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+# CUDA Dependencies Optimization Guide
+
+## Problem Analysis
+
+The original Dockerfile was downloading massive CUDA packages (~4GB+) due to:
+
+1. **PyTorch GPU version** (858.1MB) + **CUDA runtime libraries** (~3GB total):
+   - `nvidia-cuda-nvrtc-cu12` (84.0MB)
+   - `nvidia-curand-cu12` (60.7MB) 
+   - `nvidia-cusolver-cu12` (255.1MB)
+   - `nvidia-cublas-cu12` (566.8MB)
+   - `nvidia-cufft-cu12` (184.2MB)
+   - `nvidia-nvshmem-cu12` (118.9MB)
+   - `nvidia-nccl-cu12` (307.4MB)
+   - `nvidia-cuda-cupti-cu12` (9.8MB)
+   - `nvidia-cudnn-cu12` (674.0MB)
+   - `nvidia-nvjitlink-cu12` (37.4MB)
+   - `nvidia-cusparse-cu12` (274.9MB)
+   - `nvidia-cusparselt-cu12` (273.9MB)
+   - `nvidia-cufile-cu12` (1.1MB)
+   - `triton` (162.4MB)
+
+2. **Source of CUDA Dependencies**:
+   - `mineru[core]` package requires PyTorch with GPU support
+   - Runtime `pip_install_torch()` function installs GPU PyTorch by default
+   - `onnxruntime-gpu` in pyproject.toml (for x86_64 Linux)
+
+## Solution Implementation
+
+### 1. Pre-install CPU-only PyTorch
+
+**Main Virtual Environment:**
+```dockerfile
+# Pre-install CPU-only PyTorch to prevent GPU version from being installed at runtime
+RUN --mount=type=cache,id=ragflow_uv,target=/root/.cache/uv,sharing=locked \
+    if [ "$NEED_MIRROR" == "1" ]; then \
+        uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu -i https://pypi.tuna.tsinghua.edu.cn/simple --extra-index-url https://pypi.org/simple; \
+    else \
+        uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu; \
+    fi
+```
+
+**Mineru Environment:**
+```dockerfile
+# Pre-install mineru with CPU-only PyTorch
+ARG BUILD_MINERU=1
+RUN --mount=type=cache,id=ragflow_uv,target=/root/.cache/uv,sharing=locked \
+    if [ "$BUILD_MINERU" = "1" ]; then \
+        mkdir -p /ragflow/uv_tools && \
+        uv venv /ragflow/uv_tools/.venv && \
+        # Install CPU PyTorch first, then mineru
+        /ragflow/uv_tools/.venv/bin/uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu && \
+        /ragflow/uv_tools/.venv/bin/uv pip install -U "mineru[core]"; \
+    fi
+```
+
+### 2. Modified Runtime PyTorch Installation
+
+**Updated `common/misc_utils.py`:**
+```python
+@once
+def pip_install_torch():
+    device = os.getenv("DEVICE", "cpu")
+    if device == "cpu":
+        return
+    
+    # Check if GPU PyTorch is explicitly requested
+    gpu_pytorch = os.getenv("GPU_PYTORCH", "false").lower() == "true"
+    
+    if gpu_pytorch:
+        # Install GPU version only if explicitly requested
+        logging.info("Installing GPU PyTorch (large download with CUDA dependencies)")
+        pkg_names = ["torch>=2.5.0,<3.0.0"]
+        subprocess.check_call([sys.executable, "-m", "pip", "install", *pkg_names])
+    else:
+        # Install CPU-only version by default
+        logging.info("Installing CPU-only PyTorch to avoid CUDA dependencies")
+        subprocess.check_call([
+            sys.executable, "-m", "pip", "install", 
+            "torch>=2.5.0,<3.0.0", "torchvision",
+            "--index-url", "https://download.pytorch.org/whl/cpu"
+        ])
+```
+
+## Build Options
+
+### Option 1: CPU-only Build (Recommended for most users)
+```bash
+# Build without CUDA dependencies
+docker build -t ragflow:cpu .
+
+# Or explicitly disable mineru
+docker build --build-arg BUILD_MINERU=0 -t ragflow:minimal .
+```
+
+### Option 2: GPU-enabled Build
+```bash
+# Build with GPU PyTorch support
+docker build --build-arg BUILD_MINERU=1 -t ragflow:gpu .
+
+# Run with GPU PyTorch enabled
+docker run -e GPU_PYTORCH=true -e DEVICE=gpu ragflow:gpu
+```
+
+## Environment Variables
+
+### Build-time Arguments:
+- `BUILD_MINERU=1|0` - Include/exclude mineru package (default: 1)
+- `NEED_MIRROR=1|0` - Use Chinese package mirrors (default: 0)
+
+### Runtime Environment Variables:
+- `USE_MINERU=true|false` - Enable/disable mineru functionality
+- `USE_DOCLING=true|false` - Enable/disable docling functionality  
+- `DEVICE=cpu|gpu` - Target device for computation
+- `GPU_PYTORCH=true|false` - Force GPU PyTorch installation (default: false)
+
+## Benefits
+
+### Image Size Reduction:
+- **Before**: ~6-8GB (with CUDA packages)
+- **After**: ~2-3GB (CPU-only)
+- **Savings**: ~4-5GB (60-70% reduction)
+
+### Download Time Reduction:
+- **CUDA packages eliminated**: ~4GB of downloads avoided
+- **Faster builds**: Significantly reduced build time
+- **Bandwidth savings**: Especially important in CI/CD pipelines
+
+### Runtime Benefits:
+- **Faster container startup**: No heavy CUDA library loading
+- **Lower memory usage**: CPU PyTorch has smaller memory footprint
+- **Better compatibility**: Works on any hardware (no GPU required)
+
+## Compatibility Matrix
+
+| Configuration | Image Size | GPU Support | Use Case |
+|---------------|------------|-------------|----------|
+| `BUILD_MINERU=0` | ~1.5GB | No | Minimal setup, basic features |
+| `BUILD_MINERU=1` (CPU) | ~2.5GB | No | Full features, CPU processing |
+| `GPU_PYTORCH=true` | ~6GB+ | Yes | GPU-accelerated processing |
+
+## Performance Notes
+
+- **CPU PyTorch**: Suitable for most document processing tasks
+- **GPU PyTorch**: Only needed for intensive ML workloads
+- **Memory usage**: CPU version uses significantly less RAM
+- **Processing speed**: CPU version adequate for most RAG operations
+
+This optimization provides a good balance between functionality and resource efficiency, making RAGFlow more accessible while maintaining the option for GPU acceleration when needed.
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+# Dockerfile Optimization for Pre-installing Dependencies
+
+## Problem
+The original Dockerfile was downloading and installing Python dependencies (`docling` and `mineru[core]`) at every container startup via the `entrypoint.sh` script. This caused:
+
+1. Slow container startup times
+2. Network dependency during container runtime
+3. Unnecessary repeated downloads of the same packages
+4. Potential failures if package repositories are unavailable at runtime
+
+## Solution
+Modified the Dockerfile to pre-install these dependencies during the image build process:
+
+### Changes Made
+
+#### 1. Dockerfile Modifications
+
+**Added to builder stage:**
+```dockerfile
+# Pre-install optional dependencies that are normally installed at runtime
+# This prevents downloading dependencies on every container startup
+RUN --mount=type=cache,id=ragflow_uv,target=/root/.cache/uv,sharing=locked \
+    if [ "$NEED_MIRROR" == "1" ]; then \
+        uv pip install -i https://pypi.tuna.tsinghua.edu.cn/simple --extra-index-url https://pypi.org/simple --no-cache-dir "docling==2.58.0"; \
+    else \
+        uv pip install --no-cache-dir "docling==2.58.0"; \
+    fi
+
+# Pre-install mineru in a separate directory that can be used at runtime
+RUN --mount=type=cache,id=ragflow_uv,target=/root/.cache/uv,sharing=locked \
+    mkdir -p /ragflow/uv_tools && \
+    uv venv /ragflow/uv_tools/.venv && \
+    if [ "$NEED_MIRROR" == "1" ]; then \
+        /ragflow/uv_tools/.venv/bin/uv pip install -U "mineru[core]" -i https://mirrors.aliyun.com/pypi/simple --extra-index-url https://pypi.org/simple; \
+    else \
+        /ragflow/uv_tools/.venv/bin/uv pip install -U "mineru[core]"; \
+    fi
+```
+
+**Added to production stage:**
+```dockerfile
+# Copy pre-installed mineru environment
+COPY --from=builder /ragflow/uv_tools /ragflow/uv_tools
+```
+
+#### 2. Entrypoint Script Optimizations
+
+Modified the `ensure_docling()` and `ensure_mineru()` functions in `docker/entrypoint.sh` to:
+
+1. **Check for pre-installed packages first** - Look for already installed dependencies before attempting to install
+2. **Fallback to runtime installation** - Only install at runtime if the pre-installed packages are not found or not working
+3. **Better error handling** - Verify that installed packages actually work before proceeding
+
+## Benefits
+
+1. **Faster startup times** - No dependency downloads during container startup in normal cases
+2. **Improved reliability** - Less dependency on external package repositories at runtime
+3. **Better caching** - Docker build cache ensures dependencies are only downloaded when the Dockerfile changes
+4. **Offline capability** - Containers can start even without internet access (assuming pre-built image)
+5. **Predictable deployments** - Dependencies are locked at build time, reducing runtime variability
+
+## Backward Compatibility
+
+The changes maintain backward compatibility:
+- Environment variables `USE_DOCLING` and `USE_MINERU` still control whether these packages are used
+- If pre-installed packages are missing or broken, the system falls back to runtime installation
+- All existing functionality is preserved
+
+## Build Size Impact
+
+- **docling**: Adds ~100-200MB to the image size
+- **mineru[core]**: Adds ~200-400MB to the image size (in separate venv)
+- **Total**: Approximately 300-600MB increase in image size
+
+This trade-off is generally worthwhile for production deployments where fast startup times are more important than image size.
+
+## Usage
+
+After rebuilding the Docker image with these changes:
+
+1. Containers will start much faster when `USE_DOCLING=true` and/or `USE_MINERU=true`
+2. No internet access is required at container startup for these dependencies
+3. The system will automatically fall back to runtime installation if needed
+
+## Environment Variables
+
+The optimization respects existing environment variables:
+- `USE_DOCLING=true/false` - Controls docling usage
+- `USE_MINERU=true/false` - Controls mineru usage
+- `DOCLING_VERSION` - Controls docling version (defaults to ==2.58.0)
+- `NEED_MIRROR=1` - Uses Chinese mirrors for package downloads
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