upgrade: switch from manylinux2014 to manylinux_2_35

- Use manylinux_2_35 (GLIBC 2.35) instead of manylinux2014 (GLIBC 2.17)
- Still compatible with Google Colab (requires ≤2.35)
- Benefits: newer toolchain, better performance, modern C++ features
- Switch from yum to dnf package manager
- Remove pyzmq version cap as manylinux_2_35 has newer ZeroMQ
- Update documentation to reflect the change

.github/workflows/build-and-publish.yml

@@ -0,0 +1,11 @@
+name: CI
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  build:
+    uses: ./.github/workflows/build-reusable.yml 

.github/workflows/build-cibuildwheel.yml

@@ -0,0 +1,171 @@
+name: Build with cibuildwheel
+
+on:
+  workflow_call:
+    inputs:
+      ref:
+        description: 'Git ref to build'
+        required: false
+        type: string
+        default: ''
+
+jobs:
+  build_wheels:
+    name: Build wheels on ${{ matrix.os }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest]  # Focus on Linux/manylinux for Colab compatibility
+        
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.ref }}
+          submodules: recursive
+      
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      
+      # Build pure Python packages separately
+      - name: Build pure Python packages (leann-core, leann)
+        if: matrix.os == 'ubuntu-latest'  # Only build once
+        run: |
+          python -m pip install --upgrade pip build
+          python -m build packages/leann-core --outdir wheelhouse/
+          python -m build packages/leann --outdir wheelhouse/
+      
+      - name: Build leann-backend-hnsw wheels  
+        uses: pypa/cibuildwheel@v2.20.0
+        with:
+          package-dir: packages/leann-backend-hnsw
+          output-dir: wheelhouse
+        env:
+          CIBW_BUILD: cp39-* cp310-* cp311-* cp312-* cp313-*
+          CIBW_SKIP: "*-win32 *-manylinux_i686 pp* *musllinux*"
+          
+          # Use manylinux_2_35 for Colab compatibility with modern features
+          CIBW_MANYLINUX_X86_64_IMAGE: manylinux_2_35
+          CIBW_MANYLINUX_AARCH64_IMAGE: manylinux_2_35
+          
+          # Linux dependencies - using dnf for manylinux_2_35 (based on AlmaLinux 9)
+          CIBW_BEFORE_ALL_LINUX: |
+            dnf install -y epel-release
+            dnf install -y gcc-c++ boost-devel zeromq-devel openblas-devel cmake python3-devel
+          
+          # Install numpy before building
+          CIBW_BEFORE_BUILD: |
+            pip install numpy
+            pip install --upgrade pip setuptools wheel
+          
+          CIBW_BEFORE_BUILD_LINUX: |
+            pip install numpy
+            pip install --upgrade pip setuptools wheel swig
+          
+          CIBW_BEFORE_ALL_MACOS: |
+            brew install boost zeromq openblas cmake libomp
+          
+          # Pre-install test dependencies to avoid compilation
+          CIBW_BEFORE_TEST: |
+            pip install --only-binary :all: "pyzmq>=23.0.0"
+          
+          # Test command to verify the wheel works
+          CIBW_TEST_COMMAND: |
+            python -c "import leann_backend_hnsw; print('HNSW backend imported successfully')"
+          
+          # Skip problematic configurations
+          CIBW_TEST_SKIP: "*-macosx_arm64"  # Skip ARM64 tests on GitHub Actions
+          
+          # Test dependencies
+          CIBW_TEST_REQUIRES: "pytest numpy"
+          
+          # Environment variables for build
+          CIBW_ENVIRONMENT: |
+            CMAKE_BUILD_PARALLEL_LEVEL=8
+            Python_FIND_VIRTUALENV=ONLY
+            Python3_FIND_VIRTUALENV=ONLY
+          
+          # Linux-specific environment variables
+          CIBW_ENVIRONMENT_LINUX: |
+            CMAKE_BUILD_PARALLEL_LEVEL=8
+          
+          # macOS-specific environment variables
+          CIBW_ENVIRONMENT_MACOS: |
+            CMAKE_BUILD_PARALLEL_LEVEL=8
+            MACOSX_DEPLOYMENT_TARGET=11.0
+            CMAKE_OSX_DEPLOYMENT_TARGET=11.0
+            Python_FIND_VIRTUALENV=ONLY
+            Python3_FIND_VIRTUALENV=ONLY
+      
+      - name: Build leann-backend-diskann wheels  
+        uses: pypa/cibuildwheel@v2.20.0
+        with:
+          package-dir: packages/leann-backend-diskann
+          output-dir: wheelhouse
+        env:
+          CIBW_BUILD: cp39-* cp310-* cp311-* cp312-* cp313-*
+          CIBW_SKIP: "*-win32 *-manylinux_i686 pp* *musllinux*"
+          
+          CIBW_MANYLINUX_X86_64_IMAGE: manylinux_2_35
+          CIBW_MANYLINUX_AARCH64_IMAGE: manylinux_2_35
+          
+          CIBW_BEFORE_ALL_LINUX: |
+            dnf install -y epel-release
+            dnf install -y gcc-c++ boost-devel zeromq-devel openblas-devel cmake python3-devel
+          
+          # Install numpy before building
+          CIBW_BEFORE_BUILD: |
+            pip install numpy
+            pip install --upgrade pip setuptools wheel
+          
+          CIBW_BEFORE_BUILD_LINUX: |
+            pip install numpy
+            pip install --upgrade pip setuptools wheel swig
+          
+          CIBW_BEFORE_ALL_MACOS: |
+            brew install boost zeromq openblas cmake libomp
+          
+          # Pre-install test dependencies to avoid compilation
+          CIBW_BEFORE_TEST: |
+            pip install --only-binary :all: "pyzmq>=23.0.0"
+          
+          # Test command to verify the wheel works
+          CIBW_TEST_COMMAND: |
+            python -c "import leann_backend_diskann; print('DiskANN backend imported successfully')"
+          
+          # Skip problematic configurations
+          CIBW_TEST_SKIP: "*-macosx_arm64"  # Skip ARM64 tests on GitHub Actions
+          
+          # Test dependencies - avoid pyzmq due to manylinux2014 compatibility issues
+          CIBW_TEST_REQUIRES: "pytest numpy"
+          
+          CIBW_ENVIRONMENT: |
+            CMAKE_BUILD_PARALLEL_LEVEL=8
+            Python_FIND_VIRTUALENV=ONLY
+            Python3_FIND_VIRTUALENV=ONLY
+          
+          # Linux-specific environment variables
+          CIBW_ENVIRONMENT_LINUX: |
+            CMAKE_BUILD_PARALLEL_LEVEL=8
+            CMAKE_PREFIX_PATH=$VIRTUAL_ENV
+            Python_FIND_VIRTUALENV=ONLY
+            Python3_FIND_VIRTUALENV=ONLY
+            Python_FIND_STRATEGY=LOCATION
+            Python3_FIND_STRATEGY=LOCATION
+            Python_EXECUTABLE=$VIRTUAL_ENV/bin/python
+            Python3_EXECUTABLE=$VIRTUAL_ENV/bin/python
+          
+          # macOS-specific environment variables
+          CIBW_ENVIRONMENT_MACOS: |
+            CMAKE_BUILD_PARALLEL_LEVEL=8
+            MACOSX_DEPLOYMENT_TARGET=11.0
+            CMAKE_OSX_DEPLOYMENT_TARGET=11.0
+            Python_FIND_VIRTUALENV=ONLY
+            Python3_FIND_VIRTUALENV=ONLY
+      
+      - uses: actions/upload-artifact@v4
+        with:
+          name: wheels-${{ matrix.os }}
+          path: ./wheelhouse/*.whl 

.github/workflows/build-reusable.yml

@@ -0,0 +1,313 @@
+name: Reusable Build
+
+on:
+  workflow_call:
+    inputs:
+      ref:
+        description: 'Git ref to build'
+        required: false
+        type: string
+        default: ''
+
+jobs:
+  build:
+    name: Build ${{ matrix.os }} Python ${{ matrix.python }}
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - os: ubuntu-latest
+            python: '3.9'
+            container: 'quay.io/pypa/manylinux2014_x86_64'
+          - os: ubuntu-latest
+            python: '3.10'
+            container: 'quay.io/pypa/manylinux2014_x86_64'
+          - os: ubuntu-latest
+            python: '3.11'
+            container: 'quay.io/pypa/manylinux2014_x86_64'
+          - os: ubuntu-latest
+            python: '3.12'
+            container: 'quay.io/pypa/manylinux2014_x86_64'
+          - os: ubuntu-latest
+            python: '3.13'
+            container: 'quay.io/pypa/manylinux2014_x86_64'
+          - os: macos-latest
+            python: '3.9'
+            container: ''
+          - os: macos-latest
+            python: '3.10'
+            container: ''
+          - os: macos-latest
+            python: '3.11'
+            container: ''
+          - os: macos-latest
+            python: '3.12'
+            container: ''
+          - os: macos-latest
+            python: '3.13'
+            container: ''
+    runs-on: ${{ matrix.os }}
+    container: ${{ matrix.container }}
+    
+    steps:
+      # For manylinux2014 compatibility, we'll handle checkout differently
+      - uses: actions/checkout@v4
+        if: matrix.container == ''
+        with:
+          ref: ${{ inputs.ref }}
+          submodules: recursive
+      
+      # Manual checkout for containers to avoid Node.js compatibility issues
+      - name: Manual checkout in container
+        if: matrix.container != ''
+        run: |
+          # Install git if not available
+          yum install -y git || true
+          
+          # Configure git to handle the directory ownership issue
+          git config --global --add safe.directory ${GITHUB_WORKSPACE}
+          git config --global --add safe.directory /__w/LEANN/LEANN
+          git config --global --add safe.directory /github/workspace
+          git config --global --add safe.directory $(pwd)
+          
+          # Clone the repository manually in the container
+          git init
+          git remote add origin https://github.com/${GITHUB_REPOSITORY}.git
+          
+          # Fetch the appropriate ref
+          if [ -n "${{ inputs.ref }}" ]; then
+            git fetch --depth=1 origin ${{ inputs.ref }}
+          else
+            git fetch --depth=1 origin ${GITHUB_SHA}
+          fi
+          git checkout FETCH_HEAD
+          
+          # Initialize submodules
+          git submodule update --init --recursive
+      
+      - name: Setup Python (macOS and regular Ubuntu)
+        if: matrix.container == ''
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python }}
+      
+      - name: Setup Python (manylinux container)
+        if: matrix.container != ''
+        run: |
+          # Use the pre-installed Python version in manylinux container
+          # Convert Python version format (3.9 -> 39, 3.10 -> 310, etc.)
+          PY_VER=$(echo "${{ matrix.python }}" | sed 's/\.//g')
+          /opt/python/cp${PY_VER}-*/bin/python -m pip install --upgrade pip
+          # Create symlinks for convenience
+          ln -sf /opt/python/cp${PY_VER}-*/bin/python /usr/local/bin/python
+          ln -sf /opt/python/cp${PY_VER}-*/bin/pip /usr/local/bin/pip
+      
+      - name: Install uv (macOS and regular Ubuntu)
+        if: matrix.container == ''
+        uses: astral-sh/setup-uv@v4
+      
+      - name: Install uv (manylinux container)
+        if: matrix.container != ''
+        run: |
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          echo "$HOME/.cargo/bin" >> $GITHUB_PATH
+      
+      - name: Install system dependencies (Ubuntu - regular)
+        if: runner.os == 'Linux' && matrix.container == ''
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y libomp-dev libboost-all-dev protobuf-compiler libzmq3-dev \
+            pkg-config libopenblas-dev patchelf libabsl-dev libaio-dev libprotobuf-dev
+          
+          # Install Intel MKL for DiskANN
+          wget -q https://registrationcenter-download.intel.com/akdlm/IRC_NAS/79153e0f-74d7-45af-b8c2-258941adf58a/intel-onemkl-2025.0.0.940.sh
+          sudo sh intel-onemkl-2025.0.0.940.sh -a --components intel.oneapi.lin.mkl.devel --action install --eula accept -s
+          source /opt/intel/oneapi/setvars.sh
+          echo "MKLROOT=/opt/intel/oneapi/mkl/latest" >> $GITHUB_ENV
+          echo "LD_LIBRARY_PATH=/opt/intel/oneapi/mkl/latest/lib/intel64:$LD_LIBRARY_PATH" >> $GITHUB_ENV
+      
+      - name: Install system dependencies (manylinux container)
+        if: runner.os == 'Linux' && matrix.container != ''
+        run: |
+          # manylinux2014 uses yum instead of apt
+          # Update yum cache first
+          yum clean all
+          yum makecache
+          
+          # Install EPEL repository
+          yum install -y epel-release || true
+          
+          # Update cache again after EPEL
+          yum makecache || true
+          
+          # Install development packages
+          # Note: Some packages might have different names in CentOS 7
+          yum install -y \
+            gcc-c++ \
+            boost-devel \
+            protobuf-compiler \
+            protobuf-devel \
+            zeromq-devel \
+            pkgconfig \
+            openblas-devel \
+            cmake || {
+              echo "Some packages failed to install, trying alternatives..."
+              # Try alternative package names
+              yum install -y libzmq3-devel || true
+              yum install -y libzmq-devel || true
+          }
+          
+          # Install optional packages that might not be available
+          yum install -y libaio-devel || echo "libaio-devel not available, continuing..."
+          
+          # Verify zmq installation and create pkg-config file if needed
+          if [ ! -f /usr/lib64/pkgconfig/libzmq.pc ] && [ ! -f /usr/lib/pkgconfig/libzmq.pc ]; then
+            echo "Creating libzmq.pc file..."
+            mkdir -p /usr/lib64/pkgconfig
+            cat > /usr/lib64/pkgconfig/libzmq.pc << 'EOF'
+          prefix=/usr
+          exec_prefix=${prefix}
+          libdir=${exec_prefix}/lib64
+          includedir=${prefix}/include
+          
+          Name: libzmq
+          Description: ZeroMQ library
+          Version: 4.1.4
+          Libs: -L${libdir} -lzmq
+          Cflags: -I${includedir}
+          EOF
+          fi
+          
+          # Update PKG_CONFIG_PATH
+          echo "PKG_CONFIG_PATH=/usr/lib64/pkgconfig:/usr/lib/pkgconfig:$PKG_CONFIG_PATH" >> $GITHUB_ENV
+          
+          # Build tools are pre-installed in manylinux
+          # MKL is more complex in container, skip for now and use OpenBLAS
+      
+      - name: Install system dependencies (macOS)
+        if: runner.os == 'macOS'
+        run: |
+          brew install llvm libomp boost protobuf zeromq
+      
+      - name: Install build dependencies
+        run: |
+          if [[ -n "${{ matrix.container }}" ]]; then
+            # In manylinux container, use regular pip
+            pip install scikit-build-core numpy swig Cython pybind11 auditwheel
+          else
+            # Regular environment, use uv
+            uv pip install --system scikit-build-core numpy swig Cython pybind11
+            if [[ "$RUNNER_OS" == "Linux" ]]; then
+              uv pip install --system auditwheel
+            else
+              uv pip install --system delocate
+            fi
+          fi
+      
+      - name: Build packages
+        run: |
+          # Choose build command based on environment
+          if [[ -n "${{ matrix.container }}" ]]; then
+            BUILD_CMD="pip wheel . --no-deps -w dist"
+          else
+            BUILD_CMD="uv build --wheel --python python"
+          fi
+          
+          # Build core (platform independent)
+          if [ "${{ matrix.os }}" == "ubuntu-latest" ]; then
+            cd packages/leann-core
+            if [[ -n "${{ matrix.container }}" ]]; then
+              pip wheel . --no-deps -w dist
+            else
+              uv build
+            fi
+            cd ../..
+          fi
+          
+          # Build HNSW backend
+          cd packages/leann-backend-hnsw
+          if [ "${{ matrix.os }}" == "macos-latest" ]; then
+            CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ $BUILD_CMD
+          else
+            eval $BUILD_CMD
+          fi
+          cd ../..
+          
+          # Build DiskANN backend
+          cd packages/leann-backend-diskann
+          if [ "${{ matrix.os }}" == "macos-latest" ]; then
+            CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ $BUILD_CMD
+          else
+            eval $BUILD_CMD
+          fi
+          cd ../..
+          
+          # Build meta package (platform independent)
+          if [ "${{ matrix.os }}" == "ubuntu-latest" ]; then
+            cd packages/leann
+            if [[ -n "${{ matrix.container }}" ]]; then
+              pip wheel . --no-deps -w dist
+            else
+              uv build
+            fi
+            cd ../..
+          fi
+      
+      - name: Repair wheels (Linux)
+        if: runner.os == 'Linux'
+        run: |
+          # Repair HNSW wheel
+          cd packages/leann-backend-hnsw
+          if [ -d dist ]; then
+            # Show what platform auditwheel will use
+            auditwheel show dist/*.whl || true
+            # Let auditwheel auto-detect the appropriate manylinux tag
+            auditwheel repair dist/*.whl -w dist_repaired
+            rm -rf dist
+            mv dist_repaired dist
+          fi
+          cd ../..
+          
+          # Repair DiskANN wheel
+          cd packages/leann-backend-diskann
+          if [ -d dist ]; then
+            # Show what platform auditwheel will use
+            auditwheel show dist/*.whl || true
+            # Let auditwheel auto-detect the appropriate manylinux tag
+            auditwheel repair dist/*.whl -w dist_repaired
+            rm -rf dist
+            mv dist_repaired dist
+          fi
+          cd ../..
+      
+      - name: Repair wheels (macOS)
+        if: runner.os == 'macOS'
+        run: |
+          # Repair HNSW wheel
+          cd packages/leann-backend-hnsw
+          if [ -d dist ]; then
+            delocate-wheel -w dist_repaired -v dist/*.whl
+            rm -rf dist
+            mv dist_repaired dist
+          fi
+          cd ../..
+          
+          # Repair DiskANN wheel
+          cd packages/leann-backend-diskann
+          if [ -d dist ]; then
+            delocate-wheel -w dist_repaired -v dist/*.whl
+            rm -rf dist
+            mv dist_repaired dist
+          fi
+          cd ../..
+      
+      - name: List built packages
+        run: |
+          echo "📦 Built packages:"
+          find packages/*/dist -name "*.whl" -o -name "*.tar.gz" | sort
+      
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: packages-${{ matrix.os }}-py${{ matrix.python }}${{ matrix.container && '-manylinux' || '' }}
+          path: packages/*/dist/ 

.github/workflows/release-manual.yml

@@ -0,0 +1,118 @@
+name: Release
+
+on:
+  workflow_dispatch:
+    inputs:
+      version:
+        description: 'Version to release (e.g., 0.1.2)'
+        required: true
+        type: string
+      use_cibuildwheel:
+        description: 'Use cibuildwheel for better compatibility (recommended for Colab)'
+        required: false
+        type: boolean
+        default: false
+
+jobs:
+  update-version:
+    name: Update Version
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    outputs:
+      commit-sha: ${{ steps.push.outputs.commit-sha }}
+    
+    steps:
+      - uses: actions/checkout@v4
+      
+      - name: Validate version
+        run: |
+          if ! [[ "${{ inputs.version }}" =~ ^[0-9]+\.[0-9]+\.[0-9]+$ ]]; then
+            echo "❌ Invalid version format"
+            exit 1
+          fi
+          echo "✅ Version format valid"
+      
+      - name: Update versions and push
+        id: push
+        run: |
+          ./scripts/bump_version.sh ${{ inputs.version }}
+          git config user.name "GitHub Actions"
+          git config user.email "actions@github.com"
+          git add packages/*/pyproject.toml
+          git commit -m "chore: release v${{ inputs.version }}"
+          git push origin main
+          
+          COMMIT_SHA=$(git rev-parse HEAD)
+          echo "commit-sha=$COMMIT_SHA" >> $GITHUB_OUTPUT
+          echo "✅ Pushed version update: $COMMIT_SHA"
+
+  build-packages-reusable:
+    name: Build packages (Standard)
+    needs: update-version
+    if: ${{ !inputs.use_cibuildwheel }}
+    uses: ./.github/workflows/build-reusable.yml
+    with:
+      ref: ${{ needs.update-version.outputs.commit-sha }}
+  
+  build-packages-cibuildwheel:
+    name: Build packages (cibuildwheel)
+    needs: update-version
+    if: ${{ inputs.use_cibuildwheel }}
+    uses: ./.github/workflows/build-cibuildwheel.yml
+    with:
+      ref: ${{ needs.update-version.outputs.commit-sha }}
+
+  publish:
+    name: Publish and Release
+    needs: [update-version, build-packages-reusable, build-packages-cibuildwheel]
+    if: always() && needs.update-version.result == 'success' && (needs.build-packages-reusable.result == 'success' || needs.build-packages-cibuildwheel.result == 'success')
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: ${{ needs.update-version.outputs.commit-sha }}
+      
+      - name: Download all artifacts
+        uses: actions/download-artifact@v4
+        with:
+          path: dist-artifacts
+      
+      - name: Collect packages
+        run: |
+          mkdir -p dist
+          find dist-artifacts -name "*.whl" -exec cp {} dist/ \;
+          find dist-artifacts -name "*.tar.gz" -exec cp {} dist/ \;
+          
+          echo "📦 Packages to publish:"
+          ls -la dist/
+      
+      - name: Publish to PyPI
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+        run: |
+          if [ -z "$TWINE_PASSWORD" ]; then
+            echo "❌ PYPI_API_TOKEN not configured!"
+            exit 1
+          fi
+          
+          pip install twine
+          twine upload dist/* --skip-existing --verbose
+          
+          echo "✅ Published to PyPI!"
+      
+      - name: Create release
+        run: |
+          git tag "v${{ inputs.version }}"
+          git push origin "v${{ inputs.version }}"
+          
+          gh release create "v${{ inputs.version }}" \
+            --title "Release v${{ inputs.version }}" \
+            --notes "🚀 Released to PyPI: https://pypi.org/project/leann/${{ inputs.version }}/" \
+            --latest
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/test-manylinux.yml

@@ -0,0 +1,60 @@
+name: Test Manylinux Build
+
+on:
+  workflow_dispatch:
+  pull_request:
+    branches: [ main ]
+    paths:
+      - '.github/workflows/**'
+      - 'packages/**'
+      - 'pyproject.toml'
+  push:
+    branches:
+      - 'fix/manylinux-*'
+      - 'test/build-*'
+
+jobs:
+  build:
+    uses: ./.github/workflows/build-cibuildwheel.yml
+    
+  test-install:
+    needs: build
+    runs-on: ubuntu-22.04  # Simulating Colab environment
+    strategy:
+      matrix:
+        python-version: ['3.10', '3.11', '3.12']
+    steps:
+      - uses: actions/checkout@v4
+        
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          
+      - name: Download artifacts
+        uses: actions/download-artifact@v4
+        with:
+          pattern: wheels-*
+          path: dist
+          merge-multiple: true
+          
+      - name: Test installation
+        run: |
+          python -m pip install --upgrade pip
+          # Find and install the appropriate wheels
+          pip install dist/leann_core-*.whl
+          pip install dist/leann_backend_hnsw-*manylinux*.whl
+          pip install dist/leann-*.whl
+          
+      - name: Test import
+        run: |
+          python -c "
+          import leann
+          from leann import LeannBuilder, LeannSearcher
+          print('Successfully imported leann modules')
+          
+          # Quick functionality test
+          builder = LeannBuilder(backend_name='hnsw')
+          builder.add_text('Test document')
+          print('LeannBuilder created and used successfully')
+          " 

.gitignore

@@ -12,7 +12,6 @@ outputs/
 *.idx 
 *.map
 .history/
-scripts/
 lm_eval.egg-info/
 demo/experiment_results/**/*.json
 *.jsonl
@@ -84,4 +83,6 @@ test_*.py
 packages/leann-backend-diskann/third_party/DiskANN/_deps/
 
 *.meta.json
-*.passages.json
+*.passages.json
+
+batchtest.py

.vscode/extensions.json

@@ -1,9 +0,0 @@
-{
-    "recommendations": [
-        "llvm-vs-code-extensions.vscode-clangd",
-        "ms-python.python",
-        "ms-vscode.cmake-tools",
-        "vadimcn.vscode-lldb",
-        "eamodio.gitlens",
-    ]
-}

.vscode/launch.json

@@ -1,283 +0,0 @@
-{
-    // Use IntelliSense to learn about possible attributes.
-    // Hover to view descriptions of existing attributes.
-    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
-    "version": "0.2.0",
-    "configurations": [
-        // new emdedder
-        {
-            "name": "New Embedder",
-            "type": "debugpy",
-            "request": "launch",
-            "program": "demo/main.py",
-            "console": "integratedTerminal",
-            "args": [
-                "--search",
-                "--use-original",
-                "--domain",
-                "dpr",
-                "--nprobe",
-                "5000",
-                "--load",
-                "flat",
-                "--embedder",
-                "intfloat/multilingual-e5-small"
-            ]
-        }
-        //python /home/ubuntu/Power-RAG/faiss/demo/simple_build.py
-        {
-            "name": "main.py",
-            "type": "debugpy",
-            "request": "launch",
-            "program": "demo/main.py",
-            "console": "integratedTerminal",
-            "cwd": "${workspaceFolder}",
-            "args": [
-                "--query",
-                "1000",
-                "--load",
-                "bm25"
-            ]
-        },
-        {
-            "name": "Simple Build",
-            "type": "lldb",
-            "request": "launch",
-            "program": "${workspaceFolder}/.venv/bin/python",
-            "console": "integratedTerminal",
-            "cwd": "${workspaceFolder}",
-            "args": [
-                "faiss/demo/simple_build.py"
-            ],
-            "env": {
-                "LD_PRELOAD": "/lib/x86_64-linux-gnu/libmkl_core.so:/lib/x86_64-linux-gnu/libmkl_intel_thread.so:/lib/x86_64-linux-gnu/libmkl_intel_lp64.so:/lib/x86_64-linux-gnu/libiomp5.so"
-            }
-        },
-        //# Fix for Intel MKL error
-        //export LD_PRELOAD=/lib/x86_64-linux-gnu/libmkl_core.so:/lib/x86_64-linux-gnu/libmkl_intel_thread.so:/lib/x86_64-linux-gnu/libmkl_intel_lp64.so:/lib/x86_64-linux-gnu/libiomp5.so
-        //python faiss/demo/build_demo.py
-        {
-            "name": "Build Demo",
-            "type": "lldb",
-            "request": "launch",
-            "program": "${workspaceFolder}/.venv/bin/python",
-            "console": "integratedTerminal",
-            "cwd": "${workspaceFolder}",
-            "args": [
-                "faiss/demo/build_demo.py"
-            ],
-            "env": {
-                "LD_PRELOAD": "/lib/x86_64-linux-gnu/libmkl_core.so:/lib/x86_64-linux-gnu/libmkl_intel_thread.so:/lib/x86_64-linux-gnu/libmkl_intel_lp64.so:/lib/x86_64-linux-gnu/libiomp5.so"
-            }
-        },
-        {
-            "name": "DiskANN Serve",
-            "type": "lldb",
-            "request": "launch",
-            "program": "${workspaceFolder}/.venv/bin/python",
-            "console": "integratedTerminal",
-            "cwd": "${workspaceFolder}",
-            "args": [
-                "demo/main.py",
-                "--mode",
-                "serve",
-                "--engine",
-                "sglang",
-                "--load-indices",
-                "diskann",
-                "--domain",
-                "rpj_wiki",
-                "--lazy-load",
-                "--recompute-beighbor-embeddings",
-                "--port",
-                "8082",
-                "--diskann-search-memory-maximum",
-                "2",
-                "--diskann-graph",
-                "240",
-                "--search-only"
-            ],
-            "env": {
-                "PYTHONPATH": "${workspaceFolder}/faiss_repo/build/faiss/python:$PYTHONPATH"
-            },
-            "preLaunchTask": "CMake: build",
-        },
-        {
-            "name": "DiskANN Serve MAC",
-            "type": "lldb",
-            "request": "launch",
-            "program": "${workspaceFolder}/.venv/bin/python",
-            "console": "integratedTerminal",
-            "cwd": "${workspaceFolder}",
-            "args": [
-                "demo/main.py",
-                "--mode",
-                "serve",
-                "--engine",
-                "ollama",
-                "--load-indices",
-                "diskann",
-                "--domain",
-                "rpj_wiki",
-                "--lazy-load",
-                "--recompute-beighbor-embeddings"
-            ],
-            "preLaunchTask": "CMake: build",
-            "env": {
-                "KMP_DUPLICATE_LIB_OK": "TRUE",
-                "OMP_NUM_THREADS": "1",
-                "MKL_NUM_THREADS": "1",
-                "DYLD_INSERT_LIBRARIES": "/Users/ec2-user/Power-RAG/.venv/lib/python3.10/site-packages/torch/lib/libomp.dylib",
-                "KMP_BLOCKTIME": "0"
-            }
-        },
-        {
-            "name": "Python Debugger: Current File with Arguments",
-            "type": "debugpy",
-            "request": "launch",
-            "program": "ric/main_ric.py",
-            "console": "integratedTerminal",
-            "cwd": "${workspaceFolder}",
-            "args": [
-                "--config-name",
-                "${input:configSelection}"
-            ],
-            "justMyCode": false
-        },
-        //python ./demo/validate_equivalence.py sglang
-        {
-            "name": "Validate Equivalence",
-            "type": "debugpy",
-            "request": "launch",
-            "program": "demo/validate_equivalence.py",
-            "console": "integratedTerminal",
-            "args": [
-                "sglang"
-            ],
-        },
-        //python demo/retrieval_demo.py --engine sglang  --skip-embeddings --domain dpr --load-indices flat ivf_flat
-        {
-            "name": "Retrieval Demo",
-            "type": "debugpy",
-            "request": "launch",
-            "program": "demo/retrieval_demo.py",
-            "console": "integratedTerminal",
-            "args": [
-                "--engine",
-                "vllm",
-                "--skip-embeddings",
-                "--domain",
-                "dpr",
-                "--load-indices",
-                // "flat",
-                "ivf_flat"
-            ],
-        },
-        //python demo/retrieval_demo.py --engine sglang  --skip-embeddings --domain dpr  --load-indices  diskann --hnsw-M 64 --hnsw-efConstruction 150 --hnsw-efSearch 128  --hnsw-sq-bits 8 
-        {
-            "name": "Retrieval Demo DiskANN",
-            "type": "debugpy",
-            "request": "launch",
-            "program": "demo/retrieval_demo.py",
-            "console": "integratedTerminal",
-            "args": [
-                "--engine",
-                "sglang",
-                "--skip-embeddings",
-                "--domain",
-                "dpr",
-                "--load-indices",
-                "diskann",
-                "--hnsw-M",
-                "64",
-                "--hnsw-efConstruction",
-                "150",
-                "--hnsw-efSearch",
-                "128",
-                "--hnsw-sq-bits",
-                "8"
-            ],
-        },
-        {
-            "name": "Find Probe",
-            "type": "debugpy",
-            "request": "launch",
-            "program": "find_probe.py",
-            "console": "integratedTerminal",
-            "cwd": "${workspaceFolder}",
-        },
-        {
-            "name": "Python: Attach",
-            "type": "debugpy",
-            "request": "attach",
-            "processId": "${command:pickProcess}",
-            "justMyCode": true
-        },
-        {
-            "name": "Edge RAG",
-            "type": "lldb",
-            "request": "launch",
-            "program": "${workspaceFolder}/.venv/bin/python",
-            "console": "integratedTerminal",
-            "cwd": "${workspaceFolder}",
-            "args": [
-                "edgerag_demo.py"
-            ],
-            "env": {
-                "LD_PRELOAD": "/lib/x86_64-linux-gnu/libiomp5.so /lib/x86_64-linux-gnu/libmkl_core.so /lib/x86_64-linux-gnu/libmkl_intel_lp64.so /lib/x86_64-linux-gnu/libmkl_intel_thread.so",
-                "MKL_NUM_THREADS": "1",
-                "OMP_NUM_THREADS": "1",
-            }
-        },
-        {
-            "name": "Launch Embedding Server",
-            "type": "debugpy",
-            "request": "launch",
-            "program": "demo/embedding_server.py",
-            "console": "integratedTerminal",
-            "cwd": "${workspaceFolder}",
-            "args": [
-                "--domain",
-                "rpj_wiki",
-                "--zmq-port",
-                "5556",
-            ]
-        },
-        {
-            "name": "HNSW Serve",
-            "type": "lldb",
-            "request": "launch",
-            "program": "${workspaceFolder}/.venv/bin/python",
-            "console": "integratedTerminal",
-            "cwd": "${workspaceFolder}",
-            "args": [
-                "demo/main.py",
-                "--domain",
-                "rpj_wiki",
-                "--load",
-                "hnsw",
-                "--mode",
-                "serve",
-                "--search",
-                "--skip-pa",
-                "--recompute",
-                "--hnsw-old"
-            ],
-            "env": {
-                "LD_PRELOAD": "/lib/x86_64-linux-gnu/libmkl_core.so:/lib/x86_64-linux-gnu/libmkl_intel_thread.so:/lib/x86_64-linux-gnu/libmkl_intel_lp64.so:/lib/x86_64-linux-gnu/libiomp5.so"
-            }
-        },
-    ],
-    "inputs": [
-        {
-            "id": "configSelection",
-            "type": "pickString",
-            "description": "Select a configuration",
-            "options": [
-                "example_config",
-                "vllm_gritlm"
-            ],
-            "default": "example_config"
-        }
-    ],
-}

.vscode/settings.json

@@ -1,43 +0,0 @@
-{
-    "python.analysis.extraPaths": [
-        "./sglang_repo/python"
-    ],
-    "cmake.sourceDirectory": "${workspaceFolder}/DiskANN",
-    "cmake.configureArgs": [
-        "-DPYBIND=True",
-        "-DUPDATE_EDITABLE_INSTALL=ON",
-    ],
-    "cmake.environment": {
-        "PATH": "/Users/ec2-user/Power-RAG/.venv/bin:${env:PATH}"
-    },
-    "cmake.buildDirectory": "${workspaceFolder}/build",
-    "files.associations": {
-        "*.tcc": "cpp",
-        "deque": "cpp",
-        "string": "cpp",
-        "unordered_map": "cpp",
-        "vector": "cpp",
-        "map": "cpp",
-        "unordered_set": "cpp",
-        "atomic": "cpp",
-        "inplace_vector": "cpp",
-        "*.ipp": "cpp",
-        "forward_list": "cpp",
-        "list": "cpp",
-        "any": "cpp",
-        "system_error": "cpp",
-        "__hash_table": "cpp",
-        "__split_buffer": "cpp",
-        "__tree": "cpp",
-        "ios": "cpp",
-        "set": "cpp",
-        "__string": "cpp",
-        "string_view": "cpp",
-        "ranges": "cpp",
-        "iosfwd": "cpp"
-    },
-    "lldb.displayFormat": "auto",
-    "lldb.showDisassembly": "auto",
-    "lldb.dereferencePointers": true,
-    "lldb.consoleMode": "commands",
-}

.vscode/tasks.json

@@ -1,16 +0,0 @@
-{
-	"version": "2.0.0",
-	"tasks": [
-		{
-			"type": "cmake",
-			"label": "CMake: build",
-			"command": "build",
-			"targets": [
-				"all"
-			],
-			"group": "build",
-			"problemMatcher": [],
-			"detail": "CMake template build task"
-		}
-	]
-}

MANYLINUX_BUILD_STRATEGY.md

@@ -0,0 +1,50 @@
+# Manylinux Build Strategy
+
+## Problem
+Google Colab requires wheels compatible with `manylinux_2_35_x86_64` or earlier. Our previous builds were producing `manylinux_2_39_x86_64` wheels, which are incompatible.
+
+## Solution
+We're using `cibuildwheel` with `manylinux_2_35` images to build wheels that are compatible with Google Colab while maintaining modern toolchain features.
+
+### Key Changes
+
+1. **cibuildwheel Configuration**
+   - Using `manylinux2014` images (provides `manylinux_2_17` compatibility)
+   - Using `yum` package manager (CentOS 7 based)
+   - Installing `cmake3` and creating symlink for compatibility
+
+2. **Build Matrix**
+   - Python versions: 3.9, 3.10, 3.11, 3.12, 3.13
+   - Platforms: Linux (x86_64), macOS
+   - No Windows support (not required)
+
+3. **Dependencies**
+   - Linux: gcc-c++, boost-devel, zeromq-devel, openblas-devel, cmake3
+   - macOS: boost, zeromq, openblas, cmake (via Homebrew)
+
+4. **Environment Variables**
+   - `CMAKE_BUILD_PARALLEL_LEVEL=8`: Speed up builds
+   - `Python_FIND_VIRTUALENV=ONLY`: Help CMake find Python in cibuildwheel env
+   - `Python3_FIND_VIRTUALENV=ONLY`: Alternative variable for compatibility
+
+## Testing Strategy
+
+1. **CI Pipeline**: `test-manylinux.yml`
+   - Triggers on PR to main, manual dispatch, or push to `fix/manylinux-*` branches
+   - Builds wheels using cibuildwheel
+   - Tests installation on Ubuntu 22.04 (simulating Colab)
+
+2. **Local Testing**
+   ```bash
+   # Download built wheels
+   # Test in fresh environment
+   python -m venv test_env
+   source test_env/bin/activate
+   pip install leann_core-*.whl leann_backend_hnsw-*manylinux*.whl leann-*.whl
+   python -c "from leann import LeannBuilder; print('Success!')"
+   ```
+
+## References
+- [cibuildwheel documentation](https://cibuildwheel.readthedocs.io/)
+- [manylinux standards](https://github.com/pypa/manylinux)
+- [PEP 599 - manylinux2014](https://peps.python.org/pep-0599/) 

README.md

@@ -12,11 +12,11 @@
     The smallest vector index in the world. RAG Everything with LEANN!
 </h2>
 
-LEANN is a revolutionary vector database that democratizes personal AI. Transform your laptop into a powerful RAG system that can index and search through millions of documents while using **[97% less storage]** than traditional solutions **without accuracy loss**.
+LEANN is a revolutionary vector database that democratizes personal AI. Transform your laptop into a powerful RAG system that can index and search through millions of documents while using **97% less storage** than traditional solutions **without accuracy loss**.
 
-LEANN achieves this through *graph-based selective recomputation* with *high-degree preserving pruning*, computing embeddings on-demand instead of storing them all. [Illustration →](#️-architecture--how-it-works) | [Paper →](https://arxiv.org/abs/2506.08276)
+LEANN achieves this through *graph-based selective recomputation* with *high-degree preserving pruning*, computing embeddings on-demand instead of storing them all. [Illustration Fig →](#️-architecture--how-it-works) | [Paper →](https://arxiv.org/abs/2506.08276)
 
-**Ready to RAG Everything?** Transform your laptop into a personal AI assistant that can search your **[file system](#process-any-documents-pdf-txt-md)**, **[emails](#search-your-entire-life)**, **[browser history](#time-machine-for-the-web)**, **[chat history](#wechat-detective)**, or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
+**Ready to RAG Everything?** Transform your laptop into a personal AI assistant that can search your **[file system](#-personal-data-manager-process-any-documents-pdf-txt-md)**, **[emails](#-your-personal-email-secretary-rag-on-apple-mail)**, **[browser history](#-time-machine-for-the-web-rag-your-entire-browser-history)**, **[chat history](#-wechat-detective-unlock-your-golden-memories)**, or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
 
 
 
@@ -26,9 +26,8 @@ LEANN achieves this through *graph-based selective recomputation* with *high-deg
   <img src="assets/effects.png" alt="LEANN vs Traditional Vector DB Storage Comparison" width="70%">
 </p>
 
-**The numbers speak for themselves:** Index 60 million Wikipedia articles in just 6GB instead of 201GB. From emails to browser history, everything fits on your laptop. [See detailed benchmarks below ↓](#storage-usage-comparison)
+> **The numbers speak for themselves:** Index 60 million Wikipedia chunks in just 6GB instead of 201GB. From emails to browser history, everything fits on your laptop. [See detailed benchmarks for different applications below ↓](#storage-comparison)
 
-## Why This Matters
 
 🔒 **Privacy:** Your data never leaves your laptop. No OpenAI, no cloud, no "terms of service".
 
@@ -38,8 +37,8 @@ LEANN achieves this through *graph-based selective recomputation* with *high-deg
 
 ✨ **No Accuracy Loss:** Maintain the same search quality as heavyweight solutions while using 97% less storage.
 
-## Quick Start in 1 minute
-
+## Installation
+> `pip leann` coming soon!
 ```bash
 git clone git@github.com:yichuan-w/LEANN.git leann
 cd leann
@@ -48,33 +47,30 @@ git submodule update --init --recursive
 
 **macOS:**
 ```bash
-brew install llvm libomp boost protobuf
-export CC=$(brew --prefix llvm)/bin/clang
-export CXX=$(brew --prefix llvm)/bin/clang++
+brew install llvm libomp boost protobuf zeromq pkgconf
 
 # Install with HNSW backend (default, recommended for most users)
-uv sync
-
-# Or add DiskANN backend if you want to test more options
-uv sync --extra diskann
+# Install uv first if you don't have it:
+# curl -LsSf https://astral.sh/uv/install.sh | sh
+# See: https://docs.astral.sh/uv/getting-started/installation/#installation-methods
+CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ uv sync
 ```
 
-**Linux (Ubuntu/Debian):**
+**Linux:**
 ```bash
-sudo apt-get install libomp-dev libboost-all-dev protobuf-compiler libabsl-dev libmkl-full-dev libaio-dev
+sudo apt-get install libomp-dev libboost-all-dev protobuf-compiler libabsl-dev libmkl-full-dev libaio-dev libzmq3-dev
 
 # Install with HNSW backend (default, recommended for most users)
 uv sync
-
-# Or add DiskANN backend if you want to test more options
-uv sync --extra diskann
 ```
 
-**Ollama Setup (Optional for Local LLM):**
 
-*We support both hf-transformers and Ollama for local LLMs. Ollama is recommended for faster performance.*
+**Ollama Setup (Recommended for full privacy):**
 
-*macOS:*
+> *You can skip this installation if you only want to use OpenAI API for generation.*
+
+
+**macOS:**
 
 First, [download Ollama for macOS](https://ollama.com/download/mac).
 
@@ -83,7 +79,7 @@ First, [download Ollama for macOS](https://ollama.com/download/mac).
 ollama pull llama3.2:1b
 ```
 
-*Linux:*
+**Linux:**
 ```bash
 # Install Ollama
 curl -fsSL https://ollama.ai/install.sh | sh
@@ -95,62 +91,78 @@ ollama serve &
 ollama pull llama3.2:1b
 ```
 
-You can also replace `llama3.2:1b` to `deepseek-r1:1.5b` or `qwen3:4b` for better performance but higher memory usage.
+## Quick Start in 30s
 
-## Dead Simple API
-
-Just 3 lines of code. Our declarative API makes RAG as easy as writing a config file:
+Our declarative API makes RAG as easy as writing a config file.
+[Try in this ipynb file →](demo.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yichuan-w/LEANN/blob/main/demo.ipynb)
 
 ```python
-from leann.api import LeannBuilder, LeannSearcher
+from leann.api import LeannBuilder, LeannSearcher, LeannChat
 
-# 1. Build index (no embeddings stored!)
+# 1. Build the index (no embeddings stored!)
 builder = LeannBuilder(backend_name="hnsw")
 builder.add_text("C# is a powerful programming language")
-builder.add_text("Python is a powerful programming language")
-builder.add_text("Machine learning transforms industries")  
+builder.add_text("Python is a powerful programming language and it is very popular")
+builder.add_text("Machine learning transforms industries")
 builder.add_text("Neural networks process complex data")
-builder.add_text("Leann is a great storage saving engine for RAG on your macbook")
+builder.add_text("Leann is a great storage saving engine for RAG on your MacBook")
 builder.build_index("knowledge.leann")
 
 # 2. Search with real-time embeddings
 searcher = LeannSearcher("knowledge.leann")
-results = searcher.search("C++ programming languages", top_k=2, recompute_beighbor_embeddings=True)
-print(results)
+results = searcher.search("programming languages", top_k=2)
+
+# 3. Chat with LEANN using retrieved results
+llm_config = {
+    "type": "ollama",
+    "model": "llama3.2:1b"
+}
+
+chat = LeannChat(index_path="knowledge.leann", llm_config=llm_config)
+response = chat.ask(
+    "Compare the two retrieved programming languages and say which one is more popular today.",
+    top_k=2,
+)
 ```
 
-**That's it.** No cloud setup, no API keys, no "fine-tuning". Just your data, your questions, your laptop.
+## RAG on Everything!
 
-[Try the interactive demo →](demo.ipynb)
+LEANN supports RAG on various data sources including documents (.pdf, .txt, .md), Apple Mail, Google Search History, WeChat, and more.
 
-## Wild Things You Can Do
+### 📄 Personal Data Manager: Process Any Documents (.pdf, .txt, .md)!
 
-LEANN supports RAGing a lot of data sources, like .pdf, .txt, .md, and also supports RAGing your WeChat, Google Search History, and more.
+Ask questions directly about your personal PDFs, documents, and any directory containing your files!
 
-### Process Any Documents (.pdf, .txt, .md)
+<p align="center">
+  <img src="videos/paper_clear.gif" alt="LEANN Document Search Demo" width="600">
+</p>
 
-Above we showed the Python API, while this CLI script demonstrates the same concepts while directly processing PDFs and documents.
+The example below asks a question about summarizing two papers (uses default data in `examples/data`):
 
 ```bash
 # Drop your PDFs, .txt, .md files into examples/data/
 uv run ./examples/main_cli_example.py
+```
 
+```
 # Or use python directly
 source .venv/bin/activate
 python ./examples/main_cli_example.py
 ```
 
-Uses Ollama `qwen3:8b` by default. For other models: `--llm openai --model gpt-4o` (requires `OPENAI_API_KEY` environment variable) or `--llm hf --model Qwen/Qwen3-4B`.
 
-**Works with any text format** - research papers, personal notes, presentations. Built with LlamaIndex for document parsing.
 
-### Search Your Entire Life
+### 📧 Your Personal Email Secretary: RAG on Apple Mail!
+
+<p align="center">
+  <img src="videos/mail_clear.gif" alt="LEANN Email Search Demo" width="600">
+</p>
+
+**Note:** You need to grant full disk access to your terminal/VS Code in System Preferences → Privacy & Security → Full Disk Access.
 ```bash
-python examples/mail_reader_leann.py
-# "What did my boss say about the Christmas party last year?"
-# "Find all emails from my mom about birthday plans"
+python examples/mail_reader_leann.py --query "What's the food I ordered by doordash or Uber eat mostly?"
 ```
-**90K emails → 14MB.** Finally, search your email like you search Google.
+**780K email chunks → 78MB storage** Finally, search your email like you search Google.
 
 <details>
 <summary><strong>📋 Click to expand: Command Examples</strong></summary>
@@ -183,13 +195,16 @@ Once the index is built, you can ask questions like:
 - "Show me emails about travel expenses"
 </details>
 
-### Time Machine for the Web  
+### 🔍 Time Machine for the Web: RAG Your Entire Google Browser History!
+
+<p align="center">
+  <img src="videos/google_clear.gif" alt="LEANN Browser History Search Demo" width="600">
+</p>
+
 ```bash
-python examples/google_history_reader_leann.py
-# "What was that AI paper I read last month?"
-# "Show me all the cooking videos I watched"
+python examples/google_history_reader_leann.py --query "Tell me my browser history about machine learning?"
 ```
-**38K browser entries → 6MB.** Your browser history becomes your personal search engine.
+**38K browser entries → 6MB storage.** Your browser history becomes your personal search engine.
 
 <details>
 <summary><strong>📋 Click to expand: Command Examples</strong></summary>
@@ -238,13 +253,17 @@ Once the index is built, you can ask questions like:
 
 </details>
 
-### WeChat Detective
+### 💬 WeChat Detective: Unlock Your Golden Memories!
+
+<p align="center">
+  <img src="videos/wechat_clear.gif" alt="LEANN WeChat Search Demo" width="600">
+</p>
 
 ```bash
-python examples/wechat_history_reader_leann.py
-# "Show me all group chats about weekend plans"
+python examples/wechat_history_reader_leann.py --query "Show me all group chats about weekend plans"
 ```
-**400K messages → 64MB.** Search years of chat history in any language.
+**400K messages → 64MB storage** Search years of chat history in any language.
+
 
 <details>
 <summary><strong>🔧 Click to expand: Installation Requirements</strong></summary>
@@ -255,7 +274,13 @@ First, you need to install the WeChat exporter:
 sudo packages/wechat-exporter/wechattweak-cli install
 ```
 
-**Troubleshooting**: If you encounter installation issues, check the [WeChatTweak-CLI issues page](https://github.com/sunnyyoung/WeChatTweak-CLI/issues/41).
+**Troubleshooting:**
+- **Installation issues**: Check the [WeChatTweak-CLI issues page](https://github.com/sunnyyoung/WeChatTweak-CLI/issues/41)
+- **Export errors**: If you encounter the error below, try restarting WeChat
+```
+Failed to export WeChat data. Please ensure WeChat is running and WeChatTweak is installed.
+Failed to find or export WeChat data. Exiting.
+```
 </details>
 
 <details>
@@ -290,6 +315,73 @@ Once the index is built, you can ask questions like:
 </details>
 
 
+
+## 🖥️ Command Line Interface
+
+LEANN includes a powerful CLI for document processing and search. Perfect for quick document indexing and interactive chat.
+
+```bash
+# Build an index from documents
+leann build my-docs --docs ./documents
+
+# Search your documents  
+leann search my-docs "machine learning concepts"
+
+# Interactive chat with your documents
+leann ask my-docs --interactive
+
+# List all your indexes
+leann list
+```
+
+**Key CLI features:**
+- Auto-detects document formats (PDF, TXT, MD, DOCX)
+- Smart text chunking with overlap
+- Multiple LLM providers (Ollama, OpenAI, HuggingFace)
+- Organized index storage in `~/.leann/indexes/`
+- Support for advanced search parameters
+
+<details>
+<summary><strong>📋 Click to expand: Complete CLI Reference</strong></summary>
+
+**Build Command:**
+```bash
+leann build INDEX_NAME --docs DIRECTORY [OPTIONS]
+
+Options:
+  --backend {hnsw,diskann}     Backend to use (default: hnsw)
+  --embedding-model MODEL      Embedding model (default: facebook/contriever)
+  --graph-degree N            Graph degree (default: 32)
+  --complexity N              Build complexity (default: 64)
+  --force                     Force rebuild existing index
+  --compact                   Use compact storage (default: true)
+  --recompute                 Enable recomputation (default: true)
+```
+
+**Search Command:**
+```bash
+leann search INDEX_NAME QUERY [OPTIONS]
+
+Options:
+  --top-k N                   Number of results (default: 5)
+  --complexity N              Search complexity (default: 64)
+  --recompute-embeddings      Use recomputation for highest accuracy
+  --pruning-strategy {global,local,proportional}
+```
+
+**Ask Command:**
+```bash
+leann ask INDEX_NAME [OPTIONS]
+
+Options:
+  --llm {ollama,openai,hf}    LLM provider (default: ollama)
+  --model MODEL               Model name (default: qwen3:8b)
+  --interactive              Interactive chat mode
+  --top-k N                  Retrieval count (default: 20)
+```
+
+</details>
+
 ## 🏗️ Architecture & How It Works
 
 <p align="center">
@@ -308,18 +400,17 @@ Once the index is built, you can ask questions like:
 
 ## Benchmarks
 
-Run the comparison yourself:
-```bash
-python examples/compare_faiss_vs_leann.py
-```
 
-| System | Storage | 
-|--------|---------|
-| FAISS HNSW | 5.5 MB |
-| LEANN | 0.5 MB |
-| **Savings** | **91%** |
+📊 **[Simple Example: Compare LEANN vs FAISS →](examples/compare_faiss_vs_leann.py)**
+### Storage Comparison
+
+| System | DPR (2.1M) | Wiki (60M) | Chat (400K) | Email (780K) | Browser (38K) |
+|--------|-------------|------------|-------------|--------------|---------------|
+| Traditional vector database (e.g., FAISS) | 3.8 GB      | 201 GB     | 1.8 GB     | 2.4 GB      | 130 MB        |
+| LEANN  | 324 MB      | 6 GB       | 64 MB       | 79 MB       | 6.4 MB        |
+| Savings| 91%         | 97%        | 97%         | 97%         | 95%           |
+
 
-Same dataset, same hardware, same embedding model. LEANN just works better.
 
 ## Reproduce Our Results
 
@@ -329,33 +420,7 @@ python examples/run_evaluation.py data/indices/dpr/dpr_diskann      # DPR datase
 python examples/run_evaluation.py data/indices/rpj_wiki/rpj_wiki.index  # Wikipedia
 ```
 
-The evaluation script downloads data automatically on first run.
-
-### Storage Usage Comparison
-
-| System                | DPR (2.1M chunks) | RPJ-wiki (60M chunks) | Chat history (400K messages) | Apple emails (90K messages chunks) |Google Search History (38K entries)
-|-----------------------|------------------|------------------------|-----------------------------|------------------------------|------------------------------|
-| Traditional Vector DB(FAISS) | 3.8 GB           | 201 GB                 | 1.8G                     | 305.8 MB                     |130.4 MB                     |
-| **LEANN**             | **324 MB**       | **6 GB**               | **64 MB**                 | **14.8 MB**                  |**6.4MB**                  |
-| **Reduction**         | **91% smaller**  | **97% smaller**        | **97% smaller**             | **95% smaller**              |**95% smaller**              |
-
-<!-- ### Memory Usage Comparison
-
-| System          j      | DPR(2M docs)     | RPJ-wiki(60M docs)    | Chat history()   |
-| --------------------- | ---------------- | ---------------- | ---------------- |
-| Traditional Vector DB(LLamaindex faiss) | x GB           | x GB            | x GB           |
-| **Leann**       | **xx MB** | **x GB** | **x GB** |
-| **Reduction**   | **x%**  | **x%**  | **x%**  |
-
-### Query Performance of LEANN
-
-| Backend             | Index Size | Query Time | Recall@3 |
-| ------------------- | ---------- | ---------- | --------- |
-| DiskANN             | 1M docs    | xms       | 0.95      |
-| HNSW                | 1M docs    | xms        | 0.95      | -->
-
-*Benchmarks run on Apple M3 Pro 36 GB*
-
+The evaluation script downloads data automatically on first run. The last three results were tested with partial personal data, and you can reproduce them with your own data!
 ## 🔬 Paper
 
 If you find Leann useful, please cite:
@@ -374,87 +439,15 @@ If you find Leann useful, please cite:
 }
 ```
 
-## ✨ Features
+## ✨ [Detailed Features →](docs/features.md)
 
-### 🔥 Core Features
-
-- **🔄 Real-time Embeddings** - Eliminate heavy embedding storage with dynamic computation using optimized ZMQ servers and highly optimized search paradigm (overlapping and batching) with highly optimized embedding engine
-- **📈 Scalable Architecture** - Handles millions of documents on consumer hardware; the larger your dataset, the more LEANN can save
-- **🎯 Graph Pruning** - Advanced techniques to minimize the storage overhead of vector search to a limited footprint
-- **🏗️ Pluggable Backends** - DiskANN, HNSW/FAISS with unified API
-
-### 🛠️ Technical Highlights
-- **🔄 Recompute Mode** - Highest accuracy scenarios while eliminating vector storage overhead
-- **⚡ Zero-copy Operations** - Minimize IPC overhead by transferring distances instead of embeddings
-- **🚀 High-throughput Embedding Pipeline** - Optimized batched processing for maximum efficiency
-- **🎯 Two-level Search** - Novel coarse-to-fine search overlap for accelerated query processing (optional)
-- **💾 Memory-mapped Indices** - Fast startup with raw text mapping to reduce memory overhead
-- **🚀 MLX Support** - Ultra-fast recompute/build with quantized embedding models, accelerating building and search ([minimal example](test/build_mlx_index.py))
-
-### 🎨 Developer Experience
-
-- **Simple Python API** - Get started in minutes
-- **Extensible backend system** - Easy to add new algorithms
-- **Comprehensive examples** - From basic usage to production deployment
-
-## 🤝 Contributing
-
-We welcome contributions! Leann is built by the community, for the community.
-
-### Ways to Contribute
-
-- 🐛 **Bug Reports**: Found an issue? Let us know!
-- 💡 **Feature Requests**: Have an idea? We'd love to hear it!
-- 🔧 **Code Contributions**: PRs welcome for all skill levels
-- 📖 **Documentation**: Help make Leann more accessible
-- 🧪 **Benchmarks**: Share your performance results
+## 🤝 [Contributing →](docs/contributing.md)
 
 
-<!-- ## ❓ FAQ
-
-### Common Issues
-
-#### NCCL Topology Error
-
-**Problem**: You encounter `ncclTopoComputePaths` error during document processing:
-
-```
-ncclTopoComputePaths (system=<optimized out>, comm=comm@entry=0x5555a82fa3c0) at graph/paths.cc:688
-```
-
-**Solution**: Set these environment variables before running your script:
-
-```bash
-export NCCL_TOPO_DUMP_FILE=/tmp/nccl_topo.xml
-export NCCL_DEBUG=INFO
-export NCCL_DEBUG_SUBSYS=INIT,GRAPH
-export NCCL_IB_DISABLE=1
-export NCCL_NET_PLUGIN=none
-export NCCL_SOCKET_IFNAME=ens5
-``` -->
-
-## 📈 Roadmap
-
-### 🎯 Q2 2025
-
-- [X] DiskANN backend with MIPS/L2/Cosine support
-- [X] HNSW backend integration
-- [X] Real-time embedding pipeline
-- [X] Memory-efficient graph pruning
-
-### 🚀 Q3 2025
+## [FAQ →](docs/faq.md)
 
 
-- [ ] Advanced caching strategies
-- [ ] Add contextual-retrieval https://www.anthropic.com/news/contextual-retrieval
-- [ ] Add sleep-time-compute and summarize agent! to summarilze the file on computer!
-- [ ] Add OpenAI recompute API
-
-### 🌟 Q4 2025
-
-- [ ] Integration with LangChain/LlamaIndex
-- [ ] Visual similarity search
-- [ ] Query rewrtiting, rerank and expansion
+## 📈 [Roadmap →](docs/roadmap.md)
 
 ## 📄 License
 
@@ -462,11 +455,7 @@ MIT License - see [LICENSE](LICENSE) for details.
 
 ## 🙏 Acknowledgments
 
-- **Microsoft Research** for the DiskANN algorithm
-- **Meta AI** for FAISS and optimization insights
-- **HuggingFace** for the transformer ecosystem
-- **Our amazing contributors** who make this possible
-
+This work is done at [**Berkeley Sky Computing Lab**](https://sky.cs.berkeley.edu/)
 ---
 
 <p align="center">

demo.ipynb

@@ -1,35 +1,321 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quick Start in 30s"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from leann.api import LeannBuilder, LeannSearcher, LeannChat\n",
-    "# 1. Build index (no embeddings stored!)\n",
-    "builder = LeannBuilder(backend_name=\"hnsw\")\n",
-    "builder.add_text(\"C# is a powerful programming language but it is not very popular\")\n",
-    "builder.add_text(\"Python is a powerful programming language and it is very popular\")\n",
-    "builder.add_text(\"Machine learning transforms industries\")  \n",
-    "builder.add_text(\"Neural networks process complex data\")\n",
-    "builder.add_text(\"Leann is a great storage saving engine for RAG on your macbook\")\n",
-    "builder.build_index(\"knowledge.leann\")\n",
-    "# 2. Search with real-time embeddings\n",
-    "searcher = LeannSearcher(\"knowledge.leann\")\n",
-    "results = searcher.search(\"programming languages\", top_k=2, recompute_beighbor_embeddings=True)\n",
-    "print(results)\n",
+    "# install this if you areusing colab\n",
+    "! pip install leann"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build the index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Registering backend 'hnsw'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/yichuan/Desktop/code/LEANN/leann/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: facebook/contriever\n",
+      "WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name facebook/contriever. Creating a new one with mean pooling.\n",
+      "Writing passages: 100%|██████████| 5/5 [00:00<00:00, 27887.66chunk/s]\n",
+      "Batches: 100%|██████████| 1/1 [00:00<00:00, 13.51it/s]\n",
+      "WARNING:leann_backend_hnsw.hnsw_backend:Converting data to float32, shape: (5, 768)\n",
+      "INFO:leann_backend_hnsw.hnsw_backend:INFO: Converting HNSW index to CSR-pruned format...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "M: 64 for level: 0\n",
+      "Starting conversion: knowledge.index -> knowledge.csr.tmp\n",
+      "[0.00s] Reading Index HNSW header...\n",
+      "[0.00s]   Header read: d=768, ntotal=5\n",
+      "[0.00s] Reading HNSW struct vectors...\n",
+      "  Reading vector (dtype=<class 'numpy.float64'>, fmt='d')... Count=6, Bytes=48\n",
+      "[0.00s]   Read assign_probas (6)\n",
+      "  Reading vector (dtype=<class 'numpy.int32'>, fmt='i')... Count=7, Bytes=28\n",
+      "[0.11s]   Read cum_nneighbor_per_level (7)\n",
+      "  Reading vector (dtype=<class 'numpy.int32'>, fmt='i')... Count=5, Bytes=20\n",
+      "[0.21s]   Read levels (5)\n",
+      "[0.30s]   Probing for compact storage flag...\n",
+      "[0.30s]   Found compact flag: False\n",
+      "[0.30s]   Compact flag is False, reading original format...\n",
+      "[0.30s]   Probing for potential extra byte before non-compact offsets...\n",
+      "[0.30s]   Found and consumed an unexpected 0x00 byte.\n",
+      "  Reading vector (dtype=<class 'numpy.uint64'>, fmt='Q')... Count=6, Bytes=48\n",
+      "[0.30s]   Read offsets (6)\n",
+      "[0.40s]   Attempting to read neighbors vector...\n",
+      "  Reading vector (dtype=<class 'numpy.int32'>, fmt='i')... Count=320, Bytes=1280\n",
+      "[0.40s]   Read neighbors (320)\n",
+      "[0.50s]   Read scalar params (ep=4, max_lvl=0)\n",
+      "[0.50s] Checking for storage data...\n",
+      "[0.50s]   Found storage fourcc: 49467849.\n",
+      "[0.50s] Converting to CSR format...\n",
+      "[0.50s]   Conversion loop finished.                        \n",
+      "[0.50s] Running validation checks...\n",
+      "    Checking total valid neighbor count...\n",
+      "    OK: Total valid neighbors = 20\n",
+      "    Checking final pointer indices...\n",
+      "    OK: Final pointers match data size.\n",
+      "[0.50s] Deleting original neighbors and offsets arrays...\n",
+      "    CSR Stats: |data|=20, |level_ptr|=10\n",
+      "[0.59s] Writing CSR HNSW graph data in FAISS-compatible order...\n",
+      "   Pruning embeddings: Writing NULL storage marker.\n",
+      "[0.69s] Conversion complete.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:leann_backend_hnsw.hnsw_backend:✅ CSR conversion successful.\n",
+      "INFO:leann_backend_hnsw.hnsw_backend:INFO: Replaced original index with CSR-pruned version at 'knowledge.index'\n"
+     ]
+    }
+   ],
+   "source": [
+    "from leann.api import LeannBuilder\n",
     "\n",
-    "llm_config = {\"type\": \"ollama\", \"model\": \"qwen3:8b\"}\n",
+    "builder = LeannBuilder(backend_name=\"hnsw\")\n",
+    "builder.add_text(\"C# is a powerful programming language and it is good at game development\")\n",
+    "builder.add_text(\"Python is a powerful programming language and it is good at machine learning tasks\")\n",
+    "builder.add_text(\"Machine learning transforms industries\")\n",
+    "builder.add_text(\"Neural networks process complex data\")\n",
+    "builder.add_text(\"Leann is a great storage saving engine for RAG on your MacBook\")\n",
+    "builder.build_index(\"knowledge.leann\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Search with real-time embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:leann.api:🔍 LeannSearcher.search() called:\n",
+      "INFO:leann.api:  Query: 'programming languages'\n",
+      "INFO:leann.api:  Top_k: 2\n",
+      "INFO:leann.api:  Additional kwargs: {}\n",
+      "INFO:leann.embedding_server_manager:Port 5557 has incompatible server, trying next port...\n",
+      "INFO:leann.embedding_server_manager:Port 5558 has incompatible server, trying next port...\n",
+      "INFO:leann.embedding_server_manager:Port 5559 has incompatible server, trying next port...\n",
+      "INFO:leann.embedding_server_manager:Using port 5560 instead of 5557\n",
+      "INFO:leann.embedding_server_manager:Starting embedding server on port 5560...\n",
+      "INFO:leann.embedding_server_manager:Command: /Users/yichuan/Desktop/code/LEANN/leann/.venv/bin/python -m leann_backend_hnsw.hnsw_embedding_server --zmq-port 5560 --model-name facebook/contriever --passages-file knowledge.leann.meta.json\n",
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
+      "INFO:leann.embedding_server_manager:Server process started with PID: 4574\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[read_HNSW - CSR NL v4] Reading metadata & CSR indices (manual offset)...\n",
+      "[read_HNSW NL v4] Read levels vector, size: 5\n",
+      "[read_HNSW NL v4] Reading Compact Storage format indices...\n",
+      "[read_HNSW NL v4] Read compact_level_ptr, size: 10\n",
+      "[read_HNSW NL v4] Read compact_node_offsets, size: 6\n",
+      "[read_HNSW NL v4] Read entry_point: 4, max_level: 0\n",
+      "[read_HNSW NL v4] Read storage fourcc: 0x6c6c756e\n",
+      "[read_HNSW NL v4 FIX] Detected FileIOReader. Neighbors size field offset: 326\n",
+      "[read_HNSW NL v4] Reading neighbors data into memory.\n",
+      "[read_HNSW NL v4] Read neighbors data, size: 20\n",
+      "[read_HNSW NL v4] Finished reading metadata and CSR indices.\n",
+      "INFO: Skipping external storage loading, since is_recompute is true.\n",
+      "INFO: Registering backend 'hnsw'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:leann.embedding_server_manager:Embedding server is ready!\n",
+      "INFO:leann.api:  Launching server time: 1.078078269958496 seconds\n",
+      "INFO:leann.embedding_server_manager:Existing server process (PID 4574) is compatible\n",
+      "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: facebook/contriever\n",
+      "WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name facebook/contriever. Creating a new one with mean pooling.\n",
+      "INFO:leann.api:  Generated embedding shape: (1, 768)\n",
+      "INFO:leann.api:  Embedding time: 2.9307072162628174 seconds\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ZmqDistanceComputer initialized: d=768, metric=0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:leann.api:  Search time: 0.27327895164489746 seconds\n",
+      "INFO:leann.api:  Backend returned: labels=2 results\n",
+      "INFO:leann.api:  Processing 2 passage IDs:\n",
+      "INFO:leann.api:    1. passage_id='0' -> SUCCESS: C# is a powerful programming language and it is good at game development...\n",
+      "INFO:leann.api:    2. passage_id='1' -> SUCCESS: Python is a powerful programming language and it is good at machine learning tasks...\n",
+      "INFO:leann.api:  Final enriched results: 2 passages\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[SearchResult(id='0', score=np.float32(0.9874103), text='C# is a powerful programming language and it is good at game development', metadata={}),\n",
+       " SearchResult(id='1', score=np.float32(0.8922168), text='Python is a powerful programming language and it is good at machine learning tasks', metadata={})]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from leann.api import LeannSearcher\n",
+    "\n",
+    "searcher = LeannSearcher(\"knowledge.leann\")\n",
+    "results = searcher.search(\"programming languages\", top_k=2)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chat with LEANN using retrieved results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:leann.chat:Attempting to create LLM of type='hf' with model='Qwen/Qwen3-0.6B'\n",
+      "INFO:leann.chat:Initializing HFChat with model='Qwen/Qwen3-0.6B'\n",
+      "INFO:leann.chat:MPS is available. Using Apple Silicon GPU.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[read_HNSW - CSR NL v4] Reading metadata & CSR indices (manual offset)...\n",
+      "[read_HNSW NL v4] Read levels vector, size: 5\n",
+      "[read_HNSW NL v4] Reading Compact Storage format indices...\n",
+      "[read_HNSW NL v4] Read compact_level_ptr, size: 10\n",
+      "[read_HNSW NL v4] Read compact_node_offsets, size: 6\n",
+      "[read_HNSW NL v4] Read entry_point: 4, max_level: 0\n",
+      "[read_HNSW NL v4] Read storage fourcc: 0x6c6c756e\n",
+      "[read_HNSW NL v4 FIX] Detected FileIOReader. Neighbors size field offset: 326\n",
+      "[read_HNSW NL v4] Reading neighbors data into memory.\n",
+      "[read_HNSW NL v4] Read neighbors data, size: 20\n",
+      "[read_HNSW NL v4] Finished reading metadata and CSR indices.\n",
+      "INFO: Skipping external storage loading, since is_recompute is true.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:leann.api:🔍 LeannSearcher.search() called:\n",
+      "INFO:leann.api:  Query: 'Compare the two retrieved programming languages and tell me their advantages.'\n",
+      "INFO:leann.api:  Top_k: 2\n",
+      "INFO:leann.api:  Additional kwargs: {}\n",
+      "INFO:leann.embedding_server_manager:Port 5557 has incompatible server, trying next port...\n",
+      "INFO:leann.embedding_server_manager:Port 5558 has incompatible server, trying next port...\n",
+      "INFO:leann.embedding_server_manager:Port 5559 has incompatible server, trying next port...\n",
+      "INFO:leann.embedding_server_manager:Found compatible server on port 5560\n",
+      "INFO:leann.embedding_server_manager:Using existing compatible server on port 5560\n",
+      "INFO:leann.api:  Launching server time: 0.04932403564453125 seconds\n",
+      "INFO:leann.embedding_server_manager:Found compatible server on port 5560\n",
+      "INFO:leann.embedding_server_manager:Using existing compatible server on port 5560\n",
+      "INFO:leann.api:  Generated embedding shape: (1, 768)\n",
+      "INFO:leann.api:  Embedding time: 0.06902289390563965 seconds\n",
+      "INFO:leann.api:  Search time: 0.026793241500854492 seconds\n",
+      "INFO:leann.api:  Backend returned: labels=2 results\n",
+      "INFO:leann.api:  Processing 2 passage IDs:\n",
+      "INFO:leann.api:    1. passage_id='0' -> SUCCESS: C# is a powerful programming language and it is good at game development...\n",
+      "INFO:leann.api:    2. passage_id='1' -> SUCCESS: Python is a powerful programming language and it is good at machine learning tasks...\n",
+      "INFO:leann.api:  Final enriched results: 2 passages\n",
+      "INFO:leann.chat:Generating with HuggingFace model, config: {'max_new_tokens': 128, 'temperature': 0.7, 'top_p': 0.9, 'do_sample': True, 'pad_token_id': 151645, 'eos_token_id': 151645}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ZmqDistanceComputer initialized: d=768, metric=0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"<think>\\n\\n</think>\\n\\nBased on the context provided, here's a comparison of the two retrieved programming languages:\\n\\n**C#** is known for being a powerful programming language and is well-suited for game development. It is often used in game development and is popular among developers working on Windows applications.\\n\\n**Python**, on the other hand, is also a powerful language and is well-suited for machine learning tasks. It is widely used for data analysis, scientific computing, and other applications that require handling large datasets or performing complex calculations.\\n\\n**Advantages**:\\n- C#: Strong for game development and cross-platform compatibility.\\n- Python: Strong for\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from leann.api import LeannChat\n",
+    "\n",
+    "llm_config = {\n",
+    "    \"type\": \"hf\",\n",
+    "    \"model\": \"Qwen/Qwen3-0.6B\",\n",
+    "}\n",
     "\n",
     "chat = LeannChat(index_path=\"knowledge.leann\", llm_config=llm_config)\n",
-    "\n",
     "response = chat.ask(\n",
-    "    \"Compare the two retrieved programming languages and say which one is more popular today. Respond in a single well-formed sentence.\",\n",
+    "    \"Compare the two retrieved programming languages and tell me their advantages.\",\n",
     "    top_k=2,\n",
-    "    recompute_beighbor_embeddings=True,\n",
+    "    llm_kwargs={\"max_tokens\": 128}\n",
     ")\n",
-    "print(response)"
+    "response"
    ]
   }
  ],

docs/RELEASE.md

@@ -0,0 +1,22 @@
+# Release Guide
+
+## Setup (One-time)
+
+Add `PYPI_API_TOKEN` to GitHub Secrets:
+1. Get token: https://pypi.org/manage/account/token/
+2. Add to secrets: Settings → Secrets → Actions → `PYPI_API_TOKEN`
+
+## Release (One-click)
+
+1. Go to: https://github.com/yichuan-w/LEANN/actions/workflows/release-manual.yml
+2. Click "Run workflow"
+3. Enter version: `0.1.2`
+4. Click green "Run workflow" button
+
+That's it! The workflow will automatically:
+- ✅ Update version in all packages
+- ✅ Build all packages
+- ✅ Publish to PyPI
+- ✅ Create GitHub tag and release
+
+Check progress: https://github.com/yichuan-w/LEANN/actions 

docs/contributing.md

@@ -0,0 +1,11 @@
+# 🤝 Contributing
+
+We welcome contributions! Leann is built by the community, for the community.
+
+## Ways to Contribute
+
+- 🐛 **Bug Reports**: Found an issue? Let us know!
+- 💡 **Feature Requests**: Have an idea? We'd love to hear it!
+- 🔧 **Code Contributions**: PRs welcome for all skill levels
+- 📖 **Documentation**: Help make Leann more accessible
+- 🧪 **Benchmarks**: Share your performance results 

docs/faq.md

@@ -0,0 +1,10 @@
+# FAQ
+
+## 1. My building time seems long
+
+You can speed up the process by using a lightweight embedding model. Add this to your arguments:
+
+```bash
+--embedding-model sentence-transformers/all-MiniLM-L6-v2
+```
+**Model sizes:** `all-MiniLM-L6-v2` (30M parameters), `facebook/contriever` (~100M parameters), `Qwen3-0.6B` (600M parameters) 

docs/features.md

@@ -0,0 +1,22 @@
+# ✨ Detailed Features
+
+## 🔥 Core Features
+
+- **🔄 Real-time Embeddings** - Eliminate heavy embedding storage with dynamic computation using optimized ZMQ servers and highly optimized search paradigm (overlapping and batching) with highly optimized embedding engine
+- **📈 Scalable Architecture** - Handles millions of documents on consumer hardware; the larger your dataset, the more LEANN can save
+- **🎯 Graph Pruning** - Advanced techniques to minimize the storage overhead of vector search to a limited footprint
+- **🏗️ Pluggable Backends** - DiskANN, HNSW/FAISS with unified API
+
+## 🛠️ Technical Highlights
+- **🔄 Recompute Mode** - Highest accuracy scenarios while eliminating vector storage overhead
+- **⚡ Zero-copy Operations** - Minimize IPC overhead by transferring distances instead of embeddings
+- **🚀 High-throughput Embedding Pipeline** - Optimized batched processing for maximum efficiency
+- **🎯 Two-level Search** - Novel coarse-to-fine search overlap for accelerated query processing (optional)
+- **💾 Memory-mapped Indices** - Fast startup with raw text mapping to reduce memory overhead
+- **🚀 MLX Support** - Ultra-fast recompute/build with quantized embedding models, accelerating building and search ([minimal example](test/build_mlx_index.py))
+
+## 🎨 Developer Experience
+
+- **Simple Python API** - Get started in minutes
+- **Extensible backend system** - Easy to add new algorithms
+- **Comprehensive examples** - From basic usage to production deployment 

docs/roadmap.md

@@ -0,0 +1,21 @@
+# 📈 Roadmap
+
+## 🎯 Q2 2025
+
+- [X] DiskANN backend with MIPS/L2/Cosine support
+- [X] HNSW backend integration
+- [X] Real-time embedding pipeline
+- [X] Memory-efficient graph pruning
+
+## 🚀 Q3 2025
+
+- [ ] Advanced caching strategies
+- [ ] Add contextual-retrieval https://www.anthropic.com/news/contextual-retrieval
+- [ ] Add sleep-time-compute and summarize agent! to summarilze the file on computer!
+- [ ] Add OpenAI recompute API
+
+## 🌟 Q4 2025
+
+- [ ] Integration with LangChain/LlamaIndex
+- [ ] Visual similarity search
+- [ ] Query rewrtiting, rerank and expansion 

examples/compare_faiss_vs_leann.py

@@ -135,6 +135,7 @@ def test_leann_hnsw():
         nodes = node_parser.get_nodes_from_documents([doc])
         for node in nodes:
             all_texts.append(node.get_content())
+    print(f"Total number of chunks: {len(all_texts)}")
 
     tracker.checkpoint("After text chunking")
 

examples/email_data/LEANN_email_reader.py

@@ -96,14 +96,12 @@ class EmlxReader(BaseReader):
                                 
                                 # Create document content with metadata embedded in text
                                 doc_content = f"""
-[EMAIL METADATA]
-File: {filename}
-From: {from_addr}
-To: {to_addr}
-Subject: {subject}
-Date: {date}
-[END METADATA]
-
+[File]: {filename}
+[From]: {from_addr}
+[To]: {to_addr}
+[Subject]: {subject}
+[Date]: {date}
+[EMAIL BODY Start]:
 {body}
 """
                                 

examples/faiss_only.py

@@ -37,7 +37,7 @@ def main():
         import faiss
     except ImportError:
         print("Faiss is not installed.")
-        print("Please install it with `uv pip install faiss-cpu`")
+        print("Please install it with `uv pip install faiss-cpu` and you can  then run this script again")
         sys.exit(1)
 
     from llama_index.core import (

examples/google_history_reader_leann.py

@@ -65,12 +65,14 @@ def create_leann_index_from_multiple_chrome_profiles(profile_dirs: List[Path], i
         
         if not all_documents:
             print("No documents loaded from any source. Exiting.")
+            # highlight info that you need to close all chrome browser before running this script and high light the instruction!!
+            print("\033[91mYou need to close or quit all chrome browser before running this script\033[0m")
             return None
         
         print(f"\nTotal loaded {len(all_documents)} history documents from {len(profile_dirs)} profiles")
         
         # Create text splitter with 256 chunk size
-        text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=25)
+        text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=128)
         
         # Convert Documents to text strings and chunk them
         all_texts = []
@@ -78,7 +80,9 @@ def create_leann_index_from_multiple_chrome_profiles(profile_dirs: List[Path], i
             # Split the document into chunks
             nodes = text_splitter.get_nodes_from_documents([doc])
             for node in nodes:
-                all_texts.append(node.get_content())
+                text = node.get_content()
+                # text = '[Title] ' + doc.metadata["title"] + '\n' + text
+                all_texts.append(text)
         
         print(f"Created {len(all_texts)} text chunks from {len(all_documents)} documents")
         
@@ -218,14 +222,15 @@ async def query_leann_index(index_path: str, query: str):
             "max_tokens": 1000
         }
     )
-    print(f"Leann: {chat_response}")
+
+    print(f"Leann chat response: \033[36m{chat_response}\033[0m")
 
 async def main():
     # Parse command line arguments
     parser = argparse.ArgumentParser(description='LEANN Chrome History Reader - Create and query browser history index')
     parser.add_argument('--chrome-profile', type=str, default=DEFAULT_CHROME_PROFILE,
                        help=f'Path to Chrome profile directory (default: {DEFAULT_CHROME_PROFILE}), usually you dont need to change this')
-    parser.add_argument('--index-dir', type=str, default="./chrome_history_index_leann_test",
+    parser.add_argument('--index-dir', type=str, default="./google_history_index",
                        help='Directory to store the LEANN index (default: ./chrome_history_index_leann_test)')
     parser.add_argument('--max-entries', type=int, default=1000,
                        help='Maximum number of history entries to process (default: 1000)')

examples/history_data/history.py

@@ -74,22 +74,17 @@ class ChromeHistoryReader(BaseReader):
                 
                 # Create document content with metadata embedded in text
                 doc_content = f"""
-[BROWSING HISTORY METADATA]
-URL: {url}
-Title: {title}
-Last Visit: {last_visit}
-Visit Count: {visit_count}
-Typed Count: {typed_count}
-Hidden: {hidden}
-[END METADATA]
-
-Title: {title}
-URL: {url}
-Last visited: {last_visit}
+[Title]: {title}
+[URL of the page]: {url}
+[Last visited time]: {last_visit}
+[Visit times]: {visit_count}
+[Typed times]: {typed_count}
 """
                 
                 # Create document with embedded metadata
-                doc = Document(text=doc_content, metadata={})
+                doc = Document(text=doc_content, metadata={ "title": title[0:150]})
+                # if len(title) > 150:
+                #     print(f"Title is too long: {title}")
                 docs.append(doc)
                 count += 1
             

examples/history_data/wechat_history.py

@@ -335,14 +335,15 @@ class WeChatHistoryReader(BaseReader):
             if create_time:
                 try:
                     timestamp = datetime.fromtimestamp(create_time)
-                    time_str = timestamp.strftime('%H:%M:%S')
+                    # change to YYYY-MM-DD HH:MM:SS
+                    time_str = timestamp.strftime('%Y-%m-%d %H:%M:%S')
                 except:
                     time_str = str(create_time)
             else:
                 time_str = "Unknown"
             
-            sender = "Me" if is_sent_from_self else "Contact"
-            message_parts.append(f"[{time_str}] {sender}: {readable_text}")
+            sender = "[Me]" if is_sent_from_self else "[Contact]"
+            message_parts.append(f"({time_str}) {sender}: {readable_text}")
         
         concatenated_text = "\n".join(message_parts)
         
@@ -354,13 +355,11 @@ Messages ({len(messages)} messages, {message_group['total_length']} chars):
 
 {concatenated_text}
 """
-        
+        # TODO @yichuan give better format and rich info here!    
         doc_content = f"""
-Contact: {contact_name}
-
 {concatenated_text}
 """
-        return doc_content
+        return doc_content, contact_name
     
     def load_data(self, input_dir: str = None, **load_kwargs: Any) -> List[Document]:
         """
@@ -441,8 +440,8 @@ Contact: {contact_name}
                             if count >= max_count and max_count > 0:
                                 break
                             
-                            doc_content = self._create_concatenated_content(message_group, contact_name)
-                            doc = Document(text=doc_content, metadata={})
+                            doc_content, contact_name  = self._create_concatenated_content(message_group, contact_name)
+                            doc = Document(text=doc_content, metadata={"contact_name": contact_name})
                             docs.append(doc)
                             count += 1
                         

examples/mail_reader_leann.py

@@ -22,7 +22,7 @@ def get_mail_path():
     return os.path.join(home_dir, "Library", "Mail")
 
 # Default mail path for macOS
-# DEFAULT_MAIL_PATH = "/Users/yichuan/Library/Mail/V10/0FCA0879-FD8C-4B7E-83BF-FDDA930791C5/[Gmail].mbox/All Mail.mbox/78BA5BE1-8819-4F9A-9613-EB63772F1DD0/Data"
+DEFAULT_MAIL_PATH = "/Users/yichuan/Library/Mail/V10/0FCA0879-FD8C-4B7E-83BF-FDDA930791C5/[Gmail].mbox/All Mail.mbox/78BA5BE1-8819-4F9A-9613-EB63772F1DD0/Data"
 
 def create_leann_index_from_multiple_sources(messages_dirs: List[Path], index_path: str = "mail_index.leann", max_count: int = -1, include_html: bool = False, embedding_model: str = "facebook/contriever"):
     """
@@ -74,7 +74,7 @@ def create_leann_index_from_multiple_sources(messages_dirs: List[Path], index_pa
             print("No documents loaded from any source. Exiting.")
             return None
         
-        print(f"\nTotal loaded {len(all_documents)} email documents from {len(messages_dirs)} directories")
+        print(f"\nTotal loaded {len(all_documents)} email documents from {len(messages_dirs)} directories and starting to split them into chunks")
         
         # Create text splitter with 256 chunk size
         text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=25)
@@ -85,9 +85,11 @@ def create_leann_index_from_multiple_sources(messages_dirs: List[Path], index_pa
             # Split the document into chunks
             nodes = text_splitter.get_nodes_from_documents([doc])
             for node in nodes:
-                all_texts.append(node.get_content())
+                text = node.get_content()
+                # text = '[subject] ' + doc.metadata["subject"] + '\n' + text
+                all_texts.append(text)
         
-        print(f"Created {len(all_texts)} text chunks from {len(all_documents)} documents")
+        print(f"Finished splitting {len(all_documents)} documents into {len(all_texts)} text chunks")
         
         # Create LEANN index directory
 
@@ -156,7 +158,7 @@ def create_leann_index(mail_path: str, index_path: str = "mail_index.leann", max
         print(f"Loaded {len(documents)} email documents")
         
         # Create text splitter with 256 chunk size
-        text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=25)
+        text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=128)
         
         # Convert Documents to text strings and chunk them
         all_texts = []
@@ -216,22 +218,22 @@ async def query_leann_index(index_path: str, query: str):
     start_time = time.time()
     chat_response = chat.ask(
         query, 
-        top_k=10, 
+        top_k=20, 
         recompute_beighbor_embeddings=True,
-        complexity=12,
+        complexity=32,
         beam_width=1,
-        
     )
     end_time = time.time()
-    print(f"Time taken: {end_time - start_time} seconds")
-    print(f"Leann: {chat_response}")
+    # print(f"Time taken: {end_time - start_time} seconds")
+    # highlight the answer
+    print(f"Leann chat response: \033[36m{chat_response}\033[0m")
 
 async def main():
     # Parse command line arguments
     parser = argparse.ArgumentParser(description='LEANN Mail Reader - Create and query email index')
     # Remove --mail-path argument and auto-detect all Messages directories
     # Remove DEFAULT_MAIL_PATH
-    parser.add_argument('--index-dir', type=str, default="./mail_index_leann_raw_text_all_dicts",
+    parser.add_argument('--index-dir', type=str, default="./mail_index",
                        help='Directory to store the LEANN index (default: ./mail_index_leann_raw_text_all_dicts)')
     parser.add_argument('--max-emails', type=int, default=1000,
                        help='Maximum number of emails to process (-1 means all)')
@@ -251,6 +253,9 @@ async def main():
     mail_path = get_mail_path()
     print(f"Searching for email data in: {mail_path}")
     messages_dirs = find_all_messages_directories(mail_path)
+    # messages_dirs = find_all_messages_directories(DEFAULT_MAIL_PATH)
+    # messages_dirs = [DEFAULT_MAIL_PATH]
+    # messages_dirs = messages_dirs[:1]
     
     print('len(messages_dirs): ', len(messages_dirs))
     

examples/main_cli_example.py

@@ -1,40 +1,40 @@
 import argparse
-from llama_index.core import SimpleDirectoryReader, Settings
+from llama_index.core import SimpleDirectoryReader
 from llama_index.core.node_parser import SentenceSplitter
 import asyncio
 import dotenv
-from leann.api import LeannBuilder, LeannSearcher, LeannChat
-import shutil
+from leann.api import LeannBuilder, LeannChat
 from pathlib import Path
 
 dotenv.load_dotenv()
 
-node_parser = SentenceSplitter(
-    chunk_size=256, chunk_overlap=128, separator=" ", paragraph_separator="\n\n"
-)
-print("Loading documents...")
-documents = SimpleDirectoryReader(
-    "examples/data",
-    recursive=True,
-    encoding="utf-8",
-    required_exts=[".pdf", ".txt", ".md"],
-).load_data(show_progress=True)
-print("Documents loaded.")
-all_texts = []
-for doc in documents:
-    nodes = node_parser.get_nodes_from_documents([doc])
-    for node in nodes:
-        all_texts.append(node.get_content())
-
 
 async def main(args):
     INDEX_DIR = Path(args.index_dir)
     INDEX_PATH = str(INDEX_DIR / "pdf_documents.leann")
 
     if not INDEX_DIR.exists():
-        print(f"--- Index directory not found, building new index ---")
+        node_parser = SentenceSplitter(
+            chunk_size=256, chunk_overlap=128, separator=" ", paragraph_separator="\n\n"
+        )
 
-        print(f"\n[PHASE 1] Building Leann index...")
+        print("Loading documents...")
+        documents = SimpleDirectoryReader(
+            args.data_dir,
+            recursive=True,
+            encoding="utf-8",
+            required_exts=[".pdf", ".txt", ".md"],
+        ).load_data(show_progress=True)
+        print("Documents loaded.")
+        all_texts = []
+        for doc in documents:
+            nodes = node_parser.get_nodes_from_documents([doc])
+            for node in nodes:
+                all_texts.append(node.get_content())
+
+        print("--- Index directory not found, building new index ---")
+
+        print("\n[PHASE 1] Building Leann index...")
 
         # Use HNSW backend for better macOS compatibility
         builder = LeannBuilder(
@@ -58,22 +58,19 @@ async def main(args):
 
     print(f"\n[PHASE 2] Starting Leann chat session...")
 
-    # llm_config = {"type": "hf", "model": "Qwen/Qwen3-4B"}
+    llm_config = {"type": "hf", "model": "Qwen/Qwen3-4B"}
     llm_config = {"type": "ollama", "model": "qwen3:8b"}
+    llm_config = {"type": "openai", "model": "gpt-4o"}
 
     chat = LeannChat(index_path=INDEX_PATH, llm_config=llm_config)
-
-    query = "Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead and DLPM explore to achieve Fairness and Efiiciency trade-off?"
-
     # query = (
     #     "什么是盘古大模型以及盘古开发过程中遇到了什么阴暗面，任务令一般在什么城市颁发"
     # )
+    query = args.query
 
     print(f"You: {query}")
-    chat_response = chat.ask(
-        query, top_k=20, recompute_beighbor_embeddings=True, complexity=32
-    )
-    print(f"Leann: {chat_response}")
+    chat_response = chat.ask(query, top_k=20, recompute_embeddings=True, complexity=32)
+    print(f"Leann chat response: \033[36m{chat_response}\033[0m")
 
 
 if __name__ == "__main__":
@@ -105,6 +102,18 @@ if __name__ == "__main__":
         default="./test_doc_files",
         help="Directory where the Leann index will be stored.",
     )
+    parser.add_argument(
+        "--data-dir",
+        type=str,
+        default="examples/data",
+        help="Directory containing documents to index (PDF, TXT, MD files).",
+    )
+    parser.add_argument(
+        "--query",
+        type=str,
+        default="Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead and DLPM explore to achieve Fairness and Efiiciency trade-off?",
+        help="The query to ask the Leann chat system.",
+    )
     args = parser.parse_args()
 
     asyncio.run(main(args))

examples/wechat_history_reader_leann.py

@@ -52,7 +52,7 @@ def create_leann_index_from_multiple_wechat_exports(
                 documents = reader.load_data(
                     wechat_export_dir=str(export_dir),
                     max_count=max_count,
-                    concatenate_messages=False,  # Disable concatenation - one message per document
+                    concatenate_messages=True,  # Disable concatenation - one message per document
                 )
                 if documents:
                     print(f"Loaded {len(documents)} chat documents from {export_dir}")
@@ -74,11 +74,11 @@ def create_leann_index_from_multiple_wechat_exports(
             return None
 
         print(
-            f"\nTotal loaded {len(all_documents)} chat documents from {len(export_dirs)} exports"
+            f"\nTotal loaded {len(all_documents)} chat documents from {len(export_dirs)} exports and starting to split them into chunks"
         )
 
         # Create text splitter with 256 chunk size
-        text_splitter = SentenceSplitter(chunk_size=128, chunk_overlap=64)
+        text_splitter = SentenceSplitter(chunk_size=192, chunk_overlap=64)
 
         # Convert Documents to text strings and chunk them
         all_texts = []
@@ -86,10 +86,11 @@ def create_leann_index_from_multiple_wechat_exports(
             # Split the document into chunks
             nodes = text_splitter.get_nodes_from_documents([doc])
             for node in nodes:
-                all_texts.append(node.get_content())
+                text = '[Contact] means the message is from: ' + doc.metadata["contact_name"] + '\n' + node.get_content()
+                all_texts.append(text)
 
         print(
-            f"Created {len(all_texts)} text chunks from {len(all_documents)} documents"
+            f"Finished splitting {len(all_documents)} documents into {len(all_texts)} text chunks"
         )
 
         # Create LEANN index directory
@@ -224,7 +225,7 @@ async def query_leann_index(index_path: str, query: str):
         query,
         top_k=20,
         recompute_beighbor_embeddings=True,
-        complexity=128,
+        complexity=16,
         beam_width=1,
         llm_config={
             "type": "openai",
@@ -233,7 +234,7 @@ async def query_leann_index(index_path: str, query: str):
         },
         llm_kwargs={"temperature": 0.0, "max_tokens": 1000},
     )
-    print(f"Leann: {chat_response}")
+    print(f"Leann chat response: \033[36m{chat_response}\033[0m")
 
 
 async def main():
@@ -252,13 +253,13 @@ async def main():
     parser.add_argument(
         "--index-dir",
         type=str,
-        default="./wechat_history_june19_test",
+        default="./wechat_history_magic_test_11Debug_new",
         help="Directory to store the LEANN index (default: ./wechat_history_index_leann_test)",
     )
     parser.add_argument(
         "--max-entries",
         type=int,
-        default=5000,
+        default=50,
         help="Maximum number of chat entries to process (default: 5000)",
     )
     parser.add_argument(

packages/leann-backend-diskann/CMakeLists.txt

@@ -1,8 +1,36 @@
-# packages/leann-backend-diskann/CMakeLists.txt (最终简化版)
+# packages/leann-backend-diskann/CMakeLists.txt (simplified version)
 
 cmake_minimum_required(VERSION 3.20)
 project(leann_backend_diskann_wrapper)
 
-# 告诉 CMake 直接进入 DiskANN 子模块并执行它自己的 CMakeLists.txt
-# DiskANN 会自己处理所有事情，包括编译 Python 绑定
-add_subdirectory(src/third_party/DiskANN)
+# Find Python - scikit-build-core should provide this
+find_package(Python REQUIRED COMPONENTS Interpreter Development.Module)
+
+# Print Python information for debugging
+message(STATUS "Python_FOUND: ${Python_FOUND}")
+message(STATUS "Python_VERSION: ${Python_VERSION}")
+message(STATUS "Python_EXECUTABLE: ${Python_EXECUTABLE}")
+message(STATUS "Python_INCLUDE_DIRS: ${Python_INCLUDE_DIRS}")
+
+# Pass Python information to DiskANN through cache variables
+set(Python_EXECUTABLE ${Python_EXECUTABLE} CACHE FILEPATH "Python executable" FORCE)
+set(Python_INCLUDE_DIRS ${Python_INCLUDE_DIRS} CACHE PATH "Python include dirs" FORCE)
+set(Python_LIBRARIES ${Python_LIBRARIES} CACHE FILEPATH "Python libraries" FORCE)
+set(Python_VERSION ${Python_VERSION} CACHE STRING "Python version" FORCE)
+set(Python_FOUND ${Python_FOUND} CACHE BOOL "Python found" FORCE)
+
+# Also set Python3 variables for compatibility
+set(Python3_EXECUTABLE ${Python_EXECUTABLE} CACHE FILEPATH "Python3 executable" FORCE)
+set(Python3_INCLUDE_DIRS ${Python_INCLUDE_DIRS} CACHE PATH "Python3 include dirs" FORCE)
+set(Python3_LIBRARIES ${Python_LIBRARIES} CACHE FILEPATH "Python3 libraries" FORCE)
+set(Python3_VERSION ${Python_VERSION} CACHE STRING "Python3 version" FORCE)
+set(Python3_FOUND ${Python_FOUND} CACHE BOOL "Python3 found" FORCE)
+set(Python3_Development_FOUND TRUE CACHE BOOL "Python3 development found" FORCE)
+
+# Set Python finding strategy
+set(Python_FIND_VIRTUALENV ONLY CACHE STRING "" FORCE)
+set(Python3_FIND_VIRTUALENV ONLY CACHE STRING "" FORCE)
+
+# Tell CMake to directly enter the DiskANN submodule and execute its own CMakeLists.txt
+# DiskANN will handle everything itself, including compiling Python bindings
+add_subdirectory(third_party/DiskANN)

packages/leann-backend-diskann/leann_backend_diskann/diskann_backend.py

@@ -1,10 +1,12 @@
 import numpy as np
 import os
 import struct
+import sys
 from pathlib import Path
-from typing import Dict, Any, List, Literal
+from typing import Dict, Any, List, Literal, Optional
 import contextlib
-import pickle
+
+import logging
 
 from leann.searcher_base import BaseSearcher
 from leann.registry import register_backend
@@ -14,6 +16,46 @@ from leann.interface import (
     LeannBackendSearcherInterface,
 )
 
+logger = logging.getLogger(__name__)
+
+
+@contextlib.contextmanager
+def suppress_cpp_output_if_needed():
+    """Suppress C++ stdout/stderr based on LEANN_LOG_LEVEL"""
+    log_level = os.getenv("LEANN_LOG_LEVEL", "WARNING").upper()
+
+    # Only suppress if log level is WARNING or higher (ERROR, CRITICAL)
+    should_suppress = log_level in ["WARNING", "ERROR", "CRITICAL"]
+
+    if not should_suppress:
+        # Don't suppress, just yield
+        yield
+        return
+
+    # Save original file descriptors
+    stdout_fd = sys.stdout.fileno()
+    stderr_fd = sys.stderr.fileno()
+
+    # Save original stdout/stderr
+    stdout_dup = os.dup(stdout_fd)
+    stderr_dup = os.dup(stderr_fd)
+
+    try:
+        # Redirect to /dev/null
+        devnull = os.open(os.devnull, os.O_WRONLY)
+        os.dup2(devnull, stdout_fd)
+        os.dup2(devnull, stderr_fd)
+        os.close(devnull)
+
+        yield
+
+    finally:
+        # Restore original file descriptors
+        os.dup2(stdout_dup, stdout_fd)
+        os.dup2(stderr_dup, stderr_fd)
+        os.close(stdout_dup)
+        os.close(stderr_dup)
+
 
 def _get_diskann_metrics():
     from . import _diskannpy as diskannpy  # type: ignore
@@ -65,22 +107,20 @@ class DiskannBuilder(LeannBackendBuilderInterface):
         index_dir.mkdir(parents=True, exist_ok=True)
 
         if data.dtype != np.float32:
+            logger.warning(f"Converting data to float32, shape: {data.shape}")
             data = data.astype(np.float32)
 
         data_filename = f"{index_prefix}_data.bin"
         _write_vectors_to_bin(data, index_dir / data_filename)
 
-        label_map = {i: str_id for i, str_id in enumerate(ids)}
-        label_map_file = index_dir / "leann.labels.map"
-        with open(label_map_file, "wb") as f:
-            pickle.dump(label_map, f)
-
         build_kwargs = {**self.build_params, **kwargs}
         metric_enum = _get_diskann_metrics().get(
             build_kwargs.get("distance_metric", "mips").lower()
         )
         if metric_enum is None:
-            raise ValueError("Unsupported distance_metric.")
+            raise ValueError(
+                f"Unsupported distance_metric '{build_kwargs.get('distance_metric', 'unknown')}'."
+            )
 
         try:
             from . import _diskannpy as diskannpy  # type: ignore
@@ -102,36 +142,40 @@ class DiskannBuilder(LeannBackendBuilderInterface):
             temp_data_file = index_dir / data_filename
             if temp_data_file.exists():
                 os.remove(temp_data_file)
+                logger.debug(f"Cleaned up temporary data file: {temp_data_file}")
 
 
 class DiskannSearcher(BaseSearcher):
     def __init__(self, index_path: str, **kwargs):
         super().__init__(
             index_path,
-            backend_module_name="leann_backend_diskann.embedding_server",
+            backend_module_name="leann_backend_diskann.diskann_embedding_server",
             **kwargs,
         )
-        from . import _diskannpy as diskannpy  # type: ignore
 
-        distance_metric = kwargs.get("distance_metric", "mips").lower()
-        metric_enum = _get_diskann_metrics().get(distance_metric)
-        if metric_enum is None:
-            raise ValueError(f"Unsupported distance_metric '{distance_metric}'.")
+        # Initialize DiskANN index with suppressed C++ output based on log level
+        with suppress_cpp_output_if_needed():
+            from . import _diskannpy as diskannpy  # type: ignore
 
-        self.num_threads = kwargs.get("num_threads", 8)
-        self.zmq_port = kwargs.get("zmq_port", 6666)
+            distance_metric = kwargs.get("distance_metric", "mips").lower()
+            metric_enum = _get_diskann_metrics().get(distance_metric)
+            if metric_enum is None:
+                raise ValueError(f"Unsupported distance_metric '{distance_metric}'.")
 
-        full_index_prefix = str(self.index_dir / self.index_path.stem)
-        self._index = diskannpy.StaticDiskFloatIndex(
-            metric_enum,
-            full_index_prefix,
-            self.num_threads,
-            kwargs.get("num_nodes_to_cache", 0),
-            1,
-            self.zmq_port,
-            "",
-            "",
-        )
+            self.num_threads = kwargs.get("num_threads", 8)
+
+            fake_zmq_port = 6666
+            full_index_prefix = str(self.index_dir / self.index_path.stem)
+            self._index = diskannpy.StaticDiskFloatIndex(
+                metric_enum,
+                full_index_prefix,
+                self.num_threads,
+                kwargs.get("num_nodes_to_cache", 0),
+                1,
+                fake_zmq_port,  # Initial port, can be updated at runtime
+                "",
+                "",
+            )
 
     def search(
         self,
@@ -142,7 +186,7 @@ class DiskannSearcher(BaseSearcher):
         prune_ratio: float = 0.0,
         recompute_embeddings: bool = False,
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
-        zmq_port: int = 5557,
+        zmq_port: Optional[int] = None,
         batch_recompute: bool = False,
         dedup_node_dis: bool = False,
         **kwargs,
@@ -161,7 +205,7 @@ class DiskannSearcher(BaseSearcher):
                 - "global": Use global pruning strategy (default)
                 - "local": Use local pruning strategy
                 - "proportional": Not supported in DiskANN, falls back to global
-            zmq_port: ZMQ port for embedding server
+            zmq_port: ZMQ port for embedding server communication. Must be provided if recompute_embeddings is True.
             batch_recompute: Whether to batch neighbor recomputation (DiskANN-specific)
             dedup_node_dis: Whether to cache and reuse distance computations (DiskANN-specific)
             **kwargs: Additional DiskANN-specific parameters (for legacy compatibility)
@@ -169,22 +213,25 @@ class DiskannSearcher(BaseSearcher):
         Returns:
             Dict with 'labels' (list of lists) and 'distances' (ndarray)
         """
+        # Handle zmq_port compatibility: DiskANN can now update port at runtime
+        if recompute_embeddings:
+            if zmq_port is None:
+                raise ValueError(
+                    "zmq_port must be provided if recompute_embeddings is True"
+                )
+            current_port = self._index.get_zmq_port()
+            if zmq_port != current_port:
+                logger.debug(
+                    f"Updating DiskANN zmq_port from {current_port} to {zmq_port}"
+                )
+                self._index.set_zmq_port(zmq_port)
+
         # DiskANN doesn't support "proportional" strategy
         if pruning_strategy == "proportional":
             raise NotImplementedError(
                 "DiskANN backend does not support 'proportional' pruning strategy. Use 'global' or 'local' instead."
             )
 
-        # Use recompute_embeddings parameter
-        use_recompute = recompute_embeddings
-        if use_recompute:
-            meta_file_path = self.index_dir / f"{self.index_path.name}.meta.json"
-            if not meta_file_path.exists():
-                raise RuntimeError(
-                    f"FATAL: Recompute enabled but metadata file not found: {meta_file_path}"
-                )
-            self._ensure_server_running(str(meta_file_path), port=zmq_port, **kwargs)
-
         if query.dtype != np.float32:
             query = query.astype(np.float32)
 
@@ -194,28 +241,26 @@ class DiskannSearcher(BaseSearcher):
         else:  # "global"
             use_global_pruning = True
 
-        labels, distances = self._index.batch_search(
-            query,
-            query.shape[0],
-            top_k,
-            complexity,
-            beam_width,
-            self.num_threads,
-            kwargs.get("USE_DEFERRED_FETCH", False),
-            kwargs.get("skip_search_reorder", False),
-            use_recompute,
-            dedup_node_dis,
-            prune_ratio,
-            batch_recompute,
-            use_global_pruning,
-        )
+        # Perform search with suppressed C++ output based on log level
+        with suppress_cpp_output_if_needed():
+            labels, distances = self._index.batch_search(
+                query,
+                query.shape[0],
+                top_k,
+                complexity,
+                beam_width,
+                self.num_threads,
+                kwargs.get("USE_DEFERRED_FETCH", False),
+                kwargs.get("skip_search_reorder", False),
+                recompute_embeddings,
+                dedup_node_dis,
+                prune_ratio,
+                batch_recompute,
+                use_global_pruning,
+            )
 
         string_labels = [
-            [
-                self.label_map.get(int_label, f"unknown_{int_label}")
-                for int_label in batch_labels
-            ]
-            for batch_labels in labels
+            [str(int_label) for int_label in batch_labels] for batch_labels in labels
         ]
 
         return {"labels": string_labels, "distances": distances}

packages/leann-backend-diskann/leann_backend_diskann/diskann_embedding_server.py

@@ -0,0 +1,283 @@
+"""
+DiskANN-specific embedding server
+"""
+
+import argparse
+import threading
+import time
+import os
+import zmq
+import numpy as np
+import json
+from pathlib import Path
+from typing import Optional
+import sys
+import logging
+
+# Set up logging based on environment variable
+LOG_LEVEL = os.getenv("LEANN_LOG_LEVEL", "WARNING").upper()
+logger = logging.getLogger(__name__)
+
+# Force set logger level (don't rely on basicConfig in subprocess)
+log_level = getattr(logging, LOG_LEVEL, logging.WARNING)
+logger.setLevel(log_level)
+
+# Ensure we have a handler if none exists
+if not logger.handlers:
+    handler = logging.StreamHandler()
+    formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+    logger.propagate = False
+
+
+def create_diskann_embedding_server(
+    passages_file: Optional[str] = None,
+    zmq_port: int = 5555,
+    model_name: str = "sentence-transformers/all-mpnet-base-v2",
+    embedding_mode: str = "sentence-transformers",
+):
+    """
+    Create and start a ZMQ-based embedding server for DiskANN backend.
+    Uses ROUTER socket and protobuf communication as required by DiskANN C++ implementation.
+    """
+    logger.info(f"Starting DiskANN server on port {zmq_port} with model {model_name}")
+    logger.info(f"Using embedding mode: {embedding_mode}")
+
+    # Add leann-core to path for unified embedding computation
+    current_dir = Path(__file__).parent
+    leann_core_path = current_dir.parent.parent / "leann-core" / "src"
+    sys.path.insert(0, str(leann_core_path))
+
+    try:
+        from leann.embedding_compute import compute_embeddings
+        from leann.api import PassageManager
+
+        logger.info("Successfully imported unified embedding computation module")
+    except ImportError as e:
+        logger.error(f"Failed to import embedding computation module: {e}")
+        return
+    finally:
+        sys.path.pop(0)
+
+    # Check port availability
+    import socket
+
+    def check_port(port):
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+            return s.connect_ex(("localhost", port)) == 0
+
+    if check_port(zmq_port):
+        logger.error(f"Port {zmq_port} is already in use")
+        return
+
+    # Only support metadata file, fail fast for everything else
+    if not passages_file or not passages_file.endswith(".meta.json"):
+        raise ValueError("Only metadata files (.meta.json) are supported")
+
+    # Load metadata to get passage sources
+    with open(passages_file, "r") as f:
+        meta = json.load(f)
+
+    passages = PassageManager(meta["passage_sources"])
+    logger.info(
+        f"Loaded PassageManager with {len(passages.global_offset_map)} passages from metadata"
+    )
+
+    # Import protobuf after ensuring the path is correct
+    try:
+        from . import embedding_pb2
+    except ImportError as e:
+        logger.error(f"Failed to import protobuf module: {e}")
+        return
+
+    def zmq_server_thread():
+        """ZMQ server thread using REP socket for universal compatibility"""
+        context = zmq.Context()
+        socket = context.socket(
+            zmq.REP
+        )  # REP socket for both BaseSearcher and DiskANN C++ REQ clients
+        socket.bind(f"tcp://*:{zmq_port}")
+        logger.info(f"DiskANN ZMQ REP server listening on port {zmq_port}")
+
+        socket.setsockopt(zmq.RCVTIMEO, 300000)
+        socket.setsockopt(zmq.SNDTIMEO, 300000)
+
+        while True:
+            try:
+                # REP socket receives single-part messages
+                message = socket.recv()
+
+                # Check for empty messages - REP socket requires response to every request
+                if len(message) == 0:
+                    logger.debug("Received empty message, sending empty response")
+                    socket.send(b"")  # REP socket must respond to every request
+                    continue
+
+                logger.debug(f"Received ZMQ request of size {len(message)} bytes")
+                logger.debug(f"Message preview: {message[:50]}")  # Show first 50 bytes
+
+                e2e_start = time.time()
+
+                # Try protobuf first (for DiskANN C++ node_ids requests - primary use case)
+                texts = []
+                node_ids = []
+                is_text_request = False
+
+                try:
+                    req_proto = embedding_pb2.NodeEmbeddingRequest()
+                    req_proto.ParseFromString(message)
+                    node_ids = list(req_proto.node_ids)
+
+                    if not node_ids:
+                        raise RuntimeError(
+                            f"PROTOBUF: Received empty node_ids! Message size: {len(message)}"
+                        )
+
+                    logger.info(
+                        f"✅ PROTOBUF: Node ID request for {len(node_ids)} node embeddings: {node_ids[:10]}"
+                    )
+                except Exception as protobuf_error:
+                    logger.debug(f"Protobuf parsing failed: {protobuf_error}")
+                    # Fallback to msgpack (for BaseSearcher direct text requests)
+                    try:
+                        import msgpack
+
+                        request = msgpack.unpackb(message)
+                        # For BaseSearcher compatibility, request is a list of texts directly
+                        if isinstance(request, list) and all(
+                            isinstance(item, str) for item in request
+                        ):
+                            texts = request
+                            is_text_request = True
+                            logger.info(
+                                f"✅ MSGPACK: Direct text request for {len(texts)} texts"
+                            )
+                        else:
+                            raise ValueError("Not a valid msgpack text request")
+                    except Exception as msgpack_error:
+                        raise RuntimeError(
+                            f"Both protobuf and msgpack parsing failed! Protobuf: {protobuf_error}, Msgpack: {msgpack_error}"
+                        )
+
+                # Look up texts by node IDs (only if not direct text request)
+                if not is_text_request:
+                    for nid in node_ids:
+                        try:
+                            passage_data = passages.get_passage(str(nid))
+                            txt = passage_data["text"]
+                            if not txt:
+                                raise RuntimeError(
+                                    f"FATAL: Empty text for passage ID {nid}"
+                                )
+                            texts.append(txt)
+                        except KeyError as e:
+                            logger.error(f"Passage ID {nid} not found: {e}")
+                            raise e
+                        except Exception as e:
+                            logger.error(f"Exception looking up passage ID {nid}: {e}")
+                            raise
+
+                    # Debug logging
+                    logger.debug(f"Processing {len(texts)} texts")
+                    logger.debug(
+                        f"Text lengths: {[len(t) for t in texts[:5]]}"
+                    )  # Show first 5
+
+                # Process embeddings using unified computation
+                embeddings = compute_embeddings(texts, model_name, mode=embedding_mode)
+                logger.info(
+                    f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
+                )
+
+                # Prepare response based on request type
+                if is_text_request:
+                    # For BaseSearcher compatibility: return msgpack format
+                    import msgpack
+
+                    response_data = msgpack.packb(embeddings.tolist())
+                else:
+                    # For DiskANN C++ compatibility: return protobuf format
+                    resp_proto = embedding_pb2.NodeEmbeddingResponse()
+                    hidden_contiguous = np.ascontiguousarray(
+                        embeddings, dtype=np.float32
+                    )
+
+                    # Serialize embeddings data
+                    resp_proto.embeddings_data = hidden_contiguous.tobytes()
+                    resp_proto.dimensions.append(hidden_contiguous.shape[0])
+                    resp_proto.dimensions.append(hidden_contiguous.shape[1])
+
+                    response_data = resp_proto.SerializeToString()
+
+                # Send response back to the client
+                socket.send(response_data)
+
+                e2e_end = time.time()
+                logger.info(f"⏱️  ZMQ E2E time: {e2e_end - e2e_start:.6f}s")
+
+            except zmq.Again:
+                logger.debug("ZMQ socket timeout, continuing to listen")
+                continue
+            except Exception as e:
+                logger.error(f"Error in ZMQ server loop: {e}")
+                import traceback
+
+                traceback.print_exc()
+                raise
+
+    zmq_thread = threading.Thread(target=zmq_server_thread, daemon=True)
+    zmq_thread.start()
+    logger.info(f"Started DiskANN ZMQ server thread on port {zmq_port}")
+
+    # Keep the main thread alive
+    try:
+        while True:
+            time.sleep(1)
+    except KeyboardInterrupt:
+        logger.info("DiskANN Server shutting down...")
+        return
+
+
+if __name__ == "__main__":
+    import signal
+    import sys
+
+    def signal_handler(sig, frame):
+        logger.info(f"Received signal {sig}, shutting down gracefully...")
+        sys.exit(0)
+
+    # Register signal handlers for graceful shutdown
+    signal.signal(signal.SIGTERM, signal_handler)
+    signal.signal(signal.SIGINT, signal_handler)
+
+    parser = argparse.ArgumentParser(description="DiskANN Embedding service")
+    parser.add_argument("--zmq-port", type=int, default=5555, help="ZMQ port to run on")
+    parser.add_argument(
+        "--passages-file",
+        type=str,
+        help="Metadata JSON file containing passage sources",
+    )
+    parser.add_argument(
+        "--model-name",
+        type=str,
+        default="sentence-transformers/all-mpnet-base-v2",
+        help="Embedding model name",
+    )
+    parser.add_argument(
+        "--embedding-mode",
+        type=str,
+        default="sentence-transformers",
+        choices=["sentence-transformers", "openai", "mlx"],
+        help="Embedding backend mode",
+    )
+
+    args = parser.parse_args()
+
+    # Create and start the DiskANN embedding server
+    create_diskann_embedding_server(
+        passages_file=args.passages_file,
+        zmq_port=args.zmq_port,
+        model_name=args.model_name,
+        embedding_mode=args.embedding_mode,
+    )

packages/leann-backend-diskann/leann_backend_diskann/embedding_server.py

@@ -1,741 +0,0 @@
-#!/usr/bin/env python3
-"""
-Embedding server for leann-backend-diskann - Fixed ZMQ REQ-REP pattern
-"""
-
-import pickle
-import argparse
-import time
-import json
-from typing import Dict, Any, Optional, Union
-
-from transformers import AutoTokenizer, AutoModel
-import os
-from contextlib import contextmanager
-import zmq
-import numpy as np
-import msgpack
-from pathlib import Path
-import logging
-
-RED = "\033[91m"
-
-# Set up logging based on environment variable
-LOG_LEVEL = os.getenv('LEANN_LOG_LEVEL', 'INFO').upper()
-logging.basicConfig(
-    level=getattr(logging, LOG_LEVEL, logging.INFO),
-    format='%(asctime)s - %(levelname)s - %(message)s'
-)
-logger = logging.getLogger(__name__)
-RESET = "\033[0m"
-
-# --- New Passage Loader from HNSW backend ---
-class SimplePassageLoader:
-    """
-    Simple passage loader that replaces config.py dependencies
-    """
-    def __init__(self, passages_data: Optional[Dict[str, Any]] = None):
-        self.passages_data = passages_data or {}
-        self._meta_path = ''
-    
-    def __getitem__(self, passage_id: Union[str, int]) -> Dict[str, str]:
-        """Get passage by ID"""
-        str_id = str(passage_id)
-        if str_id in self.passages_data:
-            return {"text": self.passages_data[str_id]}
-        else:
-            # Return empty text for missing passages
-            return {"text": ""}
-    
-    def __len__(self) -> int:
-        return len(self.passages_data)
-    
-    def keys(self):
-        return self.passages_data.keys()
-
-def load_passages_from_metadata(meta_file: str) -> SimplePassageLoader:
-    """
-    Load passages using metadata file with PassageManager for lazy loading
-    """
-    # Load metadata to get passage sources
-    with open(meta_file, 'r') as f:
-        meta = json.load(f)
-    
-    # Import PassageManager dynamically to avoid circular imports
-    import sys
-    from pathlib import Path
-    
-    # Find the leann package directory relative to this file
-    current_dir = Path(__file__).parent
-    leann_core_path = current_dir.parent.parent / "leann-core" / "src"
-    sys.path.insert(0, str(leann_core_path))
-    
-    try:
-        from leann.api import PassageManager
-        passage_manager = PassageManager(meta['passage_sources'])
-    finally:
-        sys.path.pop(0)
-    
-    # Load label map 
-    passages_dir = Path(meta_file).parent
-    label_map_file = passages_dir / "leann.labels.map"
-    
-    if label_map_file.exists():
-        import pickle
-        with open(label_map_file, 'rb') as f:
-            label_map = pickle.load(f)
-        print(f"Loaded label map with {len(label_map)} entries")
-    else:
-        raise FileNotFoundError(f"Label map file not found: {label_map_file}")
-    
-    print(f"Initialized lazy passage loading for {len(label_map)} passages")
-    
-    class LazyPassageLoader(SimplePassageLoader):
-        def __init__(self, passage_manager, label_map):
-            self.passage_manager = passage_manager
-            self.label_map = label_map
-            # Initialize parent with empty data
-            super().__init__({})
-        
-        def __getitem__(self, passage_id: Union[str, int]) -> Dict[str, str]:
-            """Get passage by ID with lazy loading"""
-            try:
-                int_id = int(passage_id)
-                if int_id in self.label_map:
-                    string_id = self.label_map[int_id]
-                    passage_data = self.passage_manager.get_passage(string_id)
-                    if passage_data and passage_data.get("text"):
-                        return {"text": passage_data["text"]}
-                    else:
-                        raise RuntimeError(f"FATAL: Empty text for ID {int_id} -> {string_id}")
-                else:
-                    raise RuntimeError(f"FATAL: ID {int_id} not found in label_map")
-            except Exception as e:
-                raise RuntimeError(f"FATAL: Exception getting passage {passage_id}: {e}")
-        
-        def __len__(self) -> int:
-            return len(self.label_map)
-        
-        def keys(self):
-            return self.label_map.keys()
-    
-    loader = LazyPassageLoader(passage_manager, label_map)
-    loader._meta_path = meta_file
-    return loader
-
-def load_passages_from_file(passages_file: str) -> SimplePassageLoader:
-    """
-    Load passages from a JSONL file with label map support
-    Expected format: {"id": "passage_id", "text": "passage_text", "metadata": {...}} (one per line)
-    """
-    
-    if not os.path.exists(passages_file):
-        raise FileNotFoundError(f"Passages file {passages_file} not found.")
-    
-    if not passages_file.endswith('.jsonl'):
-        raise ValueError(f"Expected .jsonl file format, got: {passages_file}")
-    
-    # Load label map (int -> string_id)
-    passages_dir = Path(passages_file).parent
-    label_map_file = passages_dir / "leann.labels.map"
-    
-    label_map = {}
-    if label_map_file.exists():
-        with open(label_map_file, 'rb') as f:
-            label_map = pickle.load(f)
-        print(f"Loaded label map with {len(label_map)} entries")
-    else:
-        raise FileNotFoundError(f"Label map file not found: {label_map_file}")
-    
-    # Load passages by string ID
-    string_id_passages = {}
-    with open(passages_file, 'r', encoding='utf-8') as f:
-        for line in f:
-            if line.strip():
-                passage = json.loads(line)
-                string_id_passages[passage['id']] = passage['text']
-    
-    # Create int ID -> text mapping using label map
-    passages_data = {}
-    for int_id, string_id in label_map.items():
-        if string_id in string_id_passages:
-            passages_data[str(int_id)] = string_id_passages[string_id]
-        else:
-            print(f"WARNING: String ID {string_id} from label map not found in passages")
-    
-    print(f"Loaded {len(passages_data)} passages from JSONL file {passages_file} using label map")
-    return SimplePassageLoader(passages_data)
-
-def create_embedding_server_thread(
-    zmq_port=5555,
-    model_name="sentence-transformers/all-mpnet-base-v2",
-    max_batch_size=128,
-    passages_file: Optional[str] = None,
-    embedding_mode: str = "sentence-transformers",
-    enable_warmup: bool = False,
-):
-    """
-    Create and run embedding server in the current thread
-    This function is designed to be called in a separate thread
-    """
-    logger.info(f"Initializing embedding server thread on port {zmq_port}")
-    
-    try:
-        # Check if port is already occupied
-        import socket
-        def check_port(port):
-            with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-                return s.connect_ex(('localhost', port)) == 0
-
-        if check_port(zmq_port):
-            print(f"{RED}Port {zmq_port} is already in use{RESET}")
-            return
-
-        # Auto-detect mode based on model name if not explicitly set
-        if embedding_mode == "sentence-transformers" and model_name.startswith("text-embedding-"):
-            embedding_mode = "openai"
-        
-        if embedding_mode == "mlx":
-            from leann.api import compute_embeddings_mlx
-            import torch
-            logger.info("Using MLX for embeddings")
-            # Set device to CPU for compatibility with DeviceTimer class
-            device = torch.device("cpu")
-            cuda_available = False
-            mps_available = False
-        elif embedding_mode == "openai":
-            from leann.api import compute_embeddings_openai
-            import torch
-            logger.info("Using OpenAI API for embeddings")
-            # Set device to CPU for compatibility with DeviceTimer class
-            device = torch.device("cpu")
-            cuda_available = False
-            mps_available = False
-        elif embedding_mode == "sentence-transformers":
-            # Initialize model
-            tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
-            import torch
-
-            # Select device
-            mps_available = hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()
-            cuda_available = torch.cuda.is_available()
-            
-            if cuda_available:
-                device = torch.device("cuda")
-                logger.info("Using CUDA device")
-            elif mps_available:
-                device = torch.device("mps")
-                logger.info("Using MPS device (Apple Silicon)")
-            else:
-                device = torch.device("cpu")
-                logger.info("Using CPU device")
-            
-            # Load model
-            logger.info(f"Loading model {model_name}")
-            model = AutoModel.from_pretrained(model_name).to(device).eval()
-
-            # Optimize model
-            if cuda_available or mps_available:
-                try:
-                    model = model.half()
-                    model = torch.compile(model)
-                    logger.info(f"Using FP16 precision with model: {model_name}")
-                except Exception as e:
-                    print(f"WARNING: Model optimization failed: {e}")
-        else:
-            raise ValueError(f"Unsupported embedding mode: {embedding_mode}. Supported modes: sentence-transformers, mlx, openai")
-
-        # Load passages from file if provided
-        if passages_file and os.path.exists(passages_file):
-            # Check if it's a metadata file or a single passages file
-            if passages_file.endswith('.meta.json'):
-                passages = load_passages_from_metadata(passages_file)
-            else:
-                # Try to find metadata file in same directory
-                passages_dir = Path(passages_file).parent
-                meta_files = list(passages_dir.glob("*.meta.json"))
-                if meta_files:
-                    print(f"Found metadata file: {meta_files[0]}, using lazy loading")
-                    passages = load_passages_from_metadata(str(meta_files[0]))
-                else:
-                    # Fallback to original single file loading (will cause warnings)
-                    print("WARNING: No metadata file found, using single file loading (may cause missing passage warnings)")
-                    passages = load_passages_from_file(passages_file)
-        else:
-            print("WARNING: No passages file provided or file not found. Using an empty passage loader.")
-            passages = SimplePassageLoader()
-
-        logger.info(f"Loaded {len(passages)} passages.")
-
-        def client_warmup(zmq_port):
-            """Perform client-side warmup for DiskANN server"""
-            time.sleep(2)
-            print(f"Performing client-side warmup with model {model_name}...")
-            
-            # Get actual passage IDs from the loaded passages
-            sample_ids = []
-            if hasattr(passages, 'keys') and len(passages) > 0:
-                available_ids = list(passages.keys())
-                # Take up to 5 actual IDs, but at least 1
-                sample_ids = available_ids[:min(5, len(available_ids))]
-                print(f"Using actual passage IDs for warmup: {sample_ids}")
-            else:
-                print("No passages available for warmup, skipping warmup...")
-                return
-
-            try:
-                context = zmq.Context()
-                socket = context.socket(zmq.REQ)
-                socket.connect(f"tcp://localhost:{zmq_port}")
-                socket.setsockopt(zmq.RCVTIMEO, 30000)
-                socket.setsockopt(zmq.SNDTIMEO, 30000)
-
-                try:
-                    ids_to_send = [int(x) for x in sample_ids]
-                except ValueError:
-                    print("Warning: Could not convert sample IDs to integers, skipping warmup")
-                    return
-
-                if not ids_to_send:
-                    print("Skipping warmup send.")
-                    return
-
-                # Use protobuf format for warmup
-                from . import embedding_pb2
-                req_proto = embedding_pb2.NodeEmbeddingRequest()
-                req_proto.node_ids.extend(ids_to_send)
-                request_bytes = req_proto.SerializeToString()
-
-                for i in range(3):
-                    print(f"Sending warmup request {i + 1}/3 via ZMQ (Protobuf)...")
-                    socket.send(request_bytes)
-                    response_bytes = socket.recv()
-                    
-                    resp_proto = embedding_pb2.NodeEmbeddingResponse()
-                    resp_proto.ParseFromString(response_bytes)
-                    embeddings_count = resp_proto.dimensions[0] if resp_proto.dimensions else 0
-                    print(f"Warmup request {i + 1}/3 successful, received {embeddings_count} embeddings")
-                    time.sleep(0.1)
-
-                print("Client-side Protobuf ZMQ warmup complete")
-                socket.close()
-                context.term()
-            except Exception as e:
-                print(f"Error during Protobuf ZMQ warmup: {e}")
-
-        class DeviceTimer:
-            """Device timer"""
-            def __init__(self, name="", device=device):
-                self.name = name
-                self.device = device
-                self.start_time = 0
-                self.end_time = 0
-                
-                if embedding_mode == "sentence-transformers" and torch.cuda.is_available():
-                    self.start_event = torch.cuda.Event(enable_timing=True)
-                    self.end_event = torch.cuda.Event(enable_timing=True)
-                else:
-                    self.start_event = None
-                    self.end_event = None
-
-            @contextmanager
-            def timing(self):
-                self.start()
-                yield
-                self.end()
-
-            def start(self):
-                if embedding_mode == "sentence-transformers" and torch.cuda.is_available():
-                    torch.cuda.synchronize()
-                    self.start_event.record()
-                else:
-                    if embedding_mode == "sentence-transformers" and self.device.type == "mps":
-                        torch.mps.synchronize()
-                    self.start_time = time.time()
-
-            def end(self):
-                if embedding_mode == "sentence-transformers" and torch.cuda.is_available():
-                    self.end_event.record()
-                    torch.cuda.synchronize()
-                else:
-                    if embedding_mode == "sentence-transformers" and self.device.type == "mps":
-                        torch.mps.synchronize()
-                    self.end_time = time.time()
-
-            def elapsed_time(self):
-                if embedding_mode == "sentence-transformers" and torch.cuda.is_available():
-                    return self.start_event.elapsed_time(self.end_event) / 1000.0
-                else:
-                    return self.end_time - self.start_time
-
-            def print_elapsed(self):
-                elapsed = self.elapsed_time()
-                print(f"[{self.name}] Elapsed time: {elapsed:.3f}s")
-
-        def process_batch_pytorch(texts_batch, ids_batch, missing_ids):
-            """Process text batch"""
-            if not texts_batch:
-                return np.array([])
-
-            # Filter out empty texts and their corresponding IDs
-            valid_texts = []
-            valid_ids = []
-            for i, text in enumerate(texts_batch):
-                if text.strip():  # Only include non-empty texts
-                    valid_texts.append(text)
-                    valid_ids.append(ids_batch[i])
-
-            if not valid_texts:
-                print("WARNING: No valid texts in batch")
-                return np.array([])
-
-            # Tokenize
-            token_timer = DeviceTimer("tokenization")
-            with token_timer.timing():
-                inputs = tokenizer(
-                    valid_texts,
-                    padding=True,
-                    truncation=True,
-                    max_length=512,
-                    return_tensors="pt"
-                ).to(device)
-
-            # Compute embeddings
-            embed_timer = DeviceTimer("embedding computation")
-            with embed_timer.timing():
-                with torch.no_grad():
-                    outputs = model(**inputs)
-                    hidden_states = outputs.last_hidden_state
-                    
-                    # Mean pooling
-                    attention_mask = inputs['attention_mask']
-                    mask_expanded = attention_mask.unsqueeze(-1).expand(hidden_states.size()).float()
-                    sum_embeddings = torch.sum(hidden_states * mask_expanded, 1)
-                    sum_mask = torch.clamp(mask_expanded.sum(1), min=1e-9)
-                    batch_embeddings = sum_embeddings / sum_mask
-                embed_timer.print_elapsed()
-
-            return batch_embeddings.cpu().numpy()
-
-        # ZMQ server main loop - modified to use REP socket
-        context = zmq.Context()
-        socket = context.socket(zmq.ROUTER)  # Changed to REP socket
-        socket.bind(f"tcp://127.0.0.1:{zmq_port}")
-        print(f"INFO: ZMQ ROUTER server listening on port {zmq_port}")
-
-        # Set timeouts
-        socket.setsockopt(zmq.RCVTIMEO, 5000)  # 5 second receive timeout
-        socket.setsockopt(zmq.SNDTIMEO, 300000)  # 300 second send timeout
-
-        from . import embedding_pb2
-
-        print(f"INFO: Embedding server ready to serve requests")
-
-        # Start warmup thread if enabled
-        if enable_warmup and len(passages) > 0:
-            import threading
-            print(f"Warmup enabled: starting warmup thread")
-            warmup_thread = threading.Thread(target=client_warmup, args=(zmq_port,))
-            warmup_thread.daemon = True
-            warmup_thread.start()
-        else:
-            print(f"Warmup disabled or no passages available (enable_warmup={enable_warmup}, passages={len(passages)})")
-
-        while True:
-            try:
-                parts = socket.recv_multipart()
-
-                # --- Restore robust message format detection ---
-                # Must check parts length to avoid IndexError
-                if len(parts) >= 3:
-                    identity = parts[0]
-                    # empty = parts[1]  # We usually don't care about the middle empty frame
-                    message = parts[2]
-                elif len(parts) == 2:
-                    # Can also handle cases without empty frame
-                    identity = parts[0]
-                    message = parts[1]
-                else:
-                    # If received message format is wrong, print warning and ignore it instead of crashing
-                    print(f"WARNING: Received unexpected message format with {len(parts)} parts. Ignoring.")
-                    continue
-                print(f"INFO: Received ZMQ request from client {identity.hex()[:8]}, size {len(message)} bytes")
-
-                # Handle control messages (MessagePack format)
-                try:
-                    request_payload = msgpack.unpackb(message)
-                    if isinstance(request_payload, list) and len(request_payload) >= 1:
-                        if request_payload[0] == "__QUERY_META_PATH__":
-                            # Return the current meta path being used by the server
-                            current_meta_path = getattr(passages, '_meta_path', '') if hasattr(passages, '_meta_path') else ''
-                            response = [current_meta_path]
-                            socket.send_multipart([identity, b'', msgpack.packb(response)])
-                            continue
-                            
-                        elif request_payload[0] == "__UPDATE_META_PATH__" and len(request_payload) >= 2:
-                            # Update the server's meta path and reload passages
-                            new_meta_path = request_payload[1]
-                            try:
-                                print(f"INFO: Updating server meta path to: {new_meta_path}")
-                                # Reload passages from the new meta file
-                                passages = load_passages_from_metadata(new_meta_path)
-                                # Store the meta path for future queries
-                                passages._meta_path = new_meta_path
-                                response = ["SUCCESS"]
-                                print(f"INFO: Successfully updated meta path and reloaded {len(passages)} passages")
-                            except Exception as e:
-                                print(f"ERROR: Failed to update meta path: {e}")
-                                response = ["FAILED", str(e)]
-                            socket.send_multipart([identity, b'', msgpack.packb(response)])
-                            continue
-                            
-                        elif request_payload[0] == "__QUERY_MODEL__":
-                            # Return the current model being used by the server
-                            response = [model_name]
-                            socket.send_multipart([identity, b'', msgpack.packb(response)])
-                            continue
-                            
-                        elif request_payload[0] == "__UPDATE_MODEL__" and len(request_payload) >= 2:
-                            # Update the server's embedding model
-                            new_model_name = request_payload[1]
-                            try:
-                                print(f"INFO: Updating server model from {model_name} to: {new_model_name}")
-                                
-                                # Clean up old model to free memory
-                                if not use_mlx:
-                                    print("INFO: Releasing old model from memory...")
-                                    old_model = model
-                                    old_tokenizer = tokenizer
-                                    
-                                    # Load new tokenizer first
-                                    print(f"Loading new tokenizer for {new_model_name}...")
-                                    tokenizer = AutoTokenizer.from_pretrained(new_model_name, use_fast=True)
-                                    
-                                    # Load new model
-                                    print(f"Loading new model {new_model_name}...")
-                                    model = AutoModel.from_pretrained(new_model_name).to(device).eval()
-                                    
-                                    # Optimize new model
-                                    if cuda_available or mps_available:
-                                        try:
-                                            model = model.half()
-                                            model = torch.compile(model)
-                                            print(f"INFO: Using FP16 precision with model: {new_model_name}")
-                                        except Exception as e:
-                                            print(f"WARNING: Model optimization failed: {e}")
-                                    
-                                    # Now safely delete old model after new one is loaded
-                                    del old_model
-                                    del old_tokenizer
-                                    
-                                    # Clear GPU cache if available
-                                    if device.type == "cuda":
-                                        torch.cuda.empty_cache()
-                                        print("INFO: Cleared CUDA cache")
-                                    elif device.type == "mps":
-                                        torch.mps.empty_cache()
-                                        print("INFO: Cleared MPS cache")
-                                    
-                                    # Force garbage collection
-                                    import gc
-                                    gc.collect()
-                                    print("INFO: Memory cleanup completed")
-                                
-                                # Update model name
-                                model_name = new_model_name
-                                
-                                response = ["SUCCESS"]
-                                print(f"INFO: Successfully updated model to: {new_model_name}")
-                            except Exception as e:
-                                print(f"ERROR: Failed to update model: {e}")
-                                response = ["FAILED", str(e)]
-                            socket.send_multipart([identity, b'', msgpack.packb(response)])
-                            continue
-                except:
-                    # Not a control message, continue with normal protobuf processing
-                    pass
-
-                e2e_start = time.time()
-                lookup_timer = DeviceTimer("text lookup")
-
-                # Parse request
-                req_proto = embedding_pb2.NodeEmbeddingRequest()
-                req_proto.ParseFromString(message)
-                node_ids = req_proto.node_ids
-                print(f"INFO: Request for {len(node_ids)} node embeddings: {list(node_ids)}")
-
-                # Add debug information
-                if len(node_ids) > 0:
-                    print(f"DEBUG: Node ID range: {min(node_ids)} to {max(node_ids)}")
-                
-                # Look up texts
-                texts = []
-                missing_ids = []
-                with lookup_timer.timing():
-                    for nid in node_ids:
-                        txtinfo = passages[nid]
-                        txt = txtinfo["text"]
-                        if txt:
-                            texts.append(txt)
-                        else:
-                            # If text is empty, we still need a placeholder for batch processing,
-                            # but record its ID as missing
-                            texts.append("") 
-                            missing_ids.append(nid)
-                lookup_timer.print_elapsed()
-
-                if missing_ids:
-                    print(f"WARNING: Missing passages for IDs: {missing_ids}")
-
-                # Process batch
-                total_size = len(texts)
-                print(f"INFO: Total batch size: {total_size}, max_batch_size: {max_batch_size}")
-                
-                all_embeddings = []
-                
-                if total_size > max_batch_size:
-                    print(f"INFO: Splitting batch of size {total_size} into chunks of {max_batch_size}")
-                    for i in range(0, total_size, max_batch_size):
-                        end_idx = min(i + max_batch_size, total_size)
-                        print(f"INFO: Processing chunk {i//max_batch_size + 1}/{(total_size + max_batch_size - 1)//max_batch_size}: items {i} to {end_idx-1}")
-                        
-                        chunk_texts = texts[i:end_idx]
-                        chunk_ids = node_ids[i:end_idx]
-                        
-                        if embedding_mode == "mlx":
-                            embeddings_chunk = compute_embeddings_mlx(chunk_texts, model_name, batch_size=16)
-                        elif embedding_mode == "openai":
-                            embeddings_chunk = compute_embeddings_openai(chunk_texts, model_name)
-                        else:  # sentence-transformers
-                            embeddings_chunk = process_batch_pytorch(chunk_texts, chunk_ids, missing_ids)
-                        all_embeddings.append(embeddings_chunk)
-                        
-                        if embedding_mode == "sentence-transformers":
-                            if cuda_available:
-                                torch.cuda.empty_cache()
-                            elif device.type == "mps":
-                                torch.mps.empty_cache()
-                            
-                    hidden = np.vstack(all_embeddings)
-                    print(f"INFO: Combined embeddings shape: {hidden.shape}")
-                else:
-                    if embedding_mode == "mlx":
-                        hidden = compute_embeddings_mlx(texts, model_name, batch_size=16)
-                    elif embedding_mode == "openai":
-                        hidden = compute_embeddings_openai(texts, model_name)
-                    else:  # sentence-transformers
-                        hidden = process_batch_pytorch(texts, node_ids, missing_ids)
-
-                # Serialize response
-                ser_start = time.time()
-
-                resp_proto = embedding_pb2.NodeEmbeddingResponse()
-                hidden_contiguous = np.ascontiguousarray(hidden, dtype=np.float32)
-                resp_proto.embeddings_data = hidden_contiguous.tobytes()
-                resp_proto.dimensions.append(hidden_contiguous.shape[0])
-                resp_proto.dimensions.append(hidden_contiguous.shape[1])
-                resp_proto.missing_ids.extend(missing_ids)
-
-                response_data = resp_proto.SerializeToString()
-                
-                # REP socket sends a single response
-                socket.send_multipart([identity, b'', response_data])
-
-                ser_end = time.time()
-
-                print(f"INFO: Serialize time: {ser_end - ser_start:.6f} seconds")
-
-                if embedding_mode == "sentence-transformers":
-                    if device.type == "cuda":
-                        torch.cuda.synchronize()
-                    elif device.type == "mps":
-                        torch.mps.synchronize()
-                e2e_end = time.time()
-                print(f"INFO: ZMQ E2E time: {e2e_end - e2e_start:.6f} seconds")
-
-            except zmq.Again:
-                print("INFO: ZMQ socket timeout, continuing to listen")
-                continue
-            except Exception as e:
-                print(f"ERROR: Error in ZMQ server: {e}")
-                try:
-                    # Send empty response to maintain REQ-REP state
-                    empty_resp = embedding_pb2.NodeEmbeddingResponse()
-                    socket.send(empty_resp.SerializeToString())
-                except:
-                    # If sending fails, recreate socket
-                    socket.close()
-                    socket = context.socket(zmq.REP)
-                    socket.bind(f"tcp://127.0.0.1:{zmq_port}")
-                    socket.setsockopt(zmq.RCVTIMEO, 5000)
-                    socket.setsockopt(zmq.SNDTIMEO, 300000)
-                    print("INFO: ZMQ socket recreated after error")
-
-    except Exception as e:
-        print(f"ERROR: Failed to start embedding server: {e}")
-        raise
-
-
-def create_embedding_server(
-    domain="demo",
-    load_passages=True,
-    load_embeddings=False,
-    use_fp16=True,
-    use_int8=False,
-    use_cuda_graphs=False,
-    zmq_port=5555,
-    max_batch_size=128,
-    lazy_load_passages=False,
-    model_name="sentence-transformers/all-mpnet-base-v2",
-    passages_file: Optional[str] = None,
-    embedding_mode: str = "sentence-transformers",
-    enable_warmup: bool = False,
-):
-    """
-    原有的 create_embedding_server 函数保持不变
-    这个是阻塞版本，用于直接运行
-    """
-    create_embedding_server_thread(zmq_port, model_name, max_batch_size, passages_file, embedding_mode, enable_warmup)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Embedding service")
-    parser.add_argument("--zmq-port", type=int, default=5555, help="ZMQ port to run on")
-    parser.add_argument("--domain", type=str, default="demo", help="Domain name")
-    parser.add_argument("--passages-file", type=str, help="JSON file containing passage ID to text mapping")
-    parser.add_argument("--load-passages", action="store_true", default=True)
-    parser.add_argument("--load-embeddings", action="store_true", default=False)
-    parser.add_argument("--use-fp16", action="store_true", default=False)
-    parser.add_argument("--use-int8", action="store_true", default=False)
-    parser.add_argument("--use-cuda-graphs", action="store_true", default=False)
-    parser.add_argument("--max-batch-size", type=int, default=128, help="Maximum batch size before splitting")
-    parser.add_argument("--lazy-load-passages", action="store_true", default=True)
-    parser.add_argument("--model-name", type=str, default="sentence-transformers/all-mpnet-base-v2", 
-                        help="Embedding model name")
-    parser.add_argument("--embedding-mode", type=str, default="sentence-transformers", 
-                        choices=["sentence-transformers", "mlx", "openai"],
-                        help="Embedding backend mode")
-    parser.add_argument("--use-mlx", action="store_true", default=False, help="Use MLX backend for embeddings (deprecated: use --embedding-mode mlx)")
-    parser.add_argument("--disable-warmup", action="store_true", default=False, help="Disable warmup requests on server start")
-    args = parser.parse_args()
-    
-    # Handle backward compatibility with use_mlx
-    embedding_mode = args.embedding_mode
-    if args.use_mlx:
-        embedding_mode = "mlx"
-
-    create_embedding_server(
-        domain=args.domain,
-        load_passages=args.load_passages,
-        load_embeddings=args.load_embeddings,
-        use_fp16=args.use_fp16,
-        use_int8=args.use_int8,
-        use_cuda_graphs=args.use_cuda_graphs,
-        zmq_port=args.zmq_port,
-        max_batch_size=args.max_batch_size,
-        lazy_load_passages=args.lazy_load_passages,
-        model_name=args.model_name,
-        passages_file=args.passages_file,
-        embedding_mode=embedding_mode,
-        enable_warmup=not args.disable_warmup,
-    )

packages/leann-backend-diskann/pyproject.toml

@@ -4,16 +4,21 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-diskann"
-version = "0.1.0"
-dependencies = ["leann-core==0.1.0", "numpy"]
+version = "0.1.8"
+dependencies = ["leann-core==0.1.8", "numpy"]
 
 [tool.scikit-build]
-# 关键：简化的 CMake 路径
+# Key: simplified CMake path
 cmake.source-dir = "third_party/DiskANN"
-# 关键：Python 包在根目录，路径完全匹配
+# Key: Python package in root directory, paths match exactly
 wheel.packages = ["leann_backend_diskann"]
-# 使用默认的 redirect 模式
+# Use default redirect mode
 editable.mode = "redirect"
 cmake.build-type = "Release"
 build.verbose = true
-build.tool-args = ["-j8"]
+build.tool-args = ["-j8"]
+wheel.exclude = ["CMakeLists.txt", "src", "third_party/**", "*.o", "*.so"]
+sdist.include = ["CMakeLists.txt", "src", "third_party", "leann_backend_diskann/*.txt"]
+
+[tool.scikit-build.cmake.define]
+CMAKE_BUILD_PARALLEL_LEVEL = "8"

packages/leann-backend-hnsw/CMakeLists.txt

@@ -1,6 +1,7 @@
-# 最终简化版
 cmake_minimum_required(VERSION 3.24)
 project(leann_backend_hnsw_wrapper)
+set(CMAKE_C_COMPILER_WORKS 1)
+set(CMAKE_CXX_COMPILER_WORKS 1)
 
 # Set OpenMP path for macOS
 if(APPLE)
@@ -11,15 +12,9 @@ if(APPLE)
     set(OpenMP_omp_LIBRARY "/opt/homebrew/opt/libomp/lib/libomp.dylib")
 endif()
 
-# Build ZeroMQ from source
-set(ZMQ_BUILD_TESTS OFF CACHE BOOL "" FORCE)
-set(ENABLE_DRAFTS OFF CACHE BOOL "" FORCE)
-set(ENABLE_PRECOMPILED OFF CACHE BOOL "" FORCE)
-set(WITH_PERF_TOOL OFF CACHE BOOL "" FORCE)
-set(WITH_DOCS OFF CACHE BOOL "" FORCE)
-set(BUILD_SHARED OFF CACHE BOOL "" FORCE)
-set(BUILD_STATIC ON CACHE BOOL "" FORCE)
-add_subdirectory(third_party/libzmq)
+# Use system ZeroMQ instead of building from source
+find_package(PkgConfig REQUIRED)
+pkg_check_modules(ZMQ REQUIRED libzmq)
 
 # Add cppzmq headers
 include_directories(third_party/cppzmq)
@@ -29,6 +24,39 @@ set(MSGPACK_USE_BOOST OFF CACHE BOOL "" FORCE)
 add_compile_definitions(MSGPACK_NO_BOOST)
 include_directories(third_party/msgpack-c/include)
 
+# Find Python for our own use (not for Faiss)
+if(DEFINED SKBUILD)
+    message(STATUS "Building with scikit-build")
+    # scikit-build-core provides Python information
+endif()
+
+# Find Python - scikit-build-core should provide this
+find_package(Python REQUIRED COMPONENTS Interpreter Development.Module NumPy)
+
+# Print Python information for debugging
+message(STATUS "Python_FOUND: ${Python_FOUND}")
+message(STATUS "Python_VERSION: ${Python_VERSION}")
+message(STATUS "Python_EXECUTABLE: ${Python_EXECUTABLE}")
+message(STATUS "Python_INCLUDE_DIRS: ${Python_INCLUDE_DIRS}")
+message(STATUS "Python_NumPy_INCLUDE_DIRS: ${Python_NumPy_INCLUDE_DIRS}")
+
+# Pass Python information to faiss through cache variables
+set(Python_EXECUTABLE ${Python_EXECUTABLE} CACHE FILEPATH "Python executable" FORCE)
+set(Python_INCLUDE_DIRS ${Python_INCLUDE_DIRS} CACHE PATH "Python include dirs" FORCE)
+set(Python_NumPy_INCLUDE_DIRS ${Python_NumPy_INCLUDE_DIRS} CACHE PATH "NumPy include dirs" FORCE)
+set(Python_VERSION ${Python_VERSION} CACHE STRING "Python version" FORCE)
+set(Python_FOUND ${Python_FOUND} CACHE BOOL "Python found" FORCE)
+
+# Also set Python3 variables for compatibility
+set(Python3_EXECUTABLE ${Python_EXECUTABLE} CACHE FILEPATH "Python3 executable" FORCE)
+set(Python3_INCLUDE_DIRS ${Python_INCLUDE_DIRS} CACHE PATH "Python3 include dirs" FORCE)
+set(Python3_NumPy_INCLUDE_DIRS ${Python_NumPy_INCLUDE_DIRS} CACHE PATH "NumPy include dirs" FORCE)
+set(Python3_VERSION ${Python_VERSION} CACHE STRING "Python3 version" FORCE)
+set(Python3_FOUND ${Python_FOUND} CACHE BOOL "Python3 found" FORCE)
+set(Python3_Development_FOUND TRUE CACHE BOOL "Python3 development found" FORCE)
+set(Python3_NumPy_FOUND TRUE CACHE BOOL "Python3 NumPy found" FORCE)
+
+# Faiss configuration - streamlined build
 set(FAISS_ENABLE_PYTHON ON CACHE BOOL "" FORCE)
 set(FAISS_ENABLE_GPU OFF CACHE BOOL "" FORCE)
 set(FAISS_ENABLE_EXTRAS OFF CACHE BOOL "" FORCE)
@@ -36,4 +64,28 @@ set(BUILD_TESTING OFF CACHE BOOL "" FORCE)
 set(FAISS_ENABLE_C_API OFF CACHE BOOL "" FORCE)
 set(FAISS_OPT_LEVEL "generic" CACHE STRING "" FORCE)
 
+# Disable additional SIMD versions to speed up compilation
+set(FAISS_ENABLE_AVX2 OFF CACHE BOOL "" FORCE)
+set(FAISS_ENABLE_AVX512 OFF CACHE BOOL "" FORCE)
+
+# Additional optimization options from INSTALL.md
+set(CMAKE_BUILD_TYPE "Release" CACHE STRING "" FORCE)
+set(BUILD_SHARED_LIBS OFF CACHE BOOL "" FORCE)  # Static library is faster to build
+
+# Avoid building demos and benchmarks
+set(BUILD_DEMOS OFF CACHE BOOL "" FORCE)
+set(BUILD_BENCHS OFF CACHE BOOL "" FORCE)
+
+# NEW: Tell Faiss to only build the generic version
+set(FAISS_BUILD_GENERIC ON CACHE BOOL "" FORCE)
+set(FAISS_BUILD_AVX2 OFF CACHE BOOL "" FORCE)
+set(FAISS_BUILD_AVX512 OFF CACHE BOOL "" FORCE)
+
+# IMPORTANT: Disable building AVX versions to speed up compilation
+set(FAISS_BUILD_AVX_VERSIONS OFF CACHE BOOL "" FORCE)
+
+# Force Faiss to use our Python settings
+set(Python_FIND_VIRTUALENV ONLY CACHE STRING "" FORCE)
+set(Python3_FIND_VIRTUALENV ONLY CACHE STRING "" FORCE)
+
 add_subdirectory(third_party/faiss)

packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py

@@ -1,10 +1,9 @@
 import numpy as np
 import os
 from pathlib import Path
-from typing import Dict, Any, List, Literal
-import pickle
+from typing import Dict, Any, List, Literal, Optional
 import shutil
-import time
+import logging
 
 from leann.searcher_base import BaseSearcher
 from .convert_to_csr import convert_hnsw_graph_to_csr
@@ -16,6 +15,8 @@ from leann.interface import (
     LeannBackendSearcherInterface,
 )
 
+logger = logging.getLogger(__name__)
+
 
 def get_metric_map():
     from . import faiss  # type: ignore
@@ -57,13 +58,9 @@ class HNSWBuilder(LeannBackendBuilderInterface):
         index_dir.mkdir(parents=True, exist_ok=True)
 
         if data.dtype != np.float32:
+            logger.warning(f"Converting data to float32, shape: {data.shape}")
             data = data.astype(np.float32)
 
-        label_map = {i: str_id for i, str_id in enumerate(ids)}
-        label_map_file = index_dir / "leann.labels.map"
-        with open(label_map_file, "wb") as f:
-            pickle.dump(label_map, f)
-
         metric_enum = get_metric_map().get(self.distance_metric.lower())
         if metric_enum is None:
             raise ValueError(f"Unsupported distance_metric '{self.distance_metric}'.")
@@ -85,7 +82,7 @@ class HNSWBuilder(LeannBackendBuilderInterface):
     def _convert_to_csr(self, index_file: Path):
         """Convert built index to CSR format"""
         mode_str = "CSR-pruned" if self.is_recompute else "CSR-standard"
-        print(f"INFO: Converting HNSW index to {mode_str} format...")
+        logger.info(f"INFO: Converting HNSW index to {mode_str} format...")
 
         csr_temp_file = index_file.with_suffix(".csr.tmp")
 
@@ -94,11 +91,11 @@ class HNSWBuilder(LeannBackendBuilderInterface):
         )
 
         if success:
-            print("✅ CSR conversion successful.")
-            index_file_old = index_file.with_suffix(".old")
-            shutil.move(str(index_file), str(index_file_old))
+            logger.info("✅ CSR conversion successful.")
+            # index_file_old = index_file.with_suffix(".old")
+            # shutil.move(str(index_file), str(index_file_old))
             shutil.move(str(csr_temp_file), str(index_file))
-            print(
+            logger.info(
                 f"INFO: Replaced original index with {mode_str} version at '{index_file}'"
             )
         else:
@@ -135,31 +132,22 @@ class HNSWSearcher(BaseSearcher):
 
         hnsw_config = faiss.HNSWIndexConfig()
         hnsw_config.is_compact = self.is_compact
-        hnsw_config.is_recompute = self.is_pruned or kwargs.get("is_recompute", False)
-
-        if self.is_pruned and not hnsw_config.is_recompute:
-            raise RuntimeError("Index is pruned but recompute is disabled.")
+        hnsw_config.is_recompute = (
+            self.is_pruned
+        )  # In C++ code, it's called is_recompute, but it's only for loading IIUC.
 
         self._index = faiss.read_index(str(index_file), faiss.IO_FLAG_MMAP, hnsw_config)
-        
-        # Load label mapping
-        label_map_file = self.index_dir / "leann.labels.map"
-        if not label_map_file.exists():
-            raise FileNotFoundError(f"Label map file not found at {label_map_file}")
-        
-        with open(label_map_file, "rb") as f:
-            self.label_map = pickle.load(f)
 
     def search(
         self,
         query: np.ndarray,
         top_k: int,
+        zmq_port: Optional[int] = None,
         complexity: int = 64,
         beam_width: int = 1,
         prune_ratio: float = 0.0,
-        recompute_embeddings: bool = False,
+        recompute_embeddings: bool = True,
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
-        zmq_port: int = 5557,
         batch_size: int = 0,
         **kwargs,
     ) -> Dict[str, Any]:
@@ -177,7 +165,7 @@ class HNSWSearcher(BaseSearcher):
                 - "global": Use global PQ queue size for selection (default)
                 - "local": Local pruning, sort and select best candidates
                 - "proportional": Base selection on new neighbor count ratio
-            zmq_port: ZMQ port for embedding server
+            zmq_port: ZMQ port for embedding server communication. Must be provided if recompute_embeddings is True.
             batch_size: Neighbor processing batch size, 0=disabled (HNSW-specific)
             **kwargs: Additional HNSW-specific parameters (for legacy compatibility)
 
@@ -186,15 +174,14 @@ class HNSWSearcher(BaseSearcher):
         """
         from . import faiss  # type: ignore
 
-        # Use recompute_embeddings parameter
-        use_recompute = recompute_embeddings or self.is_pruned
-        if use_recompute:
-            meta_file_path = self.index_dir / f"{self.index_path.name}.meta.json"
-            if not meta_file_path.exists():
-                raise RuntimeError(
-                    f"FATAL: Recompute enabled but metadata file not found: {meta_file_path}"
+        if not recompute_embeddings:
+            if self.is_pruned:
+                raise RuntimeError("Recompute is required for pruned index.")
+        if recompute_embeddings:
+            if zmq_port is None:
+                raise ValueError(
+                    "zmq_port must be provided if recompute_embeddings is True"
                 )
-            self._ensure_server_running(str(meta_file_path), port=zmq_port, **kwargs)
 
         if query.dtype != np.float32:
             query = query.astype(np.float32)
@@ -202,7 +189,10 @@ class HNSWSearcher(BaseSearcher):
             faiss.normalize_L2(query)
 
         params = faiss.SearchParametersHNSW()
-        params.zmq_port = zmq_port
+        if zmq_port is not None:
+            params.zmq_port = (
+                zmq_port  # C++ code won't use this if recompute_embeddings is False
+            )
         params.efSearch = complexity
         params.beam_size = beam_width
 
@@ -239,11 +229,7 @@ class HNSWSearcher(BaseSearcher):
         )
 
         string_labels = [
-            [
-                self.label_map.get(int_label, f"unknown_{int_label}")
-                for int_label in batch_labels
-            ]
-            for batch_labels in labels
+            [str(int_label) for int_label in batch_labels] for batch_labels in labels
         ]
 
         return {"labels": string_labels, "distances": distances}

packages/leann-backend-hnsw/pyproject.toml

@@ -6,13 +6,24 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-hnsw"
-version = "0.1.0"
+version = "0.1.8"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
-dependencies = ["leann-core==0.1.0", "numpy"]
+dependencies = [
+    "leann-core==0.1.8", 
+    "numpy",
+    "pyzmq>=23.0.0",
+    "msgpack>=1.0.0",
+]
 
 [tool.scikit-build]
-wheel.packages = ["leann_backend_hnsw"]
-editable.mode = "redirect"
-cmake.build-type = "Release"
-build.verbose = true
-build.tool-args = ["-j8"]
+wheel.exclude = ["CMakeLists.txt", "src", "third_party"]
+sdist.include = ["CMakeLists.txt", "src", "third_party", "leann_backend_hnsw/*.txt"]
+cmake.args = ["-DCMAKE_BUILD_TYPE=Release"]
+# Ensure CMake can find system libraries
+build-dir = "build/{cache_tag}"
+minimum-version = "build-system.requires"
+
+# CMake definitions to optimize compilation
+[tool.scikit-build.cmake.define]
+CMAKE_BUILD_PARALLEL_LEVEL = "8"
+SKBUILD_SOABI = "YES"

packages/leann-core/pyproject.toml

@@ -4,16 +4,27 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann-core"
-version = "0.1.0"
-description = "Core API and plugin system for Leann."
+version = "0.1.8"
+description = "Core API and plugin system for LEANN"
 readme = "README.md"
 requires-python = ">=3.9"
 license = { text = "MIT" }
 
+# All required dependencies included
 dependencies = [
     "numpy>=1.20.0",
-    "tqdm>=4.60.0"
+    "tqdm>=4.60.0",
+    "psutil>=5.8.0",
+    "pyzmq>=23.0.0,<27",  # Cap at 26.x for manylinux2014 compatibility
+    "msgpack>=1.0.0",
+    "torch>=2.0.0",
+    "sentence-transformers>=2.2.0",
+    "llama-index-core>=0.12.0",
+    "python-dotenv>=1.0.0",
 ]
 
+[project.scripts]
+leann = "leann.cli:main"
+
 [tool.setuptools.packages.find]
 where = ["src"]

packages/leann-core/src/leann/api.py

@@ -5,16 +5,18 @@ with the correct, original embedding logic from the user's reference code.
 
 import json
 import pickle
+from leann.interface import LeannBackendSearcherInterface
 import numpy as np
+import time
 from pathlib import Path
 from typing import List, Dict, Any, Optional, Literal
 from dataclasses import dataclass, field
-import uuid
-import torch
-
 from .registry import BACKEND_REGISTRY
 from .interface import LeannBackendFactoryInterface
 from .chat import get_llm
+import logging
+
+logger = logging.getLogger(__name__)
 
 
 def compute_embeddings(
@@ -22,7 +24,8 @@ def compute_embeddings(
     model_name: str,
     mode: str = "sentence-transformers",
     use_server: bool = True,
-    use_mlx: bool = False  # Backward compatibility: if True, override mode to 'mlx',
+    port: Optional[int] = None,
+    is_build=False,
 ) -> np.ndarray:
     """
     Computes embeddings using different backends.
@@ -39,251 +42,63 @@ def compute_embeddings(
     Returns:
         numpy array of embeddings
     """
-    # Override mode for backward compatibility
-    if use_mlx:
-        mode = "mlx"
-
-    # Auto-detect mode based on model name if not explicitly set
-    if mode == "sentence-transformers" and model_name.startswith("text-embedding-"):
-        mode = "openai"
-
-    if mode == "mlx":
-        return compute_embeddings_mlx(chunks, model_name, batch_size=16)
-    elif mode == "openai":
-        return compute_embeddings_openai(chunks, model_name)
-    elif mode == "sentence-transformers":
-        return compute_embeddings_sentence_transformers(
-            chunks, model_name, use_server=use_server
-        )
+    if use_server:
+        # Use embedding server (for search/query)
+        if port is None:
+            raise ValueError("port is required when use_server is True")
+        return compute_embeddings_via_server(chunks, model_name, port=port)
     else:
-        raise ValueError(
-            f"Unsupported embedding mode: {mode}. Supported modes: sentence-transformers, mlx, openai"
+        # Use direct computation (for build_index)
+        from .embedding_compute import (
+            compute_embeddings as compute_embeddings_direct,
+        )
+
+        return compute_embeddings_direct(
+            chunks,
+            model_name,
+            mode=mode,
+            is_build=is_build,
         )
 
 
-def compute_embeddings_sentence_transformers(
-    chunks: List[str], model_name: str, use_server: bool = True
+def compute_embeddings_via_server(
+    chunks: List[str], model_name: str, port: int
 ) -> np.ndarray:
     """Computes embeddings using sentence-transformers.
 
     Args:
         chunks: List of text chunks to embed
         model_name: Name of the sentence transformer model
-        use_server: If True, use embedding server (good for search). If False, use direct computation (good for build).
     """
-    if not use_server:
-        print(
-            f"INFO: Computing embeddings for {len(chunks)} chunks using SentenceTransformer model '{model_name}' (direct)..."
-        )
-        return _compute_embeddings_sentence_transformers_direct(chunks, model_name)
-
-    print(
-        f"INFO: Computing embeddings for {len(chunks)} chunks using SentenceTransformer model '{model_name}' (via embedding server)..."
+    logger.info(
+        f"Computing embeddings for {len(chunks)} chunks using SentenceTransformer model '{model_name}' (via embedding server)..."
     )
+    import zmq
+    import msgpack
+    import numpy as np
 
-    # Use embedding server for sentence-transformers too
-    # This avoids loading the model twice (once in API, once in server)
-    try:
-        # Import ZMQ client functionality and server manager
-        import zmq
-        import msgpack
-        import numpy as np
-        from .embedding_server_manager import EmbeddingServerManager
+    # Connect to embedding server
+    context = zmq.Context()
+    socket = context.socket(zmq.REQ)
+    socket.connect(f"tcp://localhost:{port}")
 
-        # Ensure embedding server is running
-        port = 5557
-        server_manager = EmbeddingServerManager(
-            backend_module_name="leann_backend_hnsw.hnsw_embedding_server"
-        )
+    # Send chunks to server for embedding computation
+    request = chunks
+    socket.send(msgpack.packb(request))
 
-        server_started = server_manager.start_server(
-            port=port,
-            model_name=model_name,
-            embedding_mode="sentence-transformers",
-            enable_warmup=False,
-        )
+    # Receive embeddings from server
+    response = socket.recv()
+    embeddings_list = msgpack.unpackb(response)
 
-        if not server_started:
-            raise RuntimeError(f"Failed to start embedding server on port {port}")
+    # Convert back to numpy array
+    embeddings = np.array(embeddings_list, dtype=np.float32)
 
-        # Connect to embedding server
-        context = zmq.Context()
-        socket = context.socket(zmq.REQ)
-        socket.connect(f"tcp://localhost:{port}")
-
-        # Send chunks to server for embedding computation
-        request = chunks
-        socket.send(msgpack.packb(request))
-
-        # Receive embeddings from server
-        response = socket.recv()
-        embeddings_list = msgpack.unpackb(response)
-
-        # Convert back to numpy array
-        embeddings = np.array(embeddings_list, dtype=np.float32)
-
-        socket.close()
-        context.term()
-
-        return embeddings
-
-    except Exception as e:
-        # Fallback to direct sentence-transformers if server connection fails
-        print(
-            f"Warning: Failed to connect to embedding server, falling back to direct computation: {e}"
-        )
-        return _compute_embeddings_sentence_transformers_direct(chunks, model_name)
-
-
-def _compute_embeddings_sentence_transformers_direct(
-    chunks: List[str], model_name: str
-) -> np.ndarray:
-    """Direct sentence-transformers computation (fallback)."""
-    try:
-        from sentence_transformers import SentenceTransformer
-    except ImportError as e:
-        raise RuntimeError(
-            "sentence-transformers not available. Install with: uv pip install sentence-transformers"
-        ) from e
-
-    # Load model using sentence-transformers
-    model = SentenceTransformer(model_name)
-
-    model = model.half()
-    print(
-        f"INFO: Computing embeddings for {len(chunks)} chunks using SentenceTransformer model '{model_name}' (direct)..."
-    )
-    # use acclerater GPU or MAC GPU
-
-    if torch.cuda.is_available():
-        model = model.to("cuda")
-    elif torch.backends.mps.is_available():
-        model = model.to("mps")
-
-    # Generate embeddings
-    # give use an warning if OOM here means we need to turn down the batch size
-    embeddings = model.encode(
-        chunks, convert_to_numpy=True, show_progress_bar=True, batch_size=16
-    )
+    socket.close()
+    context.term()
 
     return embeddings
 
 
-def compute_embeddings_openai(chunks: List[str], model_name: str) -> np.ndarray:
-    """Computes embeddings using OpenAI API."""
-    try:
-        import openai
-        import os
-    except ImportError as e:
-        raise RuntimeError(
-            "openai not available. Install with: uv pip install openai"
-        ) from e
-
-    # Get API key from environment
-    api_key = os.getenv("OPENAI_API_KEY")
-    if not api_key:
-        raise RuntimeError("OPENAI_API_KEY environment variable not set")
-
-    client = openai.OpenAI(api_key=api_key)
-
-    print(
-        f"INFO: Computing embeddings for {len(chunks)} chunks using OpenAI model '{model_name}'..."
-    )
-
-    # OpenAI has a limit on batch size and input length
-    max_batch_size = 100  # Conservative batch size
-    all_embeddings = []
-    
-    try:
-        from tqdm import tqdm
-        total_batches = (len(chunks) + max_batch_size - 1) // max_batch_size
-        batch_range = range(0, len(chunks), max_batch_size)
-        batch_iterator = tqdm(batch_range, desc="Computing embeddings", unit="batch", total=total_batches)
-    except ImportError:
-        # Fallback without progress bar
-        batch_iterator = range(0, len(chunks), max_batch_size)
-    
-    for i in batch_iterator:
-        batch_chunks = chunks[i:i + max_batch_size]
-        
-        try:
-            response = client.embeddings.create(model=model_name, input=batch_chunks)
-            batch_embeddings = [embedding.embedding for embedding in response.data]
-            all_embeddings.extend(batch_embeddings)
-        except Exception as e:
-            print(f"ERROR: Failed to get embeddings for batch starting at {i}: {e}")
-            raise
-
-    embeddings = np.array(all_embeddings, dtype=np.float32)
-    print(
-        f"INFO: Generated {len(embeddings)} embeddings with dimension {embeddings.shape[1]}"
-    )
-    return embeddings
-
-
-def compute_embeddings_mlx(chunks: List[str], model_name: str, batch_size: int = 16) -> np.ndarray:
-    """Computes embeddings using an MLX model."""
-    try:
-        import mlx.core as mx
-        from mlx_lm.utils import load
-        from tqdm import tqdm
-    except ImportError as e:
-        raise RuntimeError(
-            "MLX or related libraries not available. Install with: uv pip install mlx mlx-lm"
-        ) from e
-
-    print(
-        f"INFO: Computing embeddings for {len(chunks)} chunks using MLX model '{model_name}' with batch_size={batch_size}..."
-    )
-
-    # Load model and tokenizer
-    model, tokenizer = load(model_name)
-
-    # Process chunks in batches with progress bar
-    all_embeddings = []
-    
-    try:
-        from tqdm import tqdm
-        batch_iterator = tqdm(range(0, len(chunks), batch_size), desc="Computing embeddings", unit="batch")
-    except ImportError:
-        batch_iterator = range(0, len(chunks), batch_size)
-    
-    for i in batch_iterator:
-        batch_chunks = chunks[i:i + batch_size]
-        
-        # Tokenize all chunks in the batch
-        batch_token_ids = []
-        for chunk in batch_chunks:
-            token_ids = tokenizer.encode(chunk)  # type: ignore
-            batch_token_ids.append(token_ids)
-        
-        # Pad sequences to the same length for batch processing
-        max_length = max(len(ids) for ids in batch_token_ids)
-        padded_token_ids = []
-        for token_ids in batch_token_ids:
-            # Pad with tokenizer.pad_token_id or 0
-            padded = token_ids + [0] * (max_length - len(token_ids))
-            padded_token_ids.append(padded)
-        
-        # Convert to MLX array with batch dimension
-        input_ids = mx.array(padded_token_ids)
-
-        # Get embeddings for the batch
-        embeddings = model(input_ids)
-
-        # Mean pooling for each sequence in the batch
-        pooled = embeddings.mean(axis=1)  # Shape: (batch_size, hidden_size)
-
-        # Convert batch embeddings to numpy
-        for j in range(len(batch_chunks)):
-            pooled_list = pooled[j].tolist()  # Convert to list
-            pooled_numpy = np.array(pooled_list, dtype=np.float32)
-            all_embeddings.append(pooled_numpy)
-
-    # Stack numpy arrays
-    return np.stack(all_embeddings)
-
-
 @dataclass
 class SearchResult:
     id: str
@@ -299,25 +114,24 @@ class PassageManager:
         self.global_offset_map = {}  # Combined map for fast lookup
 
         for source in passage_sources:
-            if source["type"] == "jsonl":
-                passage_file = source["path"]
-                index_file = source["index_path"]
-                if not Path(index_file).exists():
-                    raise FileNotFoundError(
-                        f"Passage index file not found: {index_file}"
-                    )
-                with open(index_file, "rb") as f:
-                    offset_map = pickle.load(f)
-                    self.offset_maps[passage_file] = offset_map
-                    self.passage_files[passage_file] = passage_file
+            assert source["type"] == "jsonl", "only jsonl is supported"
+            passage_file = source["path"]
+            index_file = source["index_path"]  # .idx file
+            if not Path(index_file).exists():
+                raise FileNotFoundError(f"Passage index file not found: {index_file}")
+            with open(index_file, "rb") as f:
+                offset_map = pickle.load(f)
+                self.offset_maps[passage_file] = offset_map
+                self.passage_files[passage_file] = passage_file
 
-                    # Build global map for O(1) lookup
-                    for passage_id, offset in offset_map.items():
-                        self.global_offset_map[passage_id] = (passage_file, offset)
+                # Build global map for O(1) lookup
+                for passage_id, offset in offset_map.items():
+                    self.global_offset_map[passage_id] = (passage_file, offset)
 
     def get_passage(self, passage_id: str) -> Dict[str, Any]:
         if passage_id in self.global_offset_map:
             passage_file, offset = self.global_offset_map[passage_id]
+            # Lazy file opening - only open when needed
             with open(passage_file, "r", encoding="utf-8") as f:
                 f.seek(offset)
                 return json.loads(f.readline())
@@ -328,7 +142,7 @@ class LeannBuilder:
     def __init__(
         self,
         backend_name: str,
-        embedding_model: str = "facebook/contriever-msmarco",
+        embedding_model: str = "facebook/contriever",
         dimensions: Optional[int] = None,
         embedding_mode: str = "sentence-transformers",
         **backend_kwargs,
@@ -344,14 +158,12 @@ class LeannBuilder:
         self.dimensions = dimensions
         self.embedding_mode = embedding_mode
         self.backend_kwargs = backend_kwargs
-        if 'mlx' in self.embedding_model:
-            self.embedding_mode = "mlx"
         self.chunks: List[Dict[str, Any]] = []
 
     def add_text(self, text: str, metadata: Optional[Dict[str, Any]] = None):
         if metadata is None:
             metadata = {}
-        passage_id = metadata.get("id", str(uuid.uuid4()))
+        passage_id = metadata.get("id", str(len(self.chunks)))
         chunk_data = {"id": passage_id, "text": text, "metadata": metadata}
         self.chunks.append(chunk_data)
 
@@ -377,10 +189,13 @@ class LeannBuilder:
         with open(passages_file, "w", encoding="utf-8") as f:
             try:
                 from tqdm import tqdm
-                chunk_iterator = tqdm(self.chunks, desc="Writing passages", unit="chunk")
+
+                chunk_iterator = tqdm(
+                    self.chunks, desc="Writing passages", unit="chunk"
+                )
             except ImportError:
                 chunk_iterator = self.chunks
-            
+
             for chunk in chunk_iterator:
                 offset = f.tell()
                 json.dump(
@@ -398,7 +213,11 @@ class LeannBuilder:
             pickle.dump(offset_map, f)
         texts_to_embed = [c["text"] for c in self.chunks]
         embeddings = compute_embeddings(
-            texts_to_embed, self.embedding_model, self.embedding_mode, use_server=False
+            texts_to_embed,
+            self.embedding_model,
+            self.embedding_mode,
+            use_server=False,
+            is_build=True,
         )
         string_ids = [chunk["id"] for chunk in self.chunks]
         current_backend_kwargs = {**self.backend_kwargs, "dimensions": self.dimensions}
@@ -472,7 +291,7 @@ class LeannBuilder:
                 f"Dimension mismatch: expected {self.dimensions}, got {embedding_dim}"
             )
 
-        print(
+        logger.info(
             f"Building index from precomputed embeddings: {len(ids)} items, {embedding_dim} dimensions"
         )
 
@@ -480,7 +299,7 @@ class LeannBuilder:
         if len(self.chunks) != len(ids):
             # If no text chunks provided, create placeholder text entries
             if not self.chunks:
-                print("No text chunks provided, creating placeholder entries...")
+                logger.info("No text chunks provided, creating placeholder entries...")
                 for id_val in ids:
                     self.add_text(
                         f"Document {id_val}",
@@ -555,15 +374,19 @@ class LeannBuilder:
         with open(leann_meta_path, "w", encoding="utf-8") as f:
             json.dump(meta_data, f, indent=2)
 
-        print(f"Index built successfully from precomputed embeddings: {index_path}")
+        logger.info(
+            f"Index built successfully from precomputed embeddings: {index_path}"
+        )
 
 
 class LeannSearcher:
     def __init__(self, index_path: str, enable_warmup: bool = False, **backend_kwargs):
-        meta_path_str = f"{index_path}.meta.json"
-        if not Path(meta_path_str).exists():
-            raise FileNotFoundError(f"Leann metadata file not found at {meta_path_str}")
-        with open(meta_path_str, "r", encoding="utf-8") as f:
+        self.meta_path_str = f"{index_path}.meta.json"
+        if not Path(self.meta_path_str).exists():
+            raise FileNotFoundError(
+                f"Leann metadata file not found at {self.meta_path_str}"
+            )
+        with open(self.meta_path_str, "r", encoding="utf-8") as f:
             self.meta_data = json.load(f)
         backend_name = self.meta_data["backend_name"]
         self.embedding_model = self.meta_data["embedding_model"]
@@ -571,16 +394,15 @@ class LeannSearcher:
         self.embedding_mode = self.meta_data.get(
             "embedding_mode", "sentence-transformers"
         )
-        # Backward compatibility with use_mlx
-        if self.meta_data.get("use_mlx", False):
-            self.embedding_mode = "mlx"
         self.passage_manager = PassageManager(self.meta_data.get("passage_sources", []))
         backend_factory = BACKEND_REGISTRY.get(backend_name)
         if backend_factory is None:
             raise ValueError(f"Backend '{backend_name}' not found.")
         final_kwargs = {**self.meta_data.get("backend_kwargs", {}), **backend_kwargs}
         final_kwargs["enable_warmup"] = enable_warmup
-        self.backend_impl = backend_factory.searcher(index_path, **final_kwargs)
+        self.backend_impl: LeannBackendSearcherInterface = backend_factory.searcher(
+            index_path, **final_kwargs
+        )
 
     def search(
         self,
@@ -589,26 +411,39 @@ class LeannSearcher:
         complexity: int = 64,
         beam_width: int = 1,
         prune_ratio: float = 0.0,
-        recompute_embeddings: bool = False,
+        recompute_embeddings: bool = True,
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
-        zmq_port: int = 5557,
+        expected_zmq_port: int = 5557,
         **kwargs,
     ) -> List[SearchResult]:
-        print("🔍 DEBUG LeannSearcher.search() called:")
-        print(f"  Query: '{query}'")
-        print(f"  Top_k: {top_k}")
-        print(f"  Additional kwargs: {kwargs}")
+        logger.info("🔍 LeannSearcher.search() called:")
+        logger.info(f"  Query: '{query}'")
+        logger.info(f"  Top_k: {top_k}")
+        logger.info(f"  Additional kwargs: {kwargs}")
 
-        # Use backend's compute_query_embedding method
-        # This will automatically use embedding server if available and needed
-        import time
+        zmq_port = None
+
+        start_time = time.time()
+        if recompute_embeddings:
+            zmq_port = self.backend_impl._ensure_server_running(
+                self.meta_path_str,
+                port=expected_zmq_port,
+                **kwargs,
+            )
+            del expected_zmq_port
+        zmq_time = time.time() - start_time
+        logger.info(f"  Launching server time: {zmq_time} seconds")
 
         start_time = time.time()
 
-        query_embedding = self.backend_impl.compute_query_embedding(query, zmq_port)
-        print(f"  Generated embedding shape: {query_embedding.shape}")
+        query_embedding = self.backend_impl.compute_query_embedding(
+            query,
+            use_server_if_available=recompute_embeddings,
+            zmq_port=zmq_port,
+        )
+        # logger.info(f"  Generated embedding shape: {query_embedding.shape}")
         embedding_time = time.time() - start_time
-        print(f"  Embedding time: {embedding_time} seconds")
+        # logger.info(f"  Embedding time: {embedding_time} seconds")
 
         start_time = time.time()
         results = self.backend_impl.search(
@@ -623,14 +458,14 @@ class LeannSearcher:
             **kwargs,
         )
         search_time = time.time() - start_time
-        print(f"  Search time: {search_time} seconds")
-        print(
+        # logger.info(f"  Search time: {search_time} seconds")
+        logger.info(
             f"  Backend returned: labels={len(results.get('labels', [[]])[0])} results"
         )
 
         enriched_results = []
         if "labels" in results and "distances" in results:
-            print(f"  Processing {len(results['labels'][0])} passage IDs:")
+            logger.info(f"  Processing {len(results['labels'][0])} passage IDs:")
             for i, (string_id, dist) in enumerate(
                 zip(results["labels"][0], results["distances"][0])
             ):
@@ -644,15 +479,25 @@ class LeannSearcher:
                             metadata=passage_data.get("metadata", {}),
                         )
                     )
-                    print(
-                        f"    {i + 1}. passage_id='{string_id}' -> SUCCESS: {passage_data['text']}..."
+                    
+                    # Color codes for better logging
+                    GREEN = "\033[92m"
+                    BLUE = "\033[94m"
+                    YELLOW = "\033[93m"
+                    RESET = "\033[0m"
+                    
+                    # Truncate text for display (first 100 chars)
+                    display_text = passage_data['text']
+                    logger.info(
+                        f"   {GREEN}✓{RESET} {BLUE}[{i + 1:2d}]{RESET} {YELLOW}ID:{RESET} '{string_id}' {YELLOW}Score:{RESET} {dist:.4f} {YELLOW}Text:{RESET} {display_text}"
                     )
                 except KeyError:
-                    print(
-                        f"    {i + 1}. passage_id='{string_id}' -> ERROR: Passage not found in PassageManager!"
+                    RED = "\033[91m"
+                    logger.error(
+                        f"   {RED}✗{RESET} [{i + 1:2d}] ID: '{string_id}' -> {RED}ERROR: Passage not found!{RESET}"
                     )
 
-        print(f"  Final enriched results: {len(enriched_results)} passages")
+        logger.info(f"  {GREEN}✓ Final enriched results: {len(enriched_results)} passages{RESET}")
         return enriched_results
 
 
@@ -674,15 +519,15 @@ class LeannChat:
         complexity: int = 64,
         beam_width: int = 1,
         prune_ratio: float = 0.0,
-        recompute_embeddings: bool = False,
+        recompute_embeddings: bool = True,
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
-        zmq_port: int = 5557,
         llm_kwargs: Optional[Dict[str, Any]] = None,
+        expected_zmq_port: int = 5557,
         **search_kwargs,
     ):
         if llm_kwargs is None:
             llm_kwargs = {}
-
+        search_time = time.time()
         results = self.searcher.search(
             question,
             top_k=top_k,
@@ -691,9 +536,11 @@ class LeannChat:
             prune_ratio=prune_ratio,
             recompute_embeddings=recompute_embeddings,
             pruning_strategy=pruning_strategy,
-            zmq_port=zmq_port,
+            expected_zmq_port=expected_zmq_port,
             **search_kwargs,
         )
+        search_time = time.time() - search_time
+        # logger.info(f"  Search time: {search_time} seconds")
         context = "\n\n".join([r.text for r in results])
         prompt = (
             "Here is some retrieved context that might help answer your question:\n\n"

packages/leann-core/src/leann/chat.py

@@ -9,6 +9,7 @@ from typing import Dict, Any, Optional, List
 import logging
 import os
 import difflib
+import torch
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -28,6 +29,68 @@ def check_ollama_models() -> List[str]:
         return []
 
 
+def check_ollama_model_exists_remotely(model_name: str) -> tuple[bool, list[str]]:
+    """Check if a model exists in Ollama's remote library and return available tags
+    
+    Returns:
+        (model_exists, available_tags): bool and list of matching tags
+    """
+    try:
+        import requests
+        import re
+        
+        # Split model name and tag
+        if ':' in model_name:
+            base_model, requested_tag = model_name.split(':', 1)
+        else:
+            base_model, requested_tag = model_name, None
+        
+        # First check if base model exists in library
+        library_response = requests.get("https://ollama.com/library", timeout=8)
+        if library_response.status_code != 200:
+            return True, []  # Assume exists if can't check
+            
+        # Extract model names from library page
+        models_in_library = re.findall(r'href="/library/([^"]+)"', library_response.text)
+        
+        if base_model not in models_in_library:
+            return False, []  # Base model doesn't exist
+        
+        # If base model exists, get available tags
+        tags_response = requests.get(f"https://ollama.com/library/{base_model}/tags", timeout=8)
+        if tags_response.status_code != 200:
+            return True, []  # Base model exists but can't get tags
+            
+        # Extract tags for this model - be more specific to avoid HTML artifacts
+        tag_pattern = rf'{re.escape(base_model)}:[a-zA-Z0-9\.\-_]+'
+        raw_tags = re.findall(tag_pattern, tags_response.text)
+        
+        # Clean up tags - remove HTML artifacts and duplicates
+        available_tags = []
+        seen = set()
+        for tag in raw_tags:
+            # Skip if it looks like HTML (contains < or >)
+            if '<' in tag or '>' in tag:
+                continue
+            if tag not in seen:
+                seen.add(tag)
+                available_tags.append(tag)
+        
+        # Check if exact model exists
+        if requested_tag is None:
+            # User just requested base model, suggest tags
+            return True, available_tags[:10]  # Return up to 10 tags
+        else:
+            exact_match = model_name in available_tags
+            return exact_match, available_tags[:10]
+            
+    except Exception:
+        pass
+    
+    # If scraping fails, assume model might exist (don't block user)
+    return True, []
+
+
 def search_ollama_models_fuzzy(query: str, available_models: List[str]) -> List[str]:
     """Use intelligent fuzzy search for Ollama models"""
     if not available_models:
@@ -243,24 +306,66 @@ def validate_model_and_suggest(model_name: str, llm_type: str) -> Optional[str]:
     if llm_type == "ollama":
         available_models = check_ollama_models()
         if available_models and model_name not in available_models:
-            # Use intelligent fuzzy search based on locally installed models
-            suggestions = search_ollama_models_fuzzy(model_name, available_models)
-            
             error_msg = f"Model '{model_name}' not found in your local Ollama installation."
-            if suggestions:
-                error_msg += "\n\nDid you mean one of these installed models?\n"
-                for i, suggestion in enumerate(suggestions, 1):
-                    error_msg += f"  {i}. {suggestion}\n"
-            else:
-                error_msg += "\n\nYour installed models:\n"
-                for i, model in enumerate(available_models[:8], 1):
-                    error_msg += f"  {i}. {model}\n"
-                if len(available_models) > 8:
-                    error_msg += f"  ... and {len(available_models) - 8} more\n"
             
-            error_msg += "\nTo list all models: ollama list"
-            error_msg += "\nTo download a new model: ollama pull <model_name>"
-            error_msg += "\nBrowse models: https://ollama.com/library"
+            # Check if the model exists remotely and get available tags
+            model_exists_remotely, available_tags = check_ollama_model_exists_remotely(model_name)
+            
+            if model_exists_remotely and model_name in available_tags:
+                # Exact model exists remotely - suggest pulling it
+                error_msg += f"\n\nTo install the requested model:\n"
+                error_msg += f"  ollama pull {model_name}\n"
+                
+                # Show local alternatives
+                suggestions = search_ollama_models_fuzzy(model_name, available_models)
+                if suggestions:
+                    error_msg += "\nOr use one of these similar installed models:\n"
+                    for i, suggestion in enumerate(suggestions, 1):
+                        error_msg += f"  {i}. {suggestion}\n"
+                        
+            elif model_exists_remotely and available_tags:
+                # Base model exists but requested tag doesn't - suggest correct tags
+                base_model = model_name.split(':')[0]
+                requested_tag = model_name.split(':', 1)[1] if ':' in model_name else None
+                
+                error_msg += f"\n\nModel '{base_model}' exists, but tag '{requested_tag}' is not available."
+                error_msg += f"\n\nAvailable {base_model} models you can install:\n"
+                for i, tag in enumerate(available_tags[:8], 1):
+                    error_msg += f"  {i}. ollama pull {tag}\n"
+                if len(available_tags) > 8:
+                    error_msg += f"  ... and {len(available_tags) - 8} more variants\n"
+                    
+                # Also show local alternatives
+                suggestions = search_ollama_models_fuzzy(model_name, available_models)
+                if suggestions:
+                    error_msg += "\nOr use one of these similar installed models:\n"
+                    for i, suggestion in enumerate(suggestions, 1):
+                        error_msg += f"  {i}. {suggestion}\n"
+                        
+            else:
+                # Model doesn't exist remotely - show fuzzy suggestions
+                suggestions = search_ollama_models_fuzzy(model_name, available_models)
+                error_msg += f"\n\nModel '{model_name}' was not found in Ollama's library."
+                
+                if suggestions:
+                    error_msg += "\n\nDid you mean one of these installed models?\n"
+                    for i, suggestion in enumerate(suggestions, 1):
+                        error_msg += f"  {i}. {suggestion}\n"
+                else:
+                    error_msg += "\n\nYour installed models:\n"
+                    for i, model in enumerate(available_models[:8], 1):
+                        error_msg += f"  {i}. {model}\n"
+                    if len(available_models) > 8:
+                        error_msg += f"  ... and {len(available_models) - 8} more\n"
+            
+            error_msg += "\n\nCommands:"
+            error_msg += "\n  ollama list                    # List installed models"
+            if model_exists_remotely and available_tags:
+                if model_name in available_tags:
+                    error_msg += f"\n  ollama pull {model_name}          # Install requested model"
+                else:
+                    error_msg += f"\n  ollama pull {available_tags[0]}    # Install recommended variant"
+            error_msg += "\n  https://ollama.com/library     # Browse available models"
             return error_msg
             
     elif llm_type == "hf":
@@ -375,8 +480,9 @@ class OllamaChat(LLMInterface):
             "stream": False,  # Keep it simple for now
             "options": kwargs,
         }
-        logger.info(f"Sending request to Ollama: {payload}")
+        logger.debug(f"Sending request to Ollama: {payload}")
         try:
+            logger.info(f"Sending request to Ollama and waiting for response...")
             response = requests.post(full_url, data=json.dumps(payload))
             response.raise_for_status()
 
@@ -396,7 +502,7 @@ class OllamaChat(LLMInterface):
 
 
 class HFChat(LLMInterface):
-    """LLM interface for local Hugging Face Transformers models."""
+    """LLM interface for local Hugging Face Transformers models with proper chat templates."""
 
     def __init__(self, model_name: str = "deepseek-ai/deepseek-llm-7b-chat"):
         logger.info(f"Initializing HFChat with model='{model_name}'")
@@ -407,7 +513,7 @@ class HFChat(LLMInterface):
             raise ValueError(model_error)
             
         try:
-            from transformers.pipelines import pipeline
+            from transformers import AutoTokenizer, AutoModelForCausalLM
             import torch
         except ImportError:
             raise ImportError(
@@ -416,54 +522,101 @@ class HFChat(LLMInterface):
 
         # Auto-detect device
         if torch.cuda.is_available():
-            device = "cuda"
+            self.device = "cuda"
             logger.info("CUDA is available. Using GPU.")
         elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
-            device = "mps"
+            self.device = "mps"
             logger.info("MPS is available. Using Apple Silicon GPU.")
         else:
-            device = "cpu"
+            self.device = "cpu"
             logger.info("No GPU detected. Using CPU.")
 
-        self.pipeline = pipeline("text-generation", model=model_name, device=device)
+        # Load tokenizer and model
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=torch.float16 if self.device != "cpu" else torch.float32,
+            device_map="auto" if self.device != "cpu" else None,
+            trust_remote_code=True
+        )
+        
+        # Move model to device if not using device_map
+        if self.device != "cpu" and "device_map" not in str(self.model):
+            self.model = self.model.to(self.device)
+        
+        # Set pad token if not present
+        if self.tokenizer.pad_token is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
 
     def ask(self, prompt: str, **kwargs) -> str:
-        # Map OpenAI-style arguments to Hugging Face equivalents
-        if "max_tokens" in kwargs:
-            # Prefer user-provided max_new_tokens if both are present
-            kwargs.setdefault("max_new_tokens", kwargs["max_tokens"])
-            # Remove the unsupported key to avoid errors in Transformers
-            kwargs.pop("max_tokens")
+        print('kwargs in HF: ', kwargs)
+        # Check if this is a Qwen model and add /no_think by default
+        is_qwen_model = "qwen" in self.model.config._name_or_path.lower()
+        
+        # For Qwen models, automatically add /no_think to the prompt
+        if is_qwen_model and "/no_think" not in prompt and "/think" not in prompt:
+            prompt = prompt + " /no_think"
+        
+        # Prepare chat template
+        messages = [{"role": "user", "content": prompt}]
+        
+        # Apply chat template if available
+        if hasattr(self.tokenizer, "apply_chat_template"):
+            try:
+                formatted_prompt = self.tokenizer.apply_chat_template(
+                    messages, 
+                    tokenize=False, 
+                    add_generation_prompt=True
+                )
+            except Exception as e:
+                logger.warning(f"Chat template failed, using raw prompt: {e}")
+                formatted_prompt = prompt
+        else:
+            # Fallback for models without chat template
+            formatted_prompt = prompt
 
-        # Handle temperature=0 edge-case for greedy decoding
-        if "temperature" in kwargs and kwargs["temperature"] == 0.0:
-            # Remove unsupported zero temperature and use deterministic generation
-            kwargs.pop("temperature")
-            kwargs.setdefault("do_sample", False)
+        # Tokenize input
+        inputs = self.tokenizer(
+            formatted_prompt, 
+            return_tensors="pt", 
+            padding=True,
+            truncation=True,
+            max_length=2048
+        )
+        
+        # Move inputs to device
+        if self.device != "cpu":
+            inputs = {k: v.to(self.device) for k, v in inputs.items()}
 
-        # Sensible defaults for text generation
-        params = {"max_length": 500, "num_return_sequences": 1, **kwargs}
-        logger.info(f"Generating text with Hugging Face model with params: {params}")
-        results = self.pipeline(prompt, **params)
+        # Set generation parameters
+        generation_config = {
+            "max_new_tokens": kwargs.get("max_tokens", kwargs.get("max_new_tokens", 512)),
+            "temperature": kwargs.get("temperature", 0.7),
+            "top_p": kwargs.get("top_p", 0.9),
+            "do_sample": kwargs.get("temperature", 0.7) > 0,
+            "pad_token_id": self.tokenizer.eos_token_id,
+            "eos_token_id": self.tokenizer.eos_token_id,
+        }
+        
+        # Handle temperature=0 for greedy decoding
+        if generation_config["temperature"] == 0.0:
+            generation_config["do_sample"] = False
+            generation_config.pop("temperature")
 
-        # Handle different response formats from transformers
-        if isinstance(results, list) and len(results) > 0:
-            generated_text = (
-                results[0].get("generated_text", "")
-                if isinstance(results[0], dict)
-                else str(results[0])
+        logger.info(f"Generating with HuggingFace model, config: {generation_config}")
+        
+        # Generate
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                **generation_config
             )
-        else:
-            generated_text = str(results)
 
-        # Extract only the newly generated portion by removing the original prompt
-        if isinstance(generated_text, str) and generated_text.startswith(prompt):
-            response = generated_text[len(prompt) :].strip()
-        else:
-            # Fallback: return the full response if prompt removal fails
-            response = str(generated_text)
-
-        return response
+        # Decode response
+        generated_tokens = outputs[0][inputs["input_ids"].shape[1]:]
+        response = self.tokenizer.decode(generated_tokens, skip_special_tokens=True)
+        
+        return response.strip()
 
 
 class OpenAIChat(LLMInterface):

packages/leann-core/src/leann/cli.py

@@ -0,0 +1,315 @@
+import argparse
+import asyncio
+from pathlib import Path
+
+from llama_index.core import SimpleDirectoryReader
+from llama_index.core.node_parser import SentenceSplitter
+
+from .api import LeannBuilder, LeannSearcher, LeannChat
+
+
+class LeannCLI:
+    def __init__(self):
+        self.indexes_dir = Path.home() / ".leann" / "indexes"
+        self.indexes_dir.mkdir(parents=True, exist_ok=True)
+
+        self.node_parser = SentenceSplitter(
+            chunk_size=256, chunk_overlap=128, separator=" ", paragraph_separator="\n\n"
+        )
+
+    def get_index_path(self, index_name: str) -> str:
+        index_dir = self.indexes_dir / index_name
+        return str(index_dir / "documents.leann")
+
+    def index_exists(self, index_name: str) -> bool:
+        index_dir = self.indexes_dir / index_name
+        meta_file = index_dir / "documents.leann.meta.json"
+        return meta_file.exists()
+
+    def create_parser(self) -> argparse.ArgumentParser:
+        parser = argparse.ArgumentParser(
+            prog="leann",
+            description="LEANN - Local Enhanced AI Navigation",
+            formatter_class=argparse.RawDescriptionHelpFormatter,
+            epilog="""
+Examples:
+  leann build my-docs --docs ./documents    # Build index named my-docs
+  leann search my-docs "query"             # Search in my-docs index
+  leann ask my-docs "question"             # Ask my-docs index
+  leann list                              # List all stored indexes
+            """,
+        )
+
+        subparsers = parser.add_subparsers(dest="command", help="Available commands")
+
+        # Build command
+        build_parser = subparsers.add_parser("build", help="Build document index")
+        build_parser.add_argument("index_name", help="Index name")
+        build_parser.add_argument(
+            "--docs", type=str, required=True, help="Documents directory"
+        )
+        build_parser.add_argument(
+            "--backend", type=str, default="hnsw", choices=["hnsw", "diskann"]
+        )
+        build_parser.add_argument(
+            "--embedding-model", type=str, default="facebook/contriever"
+        )
+        build_parser.add_argument(
+            "--force", "-f", action="store_true", help="Force rebuild"
+        )
+        build_parser.add_argument("--graph-degree", type=int, default=32)
+        build_parser.add_argument("--complexity", type=int, default=64)
+        build_parser.add_argument("--num-threads", type=int, default=1)
+        build_parser.add_argument("--compact", action="store_true", default=True)
+        build_parser.add_argument("--recompute", action="store_true", default=True)
+
+        # Search command
+        search_parser = subparsers.add_parser("search", help="Search documents")
+        search_parser.add_argument("index_name", help="Index name")
+        search_parser.add_argument("query", help="Search query")
+        search_parser.add_argument("--top-k", type=int, default=5)
+        search_parser.add_argument("--complexity", type=int, default=64)
+        search_parser.add_argument("--beam-width", type=int, default=1)
+        search_parser.add_argument("--prune-ratio", type=float, default=0.0)
+        search_parser.add_argument("--recompute-embeddings", action="store_true")
+        search_parser.add_argument(
+            "--pruning-strategy",
+            choices=["global", "local", "proportional"],
+            default="global",
+        )
+
+        # Ask command
+        ask_parser = subparsers.add_parser("ask", help="Ask questions")
+        ask_parser.add_argument("index_name", help="Index name")
+        ask_parser.add_argument(
+            "--llm",
+            type=str,
+            default="ollama",
+            choices=["simulated", "ollama", "hf", "openai"],
+        )
+        ask_parser.add_argument("--model", type=str, default="qwen3:8b")
+        ask_parser.add_argument("--host", type=str, default="http://localhost:11434")
+        ask_parser.add_argument("--interactive", "-i", action="store_true")
+        ask_parser.add_argument("--top-k", type=int, default=20)
+        ask_parser.add_argument("--complexity", type=int, default=32)
+        ask_parser.add_argument("--beam-width", type=int, default=1)
+        ask_parser.add_argument("--prune-ratio", type=float, default=0.0)
+        ask_parser.add_argument("--recompute-embeddings", action="store_true")
+        ask_parser.add_argument(
+            "--pruning-strategy",
+            choices=["global", "local", "proportional"],
+            default="global",
+        )
+
+        # List command
+        list_parser = subparsers.add_parser("list", help="List all indexes")
+
+        return parser
+
+    def list_indexes(self):
+        print("Stored LEANN indexes:")
+
+        if not self.indexes_dir.exists():
+            print(
+                "No indexes found. Use 'leann build <name> --docs <dir>' to create one."
+            )
+            return
+
+        index_dirs = [d for d in self.indexes_dir.iterdir() if d.is_dir()]
+
+        if not index_dirs:
+            print(
+                "No indexes found. Use 'leann build <name> --docs <dir>' to create one."
+            )
+            return
+
+        print(f"Found {len(index_dirs)} indexes:")
+        for i, index_dir in enumerate(index_dirs, 1):
+            index_name = index_dir.name
+            status = "✓" if self.index_exists(index_name) else "✗"
+
+            print(f"  {i}. {index_name} [{status}]")
+            if self.index_exists(index_name):
+                meta_file = index_dir / "documents.leann.meta.json"
+                size_mb = sum(
+                    f.stat().st_size for f in index_dir.iterdir() if f.is_file()
+                ) / (1024 * 1024)
+                print(f"     Size: {size_mb:.1f} MB")
+
+        if index_dirs:
+            example_name = index_dirs[0].name
+            print(f"\nUsage:")
+            print(f'  leann search {example_name} "your query"')
+            print(f"  leann ask {example_name} --interactive")
+
+    def load_documents(self, docs_dir: str):
+        print(f"Loading documents from {docs_dir}...")
+
+        documents = SimpleDirectoryReader(
+            docs_dir,
+            recursive=True,
+            encoding="utf-8",
+            required_exts=[".pdf", ".txt", ".md", ".docx"],
+        ).load_data(show_progress=True)
+
+        all_texts = []
+        for doc in documents:
+            nodes = self.node_parser.get_nodes_from_documents([doc])
+            for node in nodes:
+                all_texts.append(node.get_content())
+
+        print(f"Loaded {len(documents)} documents, {len(all_texts)} chunks")
+        return all_texts
+
+    async def build_index(self, args):
+        docs_dir = args.docs
+        index_name = args.index_name
+        index_dir = self.indexes_dir / index_name
+        index_path = self.get_index_path(index_name)
+
+        if index_dir.exists() and not args.force:
+            print(f"Index '{index_name}' already exists. Use --force to rebuild.")
+            return
+
+        all_texts = self.load_documents(docs_dir)
+        if not all_texts:
+            print("No documents found")
+            return
+
+        index_dir.mkdir(parents=True, exist_ok=True)
+
+        print(f"Building index '{index_name}' with {args.backend} backend...")
+
+        builder = LeannBuilder(
+            backend_name=args.backend,
+            embedding_model=args.embedding_model,
+            graph_degree=args.graph_degree,
+            complexity=args.complexity,
+            is_compact=args.compact,
+            is_recompute=args.recompute,
+            num_threads=args.num_threads,
+        )
+
+        for chunk_text in all_texts:
+            builder.add_text(chunk_text)
+
+        builder.build_index(index_path)
+        print(f"Index built at {index_path}")
+
+    async def search_documents(self, args):
+        index_name = args.index_name
+        query = args.query
+        index_path = self.get_index_path(index_name)
+
+        if not self.index_exists(index_name):
+            print(
+                f"Index '{index_name}' not found. Use 'leann build {index_name} --docs <dir>' to create it."
+            )
+            return
+
+        searcher = LeannSearcher(index_path=index_path)
+        results = searcher.search(
+            query,
+            top_k=args.top_k,
+            complexity=args.complexity,
+            beam_width=args.beam_width,
+            prune_ratio=args.prune_ratio,
+            recompute_embeddings=args.recompute_embeddings,
+            pruning_strategy=args.pruning_strategy,
+        )
+
+        print(f"Search results for '{query}' (top {len(results)}):")
+        for i, result in enumerate(results, 1):
+            print(f"{i}. Score: {result.score:.3f}")
+            print(f"   {result.text[:200]}...")
+            print()
+
+    async def ask_questions(self, args):
+        index_name = args.index_name
+        index_path = self.get_index_path(index_name)
+
+        if not self.index_exists(index_name):
+            print(
+                f"Index '{index_name}' not found. Use 'leann build {index_name} --docs <dir>' to create it."
+            )
+            return
+
+        print(f"Starting chat with index '{index_name}'...")
+        print(f"Using {args.model} ({args.llm})")
+
+        llm_config = {"type": args.llm, "model": args.model}
+        if args.llm == "ollama":
+            llm_config["host"] = args.host
+
+        chat = LeannChat(index_path=index_path, llm_config=llm_config)
+
+        if args.interactive:
+            print("LEANN Assistant ready! Type 'quit' to exit")
+            print("=" * 40)
+
+            while True:
+                user_input = input("\nYou: ").strip()
+                if user_input.lower() in ["quit", "exit", "q"]:
+                    print("Goodbye!")
+                    break
+
+                if not user_input:
+                    continue
+
+                response = chat.ask(
+                    user_input,
+                    top_k=args.top_k,
+                    complexity=args.complexity,
+                    beam_width=args.beam_width,
+                    prune_ratio=args.prune_ratio,
+                    recompute_embeddings=args.recompute_embeddings,
+                    pruning_strategy=args.pruning_strategy,
+                )
+                print(f"LEANN: {response}")
+        else:
+            query = input("Enter your question: ").strip()
+            if query:
+                response = chat.ask(
+                    query,
+                    top_k=args.top_k,
+                    complexity=args.complexity,
+                    beam_width=args.beam_width,
+                    prune_ratio=args.prune_ratio,
+                    recompute_embeddings=args.recompute_embeddings,
+                    pruning_strategy=args.pruning_strategy,
+                )
+                print(f"LEANN: {response}")
+
+    async def run(self, args=None):
+        parser = self.create_parser()
+
+        if args is None:
+            args = parser.parse_args()
+
+        if not args.command:
+            parser.print_help()
+            return
+
+        if args.command == "list":
+            self.list_indexes()
+        elif args.command == "build":
+            await self.build_index(args)
+        elif args.command == "search":
+            await self.search_documents(args)
+        elif args.command == "ask":
+            await self.ask_questions(args)
+        else:
+            parser.print_help()
+
+
+def main():
+    import dotenv
+
+    dotenv.load_dotenv()
+
+    cli = LeannCLI()
+    asyncio.run(cli.run())
+
+
+if __name__ == "__main__":
+    main()

packages/leann-core/src/leann/embedding_compute.py

@@ -0,0 +1,375 @@
+"""
+Unified embedding computation module
+Consolidates all embedding computation logic using SentenceTransformer
+Preserves all optimization parameters to ensure performance
+"""
+
+import numpy as np
+import torch
+from typing import List, Dict, Any
+import logging
+import os
+
+# Set up logger with proper level
+logger = logging.getLogger(__name__)
+LOG_LEVEL = os.getenv("LEANN_LOG_LEVEL", "WARNING").upper()
+log_level = getattr(logging, LOG_LEVEL, logging.WARNING)
+logger.setLevel(log_level)
+
+# Global model cache to avoid repeated loading
+_model_cache: Dict[str, Any] = {}
+
+
+def compute_embeddings(
+    texts: List[str],
+    model_name: str,
+    mode: str = "sentence-transformers",
+    is_build: bool = False,
+    batch_size: int = 32,
+    adaptive_optimization: bool = True,
+) -> np.ndarray:
+    """
+    Unified embedding computation entry point
+
+    Args:
+        texts: List of texts to compute embeddings for
+        model_name: Model name
+        mode: Computation mode ('sentence-transformers', 'openai', 'mlx')
+        is_build: Whether this is a build operation (shows progress bar)
+        batch_size: Batch size for processing
+        adaptive_optimization: Whether to use adaptive optimization based on batch size
+
+    Returns:
+        Normalized embeddings array, shape: (len(texts), embedding_dim)
+    """
+    if mode == "sentence-transformers":
+        return compute_embeddings_sentence_transformers(
+            texts,
+            model_name,
+            is_build=is_build,
+            batch_size=batch_size,
+            adaptive_optimization=adaptive_optimization,
+        )
+    elif mode == "openai":
+        return compute_embeddings_openai(texts, model_name)
+    elif mode == "mlx":
+        return compute_embeddings_mlx(texts, model_name)
+    else:
+        raise ValueError(f"Unsupported embedding mode: {mode}")
+
+
+def compute_embeddings_sentence_transformers(
+    texts: List[str],
+    model_name: str,
+    use_fp16: bool = True,
+    device: str = "auto",
+    batch_size: int = 32,
+    is_build: bool = False,
+    adaptive_optimization: bool = True,
+) -> np.ndarray:
+    """
+    Compute embeddings using SentenceTransformer with model caching and adaptive optimization
+
+    Args:
+        texts: List of texts to compute embeddings for
+        model_name: Model name
+        use_fp16: Whether to use FP16 precision
+        device: Device to use ('auto', 'cuda', 'mps', 'cpu')
+        batch_size: Batch size for processing
+        is_build: Whether this is a build operation (shows progress bar)
+        adaptive_optimization: Whether to use adaptive optimization based on batch size
+    """
+    # Handle empty input
+    if not texts:
+        raise ValueError("Cannot compute embeddings for empty text list")
+    logger.info(
+        f"Computing embeddings for {len(texts)} texts using SentenceTransformer, model: '{model_name}'"
+    )
+
+    # Auto-detect device
+    if device == "auto":
+        if torch.cuda.is_available():
+            device = "cuda"
+        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            device = "mps"
+        else:
+            device = "cpu"
+
+    # Apply optimizations based on benchmark results
+    if adaptive_optimization:
+        # Use optimal batch_size constants for different devices based on benchmark results
+        if device == "mps":
+            batch_size = 128  # MPS optimal batch size from benchmark
+            if model_name == "Qwen/Qwen3-Embedding-0.6B":
+                batch_size = 32
+        elif device == "cuda":
+            batch_size = 256  # CUDA optimal batch size
+        # Keep original batch_size for CPU
+
+    # Create cache key
+    cache_key = f"sentence_transformers_{model_name}_{device}_{use_fp16}_optimized"
+
+    # Check if model is already cached
+    if cache_key in _model_cache:
+        logger.info(f"Using cached optimized model: {model_name}")
+        model = _model_cache[cache_key]
+    else:
+        logger.info(
+            f"Loading and caching optimized SentenceTransformer model: {model_name}"
+        )
+        from sentence_transformers import SentenceTransformer
+
+        logger.info(f"Using device: {device}")
+
+        # Apply hardware optimizations
+        if device == "cuda":
+            # TODO: Haven't tested this yet
+            torch.backends.cuda.matmul.allow_tf32 = True
+            torch.backends.cudnn.allow_tf32 = True
+            torch.backends.cudnn.benchmark = True
+            torch.backends.cudnn.deterministic = False
+            torch.cuda.set_per_process_memory_fraction(0.9)
+        elif device == "mps":
+            try:
+                if hasattr(torch.mps, "set_per_process_memory_fraction"):
+                    torch.mps.set_per_process_memory_fraction(0.9)
+            except AttributeError:
+                logger.warning(
+                    "Some MPS optimizations not available in this PyTorch version"
+                )
+        elif device == "cpu":
+            # TODO: Haven't tested this yet
+            torch.set_num_threads(min(8, os.cpu_count() or 4))
+            try:
+                torch.backends.mkldnn.enabled = True
+            except AttributeError:
+                pass
+
+        # Prepare optimized model and tokenizer parameters
+        model_kwargs = {
+            "torch_dtype": torch.float16 if use_fp16 else torch.float32,
+            "low_cpu_mem_usage": True,
+            "_fast_init": True,
+            "attn_implementation": "eager",  # Use eager attention for speed
+        }
+
+        tokenizer_kwargs = {
+            "use_fast": True,
+            "padding": True,
+            "truncation": True,
+        }
+
+        try:
+            # Try local loading first
+            model_kwargs["local_files_only"] = True
+            tokenizer_kwargs["local_files_only"] = True
+
+            model = SentenceTransformer(
+                model_name,
+                device=device,
+                model_kwargs=model_kwargs,
+                tokenizer_kwargs=tokenizer_kwargs,
+                local_files_only=True,
+            )
+            logger.info("Model loaded successfully! (local + optimized)")
+        except Exception as e:
+            logger.warning(f"Local loading failed ({e}), trying network download...")
+            # Fallback to network loading
+            model_kwargs["local_files_only"] = False
+            tokenizer_kwargs["local_files_only"] = False
+
+            model = SentenceTransformer(
+                model_name,
+                device=device,
+                model_kwargs=model_kwargs,
+                tokenizer_kwargs=tokenizer_kwargs,
+                local_files_only=False,
+            )
+            logger.info("Model loaded successfully! (network + optimized)")
+
+        # Apply additional optimizations based on mode
+        if use_fp16 and device in ["cuda", "mps"]:
+            try:
+                model = model.half()
+                logger.info(f"Applied FP16 precision: {model_name}")
+            except Exception as e:
+                logger.warning(f"FP16 optimization failed: {e}")
+
+        # Apply torch.compile optimization
+        if device in ["cuda", "mps"]:
+            try:
+                model = torch.compile(model, mode="reduce-overhead", dynamic=True)
+                logger.info(f"Applied torch.compile optimization: {model_name}")
+            except Exception as e:
+                logger.warning(f"torch.compile optimization failed: {e}")
+
+        # Set model to eval mode and disable gradients for inference
+        model.eval()
+        for param in model.parameters():
+            param.requires_grad_(False)
+
+        # Cache the model
+        _model_cache[cache_key] = model
+        logger.info(f"Model cached: {cache_key}")
+
+    # Compute embeddings with optimized inference mode
+    logger.info(f"Starting embedding computation... (batch_size: {batch_size})")
+
+    # Use torch.inference_mode for optimal performance
+    with torch.inference_mode():
+        embeddings = model.encode(
+            texts,
+            batch_size=batch_size,
+            show_progress_bar=is_build,  # Don't show progress bar in server environment
+            convert_to_numpy=True,
+            normalize_embeddings=False,
+            device=device,
+        )
+
+    logger.info(
+        f"Generated {len(embeddings)} embeddings, dimension: {embeddings.shape[1]}"
+    )
+
+    # Validate results
+    if np.isnan(embeddings).any() or np.isinf(embeddings).any():
+        raise RuntimeError(
+            f"Detected NaN or Inf values in embeddings, model: {model_name}"
+        )
+
+    return embeddings
+
+
+def compute_embeddings_openai(texts: List[str], model_name: str) -> np.ndarray:
+    # TODO: @yichuan-w add progress bar only in build mode
+    """Compute embeddings using OpenAI API"""
+    try:
+        import openai
+        import os
+    except ImportError as e:
+        raise ImportError(f"OpenAI package not installed: {e}")
+
+    api_key = os.getenv("OPENAI_API_KEY")
+    if not api_key:
+        raise RuntimeError("OPENAI_API_KEY environment variable not set")
+
+    # Cache OpenAI client
+    cache_key = "openai_client"
+    if cache_key in _model_cache:
+        client = _model_cache[cache_key]
+    else:
+        client = openai.OpenAI(api_key=api_key)
+        _model_cache[cache_key] = client
+        logger.info("OpenAI client cached")
+
+    logger.info(
+        f"Computing embeddings for {len(texts)} texts using OpenAI API, model: '{model_name}'"
+    )
+
+    # OpenAI has limits on batch size and input length
+    max_batch_size = 100  # Conservative batch size
+    all_embeddings = []
+
+    try:
+        from tqdm import tqdm
+
+        total_batches = (len(texts) + max_batch_size - 1) // max_batch_size
+        batch_range = range(0, len(texts), max_batch_size)
+        batch_iterator = tqdm(
+            batch_range, desc="Computing embeddings", unit="batch", total=total_batches
+        )
+    except ImportError:
+        # Fallback when tqdm is not available
+        batch_iterator = range(0, len(texts), max_batch_size)
+
+    for i in batch_iterator:
+        batch_texts = texts[i : i + max_batch_size]
+
+        try:
+            response = client.embeddings.create(model=model_name, input=batch_texts)
+            batch_embeddings = [embedding.embedding for embedding in response.data]
+            all_embeddings.extend(batch_embeddings)
+        except Exception as e:
+            logger.error(f"Batch {i} failed: {e}")
+            raise
+
+    embeddings = np.array(all_embeddings, dtype=np.float32)
+    logger.info(
+        f"Generated {len(embeddings)} embeddings, dimension: {embeddings.shape[1]}"
+    )
+    return embeddings
+
+
+def compute_embeddings_mlx(
+    chunks: List[str], model_name: str, batch_size: int = 16
+) -> np.ndarray:
+    # TODO: @yichuan-w add progress bar only in build mode
+    """Computes embeddings using an MLX model."""
+    try:
+        import mlx.core as mx
+        from mlx_lm.utils import load
+    except ImportError as e:
+        raise RuntimeError(
+            "MLX or related libraries not available. Install with: uv pip install mlx mlx-lm"
+        ) from e
+
+    logger.info(
+        f"Computing embeddings for {len(chunks)} chunks using MLX model '{model_name}' with batch_size={batch_size}..."
+    )
+
+    # Cache MLX model and tokenizer
+    cache_key = f"mlx_{model_name}"
+    if cache_key in _model_cache:
+        logger.info(f"Using cached MLX model: {model_name}")
+        model, tokenizer = _model_cache[cache_key]
+    else:
+        logger.info(f"Loading and caching MLX model: {model_name}")
+        model, tokenizer = load(model_name)
+        _model_cache[cache_key] = (model, tokenizer)
+        logger.info(f"MLX model cached: {cache_key}")
+
+    # Process chunks in batches with progress bar
+    all_embeddings = []
+
+    try:
+        from tqdm import tqdm
+
+        batch_iterator = tqdm(
+            range(0, len(chunks), batch_size), desc="Computing embeddings", unit="batch"
+        )
+    except ImportError:
+        batch_iterator = range(0, len(chunks), batch_size)
+
+    for i in batch_iterator:
+        batch_chunks = chunks[i : i + batch_size]
+
+        # Tokenize all chunks in the batch
+        batch_token_ids = []
+        for chunk in batch_chunks:
+            token_ids = tokenizer.encode(chunk)  # type: ignore
+            batch_token_ids.append(token_ids)
+
+        # Pad sequences to the same length for batch processing
+        max_length = max(len(ids) for ids in batch_token_ids)
+        padded_token_ids = []
+        for token_ids in batch_token_ids:
+            # Pad with tokenizer.pad_token_id or 0
+            padded = token_ids + [0] * (max_length - len(token_ids))
+            padded_token_ids.append(padded)
+
+        # Convert to MLX array with batch dimension
+        input_ids = mx.array(padded_token_ids)
+
+        # Get embeddings for the batch
+        embeddings = model(input_ids)
+
+        # Mean pooling for each sequence in the batch
+        pooled = embeddings.mean(axis=1)  # Shape: (batch_size, hidden_size)
+
+        # Convert batch embeddings to numpy
+        for j in range(len(batch_chunks)):
+            pooled_list = pooled[j].tolist()  # Convert to list
+            pooled_numpy = np.array(pooled_list, dtype=np.float32)
+            all_embeddings.append(pooled_numpy)
+
+    # Stack numpy arrays
+    return np.stack(all_embeddings)

packages/leann-core/src/leann/embedding_server_manager.py

@@ -1,14 +1,21 @@
-import threading
 import time
 import atexit
 import socket
 import subprocess
 import sys
-import zmq
-import msgpack
+import os
+import logging
 from pathlib import Path
 from typing import Optional
-import select
+import psutil
+
+# Set up logging based on environment variable
+LOG_LEVEL = os.getenv("LEANN_LOG_LEVEL", "WARNING").upper()
+logging.basicConfig(
+    level=getattr(logging, LOG_LEVEL, logging.INFO),
+    format="%(levelname)s - %(name)s - %(message)s",
+)
+logger = logging.getLogger(__name__)
 
 
 def _check_port(port: int) -> bool:
@@ -17,151 +24,135 @@ def _check_port(port: int) -> bool:
         return s.connect_ex(("localhost", port)) == 0
 
 
-def _check_server_meta_path(port: int, expected_meta_path: str) -> bool:
+def _check_process_matches_config(
+    port: int, expected_model: str, expected_passages_file: str
+) -> bool:
     """
-    Check if the existing server on the port is using the correct meta file.
-    Returns True if the server has the right meta path, False otherwise.
+    Check if the process using the port matches our expected model and passages file.
+    Returns True if matches, False otherwise.
     """
     try:
-        context = zmq.Context()
-        socket = context.socket(zmq.REQ)
-        socket.setsockopt(zmq.RCVTIMEO, 3000)  # 3 second timeout
-        socket.connect(f"tcp://localhost:{port}")
+        for proc in psutil.process_iter(["pid", "cmdline"]):
+            if not _is_process_listening_on_port(proc, port):
+                continue
 
-        # Send a special control message to query the server's meta path
-        control_request = ["__QUERY_META_PATH__"]
-        request_bytes = msgpack.packb(control_request)
-        socket.send(request_bytes)
+            cmdline = proc.info["cmdline"]
+            if not cmdline:
+                continue
 
-        # Wait for response
-        response_bytes = socket.recv()
-        response = msgpack.unpackb(response_bytes)
-
-        socket.close()
-        context.term()
-
-        # Check if the response contains the meta path and if it matches
-        if isinstance(response, list) and len(response) > 0:
-            server_meta_path = response[0]
-            # Normalize paths for comparison
-            expected_path = Path(expected_meta_path).resolve()
-            server_path = Path(server_meta_path).resolve() if server_meta_path else None
-            return server_path == expected_path
+            return _check_cmdline_matches_config(
+                cmdline, port, expected_model, expected_passages_file
+            )
 
+        logger.debug(f"No process found listening on port {port}")
         return False
 
     except Exception as e:
-        print(f"WARNING: Could not query server meta path on port {port}: {e}")
+        logger.warning(f"Could not check process on port {port}: {e}")
         return False
 
 
-def _update_server_meta_path(port: int, new_meta_path: str) -> bool:
-    """
-    Send a control message to update the server's meta path.
-    Returns True if successful, False otherwise.
-    """
+def _is_process_listening_on_port(proc, port: int) -> bool:
+    """Check if a process is listening on the given port."""
     try:
-        context = zmq.Context()
-        socket = context.socket(zmq.REQ)
-        socket.setsockopt(zmq.RCVTIMEO, 5000)  # 5 second timeout
-        socket.connect(f"tcp://localhost:{port}")
-
-        # Send a control message to update the meta path
-        control_request = ["__UPDATE_META_PATH__", new_meta_path]
-        request_bytes = msgpack.packb(control_request)
-        socket.send(request_bytes)
-
-        # Wait for response
-        response_bytes = socket.recv()
-        response = msgpack.unpackb(response_bytes)
-
-        socket.close()
-        context.term()
-
-        # Check if the update was successful
-        if isinstance(response, list) and len(response) > 0:
-            return response[0] == "SUCCESS"
-
+        connections = proc.net_connections()
+        for conn in connections:
+            if conn.laddr.port == port and conn.status == psutil.CONN_LISTEN:
+                return True
         return False
-
-    except Exception as e:
-        print(f"ERROR: Could not update server meta path on port {port}: {e}")
+    except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess):
         return False
 
 
-def _check_server_model(port: int, expected_model: str) -> bool:
+def _check_cmdline_matches_config(
+    cmdline: list, port: int, expected_model: str, expected_passages_file: str
+) -> bool:
+    """Check if command line matches our expected configuration."""
+    cmdline_str = " ".join(cmdline)
+    logger.debug(f"Found process on port {port}: {cmdline_str}")
+
+    # Check if it's our embedding server
+    is_embedding_server = any(
+        server_type in cmdline_str
+        for server_type in [
+            "embedding_server",
+            "leann_backend_diskann.embedding_server",
+            "leann_backend_hnsw.hnsw_embedding_server",
+        ]
+    )
+
+    if not is_embedding_server:
+        logger.debug(f"Process on port {port} is not our embedding server")
+        return False
+
+    # Check model name
+    model_matches = _check_model_in_cmdline(cmdline, expected_model)
+
+    # Check passages file if provided
+    passages_matches = _check_passages_in_cmdline(cmdline, expected_passages_file)
+
+    result = model_matches and passages_matches
+    logger.debug(
+        f"model_matches: {model_matches}, passages_matches: {passages_matches}, overall: {result}"
+    )
+    return result
+
+
+def _check_model_in_cmdline(cmdline: list, expected_model: str) -> bool:
+    """Check if the command line contains the expected model."""
+    if "--model-name" not in cmdline:
+        return False
+
+    model_idx = cmdline.index("--model-name")
+    if model_idx + 1 >= len(cmdline):
+        return False
+
+    actual_model = cmdline[model_idx + 1]
+    return actual_model == expected_model
+
+
+def _check_passages_in_cmdline(cmdline: list, expected_passages_file: str) -> bool:
+    """Check if the command line contains the expected passages file."""
+    if "--passages-file" not in cmdline:
+        return False  # Expected but not found
+
+    passages_idx = cmdline.index("--passages-file")
+    if passages_idx + 1 >= len(cmdline):
+        return False
+
+    actual_passages = cmdline[passages_idx + 1]
+    expected_path = Path(expected_passages_file).resolve()
+    actual_path = Path(actual_passages).resolve()
+    return actual_path == expected_path
+
+
+def _find_compatible_port_or_next_available(
+    start_port: int, model_name: str, passages_file: str, max_attempts: int = 100
+) -> tuple[int, bool]:
     """
-    Check if the existing server on the port is using the correct embedding model.
-    Returns True if the server has the right model, False otherwise.
+    Find a port that either has a compatible server or is available.
+    Returns (port, is_compatible) where is_compatible indicates if we found a matching server.
     """
-    try:
-        context = zmq.Context()
-        socket = context.socket(zmq.REQ)
-        socket.setsockopt(zmq.RCVTIMEO, 3000)  # 3 second timeout
-        socket.connect(f"tcp://localhost:{port}")
+    for port in range(start_port, start_port + max_attempts):
+        if not _check_port(port):
+            # Port is available
+            return port, False
 
-        # Send a special control message to query the server's model
-        control_request = ["__QUERY_MODEL__"]
-        request_bytes = msgpack.packb(control_request)
-        socket.send(request_bytes)
+        # Port is in use, check if it's compatible
+        if _check_process_matches_config(port, model_name, passages_file):
+            logger.info(f"Found compatible server on port {port}")
+            return port, True
+        else:
+            logger.info(f"Port {port} has incompatible server, trying next port...")
 
-        # Wait for response
-        response_bytes = socket.recv()
-        response = msgpack.unpackb(response_bytes)
-
-        socket.close()
-        context.term()
-
-        # Check if the response contains the model name and if it matches
-        if isinstance(response, list) and len(response) > 0:
-            server_model = response[0]
-            return server_model == expected_model
-
-        return False
-
-    except Exception as e:
-        print(f"WARNING: Could not query server model on port {port}: {e}")
-        return False
-
-
-def _update_server_model(port: int, new_model: str) -> bool:
-    """
-    Send a control message to update the server's embedding model.
-    Returns True if successful, False otherwise.
-    """
-    try:
-        context = zmq.Context()
-        socket = context.socket(zmq.REQ)
-        socket.setsockopt(zmq.RCVTIMEO, 30000)  # 30 second timeout for model loading
-        socket.setsockopt(zmq.SNDTIMEO, 5000)  # 5 second timeout for sending
-        socket.connect(f"tcp://localhost:{port}")
-
-        # Send a control message to update the model
-        control_request = ["__UPDATE_MODEL__", new_model]
-        request_bytes = msgpack.packb(control_request)
-        socket.send(request_bytes)
-
-        # Wait for response
-        response_bytes = socket.recv()
-        response = msgpack.unpackb(response_bytes)
-
-        socket.close()
-        context.term()
-
-        # Check if the update was successful
-        if isinstance(response, list) and len(response) > 0:
-            return response[0] == "SUCCESS"
-
-        return False
-
-    except Exception as e:
-        print(f"ERROR: Could not update server model on port {port}: {e}")
-        return False
+    raise RuntimeError(
+        f"Could not find compatible or available port in range {start_port}-{start_port + max_attempts}"
+    )
 
 
 class EmbeddingServerManager:
     """
-    A generic manager for handling the lifecycle of a backend-specific embedding server process.
+    A simplified manager for embedding server processes that avoids complex update mechanisms.
     """
 
     def __init__(self, backend_module_name: str):
@@ -175,246 +166,183 @@ class EmbeddingServerManager:
         self.backend_module_name = backend_module_name
         self.server_process: Optional[subprocess.Popen] = None
         self.server_port: Optional[int] = None
-        atexit.register(self.stop_server)
+        self._atexit_registered = False
 
-    def start_server(self, port: int, model_name: str, embedding_mode: str = "sentence-transformers", **kwargs) -> bool:
+    def start_server(
+        self,
+        port: int,
+        model_name: str,
+        embedding_mode: str = "sentence-transformers",
+        **kwargs,
+    ) -> tuple[bool, int]:
         """
         Starts the embedding server process.
 
         Args:
-            port (int): The ZMQ port for the server.
+            port (int): The preferred ZMQ port for the server.
             model_name (str): The name of the embedding model to use.
-            **kwargs: Additional arguments for the server (e.g., passages_file, distance_metric, enable_warmup).
+            **kwargs: Additional arguments for the server.
 
         Returns:
-            bool: True if the server is started successfully or already running, False otherwise.
+            tuple[bool, int]: (success, actual_port_used)
         """
-        if self.server_process and self.server_process.poll() is None:
-            # Even if we have a running process, check if model/meta path match
-            if self.server_port is not None:
-                port_in_use = _check_port(self.server_port)
-                if port_in_use:
-                    print(
-                        f"INFO: Checking compatibility of existing server process (PID {self.server_process.pid})"
-                    )
+        passages_file = kwargs.get("passages_file")
+        assert isinstance(passages_file, str), "passages_file must be a string"
 
-                    # Check model compatibility
-                    model_matches = _check_server_model(self.server_port, model_name)
-                    if model_matches:
-                        print(
-                            f"✅ Existing server already using correct model: {model_name}"
-                        )
-                        
-                        # Still check meta path if provided
-                        passages_file = kwargs.get("passages_file")
-                        if passages_file and str(passages_file).endswith(
-                            ".meta.json"
-                        ):
-                            meta_matches = _check_server_meta_path(
-                                self.server_port, str(passages_file)
-                            )
-                            if not meta_matches:
-                                print("⚠️  Updating meta path to: {passages_file}")
-                                _update_server_meta_path(
-                                    self.server_port, str(passages_file)
-                                )
-                        
-                        return True
-                    else:
-                        print(
-                            f"⚠️  Existing server has different model. Attempting to update to: {model_name}"
-                        )
-                        if not _update_server_model(self.server_port, model_name):
-                            print(
-                                "❌ Failed to update existing server model. Restarting server..."
-                            )
-                            self.stop_server()
-                            # Continue to start new server below
-                        else:
-                            print(
-                                f"✅ Successfully updated existing server model to: {model_name}"
-                            )
+        # Check if we have a compatible running server
+        if self._has_compatible_running_server(model_name, passages_file):
+            assert self.server_port is not None, (
+                "a compatible running server should set server_port"
+            )
+            return True, self.server_port
 
-                            # Also check meta path if provided
-                            passages_file = kwargs.get("passages_file")
-                            if passages_file and str(passages_file).endswith(
-                                ".meta.json"
-                            ):
-                                meta_matches = _check_server_meta_path(
-                                    self.server_port, str(passages_file)
-                                )
-                                if not meta_matches:
-                                    print("⚠️  Updating meta path to: {passages_file}")
-                                    _update_server_meta_path(
-                                        self.server_port, str(passages_file)
-                                    )
+        # Find available port (compatible or free)
+        try:
+            actual_port, is_compatible = _find_compatible_port_or_next_available(
+                port, model_name, passages_file
+            )
+        except RuntimeError as e:
+            logger.error(str(e))
+            return False, port
 
-                            return True
-                else:
-                    # Server process exists but port not responding - restart
-                    print("⚠️  Server process exists but not responding. Restarting...")
-                    self.stop_server()
-                    # Continue to start new server below
-            else:
-                # No port stored - restart
-                print("⚠️  No port information stored. Restarting server...")
-                self.stop_server()
-                # Continue to start new server below
+        if is_compatible:
+            logger.info(f"Using existing compatible server on port {actual_port}")
+            self.server_port = actual_port
+            self.server_process = None  # We don't own this process
+            return True, actual_port
 
-        if _check_port(port):
-            # Port is in use, check if it's using the correct meta file and model
-            passages_file = kwargs.get("passages_file")
+        if actual_port != port:
+            logger.info(f"Using port {actual_port} instead of {port}")
 
-            print(f"INFO: Port {port} is in use. Checking server compatibility...")
+        # Start new server
+        return self._start_new_server(actual_port, model_name, embedding_mode, **kwargs)
 
-            # Check model compatibility first
-            model_matches = _check_server_model(port, model_name)
-            if model_matches:
-                print(
-                    f"✅ Existing server on port {port} is using correct model: {model_name}"
-                )
-            else:
-                print(
-                    f"⚠️  Existing server on port {port} has different model. Attempting to update to: {model_name}"
-                )
-                if not _update_server_model(port, model_name):
-                    raise RuntimeError(
-                        f"❌ Failed to update server model to {model_name}. Consider using a different port."
-                    )
-                print(f"✅ Successfully updated server model to: {model_name}")
+    def _has_compatible_running_server(
+        self, model_name: str, passages_file: str
+    ) -> bool:
+        """Check if we have a compatible running server."""
+        if not (
+            self.server_process
+            and self.server_process.poll() is None
+            and self.server_port
+        ):
+            return False
 
-            # Check meta path compatibility if provided
-            if passages_file and str(passages_file).endswith(".meta.json"):
-                meta_matches = _check_server_meta_path(port, str(passages_file))
-                if not meta_matches:
-                    print(
-                        f"⚠️  Existing server on port {port} has different meta path. Attempting to update..."
-                    )
-                    if not _update_server_meta_path(port, str(passages_file)):
-                        raise RuntimeError(
-                            "❌ Failed to update server meta path. This may cause data synchronization issues."
-                        )
-                    print(
-                        f"✅ Successfully updated server meta path to: {passages_file}"
-                    )
-                else:
-                    print(
-                        f"✅ Existing server on port {port} is using correct meta path: {passages_file}"
-                    )
-
-            print(f"✅ Server on port {port} is compatible and ready to use.")
+        if _check_process_matches_config(self.server_port, model_name, passages_file):
+            logger.info(
+                f"Existing server process (PID {self.server_process.pid}) is compatible"
+            )
             return True
 
-        print(
-            f"INFO: Starting session-level embedding server for '{self.backend_module_name}'..."
+        logger.info(
+            "Existing server process is incompatible. Should start a new server."
         )
+        return False
+
+    def _start_new_server(
+        self, port: int, model_name: str, embedding_mode: str, **kwargs
+    ) -> tuple[bool, int]:
+        """Start a new embedding server on the given port."""
+        logger.info(f"Starting embedding server on port {port}...")
+
+        command = self._build_server_command(port, model_name, embedding_mode, **kwargs)
 
         try:
-            command = [
-                sys.executable,
-                "-m",
-                self.backend_module_name,
-                "--zmq-port",
-                str(port),
-                "--model-name",
-                model_name,
-            ]
-
-            # Add extra arguments for specific backends
-            if "passages_file" in kwargs and kwargs["passages_file"]:
-                command.extend(["--passages-file", str(kwargs["passages_file"])])
-            # if "distance_metric" in kwargs and kwargs["distance_metric"]:
-            #     command.extend(["--distance-metric", kwargs["distance_metric"]])
-            if embedding_mode != "sentence-transformers":
-                command.extend(["--embedding-mode", embedding_mode])
-            if "enable_warmup" in kwargs and not kwargs["enable_warmup"]:
-                command.extend(["--disable-warmup"])
-
-            project_root = Path(__file__).parent.parent.parent.parent.parent
-            print(f"INFO: Running command from project root: {project_root}")
-            print(f"INFO: Command: {' '.join(command)}")  # Debug: show actual command
-
-            self.server_process = subprocess.Popen(
-                command,
-                cwd=project_root,
-                stdout=subprocess.PIPE,
-                stderr=subprocess.STDOUT,  # Merge stderr into stdout for easier monitoring
-                text=True,
-                encoding="utf-8",
-                bufsize=1,  # Line buffered
-                universal_newlines=True,
-            )
-            self.server_port = port
-            print(f"INFO: Server process started with PID: {self.server_process.pid}")
-
-            max_wait, wait_interval = 120, 0.5
-            for _ in range(int(max_wait / wait_interval)):
-                if _check_port(port):
-                    print("✅ Embedding server is up and ready for this session.")
-                    log_thread = threading.Thread(target=self._log_monitor, daemon=True)
-                    log_thread.start()
-                    return True
-                if self.server_process.poll() is not None:
-                    print(
-                        "❌ ERROR: Server process terminated unexpectedly during startup."
-                    )
-                    self._print_recent_output()
-                    return False
-                time.sleep(wait_interval)
-
-            print(
-                f"❌ ERROR: Server process failed to start listening within {max_wait} seconds."
-            )
-            self.stop_server()
-            return False
-
+            self._launch_server_process(command, port)
+            return self._wait_for_server_ready(port)
         except Exception as e:
-            print(f"❌ ERROR: Failed to start embedding server process: {e}")
-            return False
+            logger.error(f"Failed to start embedding server: {e}")
+            return False, port
 
-    def _print_recent_output(self):
-        """Print any recent output from the server process."""
-        if not self.server_process or not self.server_process.stdout:
-            return
-        try:
-            # Read any available output
+    def _build_server_command(
+        self, port: int, model_name: str, embedding_mode: str, **kwargs
+    ) -> list:
+        """Build the command to start the embedding server."""
+        command = [
+            sys.executable,
+            "-m",
+            self.backend_module_name,
+            "--zmq-port",
+            str(port),
+            "--model-name",
+            model_name,
+        ]
 
-            if select.select([self.server_process.stdout], [], [], 0)[0]:
-                output = self.server_process.stdout.read()
-                if output:
-                    print(f"[{self.backend_module_name} OUTPUT]: {output}")
-        except Exception as e:
-            print(f"Error reading server output: {e}")
+        if kwargs.get("passages_file"):
+            command.extend(["--passages-file", str(kwargs["passages_file"])])
+        if embedding_mode != "sentence-transformers":
+            command.extend(["--embedding-mode", embedding_mode])
 
-    def _log_monitor(self):
-        """Monitors and prints the server's stdout and stderr."""
-        if not self.server_process:
-            return
-        try:
-            if self.server_process.stdout:
-                while True:
-                    line = self.server_process.stdout.readline()
-                    if not line:
-                        break
-                    print(
-                        f"[{self.backend_module_name} LOG]: {line.strip()}", flush=True
-                    )
-        except Exception as e:
-            print(f"Log monitor error: {e}")
+        return command
+
+    def _launch_server_process(self, command: list, port: int) -> None:
+        """Launch the server process."""
+        project_root = Path(__file__).parent.parent.parent.parent.parent
+        logger.info(f"Command: {' '.join(command)}")
+
+        # Let server output go directly to console
+        # The server will respect LEANN_LOG_LEVEL environment variable
+        self.server_process = subprocess.Popen(
+            command,
+            cwd=project_root,
+            stdout=None,  # Direct to console
+            stderr=None,  # Direct to console
+        )
+        self.server_port = port
+        logger.info(f"Server process started with PID: {self.server_process.pid}")
+
+        # Register atexit callback only when we actually start a process
+        if not self._atexit_registered:
+            # Use a lambda to avoid issues with bound methods
+            atexit.register(lambda: self.stop_server() if self.server_process else None)
+            self._atexit_registered = True
+
+    def _wait_for_server_ready(self, port: int) -> tuple[bool, int]:
+        """Wait for the server to be ready."""
+        max_wait, wait_interval = 120, 0.5
+        for _ in range(int(max_wait / wait_interval)):
+            if _check_port(port):
+                logger.info("Embedding server is ready!")
+                return True, port
+
+            if self.server_process and self.server_process.poll() is not None:
+                logger.error("Server terminated during startup.")
+                return False, port
+
+            time.sleep(wait_interval)
+
+        logger.error(f"Server failed to start within {max_wait} seconds.")
+        self.stop_server()
+        return False, port
 
     def stop_server(self):
         """Stops the embedding server process if it's running."""
-        if self.server_process and self.server_process.poll() is None:
-            print(
-                f"INFO: Terminating session server process (PID: {self.server_process.pid})..."
+        if not self.server_process:
+            return
+
+        if self.server_process.poll() is not None:
+            # Process already terminated
+            self.server_process = None
+            return
+
+        logger.info(
+            f"Terminating server process (PID: {self.server_process.pid}) for backend {self.backend_module_name}..."
+        )
+        self.server_process.terminate()
+
+        try:
+            self.server_process.wait(timeout=5)
+            logger.info(f"Server process {self.server_process.pid} terminated.")
+        except subprocess.TimeoutExpired:
+            logger.warning(
+                f"Server process {self.server_process.pid} did not terminate gracefully, killing it."
             )
-            self.server_process.terminate()
-            try:
-                self.server_process.wait(timeout=5)
-                print("INFO: Server process terminated.")
-            except subprocess.TimeoutExpired:
-                print(
-                    "WARNING: Server process did not terminate gracefully, killing it."
-                )
-                self.server_process.kill()
+            self.server_process.kill()
+
+        # Clean up process resources to prevent resource tracker warnings
+        try:
+            self.server_process.wait()  # Ensure process is fully cleaned up
+        except Exception:
+            pass
+
         self.server_process = None

packages/leann-core/src/leann/interface.py

@@ -1,6 +1,6 @@
 from abc import ABC, abstractmethod
 import numpy as np
-from typing import Dict, Any, List, Literal
+from typing import Dict, Any, List, Literal, Optional
 
 
 class LeannBackendBuilderInterface(ABC):
@@ -34,6 +34,13 @@ class LeannBackendSearcherInterface(ABC):
         """
         pass
 
+    @abstractmethod
+    def _ensure_server_running(
+        self, passages_source_file: str, port: Optional[int], **kwargs
+    ) -> int:
+        """Ensure server is running"""
+        pass
+
     @abstractmethod
     def search(
         self,
@@ -44,7 +51,7 @@ class LeannBackendSearcherInterface(ABC):
         prune_ratio: float = 0.0,
         recompute_embeddings: bool = False,
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
-        zmq_port: int = 5557,
+        zmq_port: Optional[int] = None,
         **kwargs,
     ) -> Dict[str, Any]:
         """Search for nearest neighbors
@@ -57,7 +64,7 @@ class LeannBackendSearcherInterface(ABC):
             prune_ratio: Ratio of neighbors to prune via approximate distance (0.0-1.0)
             recompute_embeddings: Whether to fetch fresh embeddings from server vs use stored PQ codes
             pruning_strategy: PQ candidate selection strategy - "global" (default), "local", or "proportional"
-            zmq_port: ZMQ port for embedding server communication
+            zmq_port: ZMQ port for embedding server communication. Must be provided if recompute_embeddings is True.
             **kwargs: Backend-specific parameters
 
         Returns:
@@ -67,7 +74,10 @@ class LeannBackendSearcherInterface(ABC):
 
     @abstractmethod
     def compute_query_embedding(
-        self, query: str, zmq_port: int = 5557, use_server_if_available: bool = True
+        self,
+        query: str,
+        use_server_if_available: bool = True,
+        zmq_port: Optional[int] = None,
     ) -> np.ndarray:
         """Compute embedding for a query string
 

packages/leann-core/src/leann/registry.py

@@ -7,30 +7,37 @@ import importlib.metadata
 if TYPE_CHECKING:
     from leann.interface import LeannBackendFactoryInterface
 
-BACKEND_REGISTRY: Dict[str, 'LeannBackendFactoryInterface'] = {}
+BACKEND_REGISTRY: Dict[str, "LeannBackendFactoryInterface"] = {}
+
 
 def register_backend(name: str):
     """A decorator to register a new backend class."""
+
     def decorator(cls):
         print(f"INFO: Registering backend '{name}'")
         BACKEND_REGISTRY[name] = cls
         return cls
+
     return decorator
 
+
 def autodiscover_backends():
     """Automatically discovers and imports all 'leann-backend-*' packages."""
-    print("INFO: Starting backend auto-discovery...")
+    # print("INFO: Starting backend auto-discovery...")
     discovered_backends = []
     for dist in importlib.metadata.distributions():
-        dist_name = dist.metadata['name']
-        if dist_name.startswith('leann-backend-'):
-            backend_module_name = dist_name.replace('-', '_')
+        dist_name = dist.metadata["name"]
+        if dist_name.startswith("leann-backend-"):
+            backend_module_name = dist_name.replace("-", "_")
             discovered_backends.append(backend_module_name)
-            
-    for backend_module_name in sorted(discovered_backends): # sort for deterministic loading
+
+    for backend_module_name in sorted(
+        discovered_backends
+    ):  # sort for deterministic loading
         try:
             importlib.import_module(backend_module_name)
             # Registration message is printed by the decorator
         except ImportError as e:
-            print(f"WARN: Could not import backend module '{backend_module_name}': {e}")
-    print("INFO: Backend auto-discovery finished.")
+            # print(f"WARN: Could not import backend module '{backend_module_name}': {e}")
+            pass
+    # print("INFO: Backend auto-discovery finished.")

packages/leann-core/src/leann/searcher_base.py

@@ -1,8 +1,7 @@
 import json
-import pickle
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Dict, Any, Literal
+from typing import Dict, Any, Literal, Optional
 
 import numpy as np
 
@@ -43,10 +42,10 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
                 "WARNING: embedding_model not found in meta.json. Recompute will fail."
             )
 
-        self.label_map = self._load_label_map()
+        self.embedding_mode = self.meta.get("embedding_mode", "sentence-transformers")
 
         self.embedding_server_manager = EmbeddingServerManager(
-            backend_module_name=backend_module_name
+            backend_module_name=backend_module_name,
         )
 
     def _load_meta(self) -> Dict[str, Any]:
@@ -58,17 +57,9 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
         with open(meta_path, "r", encoding="utf-8") as f:
             return json.load(f)
 
-    def _load_label_map(self) -> Dict[int, str]:
-        """Loads the mapping from integer IDs to string IDs."""
-        label_map_file = self.index_dir / "leann.labels.map"
-        if not label_map_file.exists():
-            raise FileNotFoundError(f"Label map file not found: {label_map_file}")
-        with open(label_map_file, "rb") as f:
-            return pickle.load(f)
-
     def _ensure_server_running(
         self, passages_source_file: str, port: int, **kwargs
-    ) -> None:
+    ) -> int:
         """
         Ensures the embedding server is running if recompute is needed.
         This is a helper for subclasses.
@@ -78,21 +69,26 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
                 "Cannot use recompute mode without 'embedding_model' in meta.json."
             )
 
-        embedding_mode = self.meta.get("embedding_mode", "sentence-transformers")
-        
-        server_started = self.embedding_server_manager.start_server(
+        server_started, actual_port = self.embedding_server_manager.start_server(
             port=port,
             model_name=self.embedding_model,
+            embedding_mode=self.embedding_mode,
             passages_file=passages_source_file,
             distance_metric=kwargs.get("distance_metric"),
-            embedding_mode=embedding_mode,
             enable_warmup=kwargs.get("enable_warmup", False),
         )
         if not server_started:
-            raise RuntimeError(f"Failed to start embedding server on port {port}")
+            raise RuntimeError(
+                f"Failed to start embedding server on port {actual_port}"
+            )
+
+        return actual_port
 
     def compute_query_embedding(
-        self, query: str, zmq_port: int = 5557, use_server_if_available: bool = True
+        self,
+        query: str,
+        use_server_if_available: bool = True,
+        zmq_port: int = 5557,
     ) -> np.ndarray:
         """
         Compute embedding for a query string.
@@ -106,12 +102,21 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
             Query embedding as numpy array
         """
         # Try to use embedding server if available and requested
-        if (
-            use_server_if_available
-            and self.embedding_server_manager
-            and self.embedding_server_manager.server_process
-        ):
+        if use_server_if_available:
             try:
+                # TODO: Maybe we can directly use this port here?
+                # For this internal method, it's ok to assume that the server is running
+                # on that port?
+
+                # Ensure we have a server with passages_file for compatibility
+                passages_source_file = (
+                    self.index_dir / f"{self.index_path.name}.meta.json"
+                )
+                # Convert to absolute path to ensure server can find it
+                zmq_port = self._ensure_server_running(
+                    str(passages_source_file.resolve()), zmq_port
+                )
+
                 return self._compute_embedding_via_server([query], zmq_port)[
                     0:1
                 ]  # Return (1, D) shape
@@ -120,7 +125,7 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
                 print("⏭️ Falling back to direct model loading...")
 
         # Fallback to direct computation
-        from .api import compute_embeddings
+        from .embedding_compute import compute_embeddings
 
         embedding_mode = self.meta.get("embedding_mode", "sentence-transformers")
         return compute_embeddings([query], self.embedding_model, embedding_mode)
@@ -167,7 +172,7 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
         prune_ratio: float = 0.0,
         recompute_embeddings: bool = False,
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
-        zmq_port: int = 5557,
+        zmq_port: Optional[int] = None,
         **kwargs,
     ) -> Dict[str, Any]:
         """
@@ -181,7 +186,7 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
             prune_ratio: Ratio of neighbors to prune via approximate distance (0.0-1.0)
             recompute_embeddings: Whether to fetch fresh embeddings from server vs use stored PQ codes
             pruning_strategy: PQ candidate selection strategy - "global" (default), "local", or "proportional"
-            zmq_port: ZMQ port for embedding server communication
+            zmq_port: ZMQ port for embedding server communication. Must be provided if recompute_embeddings is True.
             **kwargs: Backend-specific parameters (e.g., batch_size, dedup_node_dis, etc.)
 
         Returns:

packages/leann/README.md

@@ -0,0 +1,40 @@
+# LEANN - The smallest vector index in the world
+
+LEANN is a revolutionary vector database that democratizes personal AI. Transform your laptop into a powerful RAG system that can index and search through millions of documents while using **97% less storage** than traditional solutions **without accuracy loss**.
+
+## Installation
+
+```bash
+# Default installation (HNSW backend, recommended)
+uv pip install leann
+
+# With DiskANN backend (for large-scale deployments)
+uv pip install leann[diskann]
+```
+
+## Quick Start
+
+```python
+from leann import LeannBuilder, LeannSearcher, LeannChat
+
+# Build an index
+builder = LeannBuilder(backend_name="hnsw")
+builder.add_text("LEANN saves 97% storage compared to traditional vector databases.")
+builder.build_index("my_index.leann")
+
+# Search
+searcher = LeannSearcher("my_index.leann")
+results = searcher.search("storage savings", top_k=3)
+
+# Chat with your data
+chat = LeannChat("my_index.leann", llm_config={"type": "ollama", "model": "llama3.2:1b"})
+response = chat.ask("How much storage does LEANN save?")
+```
+
+## Documentation
+
+For full documentation, visit [https://leann.readthedocs.io](https://leann.readthedocs.io)
+
+## License
+
+MIT License 

packages/leann/__init__.py

@@ -0,0 +1,12 @@
+"""
+LEANN - Low-storage Embedding Approximation for Neural Networks
+
+A revolutionary vector database that democratizes personal AI.
+"""
+
+__version__ = "0.1.0"
+
+# Re-export main API from leann-core
+from leann_core import LeannBuilder, LeannSearcher, LeannChat
+
+__all__ = ["LeannBuilder", "LeannSearcher", "LeannChat"]

packages/leann/pyproject.toml

@@ -0,0 +1,42 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "leann"
+version = "0.1.8"
+description = "LEANN - The smallest vector index in the world. RAG Everything with LEANN!"
+readme = "README.md"
+requires-python = ">=3.9"
+license = { text = "MIT" }
+authors = [
+    { name = "LEANN Team" }
+]
+keywords = ["vector-database", "rag", "embeddings", "search", "ai"]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Developers",
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+]
+
+# Default installation: core + hnsw
+dependencies = [
+    "leann-core>=0.1.0",
+    "leann-backend-hnsw>=0.1.0",
+]
+
+[project.optional-dependencies]
+diskann = [
+    "leann-backend-diskann>=0.1.0",
+]
+
+[project.urls]
+Homepage = "https://github.com/yourusername/leann"
+Documentation = "https://leann.readthedocs.io"
+Repository = "https://github.com/yourusername/leann"
+Issues = "https://github.com/yourusername/leann/issues" 

pyproject.toml

@@ -33,8 +33,9 @@ dependencies = [
     "msgpack>=1.1.1",
     "llama-index-vector-stores-faiss>=0.4.0",
     "llama-index-embeddings-huggingface>=0.5.5",
-    "mlx>=0.26.3",
-    "mlx-lm>=0.26.0",
+    "mlx>=0.26.3; sys_platform == 'darwin'",
+    "mlx-lm>=0.26.0; sys_platform == 'darwin'",
+    "psutil>=5.8.0",
 ]
 
 [project.optional-dependencies]
@@ -59,3 +60,29 @@ py-modules = []
 leann-core = { path = "packages/leann-core", editable = true }
 leann-backend-diskann = { path = "packages/leann-backend-diskann", editable = true }
 leann-backend-hnsw = { path = "packages/leann-backend-hnsw", editable = true }
+
+[tool.cibuildwheel]
+# Skip 32-bit and PyPy builds
+skip = "*-win32 *-manylinux_i686 pp* *musllinux*"
+
+# Use manylinux_2_35 for Colab compatibility while keeping modern features
+manylinux-x86_64-image = "manylinux_2_35"
+manylinux-aarch64-image = "manylinux_2_35"
+
+# Linux system dependencies
+[tool.cibuildwheel.linux]
+before-all = """
+  yum install -y epel-release
+  yum install -y gcc-c++ boost-devel zeromq-devel openblas-devel cmake3 python3-devel
+  ln -sf /usr/bin/cmake3 /usr/bin/cmake
+"""
+
+# macOS system dependencies
+[tool.cibuildwheel.macos]
+before-all = "brew install boost zeromq openblas cmake libomp"
+# Set minimum macOS version
+environment = { MACOSX_DEPLOYMENT_TARGET = "11.0", CMAKE_OSX_DEPLOYMENT_TARGET = "11.0" }
+
+# Environment variables configuration
+[tool.cibuildwheel.environment]
+CMAKE_BUILD_PARALLEL_LEVEL = "8"

research/micro/analyze_HNSW.py

@@ -1,12 +0,0 @@
-import faiss
-hnsw_index = faiss.read_index("/opt/dlami/nvme/scaling_out/indices/rpj_wiki/facebook/contriever-msmarco/hnsw/hnsw_IP_M30_efC128.index", faiss.IO_FLAG_ONDISK_SAME_DIR)
-
-# print total number of nodes
-print(hnsw_index.ntotal)
-
-# print  stats of the graph
-print(hnsw_index.hnsw.print_neighbor_stats(0))
-
-
-# save_degree_distribution
-hnsw_index.hnsw.save_degree_distribution(0, "degree_distribution_HNSW_M30.txt")

research/micro/analyze_NSG.py

@@ -1,11 +0,0 @@
-import faiss
-nsg_index = faiss.read_index("/opt/dlami/nvme/scaling_out/indices/rpj_wiki/facebook/contriever-msmarco/nsg_R16.index", faiss.IO_FLAG_ONDISK_SAME_DIR)
-
-# print total number of nodes
-print(nsg_index.ntotal)
-
-# print stats of the graph
-print(nsg_index.nsg.print_neighbor_stats(0))
-
-# save degree distribution
-nsg_index.nsg.save_degree_distribution("degree_distribution_NSG_R60.txt")

research/micro/bnbtest.py

@@ -1,63 +0,0 @@
-import torch
-import torch.nn as nn
-import time
-
-# import bitsandbytes as bnb
-from bitsandbytes.nn import Linear8bitLt
-
-# set default to half
-import torch
-torch.set_default_dtype(torch.float16)
-
-M = 2048
-N = 2048
-
-bsz =  2048
-import torch_int
-from torch_int.nn.linear import W8A8BFP32OFP32Linear, W8A8B8O8Linear, W8A8B8O8LinearReLU
-
-fp16_model = nn.Sequential(
-    nn.Linear(M, N),
-    # nn.Linear(2048, 2048)
-)
-
-int8_model = nn.Sequential(
-    Linear8bitLt(M, N, has_fp16_weights=False),
-    # Linear8bitLt(2048, 2048, has_fp16_weights=False)
-)
-
-int8_model.load_state_dict(fp16_model.state_dict())
-int8_model = int8_model.to(0) # Quantization happens here
-fp16_model = fp16_model.to(0) # Move fp16 model to GPU as well
-
-# Create random input tensor
-input_tensor = torch.randn(bsz, M, device=0)  # Batch of 1000 vectors
-
-# Speed test function
-def speed_test(model, input_tensor, name, num_iterations=100):
-    # Warmup
-    for _ in range(10):
-        _ = model(input_tensor)
-    
-    # Actual timing
-    torch.cuda.synchronize()
-    start_time = time.time()
-    
-    for _ in range(num_iterations):
-        _ = model(input_tensor)
-    
-    torch.cuda.synchronize()
-    end_time = time.time()
-    
-    avg_time = (end_time - start_time) / num_iterations
-    print(f"{name} model: {avg_time:.6f} seconds per iteration")
-    return avg_time
-
-# Run speed tests
-with torch.no_grad():  # Disable gradient calculation for inference
-    fp16_time = speed_test(fp16_model, input_tensor, "FP16")
-    int8_time = speed_test(int8_model, input_tensor, "INT8")
-    
-    # Calculate speedup
-    speedup = fp16_time / int8_time
-    print(f"INT8 is {speedup:.2f}x faster than FP16")

research/micro/data/transformer-batching-microbenchmarks.csv

@@ -1,89 +0,0 @@
-n,d,seqlen,bs,latency,h,flop,io,intensity,throughput,series
-3,256,256,2048,0.009623501679245285,768,618475290624,167.48502132816208,3692720015.912285,64267177503366.266,dense
-3,256,256,1024,0.004853848615384615,768,309237645312,166.15392854317415,1861151572.059558,63709783682138.234,dense
-3,256,256,512,0.0024687246971962615,768,154618822656,163.57953256539062,945221081.3366361,62631051097597.516,dense
-3,256,256,256,0.0012845360838052097,768,77309411328,157.64931990085577,490388486.1451936,60184694149645.54,dense
-3,256,256,128,0.0006901147179878049,768,38654705664,147.57393422494675,261934506.70684624,56012000116019.945,dense
-3,256,256,64,0.0003363830693015702,768,19327352832,153.1328437752606,126212981.84970059,57456378146882.51,dense
-3,256,256,32,0.00018671159748991485,768,9663676416,141.10249365427362,68486928.65540518,51757237075334.75,dense
-3,256,256,16,0.00012353640857142858,768,4831838208,111.40488993609125,43371868.24359184,39112665358133.98,dense
-3,256,256,8,9.774760007849294e-05,768,2415919104,76.43260800265766,31608487.09906635,24715891766754.14,dense
-3,256,256,4,6.672271167474822e-05,768,1207959552,64.82614227498455,18633833.660438772,18104173551704.773,dense
-3,256,256,2,4.9758770289855074e-05,768,603979776,55.317122669351576,10918495.880745342,12138157202874.861,dense
-3,256,1,2048,9.785507940251571e-05,768,2415919104,76.34865809334705,31643242.518371396,24688745017132.86,dense
-3,256,1,1024,6.692813470149253e-05,768,1207959552,64.62717090938949,18691202.70936228,18048606275785.867,dense
-3,256,1,512,4.9680950036205655e-05,768,603979776,55.40377142534654,10901419.893658841,12157170415618.898,dense
-3,256,1,256,4.2781118741058655e-05,768,301989888,45.95672244805227,6571179.83862661,7058952568020.829,dense
-3,256,1,128,5.0662328255350016e-05,768,150994944,31.046026784880404,4863583.512513602,2980418571348.519,dense
-3,256,1,64,4.475009253945481e-05,768,75497472,30.75426042497223,2454862.219307235,1687090857598.4766,dense
-3,256,1,32,4.51682671454219e-05,768,37748736,28.29313765537115,1334201.1218340008,835735758435.5786,dense
-3,256,1,16,5.03585186661834e-05,768,18874368,24.401035466223117,773506.846712577,374799904761.1871,dense
-3,256,1,8,5.023459565217391e-05,768,9437184,23.972005435021096,393675.19858030166,187862246674.45105,dense
-3,256,1,4,5.053219391083726e-05,768,4718592,23.58765586356967,200044.97383259286,93377936614.54384,dense
-3,256,1,2,4.4607398995335484e-05,768,2359296,26.58285456464288,88752.54515134107,52890239133.797226,dense
-12,256,256,2048,0.14480779847058822,3072,9895604649984,44.620009282941716,221775046868.20184,68336130750540.26,dense
-12,256,256,1024,0.07254347629166667,3072,4947802324992,44.664248332585096,110777691547.58836,68204648824643.82,dense
-12,256,256,512,0.036310761444444443,3072,2473901162496,44.876147984203506,55127306456.13385,68131349056975.164,dense
-12,256,256,256,0.01821551906896552,3072,1236950581248,45.24607467289738,27338295977.947884,67906414116709.98,dense
-12,256,256,128,0.009229417903030302,3072,618475290624,45.67217092440895,13541622351.335684,67011299859001.46,dense
-12,256,256,64,0.004754550595394737,3072,309237645312,46.31372736116993,6677019167.566916,65040352207320.695,dense
-12,256,256,32,0.002405752659340659,3072,154618822656,49.68826015254682,3111777755.5766335,64270456921525.82,dense
-12,256,256,16,0.0012287219045005488,3072,77309411328,56.323579604557374,1372594069.3184311,62918558743709.18,dense
-12,256,256,8,0.0006206816149425287,3072,38654705664,70.95456179103653,544781120.315271,62277832520589.78,dense
-12,256,256,4,0.0003875502697142857,3072,19327352832,81.16954743236613,238110885.71245712,49870569942445.75,dense
-12,256,256,2,0.00027502018627941914,3072,9663676416,91.50537035282076,105607751.53129694,35138062215483.168,dense
-12,256,1,2048,0.0006202853873290136,3072,38654705664,70.99988634205897,544433345.6784943,62317614526515.766,dense
-12,256,1,1024,0.00038721467732724153,3072,19327352832,81.2398957010995,237904697.74985722,49913791918755.53,dense
-12,256,1,512,0.000274364799,3072,9663676416,91.72395326121995,105356082.81599998,35221998052308.45,dense
-12,256,1,256,0.00012488918589482266,3072,4831838208,176.31707535146046,27404255.647778228,38689003962834.75,dense
-12,256,1,128,8.976711102514506e-05,3072,2415919104,227.78088507574267,10606329.425740216,26913187652026.21,dense
-12,256,1,64,8.715176287471176e-05,3072,1207959552,225.59268282689945,5354604.31102229,13860414432884.701,dense
-12,256,1,32,8.523013435114503e-05,3072,603979776,226.06539514085782,2671703.8033338524,7086458100741.991,dense
-12,256,1,16,7.901561645904116e-05,3072,301989888,241.35704882952732,1251216.3595988373,3821901309300.556,dense
-12,256,1,8,7.827949114210329e-05,3072,150994944,242.37091635608994,622991.1833900034,1928920867994.581,dense
-12,256,1,4,7.779445951035782e-05,3072,75497472,243.25022783249054,310369.58391664835,970473636235.5986,dense
-12,256,1,2,7.758845406626506e-05,3072,37748736,243.57933441822672,154975.11761480253,486525172518.07056,dense
-3,256,256,2048,0.00507974918466899,768,206158430208,475.59810852303485,433471930.42508715,40584371927298.98,qk_init
-3,256,256,1024,0.0025616677649325623,768,103079215104,471.5519977009198,218595649.27424532,40239103803811.82,qk_init
-3,256,256,512,0.0013029336670480549,768,51539607552,463.55374128015677,111183672.92143403,39556585922573.38,qk_init
-3,256,256,256,0.0006738189029345373,768,25769803776,448.1766342333362,57499213.050413854,38244406121244.69,qk_init
-3,256,256,128,0.000358254672959467,768,12884901888,421.47375986100144,30571065.425874516,35965760841472.125,qk_init
-3,256,256,64,0.0002007051105022831,768,6442450944,376.1611839930762,17126836.096194826,32099087700742.5,qk_init
-3,256,256,32,0.00012189697230142565,768,3221225472,309.6773881032524,10401874.969721656,26425803784810.87,qk_init
-3,256,256,16,8.453561698040722e-05,768,1610612736,223.2711923587723,7213705.982328083,19052475081281.902,qk_init
-3,256,256,8,6.407660705009276e-05,768,805306368,147.2797083750448,5467870.468274581,12567868448003.822,qk_init
-3,256,256,4,5.036328747284576e-05,768,402653184,93.69110391262903,4297667.197682838,7994974200544.344,qk_init
-3,256,256,2,4.5488761135057476e-05,768,201326592,51.865470527877875,3881707.616858238,4425853485045.578,qk_init
-12,256,256,2048,0.020202365999999996,3072,824633720832,478.3437947812648,1723935231.9999998,40818670488001.266,qk_init
-12,256,256,1024,0.010124155888157895,3072,412316860416,477.2583770318811,863927969.1228071,40726048173387.19,qk_init
-12,256,256,512,0.005085633937062937,3072,206158430208,475.04777848703077,433974095.9627039,40537410430893.29,qk_init
-12,256,256,256,0.0025654916853281853,3072,103079215104,470.84913933193053,218921957.14800516,40179126556324.74,qk_init
-12,256,256,128,0.0013045765704467354,3072,51539607552,462.9699702434292,111323867.34478809,39506770794105.96,qk_init
-12,256,256,64,0.0006742801519939804,3072,25769803776,447.87005387442576,57538572.970153,38218244597284.33,qk_init
-12,256,256,32,0.00035831976790671853,3072,12884901888,421.3971919051604,30576620.194706645,35959227042573.69,qk_init
-12,256,256,16,0.0002005369068918302,3072,6442450944,376.4766953382971,17112482.721436176,32126011335534.68,qk_init
-12,256,256,8,0.00012179187250509165,3072,3221225472,309.94462293386505,10392906.453767821,26448607823689.82,qk_init
-12,256,256,4,8.452507263643351e-05,3072,1610612736,223.2990450204527,7212806.198308992,19054851841745.297,qk_init
-12,256,256,2,6.412381767545489e-05,3072,805306368,147.17127491946468,5471899.108305484,12558615459794.32,qk_init
-3,256,256,2048,0.0016183739398395718,768,805306368,811597824.0,0.9922480620155039,1265467.7325087283,qk_ar
-3,256,256,1024,0.0008322699728813558,768,402653184,405798912.0,0.9922480620155039,1230369.9921491416,qk_ar
-3,256,256,512,0.00043886859397590365,768,201326592,202899456.0,0.9922480620155039,1166636.2255762408,qk_ar
-3,256,256,256,0.00024185948322147648,768,100663296,101449728.0,0.9922480620155039,1058465.8355760013,qk_ar
-3,256,256,128,0.00014308985100166944,768,50331648,50724864.0,0.9922480620155039,894542.82818777,qk_ar
-3,256,256,64,9.382939365815932e-05,768,25165824,25362432.0,0.9922480620155039,682089.028872613,qk_ar
-3,256,256,32,6.856070612244899e-05,768,12582912,12681216.0,0.9922480620155039,466739.6503012703,qk_ar
-3,256,256,16,5.452260553129549e-05,768,6291456,6340608.0,0.9922480620155039,293456.26174846216,qk_ar
-3,256,256,8,4.608557533261417e-05,768,3145728,3170304.0,0.9922480620155039,173590.1080166944,qk_ar
-3,256,256,4,4.386146957766642e-05,768,1572864,1585152.0,0.9922480620155039,91196.21477609445,qk_ar
-3,256,256,2,4.330941094420601e-05,768,786432,792576.0,0.9922480620155039,46179.33969539622,qk_ar
-12,256,256,2048,0.006347041645299144,3072,3221225472,3246391296.0,0.9922480620155039,322670.011392918,qk_ar
-12,256,256,1024,0.0031943104467592586,3072,1610612736,1623195648.0,0.9922480620155039,320569.96872013,qk_ar
-12,256,256,512,0.0016183416350267381,3072,805306368,811597824.0,0.9922480620155039,316373.2483416833,qk_ar
-12,256,256,256,0.0008325934893977947,3072,402653184,405798912.0,0.9922480620155039,307472.9784221131,qk_ar
-12,256,256,128,0.0004389725746987952,3072,201326592,202899456.0,0.9922480620155039,291589.9702568624,qk_ar
-12,256,256,64,0.00024191767449664432,3072,100663296,101449728.0,0.9922480620155039,264552.8076159138,qk_ar
-12,256,256,32,0.0001431546143572621,3072,50331648,50724864.0,0.9922480620155039,223534.53392804778,qk_ar
-12,256,256,16,9.404283597678917e-05,3072,25165824,25362432.0,0.9922480620155039,170135.23501087292,qk_ar
-12,256,256,8,6.855550037091989e-05,3072,12582912,12681216.0,0.9922480620155039,116693.773026467,qk_ar
-12,256,256,4,5.4802094978165945e-05,3072,6291456,6340608.0,0.9922480620155039,72989.91036006316,qk_ar
-12,256,256,2,4.608510707869206e-05,3072,3145728,3170304.0,0.9922480620155039,43397.96795057727,qk_ar

research/micro/embedd_micro.py

@@ -1,594 +0,0 @@
-# python embedd_micro.py --use_int8 Fastest
-
-import argparse
-import time
-from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-import torch
-from torch import nn
-from torchao import quantize_
-from transformers import AutoModel, BitsAndBytesConfig
-from tqdm import tqdm
-from contextlib import contextmanager
-
-@dataclass
-class BenchmarkConfig:
-    model_path: str
-    batch_sizes: List[int]
-    seq_length: int
-    num_runs: int
-    use_fp16: bool = True
-    use_int4: bool = False
-    use_int8: bool = False  # Add this parameter
-    use_cuda_graphs: bool = False
-    use_flash_attention: bool = False
-    use_linear8bitlt: bool = False
-
-
-class CUDAGraphContainer:
-    """Container for managing CUDA graphs for different batch sizes."""
-    
-    def __init__(self, model: nn.Module, seq_length: int):
-        self.model = model
-        self.seq_length = seq_length
-        self.graphs: Dict[int, CUDAGraphWrapper] = {}
-    
-    def get_or_create(self, batch_size: int) -> 'CUDAGraphWrapper':
-        if batch_size not in self.graphs:
-            self.graphs[batch_size] = CUDAGraphWrapper(
-                self.model, batch_size, self.seq_length
-            )
-        return self.graphs[batch_size]
-
-
-class CUDAGraphWrapper:
-    """Wrapper for CUDA graph capture and replay."""
-    
-    def __init__(self, model: nn.Module, batch_size: int, seq_length: int):
-        self.model = model
-        self.static_input = self._create_random_batch(batch_size, seq_length)
-        self.static_attention_mask = torch.ones_like(self.static_input)
-        
-        # Warm up
-        self._warmup()
-        
-        # Capture graph
-        self.graph = torch.cuda.CUDAGraph()
-        with torch.cuda.graph(self.graph):
-            self.static_output = self.model(
-                input_ids=self.static_input,
-                attention_mask=self.static_attention_mask
-            )
-    
-    def _create_random_batch(self, batch_size: int, seq_length: int) -> torch.Tensor:
-        return torch.randint(
-            0, 1000, (batch_size, seq_length), 
-            device="cuda", 
-            dtype=torch.long
-        )
-    
-    def _warmup(self, num_warmup: int = 3):
-        with torch.no_grad():
-            for _ in range(num_warmup):
-                self.model(
-                    input_ids=self.static_input,
-                    attention_mask=self.static_attention_mask
-                )
-    
-    def __call__(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
-        self.static_input.copy_(input_ids)
-        self.static_attention_mask.copy_(attention_mask)
-        self.graph.replay()
-        return self.static_output
-
-
-class ModelOptimizer:
-    """Applies various optimizations to the model."""
-    
-    @staticmethod
-    def optimize(model: nn.Module, config: BenchmarkConfig) -> nn.Module:
-        print("\nApplying model optimizations:")
-        
-        if model is None:
-            raise ValueError("Cannot optimize None model")
-        
-        # Move to GPU
-        model = model.cuda()
-        print("- Model moved to GPU")
-        
-        # FP16
-        if config.use_fp16 and not config.use_int4:
-            model = model.half()
-            # use torch compile
-            model = torch.compile(model)
-            print("- Using FP16 precision")
-        
-        # Check if using SDPA
-        if torch.version.cuda and float(torch.version.cuda[:3]) >= 11.6:
-            if hasattr(torch.nn.functional, 'scaled_dot_product_attention'):
-                print("- Using PyTorch SDPA (scaled_dot_product_attention)")
-            else:
-                print("- PyTorch SDPA not available")
-        
-        # Flash Attention
-        if config.use_flash_attention:
-            try:
-                from flash_attn.flash_attention import FlashAttention
-                print("- Flash Attention 2 available")
-                if hasattr(model.config, "attention_mode"):
-                    model.config.attention_mode = "flash_attention_2"
-                    print("  - Enabled Flash Attention 2 mode")
-            except ImportError:
-                print("- Flash Attention not available")
-        
-        # Memory efficient attention
-        try:
-            from xformers.ops import memory_efficient_attention
-            if hasattr(model, 'enable_xformers_memory_efficient_attention'):
-                model.enable_xformers_memory_efficient_attention()
-                print("- Enabled xformers memory efficient attention")
-            else:
-                print("- Model doesn't support xformers")
-        except (ImportError, AttributeError):
-            print("- Xformers not available")
-        
-        model.eval()
-        print("- Model set to eval mode")
-        
-        return model
-
-
-class Timer:
-    """Handles accurate GPU timing using CUDA events."""
-    
-    def __init__(self):
-        self.start_event = torch.cuda.Event(enable_timing=True)
-        self.end_event = torch.cuda.Event(enable_timing=True)
-    
-    @contextmanager
-    def timing(self):
-        self.start_event.record()
-        yield
-        self.end_event.record()
-        self.end_event.synchronize()
-    
-    def elapsed_time(self) -> float:
-        return self.start_event.elapsed_time(self.end_event) / 1000  # ms to seconds
-
-
-class Benchmark:
-    """Main benchmark runner."""
-    
-    def __init__(self, config: BenchmarkConfig):
-        self.config = config
-        try:
-            self.model = self._load_model()
-            if self.model is None:
-                raise ValueError("Model initialization failed - model is None")
-            
-            self.cuda_graphs = (
-                CUDAGraphContainer(self.model, config.seq_length)
-                if config.use_cuda_graphs
-                else None
-            )
-            self.timer = Timer()
-        except Exception as e:
-            print(f"ERROR in benchmark initialization: {str(e)}")
-            raise
-    
-    def _load_model(self) -> nn.Module:
-        print(f"Loading model from {self.config.model_path}...")
-        
-        try:
-            # Int4 quantization using HuggingFace integration
-            if self.config.use_int4:
-                import bitsandbytes as bnb
-                print(f"- bitsandbytes version: {bnb.__version__}")
-                
-                # 检查是否使用自定义的8bit量化
-                if hasattr(self.config, 'use_linear8bitlt') and self.config.use_linear8bitlt:
-                    print("- Using custom Linear8bitLt replacement for all linear layers")
-                    
-                    # 加载原始模型（不使用量化配置）
-                    import bitsandbytes as bnb
-                    import torch
-                    # set default to half
-                    torch.set_default_dtype(torch.float16)
-                    compute_dtype = torch.float16 if self.config.use_fp16 else torch.float32
-                    model = AutoModel.from_pretrained(
-                        self.config.model_path,
-                        torch_dtype=compute_dtype,
-                    )
-                    
-                    # 定义替换函数
-                    def replace_linear_with_linear8bitlt(model):
-                        """递归地将模型中的所有nn.Linear层替换为Linear8bitLt"""
-                        for name, module in list(model.named_children()):
-                            if isinstance(module, nn.Linear):
-                                # 获取原始线性层的参数
-                                in_features = module.in_features
-                                out_features = module.out_features
-                                bias = module.bias is not None
-                                
-                                # 创建8bit线性层
-                                # print size
-                                print(f"in_features: {in_features}, out_features: {out_features}")
-                                new_module = bnb.nn.Linear8bitLt(
-                                    in_features, 
-                                    out_features, 
-                                    bias=bias, 
-                                    has_fp16_weights=False
-                                )
-                                
-                                # 复制权重和偏置
-                                new_module.weight.data = module.weight.data
-                                if bias:
-                                    new_module.bias.data = module.bias.data
-                                    
-                                # 替换模块
-                                setattr(model, name, new_module)
-                            else:
-                                # 递归处理子模块
-                                replace_linear_with_linear8bitlt(module)
-                        
-                        return model
-                    
-                    # 替换所有线性层
-                    model = replace_linear_with_linear8bitlt(model)
-                    # add torch compile
-                    model = torch.compile(model)
-                    
-                    # 将模型移到GPU（量化发生在这里）
-                    device = "cuda" if torch.cuda.is_available() else "cpu"
-                    model = model.to(device)
-                    
-                    print("- All linear layers replaced with Linear8bitLt")
-                    
-                else:
-                    # 使用原来的Int4量化方法
-                    print("- Using bitsandbytes for Int4 quantization")
-                    
-                    # Create quantization config
-                    
-                    compute_dtype = torch.float16 if self.config.use_fp16 else torch.float32
-                    quantization_config = BitsAndBytesConfig(
-                        load_in_4bit=True,
-                        bnb_4bit_compute_dtype=compute_dtype,
-                        bnb_4bit_use_double_quant=True,
-                        bnb_4bit_quant_type="nf4"
-                    )
-                    
-                    print("- Quantization config:", quantization_config)
-                    
-                    # Load model directly with quantization config
-                    model = AutoModel.from_pretrained(
-                        self.config.model_path,
-                        quantization_config=quantization_config,
-                        torch_dtype=compute_dtype,
-                        device_map="auto"  # Let HF decide on device mapping
-                    )
-                
-                # Check if model loaded successfully
-                if model is None:
-                    raise ValueError("Model loading returned None")
-                    
-                print(f"- Model type: {type(model)}")
-                
-                # Apply optimizations directly here
-                print("\nApplying model optimizations:")
-                
-                if hasattr(self.config, 'use_linear8bitlt') and self.config.use_linear8bitlt:
-                    print("- Model moved to GPU with Linear8bitLt quantization")
-                else:
-                    # Skip moving to GPU since device_map="auto" already did that
-                    print("- Model already on GPU due to device_map='auto'")
-                
-                # Skip FP16 conversion since we specified compute_dtype
-                print(f"- Using {compute_dtype} for compute dtype")
-                
-                # Check CUDA and SDPA
-                if torch.version.cuda and float(torch.version.cuda[:3]) >= 11.6:
-                    if hasattr(torch.nn.functional, 'scaled_dot_product_attention'):
-                        print("- Using PyTorch SDPA (scaled_dot_product_attention)")
-                    else:
-                        print("- PyTorch SDPA not available")
-                
-                # Try xformers if available
-                try:
-                    from xformers.ops import memory_efficient_attention
-                    if hasattr(model, 'enable_xformers_memory_efficient_attention'):
-                        model.enable_xformers_memory_efficient_attention()
-                        print("- Enabled xformers memory efficient attention")
-                    else:
-                        print("- Model doesn't support xformers")
-                except (ImportError, AttributeError):
-                    print("- Xformers not available")
-                
-                # Set to eval mode
-                model.eval()
-                print("- Model set to eval mode")
-            # Int8 quantization using HuggingFace integration
-            # Int8 quantization using TorchAO
-            elif self.config.use_int8:
-                print("- Using TorchAO for Int8 dynamic activation and Int8 weight quantization")
-                
-                # Import the quantize_ function and the quantization config
-                from torchao.quantization import quantize_, int8_dynamic_activation_int8_weight
-                print("- Successfully imported TorchAO")
-                
-                # Load model normally first
-                # set default to half
-                import torch
-                torch.set_default_dtype(torch.bfloat16)
-                model = AutoModel.from_pretrained(
-                    self.config.model_path,
-                    device_map="auto"
-                )
-                
-                print("- Model loaded in full precision")
-                print(f"- Model type: {type(model)}")
-                
-                # Apply quantization - call the function to get the config, then apply it
-                # quantize_(model, int8_dynamic_activation_int8_weight())
-                # from torchao.quantization import quantize_, Int8DynamicActivationInt8WeightConfig,int8_dynamic_activation_int8_semi_sparse_weight,int4_weight_only,Int8DynActInt4WeightGPTQQuantizer,int8_dynamic_activation_int4_weight,Int8DynamicActivationInt4WeightConfig,Int4DynamicActivationInt4WeightConfig
-                from torchao.quantization import quantize_, Int8DynamicActivationInt8WeightConfig
-                quantize_(model, Int8DynamicActivationInt8WeightConfig())
-                print("- Model successfully quantized with int8 weights and int8 activations")
-                # add torch compile
-                model = torch.compile(model)
-                # For older PyTorch versions that have issues with tensor subclasses
-                from torchao.utils import unwrap_tensor_subclass
-                import torch
-                if hasattr(torch, '_version') and not torch.version >= "2.5.0":
-                    print("- Unwrapping tensor subclasses for compatibility with older PyTorch")
-                    unwrap_tensor_subclass(model)
-                
-                # Apply optimizations
-                if torch.version.cuda and float(torch.version.cuda[:3]) >= 11.6:
-                    if hasattr(torch.nn.functional, 'scaled_dot_product_attention'):
-                        print("- Using PyTorch SDPA (scaled_dot_product_attention)")
-                    else:
-                        print("- PyTorch SDPA not available")
-                
-                # Set to eval mode
-                model.eval()
-                print("- Model set to eval mode")
-                
-                # For better performance with int8 dynamic quantization
-                torch._inductor.config.force_fuse_int_mm_with_mul = True
-                print("- Enabled fusion of int matmul with mul operations")
-
-                
-                
-            else:
-                # Standard loading for FP16/FP32
-                model = AutoModel.from_pretrained(self.config.model_path)
-                print("- Model loaded in standard precision")
-                print(f"- Model type: {type(model)}")
-                
-                # Apply standard optimizations
-                # set default to half
-                import torch
-                torch.set_default_dtype(torch.bfloat16)
-                model = ModelOptimizer.optimize(model, self.config)
-                model = model.half()
-                # add torch compile
-                model = torch.compile(model)
-            
-            # Final check to ensure model is not None
-            if model is None:
-                raise ValueError("Model is None after optimization")
-                
-            print(f"- Final model type: {type(model)}")
-            return model
-        
-        except Exception as e:
-            print(f"ERROR loading model: {str(e)}")
-            import traceback
-            traceback.print_exc()
-            raise
-    
-    def _create_random_batch(self, batch_size: int) -> torch.Tensor:
-        return torch.randint(
-            0, 1000,
-            (batch_size, self.config.seq_length),
-            device="cuda",
-            dtype=torch.long
-        )
-    
-    def _run_inference(
-        self,
-        input_ids: torch.Tensor,
-        cuda_graph_wrapper: Optional[CUDAGraphWrapper] = None
-    ) -> Tuple[float, torch.Tensor]:
-        attention_mask = torch.ones_like(input_ids)
-        
-        with torch.no_grad(), self.timer.timing():
-            if cuda_graph_wrapper is not None:
-                output = cuda_graph_wrapper(input_ids, attention_mask)
-            else:
-                output = self.model(input_ids=input_ids, attention_mask=attention_mask)
-        
-        return self.timer.elapsed_time(), output
-    
-    def run(self) -> Dict[int, Dict[str, float]]:
-        results = {}
-        
-        # Reset peak memory stats
-        torch.cuda.reset_peak_memory_stats()
-        
-        for batch_size in self.config.batch_sizes:
-            print(f"\nTesting batch size: {batch_size}")
-            times = []
-            
-            # Get or create CUDA graph for this batch size
-            cuda_graph_wrapper = (
-                self.cuda_graphs.get_or_create(batch_size)
-                if self.cuda_graphs is not None
-                else None
-            )
-            
-            # Pre-allocate input tensor
-            input_ids = self._create_random_batch(batch_size)
-            print(f"Input shape: {input_ids.shape}")
-            
-            # Run benchmark
-            for i in tqdm(range(self.config.num_runs), desc=f"Batch size {batch_size}"):
-                try:
-                    elapsed_time, output = self._run_inference(input_ids, cuda_graph_wrapper)
-                    if i == 0:  # Only print on first run
-                        print(f"Output shape: {output.last_hidden_state.shape}")
-                    times.append(elapsed_time)
-                except Exception as e:
-                    print(f"Error during inference: {e}")
-                    break
-            
-            if not times:
-                print(f"No successful runs for batch size {batch_size}, skipping")
-                continue
-                
-            # Calculate statistics
-            avg_time = np.mean(times)
-            std_time = np.std(times)
-            throughput = batch_size / avg_time
-            
-            results[batch_size] = {
-                "avg_time": avg_time,
-                "std_time": std_time,
-                "throughput": throughput,
-            }
-            
-            print(f"Avg Time: {avg_time:.4f}s ± {std_time:.4f}s")
-            print(f"Throughput: {throughput:.2f} sequences/second")
-        
-        # Log memory usage
-        peak_memory_gb = torch.cuda.max_memory_allocated() / (1024 ** 3)
-        print(f"\nPeak GPU memory usage: {peak_memory_gb:.2f} GB")
-        
-        # Add memory info to results
-        for batch_size in results:
-            results[batch_size]["peak_memory_gb"] = peak_memory_gb
-        
-        return results
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Model Inference Benchmark")
-    parser.add_argument(
-        "--model_path",
-        type=str,
-        default="facebook/contriever",
-        help="Path to the model",
-    )
-    parser.add_argument(
-        "--batch_sizes",
-        type=str,
-        default="1,2,4,8,10,16,20,32,40,64,128,256,512,1024,2048,4096,8192",
-        help="Comma-separated list of batch sizes",
-    )
-    parser.add_argument(
-        "--seq_length",
-        type=int,
-        default=256,
-        help="Sequence length for input",
-    )
-    parser.add_argument(
-        "--num_runs",
-        type=int,
-        default=5,
-        help="Number of runs for each batch size",
-    )
-    parser.add_argument(
-        "--use_fp16",
-        action="store_true",
-        help="Enable FP16 inference",
-    )
-    parser.add_argument(
-        "--use_int4",
-        action="store_true",
-        help="Enable INT4 quantization using bitsandbytes",
-    )
-    parser.add_argument(
-        "--use_int8",
-        action="store_true",
-        help="Enable INT8 quantization for both activations and weights using bitsandbytes",
-    )
-    parser.add_argument(
-        "--use_cuda_graphs",
-        action="store_true",
-        help="Enable CUDA Graphs optimization",
-    )
-    parser.add_argument(
-        "--use_flash_attention",
-        action="store_true",
-        help="Enable Flash Attention 2 if available",
-    )
-    parser.add_argument(
-        "--use_linear8bitlt",
-        action="store_true",
-        help="Enable Linear8bitLt quantization for all linear layers",
-    )
-    
-    args = parser.parse_args()
-    
-    # Print arguments for debugging
-    print("\nCommand line arguments:")
-    for arg, value in vars(args).items():
-        print(f"- {arg}: {value}")
-    
-    config = BenchmarkConfig(
-        model_path=args.model_path,
-        batch_sizes=[int(bs) for bs in args.batch_sizes.split(",")],
-        seq_length=args.seq_length,
-        num_runs=args.num_runs,
-        use_fp16=args.use_fp16,
-        use_int4=args.use_int4,
-        use_int8=args.use_int8,  # Add this line
-        use_cuda_graphs=args.use_cuda_graphs,
-        use_flash_attention=args.use_flash_attention,
-        use_linear8bitlt=args.use_linear8bitlt,
-    )
-        
-    # Print configuration for debugging
-    print("\nBenchmark configuration:")
-    for field, value in vars(config).items():
-        print(f"- {field}: {value}")
-    
-    try:
-        benchmark = Benchmark(config)
-        results = benchmark.run()
-        
-        # Save results to file
-        import json
-        import os
-        
-        # Create results directory if it doesn't exist
-        os.makedirs("results", exist_ok=True)
-        
-        # Generate filename based on configuration
-        precision_type = "int4" if config.use_int4 else "fp16" if config.use_fp16 else "fp32"
-        model_name = os.path.basename(config.model_path)
-        output_file = f"results/benchmark_{model_name}_{precision_type}.json"
-        
-        # Save results
-        with open(output_file, "w") as f:
-            json.dump(
-                {
-                    "config": {k: str(v) if isinstance(v, list) else v for k, v in vars(config).items()},
-                    "results": {str(k): v for k, v in results.items()}
-                }, 
-                f, 
-                indent=2
-            )
-        print(f"Results saved to {output_file}")
-        
-    except Exception as e:
-        print(f"Benchmark failed: {e}")
-        import traceback
-        traceback.print_exc()
-
-
-if __name__ == "__main__":
-    main()

research/micro/embedd_micro_seq.py

@@ -1,376 +0,0 @@
-import argparse
-import time
-from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-import torch
-from torch import nn
-from transformers import AutoModel
-from tqdm import tqdm
-from contextlib import contextmanager
-import math
-
-@dataclass
-class BenchmarkConfig:
-    model_path: str
-    batch_sizes: List[int]
-    seq_length: int
-    num_runs: int
-    use_fp16: bool = True
-    use_cuda_graphs: bool = False
-    use_flash_attention: bool = False
-    max_batch_size: int = 256  # Maximum batch size before splitting
-
-
-class CUDAGraphContainer:
-    """Container for managing CUDA graphs for different batch sizes."""
-    
-    def __init__(self, model: nn.Module, seq_length: int, max_batch_size: int):
-        self.model = model
-        self.seq_length = seq_length
-        self.max_batch_size = max_batch_size
-        self.graphs: Dict[int, CUDAGraphWrapper] = {}
-    
-    def get_or_create(self, batch_size: int) -> 'CUDAGraphWrapper':
-        # For CUDA graphs, we always use the actual batch size or max_batch_size
-        effective_batch_size = min(batch_size, self.max_batch_size)
-        
-        if effective_batch_size not in self.graphs:
-            self.graphs[effective_batch_size] = CUDAGraphWrapper(
-                self.model, effective_batch_size, self.seq_length
-            )
-        return self.graphs[effective_batch_size]
-
-
-class CUDAGraphWrapper:
-    """Wrapper for CUDA graph capture and replay."""
-    
-    def __init__(self, model: nn.Module, batch_size: int, seq_length: int):
-        self.model = model
-        self.static_input = self._create_random_batch(batch_size, seq_length)
-        self.static_attention_mask = torch.ones_like(self.static_input)
-        
-        # Warm up
-        self._warmup()
-        
-        # Capture graph
-        self.graph = torch.cuda.CUDAGraph()
-        with torch.cuda.graph(self.graph):
-            self.static_output = self.model(
-                input_ids=self.static_input,
-                attention_mask=self.static_attention_mask
-            )
-    
-    def _create_random_batch(self, batch_size: int, seq_length: int) -> torch.Tensor:
-        return torch.randint(
-            0, 1000, (batch_size, seq_length), 
-            device="cuda", 
-            dtype=torch.long
-        )
-    
-    def _warmup(self, num_warmup: int = 3):
-        with torch.no_grad():
-            for _ in range(num_warmup):
-                self.model(
-                    input_ids=self.static_input,
-                    attention_mask=self.static_attention_mask
-                )
-    
-    def __call__(self, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
-        self.static_input.copy_(input_ids)
-        self.static_attention_mask.copy_(attention_mask)
-        self.graph.replay()
-        return self.static_output
-
-
-class ModelOptimizer:
-    """Applies various optimizations to the model."""
-    
-    @staticmethod
-    def optimize(model: nn.Module, config: BenchmarkConfig) -> nn.Module:
-        print("\nApplying model optimizations:")
-        
-        # Move to GPU
-        model = model.cuda()
-        print("- Model moved to GPU")
-        
-        # FP16
-        if config.use_fp16:
-            model = model.half()
-            print("- Using FP16 precision")
-        
-        # Check if using SDPA
-        if torch.version.cuda and float(torch.version.cuda[:3]) >= 11.6:
-            if hasattr(torch.nn.functional, 'scaled_dot_product_attention'):
-                print("- Using PyTorch SDPA (scaled_dot_product_attention)")
-                # No need to do anything as it's automatically enabled
-            else:
-                print("- PyTorch SDPA not available")
-        
-        # Flash Attention
-        if config.use_flash_attention:
-            try:
-                from flash_attn.flash_attention import FlashAttention
-                print("- Flash Attention 2 available")
-                if hasattr(model.config, "attention_mode"):
-                    model.config.attention_mode = "flash_attention_2"
-                    print("  - Enabled Flash Attention 2 mode")
-            except ImportError:
-                print("- Flash Attention not available")
-        
-        # Optimize LayerNorm
-        try:
-            num_layernorms = 0
-            for module in model.modules():
-                if isinstance(module, torch.nn.LayerNorm):
-                    module.forward = torch.jit.script(module.forward)
-                    num_layernorms += 1
-            if num_layernorms > 0:
-                print(f"- Optimized {num_layernorms} LayerNorm modules with TorchScript")
-        except Exception as e:
-            print(f"- LayerNorm optimization failed: {e}")
-        
-        # Memory efficient attention
-        try:
-            from xformers.ops import memory_efficient_attention
-            model.enable_xformers_memory_efficient_attention()
-            print("- Enabled xformers memory efficient attention")
-        except (ImportError, AttributeError):
-            print("- Xformers not available")
-        
-        model.eval()
-        print("- Model set to eval mode")
-        
-        return model
-
-
-class Timer:
-    """Handles accurate GPU timing using CUDA events."""
-    
-    def __init__(self):
-        self.start_event = torch.cuda.Event(enable_timing=True)
-        self.end_event = torch.cuda.Event(enable_timing=True)
-    
-    @contextmanager
-    def timing(self):
-        self.start_event.record()
-        yield
-        self.end_event.record()
-        self.end_event.synchronize()
-    
-    def elapsed_time(self) -> float:
-        return self.start_event.elapsed_time(self.end_event) / 1000  # ms to seconds
-
-
-class Benchmark:
-    """Main benchmark runner."""
-    
-    def __init__(self, config: BenchmarkConfig):
-        self.config = config
-        self.model = self._load_model()
-        self.cuda_graphs = (
-            CUDAGraphContainer(self.model, config.seq_length, config.max_batch_size)
-            if config.use_cuda_graphs
-            else None
-        )
-        self.timer = Timer()
-    
-    def _load_model(self) -> nn.Module:
-        print(f"Loading model from {self.config.model_path}...")
-        model = AutoModel.from_pretrained(self.config.model_path)
-        return ModelOptimizer.optimize(model, self.config)
-    
-    def _create_random_batch(self, batch_size: int) -> torch.Tensor:
-        return torch.randint(
-            0, 1000,
-            (batch_size, self.config.seq_length),
-            device="cuda",
-            dtype=torch.long
-        )
-    
-    def _run_inference(
-        self,
-        input_ids: torch.Tensor,
-        cuda_graph_wrapper: Optional[CUDAGraphWrapper] = None
-    ) -> Tuple[float, torch.Tensor]:
-        attention_mask = torch.ones_like(input_ids)
-        original_batch_size = input_ids.shape[0]
-        print(f"Original input_ids shape: {input_ids.shape}")
-        
-        # Split large batches to avoid OOM
-        max_batch_size = self.config.max_batch_size
-        if original_batch_size > max_batch_size:
-            print(f"Splitting batch of size {original_batch_size} into chunks of {max_batch_size}")
-            total_time = 0
-            outputs = []
-            
-            with torch.no_grad():
-                for i in range(0, original_batch_size, max_batch_size):
-                    end_idx = min(i + max_batch_size, original_batch_size)
-                    batch_slice = input_ids[i:end_idx]
-                    mask_slice = attention_mask[i:end_idx]
-                    
-                    print(f"Processing chunk {i//max_batch_size + 1}: shape {batch_slice.shape}")
-                    
-                    # Use CUDA graph if available (with the smaller batch size)
-                    chunk_cuda_graph = None
-                    if cuda_graph_wrapper is not None:
-                        chunk_cuda_graph = self.cuda_graphs.get_or_create(batch_slice.shape[0])
-                    
-                    with self.timer.timing():
-                        if chunk_cuda_graph is not None:
-                            chunk_output = chunk_cuda_graph(batch_slice, mask_slice)
-                        else:
-                            chunk_output = self.model(input_ids=batch_slice, attention_mask=mask_slice)
-                    
-                    total_time += self.timer.elapsed_time()
-                    outputs.append(chunk_output.last_hidden_state)
-                
-                # Combine outputs
-                combined_output = torch.cat(outputs, dim=0)
-                print(f"Combined output shape: {combined_output.shape}")
-                
-                # Create a wrapper object similar to model output to maintain consistency
-                class DummyOutput:
-                    def __init__(self, hidden_states):
-                        self.last_hidden_state = hidden_states
-                
-                output = DummyOutput(combined_output)
-                return total_time, output
-        else:
-            # Process normally for small batches
-            with torch.no_grad(), self.timer.timing():
-                if cuda_graph_wrapper is not None:
-                    output = cuda_graph_wrapper(input_ids, attention_mask)
-                else:
-                    output = self.model(input_ids=input_ids, attention_mask=attention_mask)
-            
-            print(f"Output shape: {output.last_hidden_state.shape}")
-            return self.timer.elapsed_time(), output
-    
-    def run(self) -> Dict[int, Dict[str, float]]:
-        results = {}
-        
-        for batch_size in self.config.batch_sizes:
-            print(f"\nTesting batch size: {batch_size}")
-            times = []
-            
-            # Get or create CUDA graph for this batch size
-            cuda_graph_wrapper = None
-            if self.cuda_graphs is not None:
-                if batch_size <= self.config.max_batch_size:
-                    cuda_graph_wrapper = self.cuda_graphs.get_or_create(batch_size)
-                else:
-                    # For large batches, we'll use the max_batch_size graph in chunks
-                    cuda_graph_wrapper = True  # Just a flag to indicate we want to use CUDA graphs
-            
-            # Pre-allocate input tensor
-            input_ids = self._create_random_batch(batch_size)
-            
-            # Run benchmark
-            for run_idx in tqdm(range(self.config.num_runs), desc=f"Batch size {batch_size}"):
-                elapsed_time, _ = self._run_inference(input_ids, cuda_graph_wrapper)
-                times.append(elapsed_time)
-                print(f"Run {run_idx+1}: {elapsed_time:.4f}s")
-            
-            # Calculate statistics
-            avg_time = np.mean(times)
-            std_time = np.std(times)
-            throughput = batch_size / avg_time
-            
-            results[batch_size] = {
-                "avg_time": avg_time,
-                "std_time": std_time,
-                "throughput": throughput,
-            }
-            
-            print(f"Avg Time: {avg_time:.4f}s ± {std_time:.4f}s")
-            print(f"Throughput: {throughput:.2f} sequences/second")
-        
-        return results
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Model Inference Benchmark")
-    parser.add_argument(
-        "--model_path",
-        type=str,
-        default="facebook/contriever",
-        help="Path to the model",
-    )
-    parser.add_argument(
-        "--batch_sizes",
-        type=str,
-        default="1,2,4,8,16,32,64,128,256,512,1024,2048,4096",
-        help="Comma-separated list of batch sizes",
-    )
-    parser.add_argument(
-        "--seq_length",
-        type=int,
-        default=256,
-        help="Sequence length for input",
-    )
-    parser.add_argument(
-        "--num_runs",
-        type=int,
-        default=5,
-        help="Number of runs for each batch size",
-    )
-    parser.add_argument(
-        "--no_fp16",
-        action="store_true",
-        help="Disable FP16 inference",
-    )
-    parser.add_argument(
-        "--use_cuda_graphs",
-        action="store_true",
-        help="Enable CUDA Graphs optimization",
-    )
-    parser.add_argument(
-        "--use_flash_attention",
-        action="store_true",
-        help="Enable Flash Attention 2 if available",
-    )
-    parser.add_argument(
-        "--max_batch_size",
-        type=int,
-        default=256,
-        help="Maximum batch size before splitting to prevent OOM",
-    )
-    
-    args = parser.parse_args()
-    
-    config = BenchmarkConfig(
-        model_path=args.model_path,
-        batch_sizes=[int(bs) for bs in args.batch_sizes.split(",")],
-        seq_length=args.seq_length,
-        num_runs=args.num_runs,
-        use_fp16=not args.no_fp16,
-        use_cuda_graphs=args.use_cuda_graphs,
-        use_flash_attention=args.use_flash_attention,
-        max_batch_size=args.max_batch_size,
-    )
-    
-    benchmark = Benchmark(config)
-    results = benchmark.run()
-    
-    # Print overall summary
-    print("\n===== BENCHMARK SUMMARY =====")
-    print(f"Model: {config.model_path}")
-    print(f"Sequence Length: {config.seq_length}")
-    print(f"FP16: {config.use_fp16}")
-    print(f"CUDA Graphs: {config.use_cuda_graphs}")
-    print(f"Flash Attention: {config.use_flash_attention}")
-    print(f"Max Batch Size: {config.max_batch_size}")
-    print("\nResults:")
-    
-    print("\nBatch Size | Avg Time (s) | Throughput (seq/s)")
-    print("-" * 50)
-    for bs in sorted(results.keys()):
-        r = results[bs]
-        print(f"{bs:^10} | {r['avg_time']:^12.4f} | {r['throughput']:^17.2f}")
-
-
-if __name__ == "__main__":
-    main()

research/micro/int4benchmark.py

@@ -1,218 +0,0 @@
-import torch
-import torch.nn as nn
-import time
-import torch.nn.functional as F
-
-# Import necessary functions from the quantize.py file
-def get_group_qparams(w, n_bit=4, groupsize=128):
-    # needed for GPTQ with padding
-    if groupsize > w.shape[-1]:
-        groupsize = w.shape[-1]
-    assert groupsize > 1
-    assert w.shape[-1] % groupsize == 0
-    assert w.dim() == 2
-
-    to_quant = w.reshape(-1, groupsize)
-    assert torch.isnan(to_quant).sum() == 0
-
-    max_val = to_quant.amax(dim=1, keepdim=True)
-    min_val = to_quant.amin(dim=1, keepdim=True)
-    max_int = 2**n_bit - 1
-    scales = (max_val - min_val).clamp(min=1e-6) / max_int
-    zeros = min_val + scales * (2 ** (n_bit - 1))
-    return scales.to(torch.bfloat16).reshape(w.shape[0], -1), zeros.to(
-        torch.bfloat16
-    ).reshape(w.shape[0], -1)
-
-def pack_scales_and_zeros(scales, zeros):
-    assert scales.shape == zeros.shape
-    assert scales.dtype == torch.bfloat16
-    assert zeros.dtype == torch.bfloat16
-    return (
-        torch.cat(
-            [
-                scales.reshape(scales.size(0), scales.size(1), 1),
-                zeros.reshape(zeros.size(0), zeros.size(1), 1),
-            ],
-            2,
-        )
-        .transpose(0, 1)
-        .contiguous()
-    )
-
-def group_quantize_tensor(w, n_bit=4, groupsize=128):
-    scales, zeros = get_group_qparams(w, n_bit, groupsize)
-    w_int32 = group_quantize_tensor_from_qparams(w, scales, zeros, n_bit, groupsize)
-    scales_and_zeros = pack_scales_and_zeros(scales, zeros)
-    return w_int32, scales_and_zeros
-
-def group_quantize_tensor_from_qparams(w, scales, zeros, n_bit=4, groupsize=128):
-    assert groupsize > 1
-    # needed for GPTQ single column quantize
-    if groupsize > w.shape[-1] and scales.shape[-1] == 1:
-        groupsize = w.shape[-1]
-
-    assert w.shape[-1] % groupsize == 0
-    assert w.dim() == 2
-
-    to_quant = w.reshape(-1, groupsize)
-    assert torch.isnan(to_quant).sum() == 0
-
-    scales = scales.reshape(-1, 1)
-    zeros = zeros.reshape(-1, 1)
-    min_val = zeros - scales * (2 ** (n_bit - 1))
-    max_int = 2**n_bit - 1
-    min_int = 0
-    w_int32 = (
-        to_quant.sub(min_val)
-        .div(scales)
-        .round()
-        .clamp_(min_int, max_int)
-        .to(torch.int32)
-        .reshape_as(w)
-    )
-
-    return w_int32
-
-def prepare_int4_weight_and_scales_and_zeros(weight_bf16, groupsize, inner_k_tiles):
-    weight_int32, scales_and_zeros = group_quantize_tensor(
-        weight_bf16, n_bit=4, groupsize=groupsize
-    )
-    weight_int4pack = torch.ops.aten._convert_weight_to_int4pack(weight_int32, inner_k_tiles)
-    return weight_int4pack, scales_and_zeros
-
-def linear_forward_int4(x, weight_int4pack, scales_and_zeros, out_features, groupsize):
-    origin_x_size = x.size()
-    x = x.reshape(-1, origin_x_size[-1])
-    c = torch.ops.aten._weight_int4pack_mm(x, weight_int4pack, groupsize, scales_and_zeros)
-    new_shape = origin_x_size[:-1] + (out_features,)
-    c = c.reshape(new_shape)
-    return c
-
-class WeightOnlyInt4Linear(torch.nn.Module):
-    __constants__ = ['in_features', 'out_features']
-    in_features: int
-    out_features: int
-    weight: torch.Tensor
-
-    def __init__(
-            self, in_features: int, out_features: int,
-            bias=False, device=None, dtype=None, groupsize: int = 128, inner_k_tiles: int = 8
-    ) -> None:
-        super().__init__()
-        self.in_features = in_features
-        self.out_features = out_features
-        self.groupsize = groupsize
-        self.inner_k_tiles = inner_k_tiles
-
-        assert out_features % 8 == 0, "require out_features % 8 == 0"
-        assert in_features % (inner_k_tiles * 16) == 0, "require in_features % (innerKTiles * 16) == 0"
-        self.register_buffer(
-            "weight",
-            torch.empty((out_features // 8, in_features // (inner_k_tiles * 16), 32, inner_k_tiles // 2), dtype=torch.int32)
-        )
-        self.register_buffer(
-            "scales_and_zeros",
-            torch.empty((in_features // groupsize, out_features, 2), dtype=torch.bfloat16)
-        )
-
-    def forward(self, input: torch.Tensor) -> torch.Tensor:
-        input = input.to(torch.bfloat16)
-        return linear_forward_int4(
-            input,
-            self.weight, self.scales_and_zeros, self.out_features, self.groupsize
-        )
-
-# Define dimensions that satisfy the requirements for INT4 quantization
-# in_features must be divisible by inner_k_tiles * 16
-# out_features must be divisible by 8
-in_features = 1024  # Must be divisible by inner_k_tiles * 16
-out_features = 2048  # Must be divisible by 8
-groupsize = 128
-inner_k_tiles = 8
-
-# Create models
-fp16_model = nn.Sequential(
-    nn.Linear(in_features, out_features, bias=False)
-)
-
-# Create INT4 model
-int4_model = nn.Sequential(
-    WeightOnlyInt4Linear(in_features, out_features, bias=False, 
-                         groupsize=groupsize, inner_k_tiles=inner_k_tiles)
-)
-
-# Quantize the weights and set up the INT4 model
-with torch.no_grad():
-    # Convert FP16 weights to INT4
-    fp16_weight = fp16_model[0].weight.data.to(torch.bfloat16)
-    weight_int4pack, scales_and_zeros = prepare_int4_weight_and_scales_and_zeros(
-        fp16_weight, groupsize, inner_k_tiles
-    )
-    
-    # Set the quantized weights in the INT4 model
-    int4_model[0].weight.copy_(weight_int4pack)
-    int4_model[0].scales_and_zeros.copy_(scales_and_zeros)
-
-# Move models to GPU
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-fp16_model = fp16_model.to(device)
-int4_model = int4_model.to(device)
-
-# Create random input tensor
-batch_size = 1024
-input_tensor = torch.randn(batch_size, in_features, device=device)
-input_tensor_bf16 = input_tensor.to(torch.bfloat16)
-
-# Speed test function
-def speed_test(model, input_tensor, name, num_iterations=100):
-    # Warmup
-    for _ in range(10):
-        _ = model(input_tensor)
-    
-    # Actual timing
-    torch.cuda.synchronize()
-    start_time = time.time()
-    
-    for _ in range(num_iterations):
-        _ = model(input_tensor)
-    
-    torch.cuda.synchronize()
-    end_time = time.time()
-    
-    avg_time = (end_time - start_time) / num_iterations
-    print(f"{name} model: {avg_time:.6f} seconds per iteration")
-    return avg_time
-
-# Run speed tests
-with torch.no_grad():  # Disable gradient calculation for inference
-    print(f"Running benchmark with batch_size={batch_size}, in_features={in_features}, out_features={out_features}")
-    print(f"INT4 parameters: groupsize={groupsize}, inner_k_tiles={inner_k_tiles}")
-    
-    fp16_time = speed_test(fp16_model, input_tensor_bf16, "FP16")
-    int4_time = speed_test(int4_model, input_tensor, "INT4")
-    
-    # Calculate speedup
-    speedup = fp16_time / int4_time
-    print(f"INT4 is {speedup:.2f}x faster than FP16")
-    
-    # Calculate memory savings
-    fp16_memory = fp16_model[0].weight.nelement() * fp16_model[0].weight.element_size()
-    int4_memory = (int4_model[0].weight.nelement() * int4_model[0].weight.element_size() + 
-                  int4_model[0].scales_and_zeros.nelement() * int4_model[0].scales_and_zeros.element_size())
-    
-    memory_reduction = fp16_memory / int4_memory
-    print(f"Memory reduction: {memory_reduction:.2f}x ({fp16_memory/1024/1024:.2f} MB vs {int4_memory/1024/1024:.2f} MB)")
-    
-    # Check accuracy
-    with torch.no_grad():
-        fp16_output = fp16_model(input_tensor_bf16)
-        int4_output = int4_model(input_tensor)
-        
-        # Calculate error metrics
-        abs_error = torch.abs(fp16_output - int4_output)
-        rel_error = abs_error / (torch.abs(fp16_output) + 1e-7)
-        
-        print(f"Mean absolute error: {abs_error.mean().item():.6f}")
-        print(f"Max absolute error: {abs_error.max().item():.6f}")
-        print(f"Mean relative error: {rel_error.mean().item():.6f}") 

research/micro/int8.py

@@ -1,83 +0,0 @@
-import torch
-import nvmath.bindings.cublas
-import ctypes
-
-# 创建 CUBLAS 句柄
-handle = nvmath.bindings.cublas.create()
-
-# 准备数据 - 使用 uint8 类型，并确保内存连续
-m, n, k = 64, 32, 48
-a = (torch.rand(m, k, device="cuda") * 255).to(torch.uint8).contiguous()
-b = (torch.rand(k, n, device="cuda") * 255).to(torch.uint8).contiguous()
-c = torch.zeros(m, n, device="cuda", dtype=torch.uint8).contiguous()
-
-# 确保张量在 CUDA 上
-assert a.is_cuda and b.is_cuda and c.is_cuda
-# 确保张量是连续的
-assert a.is_contiguous() and b.is_contiguous() and c.is_contiguous()
-
-# 获取指针
-a_ptr = a.data_ptr()
-b_ptr = b.data_ptr()
-c_ptr = c.data_ptr()
-
-# 设置参数
-transa = 0  # CUBLAS_OP_N (不转置)
-transb = 0  # CUBLAS_OP_N (不转置)
-transc = 0  # CUBLAS_OP_N (不转置)
-
-# 设置偏置值
-a_bias = 0
-b_bias = 0
-c_bias = 0
-
-# 设置正确的 leading dimensions
-lda = k  # A 的 leading dimension
-ldb = n  # B 的 leading dimension
-ldc = n  # C 的 leading dimension
-
-c_mult = 1
-c_shift = 0
-
-# 打印调试信息
-print(f"a shape: {a.shape}, a_ptr: {a_ptr}")
-print(f"b shape: {b.shape}, b_ptr: {b_ptr}")
-print(f"c shape: {c.shape}, c_ptr: {c_ptr}")
-
-try:
-    # 调用 uint8gemm_bias
-    nvmath.bindings.cublas.uint8gemm_bias(
-        handle,
-        transa, transb, transc,
-        m, n, k,
-        a_ptr, a_bias, lda,
-        b_ptr, b_bias, ldb,
-        c_ptr, c_bias, ldc,
-        c_mult, c_shift
-    )
-except Exception as e:
-    print(f"Error: {e}")
-    # 尝试使用 ctypes 转换指针
-    a_ptr_c = ctypes.c_void_p(a_ptr).value
-    b_ptr_c = ctypes.c_void_p(b_ptr).value
-    c_ptr_c = ctypes.c_void_p(c_ptr).value
-    
-    print(f"Using ctypes: a_ptr: {a_ptr_c}, b_ptr: {b_ptr_c}, c_ptr: {c_ptr_c}")
-    
-    # 再次尝试调用
-    nvmath.bindings.cublas.uint8gemm_bias(
-        handle,
-        transa, transb, transc,
-        m, n, k,
-        a_ptr_c, a_bias, lda,
-        b_ptr_c, b_bias, ldb,
-        c_ptr_c, c_bias, ldc,
-        c_mult, c_shift
-    )
-
-# 销毁 CUBLAS 句柄
-nvmath.bindings.cublas.destroy(handle)
-
-# 打印结果
-print("Result:")
-print(c)

research/micro/llm_compress.py

@@ -1,23 +0,0 @@
-from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
-from llmcompressor.modifiers.quantization import GPTQModifier
-from llmcompressor import oneshot
-
-# Select quantization algorithm. In this case, we:
-#   * apply SmoothQuant to make the activations easier to quantize
-#   * quantize the weights to int8 with GPTQ (static per channel)
-#   * quantize the activations to int8 (dynamic per token)
-recipe = [
-    SmoothQuantModifier(smoothing_strength=0.8),
-    GPTQModifier(scheme="W8A8", targets="Linear", ignore=["lm_head"]),
-]
-
-# Apply quantization using the built in open_platypus dataset.
-#   * See examples for demos showing how to pass a custom calibration set
-oneshot(
-    model="facebook/contriever",
-    dataset="open_platypus",
-    recipe=recipe,
-    output_dir="contriever-INT4",
-    max_seq_length=2048,
-    num_calibration_samples=512,
-)

research/micro/nvmath_test.py

@@ -1,41 +0,0 @@
-# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. ALL RIGHTS RESERVED.
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-This example demonstrates basic matrix multiplication of FP8 tensors.
-
-In narrow-precision operations, quantization scales must be provided for each tensor. These
-scales are used to dequantize input operands and quantize the result. Without proper
-scaling, the results of FP8 operations will likely exceed the type's range.
-
-FP8 is only supported with cuBLAS 12.8 or newer and on devices with compute
-capability 8.9 or higher.
-"""
-
-import torch
-
-import nvmath
-
-# Prepare sample input data. Note that N, M and K must be divisible by 16 for FP8.
-# cuBLAS requires B to be column-major, so we first create a row-major tensor and then
-# transpose it.
-m, n, k = 64, 32, 48
-a = (torch.rand(m, k, device="cuda") * 10).type(torch.float8_e4m3fn)
-b = (torch.rand(n, k, device="cuda") * 10).type(torch.float8_e4m3fn).T
-
-# Prepare quantization scales. The scales must allow the result to fit within the dynamic
-# range of the data type used. Scales can be provided either as a dictionary or as a
-# MatmulQuantizationScales object. Note that scales are only allowed for FP8 operands.
-scales = {"a": 1, "b": 1, "d": 0.1}
-
-# Perform the multiplication. The result of the multiplication will be:
-# (scales.a * A) @ (scales.b * B) * scales.d
-result = nvmath.linalg.advanced.matmul(a, b, quantization_scales=scales)
-
-# Check how scaling helped to fit into the dynamic range of float8_e4m3fn type.
-result_without_scaling = nvmath.linalg.advanced.matmul(a, b, quantization_scales={"a": 1, "b": 1, "d": 1})
-print("Without scaling, most of the elements were clamped to the maximum value of float8_e4m3fn type (448):")
-print(result_without_scaling)
-print(f"\nWith D scale set to {scales['d']}, they were scaled down to fit into the dynamic range of float8_e4m3fn:")
-print(result)

research/micro/save_small_model.py

@@ -1,58 +0,0 @@
-import os
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from pathlib import Path
-
-def save_model_in_pth_format(model_name, output_dir):
-    """
-    Download a model from Hugging Face and save it in PTH format
-    for use with quantization benchmarks.
-    
-    Args:
-        model_name: Name of the model on Hugging Face
-        output_dir: Directory to save the model
-    """
-    print(f"Loading model {model_name}...")
-    
-    # Create output directory if it doesn't exist
-    os.makedirs(output_dir, exist_ok=True)
-    
-    # Load tokenizer and model
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name,
-        torch_dtype=torch.bfloat16,
-        low_cpu_mem_usage=True
-    )
-    
-    # Save tokenizer
-    tokenizer.save_pretrained(output_dir)
-    
-    # Extract and save the model weights in PTH format
-    model_state_dict = model.state_dict()
-    
-    # Save the model weights
-    model_path = Path(output_dir) / "model.pth"
-    torch.save(model_state_dict, model_path)
-    
-    print(f"Model saved to {model_path}")
-    
-    # Print model size information
-    param_count = sum(p.numel() for p in model.parameters())
-    model_size_mb = sum(p.numel() * p.element_size() for p in model.parameters()) / (1024 * 1024)
-    
-    print(f"Model parameters: {param_count:,}")
-    print(f"Model size: {model_size_mb:.2f} MB")
-    
-    return model_path
-
-if __name__ == "__main__":
-    # Use a small model for testing
-    model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
-    output_dir = "./tinyllama-1.1b-chat"
-    
-    model_path = save_model_in_pth_format(model_name, output_dir)
-    
-    print("\nYou can now use this model with the INT4 benchmark script.")
-    print("Example command:")
-    print(f"python int4benchmark.py --model_path {model_path}") 

research/micro/transformer-batching-benchmark.ipynb

@@ -1,677 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cab91cfc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ubuntu/Power-RAG/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
-   "source": [
-    "import copy\n",
-    "import dataclasses\n",
-    "import os\n",
-    "import time\n",
-    "import pathlib\n",
-    "import itertools\n",
-    "import multiprocessing\n",
-    "import scipy\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import pickle\n",
-    "import gzip\n",
-    "import threading\n",
-    "import queue\n",
-    "import pytz\n",
-    "import traceback\n",
-    "from datetime import datetime\n",
-    "from tqdm.auto import tqdm, trange\n",
-    "from typing import Any\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.ticker as mtick\n",
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format='retina'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8d24fbd7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sat Apr 12 00:10:05 2025       \n",
-      "+-----------------------------------------------------------------------------------------+\n",
-      "| NVIDIA-SMI 550.120                Driver Version: 550.120        CUDA Version: 12.4     |\n",
-      "|-----------------------------------------+------------------------+----------------------+\n",
-      "| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n",
-      "| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n",
-      "|                                         |                        |               MIG M. |\n",
-      "|=========================================+========================+======================|\n",
-      "|   0  NVIDIA A10G                    Off |   00000000:00:1E.0 Off |                    0 |\n",
-      "|  0%   27C    P8             15W /  300W |       4MiB /  23028MiB |      0%      Default |\n",
-      "|                                         |                        |                  N/A |\n",
-      "+-----------------------------------------+------------------------+----------------------+\n",
-      "                                                                                         \n",
-      "+-----------------------------------------------------------------------------------------+\n",
-      "| Processes:                                                                              |\n",
-      "|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |\n",
-      "|        ID   ID                                                               Usage      |\n",
-      "|=========================================================================================|\n",
-      "|  No running processes found                                                             |\n",
-      "+-----------------------------------------------------------------------------------------+\n"
-     ]
-    }
-   ],
-   "source": [
-    "!nvidia-smi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "538b2c11",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def benchmark(f, *, f_setup=None, min_repeat: int, min_secs: float, tqdm_kwargs: dict | None=None) -> np.ndarray:\n",
-    "    latency = []\n",
-    "    \n",
-    "    # First run, ignore min_secs\n",
-    "    if f_setup is not None:\n",
-    "        f_setup()\n",
-    "    st = time.perf_counter_ns()\n",
-    "    f()\n",
-    "    ed = time.perf_counter_ns()\n",
-    "    latency.append((ed-st)/1e9)\n",
-    "    \n",
-    "    # Subsequent runs, until reaching both min_repeat and min_secs\n",
-    "    min_nanos = int(min_secs * 1e9)\n",
-    "    start_nanos = time.perf_counter_ns()\n",
-    "    while True:\n",
-    "        now_nanos = time.perf_counter_ns()\n",
-    "        if len(latency) > min_repeat and now_nanos - start_nanos > min_nanos:\n",
-    "            break\n",
-    "        if f_setup is not None:\n",
-    "            f_setup()\n",
-    "        st = time.perf_counter_ns()\n",
-    "        f()\n",
-    "        ed = time.perf_counter_ns()\n",
-    "        latency.append((ed-st)/1e9)\n",
-    "    return np.array(latency)\n",
-    "\n",
-    "def tail_mean(xs, skip=0.2):\n",
-    "    return xs[int(len(xs) * skip):].mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "02c9c9b1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<torch.autograd.grad_mode.set_grad_enabled at 0x7c5afc12b850>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "torch.set_grad_enabled(False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "3405fdc7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nd_list = list(itertools.chain(itertools.product([12, 3], [256])))\n",
-    "seqlen_list = [256]\n",
-    "bs_list = [2,4,8,16,32,64,128,256,512,1024,2048]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "10dc981a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(12, 256), (3, 256)]\n",
-      "[256]\n",
-      "[2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(nd_list)\n",
-    "print(seqlen_list)\n",
-    "print(bs_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "7e0ee385",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def benchmark_dense(out, nd_list, seqlen_list, bs_list):\n",
-    "    seqlen_list = [1] + seqlen_list\n",
-    "    total = len(list(itertools.product(nd_list, seqlen_list, bs_list)))\n",
-    "    pbar = tqdm(total=total)\n",
-    "    for (n, d), seqlen in reversed(list(itertools.product(nd_list, seqlen_list))):\n",
-    "        h = n * d\n",
-    "        maxbs = max(bs_list)\n",
-    "        print(maxbs, n, d, seqlen)\n",
-    "        cache = torch.empty(int(256e6 // 4), dtype=torch.int, device=\"cuda:0\")\n",
-    "        X = torch.rand((maxbs, seqlen, h), dtype=torch.bfloat16, device=\"cuda:0\")\n",
-    "        W = torch.rand((h, h), dtype=torch.bfloat16, device=\"cuda:0\")\n",
-    "        torch.cuda.synchronize()\n",
-    "        for bs in reversed(bs_list):\n",
-    "            pbar.set_postfix(n=n, h=h, d=d, seqlen=seqlen, bs=bs)\n",
-    "            def run():\n",
-    "                torch.matmul(X[:bs], W)\n",
-    "                torch.cuda.synchronize()\n",
-    "            def clear_cache():\n",
-    "                cache.zero_()\n",
-    "                torch.cuda.synchronize()\n",
-    "            latency = benchmark(run, f_setup=clear_cache, min_repeat=20, min_secs=2)\n",
-    "            l = tail_mean(latency)\n",
-    "            out.append({\n",
-    "                \"n\": n,\n",
-    "                \"d\": d,\n",
-    "                \"seqlen\": seqlen,\n",
-    "                \"bs\": bs,\n",
-    "                \"latency\": l\n",
-    "            })\n",
-    "            pbar.update()\n",
-    "        del cache, X, W\n",
-    "        torch.cuda.empty_cache()\n",
-    "    pbar.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c206a502",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def benchmark_qk_init(out, nd_list, seqlen_list, bs_list):\n",
-    "    total = len(list(itertools.product(nd_list, seqlen_list, bs_list)))\n",
-    "    pbar = tqdm(total=total)\n",
-    "    for (n, d), seqlen in reversed(list(itertools.product(nd_list, seqlen_list))):\n",
-    "        h = n * d\n",
-    "        try:\n",
-    "            maxbs = max(b for b in bs_list if b*n*seqlen*d*2*2+b*n*seqlen**2*2 < 80e9)\n",
-    "        except ValueError:\n",
-    "            pbar.update(len(bs_list))\n",
-    "            continue\n",
-    "        cache = torch.empty(int(256e6 // 4), dtype=torch.int, device=\"cuda:0\")\n",
-    "        Qmax = torch.rand((maxbs, n, seqlen, d), dtype=torch.bfloat16, device=\"cuda:0\")\n",
-    "        Kmax = torch.rand((maxbs, n, seqlen, d), dtype=torch.bfloat16, device=\"cuda:0\")\n",
-    "        torch.cuda.synchronize()\n",
-    "        for bs in reversed(bs_list):\n",
-    "            pbar.set_postfix(n=n, h=h, d=d, seqlen=seqlen, bs=bs)\n",
-    "            if bs > maxbs:\n",
-    "                pbar.update()\n",
-    "                continue\n",
-    "            Q = Qmax[:bs]\n",
-    "            K = Kmax[:bs]\n",
-    "            def run():\n",
-    "                torch.bmm(Q.view(bs * n, seqlen, d), K.view(bs * n, seqlen, d).transpose(1, 2))\n",
-    "                torch.cuda.synchronize()\n",
-    "            def clear_cache():\n",
-    "                cache.zero_()\n",
-    "                torch.cuda.synchronize()\n",
-    "            latency = benchmark(run, f_setup=clear_cache, min_repeat=20, min_secs=2)\n",
-    "            l = tail_mean(latency)\n",
-    "            out.append({\n",
-    "                \"n\": n,\n",
-    "                \"d\": d,\n",
-    "                \"seqlen\": seqlen,\n",
-    "                \"bs\": bs,\n",
-    "                \"latency\": l\n",
-    "            })\n",
-    "            pbar.update()\n",
-    "        del cache, Q, K, Qmax, Kmax\n",
-    "        torch.cuda.empty_cache()\n",
-    "    pbar.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "a3a2103c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def benchmark_qk_ar(out, nd_list, seqlen_list, bs_list):\n",
-    "    total = len(list(itertools.product(nd_list, seqlen_list, bs_list)))\n",
-    "    pbar = tqdm(total=total)\n",
-    "    for (n, d), seqlen in reversed(list(itertools.product(nd_list, seqlen_list))):\n",
-    "        h = n * d\n",
-    "        try:\n",
-    "            maxbs = max(b for b in bs_list if b*n*(1+seqlen)*d*2+b*n*seqlen*2 < 80e9)\n",
-    "        except ValueError:\n",
-    "            pbar.update(len(bs_list))\n",
-    "            continue\n",
-    "        cache = torch.empty(int(256e6 // 4), dtype=torch.int, device=\"cuda:0\")\n",
-    "        Qmax = torch.rand((maxbs, n, 1, d), dtype=torch.bfloat16, device=\"cuda:0\")\n",
-    "        Kmax = torch.rand((maxbs, n, seqlen, d), dtype=torch.bfloat16, device=\"cuda:0\")\n",
-    "        torch.cuda.synchronize()\n",
-    "        for bs in reversed(bs_list):\n",
-    "            pbar.set_postfix(n=n, h=h, d=d, seqlen=seqlen, bs=bs)\n",
-    "            if bs > maxbs:\n",
-    "                pbar.update()\n",
-    "                continue\n",
-    "            Q = Qmax[:bs]\n",
-    "            K = Kmax[:bs]\n",
-    "            def run():\n",
-    "                torch.bmm(Q.view(bs * n, 1, d), K.view(bs * n, seqlen, d).transpose(1, 2))\n",
-    "                torch.cuda.synchronize()\n",
-    "            def clear_cache():\n",
-    "                cache.zero_()\n",
-    "                torch.cuda.synchronize()\n",
-    "            latency = benchmark(run, f_setup=clear_cache, min_repeat=20, min_secs=2)\n",
-    "            l = tail_mean(latency)\n",
-    "            out.append({\n",
-    "                \"n\": n,\n",
-    "                \"d\": d,\n",
-    "                \"seqlen\": seqlen,\n",
-    "                \"bs\": bs,\n",
-    "                \"latency\": l\n",
-    "            })\n",
-    "            pbar.update()\n",
-    "        del cache, Q, K, Qmax, Kmax\n",
-    "        torch.cuda.empty_cache()\n",
-    "    pbar.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "3aaad98a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "18137de3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/22 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 22/22 [00:44<00:00,  2.04s/it, bs=2, d=256, h=3072, n=12, seqlen=256]   \n"
-     ]
-    }
-   ],
-   "source": [
-    "db = []\n",
-    "benchmark_qk_init(db, nd_list, seqlen_list, bs_list)\n",
-    "data[\"qk_init\"] = db"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "26c76e15",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 22/22 [00:44<00:00,  2.01s/it, bs=2, d=256, h=3072, n=12, seqlen=256]   \n"
-     ]
-    }
-   ],
-   "source": [
-    "db = []\n",
-    "benchmark_qk_ar(db, nd_list, seqlen_list, bs_list)\n",
-    "data[\"qk_ar\"] = db"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "313e36eb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/44 [00:00<?, ?it/s, bs=2048, d=256, h=768, n=3, seqlen=256]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2048 3 256 256\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 25%|██▌       | 11/44 [00:22<01:06,  2.00s/it, bs=2048, d=256, h=768, n=3, seqlen=1] "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2048 3 256 1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 22/44 [00:44<00:44,  2.00s/it, bs=2048, d=256, h=3072, n=12, seqlen=256]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2048 12 256 256\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 75%|███████▌  | 33/44 [01:07<00:22,  2.02s/it, bs=2048, d=256, h=3072, n=12, seqlen=1]  "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2048 12 256 1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 44/44 [01:29<00:00,  2.03s/it, bs=2, d=256, h=3072, n=12, seqlen=1]   \n"
-     ]
-    }
-   ],
-   "source": [
-    "db = []\n",
-    "benchmark_dense(db, nd_list, seqlen_list, bs_list)\n",
-    "data[\"dense\"] = db"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "50c37959",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with gzip.open(\"data/20230516-transformer-batching1.pkl.gz\", \"wb\") as f:\n",
-    "    pickle.dump(data, f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "828ddb54",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_dense = (\n",
-    "    pd.DataFrame.from_dict(data[\"dense\"])\n",
-    "    .assign(h=lambda x: x[\"n\"] * x[\"d\"])\n",
-    "    .assign(flop=lambda x: (x[\"bs\"] * x[\"seqlen\"] * x[\"h\"]**2) * 2)\n",
-    "    .assign(io=lambda x: (x[\"bs\"]*x[\"seqlen\"]*x[\"h\"]*2 + x[\"h\"]**2) * 2/x['latency']/1e9)\n",
-    "    .assign(intensity=lambda x: x[\"flop\"] / x[\"io\"])\n",
-    "    .assign(throughput=lambda x: x[\"flop\"] / x[\"latency\"])\n",
-    "    .assign(series=\"dense\")\n",
-    ")\n",
-    "df_qk_init = (\n",
-    "    pd.DataFrame.from_dict(data[\"qk_init\"])\n",
-    "    .assign(h=lambda x: x[\"n\"] * x[\"d\"])\n",
-    "    .assign(flop=lambda x: (x[\"bs\"]*x[\"n\"]*x[\"d\"]*x[\"seqlen\"]**2) * 2)\n",
-    "    .assign(io=lambda x: (x[\"bs\"]*x[\"n\"]*(x[\"seqlen\"]*x[\"d\"]*2 + x[\"seqlen\"]**2)) * 2/x['latency']/1e9)\n",
-    "    .assign(intensity=lambda x: x[\"flop\"] / x[\"io\"])\n",
-    "    .assign(throughput=lambda x: x[\"flop\"] / x[\"latency\"])\n",
-    "    .assign(series=\"qk_init\")\n",
-    ")\n",
-    "df_qk_ar = (\n",
-    "    pd.DataFrame.from_dict(data[\"qk_ar\"])\n",
-    "    .assign(h=lambda x: x[\"n\"] * x[\"d\"])\n",
-    "    .assign(flop=lambda x: (x[\"bs\"]*x[\"n\"]*x[\"d\"]*x[\"seqlen\"]) * 2)\n",
-    "    .assign(io=lambda x: (x[\"bs\"]*x[\"n\"]*(x[\"d\"] + x[\"seqlen\"]*x[\"d\"] + x[\"seqlen\"])) * 2)\n",
-    "    .assign(intensity=lambda x: x[\"flop\"] / x[\"io\"])\n",
-    "    .assign(throughput=lambda x: x[\"bs\"] / x[\"latency\"])\n",
-    "    .assign(series=\"qk_ar\")\n",
-    ")\n",
-    "pd.concat([df_dense, df_qk_init, df_qk_ar]).to_csv(\"data/transformer-batching-microbenchmarks.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "c296a395",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<module 'pandas' from '/home/ubuntu/Power-RAG/.venv/lib/python3.10/site-packages/pandas/__init__.py'>"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a25cdd5a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "63b8a531",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import transformers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "af90eff1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _gen_opt_cfg(n_layers: int, d_model: int, n_heads: int, **kwargs) -> transformers.OPTConfig:\n",
-    "    return transformers.OPTConfig(\n",
-    "        num_hidden_layers=n_layers,\n",
-    "        hidden_size=d_model,\n",
-    "        ffn_dim=d_model*4,\n",
-    "        num_attention_heads=n_heads,\n",
-    "        **kwargs\n",
-    "    )\n",
-    "optcfg = {\n",
-    "    # https://arxiv.org/pdf/2205.01068.pdf   Table 2.1\n",
-    "    \"125m\": _gen_opt_cfg(12,   768, 12),\n",
-    "    \"350m\": _gen_opt_cfg(24,  1024, 16),\n",
-    "    \"760m\": _gen_opt_cfg(24,  1536, 16),\n",
-    "    \"1.3b\": _gen_opt_cfg(24,  2048, 32),\n",
-    "    \"2.7b\": _gen_opt_cfg(32,  2560, 32),\n",
-    "    \"6.7b\": _gen_opt_cfg(32,  4096, 32),\n",
-    "     \"13b\": _gen_opt_cfg(40,  5120, 40),\n",
-    "     \"13b_1layer\": _gen_opt_cfg(1,  5120, 40),\n",
-    "     \"30b\": _gen_opt_cfg(48,  7168, 56),\n",
-    "     \"66b\": _gen_opt_cfg(64,  9216, 72),\n",
-    "    \"175b\": _gen_opt_cfg(96, 12288, 96),\n",
-    "    \"175b_1layer\": _gen_opt_cfg(1, 12288, 96),\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5b9ebbec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def greedy_sample_one(model, input_ids, attention_mask=None, past_key_values=None):\n",
-    "    bs, tgt_len = input_ids.shape\n",
-    "    if past_key_values is not None:\n",
-    "        _bs, _num_heads, src_len, _head_dims = past_key_values[0][0].shape\n",
-    "        assert bs == _bs\n",
-    "    else:\n",
-    "        src_len = 0\n",
-    "    if attention_mask is None:\n",
-    "        attention_mask = torch.ones((bs, src_len + tgt_len), device=model.device)\n",
-    "    ret = model(\n",
-    "        input_ids=input_ids,\n",
-    "        attention_mask=attention_mask,\n",
-    "        past_key_values=past_key_values,\n",
-    "        use_cache=True, output_hidden_states=False, return_dict=True,\n",
-    "    )\n",
-    "    return ret\n",
-    "\n",
-    "def time_greedy_generate(model, input_ids, new_tokens):\n",
-    "    ts = []\n",
-    "    output = input_ids\n",
-    "    past_key_values = None\n",
-    "    cache = torch.empty(int(256e6 // 4), dtype=torch.int, device=model.device)\n",
-    "    attention_mask = torch.ones(input_ids.shape, device=model.device)    \n",
-    "    for _ in range(new_tokens):\n",
-    "        cache.zero_()\n",
-    "        torch.cuda.synchronize()\n",
-    "        st = time.perf_counter_ns()\n",
-    "        \n",
-    "        ret = greedy_sample_one(model, input_ids, attention_mask, past_key_values)\n",
-    "        input_ids = torch.argmax(ret.logits[:, -1, :], axis=-1)[:, None]\n",
-    "        output = torch.cat([output, input_ids], axis=1)\n",
-    "        past_key_values = ret.past_key_values\n",
-    "        attention_mask = torch.cat([attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1)\n",
-    "        \n",
-    "        torch.cuda.synchronize()\n",
-    "        ed = time.perf_counter_ns()\n",
-    "        ts.append((ed-st)/1e9)\n",
-    "    return np.array(ts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fc92f940",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "opt_config = optcfg[\"6.7b\"]\n",
-    "\n",
-    "torch.set_default_dtype(torch.bfloat16)\n",
-    "with transformers.modeling_utils.no_init_weights():\n",
-    "    model = transformers.models.opt.OPTForCausalLM(opt_config).to(\"cuda\")\n",
-    "torch.set_default_dtype(torch.float32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c19fa396",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "db = {}\n",
-    "input_tokens = 200\n",
-    "new_tokens = 500\n",
-    "for bs in tqdm(list(itertools.chain(range(1, 8), range(8, 16, 2), [16]))):\n",
-    "    x = torch.randint(1000, 10000, (bs, input_tokens), device=model.device)\n",
-    "    stack = []\n",
-    "    for _ in range(10):\n",
-    "        l = time_greedy_generate(model, x, new_tokens=new_tokens)\n",
-    "        stack.append(l)\n",
-    "    db[bs] = np.median(np.stack(stack), axis=0)\n",
-    "    del x\n",
-    "    torch.cuda.empty_cache()\n",
-    "del model\n",
-    "torch.cuda.empty_cache()\n",
-    "\n",
-    "with gzip.open(\"data/20230516-e2e-text-generation-batch.pkl.gz\", \"wb\") as f:\n",
-    "    pickle.dump(db, f)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

research/paper_plot/acc_fig.py

@@ -1,165 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-
-# Set plot parameters
-plt.rcParams["font.family"] = "Helvetica"
-plt.rcParams["ytick.direction"] = "in"
-plt.rcParams["hatch.linewidth"] = 1.5
-plt.rcParams["font.weight"] = "bold"
-plt.rcParams["axes.labelweight"] = "bold"
-plt.rcParams["text.usetex"] = True
-
-# Path settings
-FIGURE_PATH = "./paper_plot/figures"
-
-# Load accuracy data
-acc_data = pd.read_csv("./paper_plot/data/acc.csv")
-
-# Create figure with 4 subplots (one for each dataset)
-fig, axs = plt.subplots(1, 4)
-fig.set_size_inches(9, 2.5)
-
-# Reduce the spacing between subplots
-# plt.subplots_adjust(wspace=0.2)  # Reduced from 0.3 to 0.1
-
-# Define datasets and their columns
-datasets = ["NQ", "TriviaQA", "GPQA", "HotpotQA"]
-metrics = ["Exact Match", "F1"]
-
-# Define bar settings - make bars thicker
-# total_width, n = 0.9, 3  # increased total width and n for three models
-# width = total_width / n
-# The 'width' variable below now defines the distance between the centers of adjacent bars within a group.
-# It's also used as the base for calculating the actual plotted bar width.
-# Original 2 bars had centers 1.0 apart. For 3 bars, we need a smaller distance.
-# A value of 0.64 for distance between centers, with a scaling factor of 0.8 for bar width,
-# results in an actual bar width of ~0.51, and a group span of ~1.79, similar to original's ~1.76.
-n = 3 # Number of models
-width = 0.64  # Distance between centers of adjacent bars in a group
-bar_width_plotting_factor = 0.8 # Bar takes 80% of the space defined by 'width'
-
-# Colors and hatches
-edgecolors = ["dimgrey", "#63B8B6", "tomato"]  # Added color for PQ 5
-hatches = ["/////", "xxxxx", "\\\\\\\\\\"]  # Added hatch for PQ 5
-labels = ["BM25", "PQ Compressed", "Ours"] # Added PQ 5
-
-# Create plots for each dataset
-for i, dataset in enumerate(datasets):
-    ax = axs[i]
-    
-    # Get data for this dataset and convert to percentages
-    em_values = [
-        acc_data.loc[0, f"{dataset} Exact Match"] * 100, 
-        acc_data.loc[1, f"{dataset} Exact Match"] * 100,
-        acc_data.loc[2, f"{dataset} Exact Match"] * 100  # Added PQ 5 EM data
-    ]
-    f1_values = [
-        acc_data.loc[0, f"{dataset} F1"] * 100, 
-        acc_data.loc[1, f"{dataset} F1"] * 100,
-        acc_data.loc[2, f"{dataset} F1"] * 100  # Added PQ 5 F1 data
-    ]
-    
-    # Define x positions for bars
-    # For EM: center - width, center, center + width
-    # For F1: center - width, center, center + width
-    group_centers = [1.0, 3.0] # Centers for EM and F1 groups
-    bar_offsets = [-width, 0, width]
-
-    # Plot all bars on the same axis
-    for metric_idx, metric_group_center in enumerate(group_centers):
-        values_to_plot = em_values if metric_idx == 0 else f1_values
-        for j, model_label in enumerate(labels):
-            x_pos = metric_group_center + bar_offsets[j]
-            bar_value = values_to_plot[j]
-            
-            ax.bar(
-                x_pos,
-                bar_value,
-                width=width * bar_width_plotting_factor, # Use the new factor for bar width
-                color="white",
-                edgecolor=edgecolors[j],
-                hatch=hatches[j],
-                linewidth=1.5,
-                label=model_label if i == 0 and metric_idx == 0 else None # Label only once
-            )
-            
-            # Add value on top of bar
-            ax.text(x_pos, bar_value + (0.1 if dataset == "GPQA" else 0.1), 
-                    f"{bar_value:.1f}", ha='center', va='bottom', 
-                    fontsize=9, fontweight='bold') # Reduced fontsize for text on bars
-    
-    # Set x-ticks and labels
-    ax.set_xticks(group_centers)  # Position ticks at the center of each group
-    xticklabels = ax.set_xticklabels(metrics, fontsize=12)
-
-    # Now, shift these labels slightly to the right
-    # Adjust this value to control the amount of shift (in data coordinates)
-    # Given your group_centers are 1.0 and 3.0, a small value like 0.05 to 0.15 might be appropriate.
-    # horizontal_shift = 0.7  # Try adjusting this value
-
-    # for label in xticklabels:
-    #     # Get the current x position (which is the tick location)
-    #     current_x_pos = label.get_position()[0]
-    #     # Set the new x position by adding the shift
-    #     label.set_position((current_x_pos + horizontal_shift, label.get_position()[1]))
-    #     # Ensure the label remains horizontally centered on this new x position
-    #     # (set_xticklabels defaults to 'center', so this re-affirms it if needed)
-    #     label.set_horizontalalignment('center')
-
-    # Set title
-    ax.set_title(dataset, fontsize=14)
-    
-    # Set y-label for all subplots
-    if i == 0:
-        ax.set_ylabel("Accuracy (\%)", fontsize=12, fontweight="bold")
-    else:
-        # Hide y-tick labels for non-first subplots to save space
-        ax.tick_params(axis='y', labelsize=10)
-    
-    # Set y-limits based on data range
-    all_values = em_values + f1_values
-    max_val = max(all_values)
-    min_val = min(all_values)
-    
-    # Special handling for GPQA which has very low values
-    if dataset == "GPQA":
-        ax.set_ylim(0, 10.0)  # Set a fixed range for GPQA
-    else:
-        # Reduce the extra space above the bars
-        ax.set_ylim(min_val * 0.9, max_val * 1.1) # Adjusted upper limit for text
-    
-    # Format y-ticks as percentages
-    ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: ' {:.0f}'.format(y)))
-    
-    # Set x-limits to properly space the bars with less blank space
-    # ax.set_xlim(group_centers[0] - total_width, group_centers[1] + total_width)
-    # Set xlim to be similar to original (0,4) for group_centers (1,3) => margin of 1.0
-    ax.set_xlim(group_centers[0] - 1.0, group_centers[1] + 1.0)
-    
-    # Add a box around the subplot
-    # for spine in ax.spines.values():
-    #     spine.set_visible(True)
-    #     spine.set_linewidth(1.0)
-    
-    # Add legend to first subplot
-    if i == 0:
-        ax.legend(
-            bbox_to_anchor=(2.21, 1.35), # Adjusted anchor if needed
-            ncol=3, # Changed to 3 columns for three labels
-            loc="upper center",
-            labelspacing=0.1,
-            edgecolor="black",
-            facecolor="white",
-            framealpha=1,
-            shadow=False,
-            fancybox=False,
-            handlelength=1.0,
-            handletextpad=0.6,
-            columnspacing=0.8,
-            prop={"weight": "bold", "size": 12},
-        )
-
-# Save figure with tight layout but no additional padding
-plt.savefig(FIGURE_PATH + "/accuracy_comparison.pdf", bbox_inches='tight', pad_inches=0.05)
-plt.show()

research/paper_plot/analyze_visits.py

@@ -1,309 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Motto: Were It to Benefit My Country, I Would Lay Down My Life!
-# \file: /hnsw_degree_visit_plot_binned_academic.py
-# \brief: Generates a binned bar plot of HNSW node average per-query visit probability
-#         per degree bin, styled for academic publications, with caching.
-# Author: raphael hao (Original script by user, styling and caching adapted by Gemini)
-
-# %%
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import re
-from collections import Counter
-import os # For robust filepath manipulation
-import math # For calculating scaling factor
-import pickle # For caching data
-
-# %%
-# --- Matplotlib parameters for academic paper style (from reference) ---
-plt.rcParams["font.family"] = "Helvetica"
-plt.rcParams["ytick.direction"] = "in"
-plt.rcParams["hatch.linewidth"] = 1.5
-plt.rcParams["font.weight"] = "bold"
-plt.rcParams["axes.labelweight"] = "bold"
-plt.rcParams["text.usetex"] = True # Use LaTeX for text rendering (if available)
-
-# --- Define styles from reference ---
-edgecolors_ref = ["dimgrey", "#63B8B6", "tomato", "silver", "slategray"]
-
-# %%
-# --- File Paths ---
-degree_file = '/opt/dlami/nvme/scaling_out/indices/rpj_wiki/facebook/contriever-msmarco/hnsw/degree_distribution.txt'
-visit_log_file = './re.log'
-output_image_file = './paper_plot/figures/hnsw_visit_count_per_degree_corrected.pdf'
-# --- CACHE FILE PATH: Keep this consistent ---
-CACHE_FILE_PATH = './binned_plot_data_cache.pkl'
-
-# --- Configuration ---
-# Set to True to bypass cache and force recomputation.
-# Otherwise, delete CACHE_FILE_PATH manually to force recomputation.
-FORCE_RECOMPUTE = False
-NUMBER_OF_QUERIES = 1000.0 # Number of queries the visit_counts are based on
-
-# Create directory for figures if it doesn't exist
-output_dir = os.path.dirname(output_image_file)
-if output_dir and not os.path.exists(output_dir):
-    os.makedirs(output_dir)
-    print(f"Created directory: {output_dir}")
-
-# %%
-# --- Attempt to load data from cache or compute ---
-df_plot_data = None
-bin_size_for_plot = None # Will hold the bin_size associated with df_plot_data
-
-if not FORCE_RECOMPUTE and os.path.exists(CACHE_FILE_PATH):
-    try:
-        with open(CACHE_FILE_PATH, 'rb') as f:
-            cache_content = pickle.load(f)
-            df_plot_data = cache_content['data']
-            bin_size_for_plot = cache_content['bin_size']
-        # Basic validation of cached data
-        # Expecting 'average_visit_count_per_node_in_bin' (raw average over NUMBER_OF_QUERIES)
-        if not isinstance(df_plot_data, pd.DataFrame) or \
-           'degree_bin_label' not in df_plot_data.columns or \
-           'average_visit_count_per_node_in_bin' not in df_plot_data.columns or \
-           not isinstance(bin_size_for_plot, int):
-            print("Cached data is not in the expected format or missing 'average_visit_count_per_node_in_bin'. Recomputing.")
-            df_plot_data = None # Invalidate to trigger recomputation
-        else:
-            print(f"Successfully loaded binned data from cache: {CACHE_FILE_PATH}")
-
-        # --- Modify the label loaded from cache for display purpose ---
-        # This modification only happens when data is loaded from cache and meets specific conditions.
-        # Assumption: If the bin_size_for_plot in cache is 5,
-        # then the original label "0-4" actually represents nodes with degree 1-4 (because you guarantee no 0-degree nodes).
-        if df_plot_data is not None and 'degree_bin_label' in df_plot_data.columns and bin_size_for_plot == 5:
-            # Check if "0-4" label exists
-            if '0-4' in df_plot_data['degree_bin_label'].values:
-                # Use .loc to ensure the modification is on the original DataFrame
-                df_plot_data.loc[df_plot_data['degree_bin_label'] == '0-4', 'degree_bin_label'] = '1-4'
-                print("Modified degree_bin_label from '0-4' to '1-4' for display purpose.")
-    except Exception as e:
-        print(f"Error loading from cache: {e}. Recomputing.")
-        df_plot_data = None # Invalidate to trigger recomputation
-
-if df_plot_data is None:
-    print("Cache not found, invalid, or recompute forced. Computing data from scratch...")
-    # --- 1. Read Degree Distribution File ---
-    degrees_data = []
-    try:
-        with open(degree_file, 'r') as f:
-            for i, line in enumerate(f):
-                line_stripped = line.strip()
-                if line_stripped:
-                    degrees_data.append({'node_id': i, 'degree': int(line_stripped)})
-    except FileNotFoundError:
-        print(f"Error: Degree file '{degree_file}' not found. Using dummy data for degrees.")
-        degrees_data = [{'node_id': i, 'degree': (i % 20) + 1 } for i in range(200)]
-        degrees_data.extend([{'node_id': 200+i, 'degree': i} for i in range(58, 67)]) # For 60-64 bin
-        degrees_data.extend([{'node_id': 300+i, 'degree': (i % 5)+1} for i in range(10)]) # Low degrees
-        degrees_data.extend([{'node_id': 400+i, 'degree': 80 + (i%5)} for i in range(10)]) # High degrees
-
-
-    if not degrees_data:
-        print(f"Critical Error: No data loaded or generated for degrees. Exiting.")
-        exit()
-    df_degrees = pd.DataFrame(degrees_data)
-    print(f"Successfully loaded/generated {len(df_degrees)} degree entries.")
-
-    # --- 2. Read Visit Log File and Count Frequencies ---
-    visit_counts = Counter()
-    node_id_pattern = re.compile(r"Vis(i)?ted node: (\d+)")
-    try:
-        with open(visit_log_file, 'r') as f_log:
-            for line_num, line in enumerate(f_log, 1):
-                match = node_id_pattern.search(line)
-                if match:
-                    try:
-                        node_id = int(match.group(2))
-                        visit_counts[node_id] += 1 # Increment visit count for the node
-                    except ValueError:
-                        print(f"Warning: Non-integer node_id in log '{visit_log_file}' line {line_num}: {line.strip()}")
-    except FileNotFoundError:
-        print(f"Warning: Visit log file '{visit_log_file}' not found. Using dummy visit counts.")
-        if not df_degrees.empty:
-            for node_id_val in df_degrees['node_id'].sample(frac=0.9, random_state=1234): # Seed for reproducibility
-                degree_val = df_degrees[df_degrees['node_id'] == node_id_val]['degree'].iloc[0]
-                # Generate visit counts to test different probability magnitudes
-                if node_id_val % 23 == 0: # Very low probability
-                     lambda_val = 0.0005 * (100 / (max(1,degree_val) + 1)) # avg visits over 1k queries
-                elif node_id_val % 11 == 0: # Low probability
-                     lambda_val = 0.05 * (100 / (max(1,degree_val) + 1))
-                elif node_id_val % 5 == 0: # Moderate probability
-                     lambda_val = 2.5 * (100 / (max(1,degree_val) + 1))
-                else: # Higher probability (but still < 1000 visits for a single node usually)
-                     lambda_val = 50 * (100 / (max(1,degree_val) + 1))
-                visit_counts[node_id_val] = np.random.poisson(lambda_val)
-                if visit_counts[node_id_val] < 0: visit_counts[node_id_val] = 0
-
-    if not visit_counts:
-        print(f"Warning: No visit data parsed/generated. Plot may show zero visits.")
-        df_visits = pd.DataFrame(columns=['node_id', 'visit_count'])
-    else:
-        df_visits_list = [{'node_id': nid, 'visit_count': count} for nid, count in visit_counts.items()]
-        df_visits = pd.DataFrame(df_visits_list)
-    print(f"Parsed/generated {len(df_visits)} unique visited nodes, totaling {sum(visit_counts.values())} visits (simulated over {NUMBER_OF_QUERIES} queries).")
-
-    # --- 3. Merge Degree Data with Visit Data ---
-    df_merged = pd.merge(df_degrees, df_visits, on='node_id', how='left')
-    df_merged['visit_count'] = df_merged['visit_count'].fillna(0).astype(float) # visit_count is total over NUMBER_OF_QUERIES
-    print(f"Merged data contains {len(df_merged)} entries.")
-
-    # --- 5. Binning Degrees and Calculating Average Visit Count per Node in Bin (over NUMBER_OF_QUERIES) ---
-    current_bin_size = 5
-    bin_size_for_plot = current_bin_size
-
-    if not df_degrees.empty:
-        print(f"\nBinning degrees into groups of {current_bin_size} for average visit count calculation...")
-
-        df_merged_with_bins = df_merged.copy()
-        df_merged_with_bins['degree_bin_start'] = (df_merged_with_bins['degree'] // current_bin_size) * current_bin_size
-        
-        df_binned_analysis = df_merged_with_bins.groupby('degree_bin_start').agg(
-            total_visit_count_in_bin=('visit_count', 'sum'),
-            node_count_in_bin=('node_id', 'nunique')
-        ).reset_index()
-
-        # This is the average number of times a node in this bin was visited over NUMBER_OF_QUERIES queries.
-        # This value is what gets cached.
-        df_binned_analysis['average_visit_count_per_node_in_bin'] = 0.0
-        df_binned_analysis.loc[df_binned_analysis['node_count_in_bin'] > 0, 'average_visit_count_per_node_in_bin'] = \
-            df_binned_analysis['total_visit_count_in_bin'] / df_binned_analysis['node_count_in_bin']
-        
-        df_binned_analysis['degree_bin_label'] = df_binned_analysis['degree_bin_start'].astype(str) + '-' + \
-                                                 (df_binned_analysis['degree_bin_start'] + current_bin_size - 1).astype(str)
-        
-        bin_to_drop_label = '60-64'
-        original_length = len(df_binned_analysis)
-        df_plot_data_intermediate = df_binned_analysis[df_binned_analysis['degree_bin_label'] != bin_to_drop_label].copy()
-        if len(df_plot_data_intermediate) < original_length:
-            print(f"\nManually dropped the bin: '{bin_to_drop_label}'")
-        else:
-            print(f"\nNote: Bin '{bin_to_drop_label}' not found for dropping or already removed.")
-        
-        df_plot_data = df_plot_data_intermediate
-        
-        print(f"\nBinned data (average visit count per node in bin over {NUMBER_OF_QUERIES} queries) for plotting prepared:")
-        print(df_plot_data[['degree_bin_label', 'average_visit_count_per_node_in_bin']].head())
-
-        if df_plot_data is not None and not df_plot_data.empty:
-            try:
-                with open(CACHE_FILE_PATH, 'wb') as f:
-                    pickle.dump({'data': df_plot_data, 'bin_size': bin_size_for_plot}, f)
-                print(f"Saved computed binned data to cache: {CACHE_FILE_PATH}")
-            except Exception as e:
-                print(f"Error saving data to cache: {e}")
-        elif df_plot_data is None or df_plot_data.empty:
-             print("Computed data for binned plot is empty, not saving to cache.")
-    else:
-        print("Degree data (df_degrees) is empty. Cannot perform binning.")
-        df_plot_data = pd.DataFrame()
-        bin_size_for_plot = current_bin_size
-
-# %%
-# --- 6. Plotting (Binned Bar Chart - Academic Style) ---
-
-if df_plot_data is not None and not df_plot_data.empty and 'average_visit_count_per_node_in_bin' in df_plot_data.columns:
-    base_name, ext = os.path.splitext(output_image_file)
-    # --- OUTPUT PDF FILE NAME: Keep this consistent ---
-    binned_output_image_file = base_name + ext
-
-    fig, ax = plt.subplots(figsize=(6, 2.5)) # Adjusted figure size
-
-    df_plot_data_plotting = df_plot_data.copy()
-    # Calculate per-query probability: (avg visits over N queries) / N
-    df_plot_data_plotting['per_query_visit_probability'] = \
-        df_plot_data_plotting['average_visit_count_per_node_in_bin'] / NUMBER_OF_QUERIES
-    
-    max_probability = df_plot_data_plotting['per_query_visit_probability'].max()
-    
-    y_axis_values_to_plot = df_plot_data_plotting['per_query_visit_probability']
-    y_axis_label = r"Per-Query Node Visit Probability in Bin" # Base label
-
-    apply_scaling_to_label_and_values = False # Initialize flag
-    exponent_for_label_display = 0 # Initialize exponent
-
-    if pd.notna(max_probability) and max_probability > 0:
-        potential_exponent = math.floor(math.log10(max_probability))
-        
-        if potential_exponent <= -4 or potential_exponent >= 0: 
-            apply_scaling_to_label_and_values = True
-            exponent_for_label_display = potential_exponent
-            # No specific adjustment for potential_exponent >=0 here, it's handled by the general logic.
-
-        if apply_scaling_to_label_and_values:
-            y_axis_label = rf"Visit Probability ($\times 10^{{{exponent_for_label_display}}}$)"
-            y_axis_values_to_plot = df_plot_data_plotting['per_query_visit_probability'] / (10**exponent_for_label_display)
-            print(f"Plotting with Max per-query probability: {max_probability:.2e}, Exponent for label: {exponent_for_label_display}. Y-axis values scaled for plot.")
-        else:
-            print(f"Plotting with Max per-query probability: {max_probability:.2e}. Plotting direct probabilities without label scaling (exponent {potential_exponent} is within no-scale range [-3, -1]).")
-
-    elif pd.notna(max_probability) and max_probability == 0:
-        print("Max per-query probability is 0. Plotting direct probabilities (all zeros).")
-    else:
-        print(f"Max per-query probability is NaN or invalid ({max_probability}). Plotting direct probabilities without scaling if possible.")
-    
-    ax.bar(
-        df_plot_data_plotting['degree_bin_label'],
-        y_axis_values_to_plot,
-        color='white',
-        edgecolor=edgecolors_ref[0],
-        linewidth=1.5,
-        width=0.8
-    )
-    
-    ax.set_xlabel('Node Degree', fontsize=10.5, labelpad=6)
-    # MODIFIED LINE: Added labelpad to move the y-axis label to the left
-    ax.set_ylabel(y_axis_label, fontsize=10.5, labelpad=10) 
-
-    ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, pos: f"{x:.0f}%"))
-
-    num_bins = len(df_plot_data_plotting)
-    if num_bins > 12: 
-        ax.set_xticks(ax.get_xticks())
-        ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right", fontsize=9)
-    elif num_bins > 8:
-         ax.tick_params(axis='x', labelsize=9)
-    else:
-        ax.tick_params(axis='x', labelsize=10)
-    
-    ax.tick_params(axis='y', labelsize=10)
-
-    padding_factor = 0.05
-    current_max_y_on_axis = y_axis_values_to_plot.max()
-    
-    upper_y_limit = 0.1 # Default small upper limit
-    if pd.notna(current_max_y_on_axis):
-        if current_max_y_on_axis > 0:
-             # Adjust minimum visible range based on whether scaling was applied and the exponent
-            min_meaningful_limit = 0.01
-            if apply_scaling_to_label_and_values and exponent_for_label_display >= 0 : # Numbers on axis are smaller due to positive exponent scaling
-                 min_meaningful_limit = 0.1 # If original numbers were e.g. 2500 (2.5 x 10^3), scaled axis is 2.5, 0.1 is fine
-            elif not apply_scaling_to_label_and_values and pd.notna(max_probability) and max_probability >=1: # Direct large probabilities
-                 min_meaningful_limit = 1 # If max prob is 2.5 (250%), axis value 2.5, needs larger base limit
-            
-            upper_y_limit = max(min_meaningful_limit, current_max_y_on_axis * (1 + padding_factor))
-
-        else: # current_max_y_on_axis is 0
-            upper_y_limit = 0.1 
-        ax.set_ylim(0, upper_y_limit)
-    else:
-        ax.set_ylim(0, 1.0) # Default for empty or NaN data
-
-    plt.tight_layout()
-    plt.savefig(binned_output_image_file, bbox_inches="tight", dpi=300)
-    print(f"Binned bar chart saved to {binned_output_image_file}")
-    plt.show()
-    plt.close(fig)
-else:
-    if df_plot_data is None:
-        print("Data for plotting (df_plot_data) is None. Skipping plot generation.")
-    elif df_plot_data.empty:
-        print("Data for plotting (df_plot_data) is empty. Skipping plot generation.")
-    elif 'average_visit_count_per_node_in_bin' not in df_plot_data.columns: 
-        print("Essential column 'average_visit_count_per_node_in_bin' is missing in df_plot_data. Skipping plot generation.")
-
-# %%
-print("Script finished.")

research/paper_plot/b.md

@@ -1,7 +0,0 @@
-In this paper, we present LiteANN, a storage-efficient approximate nearest neighbor (ANN) search index optimized for resource-constrained personal devices. LiteANN combines a compact graph-based structure with an efficient on-the-fly recomputation strategy to enable fast and accurate retrieval wih minimal storage overhead. Our evaluation shows that LiteANN reduces index size to under 5% of the original raw data – up to 50× smaller than standard indexes – while achieving 90% top-3 recall in under 2 seconds on real-world question-answering benchmarks.
-
-
-
-
-
-

research/paper_plot/cache_degree_data.py

@@ -1,81 +0,0 @@
-import numpy as np
-import os
-
-# --- Configuration for Data Paths and Labels (Mirrors plotting script for consistency) ---
-BIG_GRAPH_PATHS = [
-    "/opt/dlami/nvme/scaling_out/indices/rpj_wiki/facebook/contriever-msmarco/hnsw/",
-    "/opt/dlami/nvme/scaling_out/embeddings/facebook/contriever-msmarco/rpj_wiki/1-shards/indices/99_4_degree_based_hnsw_IP_M32_efC256/",
-    "/opt/dlami/nvme/scaling_out/embeddings/facebook/contriever-msmarco/rpj_wiki/1-shards/indices/d9_hnsw_IP_M8_efC128/",
-    "/opt/dlami/nvme/scaling_out/embeddings/facebook/contriever-msmarco/rpj_wiki/1-shards/indices/half_edges_IP_M32_efC128/"
-]
-STATS_FILE_NAME = "degree_distribution.txt"
-BIG_GRAPH_LABELS = [  # These will be used as keys in the cached file
-    "HNSW-Base",
-    "DegreeGuide",
-    "HNSW-D9", 
-    "RandCut",
-]
-# Average degrees are static and can be directly used in the plotting script or also cached.
-# For simplicity here, we'll focus on caching the dynamic degree arrays.
-# BIG_GRAPH_AVG_DEG = [18, 9, 9, 9] 
-
-# --- Cache File Configuration ---
-DATA_CACHE_DIR = "./paper_plot/data/"
-CACHE_FILE_NAME = "big_graph_degree_data.npz" # Using .npz for multiple arrays
-
-def create_degree_data_cache():
-    """
-    Reads degree distribution data from specified text files and saves it
-    into a compressed NumPy (.npz) cache file.
-    """
-    os.makedirs(DATA_CACHE_DIR, exist_ok=True)
-    cache_file_path = os.path.join(DATA_CACHE_DIR, CACHE_FILE_NAME)
-
-    cached_data = {}
-    print(f"Starting data caching process for {len(BIG_GRAPH_PATHS)} graph types...")
-
-    for i, base_path in enumerate(BIG_GRAPH_PATHS):
-        method_label = BIG_GRAPH_LABELS[i]
-        degree_file_path = os.path.join(base_path, STATS_FILE_NAME)
-        
-        print(f"Processing: {method_label} from {degree_file_path}")
-        
-        try:
-            # Load degrees as integers
-            degrees = np.loadtxt(degree_file_path, dtype=int)
-            
-            if degrees.size == 0:
-                print(f"  [WARN] Degree file is empty: {degree_file_path}. Storing as empty array for {method_label}.")
-                # Store an empty array or handle as needed. For npz, an empty array is fine.
-                cached_data[method_label] = np.array([], dtype=int) 
-            else:
-                # Store the loaded degrees array with the method label as the key
-                cached_data[method_label] = degrees
-                print(f"  [INFO] Loaded {len(degrees)} degrees for {method_label}. Max degree: {np.max(degrees) if degrees.size > 0 else 'N/A'}")
-                
-        except FileNotFoundError:
-            print(f"  [ERROR] Degree file not found: {degree_file_path}. Skipping {method_label}.")
-            # Optionally store a placeholder or skip. For robustness, store None or an empty array.
-            # Storing None might require special handling when loading. Empty array is safer for np.load.
-            cached_data[method_label] = np.array([], dtype=int) # Store empty array if file not found
-        except Exception as e:
-            print(f"  [ERROR] An error occurred loading {degree_file_path} for {method_label}: {e}")
-            cached_data[method_label] = np.array([], dtype=int) # Store empty array on other errors
-
-    if not cached_data:
-        print("[ERROR] No data was successfully processed or loaded. Cache file will not be created.")
-        return
-
-    try:
-        # Save all collected degree arrays into a single .npz file.
-        # Using savez_compressed for potentially smaller file size.
-        np.savez_compressed(cache_file_path, **cached_data)
-        print(f"\n[SUCCESS] Degree distribution data successfully cached to: {os.path.abspath(cache_file_path)}")
-        print("Cached arrays (keys):", list(cached_data.keys()))
-    except Exception as e:
-        print(f"\n[ERROR] Failed to save data to cache file {cache_file_path}: {e}")
-
-if __name__ == "__main__":
-    print("--- Degree Distribution Data Caching Script ---")
-    create_degree_data_cache()
-    print("--- Caching script finished. ---")

research/paper_plot/data/acc.csv

@@ -1,4 +0,0 @@
-Model,NQ Exact Match,NQ F1,TriviaQA Exact Match,TriviaQA F1,GPQA Exact Match,GPQA F1,HotpotQA Exact Match,HotpotQA F1
-BM25,0.192,0.277,0.406,0.474,0.020089,0.04524,0.162,0.239
-PQ 5,0.2075,0.291,0.422,0.495,0.0201,0.0445,0.148,0.219
-Ours,0.265,0.361,0.533,0.604,0.02008,0.0452,0.182,0.2729

research/paper_plot/data/big_graph_degree_data.npz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1296720e79196bbdf38f051043c1b054667803726a24036c0b6a87cedb204ea5
-size 227482438

research/paper_plot/data/branches.csv

@@ -1,21 +0,0 @@
-2,1,512,1024,0.541,0.326,1.659509202
-2,2,512,1024,0.979,0.621,1.576489533
-2,4,512,1024,1.846,0.977,1.889457523
-2,8,512,1024,3.575,1.943,1.83993824
-2,16,512,1024,7.035,3.733,1.884543263
-2,32,512,1024,15.655,8.517,1.838088529
-2,64,512,1024,32.772,17.43,1.88020654
-4,1,512,1024,2.675,1.38,1.938405797
-4,2,512,1024,5.397,2.339,2.307396323
-4,4,512,1024,10.672,4.944,2.158576052
-4,8,512,1024,21.061,9.266,2.272933305
-4,16,512,1024,46.332,18.334,2.527108105
-4,32,512,1024,99.607,36.156,2.754923111
-4,64,512,1024,186.348,72.356,2.575432583
-8,1,512,1024,7.325,4.087,1.792268167
-8,2,512,1024,14.109,7.491,1.883460152
-8,4,512,1024,28.499,14.013,2.033754371
-8,8,512,1024,65.222,27.453,2.375769497
-8,16,512,1024,146.294,52.55,2.783901047
-8,32,512,1024,277.099,103.61,2.674442621
-8,64,512,1024,512.979,208.36,2.461984066

research/paper_plot/data/latency_ablation.csv

@@ -1,9 +0,0 @@
-Dataset,Metric,Original,original + batch,original + two_level,original + two_level + batch
-NQ,Latency,6.9,5.8,4.2,3.7
-NQ,SpeedUp,1,1.18965517,1.64285714,1.86486486
-TriviaQA,Latency,17.054,14.542,12.046,10.83
-TriviaQA,SpeedUp,1,1.17274103,1.41573967,1.57469990
-GPQA,Latency,9.164,7.639,6.798,5.77
-GPQA,SpeedUp,1,1.19963346,1.34804354,1.58821490
-HotpotQA,Latency,60.279,39.827,50.664,29.868
-HotpotQA,SpeedUp,1,1.51352098,1.18977972,2.01817999

research/paper_plot/data/main_latency.csv

@@ -1,25 +0,0 @@
-Dataset,Hardware,Recall_target,HNSW,IVF,DiskANN,IVF-Disk,IVF-Recompute,Our,BM25,LLM_Gen_Time_1B,LLM_Gen_Time_3B,LLM_Gen_Time_7B
-NQ,A10,85%,0.046,1.656,0.017,2.996,482.53,3.323,0.021,0.085,0.217,0.472
-NQ,A10,90%,0.051,2.552,0.028,3.437,769.04,4.616,0,0.085,0.217,0.472
-NQ,A10,95%,0.055,5.163,0.070,5.602,1436.26,19.494,0,0.085,0.217,0.472
-NQ,MAC,85%,0,0,0.152,2.199,1535.10,7.971,0.033,0.316,0.717,1.468
-NQ,MAC,90%,0,0,0.37,2.936,2446.60,13.843,0,0.316,0.717,1.468
-NQ,MAC,95%,0,0,1.207,4.191,4569.29,44.363,0,0.316,0.717,1.468
-TriviaQA,A10,85%,0.042,1.772,0.032,2.464,560.5,3.752,0.033,0.139,0.156,0.315
-TriviaQA,A10,90%,0.043,3.541,0.057,3.651,997.81,5.777,0,0.139,0.156,0.315
-TriviaQA,A10,95%,0.053,7.168,0.090,5.458,2005.33,20.944,0,0.139,0.156,0.315
-TriviaQA,MAC,85%,0,0,0.481,1.875,1783.14787,8.889,0.036,0.325,0.692,1.415
-TriviaQA,MAC,90%,0,0,0.984,2.639,3174.410301,17.145,0,0.325,0.692,1.415
-TriviaQA,MAC,95%,0,0,1.578,3.884,6379.712245,47.909,0,0.325,0.692,1.415
-GPQA,A10,85%,0.041,0.134,0.024,0.048,40.16,1.897,0.137,0.443,0.396,0.651
-GPQA,A10,90%,0.042,0.174,0.034,0.06,54.71,1.733,0,0.443,0.396,0.651
-GPQA,A10,95%,0.045,0.292,0.051,0.11,97.67,4.033,0,0.443,0.396,0.651
-GPQA,MAC,85%,0,0,0.144,0.087,127.7707505,4.762,0.100,0.37,0.813,1.676
-GPQA,MAC,90%,0,0,0.288,0.108,174.0647409,5.223,0,0.37,0.813,1.676
-GPQA,MAC,95%,0,0,0.497,0.132,310.7380142,9.715,0,0.37,0.813,1.676
-HotpotQA,A10,85%,0.044,2.519,0.054,4.048,724.26,10.358,0.70,0.144,0.196,0.420
-HotpotQA,A10,90%,0.049,3.867,0.109,5.045,1173.67,15.515,0,0.144,0.196,0.420
-HotpotQA,A10,95%,0.07,10.928,0.412,8.659,3079.57,61.757,0,0.144,0.196,0.420
-HotpotQA,MAC,85%,0,0,0.974,2.844,2304.125187,23.636,0.052,0.144,0.196,0.420
-HotpotQA,MAC,90%,0,0,1.913,3.542,3415.736201,44.803,0,0.144,0.196,0.420
-HotpotQA,MAC,95%,0,0,5.783,6.764,9797.244043,140.62,0,0.144,0.196,0.420

research/paper_plot/data/main_latency_small.csv

@@ -1,25 +0,0 @@
-Dataset,Hardware,Recall_target,HNSW,IVF,DiskANN,IVF-Disk,IVF-Recompute,Our,
-NQ,A10,85%,0.046,1.656,0.017,2.996,482.53,4.243,
-NQ,A10,90%,0.051,2.552,0.028,3.437,769.04,8.136,
-NQ,A10,95%,0.055,5.163,0.070,5.602,1436.26,27.275,
-NQ,MAC,85%,0,0,0.152,2.199,1535.10,10.672,
-NQ,MAC,90%,0,0,0.37,2.936,2446.60,19.941,
-NQ,MAC,95%,0,0,1.207,4.191,4569.29,61.383,
-TriviaQA,A10,85%,0.042,1.772,0.032,2.464,560.5,5.612,
-TriviaQA,A10,90%,0.043,3.541,0.057,3.651,997.81,10.737,
-TriviaQA,A10,95%,0.053,7.168,0.090,5.458,2005.33,36.387,
-TriviaQA,MAC,85%,0,0,0.481,1.875,1783.14787,12.825,
-TriviaQA,MAC,90%,0,0,0.984,2.639,3174.410301,24.977,
-TriviaQA,MAC,95%,0,0,1.578,3.884,6379.712245,85.734,
-GPQA,A10,85%,0.041,0.134,0.024,0.048,40.16,2.269,
-GPQA,A10,90%,0.042,0.174,0.034,0.06,54.71,3.200,
-GPQA,A10,95%,0.045,0.292,0.051,0.11,97.67,7.445,
-GPQA,MAC,85%,0,0,0.144,0.087,127.7707505,6.123,
-GPQA,MAC,90%,0,0,0.288,0.108,174.0647409,8.507,
-GPQA,MAC,95%,0,0,0.497,0.132,310.7380142,19.577,
-HotpotQA,A10,85%,0.044,2.519,0.054,4.048,724.26,14.713,
-HotpotQA,A10,90%,0.049,3.867,0.109,5.045,1173.67,33.561,
-HotpotQA,A10,95%,0.07,10.928,0.412,8.659,3079.57,68.626,
-HotpotQA,MAC,85%,0,0,0.974,2.844,2304.125187,34.783,
-HotpotQA,MAC,90%,0,0,1.913,3.542,3415.736201,53.004,
-HotpotQA,MAC,95%,0,0,5.783,6.764,9797.244043,95.413,

research/paper_plot/data/ram_storage.csv

@@ -1,3 +0,0 @@
-Hardware,HNSW,IVF,DiskANN,IVF-Disk,IVF-Recompute,Our,BM25
-RAM,190,171,10,0,0,0,0
-Storage,185.4,171,240,171,0.5,5,59

research/paper_plot/data/swithc_e2e.csv

@@ -1,12 +0,0 @@
-Torch,8,55.592
-Torch,16,75.439
-Torch,32,110.025
-Torch,64,186.496
-Tutel,8,56.718
-Tutel,16,82.121
-Tutel,32,125.070
-Tutel,64,216.191
-BRT,8,56.725
-BRT,16,79.291
-BRT,32,93.180
-BRT,64,118.923

research/paper_plot/data/vary_cache.csv

@@ -1,6 +0,0 @@
-Disk cache size,0,2.5%(180G*2.5%),5%,8%,10%
-Latency,,,,, 
-NQ,4.616,4.133,3.826,3.511,3.323
-TriviaQA,5.777,4.979,4.553,4.141,3.916
-GPQA,1.733,1.593,1.468,1.336,1.259
-Hotpot,15.515,13.479,12.383,11.216,10.606 

research/paper_plot/disk_cache.py

@@ -1,151 +0,0 @@
-import matplotlib
-from matplotlib.axes import Axes
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
-from matplotlib.lines import Line2D
-
-# plt.rcParams["font.family"] = "Helvetica"
-plt.rcParams["ytick.direction"] = "in"
-plt.rcParams["hatch.linewidth"] = 1
-plt.rcParams["font.weight"] = "bold"
-plt.rcParams["axes.labelweight"] = "bold"
-plt.rcParams["text.usetex"] = True
-plt.rcParams["font.family"] = "sans-serif"  # Use generic sans-serif family
-plt.rcParams['text.latex.preamble'] = r"""
-\usepackage{helvet} % Use Helvetica font for text
-\usepackage{sfmath} % Use sans-serif font for math
-\renewcommand{\familydefault}{\sfdefault} % Set sans-serif as default text font
-\usepackage[T1]{fontenc} % Recommended for font encoding
-"""
-# plt.rcParams['mathtext.fontset'] = 'dejavusans'
-SAVE_PTH = "./paper_plot/figures"
-font_size = 16
-
-# New data in dictionary format
-datasets = ["NQ", "TriviaQA", "GPQA", "Hotpot"]
-
-cache_ratios = ["4.2G\n (0\%)", "8.7G\n (2.5\%)", "13.2G\n (5\%)", "18.6G\n (8\%)", "22.2G\n (10\%)"]
-latency_data = {
-    "NQ": [4.616, 4.133, 3.826, 3.511, 3.323],
-    "TriviaQA": [5.777, 4.979, 4.553, 4.141, 3.916],
-    "GPQA": [1.733, 1.593, 1.468, 1.336, 1.259],
-    "Hotpot": [15.515, 13.479, 12.383, 11.216, 10.606],
-}
-cache_hit_counts = {
-    "NQ": [0, 14.81, 23.36, 31.99, 36.73],
-    "TriviaQA": [0, 18.55, 27.99, 37.06, 41.86],
-    "GPQA": [0, 10.99, 20.31, 29.71, 35.01],
-    "Hotpot": [0, 17.47, 26.91, 36.2, 41.06]
-}
-
-# Create the figure with 4 subplots in a 2x2 grid
-fig, axes_grid = plt.subplots(2, 2, figsize=(7,6))
-axes = axes_grid.flatten()  # Flatten the 2x2 grid to a 1D array
-
-# Bar style settings
-width = 0.7
-x = np.arange(len(cache_ratios))
-
-# Define hatch patterns for different cache ratios
-hatch_patterns = ['//', '//', '//', '//', '//']
-
-# Find max cache hit value across all datasets for unified y-axis
-all_hit_counts = []
-for dataset in datasets:
-    all_hit_counts.extend(cache_hit_counts[dataset])
-max_unified_hit = max(all_hit_counts) * 1.13
-
-for i, dataset in enumerate(datasets):
-    latencies = latency_data[dataset]
-    hit_counts = cache_hit_counts[dataset]
-
-    for j, val in enumerate(latencies):
-        container = axes[i].bar(
-            x[j],
-            val,
-            width=width,
-            color="white",
-            edgecolor="black",
-            linewidth=1.0,
-            zorder=10,
-        )
-        axes[i].bar_label(
-            container,
-            [f"{val:.2f}"],
-            fontsize=10,
-            zorder=200,
-            fontweight="bold",
-        )
-
-    axes[i].set_title(dataset, fontsize=font_size)
-    axes[i].set_xticks(x)
-    axes[i].set_xticklabels(cache_ratios, fontsize=12, rotation=0, ha='center', fontweight="bold")
-
-    max_val_ratios = [1.35, 1.65, 1.45, 1.75]
-    max_val = max(latencies) * max_val_ratios[i]
-    axes[i].set_ylim(0, max_val)
-    axes[i].tick_params(axis='y', labelsize=12)
-
-    if i % 2 == 0:
-        axes[i].set_ylabel("Latency (s)", fontsize=font_size)
-        axes[i].yaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%.1f'))
-
-    ax2: Axes = axes[i].twinx()
-    ax2.plot(x, hit_counts,
-        linestyle='--',
-        marker='o',
-        markersize=6,
-        linewidth=1.5,
-        color='k',
-        markerfacecolor='none',
-        zorder=20)
-
-    ax2.set_ylim(0, max_unified_hit)
-    ax2.tick_params(axis='y', labelsize=12)
-    if i % 2 == 1:
-        ax2.set_ylabel(r"Cache Hit (\%)", fontsize=font_size)
-
-    for j, val in enumerate(hit_counts):
-        if val > 0:
-            ax2.annotate(f"{val:.1f}%",
-                         (x[j], val),
-                         textcoords="offset points",
-                         xytext=(0, 5),
-                         ha='center',
-                         va='bottom',
-                         fontsize=10,
-                         fontweight='bold')
-
-# Create legend for both plots
-bar_patch = mpatches.Patch(facecolor='white', edgecolor='black', label='Latency')
-line_patch = Line2D([0], [0], color='black', linestyle='--', label='Cache Hit Rate')
-
-# --- MODIFICATION FOR LEGEND AT THE TOP ---
-fig.legend(handles=[bar_patch, line_patch],
-           loc='upper center',        # Position the legend at the upper center
-           bbox_to_anchor=(0.5, 0.995), # Anchor point (0.5 means horizontal center of figure,
-                                      # 0.97 means 97% from the bottom, so near the top)
-           ncol=3,
-           fontsize=font_size-2)
-# --- END OF MODIFICATION ---
-
-# Set common x-axis label - you might want to add this back if needed
-# fig.text(0.5, 0.02, "Disk Cache Size", ha='center', fontsize=font_size, fontweight='bold') # Adjusted y for potential bottom label
-
-# --- MODIFICATION FOR TIGHT LAYOUT ---
-# Adjust rect to make space for the legend at the top.
-# (left, bottom, right, top_for_subplots)
-# We want subplots to occupy space from y=0 up to y=0.93 (or similar)
-# leaving the top portion (0.93 to 1.0) for the legend.
-plt.tight_layout(rect=(0, 0, 1, 0.93)) # Ensure subplots are below the legend
-# --- END OF MODIFICATION ---
-
-# Create directory if it doesn't exist (optional, good practice)
-import os
-if not os.path.exists(SAVE_PTH):
-    os.makedirs(SAVE_PTH)
-
-plt.savefig(f"{SAVE_PTH}/disk_cache_latency.pdf", dpi=300) # Changed filename slightly for testing
-print(f"Save to {SAVE_PTH}/disk_cache_latency.pdf")
-# plt.show() # Optional: to display the plot