mirror of
https://github.com/open-webui/open-webui.git
synced 2026-09-07 08:27:05 +00:00
refac
This commit is contained in:
Timothy Jaeryang Baek
parent
7fc979f95b
commit
cb942bb94c
20 changed files with 503 additions and 182 deletions
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@ -18,3 +18,6 @@ uploads
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**/*.db
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_test
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backend/data/*
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.venv
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.git
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21
Dockerfile
21
Dockerfile
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@ -123,23 +123,30 @@ RUN echo -n 00000000-0000-0000-0000-000000000000 > $HOME/.cache/chroma/telemetry
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# Make sure the user has access to the app and root directory
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RUN chown -R $UID:$GID /app $HOME
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# Install common system dependencies
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# Slim cannot bundle a local model server or GPU runtime.
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RUN if [ "$USE_SLIM" = "true" ] && { [ "$USE_CUDA" = "true" ] || [ "$USE_OLLAMA" = "true" ]; }; then \
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echo "USE_SLIM cannot be combined with USE_CUDA or USE_OLLAMA" >&2; exit 1; fi
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# Keep the slim runtime free of local document/audio processing tools.
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RUN apt-get update && \
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apt-get install -y --no-install-recommends \
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git build-essential pandoc gcc curl jq ca-certificates \
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libmariadb-dev \
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ffmpeg libsm6 libxext6 zstd \
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&& rm -rf /var/lib/apt/lists/*
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git curl jq ca-certificates zstd \
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&& if [ "$USE_SLIM" != "true" ]; then \
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apt-get install -y --no-install-recommends \
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build-essential pandoc gcc libmariadb-dev ffmpeg libsm6 libxext6; \
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fi && rm -rf /var/lib/apt/lists/*
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# install python dependencies
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COPY --chown=$UID:$GID ./backend/requirements.txt ./requirements.txt
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COPY --chown=$UID:$GID ./backend/requirements*.txt ./
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# Set UV_LINK_MODE to copy to prevent 0-byte file corruption in QEMU arm64 cross-builds
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ENV UV_LINK_MODE=copy
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RUN set -e; \
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pip3 install --no-cache-dir uv; \
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if [ "$USE_CUDA" = "true" ]; then \
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if [ "$USE_SLIM" = "true" ]; then \
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uv pip install --system -r requirements-slim.txt --no-cache-dir; \
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elif [ "$USE_CUDA" = "true" ]; then \
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# If you use CUDA the whisper and embedding model will be downloaded on first use
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# fix: pin torch<=2.9.1 - torch 2.10.0 aarch64 wheels cause SIGILL on ARM devices (RPi 4 Cortex-A72) #21349
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pip3 install 'torch<=2.9.1' torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir; \
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@ -17,6 +17,7 @@ from authlib.integrations.starlette_client import OAuth
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from pydantic import BaseModel
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from open_webui.env import (
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USE_SLIM,
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DATA_DIR,
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DATABASE_URL,
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ENABLE_ADMIN_CHAT_ACCESS,
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@ -644,7 +645,7 @@ SSL_ASSERT_FINGERPRINT = os.getenv('SSL_ASSERT_FINGERPRINT', None)
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ELASTICSEARCH_INDEX_PREFIX = os.getenv('ELASTICSEARCH_INDEX_PREFIX', 'open_webui_collections')
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# Pgvector
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PGVECTOR_DB_URL = os.getenv('PGVECTOR_DB_URL', DATABASE_URL)
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if VECTOR_DB == 'pgvector' and not PGVECTOR_DB_URL.startswith('postgres'):
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if not USE_SLIM and VECTOR_DB == 'pgvector' and not PGVECTOR_DB_URL.startswith('postgres'):
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raise ValueError(
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'Pgvector requires setting PGVECTOR_DB_URL or using Postgres with vector extension as the primary database.'
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)
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@ -805,7 +806,7 @@ ORACLE_DB_POOL_MAX = int(os.getenv('ORACLE_DB_POOL_MAX', 10))
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ORACLE_DB_POOL_INCREMENT = int(os.getenv('ORACLE_DB_POOL_INCREMENT', 1))
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if VECTOR_DB == 'oracle23ai':
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if not USE_SLIM and VECTOR_DB == 'oracle23ai':
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if not ORACLE_DB_USER or not ORACLE_DB_PASSWORD or not ORACLE_DB_DSN:
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raise ValueError('Oracle23ai requires setting ORACLE_DB_USER, ORACLE_DB_PASSWORD, and ORACLE_DB_DSN.')
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if ORACLE_DB_USE_WALLET and (not ORACLE_WALLET_DIR or not ORACLE_WALLET_PASSWORD):
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@ -42,12 +42,13 @@ except ImportError:
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print('dotenv not installed, skipping...')
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DOCKER = os.getenv('DOCKER', 'False').lower() == 'true'
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USE_SLIM = os.getenv('USE_SLIM_DOCKER', 'False').lower() == 'true'
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USE_CUDA = os.getenv('USE_CUDA_DOCKER', 'false')
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DEVICE_TYPE = 'cpu'
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_cuda_error: Optional[str] = None
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if USE_CUDA.lower() == 'true':
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if not USE_SLIM and USE_CUDA.lower() == 'true':
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try:
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import torch # noqa: E402
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@ -59,7 +60,7 @@ if USE_CUDA.lower() == 'true':
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os.environ['USE_CUDA_DOCKER'] = 'false'
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USE_CUDA = 'false'
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if sys.platform == 'darwin' and DEVICE_TYPE == 'cpu':
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if not USE_SLIM and sys.platform == 'darwin' and DEVICE_TYPE == 'cpu':
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try:
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import torch # noqa: E402
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@ -844,7 +845,7 @@ MINERU_MAX_MARKDOWN_BYTES = (
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# When enabled, skips pydub-based preprocessing (format conversion, compression,
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# and chunked splitting) before sending files to processing engines. Useful when
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# the upstream provider handles these steps or when ffmpeg is unavailable.
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BYPASS_PYDUB_PREPROCESSING = os.getenv('BYPASS_PYDUB_PREPROCESSING', 'False').lower() == 'true'
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BYPASS_PYDUB_PREPROCESSING = USE_SLIM or os.getenv('BYPASS_PYDUB_PREPROCESSING', 'False').lower() == 'true'
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# When disabled (default), the OpenAI catch-all proxy endpoint (/{path:path})
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# is blocked. Enable only if you need direct passthrough to upstream OpenAI-
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@ -75,6 +75,7 @@ from open_webui.config import (
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)
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from open_webui.constants import ERROR_MESSAGES, TASKS
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from open_webui.env import (
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USE_SLIM,
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AIOHTTP_CLIENT_SESSION_SSL,
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AUDIT_EXCLUDED_PATHS,
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AUDIT_INCLUDED_PATHS,
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@ -2297,6 +2298,7 @@ async def get_app_config(request: Request):
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'auto_redirect': config.get('oauth.auto_redirect'),
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},
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'features': {
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'slim': USE_SLIM,
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# --- Public: required by login/signup page pre-auth ---
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'auth': WEBUI_AUTH,
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'auth_trusted_header': bool(WEBUI_AUTH_TRUSTED_EMAIL_HEADER),
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@ -7,6 +7,7 @@ import zipfile
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import ftfy
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import requests
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from fastapi import HTTPException
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from azure.identity import DefaultAzureCredential
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from langchain_community.document_loaders import (
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AzureAIDocumentIntelligenceLoader,
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@ -20,6 +21,7 @@ from langchain_core.documents import Document
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from open_webui.env import (
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AIOHTTP_CLIENT_SESSION_SSL,
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GLOBAL_LOG_LEVEL,
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USE_SLIM,
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MINERU_MAX_MARKDOWN_BYTES,
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REQUESTS_VERIFY,
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)
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@ -671,6 +673,19 @@ class Loader:
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file_path=file_path,
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)
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else:
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if USE_SLIM:
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if file_ext == 'csv':
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return CSVLoaderWithSummary(file_path, filename, self._detect_text_encoding(file_path))
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if file_ext in ['htm', 'html']:
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return BSHTMLLoader(file_path, open_encoding=self._detect_text_encoding(file_path))
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if file_ext in ['txt', 'md', 'markdown', 'rst', 'xml'] or self._is_text_file(
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file_ext, file_content_type
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):
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return TextLoader(file_path, encoding=self._detect_text_encoding(file_path))
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raise HTTPException(
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503,
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'This file type requires an external document extractor in slim. Configure one that supports it.',
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)
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if file_ext == 'pdf':
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loader = PyPDFLoader(
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file_path,
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@ -10,8 +10,9 @@ from typing import Awaitable, Optional, Union
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from urllib.parse import quote
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import aiohttp
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import numpy as np
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import requests
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from huggingface_hub import snapshot_download
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from fastapi import HTTPException
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from langchain_classic.retrievers import (
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ContextualCompressionRetriever,
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EnsembleRetriever,
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@ -34,6 +35,7 @@ from open_webui.env import (
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ENABLE_RETRIEVAL_UNSCOPED_COLLECTIONS,
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MPS_INFERENCE_LOCK,
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OFFLINE_MODE,
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USE_SLIM,
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)
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from open_webui.models.access_grants import AccessGrants
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from open_webui.models.chats import Chats
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@ -46,7 +48,7 @@ from open_webui.models.users import UserModel
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from open_webui.retrieval.loaders.youtube import YoutubeLoader
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from open_webui.retrieval.vector.async_client import ASYNC_VECTOR_DB_CLIENT
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from open_webui.retrieval.external import retrieve_external_knowledge
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from open_webui.retrieval.vector.factory import VECTOR_DB_CLIENT
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from open_webui.retrieval.vector.factory import get_vector_db_client
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from open_webui.retrieval.vector.main import GetResult, SearchResult
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from open_webui.retrieval.web.utils import get_web_loader
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from open_webui.utils.access_control.files import get_owner_accessible_folder_files, has_access_to_file
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@ -333,7 +335,7 @@ class VectorSearchRetriever(BaseRetriever):
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def query_doc(collection_name: str, query_embedding: list[float], k: int, user: UserModel = None):
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try:
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log.debug('query_doc:doc %s', collection_name)
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result = VECTOR_DB_CLIENT.search(
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result = get_vector_db_client().search(
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collection_name=collection_name,
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vectors=[query_embedding],
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limit=k,
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@ -351,7 +353,7 @@ def query_doc(collection_name: str, query_embedding: list[float], k: int, user:
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def get_doc(collection_name: str, user: UserModel = None):
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try:
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log.debug('get_doc:doc %s', collection_name)
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result = VECTOR_DB_CLIENT.get(collection_name=collection_name)
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result = get_vector_db_client().get(collection_name=collection_name)
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if result:
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log.info('query_doc:result %s %s', result.ids, result.metadatas)
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@ -1110,6 +1112,8 @@ def get_embedding_function(
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if embedding_engine == '':
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# Sentence transformers: CPU-bound sync operation
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async def async_embedding_function(query, prefix=None, user=None):
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if USE_SLIM:
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raise HTTPException(503, 'Configure an external embedding engine (openai, ollama, azure_openai).')
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# Deferred so a missing local model degrades RAG instead of crashing boot.
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if embedding_function is None:
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raise ValueError(
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@ -1243,6 +1247,14 @@ async def generate_embeddings(
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def get_reranking_function(reranking_engine, reranking_model, reranking_function, reranking_batch_size=32):
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if USE_SLIM and reranking_model and reranking_engine != 'external':
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def unavailable(query, documents, user=None):
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raise HTTPException(
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503, 'Configure an external reranker, or clear the reranking model to use cosine scoring.'
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)
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return unavailable
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if reranking_function is None:
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return None
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if reranking_engine == 'external':
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|
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@ -1688,6 +1700,8 @@ async def get_sources_from_items(
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def get_model_path(model: str, update_model: bool = False):
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from huggingface_hub import snapshot_download
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# Construct huggingface_hub kwargs with local_files_only to return the snapshot path
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cache_dir = os.getenv('SENTENCE_TRANSFORMERS_HOME')
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|
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@ -1733,6 +1747,17 @@ from langchain_core.callbacks import Callbacks
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from langchain_core.documents import BaseDocumentCompressor, Document
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def cosine_similarity(query, documents) -> np.ndarray:
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"""Score one query against documents without loading a model runtime."""
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if len(documents) == 0:
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return np.array([], dtype=float)
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query = np.asarray(query, dtype=float).reshape(-1)
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documents = np.asarray(documents, dtype=float)
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query = query / max(np.linalg.norm(query), 1e-12)
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documents = documents / np.maximum(np.linalg.norm(documents, axis=1, keepdims=True), 1e-12)
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return documents @ query
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class RerankCompressor(BaseDocumentCompressor):
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embedding_function: Any
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top_n: int
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|
|
@ -1768,18 +1793,18 @@ class RerankCompressor(BaseDocumentCompressor):
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query: str,
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callbacks: Callbacks | None = None,
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) -> Sequence[Document]:
|
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if not documents:
|
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return []
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reranking = self.reranking_function is not None
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|
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scores = None
|
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if reranking:
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scores = await asyncio.to_thread(self.reranking_function, query, documents)
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else:
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from sentence_transformers import util as st_util
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|
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query_embedding = await self.embedding_function(query, RAG_EMBEDDING_QUERY_PREFIX)
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doc_texts = [doc.page_content for doc in documents]
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document_embedding = await self.embedding_function(doc_texts, RAG_EMBEDDING_CONTENT_PREFIX)
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scores = st_util.cos_sim(query_embedding, document_embedding)[0]
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scores = cosine_similarity(query_embedding, document_embedding)
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|
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if scores is not None:
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docs_with_scores = list(
|
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|
|
|
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|
|
@ -15,9 +15,8 @@ transparently dispatches each call to a worker thread via
|
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`asyncio.to_thread`. Async callers can `await ASYNC_VECTOR_DB_CLIENT.x(...)`
|
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in place of `VECTOR_DB_CLIENT.x(...)` and the loop stays responsive.
|
||||
|
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The original `VECTOR_DB_CLIENT` is unchanged, so callers already running
|
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inside `run_in_threadpool` (e.g. `save_docs_to_vector_db`) are not
|
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affected.
|
||||
Client initialization and calls run in the worker thread. Synchronous callers
|
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already inside `run_in_threadpool` use `get_vector_db_client()` directly.
|
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|
||||
Thread-safety expectations
|
||||
--------------------------
|
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|
|
@ -55,7 +54,7 @@ from __future__ import annotations
|
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import asyncio
|
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from typing import Dict, List, Optional, Union
|
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|
||||
from open_webui.retrieval.vector.factory import VECTOR_DB_CLIENT
|
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from open_webui.retrieval.vector.factory import get_vector_db_client
|
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from open_webui.retrieval.vector.main import (
|
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GetResult,
|
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SearchResult,
|
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|
|
@ -73,30 +72,30 @@ class AsyncVectorDBClient:
|
|||
typically swallowed by surrounding ``try/except``).
|
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"""
|
||||
|
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def __init__(self, sync_client: VectorDBBase) -> None:
|
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def __init__(self, sync_client: Optional[VectorDBBase] = None) -> None:
|
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self._sync = sync_client
|
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|
||||
@property
|
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def sync(self) -> VectorDBBase:
|
||||
"""Escape hatch for code that must call the sync client directly
|
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(e.g. already inside a worker thread)."""
|
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return self._sync
|
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return self._sync if self._sync is not None else get_vector_db_client()
|
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|
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@property
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def supports_hybrid_search(self) -> bool:
|
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return type(self._sync).hybrid_search is not VectorDBBase.hybrid_search
|
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return type(self.sync).hybrid_search is not VectorDBBase.hybrid_search
|
||||
|
||||
async def has_collection(self, collection_name: str) -> bool:
|
||||
return await asyncio.to_thread(self._sync.has_collection, collection_name)
|
||||
return await asyncio.to_thread(lambda: self.sync.has_collection(collection_name))
|
||||
|
||||
async def delete_collection(self, collection_name: str) -> None:
|
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return await asyncio.to_thread(self._sync.delete_collection, collection_name)
|
||||
return await asyncio.to_thread(lambda: self.sync.delete_collection(collection_name))
|
||||
|
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async def insert(self, collection_name: str, items: List[VectorItem]) -> None:
|
||||
return await asyncio.to_thread(self._sync.insert, collection_name, items)
|
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return await asyncio.to_thread(lambda: self.sync.insert(collection_name, items))
|
||||
|
||||
async def upsert(self, collection_name: str, items: List[VectorItem]) -> None:
|
||||
return await asyncio.to_thread(self._sync.upsert, collection_name, items)
|
||||
return await asyncio.to_thread(lambda: self.sync.upsert(collection_name, items))
|
||||
|
||||
async def search(
|
||||
self,
|
||||
|
|
@ -105,7 +104,7 @@ class AsyncVectorDBClient:
|
|||
filter: Optional[Dict] = None,
|
||||
limit: int = 10,
|
||||
) -> Optional[SearchResult]:
|
||||
return await asyncio.to_thread(self._sync.search, collection_name, vectors, filter, limit)
|
||||
return await asyncio.to_thread(lambda: self.sync.search(collection_name, vectors, filter, limit))
|
||||
|
||||
async def hybrid_search(
|
||||
self,
|
||||
|
|
@ -117,13 +116,7 @@ class AsyncVectorDBClient:
|
|||
hybrid_bm25_weight: float = 0.5,
|
||||
) -> Optional[SearchResult]:
|
||||
return await asyncio.to_thread(
|
||||
self._sync.hybrid_search,
|
||||
collection_name,
|
||||
query,
|
||||
vectors,
|
||||
filter,
|
||||
limit,
|
||||
hybrid_bm25_weight,
|
||||
lambda: self.sync.hybrid_search(collection_name, query, vectors, filter, limit, hybrid_bm25_weight)
|
||||
)
|
||||
|
||||
async def query(
|
||||
|
|
@ -132,10 +125,10 @@ class AsyncVectorDBClient:
|
|||
filter: Dict,
|
||||
limit: Optional[int] = None,
|
||||
) -> Optional[GetResult]:
|
||||
return await asyncio.to_thread(self._sync.query, collection_name, filter, limit)
|
||||
return await asyncio.to_thread(lambda: self.sync.query(collection_name, filter, limit))
|
||||
|
||||
async def get(self, collection_name: str) -> Optional[GetResult]:
|
||||
return await asyncio.to_thread(self._sync.get, collection_name)
|
||||
return await asyncio.to_thread(lambda: self.sync.get(collection_name))
|
||||
|
||||
async def delete(
|
||||
self,
|
||||
|
|
@ -143,10 +136,10 @@ class AsyncVectorDBClient:
|
|||
ids: Optional[List[str]] = None,
|
||||
filter: Optional[Dict] = None,
|
||||
) -> None:
|
||||
return await asyncio.to_thread(self._sync.delete, collection_name, ids, filter)
|
||||
return await asyncio.to_thread(lambda: self.sync.delete(collection_name, ids, filter))
|
||||
|
||||
async def reset(self) -> None:
|
||||
return await asyncio.to_thread(self._sync.reset)
|
||||
return await asyncio.to_thread(lambda: self.sync.reset())
|
||||
|
||||
|
||||
ASYNC_VECTOR_DB_CLIENT = AsyncVectorDBClient(VECTOR_DB_CLIENT)
|
||||
ASYNC_VECTOR_DB_CLIENT = AsyncVectorDBClient()
|
||||
|
|
|
|||
|
|
@ -16,6 +16,8 @@ from open_webui.config import (
|
|||
CHROMA_HTTP_SSL,
|
||||
CHROMA_TENANT,
|
||||
)
|
||||
from open_webui.env import USE_SLIM
|
||||
from fastapi import HTTPException
|
||||
from open_webui.retrieval.vector.main import (
|
||||
GetResult,
|
||||
SearchResult,
|
||||
|
|
@ -29,6 +31,8 @@ log = logging.getLogger(__name__)
|
|||
|
||||
class ChromaClient(VectorDBBase):
|
||||
def __init__(self):
|
||||
if USE_SLIM and not CHROMA_HTTP_HOST:
|
||||
raise HTTPException(503, 'Configure CHROMA_HTTP_HOST: embedded Chroma is unavailable in slim.')
|
||||
settings_dict = {
|
||||
'allow_reset': True,
|
||||
'anonymized_telemetry': False,
|
||||
|
|
@ -58,7 +62,7 @@ class ChromaClient(VectorDBBase):
|
|||
|
||||
def has_collection(self, collection_name: str) -> bool:
|
||||
try:
|
||||
self.client.get_collection(name=collection_name)
|
||||
self.client.get_collection(name=collection_name, embedding_function=None)
|
||||
return True
|
||||
except NotFoundError:
|
||||
return False
|
||||
|
|
@ -76,7 +80,7 @@ class ChromaClient(VectorDBBase):
|
|||
) -> Optional[SearchResult]:
|
||||
# Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
|
||||
try:
|
||||
collection = self.client.get_collection(name=collection_name)
|
||||
collection = self.client.get_collection(name=collection_name, embedding_function=None)
|
||||
if collection:
|
||||
result = collection.query(
|
||||
query_embeddings=vectors,
|
||||
|
|
@ -105,7 +109,7 @@ class ChromaClient(VectorDBBase):
|
|||
def query(self, collection_name: str, filter: dict, limit: Optional[int] = None) -> Optional[GetResult]:
|
||||
# Query the items from the collection based on the filter.
|
||||
try:
|
||||
collection = self.client.get_collection(name=collection_name)
|
||||
collection = self.client.get_collection(name=collection_name, embedding_function=None)
|
||||
if collection:
|
||||
result = collection.get(
|
||||
where=filter,
|
||||
|
|
@ -125,7 +129,7 @@ class ChromaClient(VectorDBBase):
|
|||
|
||||
def get(self, collection_name: str) -> Optional[GetResult]:
|
||||
# Get all the items in the collection.
|
||||
collection = self.client.get_collection(name=collection_name)
|
||||
collection = self.client.get_collection(name=collection_name, embedding_function=None)
|
||||
if collection:
|
||||
result = collection.get()
|
||||
return GetResult(
|
||||
|
|
@ -139,7 +143,9 @@ class ChromaClient(VectorDBBase):
|
|||
|
||||
def insert(self, collection_name: str, items: list[VectorItem]):
|
||||
# Insert the items into the collection, if the collection does not exist, it will be created.
|
||||
collection = self.client.get_or_create_collection(name=collection_name, metadata={'hnsw:space': 'cosine'})
|
||||
collection = self.client.get_or_create_collection(
|
||||
name=collection_name, metadata={'hnsw:space': 'cosine'}, embedding_function=None
|
||||
)
|
||||
|
||||
ids = [item['id'] for item in items]
|
||||
documents = [item['text'] for item in items]
|
||||
|
|
@ -157,7 +163,9 @@ class ChromaClient(VectorDBBase):
|
|||
|
||||
def upsert(self, collection_name: str, items: list[VectorItem]):
|
||||
# Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
|
||||
collection = self.client.get_or_create_collection(name=collection_name, metadata={'hnsw:space': 'cosine'})
|
||||
collection = self.client.get_or_create_collection(
|
||||
name=collection_name, metadata={'hnsw:space': 'cosine'}, embedding_function=None
|
||||
)
|
||||
|
||||
ids = [item['id'] for item in items]
|
||||
documents = [item['text'] for item in items]
|
||||
|
|
@ -174,7 +182,7 @@ class ChromaClient(VectorDBBase):
|
|||
):
|
||||
# Delete the items from the collection based on the ids.
|
||||
try:
|
||||
collection = self.client.get_collection(name=collection_name)
|
||||
collection = self.client.get_collection(name=collection_name, embedding_function=None)
|
||||
if collection:
|
||||
if ids:
|
||||
collection.delete(ids=ids)
|
||||
|
|
|
|||
|
|
@ -1,8 +1,12 @@
|
|||
from threading import Lock
|
||||
|
||||
from fastapi import HTTPException
|
||||
from open_webui.config import (
|
||||
ENABLE_MILVUS_MULTITENANCY_MODE,
|
||||
ENABLE_QDRANT_MULTITENANCY_MODE,
|
||||
VECTOR_DB,
|
||||
)
|
||||
from open_webui.env import USE_SLIM
|
||||
from open_webui.retrieval.vector.main import VectorDBBase
|
||||
from open_webui.retrieval.vector.type import VectorType
|
||||
|
||||
|
|
@ -88,4 +92,35 @@ class Vector:
|
|||
raise ValueError(f'Unsupported vector type: {vector_type}')
|
||||
|
||||
|
||||
VECTOR_DB_CLIENT = Vector.get_vector(VECTOR_DB)
|
||||
VECTOR_DB_CLIENT = None if USE_SLIM else Vector.get_vector(VECTOR_DB)
|
||||
_vector_client_lock = Lock()
|
||||
|
||||
|
||||
def get_vector_db_client() -> VectorDBBase:
|
||||
"""Initialize slim's remote client on first use so chat can start without it."""
|
||||
global VECTOR_DB_CLIENT
|
||||
if VECTOR_DB_CLIENT is not None:
|
||||
return VECTOR_DB_CLIENT
|
||||
with _vector_client_lock:
|
||||
if VECTOR_DB_CLIENT is None:
|
||||
from open_webui import config
|
||||
|
||||
if VECTOR_DB == VectorType.CHROMA and not config.CHROMA_HTTP_HOST:
|
||||
raise HTTPException(
|
||||
503, 'Slim requires remote vector storage. Set CHROMA_HTTP_HOST or configure another VECTOR_DB.'
|
||||
)
|
||||
if VECTOR_DB == VectorType.MILVUS and not config.MILVUS_URI.startswith(('http://', 'https://', 'tcp://')):
|
||||
raise HTTPException(503, 'Slim requires an external MILVUS_URI, not a local database file.')
|
||||
if VECTOR_DB == VectorType.QDRANT and not config.QDRANT_URI:
|
||||
raise HTTPException(503, 'Configure QDRANT_URI for remote vector storage.')
|
||||
if VECTOR_DB == VectorType.PGVECTOR and not config.PGVECTOR_DB_URL.startswith('postgres'):
|
||||
raise HTTPException(503, 'Configure PGVECTOR_DB_URL for remote vector storage.')
|
||||
try:
|
||||
VECTOR_DB_CLIENT = Vector.get_vector(VECTOR_DB)
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as exc:
|
||||
raise HTTPException(
|
||||
503, f'Unable to connect to configured vector database ({VECTOR_DB}): {exc}'
|
||||
) from exc
|
||||
return VECTOR_DB_CLIENT
|
||||
|
|
|
|||
|
|
@ -29,7 +29,9 @@ import urllib3.connection
|
|||
import urllib3.connectionpool
|
||||
import validators
|
||||
from requests.adapters import HTTPAdapter
|
||||
from fastapi import HTTPException
|
||||
from fastapi.concurrency import run_in_threadpool
|
||||
from bs4 import BeautifulSoup
|
||||
from langchain_community.document_loaders import PlaywrightURLLoader, WebBaseLoader
|
||||
from langchain_community.document_loaders.base import BaseLoader
|
||||
from langchain_core.documents import Document
|
||||
|
|
@ -58,6 +60,7 @@ from open_webui.env import (
|
|||
AIOHTTP_CLIENT_SSL_CERT_FILE,
|
||||
AIOHTTP_CLIENT_TIMEOUT,
|
||||
USER_AGENT,
|
||||
USE_SLIM,
|
||||
)
|
||||
from open_webui.retrieval.loaders.external_web import ExternalWebLoader
|
||||
from open_webui.retrieval.loaders.microsoft_web_iq import MicrosoftWebIQLoader
|
||||
|
|
@ -643,6 +646,26 @@ class SafeMicrosoftWebIQLoader(BaseLoader, RateLimitMixin, URLProcessingMixin):
|
|||
raise e
|
||||
|
||||
|
||||
class TextHtmlEvaluator:
|
||||
"""Extract rendered text without Unstructured or its local NLP dependencies."""
|
||||
|
||||
def __init__(self, remove_selectors=None):
|
||||
self.remove_selectors = remove_selectors or []
|
||||
|
||||
def text(self, html):
|
||||
soup = BeautifulSoup(html, 'lxml')
|
||||
for selector in ['script', 'style', 'noscript', *self.remove_selectors]:
|
||||
for element in soup.select(selector):
|
||||
element.decompose()
|
||||
return soup.get_text(separator='\n', strip=True)
|
||||
|
||||
def evaluate(self, page, browser, response):
|
||||
return self.text(page.content())
|
||||
|
||||
async def evaluate_async(self, page, browser, response):
|
||||
return self.text(await page.content())
|
||||
|
||||
|
||||
class SafePlaywrightURLLoader(PlaywrightURLLoader, RateLimitMixin, URLProcessingMixin):
|
||||
"""Load HTML pages safely with Playwright, supporting SSL verification, rate limiting, and remote browser connection.
|
||||
|
||||
|
|
@ -674,6 +697,8 @@ class SafePlaywrightURLLoader(PlaywrightURLLoader, RateLimitMixin, URLProcessing
|
|||
playwright_timeout: Optional[int] = 10000,
|
||||
):
|
||||
"""Initialize with additional safety parameters and remote browser support."""
|
||||
if USE_SLIM and not playwright_ws_url:
|
||||
raise HTTPException(503, 'Configure PLAYWRIGHT_WS_URL. Slim requires a remote browser.')
|
||||
|
||||
proxy_server = proxy.get('server') if proxy else None
|
||||
if trust_env and not proxy_server:
|
||||
|
|
@ -690,7 +715,8 @@ class SafePlaywrightURLLoader(PlaywrightURLLoader, RateLimitMixin, URLProcessing
|
|||
urls=web_paths,
|
||||
continue_on_failure=continue_on_failure,
|
||||
headless=headless if playwright_ws_url is None else False,
|
||||
remove_selectors=remove_selectors,
|
||||
remove_selectors=None if USE_SLIM else remove_selectors,
|
||||
evaluator=TextHtmlEvaluator(remove_selectors) if USE_SLIM else None,
|
||||
proxy=proxy,
|
||||
)
|
||||
self.verify_ssl = verify_ssl
|
||||
|
|
|
|||
|
|
@ -46,6 +46,7 @@ from open_webui.env import (
|
|||
DEVICE_TYPE,
|
||||
ENABLE_FORWARD_USER_INFO_HEADERS,
|
||||
ENV,
|
||||
USE_SLIM,
|
||||
)
|
||||
from open_webui.events import EVENTS, publish_event
|
||||
from open_webui.models.config import Config
|
||||
|
|
@ -58,9 +59,10 @@ from open_webui.utils.session_pool import get_session
|
|||
from pydantic import BaseModel
|
||||
|
||||
# pydub needs stdlib audioop (gone in 3.13); keep requires-python capped < 3.13
|
||||
from pydub import AudioSegment
|
||||
from pydub.silence import split_on_silence
|
||||
from pydub.utils import mediainfo
|
||||
if not USE_SLIM:
|
||||
from pydub import AudioSegment
|
||||
from pydub.silence import split_on_silence
|
||||
from pydub.utils import mediainfo
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
router = APIRouter()
|
||||
|
|
@ -213,6 +215,8 @@ def transcode_audio_to_mp3(audio_data: bytes, content_type_header: str, output_p
|
|||
|
||||
|
||||
def set_faster_whisper_model(model: str, auto_update: bool = False):
|
||||
if USE_SLIM:
|
||||
raise HTTPException(503, 'Configure an external speech-to-text engine. Local Whisper is unavailable in slim.')
|
||||
whisper_model = None
|
||||
if model:
|
||||
from faster_whisper import WhisperModel
|
||||
|
|
@ -285,6 +289,12 @@ async def get_audio_config(request: Request, user=Depends(get_admin_user)):
|
|||
|
||||
@router.post('/config/update')
|
||||
async def update_audio_config(request: Request, form_data: AudioConfigUpdateForm, user=Depends(get_admin_user)):
|
||||
if USE_SLIM:
|
||||
current = await Config.get_many('audio.stt.engine', 'audio.tts.engine')
|
||||
if form_data.stt.ENGINE == '' and current.get('audio.stt.engine') != '':
|
||||
raise HTTPException(400, 'Local Whisper is unavailable in slim. Select an external speech-to-text engine.')
|
||||
if form_data.tts.ENGINE == 'transformers' and current.get('audio.tts.engine') != 'transformers':
|
||||
raise HTTPException(400, 'Local TTS is unavailable in slim. Select an external text-to-speech engine.')
|
||||
await Config.upsert(
|
||||
{
|
||||
**config_updates(form_data.tts.model_dump(), TTS_CONFIG_KEYS),
|
||||
|
|
@ -292,7 +302,7 @@ async def update_audio_config(request: Request, form_data: AudioConfigUpdateForm
|
|||
}
|
||||
)
|
||||
|
||||
if form_data.stt.ENGINE == '':
|
||||
if form_data.stt.ENGINE == '' and not USE_SLIM:
|
||||
request.app.state.faster_whisper_model = await asyncio.to_thread(
|
||||
set_faster_whisper_model, form_data.stt.WHISPER_MODEL, WHISPER_MODEL_AUTO_UPDATE
|
||||
)
|
||||
|
|
@ -314,6 +324,8 @@ async def update_audio_config(request: Request, form_data: AudioConfigUpdateForm
|
|||
|
||||
|
||||
def load_speech_pipeline(request):
|
||||
if USE_SLIM:
|
||||
raise HTTPException(503, 'Configure an external text-to-speech engine. Local TTS is unavailable in slim.')
|
||||
from datasets import load_dataset
|
||||
from transformers import pipeline
|
||||
|
||||
|
|
@ -328,7 +340,9 @@ def load_speech_pipeline(request):
|
|||
|
||||
async def _raise_tts_error(exc: Exception, r=None) -> None:
|
||||
"""Raise a standardised HTTPException from a TTS provider failure."""
|
||||
code = r.status if r is not None else 500
|
||||
if isinstance(exc, HTTPException):
|
||||
raise exc
|
||||
code = r.status if r is not None and r.status >= 400 else 500
|
||||
# LICENSE covers this Open WebUI error identifier.
|
||||
# Do not alter, remove, obscure, or replace it except as LICENSE permits:
|
||||
# https://docs.openwebui.com/license.
|
||||
|
|
@ -351,8 +365,29 @@ async def _write_tts_cache(
|
|||
audio: bytes,
|
||||
body_path: Path,
|
||||
payload: dict,
|
||||
content_type: str = 'audio/mpeg',
|
||||
) -> None:
|
||||
"""Persist audio + request metadata to the speech cache."""
|
||||
if USE_SLIM:
|
||||
mime_type = content_type.split(';')[0].strip().lower()
|
||||
if mime_type not in {
|
||||
'audio/mpeg',
|
||||
'audio/mp3',
|
||||
'audio/wav',
|
||||
'audio/x-wav',
|
||||
'audio/ogg',
|
||||
'audio/opus',
|
||||
'audio/webm',
|
||||
'audio/flac',
|
||||
'audio/aac',
|
||||
'audio/mp4',
|
||||
}:
|
||||
raise HTTPException(
|
||||
502,
|
||||
f'TTS returned unsupported format {mime_type}. Configure the provider to return MP3, WAV, Ogg, or another browser-playable audio format.',
|
||||
)
|
||||
async with aiofiles.open(file_path.with_suffix('.mime'), 'w') as f:
|
||||
await f.write(content_type)
|
||||
async with aiofiles.open(file_path, 'wb') as f:
|
||||
await f.write(audio)
|
||||
async with aiofiles.open(body_path, 'w') as f:
|
||||
|
|
@ -389,6 +424,10 @@ async def _tts_openai(request, payload, file_path, file_body_path, user):
|
|||
audio_data = await r.read()
|
||||
content_type = r.headers.get('Content-Type', 'audio/mpeg')
|
||||
|
||||
if USE_SLIM:
|
||||
await _write_tts_cache(file_path, audio_data, file_body_path, payload, content_type)
|
||||
return FileResponse(file_path, media_type=content_type)
|
||||
|
||||
if not await asyncio.to_thread(transcode_audio_to_mp3, audio_data, content_type, file_path):
|
||||
async with aiofiles.open(file_path, 'wb') as f:
|
||||
await f.write(audio_data)
|
||||
|
|
@ -430,8 +469,9 @@ async def _tts_elevenlabs(request, payload, file_path, file_body_path, user):
|
|||
ssl=AIOHTTP_CLIENT_SESSION_SSL,
|
||||
) as r:
|
||||
r.raise_for_status()
|
||||
await _write_tts_cache(file_path, await r.read(), file_body_path, payload)
|
||||
return FileResponse(file_path)
|
||||
content_type = r.headers.get('Content-Type', 'audio/mpeg')
|
||||
await _write_tts_cache(file_path, await r.read(), file_body_path, payload, content_type)
|
||||
return FileResponse(file_path, media_type=content_type if USE_SLIM else None)
|
||||
except Exception as exc:
|
||||
log.exception(exc)
|
||||
await _raise_tts_error(exc, r)
|
||||
|
|
@ -465,8 +505,9 @@ async def _tts_azure(request, payload, file_path, file_body_path, user):
|
|||
ssl=AIOHTTP_CLIENT_SESSION_SSL,
|
||||
) as r:
|
||||
r.raise_for_status()
|
||||
await _write_tts_cache(file_path, await r.read(), file_body_path, payload)
|
||||
return FileResponse(file_path)
|
||||
content_type = r.headers.get('Content-Type', 'audio/mpeg')
|
||||
await _write_tts_cache(file_path, await r.read(), file_body_path, payload, content_type)
|
||||
return FileResponse(file_path, media_type=content_type if USE_SLIM else None)
|
||||
except Exception as exc:
|
||||
log.exception(exc)
|
||||
await _raise_tts_error(exc, r)
|
||||
|
|
@ -474,6 +515,8 @@ async def _tts_azure(request, payload, file_path, file_body_path, user):
|
|||
|
||||
async def _tts_transformers(request, payload, file_path, file_body_path, user):
|
||||
"""Generate speech via the local HuggingFace SpeechT5 pipeline (thread-offloaded)."""
|
||||
if USE_SLIM:
|
||||
raise HTTPException(503, 'Configure an external text-to-speech engine. Local TTS is unavailable in slim.')
|
||||
import soundfile as sf
|
||||
import torch
|
||||
|
||||
|
|
@ -558,6 +601,8 @@ _TTS_ENGINES = {
|
|||
@router.post('/speech')
|
||||
async def speech(request: Request, user=Depends(get_verified_user)):
|
||||
engine = await Config.get('audio.tts.engine')
|
||||
if USE_SLIM and engine in ('', 'transformers'):
|
||||
raise HTTPException(503, 'Configure an external text-to-speech engine.')
|
||||
if engine == '':
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
|
|
@ -572,7 +617,10 @@ async def speech(request: Request, user=Depends(get_verified_user)):
|
|||
|
||||
body = await request.body()
|
||||
name = hashlib.sha256(
|
||||
body + str(engine).encode('utf-8') + str(await Config.get('audio.tts.model')).encode('utf-8')
|
||||
body
|
||||
+ str(engine).encode('utf-8')
|
||||
+ str(await Config.get('audio.tts.model')).encode('utf-8')
|
||||
+ (b':slim' if USE_SLIM else b'')
|
||||
).hexdigest()
|
||||
|
||||
file_path = SPEECH_CACHE_DIR.joinpath(f'{name}.mp3')
|
||||
|
|
@ -587,7 +635,11 @@ async def speech(request: Request, user=Depends(get_verified_user)):
|
|||
subject_id=name,
|
||||
data={'engine': engine, 'cached': True},
|
||||
)
|
||||
return FileResponse(file_path)
|
||||
content_type = None
|
||||
if USE_SLIM:
|
||||
async with aiofiles.open(file_path.with_suffix('.mime')) as f:
|
||||
content_type = await f.read()
|
||||
return FileResponse(file_path, media_type=content_type)
|
||||
|
||||
try:
|
||||
payload = JSONCodec.loads(body)
|
||||
|
|
@ -960,7 +1012,11 @@ async def _transcribe_mistral(request, file_path, filename, metadata, file_dir,
|
|||
session = await get_session()
|
||||
if use_chat_completions:
|
||||
audio_file_to_use = file_path
|
||||
if is_audio_conversion_required(file_path):
|
||||
if USE_SLIM and Path(filename).suffix.lower() not in ('.mp3', '.wav'):
|
||||
raise HTTPException(
|
||||
400, 'Mistral chat transcription requires MP3 or WAV in slim; local conversion is unavailable.'
|
||||
)
|
||||
if not BYPASS_PYDUB_PREPROCESSING and is_audio_conversion_required(file_path):
|
||||
log.debug('Converting audio to mp3 for chat completions API')
|
||||
converted_path = await asyncio.to_thread(convert_audio_to_mp3, file_path)
|
||||
if converted_path:
|
||||
|
|
|
|||
|
|
@ -6,6 +6,8 @@ from fastapi.concurrency import run_in_threadpool
|
|||
from open_webui.constants import ERROR_MESSAGES
|
||||
from open_webui.env import MPS_INFERENCE_LOCK
|
||||
from open_webui.events import EVENTS, publish_event
|
||||
from open_webui.env import USE_SLIM
|
||||
from open_webui.retrieval.utils import cosine_similarity
|
||||
from open_webui.internal.db import get_async_session
|
||||
from open_webui.models.config import Config
|
||||
from open_webui.models.feedbacks import (
|
||||
|
|
@ -69,6 +71,8 @@ _embedding_model = None
|
|||
|
||||
def _get_embedding_model():
|
||||
global _embedding_model
|
||||
if USE_SLIM:
|
||||
return None
|
||||
if _embedding_model is None:
|
||||
try:
|
||||
from sentence_transformers import SentenceTransformer
|
||||
|
|
@ -217,6 +221,7 @@ class LeaderboardResponse(BaseModel):
|
|||
|
||||
@router.get('/leaderboard', response_model=LeaderboardResponse)
|
||||
async def get_leaderboard(
|
||||
request: Request,
|
||||
query: Optional[str] = None,
|
||||
user=Depends(get_admin_user),
|
||||
db: AsyncSession = Depends(get_async_session),
|
||||
|
|
@ -226,7 +231,17 @@ async def get_leaderboard(
|
|||
|
||||
similarities = None
|
||||
if query and query.strip():
|
||||
similarities = await run_in_threadpool(_compute_similarities, feedbacks, query.strip())
|
||||
if USE_SLIM:
|
||||
tags = list({tag for feedback in feedbacks for tag in (feedback.data or {}).get('tags', [])})
|
||||
embeddings = await request.app.state.EMBEDDING_FUNCTION([query.strip(), *tags], user=user)
|
||||
scores = cosine_similarity(embeddings[0], embeddings[1:])
|
||||
tag_scores = dict(zip(tags, scores.tolist()))
|
||||
similarities = {
|
||||
feedback.id: max((tag_scores.get(tag, 0) for tag in (feedback.data or {}).get('tags', [])), default=0)
|
||||
for feedback in feedbacks
|
||||
}
|
||||
else:
|
||||
similarities = await run_in_threadpool(_compute_similarities, feedbacks, query.strip())
|
||||
|
||||
elo_stats = _calculate_elo(feedbacks, similarities)
|
||||
tags_by_model = _get_top_tags(feedbacks)
|
||||
|
|
|
|||
|
|
@ -58,6 +58,7 @@ from open_webui.env import (
|
|||
SENTENCE_TRANSFORMERS_CROSS_ENCODER_MODEL_KWARGS,
|
||||
SENTENCE_TRANSFORMERS_CROSS_ENCODER_SIGMOID_ACTIVATION_FUNCTION,
|
||||
SENTENCE_TRANSFORMERS_MODEL_KWARGS,
|
||||
USE_SLIM,
|
||||
)
|
||||
from open_webui.events import EVENTS, publish_event
|
||||
from open_webui.internal.db import get_async_db, get_async_session
|
||||
|
|
@ -82,7 +83,7 @@ from open_webui.retrieval.utils import (
|
|||
query_doc_with_hybrid_search,
|
||||
)
|
||||
from open_webui.retrieval.vector.async_client import ASYNC_VECTOR_DB_CLIENT
|
||||
from open_webui.retrieval.vector.factory import VECTOR_DB_CLIENT
|
||||
from open_webui.retrieval.vector.factory import get_vector_db_client
|
||||
from open_webui.retrieval.vector.utils import filter_metadata
|
||||
from open_webui.retrieval.web.azure import search_azure
|
||||
from open_webui.retrieval.web.bing import search_bing
|
||||
|
|
@ -150,7 +151,7 @@ def get_ef(
|
|||
auto_update: bool = RAG_EMBEDDING_MODEL_AUTO_UPDATE,
|
||||
):
|
||||
ef = None
|
||||
if embedding_model and engine == '':
|
||||
if embedding_model and engine == '' and not USE_SLIM:
|
||||
from sentence_transformers import SentenceTransformer
|
||||
|
||||
try:
|
||||
|
|
@ -178,6 +179,17 @@ def get_rf(
|
|||
rf = None
|
||||
# Convert timeout string to int or None (system default)
|
||||
timeout_value = int(external_reranker_timeout) if external_reranker_timeout else None
|
||||
if reranking_model and engine == 'external':
|
||||
from open_webui.retrieval.models.external import ExternalReranker
|
||||
|
||||
return ExternalReranker(
|
||||
url=external_reranker_url,
|
||||
api_key=external_reranker_api_key,
|
||||
model=reranking_model,
|
||||
timeout=timeout_value,
|
||||
)
|
||||
if USE_SLIM:
|
||||
return None
|
||||
if reranking_model:
|
||||
if any(model in reranking_model for model in ['jinaai/jina-colbert-v2']):
|
||||
try:
|
||||
|
|
@ -192,55 +204,39 @@ def get_rf(
|
|||
log.error(f'ColBERT: {e}')
|
||||
raise Exception(ERROR_MESSAGES.DEFAULT(e, 'Error loading reranking model'))
|
||||
else:
|
||||
if engine == 'external':
|
||||
try:
|
||||
from open_webui.retrieval.models.external import ExternalReranker
|
||||
import sentence_transformers
|
||||
import torch
|
||||
|
||||
rf = ExternalReranker(
|
||||
url=external_reranker_url,
|
||||
api_key=external_reranker_api_key,
|
||||
model=reranking_model,
|
||||
timeout=timeout_value,
|
||||
)
|
||||
except Exception as e:
|
||||
log.error(f'ExternalReranking: {e}')
|
||||
raise Exception(ERROR_MESSAGES.DEFAULT(e, 'Error loading reranking model'))
|
||||
else:
|
||||
import sentence_transformers
|
||||
import torch
|
||||
try:
|
||||
rf = sentence_transformers.CrossEncoder(
|
||||
get_model_path(reranking_model, auto_update),
|
||||
device=DEVICE_TYPE,
|
||||
trust_remote_code=RAG_RERANKING_MODEL_TRUST_REMOTE_CODE,
|
||||
backend=SENTENCE_TRANSFORMERS_CROSS_ENCODER_BACKEND,
|
||||
model_kwargs=SENTENCE_TRANSFORMERS_CROSS_ENCODER_MODEL_KWARGS,
|
||||
activation_fn=(
|
||||
torch.nn.Sigmoid() if SENTENCE_TRANSFORMERS_CROSS_ENCODER_SIGMOID_ACTIVATION_FUNCTION else None
|
||||
),
|
||||
)
|
||||
except Exception as e:
|
||||
log.error(f'CrossEncoder: {e}')
|
||||
raise Exception(ERROR_MESSAGES.DEFAULT(e, 'CrossEncoder error'))
|
||||
|
||||
try:
|
||||
rf = sentence_transformers.CrossEncoder(
|
||||
get_model_path(reranking_model, auto_update),
|
||||
device=DEVICE_TYPE,
|
||||
trust_remote_code=RAG_RERANKING_MODEL_TRUST_REMOTE_CODE,
|
||||
backend=SENTENCE_TRANSFORMERS_CROSS_ENCODER_BACKEND,
|
||||
model_kwargs=SENTENCE_TRANSFORMERS_CROSS_ENCODER_MODEL_KWARGS,
|
||||
activation_fn=(
|
||||
torch.nn.Sigmoid()
|
||||
if SENTENCE_TRANSFORMERS_CROSS_ENCODER_SIGMOID_ACTIVATION_FUNCTION
|
||||
else None
|
||||
),
|
||||
)
|
||||
except Exception as e:
|
||||
log.error(f'CrossEncoder: {e}')
|
||||
raise Exception(ERROR_MESSAGES.DEFAULT(e, 'CrossEncoder error'))
|
||||
|
||||
# Safely adjust pad_token_id if missing as some models do not have this in config
|
||||
try:
|
||||
model_cfg = getattr(rf, 'model', None)
|
||||
if model_cfg and hasattr(model_cfg, 'config'):
|
||||
cfg = model_cfg.config
|
||||
if getattr(cfg, 'pad_token_id', None) is None:
|
||||
# Fallback to eos_token_id when available
|
||||
eos = getattr(cfg, 'eos_token_id', None)
|
||||
if eos is not None:
|
||||
cfg.pad_token_id = eos
|
||||
log.debug('Missing pad_token_id detected; set to eos_token_id=%s', eos)
|
||||
else:
|
||||
log.warning('Neither pad_token_id nor eos_token_id present in model config')
|
||||
except Exception as e2:
|
||||
log.warning(f'Failed to adjust pad_token_id on CrossEncoder: {e2}')
|
||||
# Safely adjust pad_token_id if missing as some models do not have this in config
|
||||
try:
|
||||
model_cfg = getattr(rf, 'model', None)
|
||||
if model_cfg and hasattr(model_cfg, 'config'):
|
||||
cfg = model_cfg.config
|
||||
if getattr(cfg, 'pad_token_id', None) is None:
|
||||
# Fallback to eos_token_id when available
|
||||
eos = getattr(cfg, 'eos_token_id', None)
|
||||
if eos is not None:
|
||||
cfg.pad_token_id = eos
|
||||
log.debug('Missing pad_token_id detected; set to eos_token_id=%s', eos)
|
||||
else:
|
||||
log.warning('Neither pad_token_id nor eos_token_id present in model config')
|
||||
except Exception as e2:
|
||||
log.warning(f'Failed to adjust pad_token_id on CrossEncoder: {e2}')
|
||||
|
||||
return rf
|
||||
|
||||
|
|
@ -535,6 +531,8 @@ async def unload_embedding_model(request: Request):
|
|||
|
||||
@router.post('/embedding/update')
|
||||
async def update_embedding_config(request: Request, form_data: EmbeddingModelUpdateForm, user=Depends(get_admin_user)):
|
||||
if USE_SLIM and form_data.RAG_EMBEDDING_ENGINE == '':
|
||||
raise HTTPException(400, 'Slim requires an external embedding engine (openai, ollama, azure_openai).')
|
||||
config = await get_retrieval_config()
|
||||
log.info('Updating embedding model: %s to %s', config.RAG_EMBEDDING_MODEL, form_data.RAG_EMBEDDING_MODEL)
|
||||
await unload_embedding_model(request)
|
||||
|
|
@ -952,6 +950,44 @@ class ConfigForm(BaseModel):
|
|||
async def update_rag_config(request: Request, form_data: ConfigForm, user=Depends(get_admin_user)):
|
||||
# RAG settings
|
||||
config = await get_retrieval_config()
|
||||
if USE_SLIM:
|
||||
if form_data.web:
|
||||
web_engine = (
|
||||
form_data.web.WEB_LOADER_ENGINE
|
||||
if form_data.web.WEB_LOADER_ENGINE is not None
|
||||
else config.WEB_LOADER_ENGINE
|
||||
)
|
||||
browser_url = (
|
||||
form_data.web.PLAYWRIGHT_WS_URL
|
||||
if form_data.web.PLAYWRIGHT_WS_URL is not None
|
||||
else config.PLAYWRIGHT_WS_URL
|
||||
)
|
||||
if (
|
||||
web_engine == 'playwright'
|
||||
and not browser_url
|
||||
and (web_engine != config.WEB_LOADER_ENGINE or browser_url != config.PLAYWRIGHT_WS_URL)
|
||||
):
|
||||
raise HTTPException(400, 'Configure PLAYWRIGHT_WS_URL. Slim requires a remote browser.')
|
||||
if form_data.TEXT_SPLITTER == 'token_transformers' and config.TEXT_SPLITTER != 'token_transformers':
|
||||
raise HTTPException(
|
||||
400, 'Transformers tokenization is unavailable in slim. Use character or token splitting.'
|
||||
)
|
||||
reranker_engine = (
|
||||
form_data.RAG_RERANKING_ENGINE
|
||||
if form_data.RAG_RERANKING_ENGINE is not None
|
||||
else config.RAG_RERANKING_ENGINE
|
||||
)
|
||||
reranker_model = (
|
||||
form_data.RAG_RERANKING_MODEL if form_data.RAG_RERANKING_MODEL is not None else config.RAG_RERANKING_MODEL
|
||||
)
|
||||
if (
|
||||
reranker_engine != 'external'
|
||||
and reranker_model
|
||||
and (reranker_engine != config.RAG_RERANKING_ENGINE or reranker_model != config.RAG_RERANKING_MODEL)
|
||||
):
|
||||
raise HTTPException(
|
||||
400, 'Slim requires an external reranker, or an empty reranking model for cosine scoring.'
|
||||
)
|
||||
config.RAG_TEMPLATE = form_data.RAG_TEMPLATE if form_data.RAG_TEMPLATE is not None else config.RAG_TEMPLATE
|
||||
config.TOP_K = form_data.TOP_K if form_data.TOP_K is not None else config.TOP_K
|
||||
config.BYPASS_EMBEDDING_AND_RETRIEVAL = (
|
||||
|
|
@ -1589,6 +1625,8 @@ def merge_docs_to_target_size(
|
|||
|
||||
|
||||
def get_transformers_tokenizer(request: Request, config: RetrievalConfig):
|
||||
if USE_SLIM:
|
||||
raise HTTPException(503, 'Transformers tokenization is unavailable in slim. Use character or token splitting.')
|
||||
if config.RAG_TOKENIZER_MODEL:
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
|
|
@ -1673,7 +1711,7 @@ def save_docs_to_vector_db(
|
|||
|
||||
# Check if entries with the same hash (metadata.hash) already exist
|
||||
if metadata and 'hash' in metadata:
|
||||
result = VECTOR_DB_CLIENT.query(
|
||||
result = get_vector_db_client().query(
|
||||
collection_name=collection_name,
|
||||
filter={'hash': metadata['hash']},
|
||||
)
|
||||
|
|
@ -1773,11 +1811,11 @@ def save_docs_to_vector_db(
|
|||
]
|
||||
|
||||
try:
|
||||
if VECTOR_DB_CLIENT.has_collection(collection_name=collection_name):
|
||||
if get_vector_db_client().has_collection(collection_name=collection_name):
|
||||
log.info('collection %s already exists', collection_name)
|
||||
|
||||
if overwrite:
|
||||
VECTOR_DB_CLIENT.delete_collection(collection_name=collection_name)
|
||||
get_vector_db_client().delete_collection(collection_name=collection_name)
|
||||
log.info('deleting existing collection %s', collection_name)
|
||||
elif add is False:
|
||||
log.info('collection %s already exists, overwrite is False and add is False', collection_name)
|
||||
|
|
@ -1840,7 +1878,7 @@ def save_docs_to_vector_db(
|
|||
]
|
||||
|
||||
log.info('adding to collection %s', collection_name)
|
||||
VECTOR_DB_CLIENT.insert(
|
||||
get_vector_db_client().insert(
|
||||
collection_name=collection_name,
|
||||
items=items,
|
||||
)
|
||||
|
|
@ -3035,7 +3073,7 @@ async def query_doc_handler(
|
|||
query_embedding = await request.app.state.EMBEDDING_FUNCTION(
|
||||
form_data.query, prefix=RAG_EMBEDDING_QUERY_PREFIX, user=user
|
||||
)
|
||||
# query_doc wraps a blocking VECTOR_DB_CLIENT.search call;
|
||||
# query_doc wraps a blocking get_vector_db_client().search call;
|
||||
# offload so the request's event loop stays responsive.
|
||||
return await asyncio.to_thread(
|
||||
query_doc,
|
||||
|
|
|
|||
|
|
@ -1,54 +0,0 @@
|
|||
# Minimal requirements for backend to run
|
||||
# WIP: use this as a reference to build a minimal docker image
|
||||
|
||||
fastapi==0.136.3
|
||||
uvicorn[standard]==0.51.0
|
||||
pydantic==2.13.4
|
||||
python-multipart==0.0.32
|
||||
itsdangerous==2.2.0
|
||||
|
||||
python-socketio==5.16.2
|
||||
orjson==3.11.9
|
||||
cryptography
|
||||
bcrypt==5.0.0
|
||||
argon2-cffi==25.1.0
|
||||
PyJWT[crypto]==2.13.0
|
||||
authlib==1.7.2
|
||||
joserfc==1.7.4
|
||||
|
||||
requests==2.34.2
|
||||
aiohttp==3.13.5 # do not update to 3.13.3 - broken
|
||||
aiocache
|
||||
aiofiles
|
||||
starlette-compress==1.7.1
|
||||
Brotli==1.2.0
|
||||
brotlicffi==1.2.0.1
|
||||
httpx[socks,http2,zstd,cli,brotli]==0.28.1
|
||||
starsessions[redis]==2.2.1
|
||||
|
||||
sqlalchemy==2.0.50
|
||||
aiosqlite==0.22.1
|
||||
psycopg[binary]==3.3.4
|
||||
alembic==1.18.4
|
||||
|
||||
pycrdt==0.13.1
|
||||
redis
|
||||
hiredis
|
||||
|
||||
loguru==0.7.3
|
||||
asgiref==3.11.1
|
||||
|
||||
mcp==1.27.2
|
||||
openai
|
||||
|
||||
langchain-community==0.4.2
|
||||
langchain-classic==1.0.7
|
||||
langchain-text-splitters==1.1.2
|
||||
|
||||
fake-useragent==2.2.0
|
||||
|
||||
chromadb==1.5.9
|
||||
black==26.5.1
|
||||
pydub
|
||||
chardet==7.4.3
|
||||
beautifulsoup4
|
||||
122
backend/requirements-slim.txt
Normal file
122
backend/requirements-slim.txt
Normal file
|
|
@ -0,0 +1,122 @@
|
|||
# External-services image. Keep shared package pins aligned with requirements.txt.
|
||||
# NumPy is used for cosine scoring; pandas is required by the remote Milvus client.
|
||||
numpy==2.4.6
|
||||
typer==0.25.1
|
||||
fastapi==0.136.3
|
||||
uvicorn[standard]==0.51.0
|
||||
pydantic==2.13.4
|
||||
python-multipart==0.0.32
|
||||
itsdangerous==2.2.0
|
||||
|
||||
python-socketio==5.16.2
|
||||
orjson==3.11.9
|
||||
cryptography==48.0.0
|
||||
bcrypt==5.0.0
|
||||
argon2-cffi==25.1.0
|
||||
PyJWT[crypto]==2.13.0
|
||||
authlib==1.7.2
|
||||
joserfc==1.7.4
|
||||
|
||||
requests==2.34.2
|
||||
regex==2026.5.9 # supports a per-search timeout, which `re` does not
|
||||
aiohttp==3.13.5 # do not update to 3.13.3 - broken
|
||||
aiodns==3.6.1 # keep pinned: 4.x pulls pycares 5 (c-ares 1.34.6) which breaks DNS on some hosts (#28013, #28215); opt-in via AIOHTTP_CLIENT_ASYNC_DNS_RESOLVER
|
||||
aiocache==0.12.3
|
||||
aiofiles==25.1.0
|
||||
starlette-compress==1.7.1
|
||||
Brotli==1.2.0
|
||||
httpx[socks,http2,zstd,cli,brotli]==0.28.1
|
||||
starsessions[redis]==2.2.1
|
||||
python-mimeparse==2.0.0
|
||||
|
||||
sqlalchemy[asyncio]==2.0.50
|
||||
aiosqlite==0.22.1
|
||||
psycopg[binary]==3.3.4
|
||||
alembic==1.18.4
|
||||
|
||||
pycrdt==0.13.1
|
||||
redis==8.0.1
|
||||
hiredis==3.4.0
|
||||
|
||||
APScheduler==3.11.2
|
||||
pytz==2026.2
|
||||
|
||||
loguru==0.7.3
|
||||
asgiref==3.11.1
|
||||
|
||||
# AI libraries
|
||||
tiktoken==0.13.0
|
||||
mcp==1.27.2
|
||||
|
||||
openai==2.29.0
|
||||
|
||||
langchain-community==0.4.2
|
||||
langchain-classic==1.0.7
|
||||
langchain-text-splitters==1.1.2
|
||||
|
||||
fake-useragent==2.2.0
|
||||
chromadb-client==1.5.9
|
||||
weaviate-client==4.20.3
|
||||
opensearch-py==3.2.0
|
||||
|
||||
ftfy==6.3.1
|
||||
chardet==7.4.3
|
||||
fpdf2==2.8.7
|
||||
|
||||
Markdown==3.10.2
|
||||
beautifulsoup4==4.14.3
|
||||
lxml==6.1.1
|
||||
pandas==3.0.3
|
||||
validators==0.35.0
|
||||
psutil==7.2.2
|
||||
|
||||
pillow==12.2.0
|
||||
rank-bm25==0.2.2
|
||||
|
||||
black==26.5.1
|
||||
youtube-transcript-api==1.2.4
|
||||
|
||||
ddgs==9.14.4
|
||||
|
||||
azure-ai-documentintelligence==1.0.2
|
||||
azure-identity==1.25.3
|
||||
azure-storage-blob==12.29.0
|
||||
azure-search-documents==12.0.0
|
||||
|
||||
## Google Cloud storage
|
||||
|
||||
googleapis-common-protos==1.75.0
|
||||
google-cloud-storage==3.9.0
|
||||
|
||||
## Databases
|
||||
psycopg2-binary==2.9.12
|
||||
pgvector==0.4.2
|
||||
|
||||
PyMySQL==1.2.0
|
||||
boto3==1.42.62
|
||||
# mariadb==1.1.14 should be added if you want to support MariaDB
|
||||
# valkey-glide-sync==2.3.1 # optional: install manually if VECTOR_DB=valkey
|
||||
|
||||
pymilvus==2.6.14
|
||||
qdrant-client==1.18.0
|
||||
playwright==1.60.0 # Caution: version must match docker-compose.playwright.yaml - Update the docker-compose.yaml if necessary
|
||||
elasticsearch==9.4.1
|
||||
pinecone==6.0.2
|
||||
oracledb==3.4.2
|
||||
|
||||
## LDAP
|
||||
ldap3==2.9.1
|
||||
|
||||
## Trace
|
||||
opentelemetry-api==1.42.1
|
||||
opentelemetry-sdk==1.42.1
|
||||
opentelemetry-exporter-otlp==1.42.1
|
||||
opentelemetry-instrumentation==0.63b1
|
||||
opentelemetry-instrumentation-fastapi==0.63b1
|
||||
opentelemetry-instrumentation-sqlalchemy==0.63b1
|
||||
opentelemetry-instrumentation-redis==0.63b1
|
||||
opentelemetry-instrumentation-requests==0.63b1
|
||||
opentelemetry-instrumentation-logging==0.63b1
|
||||
opentelemetry-instrumentation-httpx==0.63b1
|
||||
opentelemetry-instrumentation-aiohttp-client==0.63b1
|
||||
opentelemetry-instrumentation-system-metrics==0.63b1
|
||||
|
|
@ -11,14 +11,14 @@ set -euo pipefail
|
|||
# expansion. The two can't be combined inline (`${VAR:-default,,}` makes
|
||||
# the default literal `,,`), so we normalise once up front and the simple
|
||||
# `${VAR,,}` form stays safe under `set -u` everywhere else.
|
||||
: "${WEB_LOADER_ENGINE:=}" "${USE_OLLAMA_DOCKER:=}" "${USE_CUDA_DOCKER:=}"
|
||||
: "${USE_SLIM_DOCKER:=}" "${WEB_LOADER_ENGINE:=}" "${USE_OLLAMA_DOCKER:=}" "${USE_CUDA_DOCKER:=}"
|
||||
|
||||
SCRIPT_DIR=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)
|
||||
cd "$SCRIPT_DIR" || exit 1
|
||||
|
||||
# ── Playwright browser installation (if configured) ──────────────────────────
|
||||
|
||||
if [[ "${WEB_LOADER_ENGINE,,}" == "playwright" ]]; then
|
||||
if [[ "${USE_SLIM_DOCKER,,}" != "true" && "${WEB_LOADER_ENGINE,,}" == "playwright" ]]; then
|
||||
if [[ -z "${PLAYWRIGHT_WS_URL:-}" ]]; then
|
||||
echo "Installing Playwright Chromium browser..."
|
||||
playwright install chromium
|
||||
|
|
|
|||
|
|
@ -245,6 +245,13 @@
|
|||
}}
|
||||
>
|
||||
<h2 class="text-sm font-medium text-gray-900 dark:text-white mb-4">{$i18n.t('Audio')}</h2>
|
||||
{#if $config?.features?.slim === true}
|
||||
<p class="mb-4 text-xs text-gray-500">
|
||||
{$i18n.t(
|
||||
'Slim requires external speech providers. Local speech models and audio conversion are unavailable.'
|
||||
)}
|
||||
</p>
|
||||
{/if}
|
||||
|
||||
<div class="flex-1 min-h-0 overflow-y-auto scrollbar-hover pr-1.5">
|
||||
<AdminSettingSection title={$i18n.t('Speech-to-Text')} first>
|
||||
|
|
@ -253,7 +260,9 @@
|
|||
description={$i18n.t('Choose the transcription provider used for audio input.')}
|
||||
>
|
||||
<SettingsSelect bind:value={STT_ENGINE} placeholder={$i18n.t('Select an engine')}>
|
||||
<option value="">{$i18n.t('Whisper (Local)')}</option>
|
||||
<option value="" disabled={$config?.features?.slim === true}
|
||||
>{$i18n.t('Whisper (Local)')}</option
|
||||
>
|
||||
<option value="openai">{$i18n.t('OpenAI')}</option>
|
||||
<option value="web">{$i18n.t('Web API')}</option>
|
||||
<option value="deepgram">{$i18n.t('Deepgram')}</option>
|
||||
|
|
@ -500,7 +509,9 @@
|
|||
}}
|
||||
>
|
||||
<option value="">{$i18n.t('Web API')}</option>
|
||||
<option value="transformers">{$i18n.t('Transformers')} ({$i18n.t('Local')})</option>
|
||||
<option value="transformers" disabled={$config?.features?.slim === true}
|
||||
>{$i18n.t('Transformers')} ({$i18n.t('Local')})</option
|
||||
>
|
||||
<option value="openai">{$i18n.t('OpenAI')}</option>
|
||||
<option value="elevenlabs">{$i18n.t('ElevenLabs')}</option>
|
||||
<option value="azure">{$i18n.t('Azure AI Speech')}</option>
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
<script lang="ts">
|
||||
import { config } from '$lib/stores';
|
||||
import { toast } from 'svelte-sonner';
|
||||
|
||||
import { onMount, getContext, createEventDispatcher } from 'svelte';
|
||||
|
|
@ -421,6 +422,13 @@
|
|||
}}
|
||||
>
|
||||
<h2 class="text-sm font-medium text-gray-900 dark:text-white mb-4">{$i18n.t('Documents')}</h2>
|
||||
{#if $config?.features?.slim === true}
|
||||
<p class="mb-4 text-xs text-gray-500">
|
||||
{$i18n.t(
|
||||
'Slim requires external services for embeddings, vector storage, and document extraction. Basic text files can be read locally.'
|
||||
)}
|
||||
</p>
|
||||
{/if}
|
||||
|
||||
{#if RAGConfig}
|
||||
<div class="flex-1 min-h-0 overflow-y-auto scrollbar-hover pr-1.5">
|
||||
|
|
@ -430,7 +438,11 @@
|
|||
description={$i18n.t('Choose how uploaded documents are parsed before indexing.')}
|
||||
>
|
||||
<SettingsSelect bind:value={RAGConfig.CONTENT_EXTRACTION_ENGINE}>
|
||||
<option value="">{$i18n.t('Default')}</option>
|
||||
<option value=""
|
||||
>{$config?.features?.slim === true
|
||||
? $i18n.t('Basic text only')
|
||||
: $i18n.t('Default')}</option
|
||||
>
|
||||
<option value="external">{$i18n.t('External')}</option>
|
||||
<option value="tika">{$i18n.t('Tika')}</option>
|
||||
<option value="docling">{$i18n.t('Docling')}</option>
|
||||
|
|
@ -455,7 +467,7 @@
|
|||
/>
|
||||
</AdminSettingField>
|
||||
|
||||
{#if RAGConfig.CONTENT_EXTRACTION_ENGINE === ''}
|
||||
{#if RAGConfig.CONTENT_EXTRACTION_ENGINE === '' && $config?.features?.slim !== true}
|
||||
<AdminSettingRow
|
||||
label={$i18n.t('PDF Extract Images (OCR)')}
|
||||
description={$i18n.t('Extract images from PDFs so OCR can process image-only pages.')}
|
||||
|
|
@ -908,7 +920,7 @@
|
|||
<SettingsSelect bind:value={RAGConfig.TEXT_SPLITTER}>
|
||||
<option value="">{$i18n.t('Default')} ({$i18n.t('Character')})</option>
|
||||
<option value="token">{$i18n.t('Token')} ({$i18n.t('Tiktoken')})</option>
|
||||
<option value="token_transformers">
|
||||
<option value="token_transformers" disabled={$config?.features?.slim === true}>
|
||||
{$i18n.t('Token')} ({$i18n.t('Transformers')})
|
||||
</option>
|
||||
</SettingsSelect>
|
||||
|
|
@ -1012,7 +1024,9 @@
|
|||
}
|
||||
}}
|
||||
>
|
||||
<option value="">{$i18n.t('Default (SentenceTransformers)')}</option>
|
||||
<option value="" disabled={$config?.features?.slim === true}
|
||||
>{$i18n.t('Default (SentenceTransformers)')}</option
|
||||
>
|
||||
<option value="ollama">{$i18n.t('Ollama')}</option>
|
||||
<option value="openai">{$i18n.t('OpenAI')}</option>
|
||||
<option value="azure_openai">{$i18n.t('Azure OpenAI')}</option>
|
||||
|
|
@ -1251,7 +1265,9 @@
|
|||
}
|
||||
}}
|
||||
>
|
||||
<option value="">{$i18n.t('Default (SentenceTransformers)')}</option>
|
||||
<option value="" disabled={$config?.features?.slim === true}
|
||||
>{$i18n.t('Default (SentenceTransformers)')}</option
|
||||
>
|
||||
<option value="external">{$i18n.t('External')}</option>
|
||||
</SettingsSelect>
|
||||
</AdminSettingRow>
|
||||
|
|
|
|||
|
|
@ -336,6 +336,7 @@ type Config = {
|
|||
default_prompt_suggestions: PromptSuggestion[];
|
||||
default_prompt_suggestions_i18n?: Record<string, { suggestion_prompts: PromptSuggestion[] }>;
|
||||
features: {
|
||||
slim?: boolean;
|
||||
auth: boolean;
|
||||
auth_trusted_header: boolean;
|
||||
enable_api_keys: boolean;
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue