forked from open-webui/open-webui
125 lines
No EOL
4.9 KiB
Docker
125 lines
No EOL
4.9 KiB
Docker
# syntax=docker/dockerfile:1
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# Initialize device type args
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# use build args in the docker build commmand with --build-arg="BUILDARG=true"
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ARG USE_CUDA=false
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ARG USE_OLLAMA=false
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# Tested with cu117 for CUDA 11 and cu121 for CUDA 12 (default)
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ARG USE_CUDA_VER=cu121
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# any sentence transformer model; models to use can be found at https://huggingface.co/models?library=sentence-transformers
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# Leaderboard: https://huggingface.co/spaces/mteb/leaderboard
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# for better performance and multilangauge support use "intfloat/multilingual-e5-large" (~2.5GB) or "intfloat/multilingual-e5-base" (~1.5GB)
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# 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.
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ARG USE_EMBEDDING_MODEL=all-MiniLM-L6-v2
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######## WebUI frontend ########
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FROM --platform=$BUILDPLATFORM node:21-alpine3.19 as build
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WORKDIR /app
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COPY package.json package-lock.json ./
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RUN npm ci
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COPY . .
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RUN npm run build
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######## WebUI backend ########
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FROM python:3.11-slim-bookworm as base
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# Use args
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ARG USE_CUDA
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ARG USE_OLLAMA
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ARG USE_CUDA_VER
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ARG USE_EMBEDDING_MODEL
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## Basis ##
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ENV ENV=prod \
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PORT=8080 \
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# pass build args to the build
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USE_OLLAMA_DOCKER=${USE_OLLAMA} \
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USE_CUDA_DOCKER=${USE_CUDA} \
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USE_CUDA_DOCKER_VER=${USE_CUDA_VER} \
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USE_EMBEDDING_MODEL_DOCKER=${USE_EMBEDDING_MODEL}
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## Basis URL Config ##
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ENV OLLAMA_BASE_URL="/ollama" \
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OPENAI_API_BASE_URL=""
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## API Key and Security Config ##
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ENV OPENAI_API_KEY="" \
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WEBUI_SECRET_KEY="" \
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SCARF_NO_ANALYTICS=true \
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DO_NOT_TRACK=true
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# Use locally bundled version of the LiteLLM cost map json
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# to avoid repetitive startup connections
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ENV LITELLM_LOCAL_MODEL_COST_MAP="True"
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#### Other models #########################################################
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## whisper TTS model settings ##
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ENV WHISPER_MODEL="base" \
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WHISPER_MODEL_DIR="/app/backend/data/cache/whisper/models"
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## RAG Embedding model settings ##
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ENV RAG_EMBEDDING_MODEL="$USE_EMBEDDING_MODEL_DOCKER" \
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RAG_EMBEDDING_MODEL_DIR="/app/backend/data/cache/embedding/models" \
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SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models"
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#### Other models ##########################################################
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WORKDIR /app/backend
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RUN if [ "$USE_OLLAMA" = "true" ]; then \
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apt-get update && \
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# Install pandoc and netcat
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apt-get install -y --no-install-recommends pandoc netcat-openbsd && \
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# for RAG OCR
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apt-get install -y --no-install-recommends ffmpeg libsm6 libxext6 && \
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# install helper tools
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apt-get install -y --no-install-recommends curl && \
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# install ollama
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curl -fsSL https://ollama.com/install.sh | sh && \
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# cleanup
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rm -rf /var/lib/apt/lists/*; \
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else \
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apt-get update && \
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# Install pandoc and netcat
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apt-get install -y --no-install-recommends pandoc netcat-openbsd && \
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# for RAG OCR
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apt-get install -y --no-install-recommends ffmpeg libsm6 libxext6 && \
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# cleanup
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rm -rf /var/lib/apt/lists/*; \
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fi
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# install python dependencies
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COPY ./backend/requirements.txt ./requirements.txt
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RUN if [ "$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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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir && \
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pip3 install -r requirements.txt --no-cache-dir && \
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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'])" && \
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python -c "import os; from chromadb.utils import embedding_functions; sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=os.environ['RAG_EMBEDDING_MODEL'], device='cpu')"; \
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else \
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \
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pip3 install -r requirements.txt --no-cache-dir && \
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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'])" && \
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python -c "import os; from chromadb.utils import embedding_functions; sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=os.environ['RAG_EMBEDDING_MODEL'], device='cpu')"; \
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fi
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# copy embedding weight from build
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# RUN mkdir -p /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2
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# COPY --from=build /app/onnx /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2/onnx
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# copy built frontend files
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COPY --from=build /app/build /app/build
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COPY --from=build /app/CHANGELOG.md /app/CHANGELOG.md
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COPY --from=build /app/package.json /app/package.json
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# copy backend files
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COPY ./backend .
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EXPOSE 8080
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CMD [ "bash", "start.sh"] |