Dev

.github/workflows/format-build-frontend.yaml

@@ -34,7 +34,9 @@ jobs:
           node-version: '22'
 
       - name: Install Dependencies
-        run: npm install --force
+        run: |
+          npm install -g npm@latest
+          npm install --force
 
       - name: Format Frontend
         run: npm run format
@@ -61,7 +63,9 @@ jobs:
           node-version: '22'
 
       - name: Install Dependencies
-        run: npm ci --force
+        run: |
+          npm install -g npm@latest
+          npm ci --force
 
       - name: Run vitest
         run: npm run test:frontend

CHANGELOG_EXTRA.md

@@ -5,6 +5,12 @@ 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).
 
+## [0.7.1.1] - 2026.01.10
+
+### Changed
+
+- 合并官方 0.7.1 改动
+
 ## [0.6.43.2] - 2025.12.22
 
 ### Fixed

Dockerfile

@@ -13,6 +13,7 @@ ARG USE_CUDA_VER=cu128
 # IMPORTANT: If you change the embedding model (sentence-transformers/all-MiniLM-L6-v2) and vice versa, you aren't able to use RAG Chat with your previous documents loaded in the WebUI! You need to re-embed them.
 ARG USE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
 ARG USE_RERANKING_MODEL=""
+ARG USE_AUXILIARY_EMBEDDING_MODEL=TaylorAI/bge-micro-v2
 
 # Tiktoken encoding name; models to use can be found at https://huggingface.co/models?library=tiktoken
 ARG USE_TIKTOKEN_ENCODING_NAME="cl100k_base"
@@ -35,14 +36,15 @@ WORKDIR /app
 RUN apk add --no-cache git
 
 COPY package.json package-lock.json ./
+RUN npm install -g npm@latest
 RUN npm ci --force
 
 COPY . .
 ENV APP_BUILD_HASH=${BUILD_HASH}
 RUN npm run build
 
 ######## WebUI backend ########
-FROM python:3.11-slim-bookworm AS base
+FROM python:3.11.14-slim-bookworm AS base
 
 # Use args
 ARG USE_CUDA
@@ -52,6 +54,7 @@ ARG USE_SLIM
 ARG USE_PERMISSION_HARDENING
 ARG USE_EMBEDDING_MODEL
 ARG USE_RERANKING_MODEL
+ARG USE_AUXILIARY_EMBEDDING_MODEL
 ARG UID
 ARG GID
 
@@ -67,7 +70,8 @@ ENV ENV=prod \
     USE_SLIM_DOCKER=${USE_SLIM} \
     USE_CUDA_DOCKER_VER=${USE_CUDA_VER} \
     USE_EMBEDDING_MODEL_DOCKER=${USE_EMBEDDING_MODEL} \
-    USE_RERANKING_MODEL_DOCKER=${USE_RERANKING_MODEL}
+    USE_RERANKING_MODEL_DOCKER=${USE_RERANKING_MODEL} \
+    USE_AUXILIARY_EMBEDDING_MODEL_DOCKER=${USE_AUXILIARY_EMBEDDING_MODEL}
 
 ## Basis URL Config ##
 ENV OLLAMA_BASE_URL="/ollama" \
@@ -88,6 +92,7 @@ ENV WHISPER_MODEL="base" \
 ## RAG Embedding model settings ##
 ENV RAG_EMBEDDING_MODEL="$USE_EMBEDDING_MODEL_DOCKER" \
     RAG_RERANKING_MODEL="$USE_RERANKING_MODEL_DOCKER" \
+    AUXILIARY_EMBEDDING_MODEL="$USE_AUXILIARY_EMBEDDING_MODEL_DOCKER" \
     SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models"
 
 ## Tiktoken model settings ##
@@ -136,13 +141,15 @@ RUN pip3 install --no-cache-dir uv && \
     pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir && \
     uv pip install --system -r requirements.txt --no-cache-dir && \
     python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
+    python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ.get('AUXILIARY_EMBEDDING_MODEL', 'TaylorAI/bge-micro-v2'), device='cpu')" && \
     python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"; \
     python -c "import os; import tiktoken; tiktoken.get_encoding(os.environ['TIKTOKEN_ENCODING_NAME'])"; \
     else \
     pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \
     uv pip install --system -r requirements.txt --no-cache-dir && \
     if [ "$USE_SLIM" != "true" ]; then \
     python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
+    python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ.get('AUXILIARY_EMBEDDING_MODEL', 'TaylorAI/bge-micro-v2'), device='cpu')" && \
     python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"; \
     python -c "import os; import tiktoken; tiktoken.get_encoding(os.environ['TIKTOKEN_ENCODING_NAME'])"; \
     fi; \

backend/open_webui/config.py

@@ -20,6 +20,7 @@
 from open_webui.env import (
     DATA_DIR,
     DATABASE_URL,
+    ENABLE_DB_MIGRATIONS,
     ENV,
     REDIS_URL,
     REDIS_KEY_PREFIX,
@@ -33,7 +34,7 @@
     WEBUI_NAME,
     log,
 )
-from open_webui.internal.db import Base, get_db, Session
+from open_webui.internal.db import Base, get_db
 from open_webui.utils.redis import get_redis_connection
 
 
@@ -1433,6 +1434,14 @@ def feishu_oauth_register(oauth: OAuth):
     os.environ.get("USER_PERMISSIONS_FEATURES_API_KEYS", "False").lower() == "true"
 )
 
+USER_PERMISSIONS_FEATURES_MEMORIES = (
+    os.environ.get("USER_PERMISSIONS_FEATURES_MEMORIES", "True").lower() == "true"
+)
+
+USER_PERMISSIONS_SETTINGS_INTERFACE = (
+    os.environ.get("USER_PERMISSIONS_SETTINGS_INTERFACE", "True").lower() == "true"
+)
+
 DEFAULT_USER_PERMISSIONS = {
     "workspace": {
         "models": USER_PERMISSIONS_WORKSPACE_MODELS_ACCESS,
@@ -1490,6 +1499,10 @@ def feishu_oauth_register(oauth: OAuth):
         "web_search": USER_PERMISSIONS_FEATURES_WEB_SEARCH,
         "image_generation": USER_PERMISSIONS_FEATURES_IMAGE_GENERATION,
         "code_interpreter": USER_PERMISSIONS_FEATURES_CODE_INTERPRETER,
+        "memories": USER_PERMISSIONS_FEATURES_MEMORIES,
+    },
+    "settings": {
+        "interface": USER_PERMISSIONS_SETTINGS_INTERFACE,
     },
 }
 
@@ -1505,6 +1518,12 @@ def feishu_oauth_register(oauth: OAuth):
     os.environ.get("ENABLE_FOLDERS", "True").lower() == "true",
 )
 
+FOLDER_MAX_FILE_COUNT = PersistentConfig(
+    "FOLDER_MAX_FILE_COUNT",
+    "folders.max_file_count",
+    os.environ.get("FOLDER_MAX_FILE_COUNT", ""),
+)
+
 ENABLE_CHANNELS = PersistentConfig(
     "ENABLE_CHANNELS",
     "channels.enable",
@@ -1517,6 +1536,12 @@ def feishu_oauth_register(oauth: OAuth):
     os.environ.get("ENABLE_NOTES", "True").lower() == "true",
 )
 
+ENABLE_USER_STATUS = PersistentConfig(
+    "ENABLE_USER_STATUS",
+    "users.enable_status",
+    os.environ.get("ENABLE_USER_STATUS", "True").lower() == "true",
+)
+
 ENABLE_EVALUATION_ARENA_MODELS = PersistentConfig(
     "ENABLE_EVALUATION_ARENA_MODELS",
     "evaluation.arena.enable",
@@ -2020,6 +2045,12 @@ class BannerModel(BaseModel):
     os.environ.get("ENABLE_CODE_INTERPRETER", "True").lower() == "true",
 )
 
+ENABLE_MEMORIES = PersistentConfig(
+    "ENABLE_MEMORIES",
+    "memories.enable",
+    os.environ.get("ENABLE_MEMORIES", "True").lower() == "true",
+)
+
 CODE_INTERPRETER_ENGINE = PersistentConfig(
     "CODE_INTERPRETER_ENGINE",
     "code_interpreter.engine",
@@ -2290,6 +2321,51 @@ class BannerModel(BaseModel):
     except Exception:
         PGVECTOR_IVFFLAT_LISTS = 100
 
+# openGauss
+OPENGAUSS_DB_URL = os.environ.get("OPENGAUSS_DB_URL", DATABASE_URL)
+
+OPENGAUSS_INITIALIZE_MAX_VECTOR_LENGTH = int(
+    os.environ.get("OPENGAUSS_INITIALIZE_MAX_VECTOR_LENGTH", "1536")
+)
+
+OPENGAUSS_POOL_SIZE = os.environ.get("OPENGAUSS_POOL_SIZE", None)
+
+if OPENGAUSS_POOL_SIZE != None:
+    try:
+        OPENGAUSS_POOL_SIZE = int(OPENGAUSS_POOL_SIZE)
+    except Exception:
+        OPENGAUSS_POOL_SIZE = None
+
+OPENGAUSS_POOL_MAX_OVERFLOW = os.environ.get("OPENGAUSS_POOL_MAX_OVERFLOW", 0)
+
+if OPENGAUSS_POOL_MAX_OVERFLOW == "":
+    OPENGAUSS_POOL_MAX_OVERFLOW = 0
+else:
+    try:
+        OPENGAUSS_POOL_MAX_OVERFLOW = int(OPENGAUSS_POOL_MAX_OVERFLOW)
+    except Exception:
+        OPENGAUSS_POOL_MAX_OVERFLOW = 0
+
+OPENGAUSS_POOL_TIMEOUT = os.environ.get("OPENGAUSS_POOL_TIMEOUT", 30)
+
+if OPENGAUSS_POOL_TIMEOUT == "":
+    OPENGAUSS_POOL_TIMEOUT = 30
+else:
+    try:
+        OPENGAUSS_POOL_TIMEOUT = int(OPENGAUSS_POOL_TIMEOUT)
+    except Exception:
+        OPENGAUSS_POOL_TIMEOUT = 30
+
+OPENGAUSS_POOL_RECYCLE = os.environ.get("OPENGAUSS_POOL_RECYCLE", 3600)
+
+if OPENGAUSS_POOL_RECYCLE == "":
+    OPENGAUSS_POOL_RECYCLE = 3600
+else:
+    try:
+        OPENGAUSS_POOL_RECYCLE = int(OPENGAUSS_POOL_RECYCLE)
+    except Exception:
+        OPENGAUSS_POOL_RECYCLE = 3600
+
 # Pinecone
 PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY", None)
 PINECONE_ENVIRONMENT = os.environ.get("PINECONE_ENVIRONMENT", None)
@@ -2753,13 +2829,18 @@ class BannerModel(BaseModel):
     os.environ.get("RAG_EXTERNAL_RERANKER_TIMEOUT", ""),
 )
 
-
 RAG_TEXT_SPLITTER = PersistentConfig(
     "RAG_TEXT_SPLITTER",
     "rag.text_splitter",
     os.environ.get("RAG_TEXT_SPLITTER", ""),
 )
 
+ENABLE_MARKDOWN_HEADER_TEXT_SPLITTER = PersistentConfig(
+    "ENABLE_MARKDOWN_HEADER_TEXT_SPLITTER",
+    "rag.enable_markdown_header_text_splitter",
+    os.environ.get("ENABLE_MARKDOWN_HEADER_TEXT_SPLITTER", "True").lower() == "true",
+)
+
 TIKTOKEN_CACHE_DIR = os.environ.get("TIKTOKEN_CACHE_DIR", f"{CACHE_DIR}/tiktoken")
 TIKTOKEN_ENCODING_NAME = PersistentConfig(
     "TIKTOKEN_ENCODING_NAME",
@@ -2770,6 +2851,13 @@ class BannerModel(BaseModel):
 CHUNK_SIZE = PersistentConfig(
     "CHUNK_SIZE", "rag.chunk_size", int(os.environ.get("CHUNK_SIZE", "1000"))
 )
+
+CHUNK_MIN_SIZE_TARGET = PersistentConfig(
+    "CHUNK_MIN_SIZE_TARGET",
+    "rag.chunk_min_size_target",
+    int(os.environ.get("CHUNK_MIN_SIZE_TARGET", "0")),
+)
+
 CHUNK_OVERLAP = PersistentConfig(
     "CHUNK_OVERLAP",
     "rag.chunk_overlap",
@@ -2952,7 +3040,6 @@ class BannerModel(BaseModel):
     os.getenv("WEB_LOADER_TIMEOUT", ""),
 )
 
-
 ENABLE_WEB_LOADER_SSL_VERIFICATION = PersistentConfig(
     "ENABLE_WEB_LOADER_SSL_VERIFICATION",
     "rag.web.loader.ssl_verification",
@@ -3061,12 +3148,24 @@ class BannerModel(BaseModel):
     os.getenv("SERPLY_API_KEY", ""),
 )
 
+DDGS_BACKEND = PersistentConfig(
+    "DDGS_BACKEND",
+    "rag.web.search.ddgs_backend",
+    os.getenv("DDGS_BACKEND", "auto"),
+)
+
 JINA_API_KEY = PersistentConfig(
     "JINA_API_KEY",
     "rag.web.search.jina_api_key",
     os.getenv("JINA_API_KEY", ""),
 )
 
+JINA_API_BASE_URL = PersistentConfig(
+    "JINA_API_BASE_URL",
+    "rag.web.search.jina_api_base_url",
+    os.getenv("JINA_API_BASE_URL", ""),
+)
+
 SEARCHAPI_API_KEY = PersistentConfig(
     "SEARCHAPI_API_KEY",
     "rag.web.search.searchapi_api_key",
@@ -3201,6 +3300,12 @@ class BannerModel(BaseModel):
     os.environ.get("FIRECRAWL_API_BASE_URL", "https://api.firecrawl.dev"),
 )
 
+FIRECRAWL_TIMEOUT = PersistentConfig(
+    "FIRECRAWL_TIMEOUT",
+    "rag.web.loader.firecrawl_timeout",
+    os.environ.get("FIRECRAWL_TIMEOUT", ""),
+)
+
 EXTERNAL_WEB_SEARCH_URL = PersistentConfig(
     "EXTERNAL_WEB_SEARCH_URL",
     "rag.web.search.external_web_search_url",
@@ -3558,17 +3663,16 @@ class BannerModel(BaseModel):
     os.getenv("WHISPER_MODEL", "base"),
 )
 
+WHISPER_COMPUTE_TYPE = os.getenv("WHISPER_COMPUTE_TYPE", "int8")
 WHISPER_MODEL_DIR = os.getenv("WHISPER_MODEL_DIR", f"{CACHE_DIR}/whisper/models")
 WHISPER_MODEL_AUTO_UPDATE = (
     not OFFLINE_MODE
     and os.environ.get("WHISPER_MODEL_AUTO_UPDATE", "").lower() == "true"
 )
 
-WHISPER_VAD_FILTER = PersistentConfig(
-    "WHISPER_VAD_FILTER",
-    "audio.stt.whisper_vad_filter",
-    os.getenv("WHISPER_VAD_FILTER", "False").lower() == "true",
-)
+WHISPER_VAD_FILTER = os.getenv("WHISPER_VAD_FILTER", "False").lower() == "true"
+
+WHISPER_MULTILINGUAL = os.getenv("WHISPER_MULTILINGUAL", "False").lower() == "true"
 
 WHISPER_LANGUAGE = os.getenv("WHISPER_LANGUAGE", "").lower() or None