Dockerfile 6.6 KB

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  1. # syntax=docker/dockerfile:1
  2. # Initialize device type args
  3. # use build args in the docker build command with --build-arg="BUILDARG=true"
  4. ARG USE_CUDA=false
  5. ARG USE_OLLAMA=false
  6. # Tested with cu117 for CUDA 11 and cu121 for CUDA 12 (default)
  7. ARG USE_CUDA_VER=cu121
  8. # any sentence transformer model; models to use can be found at https://huggingface.co/models?library=sentence-transformers
  9. # Leaderboard: https://huggingface.co/spaces/mteb/leaderboard
  10. # for better performance and multilangauge support use "intfloat/multilingual-e5-large" (~2.5GB) or "intfloat/multilingual-e5-base" (~1.5GB)
  11. # 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.
  12. ARG USE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
  13. ARG USE_RERANKING_MODEL=""
  14. # Tiktoken encoding name; models to use can be found at https://huggingface.co/models?library=tiktoken
  15. ARG USE_TIKTOKEN_ENCODING_NAME="cl100k_base"
  16. ARG BUILD_HASH=dev-build
  17. # Override at your own risk - non-root configurations are untested
  18. ARG UID=0
  19. ARG GID=0
  20. ######## WebUI frontend ########
  21. FROM --platform=$BUILDPLATFORM node:22-alpine3.20 AS build
  22. ARG BUILD_HASH
  23. WORKDIR /app
  24. COPY package.json package-lock.json ./
  25. RUN npm ci
  26. COPY . .
  27. ENV APP_BUILD_HASH=${BUILD_HASH}
  28. RUN npm run build
  29. ######## WebUI backend ########
  30. FROM python:3.11-slim-bookworm AS base
  31. # Use args
  32. ARG USE_CUDA
  33. ARG USE_OLLAMA
  34. ARG USE_CUDA_VER
  35. ARG USE_EMBEDDING_MODEL
  36. ARG USE_RERANKING_MODEL
  37. ARG UID
  38. ARG GID
  39. ## Basis ##
  40. ENV ENV=prod \
  41. PORT=8080 \
  42. # pass build args to the build
  43. USE_OLLAMA_DOCKER=${USE_OLLAMA} \
  44. USE_CUDA_DOCKER=${USE_CUDA} \
  45. USE_CUDA_DOCKER_VER=${USE_CUDA_VER} \
  46. USE_EMBEDDING_MODEL_DOCKER=${USE_EMBEDDING_MODEL} \
  47. USE_RERANKING_MODEL_DOCKER=${USE_RERANKING_MODEL}
  48. ## Basis URL Config ##
  49. ENV OLLAMA_BASE_URL="/ollama" \
  50. OPENAI_API_BASE_URL=""
  51. ## API Key and Security Config ##
  52. ENV OPENAI_API_KEY="" \
  53. WEBUI_SECRET_KEY="" \
  54. SCARF_NO_ANALYTICS=true \
  55. DO_NOT_TRACK=true \
  56. ANONYMIZED_TELEMETRY=false
  57. #### Other models #########################################################
  58. ## whisper TTS model settings ##
  59. ENV WHISPER_MODEL="base" \
  60. WHISPER_MODEL_DIR="/app/backend/data/cache/whisper/models"
  61. ## RAG Embedding model settings ##
  62. ENV RAG_EMBEDDING_MODEL="$USE_EMBEDDING_MODEL_DOCKER" \
  63. RAG_RERANKING_MODEL="$USE_RERANKING_MODEL_DOCKER" \
  64. SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models"
  65. ## Tiktoken model settings ##
  66. ENV TIKTOKEN_ENCODING_NAME="cl100k_base" \
  67. TIKTOKEN_CACHE_DIR="/app/backend/data/cache/tiktoken"
  68. ## Hugging Face download cache ##
  69. ENV HF_HOME="/app/backend/data/cache/embedding/models"
  70. ## Torch Extensions ##
  71. # ENV TORCH_EXTENSIONS_DIR="/.cache/torch_extensions"
  72. #### Other models ##########################################################
  73. WORKDIR /app/backend
  74. ENV HOME=/root
  75. # Create user and group if not root
  76. RUN if [ $UID -ne 0 ]; then \
  77. if [ $GID -ne 0 ]; then \
  78. addgroup --gid $GID app; \
  79. fi; \
  80. adduser --uid $UID --gid $GID --home $HOME --disabled-password --no-create-home app; \
  81. fi
  82. RUN mkdir -p $HOME/.cache/chroma
  83. RUN echo -n 00000000-0000-0000-0000-000000000000 > $HOME/.cache/chroma/telemetry_user_id
  84. # Make sure the user has access to the app and root directory
  85. RUN chown -R $UID:$GID /app $HOME
  86. RUN if [ "$USE_OLLAMA" = "true" ]; then \
  87. apt-get update && \
  88. # Install pandoc and netcat
  89. apt-get install -y --no-install-recommends git build-essential pandoc netcat-openbsd curl && \
  90. apt-get install -y --no-install-recommends gcc python3-dev && \
  91. # for RAG OCR
  92. apt-get install -y --no-install-recommends ffmpeg libsm6 libxext6 && \
  93. # install helper tools
  94. apt-get install -y --no-install-recommends curl jq && \
  95. # install ollama
  96. curl -fsSL https://ollama.com/install.sh | sh && \
  97. # cleanup
  98. rm -rf /var/lib/apt/lists/*; \
  99. else \
  100. apt-get update && \
  101. # Install pandoc, netcat and gcc
  102. apt-get install -y --no-install-recommends git build-essential pandoc gcc netcat-openbsd curl jq && \
  103. apt-get install -y --no-install-recommends gcc python3-dev && \
  104. # for RAG OCR
  105. apt-get install -y --no-install-recommends ffmpeg libsm6 libxext6 && \
  106. # cleanup
  107. rm -rf /var/lib/apt/lists/*; \
  108. fi
  109. # install python dependencies
  110. COPY --chown=$UID:$GID ./backend/requirements.txt ./requirements.txt
  111. RUN pip3 install uv && \
  112. if [ "$USE_CUDA" = "true" ]; then \
  113. # If you use CUDA the whisper and embedding model will be downloaded on first use
  114. pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir && \
  115. uv pip install --system -r requirements.txt --no-cache-dir && \
  116. python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
  117. 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'])"; \
  118. python -c "import os; import tiktoken; tiktoken.get_encoding(os.environ['TIKTOKEN_ENCODING_NAME'])"; \
  119. else \
  120. pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \
  121. uv pip install --system -r requirements.txt --no-cache-dir && \
  122. python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
  123. 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'])"; \
  124. python -c "import os; import tiktoken; tiktoken.get_encoding(os.environ['TIKTOKEN_ENCODING_NAME'])"; \
  125. fi; \
  126. chown -R $UID:$GID /app/backend/data/
  127. # copy embedding weight from build
  128. # RUN mkdir -p /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2
  129. # COPY --from=build /app/onnx /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2/onnx
  130. # copy built frontend files
  131. COPY --chown=$UID:$GID --from=build /app/build /app/build
  132. COPY --chown=$UID:$GID --from=build /app/CHANGELOG.md /app/CHANGELOG.md
  133. COPY --chown=$UID:$GID --from=build /app/package.json /app/package.json
  134. # copy backend files
  135. COPY --chown=$UID:$GID ./backend .
  136. EXPOSE 8080
  137. HEALTHCHECK CMD curl --silent --fail http://localhost:${PORT:-8080}/health | jq -ne 'input.status == true' || exit 1
  138. USER $UID:$GID
  139. ARG BUILD_HASH
  140. ENV WEBUI_BUILD_VERSION=${BUILD_HASH}
  141. ENV DOCKER=true
  142. CMD [ "bash", "start.sh"]