kb-mcp-server

# Use a Python image with uv pre-installed FROM ghcr.io/astral-sh/uv:python3.10-bookworm-slim # Install the project into `/app` WORKDIR /app # Enable bytecode compilation ENV UV_COMPILE_BYTECODE=1 # Copy from the cache instead of linking since it's a mounted volume ENV UV_LINK_MODE=copy # Install system dependencies RUN apt-get update && \ apt-get install -y --no-install-recommends git && \ rm -rf /var/lib/apt/lists/* # Copy project files COPY . /app/ # Install txtai and other dependencies RUN uv pip install txtai[all,pipeline,graph]>=8.3.1 \ trio httpx>=0.28.1 pydantic-settings>=2.0 \ networkx>=2.8.0 matplotlib>=3.5.0 PyPDF2>=2.0.0 \ python-docx>=0.8.11 python-louvain>=0.16.0 \ fast-langdetect>=1.0.3 datasets torch>=2.0.0 \ transformers>=4.30.0 sentence-transformers>=2.2.0 \ bitsandbytes==0.42.0 beautifulsoup4>=4.10.0 \ pandas>=1.3.0 markdown>=3.3.0 # Install MCP as a normal Python package RUN uv pip install mcp==1.3.0 # Install the project RUN uv pip install -e . # Set Python path ENV PYTHONPATH=/app # Make scripts executable RUN chmod +x /app/docker-entrypoint.sh RUN chmod +x /app/download_models.py # Create volume mount points RUN mkdir -p /data/embeddings # Download Hugging Face models if specified ARG HF_TRANSFORMERS_MODELS="" ARG HF_SENTENCE_TRANSFORMERS_MODELS="" ARG HF_CACHE_DIR="" # If HF_CACHE_DIR is provided, create a symbolic link to it RUN if [ -n "$HF_CACHE_DIR" ] && [ -d "$HF_CACHE_DIR" ]; then \ mkdir -p /root/.cache/huggingface && \ ln -s "$HF_CACHE_DIR" /root/.cache/huggingface/hub; \ fi # Download models if specified RUN if [ -n "$HF_TRANSFORMERS_MODELS" ] || [ -n "$HF_SENTENCE_TRANSFORMERS_MODELS" ]; then \ python /app/download_models.py \ --transformers "$HF_TRANSFORMERS_MODELS" \ --sentence-transformers "$HF_SENTENCE_TRANSFORMERS_MODELS"; \ fi # Set environment variables ENV TXTAI_STORAGE_MODE=persistence ENV TXTAI_INDEX_PATH=/data/embeddings ENV TXTAI_DATASET_ENABLED=true ENV TXTAI_DATASET_NAME=web_questions ENV TXTAI_DATASET_SPLIT=train # Default environment variables for configuration ENV PORT=8000 ENV HOST=0.0.0.0 ENV TRANSPORT=sse ENV EMBEDDINGS_PATH=/data/embeddings ENV HF_TRANSFORMERS_MODELS=$HF_TRANSFORMERS_MODELS ENV HF_SENTENCE_TRANSFORMERS_MODELS=$HF_SENTENCE_TRANSFORMERS_MODELS # Expose port EXPOSE 8000 # Use the entrypoint script ENTRYPOINT ["/app/docker-entrypoint.sh"]