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# 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
# 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.
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 . .
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
ARG USE_CUDA_VER
ARG USE_EMBEDDING_MODEL
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## Basis ##
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 } \
USE_CUDA_DOCKER = ${ USE_CUDA } \
USE_CUDA_DOCKER_VER = ${ USE_CUDA_VER } \
USE_EMBEDDING_MODEL_DOCKER = ${ USE_EMBEDDING_MODEL }
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## Basis URL Config ##
ENV OLLAMA_BASE_URL = "/ollama" \
OPENAI_API_BASE_URL = ""
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## API Key and Security Config ##
ENV OPENAI_API_KEY = "" \
WEBUI_SECRET_KEY = "" \
SCARF_NO_ANALYTICS = true \
DO_NOT_TRACK = true
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# Use locally bundled version of the LiteLLM cost map json
# to avoid repetitive startup connections
ENV LITELLM_LOCAL_MODEL_COST_MAP = "True"
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#### Other models #########################################################
## whisper TTS model settings ##
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ENV WHISPER_MODEL = "base" \
WHISPER_MODEL_DIR = "/app/backend/data/cache/whisper/models"
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## RAG Embedding model settings ##
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"
#### Other models ##########################################################
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WORKDIR /app/backend
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RUN if [ " $USE_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/*; \
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else \
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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/*; \
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fi
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# install python dependencies
COPY ./backend/requirements.txt ./requirements.txt
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RUN pip3 install uv && \
if [ " $USE_CUDA " = "true" ] ; then \
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# If you use CUDA the whisper and embedding model will be downloaded on first use
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$USE_CUDA_DOCKER_VER --no-cache-dir && \
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uv pip install --system -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'])" && \
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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uv pip install --system -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'])" && \
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
# COPY --from=build /app/onnx /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2/onnx
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# copy built frontend files
COPY --from= build /app/build /app/build
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COPY --from= build /app/CHANGELOG.md /app/CHANGELOG.md
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" ]