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Dockerfile
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Dockerfile
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# 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=""
# Override at your own risk - non-root configurations are untested
ARG UID=0
ARG GID=0
######## WebUI frontend ########
FROM --platform=$BUILDPLATFORM node:21-alpine3.19 as build
WORKDIR /app
COPY package.json package-lock.json ./
RUN npm ci
COPY . .
RUN npm run build
######## WebUI backend ########
FROM python:3.11-slim-bookworm as base
# Use args
ARG USE_CUDA
ARG USE_OLLAMA
ARG USE_CUDA_VER
ARG USE_EMBEDDING_MODEL
ARG USE_RERANKING_MODEL
ARG UID
ARG GID
## Basis ##
ENV ENV=prod \
PORT=8080 \
# pass build args to the build
USE_OLLAMA_DOCKER=${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
# Use locally bundled version of the LiteLLM cost map json
# to avoid repetitive startup connections
ENV LITELLM_LOCAL_MODEL_COST_MAP="True"
#### 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"
#### 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 pandoc netcat-openbsd curl && \
# 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 and netcat
apt-get install -y --no-install-recommends pandoc netcat-openbsd curl jq && \
# 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
# 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:8080/health | jq -e '.status == true' || exit 1
USER $UID:$GID
CMD [ "bash", "start.sh"]