
@@ -489,6 +505,19 @@ async def lifespan(app: FastAPI):
     log.info("Installing external dependencies of functions and tools...")
     install_tool_and_function_dependencies()
 
+    app.state.redis = get_redis_connection(
+        redis_url=REDIS_URL,
+        redis_sentinels=get_sentinels_from_env(
+            REDIS_SENTINEL_HOSTS, REDIS_SENTINEL_PORT
+        ),
+        async_mode=True,
+    )
+
+    if app.state.redis is not None:
+        app.state.redis_task_command_listener = asyncio.create_task(
+            redis_task_command_listener(app)
+        )
+
     if THREAD_POOL_SIZE and THREAD_POOL_SIZE > 0:
         limiter = anyio.to_thread.current_default_thread_limiter()
         limiter.total_tokens = THREAD_POOL_SIZE
@@ -497,6 +526,9 @@ async def lifespan(app: FastAPI):
 
     yield
 
+    if hasattr(app.state, "redis_task_command_listener"):
+        app.state.redis_task_command_listener.cancel()
+
 
 app = FastAPI(
     title="Open WebUI",
@@ -508,10 +540,12 @@ app = FastAPI(
 
 oauth_manager = OAuthManager(app)
 
+app.state.instance_id = None
 app.state.config = AppConfig(
     redis_url=REDIS_URL,
     redis_sentinels=get_sentinels_from_env(REDIS_SENTINEL_HOSTS, REDIS_SENTINEL_PORT),
 )
+app.state.redis = None
 
 app.state.WEBUI_NAME = WEBUI_NAME
 app.state.LICENSE_METADATA = None
@@ -696,6 +730,9 @@ app.state.config.DOCLING_SERVER_URL = DOCLING_SERVER_URL
 app.state.config.DOCLING_OCR_ENGINE = DOCLING_OCR_ENGINE
 app.state.config.DOCLING_OCR_LANG = DOCLING_OCR_LANG
 app.state.config.DOCLING_DO_PICTURE_DESCRIPTION = DOCLING_DO_PICTURE_DESCRIPTION
+app.state.config.DOCLING_PICTURE_DESCRIPTION_MODE = DOCLING_PICTURE_DESCRIPTION_MODE
+app.state.config.DOCLING_PICTURE_DESCRIPTION_LOCAL = DOCLING_PICTURE_DESCRIPTION_LOCAL
+app.state.config.DOCLING_PICTURE_DESCRIPTION_API = DOCLING_PICTURE_DESCRIPTION_API
 app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT = DOCUMENT_INTELLIGENCE_ENDPOINT
 app.state.config.DOCUMENT_INTELLIGENCE_KEY = DOCUMENT_INTELLIGENCE_KEY
 app.state.config.MISTRAL_OCR_API_KEY = MISTRAL_OCR_API_KEY
@@ -771,6 +808,8 @@ app.state.config.BING_SEARCH_V7_ENDPOINT = BING_SEARCH_V7_ENDPOINT
 app.state.config.BING_SEARCH_V7_SUBSCRIPTION_KEY = BING_SEARCH_V7_SUBSCRIPTION_KEY
 app.state.config.EXA_API_KEY = EXA_API_KEY
 app.state.config.PERPLEXITY_API_KEY = PERPLEXITY_API_KEY
+app.state.config.PERPLEXITY_MODEL = PERPLEXITY_MODEL
+app.state.config.PERPLEXITY_SEARCH_CONTEXT_USAGE = PERPLEXITY_SEARCH_CONTEXT_USAGE
 app.state.config.SOUGOU_API_SID = SOUGOU_API_SID
 app.state.config.SOUGOU_API_SK = SOUGOU_API_SK
 app.state.config.EXTERNAL_WEB_SEARCH_URL = EXTERNAL_WEB_SEARCH_URL
@@ -959,6 +998,7 @@ app.state.config.ENABLE_RETRIEVAL_QUERY_GENERATION = ENABLE_RETRIEVAL_QUERY_GENE
 app.state.config.ENABLE_AUTOCOMPLETE_GENERATION = ENABLE_AUTOCOMPLETE_GENERATION
 app.state.config.ENABLE_TAGS_GENERATION = ENABLE_TAGS_GENERATION
 app.state.config.ENABLE_TITLE_GENERATION = ENABLE_TITLE_GENERATION
+app.state.config.ENABLE_FOLLOW_UP_GENERATION = ENABLE_FOLLOW_UP_GENERATION
 
 
 app.state.config.TITLE_GENERATION_PROMPT_TEMPLATE = TITLE_GENERATION_PROMPT_TEMPLATE
@@ -966,6 +1006,9 @@ app.state.config.TAGS_GENERATION_PROMPT_TEMPLATE = TAGS_GENERATION_PROMPT_TEMPLA
 app.state.config.IMAGE_PROMPT_GENERATION_PROMPT_TEMPLATE = (
     IMAGE_PROMPT_GENERATION_PROMPT_TEMPLATE
 )
+app.state.config.FOLLOW_UP_GENERATION_PROMPT_TEMPLATE = (
+    FOLLOW_UP_GENERATION_PROMPT_TEMPLATE
+)
 
 app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE = (
     TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE
@@ -1197,6 +1240,37 @@ async def get_base_models(request: Request, user=Depends(get_admin_user)):
     return {"data": models}
 
 
+##################################
+# Embeddings
+##################################
+
+
+@app.post("/api/embeddings")
+async def embeddings(
+    request: Request, form_data: dict, user=Depends(get_verified_user)
+):
+    """
+    OpenAI-compatible embeddings endpoint.
+
+    This handler:
+      - Performs user/model checks and dispatches to the correct backend.
+      - Supports OpenAI, Ollama, arena models, pipelines, and any compatible provider.
+
+    Args:
+        request (Request): Request context.
+        form_data (dict): OpenAI-like payload (e.g., {"model": "...", "input": [...]})
+        user (UserModel): Authenticated user.
+
+    Returns:
+        dict: OpenAI-compatible embeddings response.
+    """
+    # Make sure models are loaded in app state
+    if not request.app.state.MODELS:
+        await get_all_models(request, user=user)
+    # Use generic dispatcher in utils.embeddings
+    return await generate_embeddings(request, form_data, user)
+
+
 @app.post("/api/chat/completions")
 async def chat_completion(
     request: Request,
@@ -1338,26 +1412,30 @@ async def chat_action(
 
 
 @app.post("/api/tasks/stop/{task_id}")
-async def stop_task_endpoint(task_id: str, user=Depends(get_verified_user)):
+async def stop_task_endpoint(
+    request: Request, task_id: str, user=Depends(get_verified_user)
+):
     try:
-        result = await stop_task(task_id)
+        result = await stop_task(request, task_id)
         return result
     except ValueError as e:
         raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(e))
 
 
 @app.get("/api/tasks")
-async def list_tasks_endpoint(user=Depends(get_verified_user)):
-    return {"tasks": list_tasks()}
+async def list_tasks_endpoint(request: Request, user=Depends(get_verified_user)):
+    return {"tasks": await list_tasks(request)}
 
 
 @app.get("/api/tasks/chat/{chat_id}")
-async def list_tasks_by_chat_id_endpoint(chat_id: str, user=Depends(get_verified_user)):
+async def list_tasks_by_chat_id_endpoint(
+    request: Request, chat_id: str, user=Depends(get_verified_user)
+):
     chat = Chats.get_chat_by_id(chat_id)
     if chat is None or chat.user_id != user.id:
         return {"task_ids": []}
 
-    task_ids = list_task_ids_by_chat_id(chat_id)
+    task_ids = await list_task_ids_by_chat_id(request, chat_id)
 
     print(f"Task IDs for chat {chat_id}: {task_ids}")
     return {"task_ids": task_ids}
@@ -1628,7 +1706,20 @@ async def healthcheck_with_db():
 
 
 app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
-app.mount("/cache", StaticFiles(directory=CACHE_DIR), name="cache")
+
+
+@app.get("/cache/{path:path}")
+async def serve_cache_file(
+    path: str,
+    user=Depends(get_verified_user),
+):
+    file_path = os.path.abspath(os.path.join(CACHE_DIR, path))
+    # prevent path traversal
+    if not file_path.startswith(os.path.abspath(CACHE_DIR)):
+        raise HTTPException(status_code=404, detail="File not found")
+    if not os.path.isfile(file_path):
+        raise HTTPException(status_code=404, detail="File not found")
+    return FileResponse(file_path)
 
 
 def swagger_ui_html(*args, **kwargs):

backend/open_webui/models/users.py

@@ -95,6 +95,7 @@ class UserRoleUpdateForm(BaseModel):
 
 
 class UserUpdateForm(BaseModel):
+    role: str
     name: str
     email: str
     profile_image_url: str
@@ -369,7 +370,7 @@ class UsersTable:
         except Exception:
             return False
 
-    def update_user_api_key_by_id(self, id: str, api_key: str) -> str:
+    def update_user_api_key_by_id(self, id: str, api_key: str) -> bool:
         try:
             with get_db() as db:
                 result = db.query(User).filter_by(id=id).update({"api_key": api_key})

backend/open_webui/retrieval/loaders/main.py

@@ -2,6 +2,7 @@ import requests
 import logging
 import ftfy
 import sys
+import json
 
 from langchain_community.document_loaders import (
     AzureAIDocumentIntelligenceLoader,
@@ -76,7 +77,6 @@ known_source_ext = [
     "swift",
     "vue",
     "svelte",
-    "msg",
     "ex",
     "exs",
     "erl",
@@ -147,17 +147,32 @@ class DoclingLoader:
                 )
             }
 
-            params = {
-                "image_export_mode": "placeholder",
-                "table_mode": "accurate",
-            }
+            params = {"image_export_mode": "placeholder", "table_mode": "accurate"}
 
             if self.params:
-                if self.params.get("do_picture_classification"):
-                    params["do_picture_classification"] = self.params.get(
-                        "do_picture_classification"
+                if self.params.get("do_picture_description"):
+                    params["do_picture_description"] = self.params.get(
+                        "do_picture_description"
                     )
 
+                    picture_description_mode = self.params.get(
+                        "picture_description_mode", ""
+                    ).lower()
+
+                    if picture_description_mode == "local" and self.params.get(
+                        "picture_description_local", {}
+                    ):
+                        params["picture_description_local"] = self.params.get(
+                            "picture_description_local", {}
+                        )
+
+                    elif picture_description_mode == "api" and self.params.get(
+                        "picture_description_api", {}
+                    ):
+                        params["picture_description_api"] = self.params.get(
+                            "picture_description_api", {}
+                        )
+
                 if self.params.get("ocr_engine") and self.params.get("ocr_lang"):
                     params["ocr_engine"] = self.params.get("ocr_engine")
                     params["ocr_lang"] = [
@@ -285,17 +300,20 @@ class Loader:
             if self._is_text_file(file_ext, file_content_type):
                 loader = TextLoader(file_path, autodetect_encoding=True)
             else:
+                # Build params for DoclingLoader
+                params = self.kwargs.get("DOCLING_PARAMS", {})
+                if not isinstance(params, dict):
+                    try:
+                        params = json.loads(params)
+                    except json.JSONDecodeError:
+                        log.error("Invalid DOCLING_PARAMS format, expected JSON object")
+                        params = {}
+
                 loader = DoclingLoader(
                     url=self.kwargs.get("DOCLING_SERVER_URL"),
                     file_path=file_path,
                     mime_type=file_content_type,
-                    params={
-                        "ocr_engine": self.kwargs.get("DOCLING_OCR_ENGINE"),
-                        "ocr_lang": self.kwargs.get("DOCLING_OCR_LANG"),
-                        "do_picture_classification": self.kwargs.get(
-                            "DOCLING_DO_PICTURE_DESCRIPTION"
-                        ),
-                    },
+                    params=params,
                 )
         elif (
             self.engine == "document_intelligence"

backend/open_webui/retrieval/loaders/mistral.py

@@ -20,6 +20,14 @@ class MistralLoader:
     """
     Enhanced Mistral OCR loader with both sync and async support.
     Loads documents by processing them through the Mistral OCR API.
+
+    Performance Optimizations:
+    - Differentiated timeouts for different operations
+    - Intelligent retry logic with exponential backoff
+    - Memory-efficient file streaming for large files
+    - Connection pooling and keepalive optimization
+    - Semaphore-based concurrency control for batch processing
+    - Enhanced error handling with retryable error classification
     """
 
     BASE_API_URL = "https://api.mistral.ai/v1"
@@ -53,17 +61,40 @@ class MistralLoader:
         self.max_retries = max_retries
         self.debug = enable_debug_logging
 
-        # Pre-compute file info for performance
+        # PERFORMANCE OPTIMIZATION: Differentiated timeouts for different operations
+        # This prevents long-running OCR operations from affecting quick operations
+        # and improves user experience by failing fast on operations that should be quick
+        self.upload_timeout = min(
+            timeout, 120
+        )  # Cap upload at 2 minutes - prevents hanging on large files
+        self.url_timeout = (
+            30  # URL requests should be fast - fail quickly if API is slow
+        )
+        self.ocr_timeout = (
+            timeout  # OCR can take the full timeout - this is the heavy operation
+        )
+        self.cleanup_timeout = (
+            30  # Cleanup should be quick - don't hang on file deletion
+        )
+
+        # PERFORMANCE OPTIMIZATION: Pre-compute file info to avoid repeated filesystem calls
+        # This avoids multiple os.path.basename() and os.path.getsize() calls during processing
         self.file_name = os.path.basename(file_path)
         self.file_size = os.path.getsize(file_path)
 
+        # ENHANCEMENT: Added User-Agent for better API tracking and debugging
         self.headers = {
             "Authorization": f"Bearer {self.api_key}",
-            "User-Agent": "OpenWebUI-MistralLoader/2.0",
+            "User-Agent": "OpenWebUI-MistralLoader/2.0",  # Helps API provider track usage
         }
 
     def _debug_log(self, message: str, *args) -> None:
-        """Conditional debug logging for performance."""
+        """
+        PERFORMANCE OPTIMIZATION: Conditional debug logging for performance.
+
+        Only processes debug messages when debug mode is enabled, avoiding
+        string formatting overhead in production environments.
+        """
         if self.debug:
             log.debug(message, *args)
 
@@ -115,53 +146,118 @@ class MistralLoader:
             log.error(f"Unexpected error processing response: {e}")
             raise
 
+    def _is_retryable_error(self, error: Exception) -> bool:
+        """
+        ENHANCEMENT: Intelligent error classification for retry logic.
+
+        Determines if an error is retryable based on its type and status code.
+        This prevents wasting time retrying errors that will never succeed
+        (like authentication errors) while ensuring transient errors are retried.
+
+        Retryable errors:
+        - Network connection errors (temporary network issues)
+        - Timeouts (server might be temporarily overloaded)
+        - Server errors (5xx status codes - server-side issues)
+        - Rate limiting (429 status - temporary throttling)
+
+        Non-retryable errors:
+        - Authentication errors (401, 403 - won't fix with retry)
+        - Bad request errors (400 - malformed request)
+        - Not found errors (404 - resource doesn't exist)
+        """
+        if isinstance(error, requests.exceptions.ConnectionError):
+            return True  # Network issues are usually temporary
+        if isinstance(error, requests.exceptions.Timeout):
+            return True  # Timeouts might resolve on retry
+        if isinstance(error, requests.exceptions.HTTPError):
+            # Only retry on server errors (5xx) or rate limits (429)
+            if hasattr(error, "response") and error.response is not None:
+                status_code = error.response.status_code
+                return status_code >= 500 or status_code == 429
+            return False
+        if isinstance(
+            error, (aiohttp.ClientConnectionError, aiohttp.ServerTimeoutError)
+        ):
+            return True  # Async network/timeout errors are retryable
+        if isinstance(error, aiohttp.ClientResponseError):
+            return error.status >= 500 or error.status == 429
+        return False  # All other errors are non-retryable
+
     def _retry_request_sync(self, request_func, *args, **kwargs):
-        """Synchronous retry logic with exponential backoff."""
+        """
+        ENHANCEMENT: Synchronous retry logic with intelligent error classification.
+
+        Uses exponential backoff with jitter to avoid thundering herd problems.
+        The wait time increases exponentially but is capped at 30 seconds to
+        prevent excessive delays. Only retries errors that are likely to succeed
+        on subsequent attempts.
+        """
         for attempt in range(self.max_retries):
             try:
                 return request_func(*args, **kwargs)
-            except (requests.exceptions.RequestException, Exception) as e:
-                if attempt == self.max_retries - 1:
+            except Exception as e:
+                if attempt == self.max_retries - 1 or not self._is_retryable_error(e):
                     raise
 
-                wait_time = (2**attempt) + 0.5
+                # PERFORMANCE OPTIMIZATION: Exponential backoff with cap
+                # Prevents overwhelming the server while ensuring reasonable retry delays
+                wait_time = min((2**attempt) + 0.5, 30)  # Cap at 30 seconds
                 log.warning(
-                    f"Request failed (attempt {attempt + 1}/{self.max_retries}): {e}. Retrying in {wait_time}s..."
+                    f"Retryable error (attempt {attempt + 1}/{self.max_retries}): {e}. "
+                    f"Retrying in {wait_time}s..."
                 )
                 time.sleep(wait_time)
 
     async def _retry_request_async(self, request_func, *args, **kwargs):
-        """Async retry logic with exponential backoff."""
+        """
+        ENHANCEMENT: Async retry logic with intelligent error classification.
+
+        Async version of retry logic that doesn't block the event loop during
+        wait periods. Uses the same exponential backoff strategy as sync version.
+        """
         for attempt in range(self.max_retries):
             try:
                 return await request_func(*args, **kwargs)
-            except (aiohttp.ClientError, asyncio.TimeoutError) as e:
-                if attempt == self.max_retries - 1:
+            except Exception as e:
+                if attempt == self.max_retries - 1 or not self._is_retryable_error(e):
                     raise
 
-                wait_time = (2**attempt) + 0.5
+                # PERFORMANCE OPTIMIZATION: Non-blocking exponential backoff
+                wait_time = min((2**attempt) + 0.5, 30)  # Cap at 30 seconds
                 log.warning(
-                    f"Request failed (attempt {attempt + 1}/{self.max_retries}): {e}. Retrying in {wait_time}s..."
+                    f"Retryable error (attempt {attempt + 1}/{self.max_retries}): {e}. "
+                    f"Retrying in {wait_time}s..."
                 )
-                await asyncio.sleep(wait_time)
+                await asyncio.sleep(wait_time)  # Non-blocking wait
 
     def _upload_file(self) -> str:
-        """Uploads the file to Mistral for OCR processing (sync version)."""
+        """
+        PERFORMANCE OPTIMIZATION: Enhanced file upload with streaming consideration.
+
+        Uploads the file to Mistral for OCR processing (sync version).
+        Uses context manager for file handling to ensure proper resource cleanup.
+        Although streaming is not enabled for this endpoint, the file is opened
+        in a context manager to minimize memory usage duration.
+        """
         log.info("Uploading file to Mistral API")
         url = f"{self.BASE_API_URL}/files"
-        file_name = os.path.basename(self.file_path)
 
         def upload_request():
+            # MEMORY OPTIMIZATION: Use context manager to minimize file handle lifetime
+            # This ensures the file is closed immediately after reading, reducing memory usage
             with open(self.file_path, "rb") as f:
-                files = {"file": (file_name, f, "application/pdf")}
+                files = {"file": (self.file_name, f, "application/pdf")}
                 data = {"purpose": "ocr"}
 
+                # NOTE: stream=False is required for this endpoint
+                # The Mistral API doesn't support chunked uploads for this endpoint
                 response = requests.post(
                     url,
                     headers=self.headers,
                     files=files,
                     data=data,
-                    timeout=self.timeout,
+                    timeout=self.upload_timeout,  # Use specialized upload timeout
+                    stream=False,  # Keep as False for this endpoint
                 )
 
             return self._handle_response(response)
@@ -209,7 +305,7 @@ class MistralLoader:
                 url,
                 data=writer,
                 headers=self.headers,
-                timeout=aiohttp.ClientTimeout(total=self.timeout),
+                timeout=aiohttp.ClientTimeout(total=self.upload_timeout),
             ) as response:
                 return await self._handle_response_async(response)
 
@@ -231,7 +327,7 @@ class MistralLoader:
 
         def url_request():
             response = requests.get(
-                url, headers=signed_url_headers, params=params, timeout=self.timeout
+                url, headers=signed_url_headers, params=params, timeout=self.url_timeout
             )
             return self._handle_response(response)
 
@@ -261,7 +357,7 @@ class MistralLoader:
                 url,
                 headers=headers,
                 params=params,
-                timeout=aiohttp.ClientTimeout(total=self.timeout),
+                timeout=aiohttp.ClientTimeout(total=self.url_timeout),
             ) as response:
                 return await self._handle_response_async(response)
 
@@ -294,7 +390,7 @@ class MistralLoader:
 
         def ocr_request():
             response = requests.post(
-                url, headers=ocr_headers, json=payload, timeout=self.timeout
+                url, headers=ocr_headers, json=payload, timeout=self.ocr_timeout
             )
             return self._handle_response(response)
 
@@ -336,7 +432,7 @@ class MistralLoader:
                 url,
                 json=payload,
                 headers=headers,
-                timeout=aiohttp.ClientTimeout(total=self.timeout),
+                timeout=aiohttp.ClientTimeout(total=self.ocr_timeout),
             ) as response:
                 ocr_response = await self._handle_response_async(response)
 
@@ -353,7 +449,9 @@ class MistralLoader:
         url = f"{self.BASE_API_URL}/files/{file_id}"
 
         try:
-            response = requests.delete(url, headers=self.headers, timeout=30)
+            response = requests.delete(
+                url, headers=self.headers, timeout=self.cleanup_timeout
+            )
             delete_response = self._handle_response(response)
             log.info(f"File deleted successfully: {delete_response}")
         except Exception as e:
@@ -372,7 +470,7 @@ class MistralLoader:
                     url=f"{self.BASE_API_URL}/files/{file_id}",
                     headers=self.headers,
                     timeout=aiohttp.ClientTimeout(
-                        total=30
+                        total=self.cleanup_timeout
                     ),  # Shorter timeout for cleanup
                 ) as response:
                     return await self._handle_response_async(response)
@@ -388,29 +486,39 @@ class MistralLoader:
     async def _get_session(self):
         """Context manager for HTTP session with optimized settings."""
         connector = aiohttp.TCPConnector(
-            limit=10,  # Total connection limit
-            limit_per_host=5,  # Per-host connection limit
-            ttl_dns_cache=300,  # DNS cache TTL
+            limit=20,  # Increased total connection limit for better throughput
+            limit_per_host=10,  # Increased per-host limit for API endpoints
+            ttl_dns_cache=600,  # Longer DNS cache TTL (10 minutes)
             use_dns_cache=True,
-            keepalive_timeout=30,
+            keepalive_timeout=60,  # Increased keepalive for connection reuse
             enable_cleanup_closed=True,
+            force_close=False,  # Allow connection reuse
+            resolver=aiohttp.AsyncResolver(),  # Use async DNS resolver
+        )
+
+        timeout = aiohttp.ClientTimeout(
+            total=self.timeout,
+            connect=30,  # Connection timeout
+            sock_read=60,  # Socket read timeout
         )
 
         async with aiohttp.ClientSession(
             connector=connector,
-            timeout=aiohttp.ClientTimeout(total=self.timeout),
+            timeout=timeout,
             headers={"User-Agent": "OpenWebUI-MistralLoader/2.0"},
+            raise_for_status=False,  # We handle status codes manually
         ) as session:
             yield session
 
     def _process_results(self, ocr_response: Dict[str, Any]) -> List[Document]:
-        """Process OCR results into Document objects with enhanced metadata."""
+        """Process OCR results into Document objects with enhanced metadata and memory efficiency."""
         pages_data = ocr_response.get("pages")
         if not pages_data:
             log.warning("No pages found in OCR response.")
             return [
                 Document(
-                    page_content="No text content found", metadata={"error": "no_pages"}
+                    page_content="No text content found",
+                    metadata={"error": "no_pages", "file_name": self.file_name},
                 )
             ]
 
@@ -418,41 +526,44 @@ class MistralLoader:
         total_pages = len(pages_data)
         skipped_pages = 0
 
+        # Process pages in a memory-efficient way
         for page_data in pages_data:
             page_content = page_data.get("markdown")
             page_index = page_data.get("index")  # API uses 0-based index
 
-            if page_content is not None and page_index is not None:
-                # Clean up content efficiently
-                cleaned_content = (
-                    page_content.strip()
-                    if isinstance(page_content, str)
-                    else str(page_content)
-                )
-
-                if cleaned_content:  # Only add non-empty pages
-                    documents.append(
-                        Document(
-                            page_content=cleaned_content,
-                            metadata={
-                                "page": page_index,  # 0-based index from API
-                                "page_label": page_index
-                                + 1,  # 1-based label for convenience
-                                "total_pages": total_pages,
-                                "file_name": self.file_name,
-                                "file_size": self.file_size,
-                                "processing_engine": "mistral-ocr",
-                            },
-                        )
-                    )
-                else:
-                    skipped_pages += 1
-                    self._debug_log(f"Skipping empty page {page_index}")
-            else:
+            if page_content is None or page_index is None:
                 skipped_pages += 1
                 self._debug_log(
-                    f"Skipping page due to missing 'markdown' or 'index'. Data: {page_data}"
+                    f"Skipping page due to missing 'markdown' or 'index'. Data keys: {list(page_data.keys())}"
                 )
+                continue
+
+            # Clean up content efficiently with early exit for empty content
+            if isinstance(page_content, str):
+                cleaned_content = page_content.strip()
+            else:
+                cleaned_content = str(page_content).strip()
+
+            if not cleaned_content:
+                skipped_pages += 1
+                self._debug_log(f"Skipping empty page {page_index}")
+                continue
+
+            # Create document with optimized metadata
+            documents.append(
+                Document(
+                    page_content=cleaned_content,
+                    metadata={
+                        "page": page_index,  # 0-based index from API
+                        "page_label": page_index + 1,  # 1-based label for convenience
+                        "total_pages": total_pages,
+                        "file_name": self.file_name,
+                        "file_size": self.file_size,
+                        "processing_engine": "mistral-ocr",
+                        "content_length": len(cleaned_content),
+                    },
+                )
+            )
 
         if skipped_pages > 0:
             log.info(
@@ -467,7 +578,11 @@ class MistralLoader:
             return [
                 Document(
                     page_content="No valid text content found in document",
-                    metadata={"error": "no_valid_pages", "total_pages": total_pages},
+                    metadata={
+                        "error": "no_valid_pages",
+                        "total_pages": total_pages,
+                        "file_name": self.file_name,
+                    },
                 )
             ]
 
@@ -585,12 +700,14 @@ class MistralLoader:
     @staticmethod
     async def load_multiple_async(
         loaders: List["MistralLoader"],
+        max_concurrent: int = 5,  # Limit concurrent requests
     ) -> List[List[Document]]:
         """
-        Process multiple files concurrently for maximum performance.
+        Process multiple files concurrently with controlled concurrency.
 
         Args:
             loaders: List of MistralLoader instances
+            max_concurrent: Maximum number of concurrent requests
 
         Returns:
             List of document lists, one for each loader
@@ -598,11 +715,20 @@ class MistralLoader:
         if not loaders:
             return []
 
-        log.info(f"Starting concurrent processing of {len(loaders)} files")
+        log.info(
+            f"Starting concurrent processing of {len(loaders)} files with max {max_concurrent} concurrent"
+        )
         start_time = time.time()
 
-        # Process all files concurrently
-        tasks = [loader.load_async() for loader in loaders]
+        # Use semaphore to control concurrency
+        semaphore = asyncio.Semaphore(max_concurrent)
+
+        async def process_with_semaphore(loader: "MistralLoader") -> List[Document]:
+            async with semaphore:
+                return await loader.load_async()
+
+        # Process all files with controlled concurrency
+        tasks = [process_with_semaphore(loader) for loader in loaders]
         results = await asyncio.gather(*tasks, return_exceptions=True)
 
         # Handle any exceptions in results
@@ -624,10 +750,18 @@ class MistralLoader:
             else:
                 processed_results.append(result)
 
+        # MONITORING: Log comprehensive batch processing statistics
         total_time = time.time() - start_time
         total_docs = sum(len(docs) for docs in processed_results)
+        success_count = sum(
+            1 for result in results if not isinstance(result, Exception)
+        )
+        failure_count = len(results) - success_count
+
         log.info(
-            f"Batch processing completed in {total_time:.2f}s, produced {total_docs} total documents"
+            f"Batch processing completed in {total_time:.2f}s: "
+            f"{success_count} files succeeded, {failure_count} files failed, "
+            f"produced {total_docs} total documents"
         )
 
         return processed_results

backend/open_webui/retrieval/loaders/youtube.py

@@ -1,4 +1,5 @@
 import logging
+from xml.etree.ElementTree import ParseError
 
 from typing import Any, Dict, Generator, List, Optional, Sequence, Union
 from urllib.parse import parse_qs, urlparse
@@ -93,7 +94,6 @@ class YoutubeLoader:
                 "http": self.proxy_url,
                 "https": self.proxy_url,
             }
-            # Don't log complete URL because it might contain secrets
             log.debug(f"Using proxy URL: {self.proxy_url[:14]}...")
         else:
             youtube_proxies = None
@@ -110,11 +110,37 @@ class YoutubeLoader:
         for lang in self.language:
             try:
                 transcript = transcript_list.find_transcript([lang])
+                if transcript.is_generated:
+                    log.debug(f"Found generated transcript for language '{lang}'")
+                    try:
+                        transcript = transcript_list.find_manually_created_transcript(
+                            [lang]
+                        )
+                        log.debug(f"Found manual transcript for language '{lang}'")
+                    except NoTranscriptFound:
+                        log.debug(
+                            f"No manual transcript found for language '{lang}', using generated"
+                        )
+                        pass
+
                 log.debug(f"Found transcript for language '{lang}'")
-                transcript_pieces: List[Dict[str, Any]] = transcript.fetch()
+                try:
+                    transcript_pieces: List[Dict[str, Any]] = transcript.fetch()
+                except ParseError:
+                    log.debug(f"Empty or invalid transcript for language '{lang}'")
+                    continue
+
+                if not transcript_pieces:
+                    log.debug(f"Empty transcript for language '{lang}'")
+                    continue
+
                 transcript_text = " ".join(
                     map(
-                        lambda transcript_piece: transcript_piece.text.strip(" "),
+                        lambda transcript_piece: (
+                            transcript_piece.text.strip(" ")
+                            if hasattr(transcript_piece, "text")
+                            else ""
+                        ),
                         transcript_pieces,
                     )
                 )
@@ -131,6 +157,4 @@ class YoutubeLoader:
         log.warning(
             f"No transcript found for any of the specified languages: {languages_tried}. Verify if the video has transcripts, add more languages if needed."
         )
-        raise NoTranscriptFound(
-            f"No transcript found for any supported language. Verify if the video has transcripts, add more languages if needed."
-        )
+        raise NoTranscriptFound(self.video_id, self.language, list(transcript_list))

backend/open_webui/retrieval/vector/dbs/pgvector.py

@@ -1,12 +1,16 @@
 from typing import Optional, List, Dict, Any
 import logging
+import json
 from sqlalchemy import (
+    func,
+    literal,
     cast,
     column,
     create_engine,
     Column,
     Integer,
     MetaData,
+    LargeBinary,
     select,
     text,
     Text,
@@ -28,7 +32,12 @@ from open_webui.retrieval.vector.main import (
     SearchResult,
     GetResult,
 )
-from open_webui.config import PGVECTOR_DB_URL, PGVECTOR_INITIALIZE_MAX_VECTOR_LENGTH
+from open_webui.config import (
+    PGVECTOR_DB_URL,
+    PGVECTOR_INITIALIZE_MAX_VECTOR_LENGTH,
+    PGVECTOR_PGCRYPTO,
+    PGVECTOR_PGCRYPTO_KEY,
+)
 
 from open_webui.env import SRC_LOG_LEVELS
 
@@ -39,14 +48,27 @@ log = logging.getLogger(__name__)
 log.setLevel(SRC_LOG_LEVELS["RAG"])
 
 
+def pgcrypto_encrypt(val, key):
+    return func.pgp_sym_encrypt(val, literal(key))
+
+
+def pgcrypto_decrypt(col, key, outtype="text"):
+    return func.cast(func.pgp_sym_decrypt(col, literal(key)), outtype)
+
+
 class DocumentChunk(Base):
     __tablename__ = "document_chunk"
 
     id = Column(Text, primary_key=True)
     vector = Column(Vector(dim=VECTOR_LENGTH), nullable=True)
     collection_name = Column(Text, nullable=False)
-    text = Column(Text, nullable=True)
-    vmetadata = Column(MutableDict.as_mutable(JSONB), nullable=True)
+
+    if PGVECTOR_PGCRYPTO:
+        text = Column(LargeBinary, nullable=True)
+        vmetadata = Column(LargeBinary, nullable=True)
+    else:
+        text = Column(Text, nullable=True)
+        vmetadata = Column(MutableDict.as_mutable(JSONB), nullable=True)
 
 
 class PgvectorClient(VectorDBBase):
@@ -70,6 +92,15 @@ class PgvectorClient(VectorDBBase):
             # Ensure the pgvector extension is available
             self.session.execute(text("CREATE EXTENSION IF NOT EXISTS vector;"))
 
+            if PGVECTOR_PGCRYPTO:
+                # Ensure the pgcrypto extension is available for encryption
+                self.session.execute(text("CREATE EXTENSION IF NOT EXISTS pgcrypto;"))
+
+                if not PGVECTOR_PGCRYPTO_KEY:
+                    raise ValueError(
+                        "PGVECTOR_PGCRYPTO_KEY must be set when PGVECTOR_PGCRYPTO is enabled."
+                    )
+
             # Check vector length consistency
             self.check_vector_length()
 
@@ -147,44 +178,39 @@ class PgvectorClient(VectorDBBase):
 
     def insert(self, collection_name: str, items: List[VectorItem]) -> None:
         try:
-            new_items = []
-            for item in items:
-                vector = self.adjust_vector_length(item["vector"])
-                new_chunk = DocumentChunk(
-                    id=item["id"],
-                    vector=vector,
-                    collection_name=collection_name,
-                    text=item["text"],
-                    vmetadata=item["metadata"],
-                )
-                new_items.append(new_chunk)
-            self.session.bulk_save_objects(new_items)
-            self.session.commit()
-            log.info(
-                f"Inserted {len(new_items)} items into collection '{collection_name}'."
-            )
-        except Exception as e:
-            self.session.rollback()
-            log.exception(f"Error during insert: {e}")
-            raise
-
-    def upsert(self, collection_name: str, items: List[VectorItem]) -> None:
-        try:
-            for item in items:
-                vector = self.adjust_vector_length(item["vector"])
-                existing = (
-                    self.session.query(DocumentChunk)
-                    .filter(DocumentChunk.id == item["id"])
-                    .first()
-                )
-                if existing:
-                    existing.vector = vector
-                    existing.text = item["text"]
-                    existing.vmetadata = item["metadata"]
-                    existing.collection_name = (
-                        collection_name  # Update collection_name if necessary
+            if PGVECTOR_PGCRYPTO:
+                for item in items:
+                    vector = self.adjust_vector_length(item["vector"])
+                    # Use raw SQL for BYTEA/pgcrypto
+                    self.session.execute(
+                        text(
+                            """
+                            INSERT INTO document_chunk
+                            (id, vector, collection_name, text, vmetadata)
+                            VALUES (
+                                :id, :vector, :collection_name,
+                                pgp_sym_encrypt(:text, :key),
+                                pgp_sym_encrypt(:metadata::text, :key)
+                            )
+                            ON CONFLICT (id) DO NOTHING
+                        """
+                        ),
+                        {
+                            "id": item["id"],
+                            "vector": vector,
+                            "collection_name": collection_name,
+                            "text": item["text"],
+                            "metadata": json.dumps(item["metadata"]),
+                            "key": PGVECTOR_PGCRYPTO_KEY,
+                        },
                     )
-                else:
+                self.session.commit()
+                log.info(f"Encrypted & inserted {len(items)} into '{collection_name}'")
+
+            else:
+                new_items = []
+                for item in items:
+                    vector = self.adjust_vector_length(item["vector"])
                     new_chunk = DocumentChunk(
                         id=item["id"],
                         vector=vector,
@@ -192,11 +218,78 @@ class PgvectorClient(VectorDBBase):
                         text=item["text"],
                         vmetadata=item["metadata"],
                     )
-                    self.session.add(new_chunk)
-            self.session.commit()
-            log.info(
-                f"Upserted {len(items)} items into collection '{collection_name}'."
-            )
+                    new_items.append(new_chunk)
+                self.session.bulk_save_objects(new_items)
+                self.session.commit()
+                log.info(
+                    f"Inserted {len(new_items)} items into collection '{collection_name}'."
+                )
+        except Exception as e:
+            self.session.rollback()
+            log.exception(f"Error during insert: {e}")
+            raise
+
+    def upsert(self, collection_name: str, items: List[VectorItem]) -> None:
+        try:
+            if PGVECTOR_PGCRYPTO:
+                for item in items:
+                    vector = self.adjust_vector_length(item["vector"])
+                    self.session.execute(
+                        text(
+                            """
+                            INSERT INTO document_chunk
+                            (id, vector, collection_name, text, vmetadata)
+                            VALUES (
+                                :id, :vector, :collection_name,
+                                pgp_sym_encrypt(:text, :key),
+                                pgp_sym_encrypt(:metadata::text, :key)
+                            )
+                            ON CONFLICT (id) DO UPDATE SET
+                              vector = EXCLUDED.vector,
+                              collection_name = EXCLUDED.collection_name,
+                              text = EXCLUDED.text,
+                              vmetadata = EXCLUDED.vmetadata
+                        """
+                        ),
+                        {
+                            "id": item["id"],
+                            "vector": vector,
+                            "collection_name": collection_name,
+                            "text": item["text"],
+                            "metadata": json.dumps(item["metadata"]),
+                            "key": PGVECTOR_PGCRYPTO_KEY,
+                        },
+                    )
+                self.session.commit()
+                log.info(f"Encrypted & upserted {len(items)} into '{collection_name}'")
+            else:
+                for item in items:
+                    vector = self.adjust_vector_length(item["vector"])
+                    existing = (
+                        self.session.query(DocumentChunk)
+                        .filter(DocumentChunk.id == item["id"])
+                        .first()
+                    )
+                    if existing:
+                        existing.vector = vector
+                        existing.text = item["text"]
+                        existing.vmetadata = item["metadata"]
+                        existing.collection_name = (
+                            collection_name  # Update collection_name if necessary
+                        )
+                    else:
+                        new_chunk = DocumentChunk(
+                            id=item["id"],
+                            vector=vector,
+                            collection_name=collection_name,
+                            text=item["text"],
+                            vmetadata=item["metadata"],
+                        )
+                        self.session.add(new_chunk)
+                self.session.commit()
+                log.info(
+                    f"Upserted {len(items)} items into collection '{collection_name}'."
+                )
         except Exception as e:
             self.session.rollback()
             log.exception(f"Error during upsert: {e}")
@@ -230,16 +323,32 @@ class PgvectorClient(VectorDBBase):
                 .alias("query_vectors")
             )
 
+            result_fields = [
+                DocumentChunk.id,
+            ]
+            if PGVECTOR_PGCRYPTO:
+                result_fields.append(
+                    pgcrypto_decrypt(
+                        DocumentChunk.text, PGVECTOR_PGCRYPTO_KEY, Text
+                    ).label("text")
+                )
+                result_fields.append(
+                    pgcrypto_decrypt(
+                        DocumentChunk.vmetadata, PGVECTOR_PGCRYPTO_KEY, JSONB
+                    ).label("vmetadata")
+                )
+            else:
+                result_fields.append(DocumentChunk.text)
+                result_fields.append(DocumentChunk.vmetadata)
+            result_fields.append(
+                (DocumentChunk.vector.cosine_distance(query_vectors.c.q_vector)).label(
+                    "distance"
+                )
+            )
+
             # Build the lateral subquery for each query vector
             subq = (
-                select(
-                    DocumentChunk.id,
-                    DocumentChunk.text,
-                    DocumentChunk.vmetadata,
-                    (
-                        DocumentChunk.vector.cosine_distance(query_vectors.c.q_vector)
-                    ).label("distance"),
-                )
+                select(*result_fields)
                 .where(DocumentChunk.collection_name == collection_name)
                 .order_by(
                     (DocumentChunk.vector.cosine_distance(query_vectors.c.q_vector))
@@ -299,17 +408,43 @@ class PgvectorClient(VectorDBBase):
         self, collection_name: str, filter: Dict[str, Any], limit: Optional[int] = None
     ) -> Optional[GetResult]:
         try:
-            query = self.session.query(DocumentChunk).filter(
-                DocumentChunk.collection_name == collection_name
-            )
+            if PGVECTOR_PGCRYPTO:
+                # Build where clause for vmetadata filter
+                where_clauses = [DocumentChunk.collection_name == collection_name]
+                for key, value in filter.items():
+                    # decrypt then check key: JSON filter after decryption
+                    where_clauses.append(
+                        pgcrypto_decrypt(
+                            DocumentChunk.vmetadata, PGVECTOR_PGCRYPTO_KEY, JSONB
+                        )[key].astext
+                        == str(value)
+                    )
+                stmt = select(
+                    DocumentChunk.id,
+                    pgcrypto_decrypt(
+                        DocumentChunk.text, PGVECTOR_PGCRYPTO_KEY, Text
+                    ).label("text"),
+                    pgcrypto_decrypt(
+                        DocumentChunk.vmetadata, PGVECTOR_PGCRYPTO_KEY, JSONB
+                    ).label("vmetadata"),
+                ).where(*where_clauses)
+                if limit is not None:
+                    stmt = stmt.limit(limit)
+                results = self.session.execute(stmt).all()
+            else:
+                query = self.session.query(DocumentChunk).filter(
+                    DocumentChunk.collection_name == collection_name
+                )
 
-            for key, value in filter.items():
-                query = query.filter(DocumentChunk.vmetadata[key].astext == str(value))
+                for key, value in filter.items():
+                    query = query.filter(
+                        DocumentChunk.vmetadata[key].astext == str(value)
+                    )
 
-            if limit is not None:
-                query = query.limit(limit)
+                if limit is not None:
+                    query = query.limit(limit)
 
-            results = query.all()
+                results = query.all()
 
             if not results:
                 return None
@@ -331,20 +466,38 @@ class PgvectorClient(VectorDBBase):
         self, collection_name: str, limit: Optional[int] = None
     ) -> Optional[GetResult]:
         try:
-            query = self.session.query(DocumentChunk).filter(
-                DocumentChunk.collection_name == collection_name
-            )
-            if limit is not None:
-                query = query.limit(limit)
+            if PGVECTOR_PGCRYPTO:
+                stmt = select(
+                    DocumentChunk.id,
+                    pgcrypto_decrypt(
+                        DocumentChunk.text, PGVECTOR_PGCRYPTO_KEY, Text
+                    ).label("text"),
+                    pgcrypto_decrypt(
+                        DocumentChunk.vmetadata, PGVECTOR_PGCRYPTO_KEY, JSONB
+                    ).label("vmetadata"),
+                ).where(DocumentChunk.collection_name == collection_name)
+                if limit is not None:
+                    stmt = stmt.limit(limit)
+                results = self.session.execute(stmt).all()
+                ids = [[row.id for row in results]]
+                documents = [[row.text for row in results]]
+                metadatas = [[row.vmetadata for row in results]]
+            else:
 
-            results = query.all()
+                query = self.session.query(DocumentChunk).filter(
+                    DocumentChunk.collection_name == collection_name
+                )
+                if limit is not None:
+                    query = query.limit(limit)
 
-            if not results:
-                return None
+                results = query.all()
 
-            ids = [[result.id for result in results]]
-            documents = [[result.text for result in results]]
-            metadatas = [[result.vmetadata for result in results]]
+                if not results:
+                    return None
+
+                ids = [[result.id for result in results]]
+                documents = [[result.text for result in results]]
+                metadatas = [[result.vmetadata for result in results]]
 
             return GetResult(ids=ids, documents=documents, metadatas=metadatas)
         except Exception as e:
@@ -358,17 +511,33 @@ class PgvectorClient(VectorDBBase):
         filter: Optional[Dict[str, Any]] = None,
     ) -> None:
         try:
-            query = self.session.query(DocumentChunk).filter(
-                DocumentChunk.collection_name == collection_name
-            )
-            if ids:
-                query = query.filter(DocumentChunk.id.in_(ids))
-            if filter:
-                for key, value in filter.items():
-                    query = query.filter(
-                        DocumentChunk.vmetadata[key].astext == str(value)
-                    )
-            deleted = query.delete(synchronize_session=False)
+            if PGVECTOR_PGCRYPTO:
+                wheres = [DocumentChunk.collection_name == collection_name]
+                if ids:
+                    wheres.append(DocumentChunk.id.in_(ids))
+                if filter:
+                    for key, value in filter.items():
+                        wheres.append(
+                            pgcrypto_decrypt(
+                                DocumentChunk.vmetadata, PGVECTOR_PGCRYPTO_KEY, JSONB
+                            )[key].astext
+                            == str(value)
+                        )
+                stmt = DocumentChunk.__table__.delete().where(*wheres)
+                result = self.session.execute(stmt)
+                deleted = result.rowcount
+            else:
+                query = self.session.query(DocumentChunk).filter(
+                    DocumentChunk.collection_name == collection_name
+                )
+                if ids:
+                    query = query.filter(DocumentChunk.id.in_(ids))
+                if filter:
+                    for key, value in filter.items():
+                        query = query.filter(
+                            DocumentChunk.vmetadata[key].astext == str(value)
+                        )
+                deleted = query.delete(synchronize_session=False)
             self.session.commit()
             log.info(f"Deleted {deleted} items from collection '{collection_name}'.")
         except Exception as e:

backend/open_webui/retrieval/vector/dbs/pinecone.py

@@ -3,10 +3,19 @@ import logging
 import time  # for measuring elapsed time
 from pinecone import Pinecone, ServerlessSpec
 
+# Add gRPC support for better performance (Pinecone best practice)
+try:
+    from pinecone.grpc import PineconeGRPC
+
+    GRPC_AVAILABLE = True
+except ImportError:
+    GRPC_AVAILABLE = False
+
 import asyncio  # for async upserts
 import functools  # for partial binding in async tasks
 
 import concurrent.futures  # for parallel batch upserts
+import random  # for jitter in retry backoff
 
 from open_webui.retrieval.vector.main import (
     VectorDBBase,
@@ -47,7 +56,24 @@ class PineconeClient(VectorDBBase):
         self.cloud = PINECONE_CLOUD
 
         # Initialize Pinecone client for improved performance
-        self.client = Pinecone(api_key=self.api_key)
+        if GRPC_AVAILABLE:
+            # Use gRPC client for better performance (Pinecone recommendation)
+            self.client = PineconeGRPC(
+                api_key=self.api_key,
+                pool_threads=20,  # Improved connection pool size
+                timeout=30,  # Reasonable timeout for operations
+            )
+            self.using_grpc = True
+            log.info("Using Pinecone gRPC client for optimal performance")
+        else:
+            # Fallback to HTTP client with enhanced connection pooling
+            self.client = Pinecone(
+                api_key=self.api_key,
+                pool_threads=20,  # Improved connection pool size
+                timeout=30,  # Reasonable timeout for operations
+            )
+            self.using_grpc = False
+            log.info("Using Pinecone HTTP client (gRPC not available)")
 
         # Persistent executor for batch operations
         self._executor = concurrent.futures.ThreadPoolExecutor(max_workers=5)
@@ -91,12 +117,53 @@ class PineconeClient(VectorDBBase):
                 log.info(f"Using existing Pinecone index '{self.index_name}'")
 
             # Connect to the index
-            self.index = self.client.Index(self.index_name)
+            self.index = self.client.Index(
+                self.index_name,
+                pool_threads=20,  # Enhanced connection pool for index operations
+            )
 
         except Exception as e:
             log.error(f"Failed to initialize Pinecone index: {e}")
             raise RuntimeError(f"Failed to initialize Pinecone index: {e}")
 
+    def _retry_pinecone_operation(self, operation_func, max_retries=3):
+        """Retry Pinecone operations with exponential backoff for rate limits and network issues."""
+        for attempt in range(max_retries):
+            try:
+                return operation_func()
+            except Exception as e:
+                error_str = str(e).lower()
+                # Check if it's a retryable error (rate limits, network issues, timeouts)
+                is_retryable = any(
+                    keyword in error_str
+                    for keyword in [
+                        "rate limit",
+                        "quota",
+                        "timeout",
+                        "network",
+                        "connection",
+                        "unavailable",
+                        "internal error",
+                        "429",
+                        "500",
+                        "502",
+                        "503",
+                        "504",
+                    ]
+                )
+
+                if not is_retryable or attempt == max_retries - 1:
+                    # Don't retry for non-retryable errors or on final attempt
+                    raise
+
+                # Exponential backoff with jitter
+                delay = (2**attempt) + random.uniform(0, 1)
+                log.warning(
+                    f"Pinecone operation failed (attempt {attempt + 1}/{max_retries}), "
+                    f"retrying in {delay:.2f}s: {e}"
+                )
+                time.sleep(delay)
+
     def _create_points(
         self, items: List[VectorItem], collection_name_with_prefix: str
     ) -> List[Dict[str, Any]]:
@@ -223,7 +290,8 @@ class PineconeClient(VectorDBBase):
         elapsed = time.time() - start_time
         log.debug(f"Insert of {len(points)} vectors took {elapsed:.2f} seconds")
         log.info(
-            f"Successfully inserted {len(points)} vectors in parallel batches into '{collection_name_with_prefix}'"
+            f"Successfully inserted {len(points)} vectors in parallel batches "
+            f"into '{collection_name_with_prefix}'"
         )
 
     def upsert(self, collection_name: str, items: List[VectorItem]) -> None:
@@ -254,7 +322,8 @@ class PineconeClient(VectorDBBase):
         elapsed = time.time() - start_time
         log.debug(f"Upsert of {len(points)} vectors took {elapsed:.2f} seconds")
         log.info(
-            f"Successfully upserted {len(points)} vectors in parallel batches into '{collection_name_with_prefix}'"
+            f"Successfully upserted {len(points)} vectors in parallel batches "
+            f"into '{collection_name_with_prefix}'"
         )
 
     async def insert_async(self, collection_name: str, items: List[VectorItem]) -> None:
@@ -285,7 +354,8 @@ class PineconeClient(VectorDBBase):
                 log.error(f"Error in async insert batch: {result}")
                 raise result
         log.info(
-            f"Successfully async inserted {len(points)} vectors in batches into '{collection_name_with_prefix}'"
+            f"Successfully async inserted {len(points)} vectors in batches "
+            f"into '{collection_name_with_prefix}'"
         )
 
     async def upsert_async(self, collection_name: str, items: List[VectorItem]) -> None:
@@ -316,7 +386,8 @@ class PineconeClient(VectorDBBase):
                 log.error(f"Error in async upsert batch: {result}")
                 raise result
         log.info(
-            f"Successfully async upserted {len(points)} vectors in batches into '{collection_name_with_prefix}'"
+            f"Successfully async upserted {len(points)} vectors in batches "
+            f"into '{collection_name_with_prefix}'"
         )
 
     def search(
@@ -457,10 +528,12 @@ class PineconeClient(VectorDBBase):
                     # This is a limitation of Pinecone - be careful with ID uniqueness
                     self.index.delete(ids=batch_ids)
                     log.debug(
-                        f"Deleted batch of {len(batch_ids)} vectors by ID from '{collection_name_with_prefix}'"
+                        f"Deleted batch of {len(batch_ids)} vectors by ID "
+                        f"from '{collection_name_with_prefix}'"
                     )
                 log.info(
-                    f"Successfully deleted {len(ids)} vectors by ID from '{collection_name_with_prefix}'"
+                    f"Successfully deleted {len(ids)} vectors by ID "
+                    f"from '{collection_name_with_prefix}'"
                 )
 
             elif filter:

backend/open_webui/retrieval/web/perplexity.py

@@ -1,10 +1,20 @@
 import logging
-from typing import Optional, List
+from typing import Optional, Literal
 import requests
 
 from open_webui.retrieval.web.main import SearchResult, get_filtered_results
 from open_webui.env import SRC_LOG_LEVELS
 
+MODELS = Literal[
+    "sonar",
+    "sonar-pro",
+    "sonar-reasoning",
+    "sonar-reasoning-pro",
+    "sonar-deep-research",
+]
+SEARCH_CONTEXT_USAGE_LEVELS = Literal["low", "medium", "high"]
+
+
 log = logging.getLogger(__name__)
 log.setLevel(SRC_LOG_LEVELS["RAG"])
 
@@ -14,6 +24,8 @@ def search_perplexity(
     query: str,
     count: int,
     filter_list: Optional[list[str]] = None,
+    model: MODELS = "sonar",
+    search_context_usage: SEARCH_CONTEXT_USAGE_LEVELS = "medium",
 ) -> list[SearchResult]:
     """Search using Perplexity API and return the results as a list of SearchResult objects.
 
@@ -21,6 +33,9 @@ def search_perplexity(
       api_key (str): A Perplexity API key
       query (str): The query to search for
       count (int): Maximum number of results to return
+      filter_list (Optional[list[str]]): List of domains to filter results
+      model (str): The Perplexity model to use (sonar, sonar-pro)
+      search_context_usage (str): Search context usage level (low, medium, high)
 
     """
 
@@ -33,7 +48,7 @@ def search_perplexity(
 
         # Create payload for the API call
         payload = {
-            "model": "sonar",
+            "model": model,
             "messages": [
                 {
                     "role": "system",
@@ -43,6 +58,9 @@ def search_perplexity(
             ],
             "temperature": 0.2,  # Lower temperature for more factual responses
             "stream": False,
+            "web_search_options": {
+                "search_context_usage": search_context_usage,
+            },
         }
 
         headers = {

backend/open_webui/routers/images.py

@@ -420,7 +420,7 @@ def load_b64_image_data(b64_str):
     try:
         if "," in b64_str:
             header, encoded = b64_str.split(",", 1)
-            mime_type = header.split(";")[0]
+            mime_type = header.split(";")[0].lstrip("data:")
             img_data = base64.b64decode(encoded)
         else:
             mime_type = "image/png"
@@ -428,7 +428,7 @@ def load_b64_image_data(b64_str):
         return img_data, mime_type
     except Exception as e:
         log.exception(f"Error loading image data: {e}")
-        return None
+        return None, None
 
 
 def load_url_image_data(url, headers=None):

backend/open_webui/routers/notes.py

@@ -124,9 +124,8 @@ async def get_note_by_id(request: Request, id: str, user=Depends(get_verified_us
             status_code=status.HTTP_404_NOT_FOUND, detail=ERROR_MESSAGES.NOT_FOUND
         )
 
-    if (
-        user.role != "admin"
-        and user.id != note.user_id
+    if user.role != "admin" or (
+        user.id != note.user_id
         and not has_access(user.id, type="read", access_control=note.access_control)
     ):
         raise HTTPException(
@@ -159,9 +158,8 @@ async def update_note_by_id(
             status_code=status.HTTP_404_NOT_FOUND, detail=ERROR_MESSAGES.NOT_FOUND
         )
 
-    if (
-        user.role != "admin"
-        and user.id != note.user_id
+    if user.role != "admin" or (
+        user.id != note.user_id
         and not has_access(user.id, type="write", access_control=note.access_control)
     ):
         raise HTTPException(
@@ -199,9 +197,8 @@ async def delete_note_by_id(request: Request, id: str, user=Depends(get_verified
             status_code=status.HTTP_404_NOT_FOUND, detail=ERROR_MESSAGES.NOT_FOUND
         )
 
-    if (
-        user.role != "admin"
-        and user.id != note.user_id
+    if user.role != "admin" or (
+        user.id != note.user_id
         and not has_access(user.id, type="write", access_control=note.access_control)
     ):
         raise HTTPException(

backend/open_webui/routers/ollama.py

@@ -1232,6 +1232,9 @@ class GenerateChatCompletionForm(BaseModel):
     stream: Optional[bool] = True
     keep_alive: Optional[Union[int, str]] = None
     tools: Optional[list[dict]] = None
+    model_config = ConfigDict(
+        extra="allow",
+    )
 
 
 async def get_ollama_url(request: Request, model: str, url_idx: Optional[int] = None):
@@ -1269,7 +1272,9 @@ async def generate_chat_completion(
             detail=str(e),
         )
 
-    payload = {**form_data.model_dump(exclude_none=True)}
+    if isinstance(form_data, BaseModel):
+        payload = {**form_data.model_dump(exclude_none=True)}
+
     if "metadata" in payload:
         del payload["metadata"]
 
@@ -1285,11 +1290,7 @@ async def generate_chat_completion(
         if params:
             system = params.pop("system", None)
 
-            # Unlike OpenAI, Ollama does not support params directly in the body
-            payload["options"] = apply_model_params_to_body_ollama(
-                params, (payload.get("options", {}) or {})
-            )
-
+            payload = apply_model_params_to_body_ollama(params, payload)
             payload = apply_model_system_prompt_to_body(system, payload, metadata, user)
 
         # Check if user has access to the model
@@ -1323,7 +1324,7 @@ async def generate_chat_completion(
     prefix_id = api_config.get("prefix_id", None)
     if prefix_id:
         payload["model"] = payload["model"].replace(f"{prefix_id}.", "")
-    # payload["keep_alive"] = -1 # keep alive forever
+
     return await send_post_request(
         url=f"{url}/api/chat",
         payload=json.dumps(payload),

backend/open_webui/routers/openai.py

@@ -887,6 +887,88 @@ async def generate_chat_completion(
             await session.close()
 
 
+async def embeddings(request: Request, form_data: dict, user):
+    """
+    Calls the embeddings endpoint for OpenAI-compatible providers.
+
+    Args:
+        request (Request): The FastAPI request context.
+        form_data (dict): OpenAI-compatible embeddings payload.
+        user (UserModel): The authenticated user.
+
+    Returns:
+        dict: OpenAI-compatible embeddings response.
+    """
+    idx = 0
+    # Prepare payload/body
+    body = json.dumps(form_data)
+    # Find correct backend url/key based on model
+    await get_all_models(request, user=user)
+    model_id = form_data.get("model")
+    models = request.app.state.OPENAI_MODELS
+    if model_id in models:
+        idx = models[model_id]["urlIdx"]
+    url = request.app.state.config.OPENAI_API_BASE_URLS[idx]
+    key = request.app.state.config.OPENAI_API_KEYS[idx]
+    r = None
+    session = None
+    streaming = False
+    try:
+        session = aiohttp.ClientSession(trust_env=True)
+        r = await session.request(
+            method="POST",
+            url=f"{url}/embeddings",
+            data=body,
+            headers={
+                "Authorization": f"Bearer {key}",
+                "Content-Type": "application/json",
+                **(
+                    {
+                        "X-OpenWebUI-User-Name": user.name,
+                        "X-OpenWebUI-User-Id": user.id,
+                        "X-OpenWebUI-User-Email": user.email,
+                        "X-OpenWebUI-User-Role": user.role,
+                    }
+                    if ENABLE_FORWARD_USER_INFO_HEADERS and user
+                    else {}
+                ),
+            },
+        )
+        r.raise_for_status()
+        if "text/event-stream" in r.headers.get("Content-Type", ""):
+            streaming = True
+            return StreamingResponse(
+                r.content,
+                status_code=r.status,
+                headers=dict(r.headers),
+                background=BackgroundTask(
+                    cleanup_response, response=r, session=session
+                ),
+            )
+        else:
+            response_data = await r.json()
+            return response_data
+    except Exception as e:
+        log.exception(e)
+        detail = None
+        if r is not None:
+            try:
+                res = await r.json()
+                if "error" in res:
+                    detail = f"External: {res['error']['message'] if 'message' in res['error'] else res['error']}"
+            except Exception:
+                detail = f"External: {e}"
+        raise HTTPException(
+            status_code=r.status if r else 500,
+            detail=detail if detail else "Open WebUI: Server Connection Error",
+        )
+    finally:
+        if not streaming and session:
+            if r:
+                r.close()
+            await session.close()
+
+
 @router.api_route("/{path:path}", methods=["GET", "POST", "PUT", "DELETE"])
 async def proxy(path: str, request: Request, user=Depends(get_verified_user)):
     """

backend/open_webui/routers/retrieval.py

@@ -414,6 +414,9 @@ async def get_rag_config(request: Request, user=Depends(get_admin_user)):
         "DOCLING_OCR_ENGINE": request.app.state.config.DOCLING_OCR_ENGINE,
         "DOCLING_OCR_LANG": request.app.state.config.DOCLING_OCR_LANG,
         "DOCLING_DO_PICTURE_DESCRIPTION": request.app.state.config.DOCLING_DO_PICTURE_DESCRIPTION,
+        "DOCLING_PICTURE_DESCRIPTION_MODE": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_MODE,
+        "DOCLING_PICTURE_DESCRIPTION_LOCAL": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_LOCAL,
+        "DOCLING_PICTURE_DESCRIPTION_API": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_API,
         "DOCUMENT_INTELLIGENCE_ENDPOINT": request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT,
         "DOCUMENT_INTELLIGENCE_KEY": request.app.state.config.DOCUMENT_INTELLIGENCE_KEY,
         "MISTRAL_OCR_API_KEY": request.app.state.config.MISTRAL_OCR_API_KEY,
@@ -467,6 +470,8 @@ async def get_rag_config(request: Request, user=Depends(get_admin_user)):
             "BING_SEARCH_V7_SUBSCRIPTION_KEY": request.app.state.config.BING_SEARCH_V7_SUBSCRIPTION_KEY,
             "EXA_API_KEY": request.app.state.config.EXA_API_KEY,
             "PERPLEXITY_API_KEY": request.app.state.config.PERPLEXITY_API_KEY,
+            "PERPLEXITY_MODEL": request.app.state.config.PERPLEXITY_MODEL,
+            "PERPLEXITY_SEARCH_CONTEXT_USAGE": request.app.state.config.PERPLEXITY_SEARCH_CONTEXT_USAGE,
             "SOUGOU_API_SID": request.app.state.config.SOUGOU_API_SID,
             "SOUGOU_API_SK": request.app.state.config.SOUGOU_API_SK,
             "WEB_LOADER_ENGINE": request.app.state.config.WEB_LOADER_ENGINE,
@@ -520,6 +525,8 @@ class WebConfig(BaseModel):
     BING_SEARCH_V7_SUBSCRIPTION_KEY: Optional[str] = None
     EXA_API_KEY: Optional[str] = None
     PERPLEXITY_API_KEY: Optional[str] = None
+    PERPLEXITY_MODEL: Optional[str] = None
+    PERPLEXITY_SEARCH_CONTEXT_USAGE: Optional[str] = None
     SOUGOU_API_SID: Optional[str] = None
     SOUGOU_API_SK: Optional[str] = None
     WEB_LOADER_ENGINE: Optional[str] = None
@@ -571,6 +578,9 @@ class ConfigForm(BaseModel):
     DOCLING_OCR_ENGINE: Optional[str] = None
     DOCLING_OCR_LANG: Optional[str] = None
     DOCLING_DO_PICTURE_DESCRIPTION: Optional[bool] = None
+    DOCLING_PICTURE_DESCRIPTION_MODE: Optional[str] = None
+    DOCLING_PICTURE_DESCRIPTION_LOCAL: Optional[dict] = None
+    DOCLING_PICTURE_DESCRIPTION_API: Optional[dict] = None
     DOCUMENT_INTELLIGENCE_ENDPOINT: Optional[str] = None
     DOCUMENT_INTELLIGENCE_KEY: Optional[str] = None
     MISTRAL_OCR_API_KEY: Optional[str] = None
@@ -744,6 +754,22 @@ async def update_rag_config(
         else request.app.state.config.DOCLING_DO_PICTURE_DESCRIPTION
     )
 
+    request.app.state.config.DOCLING_PICTURE_DESCRIPTION_MODE = (
+        form_data.DOCLING_PICTURE_DESCRIPTION_MODE
+        if form_data.DOCLING_PICTURE_DESCRIPTION_MODE is not None
+        else request.app.state.config.DOCLING_PICTURE_DESCRIPTION_MODE
+    )
+    request.app.state.config.DOCLING_PICTURE_DESCRIPTION_LOCAL = (
+        form_data.DOCLING_PICTURE_DESCRIPTION_LOCAL
+        if form_data.DOCLING_PICTURE_DESCRIPTION_LOCAL is not None
+        else request.app.state.config.DOCLING_PICTURE_DESCRIPTION_LOCAL
+    )
+    request.app.state.config.DOCLING_PICTURE_DESCRIPTION_API = (
+        form_data.DOCLING_PICTURE_DESCRIPTION_API
+        if form_data.DOCLING_PICTURE_DESCRIPTION_API is not None
+        else request.app.state.config.DOCLING_PICTURE_DESCRIPTION_API
+    )
+
     request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT = (
         form_data.DOCUMENT_INTELLIGENCE_ENDPOINT
         if form_data.DOCUMENT_INTELLIGENCE_ENDPOINT is not None
@@ -907,6 +933,10 @@ async def update_rag_config(
         )
         request.app.state.config.EXA_API_KEY = form_data.web.EXA_API_KEY
         request.app.state.config.PERPLEXITY_API_KEY = form_data.web.PERPLEXITY_API_KEY
+        request.app.state.config.PERPLEXITY_MODEL = form_data.web.PERPLEXITY_MODEL
+        request.app.state.config.PERPLEXITY_SEARCH_CONTEXT_USAGE = (
+            form_data.web.PERPLEXITY_SEARCH_CONTEXT_USAGE
+        )
         request.app.state.config.SOUGOU_API_SID = form_data.web.SOUGOU_API_SID
         request.app.state.config.SOUGOU_API_SK = form_data.web.SOUGOU_API_SK
 
@@ -977,6 +1007,9 @@ async def update_rag_config(
         "DOCLING_OCR_ENGINE": request.app.state.config.DOCLING_OCR_ENGINE,
         "DOCLING_OCR_LANG": request.app.state.config.DOCLING_OCR_LANG,
         "DOCLING_DO_PICTURE_DESCRIPTION": request.app.state.config.DOCLING_DO_PICTURE_DESCRIPTION,
+        "DOCLING_PICTURE_DESCRIPTION_MODE": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_MODE,
+        "DOCLING_PICTURE_DESCRIPTION_LOCAL": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_LOCAL,
+        "DOCLING_PICTURE_DESCRIPTION_API": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_API,
         "DOCUMENT_INTELLIGENCE_ENDPOINT": request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT,
         "DOCUMENT_INTELLIGENCE_KEY": request.app.state.config.DOCUMENT_INTELLIGENCE_KEY,
         "MISTRAL_OCR_API_KEY": request.app.state.config.MISTRAL_OCR_API_KEY,
@@ -1030,6 +1063,8 @@ async def update_rag_config(
             "BING_SEARCH_V7_SUBSCRIPTION_KEY": request.app.state.config.BING_SEARCH_V7_SUBSCRIPTION_KEY,
             "EXA_API_KEY": request.app.state.config.EXA_API_KEY,
             "PERPLEXITY_API_KEY": request.app.state.config.PERPLEXITY_API_KEY,
+            "PERPLEXITY_MODEL": request.app.state.config.PERPLEXITY_MODEL,
+            "PERPLEXITY_SEARCH_CONTEXT_USAGE": request.app.state.config.PERPLEXITY_SEARCH_CONTEXT_USAGE,
             "SOUGOU_API_SID": request.app.state.config.SOUGOU_API_SID,
             "SOUGOU_API_SK": request.app.state.config.SOUGOU_API_SK,
             "WEB_LOADER_ENGINE": request.app.state.config.WEB_LOADER_ENGINE,
@@ -1321,9 +1356,14 @@ def process_file(
                     EXTERNAL_DOCUMENT_LOADER_API_KEY=request.app.state.config.EXTERNAL_DOCUMENT_LOADER_API_KEY,
                     TIKA_SERVER_URL=request.app.state.config.TIKA_SERVER_URL,
                     DOCLING_SERVER_URL=request.app.state.config.DOCLING_SERVER_URL,
-                    DOCLING_OCR_ENGINE=request.app.state.config.DOCLING_OCR_ENGINE,
-                    DOCLING_OCR_LANG=request.app.state.config.DOCLING_OCR_LANG,
-                    DOCLING_DO_PICTURE_DESCRIPTION=request.app.state.config.DOCLING_DO_PICTURE_DESCRIPTION,
+                    DOCLING_PARAMS={
+                        "ocr_engine": request.app.state.config.DOCLING_OCR_ENGINE,
+                        "ocr_lang": request.app.state.config.DOCLING_OCR_LANG,
+                        "do_picture_description": request.app.state.config.DOCLING_DO_PICTURE_DESCRIPTION,
+                        "picture_description_mode": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_MODE,
+                        "picture_description_local": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_LOCAL,
+                        "picture_description_api": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_API,
+                    },
                     PDF_EXTRACT_IMAGES=request.app.state.config.PDF_EXTRACT_IMAGES,
                     DOCUMENT_INTELLIGENCE_ENDPOINT=request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT,
                     DOCUMENT_INTELLIGENCE_KEY=request.app.state.config.DOCUMENT_INTELLIGENCE_KEY,
@@ -1740,19 +1780,14 @@ def search_web(request: Request, engine: str, query: str) -> list[SearchResult]:
             request.app.state.config.WEB_SEARCH_RESULT_COUNT,
             request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
         )
-    elif engine == "exa":
-        return search_exa(
-            request.app.state.config.EXA_API_KEY,
-            query,
-            request.app.state.config.WEB_SEARCH_RESULT_COUNT,
-            request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
-        )
     elif engine == "perplexity":
         return search_perplexity(
             request.app.state.config.PERPLEXITY_API_KEY,
             query,
             request.app.state.config.WEB_SEARCH_RESULT_COUNT,
             request.app.state.config.WEB_SEARCH_DOMAIN_FILTER_LIST,
+            model=request.app.state.config.PERPLEXITY_MODEL,
+            search_context_usage=request.app.state.config.PERPLEXITY_SEARCH_CONTEXT_USAGE,
         )
     elif engine == "sougou":
         if (

backend/open_webui/routers/tasks.py

@@ -9,6 +9,7 @@ import re
 from open_webui.utils.chat import generate_chat_completion
 from open_webui.utils.task import (
     title_generation_template,
+    follow_up_generation_template,
     query_generation_template,
     image_prompt_generation_template,
     autocomplete_generation_template,
@@ -25,6 +26,7 @@ from open_webui.utils.task import get_task_model_id
 
 from open_webui.config import (
     DEFAULT_TITLE_GENERATION_PROMPT_TEMPLATE,
+    DEFAULT_FOLLOW_UP_GENERATION_PROMPT_TEMPLATE,
     DEFAULT_TAGS_GENERATION_PROMPT_TEMPLATE,
     DEFAULT_IMAGE_PROMPT_GENERATION_PROMPT_TEMPLATE,
     DEFAULT_QUERY_GENERATION_PROMPT_TEMPLATE,
@@ -58,6 +60,8 @@ async def get_task_config(request: Request, user=Depends(get_verified_user)):
         "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,
+        "FOLLOW_UP_GENERATION_PROMPT_TEMPLATE": request.app.state.config.FOLLOW_UP_GENERATION_PROMPT_TEMPLATE,
+        "ENABLE_FOLLOW_UP_GENERATION": request.app.state.config.ENABLE_FOLLOW_UP_GENERATION,
         "ENABLE_TAGS_GENERATION": request.app.state.config.ENABLE_TAGS_GENERATION,
         "ENABLE_TITLE_GENERATION": request.app.state.config.ENABLE_TITLE_GENERATION,
         "ENABLE_SEARCH_QUERY_GENERATION": request.app.state.config.ENABLE_SEARCH_QUERY_GENERATION,
@@ -76,6 +80,8 @@ class TaskConfigForm(BaseModel):
     ENABLE_AUTOCOMPLETE_GENERATION: bool
     AUTOCOMPLETE_GENERATION_INPUT_MAX_LENGTH: int
     TAGS_GENERATION_PROMPT_TEMPLATE: str
+    FOLLOW_UP_GENERATION_PROMPT_TEMPLATE: str
+    ENABLE_FOLLOW_UP_GENERATION: bool
     ENABLE_TAGS_GENERATION: bool
     ENABLE_SEARCH_QUERY_GENERATION: bool
     ENABLE_RETRIEVAL_QUERY_GENERATION: bool
@@ -94,6 +100,13 @@ async def update_task_config(
         form_data.TITLE_GENERATION_PROMPT_TEMPLATE
     )
 
+    request.app.state.config.ENABLE_FOLLOW_UP_GENERATION = (
+        form_data.ENABLE_FOLLOW_UP_GENERATION
+    )
+    request.app.state.config.FOLLOW_UP_GENERATION_PROMPT_TEMPLATE = (
+        form_data.FOLLOW_UP_GENERATION_PROMPT_TEMPLATE
+    )
+
     request.app.state.config.IMAGE_PROMPT_GENERATION_PROMPT_TEMPLATE = (
         form_data.IMAGE_PROMPT_GENERATION_PROMPT_TEMPLATE
     )
@@ -133,6 +146,8 @@ async def update_task_config(
         "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_FOLLOW_UP_GENERATION": request.app.state.config.ENABLE_FOLLOW_UP_GENERATION,
+        "FOLLOW_UP_GENERATION_PROMPT_TEMPLATE": request.app.state.config.FOLLOW_UP_GENERATION_PROMPT_TEMPLATE,
         "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,
@@ -231,6 +246,86 @@ async def generate_title(
         )
 
 
+@router.post("/follow_up/completions")
+async def generate_follow_ups(
+    request: Request, form_data: dict, user=Depends(get_verified_user)
+):
+
+    if not request.app.state.config.ENABLE_FOLLOW_UP_GENERATION:
+        return JSONResponse(
+            status_code=status.HTTP_200_OK,
+            content={"detail": "Follow-up generation is disabled"},
+        )
+
+    if getattr(request.state, "direct", False) and hasattr(request.state, "model"):
+        models = {
+            request.state.model["id"]: request.state.model,
+        }
+    else:
+        models = request.app.state.MODELS
+
+    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.FOLLOW_UP_GENERATION_PROMPT_TEMPLATE != "":
+        template = request.app.state.config.FOLLOW_UP_GENERATION_PROMPT_TEMPLATE
+    else:
+        template = DEFAULT_FOLLOW_UP_GENERATION_PROMPT_TEMPLATE
+
+    content = follow_up_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,
+        "metadata": {
+            **(request.state.metadata if hasattr(request.state, "metadata") else {}),
+            "task": str(TASKS.FOLLOW_UP_GENERATION),
+            "task_body": form_data,
+            "chat_id": form_data.get("chat_id", None),
+        },
+    }
+
+    # Process the payload through the pipeline
+    try:
+        payload = await process_pipeline_inlet_filter(request, payload, user, models)
+    except Exception as e:
+        raise e
+
+    try:
+        return await generate_chat_completion(request, form_data=payload, user=user)
+    except Exception as e:
+        log.error("Exception occurred", exc_info=True)
+        return JSONResponse(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            content={"detail": "An internal error has occurred."},
+        )
+
+
 @router.post("/tags/completions")
 async def generate_chat_tags(
     request: Request, form_data: dict, user=Depends(get_verified_user)

backend/open_webui/routers/users.py

@@ -165,22 +165,6 @@ async def update_default_user_permissions(
     return request.app.state.config.USER_PERMISSIONS
 
 
-############################
-# UpdateUserRole
-############################
-
-
-@router.post("/update/role", response_model=Optional[UserModel])
-async def update_user_role(form_data: UserRoleUpdateForm, user=Depends(get_admin_user)):
-    if user.id != form_data.id and form_data.id != Users.get_first_user().id:
-        return Users.update_user_role_by_id(form_data.id, form_data.role)
-
-    raise HTTPException(
-        status_code=status.HTTP_403_FORBIDDEN,
-        detail=ERROR_MESSAGES.ACTION_PROHIBITED,
-    )
-
-
 ############################
 # GetUserSettingsBySessionUser
 ############################
@@ -333,11 +317,22 @@ async def update_user_by_id(
     # Prevent modification of the primary admin user by other admins
     try:
         first_user = Users.get_first_user()
-        if first_user and user_id == first_user.id and session_user.id != user_id:
-            raise HTTPException(
-                status_code=status.HTTP_403_FORBIDDEN,
-                detail=ERROR_MESSAGES.ACTION_PROHIBITED,
-            )
+        if first_user:
+            if user_id == first_user.id:
+                if session_user.id != user_id:
+                    # If the user trying to update is the primary admin, and they are not the primary admin themselves
+                    raise HTTPException(
+                        status_code=status.HTTP_403_FORBIDDEN,
+                        detail=ERROR_MESSAGES.ACTION_PROHIBITED,
+                    )
+
+                if form_data.role != "admin":
+                    # If the primary admin is trying to change their own role, prevent it
+                    raise HTTPException(
+                        status_code=status.HTTP_403_FORBIDDEN,
+                        detail=ERROR_MESSAGES.ACTION_PROHIBITED,
+                    )
+
     except Exception as e:
         log.error(f"Error checking primary admin status: {e}")
         raise HTTPException(
@@ -365,6 +360,7 @@ async def update_user_by_id(
         updated_user = Users.update_user_by_id(
             user_id,
             {
+                "role": form_data.role,
                 "name": form_data.name,
                 "email": form_data.email.lower(),
                 "profile_image_url": form_data.profile_image_url,

backend/open_webui/routers/utils.py

@@ -34,7 +34,7 @@ class CodeForm(BaseModel):
 
 
 @router.post("/code/format")
-async def format_code(form_data: CodeForm, user=Depends(get_verified_user)):
+async def format_code(form_data: CodeForm, user=Depends(get_admin_user)):
     try:
         formatted_code = black.format_str(form_data.code, mode=black.Mode())
         return {"code": formatted_code}

backend/open_webui/tasks.py

@@ -2,16 +2,87 @@
 import asyncio
 from typing import Dict
 from uuid import uuid4
+import json
+from redis.asyncio import Redis
+from fastapi import Request
+from typing import Dict, List, Optional
 
 # A dictionary to keep track of active tasks
 tasks: Dict[str, asyncio.Task] = {}
 chat_tasks = {}
 
 
-def cleanup_task(task_id: str, id=None):
+REDIS_TASKS_KEY = "open-webui:tasks"
+REDIS_CHAT_TASKS_KEY = "open-webui:tasks:chat"
+REDIS_PUBSUB_CHANNEL = "open-webui:tasks:commands"
+
+
+def is_redis(request: Request) -> bool:
+    # Called everywhere a request is available to check Redis
+    return hasattr(request.app.state, "redis") and (request.app.state.redis is not None)
+
+
+async def redis_task_command_listener(app):
+    redis: Redis = app.state.redis
+    pubsub = redis.pubsub()
+    await pubsub.subscribe(REDIS_PUBSUB_CHANNEL)
+
+    async for message in pubsub.listen():
+        if message["type"] != "message":
+            continue
+        try:
+            command = json.loads(message["data"])
+            if command.get("action") == "stop":
+                task_id = command.get("task_id")
+                local_task = tasks.get(task_id)
+                if local_task:
+                    local_task.cancel()
+        except Exception as e:
+            print(f"Error handling distributed task command: {e}")
+
+
+### ------------------------------
+### REDIS-ENABLED HANDLERS
+### ------------------------------
+
+
+async def redis_save_task(redis: Redis, task_id: str, chat_id: Optional[str]):
+    pipe = redis.pipeline()
+    pipe.hset(REDIS_TASKS_KEY, task_id, chat_id or "")
+    if chat_id:
+        pipe.sadd(f"{REDIS_CHAT_TASKS_KEY}:{chat_id}", task_id)
+    await pipe.execute()
+
+
+async def redis_cleanup_task(redis: Redis, task_id: str, chat_id: Optional[str]):
+    pipe = redis.pipeline()
+    pipe.hdel(REDIS_TASKS_KEY, task_id)
+    if chat_id:
+        pipe.srem(f"{REDIS_CHAT_TASKS_KEY}:{chat_id}", task_id)
+        if (await pipe.scard(f"{REDIS_CHAT_TASKS_KEY}:{chat_id}").execute())[-1] == 0:
+            pipe.delete(f"{REDIS_CHAT_TASKS_KEY}:{chat_id}")  # Remove if empty set
+    await pipe.execute()
+
+
+async def redis_list_tasks(redis: Redis) -> List[str]:
+    return list(await redis.hkeys(REDIS_TASKS_KEY))
+
+
+async def redis_list_chat_tasks(redis: Redis, chat_id: str) -> List[str]:
+    return list(await redis.smembers(f"{REDIS_CHAT_TASKS_KEY}:{chat_id}"))
+
+
+async def redis_send_command(redis: Redis, command: dict):
+    await redis.publish(REDIS_PUBSUB_CHANNEL, json.dumps(command))
+
+
+async def cleanup_task(request, task_id: str, id=None):
     """
     Remove a completed or canceled task from the global `tasks` dictionary.
     """
+    if is_redis(request):
+        await redis_cleanup_task(request.app.state.redis, task_id, id)
+
     tasks.pop(task_id, None)  # Remove the task if it exists
 
     # If an ID is provided, remove the task from the chat_tasks dictionary
@@ -21,7 +92,7 @@ def cleanup_task(task_id: str, id=None):
             chat_tasks.pop(id, None)
 
 
-def create_task(coroutine, id=None):
+async def create_task(request, coroutine, id=None):
     """
     Create a new asyncio task and add it to the global task dictionary.
     """
@@ -29,7 +100,9 @@ def create_task(coroutine, id=None):
     task = asyncio.create_task(coroutine)  # Create the task
 
     # Add a done callback for cleanup
-    task.add_done_callback(lambda t: cleanup_task(task_id, id))
+    task.add_done_callback(
+        lambda t: asyncio.create_task(cleanup_task(request, task_id, id))
+    )
     tasks[task_id] = task
 
     # If an ID is provided, associate the task with that ID
@@ -38,34 +111,46 @@ def create_task(coroutine, id=None):
     else:
         chat_tasks[id] = [task_id]
 
+    if is_redis(request):
+        await redis_save_task(request.app.state.redis, task_id, id)
+
     return task_id, task
 
 
-def get_task(task_id: str):
-    """
-    Retrieve a task by its task ID.
-    """
-    return tasks.get(task_id)
-
-
-def list_tasks():
+async def list_tasks(request):
     """
     List all currently active task IDs.
     """
+    if is_redis(request):
+        return await redis_list_tasks(request.app.state.redis)
     return list(tasks.keys())
 
 
-def list_task_ids_by_chat_id(id):
+async def list_task_ids_by_chat_id(request, id):
     """
     List all tasks associated with a specific ID.
     """
+    if is_redis(request):
+        return await redis_list_chat_tasks(request.app.state.redis, id)
     return chat_tasks.get(id, [])
 
 
-async def stop_task(task_id: str):
+async def stop_task(request, task_id: str):
     """
     Cancel a running task and remove it from the global task list.
     """
+    if is_redis(request):
+        # PUBSUB: All instances check if they have this task, and stop if so.
+        await redis_send_command(
+            request.app.state.redis,
+            {
+                "action": "stop",
+                "task_id": task_id,
+            },
+        )
+        # Optionally check if task_id still in Redis a few moments later for feedback?
+        return {"status": True, "message": f"Stop signal sent for {task_id}"}
+
     task = tasks.get(task_id)
     if not task:
         raise ValueError(f"Task with ID {task_id} not found.")

backend/open_webui/utils/auth.py

@@ -23,6 +23,7 @@ from open_webui.env import (
     TRUSTED_SIGNATURE_KEY,
     STATIC_DIR,
     SRC_LOG_LEVELS,
+    WEBUI_AUTH_TRUSTED_EMAIL_HEADER,
 )
 
 from fastapi import BackgroundTasks, Depends, HTTPException, Request, Response, status
@@ -157,6 +158,7 @@ def get_http_authorization_cred(auth_header: Optional[str]):
 
 def get_current_user(
     request: Request,
+    response: Response,
     background_tasks: BackgroundTasks,
     auth_token: HTTPAuthorizationCredentials = Depends(bearer_security),
 ):
@@ -225,6 +227,19 @@ def get_current_user(
                 detail=ERROR_MESSAGES.INVALID_TOKEN,
             )
         else:
+            if WEBUI_AUTH_TRUSTED_EMAIL_HEADER:
+                trusted_email = request.headers.get(WEBUI_AUTH_TRUSTED_EMAIL_HEADER)
+                if trusted_email and user.email != trusted_email:
+                    # Delete the token cookie
+                    response.delete_cookie("token")
+                    # Delete OAuth token if present
+                    if request.cookies.get("oauth_id_token"):
+                        response.delete_cookie("oauth_id_token")
+                    raise HTTPException(
+                        status_code=status.HTTP_401_UNAUTHORIZED,
+                        detail="User mismatch. Please sign in again.",
+                    )
+
             # Add user info to current span
             current_span = trace.get_current_span()
             if current_span:

backend/open_webui/utils/chat.py

@@ -320,12 +320,7 @@ async def chat_completed(request: Request, form_data: dict, user: Any):
     extra_params = {
         "__event_emitter__": get_event_emitter(metadata),
         "__event_call__": get_event_call(metadata),
-        "__user__": {
-            "id": user.id,
-            "email": user.email,
-            "name": user.name,
-            "role": user.role,
-        },
+        "__user__": user.model_dump() if isinstance(user, UserModel) else {},
         "__metadata__": metadata,
         "__request__": request,
         "__model__": model,
@@ -424,12 +419,7 @@ async def chat_action(request: Request, action_id: str, form_data: dict, user: A
                     params[key] = value
 
             if "__user__" in sig.parameters:
-                __user__ = {
-                    "id": user.id,
-                    "email": user.email,
-                    "name": user.name,
-                    "role": user.role,
-                }
+                __user__ = (user.model_dump() if isinstance(user, UserModel) else {},)
 
                 try:
                     if hasattr(function_module, "UserValves"):

backend/open_webui/utils/embeddings.py

@@ -0,0 +1,90 @@
+import random
+import logging
+import sys
+
+from fastapi import Request
+from open_webui.models.users import UserModel
+from open_webui.models.models import Models
+from open_webui.utils.models import check_model_access
+from open_webui.env import SRC_LOG_LEVELS, GLOBAL_LOG_LEVEL, BYPASS_MODEL_ACCESS_CONTROL
+
+from open_webui.routers.openai import embeddings as openai_embeddings
+from open_webui.routers.ollama import (
+    embeddings as ollama_embeddings,
+    GenerateEmbeddingsForm,
+)
+
+
+from open_webui.utils.payload import convert_embedding_payload_openai_to_ollama
+from open_webui.utils.response import convert_embedding_response_ollama_to_openai
+
+logging.basicConfig(stream=sys.stdout, level=GLOBAL_LOG_LEVEL)
+log = logging.getLogger(__name__)
+log.setLevel(SRC_LOG_LEVELS["MAIN"])
+
+
+async def generate_embeddings(
+    request: Request,
+    form_data: dict,
+    user: UserModel,
+    bypass_filter: bool = False,
+):
+    """
+    Dispatch and handle embeddings generation based on the model type (OpenAI, Ollama).
+
+    Args:
+        request (Request): The FastAPI request context.
+        form_data (dict): The input data sent to the endpoint.
+        user (UserModel): The authenticated user.
+        bypass_filter (bool): If True, disables access filtering (default False).
+
+    Returns:
+        dict: The embeddings response, following OpenAI API compatibility.
+    """
+    if BYPASS_MODEL_ACCESS_CONTROL:
+        bypass_filter = True
+
+    # Attach extra metadata from request.state if present
+    if hasattr(request.state, "metadata"):
+        if "metadata" not in form_data:
+            form_data["metadata"] = request.state.metadata
+        else:
+            form_data["metadata"] = {
+                **form_data["metadata"],
+                **request.state.metadata,
+            }
+
+    # If "direct" flag present, use only that model
+    if getattr(request.state, "direct", False) and hasattr(request.state, "model"):
+        models = {
+            request.state.model["id"]: request.state.model,
+        }
+    else:
+        models = request.app.state.MODELS
+
+    model_id = form_data.get("model")
+    if model_id not in models:
+        raise Exception("Model not found")
+    model = models[model_id]
+
+    # Access filtering
+    if not getattr(request.state, "direct", False):
+        if not bypass_filter and user.role == "user":
+            check_model_access(user, model)
+
+    # Ollama backend
+    if model.get("owned_by") == "ollama":
+        ollama_payload = convert_embedding_payload_openai_to_ollama(form_data)
+        response = await ollama_embeddings(
+            request=request,
+            form_data=GenerateEmbeddingsForm(**ollama_payload),
+            user=user,
+        )
+        return convert_embedding_response_ollama_to_openai(response)
+
+    # Default: OpenAI or compatible backend
+    return await openai_embeddings(
+        request=request,
+        form_data=form_data,
+        user=user,
+    )

backend/open_webui/utils/middleware.py

@@ -32,11 +32,17 @@ from open_webui.socket.main import (
 from open_webui.routers.tasks import (
     generate_queries,
     generate_title,
+    generate_follow_ups,
     generate_image_prompt,
     generate_chat_tags,
 )
 from open_webui.routers.retrieval import process_web_search, SearchForm
-from open_webui.routers.images import image_generations, GenerateImageForm
+from open_webui.routers.images import (
+    load_b64_image_data,
+    image_generations,
+    GenerateImageForm,
+    upload_image,
+)
 from open_webui.routers.pipelines import (
     process_pipeline_inlet_filter,
     process_pipeline_outlet_filter,
@@ -692,13 +698,8 @@ def apply_params_to_form_data(form_data, model):
         params = deep_update(params, custom_params)
 
     if model.get("ollama"):
+        # Ollama specific parameters
         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 isinstance(params, dict):
             for key, value in params.items():
@@ -726,12 +727,7 @@ async def process_chat_payload(request, form_data, user, metadata, model):
     extra_params = {
         "__event_emitter__": event_emitter,
         "__event_call__": event_call,
-        "__user__": {
-            "id": user.id,
-            "email": user.email,
-            "name": user.name,
-            "role": user.role,
-        },
+        "__user__": user.model_dump() if isinstance(user, UserModel) else {},
         "__metadata__": metadata,
         "__request__": request,
         "__model__": model,
@@ -1061,6 +1057,59 @@ async def process_chat_response(
                 )
 
             if tasks and messages:
+                if (
+                    TASKS.FOLLOW_UP_GENERATION in tasks
+                    and tasks[TASKS.FOLLOW_UP_GENERATION]
+                ):
+                    res = await generate_follow_ups(
+                        request,
+                        {
+                            "model": message["model"],
+                            "messages": messages,
+                            "message_id": metadata["message_id"],
+                            "chat_id": metadata["chat_id"],
+                        },
+                        user,
+                    )
+
+                    if res and isinstance(res, dict):
+                        if len(res.get("choices", [])) == 1:
+                            follow_ups_string = (
+                                res.get("choices", [])[0]
+                                .get("message", {})
+                                .get("content", "")
+                            )
+                        else:
+                            follow_ups_string = ""
+
+                        follow_ups_string = follow_ups_string[
+                            follow_ups_string.find("{") : follow_ups_string.rfind("}")
+                            + 1
+                        ]
+
+                        try:
+                            follow_ups = json.loads(follow_ups_string).get(
+                                "follow_ups", []
+                            )
+                            Chats.upsert_message_to_chat_by_id_and_message_id(
+                                metadata["chat_id"],
+                                metadata["message_id"],
+                                {
+                                    "followUps": follow_ups,
+                                },
+                            )
+
+                            await event_emitter(
+                                {
+                                    "type": "chat:message:follow_ups",
+                                    "data": {
+                                        "follow_ups": follow_ups,
+                                    },
+                                }
+                            )
+                        except Exception as e:
+                            pass
+
                 if TASKS.TITLE_GENERATION in tasks:
                     if tasks[TASKS.TITLE_GENERATION]:
                         res = await generate_title(
@@ -1286,12 +1335,7 @@ async def process_chat_response(
     extra_params = {
         "__event_emitter__": event_emitter,
         "__event_call__": event_caller,
-        "__user__": {
-            "id": user.id,
-            "email": user.email,
-            "name": user.name,
-            "role": user.role,
-        },
+        "__user__": user.model_dump() if isinstance(user, UserModel) else {},
         "__metadata__": metadata,
         "__request__": request,
         "__model__": model,
@@ -1835,9 +1879,11 @@ async def process_chat_response(
 
                                     value = delta.get("content")
 
-                                    reasoning_content = delta.get(
-                                        "reasoning_content"
-                                    ) or delta.get("reasoning")
+                                    reasoning_content = (
+                                        delta.get("reasoning_content")
+                                        or delta.get("reasoning")
+                                        or delta.get("thinking")
+                                    )
                                     if reasoning_content:
                                         if (
                                             not content_blocks
@@ -2230,28 +2276,21 @@ async def process_chat_response(
                                         stdoutLines = stdout.split("\n")
                                         for idx, line in enumerate(stdoutLines):
                                             if "data:image/png;base64" in line:
-                                                id = str(uuid4())
-
-                                                # ensure the path exists
-                                                os.makedirs(
-                                                    os.path.join(CACHE_DIR, "images"),
-                                                    exist_ok=True,
+                                                image_url = ""
+                                                # Extract base64 image data from the line
+                                                image_data, content_type = (
+                                                    load_b64_image_data(line)
                                                 )
-
-                                                image_path = os.path.join(
-                                                    CACHE_DIR,
-                                                    f"images/{id}.png",
-                                                )
-
-                                                with open(image_path, "wb") as f:
-                                                    f.write(
-                                                        base64.b64decode(
-                                                            line.split(",")[1]
-                                                        )
+                                                if image_data is not None:
+                                                    image_url = upload_image(
+                                                        request,
+                                                        image_data,
+                                                        content_type,
+                                                        metadata,
+                                                        user,
                                                     )
-
                                                 stdoutLines[idx] = (
-                                                    f"![Output Image {idx}](/cache/images/{id}.png)"
+                                                    f"![Output Image]({image_url})"
                                                 )
 
                                         output["stdout"] = "\n".join(stdoutLines)
@@ -2262,30 +2301,22 @@ async def process_chat_response(
                                         resultLines = result.split("\n")
                                         for idx, line in enumerate(resultLines):
                                             if "data:image/png;base64" in line:
-                                                id = str(uuid4())
-
-                                                # ensure the path exists
-                                                os.makedirs(
-                                                    os.path.join(CACHE_DIR, "images"),
-                                                    exist_ok=True,
+                                                image_url = ""
+                                                # Extract base64 image data from the line
+                                                image_data, content_type = (
+                                                    load_b64_image_data(line)
                                                 )
-
-                                                image_path = os.path.join(
-                                                    CACHE_DIR,
-                                                    f"images/{id}.png",
-                                                )
-
-                                                with open(image_path, "wb") as f:
-                                                    f.write(
-                                                        base64.b64decode(
-                                                            line.split(",")[1]
-                                                        )
+                                                if image_data is not None:
+                                                    image_url = upload_image(
+                                                        request,
+                                                        image_data,
+                                                        content_type,
+                                                        metadata,
+                                                        user,
                                                     )
-
                                                 resultLines[idx] = (
-                                                    f"![Output Image {idx}](/cache/images/{id}.png)"
+                                                    f"![Output Image]({image_url})"
                                                 )
-
                                         output["result"] = "\n".join(resultLines)
                         except Exception as e:
                             output = str(e)
@@ -2394,8 +2425,8 @@ async def process_chat_response(
                 await response.background()
 
         # background_tasks.add_task(post_response_handler, response, events)
-        task_id, _ = create_task(
-            post_response_handler(response, events), id=metadata["chat_id"]
+        task_id, _ = await create_task(
+            request, post_response_handler(response, events), id=metadata["chat_id"]
         )
         return {"status": True, "task_id": task_id}
 

backend/open_webui/utils/misc.py

@@ -208,6 +208,7 @@ def openai_chat_message_template(model: str):
 def openai_chat_chunk_message_template(
     model: str,
     content: Optional[str] = None,
+    reasoning_content: Optional[str] = None,
     tool_calls: Optional[list[dict]] = None,
     usage: Optional[dict] = None,
 ) -> dict:
@@ -220,6 +221,9 @@ def openai_chat_chunk_message_template(
     if content:
         template["choices"][0]["delta"]["content"] = content
 
+    if reasoning_content:
+        template["choices"][0]["delta"]["reasoning_content"] = reasoning_content
+
     if tool_calls:
         template["choices"][0]["delta"]["tool_calls"] = tool_calls
 
@@ -234,6 +238,7 @@ def openai_chat_chunk_message_template(
 def openai_chat_completion_message_template(
     model: str,
     message: Optional[str] = None,
+    reasoning_content: Optional[str] = None,
     tool_calls: Optional[list[dict]] = None,
     usage: Optional[dict] = None,
 ) -> dict:
@@ -241,8 +246,9 @@ def openai_chat_completion_message_template(
     template["object"] = "chat.completion"
     if message is not None:
         template["choices"][0]["message"] = {
-            "content": message,
             "role": "assistant",
+            "content": message,
+            **({"reasoning_content": reasoning_content} if reasoning_content else {}),
             **({"tool_calls": tool_calls} if tool_calls else {}),
         }
 

backend/open_webui/utils/oauth.py

@@ -538,7 +538,7 @@ class OAuthManager:
         # Redirect back to the frontend with the JWT token
 
         redirect_base_url = request.app.state.config.WEBUI_URL or request.base_url
-        if redirect_base_url.endswith("/"):
+        if isinstance(redirect_base_url, str) and redirect_base_url.endswith("/"):
             redirect_base_url = redirect_base_url[:-1]
         redirect_url = f"{redirect_base_url}/auth#token={jwt_token}"
 

backend/open_webui/utils/payload.py

@@ -175,16 +175,32 @@ def apply_model_params_to_body_ollama(params: dict, form_data: dict) -> dict:
         "num_thread": int,
     }
 
-    # Extract keep_alive from options if it exists
-    if "options" in form_data and "keep_alive" in form_data["options"]:
-        form_data["keep_alive"] = form_data["options"]["keep_alive"]
-        del form_data["options"]["keep_alive"]
+    def parse_json(value: str) -> dict:
+        """
+        Parses a JSON string into a dictionary, handling potential JSONDecodeError.
+        """
+        try:
+            return json.loads(value)
+        except Exception as e:
+            return value
 
-    if "options" in form_data and "format" in form_data["options"]:
-        form_data["format"] = form_data["options"]["format"]
-        del form_data["options"]["format"]
+    ollama_root_params = {
+        "format": lambda x: parse_json(x),
+        "keep_alive": lambda x: parse_json(x),
+        "think": bool,
+    }
 
-    return apply_model_params_to_body(params, form_data, mappings)
+    for key, value in ollama_root_params.items():
+        if (param := params.get(key, None)) is not None:
+            # Copy the parameter to new name then delete it, to prevent Ollama warning of invalid option provided
+            form_data[key] = value(param)
+            del params[key]
+
+    # Unlike OpenAI, Ollama does not support params directly in the body
+    form_data["options"] = apply_model_params_to_body(
+        params, (form_data.get("options", {}) or {}), mappings
+    )
+    return form_data
 
 
 def convert_messages_openai_to_ollama(messages: list[dict]) -> list[dict]:
@@ -279,36 +295,48 @@ def convert_payload_openai_to_ollama(openai_payload: dict) -> dict:
         openai_payload.get("messages")
     )
     ollama_payload["stream"] = openai_payload.get("stream", False)
-
     if "tools" in openai_payload:
         ollama_payload["tools"] = openai_payload["tools"]
 
-    if "format" in openai_payload:
-        ollama_payload["format"] = openai_payload["format"]
-
     # If there are advanced parameters in the payload, format them in Ollama's options field
     if openai_payload.get("options"):
         ollama_payload["options"] = openai_payload["options"]
         ollama_options = openai_payload["options"]
 
+        def parse_json(value: str) -> dict:
+            """
+            Parses a JSON string into a dictionary, handling potential JSONDecodeError.
+            """
+            try:
+                return json.loads(value)
+            except Exception as e:
+                return value
+
+        ollama_root_params = {
+            "format": lambda x: parse_json(x),
+            "keep_alive": lambda x: parse_json(x),
+            "think": bool,
+        }
+
+        # Ollama's options field can contain parameters that should be at the root level.
+        for key, value in ollama_root_params.items():
+            if (param := ollama_options.get(key, None)) is not None:
+                # Copy the parameter to new name then delete it, to prevent Ollama warning of invalid option provided
+                ollama_payload[key] = value(param)
+                del ollama_options[key]
+
         # Re-Mapping OpenAI's `max_tokens` -> Ollama's `num_predict`
         if "max_tokens" in ollama_options:
             ollama_options["num_predict"] = ollama_options["max_tokens"]
-            del ollama_options[
-                "max_tokens"
-            ]  # To prevent Ollama warning of invalid option provided
+            del ollama_options["max_tokens"]
 
         # Ollama lacks a "system" prompt option. It has to be provided as a direct parameter, so we copy it down.
+        # Comment: Not sure why this is needed, but we'll keep it for compatibility.
         if "system" in ollama_options:
             ollama_payload["system"] = ollama_options["system"]
-            del ollama_options[
-                "system"
-            ]  # To prevent Ollama warning of invalid option provided
+            del ollama_options["system"]
 
-        # Extract keep_alive from options if it exists
-        if "keep_alive" in ollama_options:
-            ollama_payload["keep_alive"] = ollama_options["keep_alive"]
-            del ollama_options["keep_alive"]
+        ollama_payload["options"] = ollama_options
 
     # If there is the "stop" parameter in the openai_payload, remap it to the ollama_payload.options
     if "stop" in openai_payload:
@@ -329,3 +357,32 @@ def convert_payload_openai_to_ollama(openai_payload: dict) -> dict:
             ollama_payload["format"] = format
 
     return ollama_payload
+
+
+def convert_embedding_payload_openai_to_ollama(openai_payload: dict) -> dict:
+    """
+    Convert an embeddings request payload from OpenAI format to Ollama format.
+
+    Args:
+        openai_payload (dict): The original payload designed for OpenAI API usage.
+
+    Returns:
+        dict: A payload compatible with the Ollama API embeddings endpoint.
+    """
+    ollama_payload = {"model": openai_payload.get("model")}
+    input_value = openai_payload.get("input")
+
+    # Ollama expects 'input' as a list, and 'prompt' as a single string.
+    if isinstance(input_value, list):
+        ollama_payload["input"] = input_value
+        ollama_payload["prompt"] = "\n".join(str(x) for x in input_value)
+    else:
+        ollama_payload["input"] = [input_value]
+        ollama_payload["prompt"] = str(input_value)
+
+    # Optionally forward other fields if present
+    for optional_key in ("options", "truncate", "keep_alive"):
+        if optional_key in openai_payload:
+            ollama_payload[optional_key] = openai_payload[optional_key]
+
+    return ollama_payload

backend/open_webui/utils/redis.py

@@ -1,7 +1,6 @@
 import socketio
-import redis
-from redis import asyncio as aioredis
 from urllib.parse import urlparse
+from typing import Optional
 
 
 def parse_redis_service_url(redis_url):
@@ -18,23 +17,46 @@ def parse_redis_service_url(redis_url):
     }
 
 
-def get_redis_connection(redis_url, redis_sentinels, decode_responses=True):
-    if redis_sentinels:
-        redis_config = parse_redis_service_url(redis_url)
-        sentinel = redis.sentinel.Sentinel(
-            redis_sentinels,
-            port=redis_config["port"],
-            db=redis_config["db"],
-            username=redis_config["username"],
-            password=redis_config["password"],
-            decode_responses=decode_responses,
-        )
+def get_redis_connection(
+    redis_url, redis_sentinels, async_mode=False, decode_responses=True
+):
+    if async_mode:
+        import redis.asyncio as redis
 
-        # Get a master connection from Sentinel
-        return sentinel.master_for(redis_config["service"])
+        # If using sentinel in async mode
+        if redis_sentinels:
+            redis_config = parse_redis_service_url(redis_url)
+            sentinel = redis.sentinel.Sentinel(
+                redis_sentinels,
+                port=redis_config["port"],
+                db=redis_config["db"],
+                username=redis_config["username"],
+                password=redis_config["password"],
+                decode_responses=decode_responses,
+            )
+            return sentinel.master_for(redis_config["service"])
+        elif redis_url:
+            return redis.from_url(redis_url, decode_responses=decode_responses)
+        else:
+            return None
     else:
-        # Standard Redis connection
-        return redis.Redis.from_url(redis_url, decode_responses=decode_responses)
+        import redis
+
+        if redis_sentinels:
+            redis_config = parse_redis_service_url(redis_url)
+            sentinel = redis.sentinel.Sentinel(
+                redis_sentinels,
+                port=redis_config["port"],
+                db=redis_config["db"],
+                username=redis_config["username"],
+                password=redis_config["password"],
+                decode_responses=decode_responses,
+            )
+            return sentinel.master_for(redis_config["service"])
+        elif redis_url:
+            return redis.Redis.from_url(redis_url, decode_responses=decode_responses)
+        else:
+            return None
 
 
 def get_sentinels_from_env(sentinel_hosts_env, sentinel_port_env):

backend/open_webui/utils/response.py

@@ -83,6 +83,7 @@ def convert_ollama_usage_to_openai(data: dict) -> dict:
 def convert_response_ollama_to_openai(ollama_response: dict) -> dict:
     model = ollama_response.get("model", "ollama")
     message_content = ollama_response.get("message", {}).get("content", "")
+    reasoning_content = ollama_response.get("message", {}).get("thinking", None)
     tool_calls = ollama_response.get("message", {}).get("tool_calls", None)
     openai_tool_calls = None
 
@@ -94,7 +95,7 @@ def convert_response_ollama_to_openai(ollama_response: dict) -> dict:
     usage = convert_ollama_usage_to_openai(data)
 
     response = openai_chat_completion_message_template(
-        model, message_content, openai_tool_calls, usage
+        model, message_content, reasoning_content, openai_tool_calls, usage
     )
     return response
 
@@ -105,6 +106,7 @@ async def convert_streaming_response_ollama_to_openai(ollama_streaming_response)
 
         model = data.get("model", "ollama")
         message_content = data.get("message", {}).get("content", None)
+        reasoning_content = data.get("message", {}).get("thinking", None)
         tool_calls = data.get("message", {}).get("tool_calls", None)
         openai_tool_calls = None
 
@@ -118,10 +120,71 @@ async def convert_streaming_response_ollama_to_openai(ollama_streaming_response)
             usage = convert_ollama_usage_to_openai(data)
 
         data = openai_chat_chunk_message_template(
-            model, message_content, openai_tool_calls, usage
+            model, message_content, reasoning_content, openai_tool_calls, usage
         )
 
         line = f"data: {json.dumps(data)}\n\n"
         yield line
 
     yield "data: [DONE]\n\n"
+
+
+def convert_embedding_response_ollama_to_openai(response) -> dict:
+    """
+    Convert the response from Ollama embeddings endpoint to the OpenAI-compatible format.
+
+    Args:
+        response (dict): The response from the Ollama API,
+            e.g. {"embedding": [...], "model": "..."}
+            or {"embeddings": [{"embedding": [...], "index": 0}, ...], "model": "..."}
+
+    Returns:
+        dict: Response adapted to OpenAI's embeddings API format.
+            e.g. {
+                "object": "list",
+                "data": [
+                    {"object": "embedding", "embedding": [...], "index": 0},
+                    ...
+                ],
+                "model": "...",
+            }
+    """
+    # Ollama batch-style output
+    if isinstance(response, dict) and "embeddings" in response:
+        openai_data = []
+        for i, emb in enumerate(response["embeddings"]):
+            openai_data.append(
+                {
+                    "object": "embedding",
+                    "embedding": emb.get("embedding"),
+                    "index": emb.get("index", i),
+                }
+            )
+        return {
+            "object": "list",
+            "data": openai_data,
+            "model": response.get("model"),
+        }
+    # Ollama single output
+    elif isinstance(response, dict) and "embedding" in response:
+        return {
+            "object": "list",
+            "data": [
+                {
+                    "object": "embedding",
+                    "embedding": response["embedding"],
+                    "index": 0,
+                }
+            ],
+            "model": response.get("model"),
+        }
+    # Already OpenAI-compatible?
+    elif (
+        isinstance(response, dict)
+        and "data" in response
+        and isinstance(response["data"], list)
+    ):
+        return response
+
+    # Fallback: return as is if unrecognized
+    return response

backend/open_webui/utils/task.py

@@ -207,6 +207,24 @@ def title_generation_template(
     return template
 
 
+def follow_up_generation_template(
+    template: str, messages: list[dict], user: Optional[dict] = None
+) -> str:
+    prompt = get_last_user_message(messages)
+    template = replace_prompt_variable(template, prompt)
+    template = replace_messages_variable(template, messages)
+
+    template = prompt_template(
+        template,
+        **(
+            {"user_name": user.get("name"), "user_location": user.get("location")}
+            if user
+            else {}
+        ),
+    )
+    return template
+
+
 def tags_generation_template(
     template: str, messages: list[dict], user: Optional[dict] = None
 ) -> str:

backend/requirements.txt

@@ -1,5 +1,5 @@
 fastapi==0.115.7
-uvicorn[standard]==0.34.0
+uvicorn[standard]==0.34.2
 pydantic==2.10.6
 python-multipart==0.0.20
 
@@ -7,14 +7,13 @@ python-socketio==5.13.0
 python-jose==3.4.0
 passlib[bcrypt]==1.7.4
 
-requests==2.32.3
+requests==2.32.4
 aiohttp==3.11.11
 async-timeout
 aiocache
 aiofiles
 starlette-compress==1.6.0
 
-
 sqlalchemy==2.0.38
 alembic==1.14.0
 peewee==3.18.1
@@ -76,13 +75,13 @@ pandas==2.2.3
 openpyxl==3.1.5
 pyxlsb==1.0.10
 xlrd==2.0.1
-validators==0.34.0
+validators==0.35.0
 psutil
 sentencepiece
 soundfile==0.13.1
-azure-ai-documentintelligence==1.0.0
+azure-ai-documentintelligence==1.0.2
 
-pillow==11.1.0
+pillow==11.2.1
 opencv-python-headless==4.11.0.86
 rapidocr-onnxruntime==1.4.4
 rank-bm25==0.2.2

backend/start.sh

@@ -14,7 +14,11 @@ if [[ "${WEB_LOADER_ENGINE,,}" == "playwright" ]]; then
     python -c "import nltk; nltk.download('punkt_tab')"
 fi
 
-KEY_FILE=.webui_secret_key
+if [ -n "${WEBUI_SECRET_KEY_FILE}" ]; then
+    KEY_FILE="${WEBUI_SECRET_KEY_FILE}"
+else
+    KEY_FILE=".webui_secret_key"
+fi
 
 PORT="${PORT:-8080}"
 HOST="${HOST:-0.0.0.0}"

backend/start_windows.bat

@@ -18,6 +18,10 @@ IF /I "%WEB_LOADER_ENGINE%" == "playwright" (
 )
 
 SET "KEY_FILE=.webui_secret_key"
+IF NOT "%WEBUI_SECRET_KEY_FILE%" == "" (
+    SET "KEY_FILE=%WEBUI_SECRET_KEY_FILE%"
+)
+
 IF "%PORT%"=="" SET PORT=8080
 IF "%HOST%"=="" SET HOST=0.0.0.0
 SET "WEBUI_SECRET_KEY=%WEBUI_SECRET_KEY%"