mirror of https://github.com/open-webui/open-webui
134 lines
5.8 KiB
Docker
134 lines
5.8 KiB
Docker
# 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_MPS=false
|
|
ARG INCLUDE_OLLAMA=false
|
|
|
|
######## WebUI frontend ########
|
|
FROM node:21-alpine3.19 as build
|
|
|
|
WORKDIR /app
|
|
|
|
#RUN apt-get update \
|
|
# && apt-get install -y --no-install-recommends wget \
|
|
# # cleanup
|
|
# && rm -rf /var/lib/apt/lists/*
|
|
|
|
# wget embedding model weight from alpine (does not exist from slim-buster)
|
|
#RUN wget "https://chroma-onnx-models.s3.amazonaws.com/all-MiniLM-L6-v2/onnx.tar.gz" -O - | \
|
|
# tar -xzf - -C /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_MPS
|
|
ARG INCLUDE_OLLAMA
|
|
|
|
## Basis ##
|
|
ENV ENV=prod \
|
|
PORT=8080 \
|
|
INCLUDE_OLLAMA_ENV=${INCLUDE_OLLAMA}
|
|
|
|
## 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
|
|
|
|
#### Preloaded models #########################################################
|
|
## whisper TTS Settings ##
|
|
ENV WHISPER_MODEL="base" \
|
|
WHISPER_MODEL_DIR="/app/backend/data/cache/whisper/models"
|
|
|
|
## RAG Embedding Model Settings ##
|
|
# 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 default model (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.
|
|
ENV RAG_EMBEDDING_MODEL="all-MiniLM-L6-v2" \
|
|
RAG_EMBEDDING_MODEL_DIR="/app/backend/data/cache/embedding/models" \
|
|
SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models" \
|
|
# device type for whisper tts and embbeding models - "cpu" (default) or "mps" (apple silicon) - choosing this right can lead to better performance
|
|
# Important:
|
|
# If you want to use CUDA you need to install the nvidia-container-toolkit (https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
|
|
# you can set this to "cuda" but its recomended to use --build-arg CUDA_ENABLED=true flag when building the image
|
|
RAG_EMBEDDING_MODEL_DEVICE_TYPE="cpu" \
|
|
DEVICE_COMPUTE_TYPE="int8"
|
|
# device type for whisper tts and embbeding models - "cpu" (default), "cuda" (nvidia gpu and CUDA required) or "mps" (apple silicon) - choosing this right can lead to better performance
|
|
#### Preloaded models ##########################################################
|
|
|
|
WORKDIR /app/backend
|
|
# install python dependencies
|
|
COPY ./backend/requirements.txt ./requirements.txt
|
|
|
|
RUN if [ "$USE_CUDA" = "true" ]; then \
|
|
export DEVICE_TYPE="cuda" && \
|
|
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 --no-cache-dir && \
|
|
pip3 install -r requirements.txt --no-cache-dir; \
|
|
elif [ "$USE_MPS" = "true" ]; then \
|
|
export DEVICE_TYPE="mps" && \
|
|
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \
|
|
pip3 install -r requirements.txt --no-cache-dir && \
|
|
python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])" && \
|
|
python -c "import os; from chromadb.utils import embedding_functions; sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=os.environ['RAG_EMBEDDING_MODEL'], device=os.environ['DEVICE_TYPE'])"; \
|
|
else \
|
|
export DEVICE_TYPE="cpu" && \
|
|
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \
|
|
pip3 install -r requirements.txt --no-cache-dir && \
|
|
python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])" && \
|
|
python -c "import os; from chromadb.utils import embedding_functions; sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=os.environ['RAG_EMBEDDING_MODEL'], device=os.environ['DEVICE_TYPE'])"; \
|
|
fi
|
|
|
|
|
|
RUN if [ "$INCLUDE_OLLAMA" = "true" ]; then \
|
|
apt-get update && \
|
|
# Install pandoc and netcat
|
|
apt-get install -y --no-install-recommends pandoc netcat-openbsd && \
|
|
# for RAG OCR
|
|
apt-get install -y --no-install-recommends ffmpeg libsm6 libxext6 && \
|
|
# install helper tools
|
|
apt-get install -y --no-install-recommends curl && \
|
|
# 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 && \
|
|
# for RAG OCR
|
|
apt-get install -y --no-install-recommends ffmpeg libsm6 libxext6 && \
|
|
# cleanup
|
|
rm -rf /var/lib/apt/lists/*; \
|
|
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 --from=build /app/build /app/build
|
|
COPY --from=build /app/CHANGELOG.md /app/CHANGELOG.md
|
|
COPY --from=build /app/package.json /app/package.json
|
|
|
|
# copy backend files
|
|
COPY ./backend .
|
|
|
|
EXPOSE 8080
|
|
|
|
CMD [ "bash", "start.sh"] |