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cuda support
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@ -37,7 +37,7 @@ ENV OPENAI_API_KEY="" \
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SCARF_NO_ANALYTICS=true \
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SCARF_NO_ANALYTICS=true \
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DO_NOT_TRACK=true
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DO_NOT_TRACK=true
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#### Preloaded models ##########################################################
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#### Preloaded models #########################################################
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## whisper TTS Settings ##
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## whisper TTS Settings ##
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ENV WHISPER_MODEL="base" \
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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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WHISPER_MODEL_DIR="/app/backend/data/cache/whisper/models"
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@ -48,19 +48,32 @@ ENV WHISPER_MODEL="base" \
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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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# 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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# 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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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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RAG_EMBEDDING_MODEL_DEVICE_TYPE="cpu" \
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RAG_EMBEDDING_MODEL_DIR="/app/backend/data/cache/embedding/models" \
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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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SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models" \
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# device type for whisper tts and embbeding models - "cpu" (default) or "mps" (apple silicon) - choosing this right can lead to better performance
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# Important:
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# If you want to use CUDA you need to install the nvidia-container-toolkit (https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
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# you can set this to "cuda" but its recomended to use --build-arg CUDA_ENABLED=true flag when building the image
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RAG_EMBEDDING_MODEL_DEVICE_TYPE="cuda"
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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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#### Preloaded models ##########################################################
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#### Preloaded models ##########################################################
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WORKDIR /app/backend
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WORKDIR /app/backend
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# install python dependencies
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# install python dependencies
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COPY ./backend/requirements.txt ./requirements.txt
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COPY ./backend/requirements.txt ./requirements.txt
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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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&& pip3 install -r requirements.txt --no-cache-dir
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RUN if [ "$RAG_EMBEDDING_MODEL_DEVICE_TYPE" = "cuda" ]; then \
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echo "CUDA enabled" && \
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 --no-cache-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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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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fi
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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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# install required packages
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# install required packages
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RUN apt-get update \
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RUN apt-get update \
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@ -71,10 +84,7 @@ RUN apt-get update \
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# cleanup
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# cleanup
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&& rm -rf /var/lib/apt/lists/*
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&& rm -rf /var/lib/apt/lists/*
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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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# 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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# RUN mkdir -p /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2
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