Merge pull request #22469 from open-webui/dev

0.8.10

CHANGELOG.md

@@ -5,6 +5,39 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.8.10] - 2026-03-08
+
+### Added
+
+- 🔐 **Custom OIDC logout endpoint.** Administrators can now configure a custom OpenID Connect logout URL via OPENID_END_SESSION_ENDPOINT, enabling logout functionality for OIDC providers that require custom endpoints like AWS Cognito. [Commit](https://github.com/open-webui/open-webui/commit/3f350f865920daf2844769a758b2d2e6a7ee3efa)
+- 🗄️ **MariaDB Vector community support.** Added MariaDB Vector as a new vector database backend, enabling deployments with VECTOR_DB=mariadb-vector; supports cosine and euclidean distance strategies with configurable HNSW indexing. [#21931](https://github.com/open-webui/open-webui/pull/21931)
+- 📝 **Task message truncation.** Chat messages sent to task models for title and tag generation can now be truncated using a filter in the prompt template, reducing token usage and processing time for long conversations. [#21499](https://github.com/open-webui/open-webui/issues/21499)
+- 🔄 **General improvements.** Various improvements were implemented across the application to enhance performance, stability, and security.
+- 🌐 Translations for Portuguese (Brazil), Spanish, and Malay were enhanced and expanded.
+
+### Fixed
+
+- 🔗 **Pipeline filter HTTP errors.** Fixed a bug where HTTP errors in pipeline inlet/outlet filters would silently corrupt the user's chat payload; errors are now properly raised before parsing the response. [#22445](https://github.com/open-webui/open-webui/pull/22445)
+- 📚 **Knowledge file embedding updates.** Fixed a bug where updating knowledge files left old embeddings in the database, causing search results to include duplicate and stale data. [#20558](https://github.com/open-webui/open-webui/issues/20558)
+- 📁 **Files list stability.** Fixed the files list ordering to use created_at with id as secondary sort, ensuring consistent ordering and preventing page crashes when managing many files. [#21879](https://github.com/open-webui/open-webui/issues/21879)
+- 📨 **Teams webhook crash.** Fixed a TypeError crash in the Teams webhook handler when user data is missing from the event payload. [#22444](https://github.com/open-webui/open-webui/pull/22444)
+- 🛠️ **Process shutdown handling.** Fixed bare except clauses in the main process that prevented clean shutdown; replaced with proper exception handling. [#22423](https://github.com/open-webui/open-webui/pull/22423)
+- 🐳 **Docker deployment startup.** Docker deployments now start correctly; the missing OpenTelemetry system metrics dependency was added. [#22447](https://github.com/open-webui/open-webui/pull/22447), [#22401](https://github.com/open-webui/open-webui/issues/22401)
+- 🛠️ **Tool access for non-admin users.** Fixed a NameError that prevented non-admin users from viewing tools; the missing has_access function is now properly imported. [#22393](https://github.com/open-webui/open-webui/issues/22393)
+- 🔐 **OAuth error handling.** Fixed a bug where bare except clauses silently caught SystemExit and KeyboardInterrupt, preventing clean process shutdown during OAuth authentication. [#22420](https://github.com/open-webui/open-webui/pull/22420)
+- 🛠️ **Exception error messages.** Fixed three locations where incorrect exception raising caused confusing TypeError messages instead of proper error descriptions, making debugging much easier. [#22446](https://github.com/open-webui/open-webui/pull/22446)
+- 📄 **YAML file processing.** Fixed an error when uploading YAML files with Docling enabled; YAML and YML files are now properly recognized as text files and processed correctly. [#22399](https://github.com/open-webui/open-webui/pull/22399), [#22263](https://github.com/open-webui/open-webui/issues/22263)
+- 📅 **Time range month names.** Fixed month names in time range labels appearing in the wrong language when OS regional settings differ from browser language; month names now consistently display in English. [#22454](https://github.com/open-webui/open-webui/pull/22454)
+- 🔐 **OAuth error URL encoding.** Fixed OAuth error messages with special characters causing malformed redirect URLs; error messages are now properly URL-encoded. [#22415](https://github.com/open-webui/open-webui/pull/22415)
+- 🛠️ **Internal tool method filtering.** Tools no longer expose internal methods starting with underscore to the LLM, reducing clutter and improving accuracy. [#22408](https://github.com/open-webui/open-webui/pull/22408)
+- 🔊 **Azure TTS locale extraction.** Fixed Azure text-to-speech using incomplete locale codes in SSML; now correctly uses full locale like "en-US" instead of just "en". [#22443](https://github.com/open-webui/open-webui/pull/22443)
+- 🎤 **Azure speech transcription errors.** Improved Azure AI Speech error handling to display user-friendly messages instead of generic connection errors; empty transcripts, no language identified, and other Azure-specific errors now show clear descriptions. [#20485](https://github.com/open-webui/open-webui/issues/20485)
+- 📊 **Analytics group filtering.** Fixed token usage analytics not being filtered by user group; the query now properly respects group filters like other analytics metrics. [#22167](https://github.com/open-webui/open-webui/pull/22167)
+- 🔍 **Web search favicon fallback.** Fixed web search sources showing broken image icons when favicons couldn't be loaded from external sources; now falls back to the default Open WebUI favicon. [#21897](https://github.com/open-webui/open-webui/pull/21897)
+- 🔄 **Custom model fallback.** Fixed custom model fallback not working when the base model is unavailable; the base model ID is now correctly retrieved from model info instead of empty params. [#22456](https://github.com/open-webui/open-webui/issues/22456)
+- 🖼️ **Pending message image display.** Fixed images in queued messages appearing blank; image thumbnails are now properly displayed in the pending message queue. [#22256](https://github.com/open-webui/open-webui/issues/22256)
+- 🛠️ **File metadata sanitization.** Fixed file uploads failing with JSON serialization errors when metadata contained non-serializable objects like callable functions; metadata is now sanitized before database insertion. [#20561](https://github.com/open-webui/open-webui/issues/20561)
+
 ## [0.8.9] - 2026-03-07
 
 ### Added

Dockerfile

@@ -127,6 +127,7 @@ RUN chown -R $UID:$GID /app $HOME
 RUN apt-get update && \
     apt-get install -y --no-install-recommends \
     git build-essential pandoc gcc netcat-openbsd curl jq \
+    libmariadb-dev \
     python3-dev \
     ffmpeg libsm6 libxext6 zstd \
     && rm -rf /var/lib/apt/lists/*

backend/open_webui/config.py

@@ -469,6 +469,12 @@ OPENID_PROVIDER_URL = PersistentConfig(
     os.environ.get("OPENID_PROVIDER_URL", ""),
 )
 
+OPENID_END_SESSION_ENDPOINT = PersistentConfig(
+    "OPENID_END_SESSION_ENDPOINT",
+    "oauth.oidc.end_session_endpoint",
+    os.environ.get("OPENID_END_SESSION_ENDPOINT", ""),
+)
+
 OPENID_REDIRECT_URI = PersistentConfig(
     "OPENID_REDIRECT_URI",
     "oauth.oidc.redirect_uri",
@@ -844,13 +850,18 @@ def load_oauth_providers():
     if FEISHU_CLIENT_ID.value:
         configured_providers.append("Feishu")
 
-    if configured_providers and not OPENID_PROVIDER_URL.value:
+    if (
+        configured_providers
+        and not OPENID_PROVIDER_URL.value
+        and not OPENID_END_SESSION_ENDPOINT.value
+    ):
         provider_list = ", ".join(configured_providers)
         log.warning(
             f"⚠️  OAuth providers configured ({provider_list}) but OPENID_PROVIDER_URL not set - logout will not work!"
         )
         log.warning(
-            f"Set OPENID_PROVIDER_URL to your OAuth provider's OpenID Connect discovery endpoint to fix logout functionality."
+            f"Set OPENID_PROVIDER_URL to your OAuth provider's OpenID Connect discovery endpoint,"
+            f" or set OPENID_END_SESSION_ENDPOINT to a custom logout URL to fix logout functionality."
         )
 
 
@@ -2359,6 +2370,81 @@ if VECTOR_DB == "chroma":
     CHROMA_HTTP_SSL = os.environ.get("CHROMA_HTTP_SSL", "false").lower() == "true"
 # this uses the model defined in the Dockerfile ENV variable. If you dont use docker or docker based deployments such as k8s, the default embedding model will be used (sentence-transformers/all-MiniLM-L6-v2)
 
+
+# MariaDB Vector (mariadb-vector)
+MARIADB_VECTOR_DB_URL = os.environ.get("MARIADB_VECTOR_DB_URL", "").strip()
+
+MARIADB_VECTOR_INITIALIZE_MAX_VECTOR_LENGTH = int(
+    os.environ.get("MARIADB_VECTOR_INITIALIZE_MAX_VECTOR_LENGTH", "1536").strip()
+    or "1536"
+)
+
+# Distance strategy:
+#   - cosine     => vec_distance_cosine(...)
+#   - euclidean  => vec_distance_euclidean(...)
+MARIADB_VECTOR_DISTANCE_STRATEGY = (
+    os.environ.get("MARIADB_VECTOR_DISTANCE_STRATEGY", "cosine").strip().lower()
+)
+
+# HNSW M parameter (MariaDB VECTOR INDEX ... M=<int>)
+MARIADB_VECTOR_INDEX_M = int(
+    os.environ.get("MARIADB_VECTOR_INDEX_M", "8").strip() or "8"
+)
+
+# Pooling (MariaDB-Vector)
+MARIADB_VECTOR_POOL_SIZE = os.environ.get("MARIADB_VECTOR_POOL_SIZE", None)
+
+if MARIADB_VECTOR_POOL_SIZE != None:
+    try:
+        MARIADB_VECTOR_POOL_SIZE = int(MARIADB_VECTOR_POOL_SIZE)
+    except Exception:
+        MARIADB_VECTOR_POOL_SIZE = None
+
+MARIADB_VECTOR_POOL_MAX_OVERFLOW = os.environ.get("MARIADB_VECTOR_POOL_MAX_OVERFLOW", 0)
+
+if MARIADB_VECTOR_POOL_MAX_OVERFLOW == "":
+    MARIADB_VECTOR_POOL_MAX_OVERFLOW = 0
+else:
+    try:
+        MARIADB_VECTOR_POOL_MAX_OVERFLOW = int(MARIADB_VECTOR_POOL_MAX_OVERFLOW)
+    except Exception:
+        MARIADB_VECTOR_POOL_MAX_OVERFLOW = 0
+
+MARIADB_VECTOR_POOL_TIMEOUT = os.environ.get("MARIADB_VECTOR_POOL_TIMEOUT", 30)
+
+if MARIADB_VECTOR_POOL_TIMEOUT == "":
+    MARIADB_VECTOR_POOL_TIMEOUT = 30
+else:
+    try:
+        MARIADB_VECTOR_POOL_TIMEOUT = int(MARIADB_VECTOR_POOL_TIMEOUT)
+    except Exception:
+        MARIADB_VECTOR_POOL_TIMEOUT = 30
+
+MARIADB_VECTOR_POOL_RECYCLE = os.environ.get("MARIADB_VECTOR_POOL_RECYCLE", 3600)
+
+if MARIADB_VECTOR_POOL_RECYCLE == "":
+    MARIADB_VECTOR_POOL_RECYCLE = 3600
+else:
+    try:
+        MARIADB_VECTOR_POOL_RECYCLE = int(MARIADB_VECTOR_POOL_RECYCLE)
+    except Exception:
+        MARIADB_VECTOR_POOL_RECYCLE = 3600
+
+ENABLE_MARIADB_VECTOR = True
+if VECTOR_DB == "mariadb-vector":
+    if not MARIADB_VECTOR_DB_URL:
+        ENABLE_MARIADB_VECTOR = False
+    else:
+        try:
+            parsed = urlparse(MARIADB_VECTOR_DB_URL)
+            scheme = (parsed.scheme or "").lower()
+            # Require official driver so VECTOR binds as float32 bytes correctly
+            if scheme != "mariadb+mariadbconnector":
+                ENABLE_MARIADB_VECTOR = False
+        except Exception:
+            ENABLE_MARIADB_VECTOR = False
+
+
 # Milvus
 MILVUS_URI = os.environ.get("MILVUS_URI", f"{DATA_DIR}/vector_db/milvus.db")
 MILVUS_DB = os.environ.get("MILVUS_DB", "default")

backend/open_webui/main.py

@@ -1732,8 +1732,8 @@ async def chat_completion(
         }
 
         # Check base model existence for custom models
-        if model_info_params.get("base_model_id"):
-            base_model_id = model_info_params.get("base_model_id")
+        if model_info and model_info.base_model_id:
+            base_model_id = model_info.base_model_id
             if base_model_id not in request.app.state.MODELS:
                 if ENABLE_CUSTOM_MODEL_FALLBACK:
                     default_models = (
@@ -1864,7 +1864,7 @@ async def chat_completion(
                                 "model": model_id,
                             },
                         )
-                except:
+                except Exception:
                     pass
 
             ctx = build_chat_response_context(
@@ -1911,7 +1911,7 @@ async def chat_completion(
                         {"type": "chat:tasks:cancel"},
                     )
 
-                except:
+                except Exception:
                     pass
         finally:
             try:

backend/open_webui/models/chat_messages.py

@@ -420,11 +420,13 @@ class ChatMessageTable:
         self,
         start_date: Optional[int] = None,
         end_date: Optional[int] = None,
+        group_id: Optional[str] = None,
         db: Optional[Session] = None,
     ) -> dict[str, dict]:
         """Aggregate token usage by user using database-level aggregation."""
         with get_db_context(db) as db:
             from sqlalchemy import func, cast, Integer
+            from open_webui.models.groups import GroupMember
 
             dialect = db.bind.dialect.name
 
@@ -464,6 +466,13 @@ class ChatMessageTable:
                 query = query.filter(ChatMessage.created_at >= start_date)
             if end_date:
                 query = query.filter(ChatMessage.created_at <= end_date)
+            if group_id:
+                group_users = (
+                    db.query(GroupMember.user_id)
+                    .filter(GroupMember.group_id == group_id)
+                    .subquery()
+                )
+                query = query.filter(ChatMessage.user_id.in_(group_users))
 
             results = query.group_by(ChatMessage.user_id).all()
 

backend/open_webui/models/files.py

@@ -4,6 +4,7 @@ from typing import Optional
 
 from sqlalchemy.orm import Session
 from open_webui.internal.db import Base, JSONField, get_db, get_db_context
+from open_webui.utils.misc import sanitize_metadata
 from pydantic import BaseModel, ConfigDict, model_validator
 from sqlalchemy import BigInteger, Column, String, Text, JSON
 
@@ -127,9 +128,16 @@ class FilesTable:
         self, user_id: str, form_data: FileForm, db: Optional[Session] = None
     ) -> Optional[FileModel]:
         with get_db_context(db) as db:
+            file_data = form_data.model_dump()
+
+            # Sanitize meta to remove non-JSON-serializable objects
+            # (e.g. callable tool functions, MCP client instances from middleware)
+            if file_data.get("meta"):
+                file_data["meta"] = sanitize_metadata(file_data["meta"])
+
             file = FileModel(
                 **{
-                    **form_data.model_dump(),
+                    **file_data,
                     "user_id": user_id,
                     "created_at": int(time.time()),
                     "updated_at": int(time.time()),
@@ -289,7 +297,7 @@ class FilesTable:
             db: Optional database session.
 
         Returns:
-            List of matching FileModel objects, ordered by updated_at descending.
+            List of matching FileModel objects, ordered by created_at descending.
         """
         with get_db_context(db) as db:
             query = db.query(File)
@@ -303,7 +311,7 @@ class FilesTable:
 
             return [
                 FileModel.model_validate(file)
-                for file in query.order_by(File.updated_at.desc())
+                for file in query.order_by(File.created_at.desc(), File.id.desc())
                 .offset(skip)
                 .limit(limit)
                 .all()

backend/open_webui/retrieval/loaders/main.py

@@ -86,6 +86,9 @@ known_source_ext = [
     "hs",
     "lhs",
     "json",
+    "yaml",
+    "yml",
+    "toml",
 ]
 
 

backend/open_webui/retrieval/utils.py

@@ -590,7 +590,9 @@ def generate_openai_batch_embeddings(
         if "data" in data:
             return [elem["embedding"] for elem in data["data"]]
         else:
-            raise "Something went wrong :/"
+            raise ValueError(
+                "Unexpected OpenAI embeddings response: missing 'data' key"
+            )
     except Exception as e:
         log.exception(f"Error generating openai batch embeddings: {e}")
         return None
@@ -767,7 +769,9 @@ def generate_ollama_batch_embeddings(
         if "embeddings" in data:
             return data["embeddings"]
         else:
-            raise "Something went wrong :/"
+            raise ValueError(
+                "Unexpected Ollama embeddings response: missing 'embeddings' key"
+            )
     except Exception as e:
         log.exception(f"Error generating ollama batch embeddings: {e}")
         return None

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

@@ -1,3 +1,7 @@
+"""
+NOTE: This vector database integration is community-supported and maintained on a best-effort basis.
+"""
+
 from elasticsearch import Elasticsearch, BadRequestError
 from typing import Optional
 import ssl

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

@@ -0,0 +1,593 @@
+"""
+NOTE: This vector database integration is community-supported and maintained on a best-effort basis.
+"""
+
+from __future__ import annotations
+
+import array
+import json
+import logging
+import math
+import re
+import sys
+from contextlib import contextmanager
+from typing import Any, Dict, List, Optional, Tuple
+
+from sqlalchemy import create_engine
+from sqlalchemy.pool import NullPool, QueuePool
+
+from open_webui.config import (
+    MARIADB_VECTOR_DB_URL,
+    MARIADB_VECTOR_DISTANCE_STRATEGY,
+    MARIADB_VECTOR_INDEX_M,
+    MARIADB_VECTOR_INITIALIZE_MAX_VECTOR_LENGTH,
+    MARIADB_VECTOR_POOL_SIZE,
+    MARIADB_VECTOR_POOL_MAX_OVERFLOW,
+    MARIADB_VECTOR_POOL_TIMEOUT,
+    MARIADB_VECTOR_POOL_RECYCLE,
+)
+from open_webui.retrieval.vector.main import (
+    GetResult,
+    SearchResult,
+    VectorDBBase,
+    VectorItem,
+)
+from open_webui.retrieval.vector.utils import process_metadata
+
+log = logging.getLogger(__name__)
+
+VECTOR_LENGTH = int(MARIADB_VECTOR_INITIALIZE_MAX_VECTOR_LENGTH)
+
+
+def _embedding_to_f32_bytes(vec: List[float]) -> bytes:
+    """
+    Convert a Python float vector into the binary payload expected by MariaDB VECTOR.
+
+    MariaDB Vector expects the vector argument to be bound as a little-endian float32
+    byte sequence. We use array('f') to avoid a numpy dependency and byteswap on
+    big-endian platforms for portability.
+    """
+    a = array.array("f", [float(x) for x in vec])  # float32
+    if sys.byteorder != "little":
+        a.byteswap()
+    return a.tobytes()
+
+
+def _safe_json(v: Any) -> Dict[str, Any]:
+    """
+    Normalize a potentially JSON-like value into a Python dict.
+
+    Accepts:
+      - dict: returned as-is
+      - str / bytes: parsed as JSON if possible
+      - None / other types: returns {}
+    """
+    if v is None:
+        return {}
+    if isinstance(v, dict):
+        return v
+    if isinstance(v, (bytes, bytearray)):
+        try:
+            v = v.decode("utf-8")
+        except Exception:
+            return {}
+    if isinstance(v, str):
+        try:
+            j = json.loads(v)
+            return j if isinstance(j, dict) else {}
+        except Exception:
+            return {}
+    return {}
+
+
+class MariaDBVectorClient(VectorDBBase):
+    """
+    MariaDB + MariaDB Vector backend using DBAPI cursor parameter binding.
+
+    IMPORTANT:
+      - Intended for: mariadb+mariadbconnector://... (official MariaDB driver).
+      - Uses qmark ("?") params and binds vectors as float32 bytes.
+      - Uses binary binding for BOTH inserts/updates and distance computations.
+    """
+
+    def __init__(
+        self,
+        db_url: Optional[str] = None,
+        vector_length: int = VECTOR_LENGTH,
+        distance_strategy: str = MARIADB_VECTOR_DISTANCE_STRATEGY,
+        index_m: int = MARIADB_VECTOR_INDEX_M,
+    ) -> None:
+        """
+        Initialize a MariaDB Vector-backed VectorDBBase implementation.
+
+        Validates URL scheme/driver requirements, ensures schema exists, and guards
+        against dimension mismatch with an existing VECTOR(n) column.
+        """
+        self.db_url = (db_url or MARIADB_VECTOR_DB_URL).strip()
+        self.vector_length = int(vector_length)
+        self.distance_strategy = (distance_strategy or "cosine").strip().lower()
+        self.index_m = int(index_m)
+
+        if self.distance_strategy not in {"cosine", "euclidean"}:
+            raise ValueError("distance_strategy must be 'cosine' or 'euclidean'")
+
+        if not self.db_url.lower().startswith("mariadb+mariadbconnector://"):
+            raise ValueError(
+                "MariaDBVectorClient requires mariadb+mariadbconnector:// (official MariaDB driver) "
+                "to ensure qmark paramstyle and correct VECTOR binding."
+            )
+
+        if isinstance(MARIADB_VECTOR_POOL_SIZE, int):
+            if MARIADB_VECTOR_POOL_SIZE > 0:
+                self.engine = create_engine(
+                    self.db_url,
+                    pool_size=MARIADB_VECTOR_POOL_SIZE,
+                    max_overflow=MARIADB_VECTOR_POOL_MAX_OVERFLOW,
+                    pool_timeout=MARIADB_VECTOR_POOL_TIMEOUT,
+                    pool_recycle=MARIADB_VECTOR_POOL_RECYCLE,
+                    pool_pre_ping=True,
+                    poolclass=QueuePool,
+                )
+            else:
+                self.engine = create_engine(
+                    self.db_url, pool_pre_ping=True, poolclass=NullPool
+                )
+        else:
+            self.engine = create_engine(self.db_url, pool_pre_ping=True)
+        self._init_schema()
+        self._check_vector_length()
+
+    @contextmanager
+    def _connect(self):
+        """
+        Yield a context-managed DBAPI connection (SQLAlchemy raw_connection()).
+
+        Callers can use:
+          with self._connect() as conn:
+            with conn.cursor() as cur:
+              ...
+        """
+        conn = self.engine.raw_connection()
+        try:
+            yield conn
+        finally:
+            try:
+                conn.close()
+            except Exception:
+                pass
+
+    def _init_schema(self) -> None:
+        """
+        Create the backing table and vector index if they do not exist.
+
+        Uses a PK definition compatible with MariaDB Vector's VECTOR INDEX key-size constraints.
+        """
+        with self._connect() as conn:
+            with conn.cursor() as cur:
+                try:
+                    dist = self.distance_strategy
+                    cur.execute(f"""
+                        CREATE TABLE IF NOT EXISTS document_chunk (
+                            -- MariaDB Vector requires the table PRIMARY KEY used with a VECTOR INDEX to be <= 256 bytes.
+                            -- VARCHAR has internal length/metadata overhead, so VARCHAR(255) can exceed the 256-byte limit.
+                            -- We use VARCHAR(254) to stay safely under the limit, and force ASCII (1 byte/char) so the byte
+                            -- size is predictable (avoid utf8mb4 where a "255 char" key could be up to 1020 bytes).
+                            -- ascii_bin gives bytewise, case-sensitive comparisons for stable ID matching.
+                            id VARCHAR(254) CHARACTER SET ascii COLLATE ascii_bin PRIMARY KEY,
+                            embedding VECTOR({self.vector_length}) NOT NULL,
+                            collection_name VARCHAR(255) NOT NULL,
+                            text LONGTEXT NULL,
+                            vmetadata JSON NULL,
+                            VECTOR INDEX (embedding) M={self.index_m} DISTANCE={dist},
+                            INDEX idx_document_chunk_collection_name (collection_name)
+                        ) ENGINE=InnoDB;
+                        """)
+                    conn.commit()
+                except Exception as e:
+                    conn.rollback()
+                    log.exception(f"Error during database initialization: {e}")
+                    raise
+
+    def _check_vector_length(self) -> None:
+        """
+        Validate that the existing VECTOR column dimension matches this client's configured dimension.
+
+        Dimension guard: if table already exists with
+        a different VECTOR(n), refuse to silently mismatch.
+        """
+        with self._connect() as conn:
+            with conn.cursor() as cur:
+                cur.execute("SHOW CREATE TABLE document_chunk")
+                row = cur.fetchone()
+                if not row or len(row) < 2:
+                    return
+                ddl = row[1]
+                m = re.search(r"vector\\((\\d+)\\)", ddl, flags=re.IGNORECASE)
+                if not m:
+                    return
+                existing = int(m.group(1))
+                if existing != int(self.vector_length):
+                    raise Exception(
+                        f"VECTOR_LENGTH {self.vector_length} does not match existing vector column dimension {existing}. "
+                        "Cannot change vector size after initialization without migrating the data."
+                    )
+
+    def adjust_vector_length(self, vector: List[float]) -> List[float]:
+        """
+        Pad or truncate a vector to match `self.vector_length`.
+        """
+        n = len(vector)
+        if n < self.vector_length:
+            return vector + [0.0] * (self.vector_length - n)
+        if n > self.vector_length:
+            return vector[: self.vector_length]
+        return vector
+
+    def _dist_fn(self) -> str:
+        """
+        Return the MariaDB Vector distance function name for the configured strategy.
+        """
+        return (
+            "vec_distance_cosine"
+            if self.distance_strategy == "cosine"
+            else "vec_distance_euclidean"
+        )
+
+    def _score_from_dist(self, dist: float) -> float:
+        """
+        Convert a DB distance value into a normalized score in (0, 1].
+
+        - cosine: score ~= 1 - cosine_distance, clamped to [0, 1]
+        - euclidean: score = 1 / (1 + dist)
+        """
+        if self.distance_strategy == "cosine":
+            score = 1.0 - dist
+            if score < 0.0:
+                score = 0.0
+            if score > 1.0:
+                score = 1.0
+            return score
+        return 1.0 / (1.0 + max(0.0, dist))
+
+    def _build_filter_sql_qmark(self, expr: Any) -> Tuple[str, List[Any]]:
+        """
+        Build a WHERE-clause fragment and qmark params from a minimal Mongo-like filter.
+
+        Supported forms:
+          - {"field": "v"}
+          - {"field": {"$in": ["a","b"]}}
+          - {"$and": [ ... ]}
+          - {"$or":  [ ... ]}
+        """
+        if not expr or not isinstance(expr, dict):
+            return "", []
+
+        if "$and" in expr:
+            parts: List[str] = []
+            params: List[Any] = []
+            for e in expr.get("$and") or []:
+                s, p = self._build_filter_sql_qmark(e)
+                if s:
+                    parts.append(s)
+                    params.extend(p)
+            return ("(" + " AND ".join(parts) + ")") if parts else "", params
+
+        if "$or" in expr:
+            parts: List[str] = []
+            params: List[Any] = []
+            for e in expr.get("$or") or []:
+                s, p = self._build_filter_sql_qmark(e)
+                if s:
+                    parts.append(s)
+                    params.extend(p)
+            return ("(" + " OR ".join(parts) + ")") if parts else "", params
+
+        clauses: List[str] = []
+        params: List[Any] = []
+        for key, value in expr.items():
+            if key.startswith("$"):
+                continue
+            json_expr = f"JSON_UNQUOTE(JSON_EXTRACT(vmetadata, '$.{key}'))"
+            if isinstance(value, dict) and "$in" in value:
+                vals = [str(v) for v in (value.get("$in") or [])]
+                if not vals:
+                    clauses.append("0=1")
+                    continue
+                ors = []
+                for v in vals:
+                    ors.append(f"{json_expr} = ?")
+                    params.append(v)
+                clauses.append("(" + " OR ".join(ors) + ")")
+            else:
+                clauses.append(f"{json_expr} = ?")
+                params.append(str(value))
+        return ("(" + " AND ".join(clauses) + ")") if clauses else "", params
+
+    def insert(self, collection_name: str, items: List[VectorItem]) -> None:
+        """
+        Insert items into the given collection (best-effort, ignores duplicates).
+
+        Uses executemany() with binary VECTOR binding for high-throughput ingestion.
+        """
+        if not items:
+            return
+        with self._connect() as conn:
+            with conn.cursor() as cur:
+                try:
+                    sql = """
+                        INSERT IGNORE INTO document_chunk
+                          (id, embedding, collection_name, text, vmetadata)
+                        VALUES
+                          (?, ?, ?, ?, ?)
+                    """
+                    params: List[Tuple[Any, ...]] = []
+                    for item in items:
+                        v = self.adjust_vector_length(item["vector"])
+                        emb = _embedding_to_f32_bytes(v)
+                        meta = process_metadata(item.get("metadata") or {})
+                        params.append(
+                            (
+                                item["id"],
+                                emb,
+                                collection_name,
+                                item.get("text"),
+                                json.dumps(meta),
+                            )
+                        )
+                    cur.executemany(sql, params)
+                    conn.commit()
+                except Exception as e:
+                    conn.rollback()
+                    log.exception(f"Error during insert: {e}")
+                    raise
+
+    def upsert(self, collection_name: str, items: List[VectorItem]) -> None:
+        """
+        Insert or update items in the given collection by primary key.
+
+        Uses executemany() and updates embedding/text/metadata on conflicts.
+        """
+        if not items:
+            return
+        with self._connect() as conn:
+            with conn.cursor() as cur:
+                try:
+                    sql = """
+                        INSERT INTO document_chunk
+                          (id, embedding, collection_name, text, vmetadata)
+                        VALUES
+                          (?, ?, ?, ?, ?)
+                        ON DUPLICATE KEY UPDATE
+                          embedding = VALUES(embedding),
+                          collection_name = VALUES(collection_name),
+                          text = VALUES(text),
+                          vmetadata = VALUES(vmetadata)
+                    """
+                    params: List[Tuple[Any, ...]] = []
+                    for item in items:
+                        v = self.adjust_vector_length(item["vector"])
+                        emb = _embedding_to_f32_bytes(v)
+                        meta = process_metadata(item.get("metadata") or {})
+                        params.append(
+                            (
+                                item["id"],
+                                emb,
+                                collection_name,
+                                item.get("text"),
+                                json.dumps(meta),
+                            )
+                        )
+                    cur.executemany(sql, params)
+                    conn.commit()
+                except Exception as e:
+                    conn.rollback()
+                    log.exception(f"Error during upsert: {e}")
+                    raise
+
+    def search(
+        self,
+        collection_name: str,
+        vectors: List[List[float]],
+        filter: Optional[Dict[str, Any]] = None,
+        limit: int = 10,
+    ) -> Optional[SearchResult]:
+        """
+        Perform a vector similarity search.
+
+        Args:
+          collection_name: Logical collection partition key.
+          vectors: One or more query vectors.
+          filter: Optional metadata filter (Mongo-like subset).
+          limit: Top-k per query vector.
+
+        Returns a SearchResult where distances are normalized scores (higher is better).
+        """
+        if not vectors:
+            return None
+
+        dist_fn = self._dist_fn()
+        ids: List[List[str]] = [[] for _ in vectors]
+        distances: List[List[float]] = [[] for _ in vectors]
+        documents: List[List[str]] = [[] for _ in vectors]
+        metadatas: List[List[Any]] = [[] for _ in vectors]
+
+        try:
+            with self._connect() as conn:
+                with conn.cursor() as cur:
+                    fsql, fparams = self._build_filter_sql_qmark(filter or {})
+                    where = "collection_name = ?"
+                    base_params: List[Any] = [collection_name]
+                    if fsql:
+                        where = where + " AND " + fsql
+                        base_params.extend(fparams)
+
+                    sql = f"""
+                        SELECT
+                          id,
+                          text,
+                          vmetadata,
+                          {dist_fn}(embedding, ?) AS dist
+                        FROM document_chunk
+                        WHERE {where}
+                        ORDER BY dist ASC
+                        LIMIT ?
+                    """
+
+                    for q_idx, q in enumerate(vectors):
+                        qv = self.adjust_vector_length(q)
+                        qbin = _embedding_to_f32_bytes(qv)
+                        params = [qbin] + list(base_params) + [int(limit)]
+                        cur.execute(sql, params)
+                        rows = cur.fetchall()
+
+                        for r in rows:
+                            rid, rtext, rmeta, rdist = r[0], r[1], r[2], r[3]
+                            ids[q_idx].append(str(rid))
+                            try:
+                                dist = float(rdist) if rdist is not None else 1.0
+                            except Exception:
+                                dist = 1.0
+                            if math.isnan(dist) or math.isinf(dist):
+                                dist = 1.0
+                            distances[q_idx].append(self._score_from_dist(dist))
+                            documents[q_idx].append(rtext)
+                            metadatas[q_idx].append(_safe_json(rmeta))
+
+                    return SearchResult(
+                        ids=ids,
+                        distances=distances,
+                        documents=documents,
+                        metadatas=metadatas,
+                    )
+        except Exception as e:
+            log.exception(f"[MARIADB_VECTOR] search() failed: {e}")
+            return None
+
+    def query(
+        self, collection_name: str, filter: Dict[str, Any], limit: Optional[int] = None
+    ) -> Optional[GetResult]:
+        """
+        Retrieve documents by metadata filter (non-vector query).
+        """
+        with self._connect() as conn:
+            with conn.cursor() as cur:
+                fsql, fparams = self._build_filter_sql_qmark(filter or {})
+                where = "collection_name = ?"
+                params: List[Any] = [collection_name]
+                if fsql:
+                    where = where + " AND " + fsql
+                    params.extend(fparams)
+                sql = f"SELECT id, text, vmetadata FROM document_chunk WHERE {where}"
+                if limit is not None:
+                    sql += " LIMIT ?"
+                    params.append(int(limit))
+                cur.execute(sql, params)
+                rows = cur.fetchall()
+                if not rows:
+                    return None
+                ids = [[str(r[0]) for r in rows]]
+                documents = [[r[1] for r in rows]]
+                metadatas = [[_safe_json(r[2]) for r in rows]]
+                return GetResult(ids=ids, documents=documents, metadatas=metadatas)
+
+    def get(
+        self, collection_name: str, limit: Optional[int] = None
+    ) -> Optional[GetResult]:
+        """
+        Retrieve documents in a collection without filtering (optionally limited).
+        """
+        with self._connect() as conn:
+            with conn.cursor() as cur:
+                sql = "SELECT id, text, vmetadata FROM document_chunk WHERE collection_name = ?"
+                params: List[Any] = [collection_name]
+                if limit is not None:
+                    sql += " LIMIT ?"
+                    params.append(int(limit))
+                cur.execute(sql, params)
+                rows = cur.fetchall()
+                if not rows:
+                    return None
+                ids = [[str(r[0]) for r in rows]]
+                documents = [[r[1] for r in rows]]
+                metadatas = [[_safe_json(r[2]) for r in rows]]
+                return GetResult(ids=ids, documents=documents, metadatas=metadatas)
+
+    def delete(
+        self,
+        collection_name: str,
+        ids: Optional[List[str]] = None,
+        filter: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Delete rows from a collection by id list and/or metadata filter.
+
+        If both are provided, they are combined with AND semantics.
+        """
+        with self._connect() as conn:
+            with conn.cursor() as cur:
+                try:
+                    where = ["collection_name = ?"]
+                    params: List[Any] = [collection_name]
+
+                    if ids:
+                        ph = ", ".join(["?"] * len(ids))
+                        where.append(f"id IN ({ph})")
+                        params.extend(ids)
+
+                    if filter:
+                        fsql, fparams = self._build_filter_sql_qmark(filter)
+                        if fsql:
+                            where.append(fsql)
+                            params.extend(fparams)
+
+                    sql = "DELETE FROM document_chunk WHERE " + " AND ".join(where)
+                    cur.execute(sql, params)
+                    conn.commit()
+                except Exception as e:
+                    conn.rollback()
+                    log.exception(f"Error during delete: {e}")
+                    raise
+
+    def reset(self) -> None:
+        """
+        Truncate the vector table (drops all collections).
+        """
+        with self._connect() as conn:
+            with conn.cursor() as cur:
+                try:
+                    cur.execute("TRUNCATE TABLE document_chunk")
+                    conn.commit()
+                except Exception as e:
+                    conn.rollback()
+                    log.exception(f"Error during reset: {e}")
+                    raise
+
+    def has_collection(self, collection_name: str) -> bool:
+        """
+        Return True if the collection contains at least one row, else False.
+        """
+        try:
+            with self._connect() as conn:
+                with conn.cursor() as cur:
+                    cur.execute(
+                        "SELECT 1 FROM document_chunk WHERE collection_name = ? LIMIT 1",
+                        (collection_name,),
+                    )
+                    return cur.fetchone() is not None
+        except Exception:
+            return False
+
+    def delete_collection(self, collection_name: str) -> None:
+        """
+        Delete all rows in a collection.
+        """
+        self.delete(collection_name)
+
+    def close(self) -> None:
+        """
+        Dispose the underlying SQLAlchemy engine.
+        """
+        try:
+            self.engine.dispose()
+        except Exception as e:
+            log.exception(f"Error during dispose the underlying SQLAlchemy engine: {e}")

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

@@ -1,3 +1,7 @@
+"""
+NOTE: This vector database integration is community-supported and maintained on a best-effort basis.
+"""
+
 from pymilvus import MilvusClient as Client
 from pymilvus import FieldSchema, DataType
 from pymilvus import connections, Collection

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

@@ -1,3 +1,7 @@
+"""
+NOTE: This vector database integration is community-supported and maintained on a best-effort basis.
+"""
+
 import logging
 from typing import Optional, Tuple, List, Dict, Any
 

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

@@ -1,3 +1,7 @@
+"""
+NOTE: This vector database integration is community-supported and maintained on a best-effort basis.
+"""
+
 from typing import Optional, List, Dict, Any
 import logging
 import re

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

@@ -1,3 +1,7 @@
+"""
+NOTE: This vector database integration is community-supported and maintained on a best-effort basis.
+"""
+
 from opensearchpy import OpenSearch
 from opensearchpy.helpers import bulk
 from typing import Optional

backend/open_webui/retrieval/vector/dbs/oracle23ai.py

@@ -1,4 +1,6 @@
 """
+NOTE: This vector database integration is community-supported and maintained on a best-effort basis.
+
 Oracle 23ai Vector Database Client - Fixed Version
 
 # .env

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

@@ -1,3 +1,7 @@
+"""
+NOTE: This vector database integration is community-supported and maintained on a best-effort basis.
+"""
+
 from typing import Optional, List, Dict, Any, Union
 import logging
 import time  # for measuring elapsed time

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

@@ -1,3 +1,7 @@
+"""
+NOTE: This vector database integration is community-supported and maintained on a best-effort basis.
+"""
+
 from typing import Optional
 import logging
 from urllib.parse import urlparse

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

@@ -1,3 +1,7 @@
+"""
+NOTE: This vector database integration is community-supported and maintained on a best-effort basis.
+"""
+
 import logging
 from typing import Optional, Tuple, List, Dict, Any
 from urllib.parse import urlparse

backend/open_webui/retrieval/vector/dbs/s3vector.py

@@ -1,3 +1,7 @@
+"""
+NOTE: This vector database integration is community-supported and maintained on a best-effort basis.
+"""
+
 from open_webui.retrieval.vector.utils import process_metadata
 from open_webui.retrieval.vector.main import (
     VectorDBBase,
@@ -61,6 +65,11 @@ class S3VectorClient(VectorDBBase):
                 dataType=data_type,
                 dimension=dimension,
                 distanceMetric=distance_metric,
+                metadataConfiguration={
+                    "nonFilterableMetadataKeys": [
+                        "text",
+                    ]
+                },
             )
             log.info(
                 f"Created S3 index: {index_name} (dim={dimension}, type={data_type}, metric={distance_metric})"

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

@@ -1,3 +1,7 @@
+"""
+NOTE: This vector database integration is community-supported and maintained on a best-effort basis.
+"""
+
 import weaviate
 import re
 import uuid

backend/open_webui/retrieval/vector/factory.py

@@ -57,6 +57,12 @@ class Vector:
                 from open_webui.retrieval.vector.dbs.opengauss import OpenGaussClient
 
                 return OpenGaussClient()
+            case VectorType.MARIADB_VECTOR:
+                from open_webui.retrieval.vector.dbs.mariadb_vector import (
+                    MariaDBVectorClient,
+                )
+
+                return MariaDBVectorClient()
             case VectorType.ELASTICSEARCH:
                 from open_webui.retrieval.vector.dbs.elasticsearch import (
                     ElasticsearchClient,

backend/open_webui/retrieval/vector/type.py

@@ -3,6 +3,7 @@ from enum import StrEnum
 
 class VectorType(StrEnum):
     MILVUS = "milvus"
+    MARIADB_VECTOR = "mariadb-vector"
     QDRANT = "qdrant"
     CHROMA = "chroma"
     PINECONE = "pinecone"

backend/open_webui/routers/analytics.py

@@ -86,7 +86,7 @@ async def get_user_analytics(
         start_date=start_date, end_date=end_date, group_id=group_id, db=db
     )
     token_usage = ChatMessages.get_token_usage_by_user(
-        start_date=start_date, end_date=end_date, db=db
+        start_date=start_date, end_date=end_date, group_id=group_id, db=db
     )
 
     # Get user info for top users

backend/open_webui/routers/audio.py

@@ -492,7 +492,7 @@ async def speech(request: Request, user=Depends(get_verified_user)):
         region = request.app.state.config.TTS_AZURE_SPEECH_REGION or "eastus"
         base_url = request.app.state.config.TTS_AZURE_SPEECH_BASE_URL
         language = request.app.state.config.TTS_VOICE
-        locale = "-".join(request.app.state.config.TTS_VOICE.split("-")[:1])
+        locale = "-".join(request.app.state.config.TTS_VOICE.split("-")[:2])
         output_format = request.app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT
 
         try:
@@ -852,17 +852,34 @@ def transcription_handler(request, file_path, metadata, user=None):
         except requests.exceptions.RequestException as e:
             log.exception(e)
             detail = None
+            status_code = getattr(r, "status_code", 500) if r else 500
 
             try:
                 if r is not None and r.status_code != 200:
                     res = r.json()
-                    if "error" in res:
+                    # Azure returns {"code": "...", "message": "...", "innerError": {...}}
+                    if "code" in res and "message" in res:
+                        azure_code = res.get("innerError", {}).get("code", res["code"])
+                        user_facing_codes = {
+                            "EmptyAudioFile",
+                            "AudioLengthLimitExceeded",
+                            "NoLanguageIdentified",
+                            "MultipleLanguagesIdentified",
+                        }
+                        if azure_code in user_facing_codes:
+                            detail = res["message"]
+                        else:
+                            log.error(
+                                f"Azure STT error [{azure_code}]: {res['message']}"
+                            )
+                            detail = "An error occurred during transcription."
+                    elif "error" in res:
                         detail = f"External: {res['error'].get('message', '')}"
             except Exception:
                 detail = f"External: {e}"
 
             raise HTTPException(
-                status_code=getattr(r, "status_code", 500) if r else 500,
+                status_code=status_code,
                 detail=detail if detail else "Open WebUI: Server Connection Error",
             )
 
@@ -1087,6 +1104,8 @@ def transcribe(
             for future in futures:
                 try:
                     results.append(future.result())
+                except HTTPException:
+                    raise
                 except Exception as transcribe_exc:
                     raise HTTPException(
                         status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
@@ -1226,6 +1245,8 @@ def transcription(
                 "filename": os.path.basename(file_path),
             }
 
+        except HTTPException:
+            raise
         except Exception as e:
             log.exception(e)
 
@@ -1234,6 +1255,8 @@ def transcription(
                 detail="Transcription failed.",
             )
 
+    except HTTPException:
+        raise
     except Exception as e:
         log.exception(e)
 

backend/open_webui/routers/auths.py

@@ -46,6 +46,7 @@ from fastapi import APIRouter, Depends, HTTPException, Request, status
 from fastapi.responses import RedirectResponse, Response, JSONResponse
 from open_webui.config import (
     OPENID_PROVIDER_URL,
+    OPENID_END_SESSION_ENDPOINT,
     ENABLE_OAUTH_SIGNUP,
     ENABLE_LDAP,
     ENABLE_PASSWORD_AUTH,
@@ -824,6 +825,19 @@ async def signout(
         response.delete_cookie("oauth_session_id")
 
         session = OAuthSessions.get_session_by_id(oauth_session_id, db=db)
+
+        # If a custom end_session_endpoint is configured (e.g. AWS Cognito), redirect
+        # there directly instead of attempting OIDC discovery.
+        if OPENID_END_SESSION_ENDPOINT.value:
+            return JSONResponse(
+                status_code=200,
+                content={
+                    "status": True,
+                    "redirect_url": OPENID_END_SESSION_ENDPOINT.value,
+                },
+                headers=response.headers,
+            )
+
         oauth_server_metadata_url = (
             request.app.state.oauth_manager.get_server_metadata_url(session.provider)
             if session

backend/open_webui/routers/files.py

@@ -583,12 +583,36 @@ def update_file_data_content_by_id(
                 request,
                 ProcessFileForm(file_id=id, content=form_data.content),
                 user=user,
+                db=db,
             )
             file = Files.get_file_by_id(id=id, db=db)
         except Exception as e:
             log.exception(e)
             log.error(f"Error processing file: {file.id}")
 
+        # Propagate content change to all knowledge collections referencing
+        # this file.  Without this the old embeddings remain in the knowledge
+        # collection and RAG returns both stale and current data (#20558).
+        knowledges = Knowledges.get_knowledges_by_file_id(id, db=db)
+        for knowledge in knowledges:
+            try:
+                # Remove old embeddings for this file from the KB collection
+                VECTOR_DB_CLIENT.delete(
+                    collection_name=knowledge.id, filter={"file_id": id}
+                )
+                # Re-add from the now-updated file-{file_id} collection
+                process_file(
+                    request,
+                    ProcessFileForm(file_id=id, collection_name=knowledge.id),
+                    user=user,
+                    db=db,
+                )
+            except Exception as e:
+                log.warning(
+                    f"Failed to update knowledge {knowledge.id} after "
+                    f"content change for file {id}: {e}"
+                )
+
         return {"content": file.data.get("content", "")}
     else:
         raise HTTPException(

backend/open_webui/routers/ollama.py

@@ -1747,7 +1747,9 @@ async def download_file_stream(
 
                                 yield f"data: {json.dumps(res)}\n\n"
                             else:
-                                raise "Ollama: Could not create blob, Please try again."
+                                raise RuntimeError(
+                                    "Ollama: Could not create blob, Please try again."
+                                )
 
 
 # url = "https://huggingface.co/TheBloke/stablelm-zephyr-3b-GGUF/resolve/main/stablelm-zephyr-3b.Q2_K.gguf"

backend/open_webui/routers/pipelines.py

@@ -92,8 +92,8 @@ async def process_pipeline_inlet_filter(request, payload, user, models):
                     json=request_data,
                     ssl=AIOHTTP_CLIENT_SESSION_SSL,
                 ) as response:
-                    payload = await response.json()
                     response.raise_for_status()
+                    payload = await response.json()
             except aiohttp.ClientResponseError as e:
                 res = (
                     await response.json()
@@ -145,8 +145,8 @@ async def process_pipeline_outlet_filter(request, payload, user, models):
                     json=request_data,
                     ssl=AIOHTTP_CLIENT_SESSION_SSL,
                 ) as response:
-                    payload = await response.json()
                     response.raise_for_status()
+                    payload = await response.json()
             except aiohttp.ClientResponseError as e:
                 try:
                     res = (

backend/open_webui/routers/tools.py

@@ -30,7 +30,11 @@ from open_webui.utils.plugin import (
 )
 from open_webui.utils.tools import get_tool_specs
 from open_webui.utils.auth import get_admin_user, get_verified_user
-from open_webui.utils.access_control import has_permission, filter_allowed_access_grants
+from open_webui.utils.access_control import (
+    has_permission,
+    has_access,
+    filter_allowed_access_grants,
+)
 from open_webui.utils.tools import get_tool_servers
 
 from open_webui.config import CACHE_DIR, BYPASS_ADMIN_ACCESS_CONTROL

backend/open_webui/utils/misc.py

@@ -566,6 +566,53 @@ def sanitize_data_for_db(obj):
     return obj
 
 
+def sanitize_metadata(metadata: dict) -> dict:
+    """
+    Return a JSON-safe copy of a metadata dict for database storage.
+
+    The middleware metadata accumulates non-serializable Python objects
+    (e.g. callable tool functions, MCP client instances) that cause
+    PostgreSQL JSON inserts to fail.  This helper strips those out while
+    preserving the primitive data needed for file-to-chat linking.
+    """
+    if not isinstance(metadata, dict):
+        return metadata
+
+    def _sanitize(obj):
+        if isinstance(obj, (str, int, float, bool, type(None))):
+            return obj
+        if isinstance(obj, dict):
+            return {
+                k: _sanitize(v)
+                for k, v in obj.items()
+                if not callable(v) and _is_serializable(v)
+            }
+        if isinstance(obj, list):
+            return [
+                _sanitize(v) for v in obj if not callable(v) and _is_serializable(v)
+            ]
+        if callable(obj):
+            return None
+        # Last resort: try to see if it's serializable
+        try:
+            json.dumps(obj)
+            return obj
+        except (TypeError, ValueError):
+            return None
+
+    def _is_serializable(obj):
+        """Quick check whether a value can survive JSON serialization."""
+        if isinstance(obj, (str, int, float, bool, type(None), dict, list)):
+            return True
+        try:
+            json.dumps(obj)
+            return True
+        except (TypeError, ValueError):
+            return False
+
+    return _sanitize(metadata)
+
+
 def extract_folders_after_data_docs(path):
     # Convert the path to a Path object if it's not already
     path = Path(path)

backend/open_webui/utils/oauth.py

@@ -620,7 +620,7 @@ class OAuthClientManager:
                             payload = json.loads(response_text)
                             error = payload.get("error")
                             error_description = payload.get("error_description", "")
-                        except:
+                        except Exception:
                             pass
                     else:
                         error_description = response_text
@@ -1706,7 +1706,9 @@ class OAuthManager:
         redirect_url = f"{redirect_base_url}/auth"
 
         if error_message:
-            redirect_url = f"{redirect_url}?error={error_message}"
+            redirect_url = (
+                f"{redirect_url}?error={urllib.parse.quote_plus(error_message)}"
+            )
             return RedirectResponse(url=redirect_url, headers=response.headers)
 
         response = RedirectResponse(url=redirect_url, headers=response.headers)

backend/open_webui/utils/task.py

@@ -142,41 +142,121 @@ def replace_prompt_variable(template: str, prompt: str) -> str:
     return template
 
 
+def truncate_content(content: str, max_chars: int, mode: str = "middletruncate") -> str:
+    """Truncate a string to max_chars using the specified mode.
+
+    Modes:
+        - middletruncate: keep beginning and end, join with '...'
+        - start: keep first max_chars characters
+        - end: keep last max_chars characters
+    """
+    if not content or len(content) <= max_chars:
+        return content
+
+    if mode == "start":
+        return content[:max_chars]
+    elif mode == "end":
+        return content[-max_chars:]
+    else:  # middletruncate
+        half = max_chars // 2
+        return f"{content[:half]}...{content[-(max_chars - half):]}"
+
+
+def apply_content_filter(messages: list[dict], filter_str: str) -> list[dict]:
+    """Apply a content filter to each message's content.
+
+    filter_str is like 'middletruncate:500', 'start:200', or 'end:200'.
+    Returns a new list with truncated content (original messages are not mutated).
+    """
+    parts = filter_str.split(":")
+    if len(parts) != 2:
+        return messages
+
+    mode = parts[0].lower()
+    try:
+        max_chars = int(parts[1])
+    except ValueError:
+        return messages
+
+    if mode not in ("middletruncate", "start", "end"):
+        return messages
+
+    result = []
+    for msg in messages:
+        new_msg = dict(msg)
+        if isinstance(new_msg.get("content"), str):
+            new_msg["content"] = truncate_content(new_msg["content"], max_chars, mode)
+        elif isinstance(new_msg.get("content"), list):
+            new_content = []
+            for item in new_msg["content"]:
+                if isinstance(item, dict) and item.get("type") == "text":
+                    new_item = dict(item)
+                    new_item["text"] = truncate_content(
+                        item.get("text", ""), max_chars, mode
+                    )
+                    new_content.append(new_item)
+                else:
+                    new_content.append(item)
+            new_msg["content"] = new_content
+        result.append(new_msg)
+    return result
+
+
 def replace_messages_variable(
     template: str, messages: Optional[list[dict]] = None
 ) -> str:
     def replacement_function(match):
-        full_match = match.group(0)
-        start_length = match.group(1)
-        end_length = match.group(2)
-        middle_length = match.group(3)
+        # Groups: (1) filter for bare MESSAGES
+        #         (2) START count, (3) filter for START
+        #         (4) END count,   (5) filter for END
+        #         (6) MIDDLE count,(7) filter for MIDDLE
+        bare_filter = match.group(1)
+        start_length = match.group(2)
+        start_filter = match.group(3)
+        end_length = match.group(4)
+        end_filter = match.group(5)
+        middle_length = match.group(6)
+        middle_filter = match.group(7)
+
         # If messages is None, handle it as an empty list
         if messages is None:
             return ""
 
-        # Process messages based on the number of messages required
-        if full_match == "{{MESSAGES}}":
-            return get_messages_content(messages)
-        elif start_length is not None:
-            return get_messages_content(messages[: int(start_length)])
+        # Select messages based on the variant
+        if start_length is not None:
+            selected = messages[: int(start_length)]
+            content_filter = start_filter
         elif end_length is not None:
-            return get_messages_content(messages[-int(end_length) :])
+            selected = messages[-int(end_length) :]
+            content_filter = end_filter
         elif middle_length is not None:
             mid = int(middle_length)
-
             if len(messages) <= mid:
-                return get_messages_content(messages)
-            # Handle middle truncation: split to get start and end portions of the messages list
-            half = mid // 2
-            start_msgs = messages[:half]
-            end_msgs = messages[-half:] if mid % 2 == 0 else messages[-(half + 1) :]
-            formatted_start = get_messages_content(start_msgs)
-            formatted_end = get_messages_content(end_msgs)
-            return f"{formatted_start}\n{formatted_end}"
-        return ""
+                selected = messages
+            else:
+                half = mid // 2
+                start_msgs = messages[:half]
+                end_msgs = messages[-half:] if mid % 2 == 0 else messages[-(half + 1) :]
+                selected = start_msgs + end_msgs
+            content_filter = middle_filter
+        else:
+            # Bare {{MESSAGES}} or {{MESSAGES|filter}}
+            selected = messages
+            content_filter = bare_filter
+
+        # Apply content filter if present
+        if content_filter:
+            selected = apply_content_filter(selected, content_filter)
+
+        return get_messages_content(selected)
 
     template = re.sub(
-        r"{{MESSAGES}}|{{MESSAGES:START:(\d+)}}|{{MESSAGES:END:(\d+)}}|{{MESSAGES:MIDDLETRUNCATE:(\d+)}}",
+        r"(?:"
+        r"\{\{MESSAGES(?:\|(\w+:\d+))?\}\}"
+        r"|\{\{MESSAGES:START:(\d+)(?:\|(\w+:\d+))?\}\}"
+        r"|\{\{MESSAGES:END:(\d+)(?:\|(\w+:\d+))?\}\}"
+        r"|\{\{MESSAGES:MIDDLETRUNCATE:(\d+)(?:\|(\w+:\d+))?\}\}"
+        r")",
         replacement_function,
         template,
     )

backend/open_webui/utils/tools.py

@@ -707,8 +707,8 @@ def get_functions_from_tool(tool: object) -> list[Callable]:
             getattr(tool, func)
         )  # checks if the attribute is callable (a method or function).
         and not func.startswith(
-            "__"
-        )  # filters out special (dunder) methods like init, str, etc. — these are usually built-in functions of an object that you might not need to use directly.
+            "_"
+        )  # filters out internal methods (starting with _) and special (dunder) methods.
         and not inspect.isclass(
             getattr(tool, func)
         )  # ensures that the callable is not a class itself, just a method or function.

backend/open_webui/utils/webhook.py

@@ -26,9 +26,13 @@ async def post_webhook(name: str, url: str, message: str, event_data: dict) -> b
         # Microsoft Teams Webhooks
         elif "webhook.office.com" in url:
             action = event_data.get("action", "undefined")
+            user_data = event_data.get("user", "{}")
+            if isinstance(user_data, dict):
+                user_dict = user_data
+            else:
+                user_dict = json.loads(user_data)
             facts = [
-                {"name": name, "value": value}
-                for name, value in json.loads(event_data.get("user", {})).items()
+                {"name": name, "value": value} for name, value in user_dict.items()
             ]
             payload = {
                 "@type": "MessageCard",

backend/requirements.txt

@@ -119,6 +119,7 @@ pgvector==0.4.2
 
 PyMySQL==1.1.2
 boto3==1.42.62
+mariadb==1.1.14
 
 pymilvus==2.6.9
 qdrant-client==1.17.0
@@ -155,3 +156,4 @@ opentelemetry-instrumentation-requests==0.61b0
 opentelemetry-instrumentation-logging==0.61b0
 opentelemetry-instrumentation-httpx==0.61b0
 opentelemetry-instrumentation-aiohttp-client==0.61b0
+opentelemetry-instrumentation-system-metrics==0.61b0

package-lock.json

@@ -1,12 +1,12 @@
 {
 	"name": "open-webui",
-	"version": "0.8.9",
+	"version": "0.8.10",
 	"lockfileVersion": 3,
 	"requires": true,
 	"packages": {
 		"": {
 			"name": "open-webui",
-			"version": "0.8.9",
+			"version": "0.8.10",
 			"dependencies": {
 				"@azure/msal-browser": "^4.5.0",
 				"@codemirror/lang-javascript": "^6.2.2",

package.json

@@ -1,6 +1,6 @@
 {
 	"name": "open-webui",
-	"version": "0.8.9",
+	"version": "0.8.10",
 	"private": true,
 	"scripts": {
 		"dev": "npm run pyodide:fetch && vite dev --host",

pyproject.toml

@@ -137,11 +137,15 @@ postgres = [
     "psycopg2-binary==2.9.11",
     "pgvector==0.4.2",
 ]
+mariadb = [
+    "mariadb==1.1.14",
+]
 
 all = [
     "pymongo",
     "psycopg2-binary==2.9.11",
     "pgvector==0.4.2",
+    "mariadb==1.1.14",
     "moto[s3]>=5.0.26",
     "gcp-storage-emulator>=2024.8.3",
     "docker~=7.1.0",

src/lib/components/chat/MessageInput.svelte

@@ -1187,6 +1187,7 @@
 									<QueuedMessageItem
 										id={queuedMessage.id}
 										content={queuedMessage.prompt}
+										files={queuedMessage.files}
 										onSendNow={onQueueSendNow}
 										onEdit={onQueueEdit}
 										onDelete={onQueueDelete}

src/lib/components/chat/MessageInput/QueuedMessageItem.svelte

@@ -1,14 +1,17 @@
 <script lang="ts">
 	import { getContext } from 'svelte';
 	import Tooltip from '$lib/components/common/Tooltip.svelte';
+	import Image from '$lib/components/common/Image.svelte';
 	import GarbageBin from '$lib/components/icons/GarbageBin.svelte';
 	import EditPencil from '$lib/components/icons/EditPencil.svelte';
 	import ArrowForward from '$lib/components/icons/ArrowForward.svelte';
+	import { WEBUI_API_BASE_URL } from '$lib/constants';
 
 	const i18n = getContext('i18n');
 
 	export let id: string;
 	export let content: string;
+	export let files: any[] = [];
 	export let onSendNow: (id: string) => void;
 	export let onEdit: (id: string) => void;
 	export let onDelete: (id: string) => void;
@@ -21,8 +24,34 @@
 	</div>
 
 	<!-- Message content -->
-	<div class="flex-1 min-w-0">
-		<p class="text-sm text-gray-600 dark:text-gray-300 truncate">{content}</p>
+	<div class="flex-1 min-w-0 flex items-center gap-2">
+		{#if files.length > 0}
+			<div class="flex items-center gap-1 shrink-0">
+				{#each files as file}
+					{#if file.type === 'image' || (file?.content_type ?? '').startsWith('image/')}
+						{@const fileUrl =
+							file.url?.startsWith('data') || file.url?.startsWith('http')
+								? file.url
+								: `${WEBUI_API_BASE_URL}/files/${file.url}${file?.content_type ? '/content' : ''}`}
+						<Image src={fileUrl} alt="" imageClassName="size-6 rounded-md object-cover" />
+					{:else}
+						<div
+							class="flex items-center px-1.5 py-0.5 rounded-md bg-gray-100 dark:bg-gray-800 text-xs text-gray-500 dark:text-gray-400"
+						>
+							<span class="max-w-[80px] truncate">{file.name ?? 'file'}</span>
+						</div>
+					{/if}
+				{/each}
+			</div>
+		{/if}
+
+		{#if content}
+			<p class="text-sm text-gray-600 dark:text-gray-300 truncate">{content}</p>
+		{:else if files.length === 0}
+			<p class="text-sm text-gray-400 dark:text-gray-500 truncate italic">
+				{$i18n.t('Empty message')}
+			</p>
+		{/if}
 	</div>
 
 	<!-- Actions -->

src/lib/components/chat/Messages/Citations.svelte

@@ -178,6 +178,9 @@
 							src="https://www.google.com/s2/favicons?sz=32&domain={citation.source.name}"
 							alt="favicon"
 							class="size-4 rounded-full shrink-0 border border-white dark:border-gray-850 bg-white dark:bg-gray-900"
+							on:error={(e) => {
+								e.target.src = '/favicon.png';
+							}}
 						/>
 					{/each}
 				</div>

src/lib/components/layout/Sidebar/ChatItem.svelte

@@ -78,10 +78,6 @@
 	let chat = null;
 
 	let mouseOver = false;
-	let draggable = false;
-	$: if (mouseOver) {
-		loadChat();
-	}
 
 	const loadChat = async () => {
 		if (!chat) {
@@ -375,7 +371,7 @@
 	id="sidebar-chat-group"
 	bind:this={itemElement}
 	class=" w-full {className} relative group"
-	draggable={draggable && !confirmEdit}
+	draggable={!confirmEdit}
 >
 	{#if confirmEdit}
 		<div

src/lib/i18n/locales/ar-BH/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "نموذج التضمين",
 	"Embedding Model Engine": "تضمين محرك النموذج",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/ar/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "نموذج التضمين",
 	"Embedding Model Engine": "تضمين محرك النموذج",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "تفعيل توليد الإكمال التلقائي لرسائل الدردشة",

src/lib/i18n/locales/bg-BG/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Модел за вграждане",
 	"Embedding Model Engine": "Двигател на модела за вграждане",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Активиране на автоматично довършване на съобщения в чата",

src/lib/i18n/locales/bn-BD/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "ইমেজ ইমেবডিং মডেল",
 	"Embedding Model Engine": "ইমেজ ইমেবডিং মডেল ইঞ্জিন",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/bo-TB/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "ཚུད་འཇུག་དཔེ་དབྱིབས།",
 	"Embedding Model Engine": "ཚུད་འཇུག་དཔེ་དབྱིབས་འཕྲུལ་འཁོར།",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "ཁ་བརྡའི་འཕྲིན་ཡིག་གི་ཆེད་དུ་རང་འཚང་བཟོ་སྐྲུན་སྒུལ་བསྐྱོད་བྱེད་པ།",

src/lib/i18n/locales/bs-BA/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Embedding model",
 	"Embedding Model Engine": "Embedding model pogon",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/ca-ES/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "Peticions concurrents d'incrustació",
 	"Embedding Model": "Model d'incrustació",
 	"Embedding Model Engine": "Motor de model d'incrustació",
+	"Empty message": "",
 	"Enable All": "Habilitar tot",
 	"Enable API Keys": "Permetre claus API",
 	"Enable autocomplete generation for chat messages": "Activar la generació automàtica per als missatges del xat",

src/lib/i18n/locales/ceb-PH/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "",
 	"Embedding Model Engine": "",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/cs-CZ/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Model pro vektorizaci",
 	"Embedding Model Engine": "Jádro modelu pro vektorizaci",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Povolit generování automatického dokončování pro zprávy v konverzaci",

src/lib/i18n/locales/da-DK/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Embedding Model",
 	"Embedding Model Engine": "Embedding Model engine",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "Aktiver API nøgler",
 	"Enable autocomplete generation for chat messages": "Aktiver autofuldførsel for chatbeskeder",

src/lib/i18n/locales/de-DE/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "Gleichzeitige Embedding Anfragen",
 	"Embedding Model": "Embedding-Modell",
 	"Embedding Model Engine": "Embedding-Modell-Engine",
+	"Empty message": "",
 	"Enable All": "Alle aktivieren",
 	"Enable API Keys": "API-Schlüssel aktivieren",
 	"Enable autocomplete generation for chat messages": "Autovervollständigung für Chat-Nachrichten aktivieren",

src/lib/i18n/locales/dg-DG/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "",
 	"Embedding Model Engine": "",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/el-GR/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Μοντέλο Ενσωμάτωσης",
 	"Embedding Model Engine": "Μηχανή Μοντέλου Ενσωμάτωσης",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Ενεργοποίηση αυτόματης συμπλήρωσης για συνομιλίες",

src/lib/i18n/locales/en-GB/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "",
 	"Embedding Model Engine": "",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/en-US/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "",
 	"Embedding Model Engine": "",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/es-ES/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "Número de Peticiones Concurrentes en Incrustración",
 	"Embedding Model": "Modelo de Incrustación",
 	"Embedding Model Engine": "Motor del Modelo de Incrustación",
+	"Empty message": "",
 	"Enable All": "Habilitar Todo",
 	"Enable API Keys": "Habilitar Claves API",
 	"Enable autocomplete generation for chat messages": "Habilitar generación de autocompletado para mensajes de chat",

src/lib/i18n/locales/et-EE/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Manustamise mudel",
 	"Embedding Model Engine": "Manustamise mudeli mootor",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Luba automaattäitmise genereerimine vestlussõnumitele",

src/lib/i18n/locales/eu-ES/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Embedding Eredua",
 	"Embedding Model Engine": "Embedding Eredu Motorea",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/fa-IR/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "مدل پیدائش",
 	"Embedding Model Engine": "محرک مدل پیدائش",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "فعال\u200cسازی تولید تکمیل خودکار برای پیام\u200cهای چت",

src/lib/i18n/locales/fi-FI/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "Samanaikaiset upotuspyynnöt",
 	"Embedding Model": "Upotusmalli",
 	"Embedding Model Engine": "Upotusmallin moottori",
+	"Empty message": "",
 	"Enable All": "Ota kaikki käyttöön",
 	"Enable API Keys": "Ota API-avaimet käyttöön",
 	"Enable autocomplete generation for chat messages": "Ota automaattinen täydennys käyttöön keskusteluviesteissä",

src/lib/i18n/locales/fr-CA/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Modèle d'embedding",
 	"Embedding Model Engine": "Moteur de modèle d'embedding",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Activer la génération des suggestions pour les messages",

src/lib/i18n/locales/fr-FR/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Modèle d'embedding",
 	"Embedding Model Engine": "Moteur de modèle d'embedding",
+	"Empty message": "",
 	"Enable All": "Activer tout",
 	"Enable API Keys": "Autoriser les clés API",
 	"Enable autocomplete generation for chat messages": "Activer la génération des suggestions pour les messages",

src/lib/i18n/locales/gl-ES/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Modelo de Embedding",
 	"Embedding Model Engine": "Motor de Modelo de Embedding",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Habilitar xeneración de autocompletado para mensaxes de chat",

src/lib/i18n/locales/he-IL/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "מודל הטמעה",
 	"Embedding Model Engine": "מנוע מודל הטמעה",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/hi-IN/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "मॉडेल अनुकूलन",
 	"Embedding Model Engine": "एंबेडिंग मॉडल इंजन",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/hr-HR/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Embedding model",
 	"Embedding Model Engine": "Embedding model pogon",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/hu-HU/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Beágyazási modell",
 	"Embedding Model Engine": "Beágyazási modell motor",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Automatikus kiegészítés engedélyezése csevegőüzenetekhez",

src/lib/i18n/locales/id-ID/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Model Penyematan",
 	"Embedding Model Engine": "Mesin Model Penyematan",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/ie-GA/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "Iarratais Chomhuaineacha a Leabú",
 	"Embedding Model": "Samhail Leabháilte",
 	"Embedding Model Engine": "Inneall Samhail Leabaithe",
+	"Empty message": "",
 	"Enable All": "Cumasaigh Gach Rud",
 	"Enable API Keys": "Cumasaigh Eochracha API",
 	"Enable autocomplete generation for chat messages": "Cumasaigh giniúint uathchríochnaithe le haghaidh teachtaireachtaí comhrá",

src/lib/i18n/locales/it-IT/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Modello Embedding",
 	"Embedding Model Engine": "Motore Modello di Embedding",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Abilita generazione autocompletamento per i messaggi di chat",

src/lib/i18n/locales/ja-JP/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "埋め込みモデル",
 	"Embedding Model Engine": "埋め込みモデルエンジン",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "API キーを有効にする",
 	"Enable autocomplete generation for chat messages": "チャットメッセージの自動補完を有効にする",

src/lib/i18n/locales/ka-GE/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "მოდელის ჩაშენება",
 	"Embedding Model Engine": "ჩაშენებული მოდელის ძრავა",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/kab-DZ/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Tamudemt n ujmak",
 	"Embedding Model Engine": "Amsedday n tmudemt n ujmak",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Rmed tasuta tawurmant tummidt i udiwenni iznan",

src/lib/i18n/locales/ko-KR/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "임베딩 모델",
 	"Embedding Model Engine": "임베딩 모델 엔진",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "채팅 메시지에 대한 자동 완성 생성 활성화",

src/lib/i18n/locales/lt-LT/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Embedding modelis",
 	"Embedding Model Engine": "Embedding modelio variklis",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/lv-LV/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Iegulšanas modelis",
 	"Embedding Model Engine": "Iegulšanas modeļa dzinējs",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "Iespējot API atslēgas",
 	"Enable autocomplete generation for chat messages": "Iespējot automātisko pabeigšanu tērzēšanas ziņojumiem",

src/lib/i18n/locales/nb-NO/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Innbyggingsmodell",
 	"Embedding Model Engine": "Motor for innbygging av modeller",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Aktiver automatisk utfylling av chatmeldinger",

src/lib/i18n/locales/nl-NL/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Embedding Model",
 	"Embedding Model Engine": "Embedding Model Engine",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Automatische aanvullingsgeneratie voor chatberichten inschakelen",

src/lib/i18n/locales/pa-IN/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "ਐਮਬੈੱਡਿੰਗ ਮਾਡਲ",
 	"Embedding Model Engine": "ਐਮਬੈੱਡਿੰਗ ਮਾਡਲ ਇੰਜਣ",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/pl-PL/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Model embeddingów",
 	"Embedding Model Engine": "Silnik modelu embeddingów",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "Włącz klucze API",
 	"Enable autocomplete generation for chat messages": "Włącz autouzupełnianie w czacie",

src/lib/i18n/locales/pt-BR/translation.json

@@ -19,11 +19,11 @@
 	"{{COUNT}} Rows": "{{COUNT}} Linhas",
 	"{{COUNT}} Sources": "{{COUNT}} Origens",
 	"{{COUNT}} words": "{{COUNT}} palavras",
-	"{{COUNT}}d_time_ago": "",
-	"{{COUNT}}h_time_ago": "",
-	"{{COUNT}}m_time_ago": "",
-	"{{COUNT}}w_time_ago": "",
-	"{{COUNT}}y_time_ago": "",
+	"{{COUNT}}d_time_ago": "{{COUNT}}d atrás",
+	"{{COUNT}}h_time_ago": "{{COUNT}}h atrás",
+	"{{COUNT}}m_time_ago": "{{COUNT}}m atrás",
+	"{{COUNT}}w_time_ago": "{{COUNT}}sem atrás",
+	"{{COUNT}}y_time_ago": "{{COUNT}}a atrás",
 	"{{LOCALIZED_DATE}} at {{LOCALIZED_TIME}}": "{{LOCALIZED_DATE}} às {{LOCALIZED_TIME}}",
 	"{{model}} download has been canceled": "O download do {{model}} foi cancelado",
 	"{{modelName}} profile image": "Imagem de perfil de {{modelName}}",
@@ -183,7 +183,7 @@
 	"Are you sure you want to delete this channel?": "Tem certeza de que deseja excluir este canal?",
 	"Are you sure you want to delete this message?": "Tem certeza de que deseja excluir esta mensagem?",
 	"Are you sure you want to delete this version? Child versions will be relinked to this version's parent.": "Tem certeza de que deseja excluir esta versão? As versões filhas serão vinculadas novamente à versão pai.",
-	"Are you sure you want to delete this?": "",
+	"Are you sure you want to delete this?": "Tem certeza de que deseja excluir isto?",
 	"Are you sure you want to unarchive all archived chats?": "Você tem certeza que deseja desarquivar todos os chats arquivados?",
 	"Arena Models": "Arena de Modelos",
 	"Artifacts": "Artefatos",
@@ -283,7 +283,7 @@
 	"Character limit for autocomplete generation input": "Limite de caracteres para entrada de geração de preenchimento automático",
 	"Chart new frontiers": "Trace novas fronteiras",
 	"Chat": "Chat",
-	"Chat archived.": "",
+	"Chat archived.": "Chat arquivado.",
 	"Chat Background Image": "Imagem de Fundo do Chat",
 	"Chat Bubble UI": "Interface de Bolha de Chat",
 	"Chat Completions": "Gerar Resposta",
@@ -487,7 +487,7 @@
 	"Default User Role": "Padrão para novos usuários",
 	"Defaults": "Padrões",
 	"Delete": "Excluir",
-	"Delete {{name}}": "",
+	"Delete {{name}}": "Excluir {{name}}",
 	"Delete a model": "Excluir um modelo",
 	"Delete All": "Excluir tudo",
 	"Delete All Chats": "Excluir Todos os Chats",
@@ -580,7 +580,7 @@
 	"Downloading stats...": "Baixando estatísticas...",
 	"Draw": "Empate",
 	"Drop any files here to upload": "Solte qualquer arquivo aqui para fazer upload",
-	"Drop files here": "",
+	"Drop files here": "Solte os arquivos aqui",
 	"Drop files here to upload": "Solte os arquivos aqui para fazer upload",
 	"DuckDuckGo": "DuckDuckGo",
 	"e.g. '30s','10m'. Valid time units are 's', 'm', 'h'.": "por exemplo, '30s', '10m'. Unidades de tempo válidas são 's', 'm', 'h'.",
@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "Solicitações Simultâneas de Embedding",
 	"Embedding Model": "Modelo de Embedding",
 	"Embedding Model Engine": "Motor do Modelo de Embedding",
+	"Empty message": "",
 	"Enable All": "Ativar tudo",
 	"Enable API Keys": "Habilitar Chaves de API",
 	"Enable autocomplete generation for chat messages": "Habilitar geração de preenchimento automático para mensagens do chat",
@@ -826,7 +827,7 @@
 	"Fade Effect for Streaming Text": "Efeito de Fade para texto em streaming",
 	"Failed to add file.": "Falha ao adicionar arquivo.",
 	"Failed to add members": "Falha ao adicionar membros",
-	"Failed to archive chat.": "",
+	"Failed to archive chat.": "Falha ao arquivar o chat.",
 	"Failed to attach file": "Falha ao anexar arquivo",
 	"Failed to clear status": "Falha ao limpar o status",
 	"Failed to connect to {{URL}} OpenAPI tool server": "Falha ao conectar ao servidor da ferramenta OpenAPI {{URL}}",
@@ -841,11 +842,11 @@
 	"Failed to generate title": "Falha ao gerar título",
 	"Failed to import models": "Falha ao importar modelos",
 	"Failed to load chat preview": "Falha ao carregar a pré-visualização do chat",
-	"Failed to load DOCX file. Please try downloading it instead.": "",
+	"Failed to load DOCX file. Please try downloading it instead.": "Não foi possível carregar o arquivo DOCX. Tente baixá-lo em vez disso.",
 	"Failed to load Excel/CSV file. Please try downloading it instead.": "Não foi possível carregar o arquivo Excel/CSV. Tente baixá-lo em vez disso.",
 	"Failed to load file content.": "Falha ao carregar o conteúdo do arquivo.",
 	"Failed to load Interface settings": "Falha ao carregar configurações da Interface",
-	"Failed to load PPTX file. Please try downloading it instead.": "",
+	"Failed to load PPTX file. Please try downloading it instead.": "Não foi possível carregar o arquivo PPTX. Tente baixá-lo em vez disso.",
 	"Failed to move chat": "Falha ao mover o chat",
 	"Failed to process URL: {{url}}": "Falha ao processar URL: {{url}}",
 	"Failed to read clipboard contents": "Falha ao ler o conteúdo da área de transferência",
@@ -900,7 +901,7 @@
 	"Focus Chat Input": "Foco na janela do Chat",
 	"Folder": "Pasta",
 	"Folder Background Image": "Imagem de fundo da pasta",
-	"Folder created successfully": "",
+	"Folder created successfully": "Pasta criada com sucesso",
 	"Folder deleted successfully": "Pasta excluída com sucesso",
 	"Folder Max File Count": "Contagem máxima de arquivos por pasta",
 	"Folder name": "Nome da pasta",
@@ -1265,7 +1266,7 @@
 	"More options": "Mais opções",
 	"More Options": "Mais opções",
 	"Move": "Mover",
-	"Moved {{name}}": "",
+	"Moved {{name}}": "{{name}} movido",
 	"My Terminal": "Meu Terminal",
 	"Name": "Nome",
 	"Name and ID are required, please fill them out": "Nome e ID são obrigatórios, por favor preencha-os",
@@ -1308,13 +1309,13 @@
 	"No file selected": "Nenhum arquivo selecionado",
 	"No files found": "Nenhum arquivo encontrado",
 	"No files in this knowledge base.": "Não existem arquivos nesta base de conhecimento.",
-	"No files yet. Upload files or run Python code to create them.": "",
+	"No files yet. Upload files or run Python code to create them.": "Nenhum arquivo ainda. Envie arquivos ou execute código Python para criá-los.",
 	"No functions found": "Nenhuma função encontrada",
 	"No groups found": "Nenhum grupo encontrado",
 	"No history available": "Não há histórico disponível.",
 	"No HTML, CSS, or JavaScript content found.": "Nenhum conteúdo HTML, CSS ou JavaScript encontrado.",
 	"No inference engine with management support found": "Nenhum mecanismo de inferência com suporte de gerenciamento encontrado",
-	"No kernel": "",
+	"No kernel": "Sem kernel",
 	"No knowledge bases found.": "Nenhuma base de conhecimento encontrada.",
 	"No knowledge found": "Nenhum conhecimento encontrado",
 	"No memories to clear": "Nenhuma memória para limpar",
@@ -1330,7 +1331,7 @@
 	"No results": "Nenhum resultado encontrado",
 	"No results found": "Nenhum resultado encontrado",
 	"No search query generated": "Nenhuma consulta de pesquisa gerada",
-	"No servers detected": "",
+	"No servers detected": "Nenhum servidor detectado",
 	"No skills found": "Nenhuma skill encontrada",
 	"No source available": "Nenhuma fonte disponível",
 	"No sources found": "Nenhuma fonte encontrada",
@@ -1486,7 +1487,7 @@
 	"Please select at least one user for Direct Message channel.": "Por favor, selecione pelo menos um usuário para o canal de Mensagens Diretas.",
 	"Please wait until all files are uploaded.": "Aguarde até que todos os arquivos sejam enviados.",
 	"Port": "Porta",
-	"Ports": "",
+	"Ports": "Portas",
 	"Positive attitude": "Atitude positiva",
 	"Prefer not to say": "Prefiro não dizer",
 	"Prefix ID": "Prefixo ID",
@@ -1516,7 +1517,7 @@
 	"Pull \"{{searchValue}}\" from Ollama.com": "Obter \"{{searchValue}}\" de Ollama.com",
 	"Pull a model from Ollama.com": "Obter um modelo de Ollama.com",
 	"Pull Model": "Obter Modelo",
-	"Pyodide file browser": "",
+	"Pyodide file browser": "Navegador de arquivos Pyodide",
 	"Query Generation Prompt": "Prompt de Geração de Consulta",
 	"Querying": "Consultando",
 	"Quick Actions": "Ações rápidas",
@@ -1584,7 +1585,7 @@
 	"Response splitting": "Divisão da Resposta",
 	"Response Watermark": "Marca d'água de resposta",
 	"Responses": "Respostas",
-	"Restart": "",
+	"Restart": "Reiniciar",
 	"Result": "Resultado",
 	"RESULT": "Resultado",
 	"Retrieval": "Recuperação",
@@ -1598,7 +1599,7 @@
 	"Role": "Função",
 	"RTL": "Direita para Esquerda",
 	"Run": "Executar",
-	"Run All": "",
+	"Run All": "Executar Tudo",
 	"Running": "Executando",
 	"Running...": "Executando...",
 	"Runs embedding tasks concurrently to speed up processing. Turn off if rate limits become an issue.": "Executa tarefas de incorporação simultaneamente para acelerar o processamento. Desative se os limites de taxa se tornarem um problema.",
@@ -1789,7 +1790,7 @@
 	"Start a new conversation": "Iniciar uma nova conversa",
 	"Start of the channel": "Início do canal",
 	"Start Tag": "Tag inicial",
-	"Starting kernel...": "",
+	"Starting kernel...": "Iniciando kernel...",
 	"Status": "Status",
 	"Status cleared successfully": "Status liberado com sucesso",
 	"Status updated successfully": "Status atualizado com sucesso",
@@ -2134,8 +2135,8 @@
 	"You're now logged in.": "Você agora está logado.",
 	"Your Account": "Sua conta",
 	"Your account status is currently pending activation.": "O status da sua conta está atualmente aguardando ativação.",
-	"Your browser does not support the audio tag.": "",
-	"Your browser does not support the video tag.": "",
+	"Your browser does not support the audio tag.": "Seu navegador não suporta a tag de áudio.",
+	"Your browser does not support the video tag.": "Seu navegador não suporta a tag de vídeo.",
 	"Your entire contribution will go directly to the plugin developer; Open WebUI does not take any percentage. However, the chosen funding platform might have its own fees.": "Toda a sua contribuição irá diretamente para o desenvolvedor do plugin; o Open WebUI não retém nenhuma porcentagem. No entanto, a plataforma de financiamento escolhida pode ter suas próprias taxas.",
 	"Your message text or inputs": "Seu texto de mensagem ou entradas",
 	"Your usage stats have been successfully synced.": "Suas estatísticas de uso foram sincronizadas com sucesso.",

src/lib/i18n/locales/pt-PT/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Modelo de Embedding",
 	"Embedding Model Engine": "Motor de Modelo de Embedding",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/ro-RO/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Model de Încapsulare",
 	"Embedding Model Engine": "Motor de Model de Încapsulare",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Activează generarea automată pentru mesajele de chat",

src/lib/i18n/locales/ru-RU/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Модель встраивания",
 	"Embedding Model Engine": "Движок модели встраивания",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Включить генерацию автозаполнения для сообщений чата",

src/lib/i18n/locales/sk-SK/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Vkladací model (Embedding Model)",
 	"Embedding Model Engine": "",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/sr-RS/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Модел уградње",
 	"Embedding Model Engine": "Мотор модела уградње",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/sv-SE/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Inbäddningsmodell",
 	"Embedding Model Engine": "Motor för inbäddningsmodell",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Aktivera automatisk komplettering av generering för chattmeddelanden",

src/lib/i18n/locales/th-TH/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "โมเดล Embedding",
 	"Embedding Model Engine": "เอ็นจินโมเดล Embedding",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "เปิดใช้งานการเติมข้อความอัตโนมัติสำหรับข้อความแชท",

src/lib/i18n/locales/tk-TM/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "",
 	"Embedding Model Engine": "",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/tr-TR/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Gömme Modeli",
 	"Embedding Model Engine": "Gömme Modeli Motoru",
+	"Empty message": "",
 	"Enable All": "Tümünü Etkinleştir",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Sohbet mesajları için otomatik tamamlama üretimini etkinleştir",

src/lib/i18n/locales/ug-CN/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "سىڭدۈرۈش مودېلى",
 	"Embedding Model Engine": "سىڭدۈرۈش مودېل ماتورى",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "سۆھبەت ئۇچۇرلىرىغا ئاپتوماتىك تولدۇرۇش قوزغىتىش",

src/lib/i18n/locales/uk-UA/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Модель вбудовування",
 	"Embedding Model Engine": "Рушій моделі вбудовування ",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Увімкнути генерацію автозаповнення для повідомлень чату",

src/lib/i18n/locales/ur-PK/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "ایمبیڈنگ ماڈل",
 	"Embedding Model Engine": "ایمبیڈنگ ماڈل انجن",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "",

src/lib/i18n/locales/uz-Cyrl-UZ/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Ўрнатиш модели",
 	"Embedding Model Engine": "Двигател моделини ўрнатиш",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Чат хабарлари учун автоматик тўлдиришни яратишни ёқинг",

src/lib/i18n/locales/uz-Latn-Uz/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "O'rnatish modeli",
 	"Embedding Model Engine": "Dvigatel modelini o'rnatish",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Chat xabarlari uchun avtomatik to‘ldirishni yaratishni yoqing",

src/lib/i18n/locales/vi-VN/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "",
 	"Embedding Model": "Mô hình embedding",
 	"Embedding Model Engine": "Trình xử lý embedding",
+	"Empty message": "",
 	"Enable All": "",
 	"Enable API Keys": "",
 	"Enable autocomplete generation for chat messages": "Bật tạo tự động hoàn thành cho tin nhắn chat",

src/lib/i18n/locales/zh-CN/translation.json

@@ -633,6 +633,7 @@
 	"Embedding Concurrent Requests": "嵌入并发请求数",
 	"Embedding Model": "嵌入模型",
 	"Embedding Model Engine": "嵌入模型引擎",
+	"Empty message": "",
 	"Enable All": "全部启用",
 	"Enable API Keys": "启用接口密钥",
 	"Enable autocomplete generation for chat messages": "启用对话输入框内容自动补全",