90 lines
3.3 KiB
Docker
90 lines
3.3 KiB
Docker
# syntax=docker/dockerfile:1
|
|
|
|
FROM node:alpine as build
|
|
|
|
WORKDIR /app
|
|
|
|
# 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
|
|
|
|
|
|
FROM python:3.11-slim-bookworm as base
|
|
|
|
ENV ENV=prod
|
|
ENV PORT ""
|
|
|
|
ENV OLLAMA_BASE_URL "/ollama"
|
|
|
|
ENV OPENAI_API_BASE_URL ""
|
|
ENV OPENAI_API_KEY ""
|
|
|
|
ENV WEBUI_SECRET_KEY ""
|
|
ENV WEBUI_AUTH_TRUSTED_EMAIL_HEADER ""
|
|
|
|
ENV SCARF_NO_ANALYTICS true
|
|
ENV DO_NOT_TRACK true
|
|
|
|
# Use locally bundled version of the LiteLLM cost map json
|
|
# to avoid repetitive startup connections
|
|
ENV LITELLM_LOCAL_MODEL_COST_MAP="True"
|
|
|
|
######## Preloaded models ########
|
|
# whisper TTS Settings
|
|
ENV WHISPER_MODEL="base"
|
|
ENV 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 persormance 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"
|
|
# 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
|
|
ENV RAG_EMBEDDING_MODEL_DEVICE_TYPE="cpu"
|
|
ENV RAG_EMBEDDING_MODEL_DIR="/app/backend/data/cache/embedding/models"
|
|
ENV SENTENCE_TRANSFORMERS_HOME $RAG_EMBEDDING_MODEL_DIR
|
|
|
|
######## Preloaded models ########
|
|
|
|
WORKDIR /app/backend
|
|
|
|
# install python dependencies
|
|
COPY ./backend/requirements.txt ./requirements.txt
|
|
|
|
RUN apt-get update && apt-get install ffmpeg libsm6 libxext6 -y
|
|
|
|
RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir
|
|
RUN pip3 install -r requirements.txt --no-cache-dir
|
|
|
|
# Install pandoc and netcat
|
|
# RUN python -c "import pypandoc; pypandoc.download_pandoc()"
|
|
RUN apt-get update \
|
|
&& apt-get install -y pandoc netcat-openbsd \
|
|
&& rm -rf /var/lib/apt/lists/*
|
|
|
|
# preload embedding model
|
|
RUN 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['RAG_EMBEDDING_MODEL_DEVICE_TYPE'])"
|
|
# preload tts model
|
|
RUN python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='auto', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"
|
|
|
|
# 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 .
|
|
|
|
CMD [ "bash", "start.sh"]
|