mirror of
https://github.com/open-webui/open-webui
synced 2024-11-22 08:07:55 +00:00
fixes and updates
This commit is contained in:
Jannik Streidl
commit
9bcb37ea10
36
.github/workflows/docker-build.yaml
vendored
36
.github/workflows/docker-build.yaml
vendored
@ -1,5 +1,4 @@
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#
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name: Create and publish a Docker image
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name: Create and publish Docker images with specific build args
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# Configures this workflow to run every time a change is pushed to the branch called `release`.
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on:
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@ -23,7 +22,7 @@ jobs:
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permissions:
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contents: read
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packages: write
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#
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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@ -41,8 +40,8 @@ jobs:
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Extract metadata for Docker images
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id: meta
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- name: Extract metadata for Docker images (default latest tag)
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id: meta-latest
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uses: docker/metadata-action@v5
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with:
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images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
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@ -53,14 +52,31 @@ jobs:
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type=sha,prefix=git-
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type=semver,pattern={{version}}
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type=semver,pattern={{major}}.{{minor}}
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flavor: |
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latest=${{ github.ref == 'refs/heads/main' }}
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latest=true
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- name: Build and push Docker image
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- name: Build and push Docker image (latest)
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uses: docker/build-push-action@v5
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with:
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context: .
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push: true
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platforms: linux/amd64,linux/arm64
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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tags: ${{ steps.meta-latest.outputs.tags }}
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labels: ${{ steps.meta-latest.outputs.labels }}
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- name: Build and push Docker image with CUDA
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uses: docker/build-push-action@v5
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with:
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context: .
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push: true
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platforms: linux/amd64,linux/arm64
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tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:cuda
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build-args: USE_CUDA=true
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- name: Build and push Docker image with Ollama
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uses: docker/build-push-action@v5
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with:
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context: .
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push: true
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platforms: linux/amd64,linux/arm64
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tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:with-ollama
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build-args: USE_OLLAMA=true
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125
Dockerfile
125
Dockerfile
@ -1,77 +1,110 @@
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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
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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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FROM node:alpine as build
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######## WebUI frontend ########
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FROM node:21-alpine3.19 as build
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WORKDIR /app
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# wget embedding model weight from alpine (does not exist from slim-buster)
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RUN wget "https://chroma-onnx-models.s3.amazonaws.com/all-MiniLM-L6-v2/onnx.tar.gz" -O - | \
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tar -xzf - -C /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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ENV ENV=prod
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ENV PORT ""
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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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ENV OLLAMA_BASE_URL "/ollama"
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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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ENV OPENAI_API_BASE_URL ""
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ENV OPENAI_API_KEY ""
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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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ENV WEBUI_SECRET_KEY ""
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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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ENV SCARF_NO_ANALYTICS true
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ENV DO_NOT_TRACK 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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######## Preloaded models ########
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# whisper TTS Settings
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ENV WHISPER_MODEL="base"
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ENV WHISPER_MODEL_DIR="/app/backend/data/cache/whisper/models"
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# RAG Embedding Model Settings
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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 persormance 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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ENV RAG_EMBEDDING_MODEL="all-MiniLM-L6-v2"
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# 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
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ENV RAG_EMBEDDING_MODEL_DEVICE_TYPE="cpu"
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ENV RAG_EMBEDDING_MODEL_DIR="/app/backend/data/cache/embedding/models"
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ENV SENTENCE_TRANSFORMERS_HOME $RAG_EMBEDDING_MODEL_DIR
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######## Preloaded models ########
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## RAG Embedding model settings ##
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ENV RAG_EMBEDDING_MODEL="$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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# install python dependencies
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COPY ./backend/requirements.txt ./requirements.txt
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RUN apt-get update && apt-get install ffmpeg libsm6 libxext6 -y
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RUN if [ "$USE_CUDA" = "true" ]; then \
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# If you use CUDA the whisper and embedding modell will be downloaded on first use
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$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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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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RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir
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RUN pip3 install -r requirements.txt --no-cache-dir
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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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# RUN python -c "import pypandoc; pypandoc.download_pandoc()"
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RUN apt-get update \
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&& apt-get install -y pandoc netcat-openbsd \
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&& rm -rf /var/lib/apt/lists/*
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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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# preload embedding model
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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'])"
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# preload tts model
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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'])"
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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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# 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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@ -81,4 +114,6 @@ 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"]
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@ -28,6 +28,7 @@ from config import (
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UPLOAD_DIR,
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WHISPER_MODEL,
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WHISPER_MODEL_DIR,
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DEVICE_TYPE,
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)
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log = logging.getLogger(__name__)
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@ -42,6 +43,10 @@ app.add_middleware(
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allow_headers=["*"],
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)
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# setting device type for whisper model
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whisper_device_type = DEVICE_TYPE if DEVICE_TYPE and DEVICE_TYPE == "cuda" else "cpu"
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log.info(f"whisper_device_type: {whisper_device_type}")
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@app.post("/transcribe")
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def transcribe(
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@ -66,7 +71,7 @@ def transcribe(
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model = WhisperModel(
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WHISPER_MODEL,
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device="auto",
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device=whisper_device_type,
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compute_type="int8",
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download_root=WHISPER_MODEL_DIR,
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)
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@ -59,7 +59,7 @@ from config import (
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UPLOAD_DIR,
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DOCS_DIR,
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RAG_EMBEDDING_MODEL,
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RAG_EMBEDDING_MODEL_DEVICE_TYPE,
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DEVICE_TYPE,
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CHROMA_CLIENT,
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CHUNK_SIZE,
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CHUNK_OVERLAP,
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@ -71,15 +71,6 @@ from constants import ERROR_MESSAGES
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log = logging.getLogger(__name__)
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log.setLevel(SRC_LOG_LEVELS["RAG"])
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#
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# if RAG_EMBEDDING_MODEL:
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# sentence_transformer_ef = SentenceTransformer(
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# model_name_or_path=RAG_EMBEDDING_MODEL,
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# cache_folder=RAG_EMBEDDING_MODEL_DIR,
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# device=RAG_EMBEDDING_MODEL_DEVICE_TYPE,
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# )
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app = FastAPI()
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app.state.PDF_EXTRACT_IMAGES = False
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@ -92,7 +83,7 @@ app.state.TOP_K = 4
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app.state.sentence_transformer_ef = (
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embedding_functions.SentenceTransformerEmbeddingFunction(
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model_name=app.state.RAG_EMBEDDING_MODEL,
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device=RAG_EMBEDDING_MODEL_DEVICE_TYPE,
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device=DEVICE_TYPE,
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)
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)
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@ -147,10 +138,9 @@ async def update_embedding_model(
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app.state.sentence_transformer_ef = (
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embedding_functions.SentenceTransformerEmbeddingFunction(
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model_name=app.state.RAG_EMBEDDING_MODEL,
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device=RAG_EMBEDDING_MODEL_DEVICE_TYPE,
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device=DEVICE_TYPE,
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)
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)
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return {
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"status": True,
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"embedding_model": app.state.RAG_EMBEDDING_MODEL,
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@ -253,6 +253,8 @@ OLLAMA_API_BASE_URL = os.environ.get(
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OLLAMA_BASE_URL = os.environ.get("OLLAMA_BASE_URL", "")
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K8S_FLAG = os.environ.get("K8S_FLAG", "")
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USE_OLLAMA_DOCKER = os.environ.get("USE_OLLAMA_DOCKER", "false")
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if OLLAMA_BASE_URL == "" and OLLAMA_API_BASE_URL != "":
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OLLAMA_BASE_URL = (
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@ -263,6 +265,11 @@ if OLLAMA_BASE_URL == "" and OLLAMA_API_BASE_URL != "":
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if ENV == "prod":
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if OLLAMA_BASE_URL == "/ollama":
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if USE_OLLAMA_DOCKER.lower() == "true":
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# if you use all-in-one docker container (Open WebUI + Ollama)
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# with the docker build arg USE_OLLAMA=true (--build-arg="USE_OLLAMA=true") this only works with http://localhost:11434
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OLLAMA_BASE_URL = "http://localhost:11434"
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else:
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OLLAMA_BASE_URL = "http://host.docker.internal:11434"
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elif K8S_FLAG:
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@ -384,10 +391,16 @@ if WEBUI_AUTH and WEBUI_SECRET_KEY == "":
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CHROMA_DATA_PATH = f"{DATA_DIR}/vector_db"
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# this uses the model defined in the Dockerfile ENV variable. If you dont use docker or docker based deployments such as k8s, the default embedding model will be used (all-MiniLM-L6-v2)
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RAG_EMBEDDING_MODEL = os.environ.get("RAG_EMBEDDING_MODEL", "all-MiniLM-L6-v2")
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log.info(f"Embedding model set: {RAG_EMBEDDING_MODEL}"),
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# device type ebbeding models - "cpu" (default), "cuda" (nvidia gpu required) or "mps" (apple silicon) - choosing this right can lead to better performance
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RAG_EMBEDDING_MODEL_DEVICE_TYPE = os.environ.get(
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"RAG_EMBEDDING_MODEL_DEVICE_TYPE", "cpu"
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)
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USE_CUDA = os.environ.get("USE_CUDA_DOCKER", "false")
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if USE_CUDA.lower() == "true":
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DEVICE_TYPE = "cuda"
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else:
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DEVICE_TYPE = "cpu"
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CHROMA_CLIENT = chromadb.PersistentClient(
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path=CHROMA_DATA_PATH,
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settings=Settings(allow_reset=True, anonymized_telemetry=False),
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@ -7,16 +7,26 @@ KEY_FILE=.webui_secret_key
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PORT="${PORT:-8080}"
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if test "$WEBUI_SECRET_KEY $WEBUI_JWT_SECRET_KEY" = " "; then
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echo No WEBUI_SECRET_KEY provided
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echo "No WEBUI_SECRET_KEY provided"
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if ! [ -e "$KEY_FILE" ]; then
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echo Generating WEBUI_SECRET_KEY
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echo "Generating WEBUI_SECRET_KEY"
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# Generate a random value to use as a WEBUI_SECRET_KEY in case the user didn't provide one.
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echo $(head -c 12 /dev/random | base64) > $KEY_FILE
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echo $(head -c 12 /dev/random | base64) > "$KEY_FILE"
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fi
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echo Loading WEBUI_SECRET_KEY from $KEY_FILE
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WEBUI_SECRET_KEY=`cat $KEY_FILE`
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echo "Loading WEBUI_SECRET_KEY from $KEY_FILE"
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WEBUI_SECRET_KEY=$(cat "$KEY_FILE")
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fi
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|
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if [ "$USE_OLLAMA_DOCKER" = "true" ]; then
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echo "USE_OLLAMA is set to true, starting ollama serve."
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ollama serve &
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fi
|
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if [ "$USE_CUDA_DOCKER" = "true" ]; then
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echo "CUDA is enabled, appending LD_LIBRARY_PATH to include torch/cudnn & cublas libraries."
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export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/lib/python3.11/site-packages/torch/lib:/usr/local/lib/python3.11/site-packages/nvidia/cublas/lib"
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fi
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WEBUI_SECRET_KEY="$WEBUI_SECRET_KEY" exec uvicorn main:app --host 0.0.0.0 --port "$PORT" --forwarded-allow-ips '*'
|
||||
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