# syntax=docker/dockerfile:1 # Initialize device type args # use build args in the docker build commmand with --build-arg="BUILDARG=true" ARG USE_CUDA=false ARG USE_OLLAMA=false # Tested with cu117 for CUDA 11 and cu121 for CUDA 12 (default) ARG USE_CUDA_VER=cu121 # any sentence transformer model; models to use can be found at https://huggingface.co/models?library=sentence-transformers # Leaderboard: https://huggingface.co/spaces/mteb/leaderboard # for better performance and multilangauge support use "intfloat/multilingual-e5-large" (~2.5GB) or "intfloat/multilingual-e5-base" (~1.5GB) # IMPORTANT: If you change the 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 BUILD_HASH=dev-build # Override at your own risk - non-root configurations are untested ARG UID=0 ARG GID=0 ######## WebUI frontend ######## FROM --platform=$BUILDPLATFORM node:22-alpine3.20 AS build ARG BUILD_HASH WORKDIR /app COPY package.json package-lock.json ./ RUN npm ci COPY . . ENV APP_BUILD_HASH=${BUILD_HASH} ENV NODE_OPTIONS="--max_old_space_size=8192" RUN npm run build ######## WebUI backend ######## FROM python:3.11-slim-bookworm AS base # Use args ARG USE_CUDA ARG USE_OLLAMA ARG USE_CUDA_VER ARG USE_EMBEDDING_MODEL ARG USE_RERANKING_MODEL ARG UID ARG GID ## Basis ## ENV ENV=prod \ PORT=8080 \ # pass build args to the build USE_OLLAMA_DOCKER=${USE_OLLAMA} \ USE_CUDA_DOCKER=${USE_CUDA} \ USE_CUDA_DOCKER_VER=${USE_CUDA_VER} \ USE_EMBEDDING_MODEL_DOCKER=${USE_EMBEDDING_MODEL} \ USE_RERANKING_MODEL_DOCKER=${USE_RERANKING_MODEL} ## Basis URL Config ## ENV OLLAMA_BASE_URL="/ollama" \ OPENAI_API_BASE_URL="" ## API Key and Security Config ## ENV OPENAI_API_KEY="" \ WEBUI_SECRET_KEY="" \ SCARF_NO_ANALYTICS=true \ DO_NOT_TRACK=true \ ANONYMIZED_TELEMETRY=false #### Other models ######################################################### ## whisper TTS model settings ## ENV WHISPER_MODEL="base" \ WHISPER_MODEL_DIR="/app/backend/data/cache/whisper/models" ## RAG Embedding model settings ## ENV RAG_EMBEDDING_MODEL="$USE_EMBEDDING_MODEL_DOCKER" \ RAG_RERANKING_MODEL="$USE_RERANKING_MODEL_DOCKER" \ SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models" ## Hugging Face download cache ## ENV HF_HOME="/app/backend/data/cache/embedding/models" ## Torch Extensions ## # ENV TORCH_EXTENSIONS_DIR="/.cache/torch_extensions" #### Other models ########################################################## WORKDIR /app/backend ENV HOME=/root # Create user and group if not root RUN if [ $UID -ne 0 ]; then \ if [ $GID -ne 0 ]; then \ addgroup --gid $GID app; \ fi; \ adduser --uid $UID --gid $GID --home $HOME --disabled-password --no-create-home app; \ fi RUN mkdir -p $HOME/.cache/chroma RUN echo -n 00000000-0000-0000-0000-000000000000 > $HOME/.cache/chroma/telemetry_user_id # Make sure the user has access to the app and root directory RUN chown -R $UID:$GID /app $HOME RUN if [ "$USE_OLLAMA" = "true" ]; then \ apt-get update && \ # Install pandoc and netcat apt-get install -y --no-install-recommends git build-essential pandoc netcat-openbsd curl && \ apt-get install -y --no-install-recommends gcc python3-dev && \ # for RAG OCR apt-get install -y --no-install-recommends ffmpeg libsm6 libxext6 && \ # install helper tools apt-get install -y --no-install-recommends curl jq && \ # install ollama curl -fsSL https://ollama.com/install.sh | sh && \ # cleanup rm -rf /var/lib/apt/lists/*; \ else \ apt-get update && \ # Install pandoc, netcat and gcc apt-get install -y --no-install-recommends git build-essential pandoc gcc netcat-openbsd curl jq && \ apt-get install -y --no-install-recommends gcc python3-dev && \ # for RAG OCR apt-get install -y --no-install-recommends ffmpeg libsm6 libxext6 && \ # cleanup rm -rf /var/lib/apt/lists/*; \ fi # install python dependencies COPY --chown=$UID:$GID ./backend/requirements.txt ./requirements.txt RUN pip3 install uv && \ if [ "$USE_CUDA" = "true" ]; then \ # If you use CUDA the whisper and embedding model will be downloaded on first use pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir && \ uv pip install --system -r requirements.txt --no-cache-dir && \ python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \ python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"; \ else \ pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \ uv pip install --system -r requirements.txt --no-cache-dir && \ python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \ python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"; \ fi; \ chown -R $UID:$GID /app/backend/data/ # copy embedding weight from build # RUN mkdir -p /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2 # COPY --from=build /app/onnx /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2/onnx # copy built frontend files COPY --chown=$UID:$GID --from=build /app/build /app/build COPY --chown=$UID:$GID --from=build /app/CHANGELOG.md /app/CHANGELOG.md COPY --chown=$UID:$GID --from=build /app/package.json /app/package.json # copy backend files COPY --chown=$UID:$GID ./backend . EXPOSE 8080 HEALTHCHECK CMD curl --silent --fail http://localhost:${PORT:-8080}/health | jq -ne 'input.status == true' || exit 1 USER $UID:$GID ARG BUILD_HASH ENV WEBUI_BUILD_VERSION=${BUILD_HASH} ENV DOCKER=true CMD [ "bash", "start.sh"]