fix ruff errors and formatting

2025-07-27 02:22:54 -07:00
parent 383c6d8d7e
commit af1790395a
35 changed files with 166 additions and 107 deletions
--- a/.github/workflows/build-and-publish.yml
+++ b/.github/workflows/build-and-publish.yml
@@ -8,4 +8,4 @@ on:

 jobs:
  build:
-    uses: ./.github/workflows/build-reusable.yml 
+    uses: ./.github/workflows/build-reusable.yml
--- a/.github/workflows/build-reusable.yml
+++ b/.github/workflows/build-reusable.yml
@@ -17,23 +17,23 @@ jobs:
      - uses: actions/checkout@v4
        with:
          ref: ${{ inputs.ref }}
-      
+
      - name: Setup Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
-      
+
      - name: Install uv
        uses: astral-sh/setup-uv@v4
-      
+
      - name: Install ruff
        run: |
          uv tool install ruff
-      
+
      - name: Run ruff check
        run: |
          ruff check .
-      
+
      - name: Run ruff format check
        run: |
          ruff format --check .
@@ -65,40 +65,40 @@ jobs:
          - os: macos-latest
            python: '3.13'
    runs-on: ${{ matrix.os }}
-    
+
    steps:
      - uses: actions/checkout@v4
        with:
          ref: ${{ inputs.ref }}
          submodules: recursive
-      
+
      - name: Setup Python
        uses: actions/setup-python@v5
        with:
          python-version: ${{ matrix.python }}
-      
+
      - name: Install uv
        uses: astral-sh/setup-uv@v4
-      
+
      - name: Install system dependencies (Ubuntu)
        if: runner.os == 'Linux'
        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" >> $GITHUB_ENV
-      
+
      - name: Install system dependencies (macOS)
        if: runner.os == 'macOS'
        run: |
          brew install llvm libomp boost protobuf zeromq
-      
+
      - name: Install build dependencies
        run: |
          uv pip install --system scikit-build-core numpy swig Cython pybind11
@@ -107,7 +107,7 @@ jobs:
          else
            uv pip install --system delocate
          fi
-      
+
      - name: Build packages
        run: |
          # Build core (platform independent)
@@ -116,7 +116,7 @@ jobs:
            uv build
            cd ../..
          fi
-          
+
          # Build HNSW backend
          cd packages/leann-backend-hnsw
          if [ "${{ matrix.os }}" == "macos-latest" ]; then
@@ -125,7 +125,7 @@ jobs:
            uv build --wheel --python python
          fi
          cd ../..
-          
+
          # Build DiskANN backend
          cd packages/leann-backend-diskann
          if [ "${{ matrix.os }}" == "macos-latest" ]; then
@@ -134,14 +134,14 @@ jobs:
            uv build --wheel --python python
          fi
          cd ../..
-          
+
          # Build meta package (platform independent)
          if [[ "${{ matrix.os }}" == ubuntu-* ]]; then
            cd packages/leann
            uv build
            cd ../..
          fi
-      
+
      - name: Repair wheels (Linux)
        if: runner.os == 'Linux'
        run: |
@@ -153,7 +153,7 @@ jobs:
            mv dist_repaired dist
          fi
          cd ../..
-          
+
          # Repair DiskANN wheel
          cd packages/leann-backend-diskann
          if [ -d dist ]; then
@@ -162,7 +162,7 @@ jobs:
            mv dist_repaired dist
          fi
          cd ../..
-      
+
      - name: Repair wheels (macOS)
        if: runner.os == 'macOS'
        run: |
@@ -174,7 +174,7 @@ jobs:
            mv dist_repaired dist
          fi
          cd ../..
-          
+
          # Repair DiskANN wheel
          cd packages/leann-backend-diskann
          if [ -d dist ]; then
@@ -183,14 +183,14 @@ jobs:
            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 }}
-          path: packages/*/dist/ 
+          path: packages/*/dist/
--- a/.github/workflows/release-manual.yml
+++ b/.github/workflows/release-manual.yml
@@ -16,10 +16,10 @@ jobs:
      contents: write
    outputs:
      commit-sha: ${{ steps.push.outputs.commit-sha }}
-    
+
    steps:
      - uses: actions/checkout@v4
-      
+
      - name: Validate version
        run: |
          # Remove 'v' prefix if present for validation
@@ -30,7 +30,7 @@ jobs:
            exit 1
          fi
          echo "✅ Version format valid: ${{ inputs.version }}"
-      
+
      - name: Update versions and push
        id: push
        run: |
@@ -38,7 +38,7 @@ jobs:
          CURRENT_VERSION=$(grep "^version" packages/leann-core/pyproject.toml | cut -d'"' -f2)
          echo "Current version: $CURRENT_VERSION"
          echo "Target version: ${{ inputs.version }}"
-          
+
          if [ "$CURRENT_VERSION" = "${{ inputs.version }}" ]; then
            echo "⚠️  Version is already ${{ inputs.version }}, skipping update"
            COMMIT_SHA=$(git rev-parse HEAD)
@@ -52,7 +52,7 @@ jobs:
            COMMIT_SHA=$(git rev-parse HEAD)
            echo "✅ Pushed version update: $COMMIT_SHA"
          fi
-          
+
          echo "commit-sha=$COMMIT_SHA" >> $GITHUB_OUTPUT

  build-packages:
@@ -60,7 +60,7 @@ jobs:
    needs: update-version
    uses: ./.github/workflows/build-reusable.yml
    with:
-      ref: 'main' 
+      ref: 'main'

  publish:
    name: Publish and Release
@@ -69,26 +69,26 @@ jobs:
    runs-on: ubuntu-latest
    permissions:
      contents: write
-    
+
    steps:
      - uses: actions/checkout@v4
        with:
-          ref: 'main' 
-      
+          ref: 'main'
+
      - 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__
@@ -98,12 +98,12 @@ jobs:
            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: |
          # Check if tag already exists
@@ -114,7 +114,7 @@ jobs:
            git push origin "v${{ inputs.version }}"
            echo "✅ Created and pushed tag v${{ inputs.version }}"
          fi
-          
+
          # Check if release already exists
          if gh release view "v${{ inputs.version }}" >/dev/null 2>&1; then
            echo "⚠️  Release v${{ inputs.version }} already exists, skipping release creation"
@@ -126,4 +126,4 @@ jobs:
            echo "✅ Created GitHub release v${{ inputs.version }}"
          fi
        env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
--- a/.gitignore
+++ b/.gitignore
@@ -9,7 +9,7 @@ demo/indices/
 outputs/
 *.pkl
 *.pdf
-*.idx 
+*.idx
 *.map
 .history/
 lm_eval.egg-info/
@@ -85,4 +85,4 @@ packages/leann-backend-diskann/third_party/DiskANN/_deps/
 *.meta.json
 *.passages.json

-batchtest.py
+batchtest.py
--- a/docs/RELEASE.md
+++ b/docs/RELEASE.md
@@ -19,4 +19,4 @@ That's it! The workflow will automatically:
 - ✅ Publish to PyPI
 - ✅ Create GitHub tag and release

-Check progress: https://github.com/yichuan-w/LEANN/actions 
+Check progress: https://github.com/yichuan-w/LEANN/actions
--- a/docs/contributing.md
+++ b/docs/contributing.md
@@ -8,4 +8,4 @@ We welcome contributions! Leann is built by the community, for the community.
 - 💡 **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 
+- 🧪 **Benchmarks**: Share your performance results
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -7,4 +7,4 @@ You can speed up the process by using a lightweight embedding model. Add this to
 ```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) 
+**Model sizes:** `all-MiniLM-L6-v2` (30M parameters), `facebook/contriever` (~100M parameters), `Qwen3-0.6B` (600M parameters)
--- a/docs/features.md
+++ b/docs/features.md
@@ -19,4 +19,4 @@

 - **Simple Python API** - Get started in minutes
 - **Extensible backend system** - Easy to add new algorithms
- **Comprehensive examples** - From basic usage to production deployment 
+- **Comprehensive examples** - From basic usage to production deployment
--- a/docs/roadmap.md
+++ b/docs/roadmap.md
@@ -18,4 +18,4 @@

 - [ ] Integration with LangChain/LlamaIndex
 - [ ] Visual similarity search
- [ ] Query rewrtiting, rerank and expansion 
+- [ ] Query rewrtiting, rerank and expansion
--- a/examples/data/PrideandPrejudice.txt
+++ b/examples/data/PrideandPrejudice.txt
@@ -1,5 +1,5 @@
 The Project Gutenberg eBook of Pride and Prejudice
-    
+
 This ebook is for the use of anyone anywhere in the United States and
 most other parts of the world at no cost and with almost no restrictions
 whatsoever. You may copy it, give it away or re-use it under the terms
@@ -14557,7 +14557,7 @@ her into Derbyshire, had been the means of uniting them.
 *** END OF THE PROJECT GUTENBERG EBOOK PRIDE AND PREJUDICE ***


-    
+

 Updated editions will replace the previous one—the old editions will
 be renamed.
@@ -14662,7 +14662,7 @@ performed, viewed, copied or distributed:
    at www.gutenberg.org. If you
    are not located in the United States, you will have to check the laws
    of the country where you are located before using this eBook.
-  
+
 1.E.2. If an individual Project Gutenberg™ electronic work is
 derived from texts not protected by U.S. copyright law (does not
 contain a notice indicating that it is posted with permission of the
@@ -14724,7 +14724,7 @@ provided that:
        Gutenberg Literary Archive Foundation at the address specified in
        Section 4, “Information about donations to the Project Gutenberg
        Literary Archive Foundation.”
-    
+
    • You provide a full refund of any money paid by a user who notifies
        you in writing (or by e-mail) within 30 days of receipt that s/he
        does not agree to the terms of the full Project Gutenberg™
@@ -14732,15 +14732,15 @@ provided that:
        copies of the works possessed in a physical medium and discontinue
        all use of and all access to other copies of Project Gutenberg™
        works.
-    
+
    • You provide, in accordance with paragraph 1.F.3, a full refund of
        any money paid for a work or a replacement copy, if a defect in the
        electronic work is discovered and reported to you within 90 days of
        receipt of the work.
-    
+
    • You comply with all other terms of this agreement for free
        distribution of Project Gutenberg™ works.
-    
+

 1.E.9. If you wish to charge a fee or distribute a Project
 Gutenberg™ electronic work or group of works on different terms than
@@ -14903,5 +14903,3 @@ This website includes information about Project Gutenberg™,
 including how to make donations to the Project Gutenberg Literary
 Archive Foundation, how to help produce our new eBooks, and how to
 subscribe to our email newsletter to hear about new eBooks.
-
-
--- a/examples/document_search.py
+++ b/examples/document_search.py
@@ -27,7 +27,10 @@ def load_sample_documents():
            "title": "Intro to Python",
            "content": "Python is a high-level, interpreted language known for simplicity.",
        },
-        {"title": "ML Basics", "content": "Machine learning builds systems that learn from data."},
+        {
+            "title": "ML Basics",
+            "content": "Machine learning builds systems that learn from data.",
+        },
        {
            "title": "Data Structures",
            "content": "Data structures like arrays, lists, and graphs organize data.",
--- a/examples/google_history_reader_leann.py
+++ b/examples/google_history_reader_leann.py
@@ -21,7 +21,9 @@ DEFAULT_CHROME_PROFILE = os.path.expanduser("~/Library/Application Support/Googl


 def create_leann_index_from_multiple_chrome_profiles(
-    profile_dirs: list[Path], index_path: str = "chrome_history_index.leann", max_count: int = -1
+    profile_dirs: list[Path],
+    index_path: str = "chrome_history_index.leann",
+    max_count: int = -1,
 ):
    """
    Create LEANN index from multiple Chrome profile data sources.
--- a/examples/history_data/wechat_history.py
+++ b/examples/history_data/wechat_history.py
@@ -474,7 +474,8 @@ Messages ({len(messages)} messages, {message_group["total_length"]} chars):
                                message_group, contact_name
                            )
                            doc = Document(
-                                text=doc_content, metadata={"contact_name": contact_name}
+                                text=doc_content,
+                                metadata={"contact_name": contact_name},
                            )
                            docs.append(doc)
                            count += 1
--- a/examples/mail_reader_leann.py
+++ b/examples/mail_reader_leann.py
@@ -315,7 +315,11 @@ async def main():

    # Create or load the LEANN index from all sources
    index_path = create_leann_index_from_multiple_sources(
-        messages_dirs, INDEX_PATH, args.max_emails, args.include_html, args.embedding_model
+        messages_dirs,
+        INDEX_PATH,
+        args.max_emails,
+        args.include_html,
+        args.embedding_model,
    )

    if index_path:
--- a/examples/mail_reader_llamaindex.py
+++ b/examples/mail_reader_llamaindex.py
@@ -92,7 +92,10 @@ def main():
        help="Directory to store the index (default: mail_index_embedded)",
    )
    parser.add_argument(
-        "--max-emails", type=int, default=10000, help="Maximum number of emails to process"
+        "--max-emails",
+        type=int,
+        default=10000,
+        help="Maximum number of emails to process",
    )
    parser.add_argument(
        "--include-html",
@@ -112,7 +115,10 @@ def main():
    else:
        print("Creating new index...")
        index = create_and_save_index(
-            mail_path, save_dir, max_count=args.max_emails, include_html=args.include_html
+            mail_path,
+            save_dir,
+            max_count=args.max_emails,
+            include_html=args.include_html,
        )
    if index:
        queries = [
--- a/examples/multi_vector_aggregator.py
+++ b/examples/multi_vector_aggregator.py
@@ -347,7 +347,9 @@ def demo_aggregation():
        print(f"\n{'=' * 20} {method.upper()} AGGREGATION {'=' * 20}")

        aggregator = MultiVectorAggregator(
-            aggregation_method=method, spatial_clustering=True, cluster_distance_threshold=100.0
+            aggregation_method=method,
+            spatial_clustering=True,
+            cluster_distance_threshold=100.0,
        )

        aggregated = aggregator.aggregate_results(mock_results, top_k=5)
--- a/packages/init.py
+++ b/packages/init.py
@@ -1 +0,0 @@
-
--- a/packages/leann-backend-diskann/pyproject.toml
+++ b/packages/leann-backend-diskann/pyproject.toml
@@ -16,4 +16,4 @@ wheel.packages = ["leann_backend_diskann"]
 editable.mode = "redirect"
 cmake.build-type = "Release"
 build.verbose = true
-build.tool-args = ["-j8"]
+build.tool-args = ["-j8"]
--- a/packages/leann-backend-diskann/third_party/embedding.proto
+++ b/packages/leann-backend-diskann/third_party/embedding.proto
@@ -2,12 +2,12 @@ syntax = "proto3";

 package protoembedding;

-message NodeEmbeddingRequest { 
-  repeated uint32 node_ids = 1; 
+message NodeEmbeddingRequest {
+  repeated uint32 node_ids = 1;
 }

 message NodeEmbeddingResponse {
  bytes embeddings_data = 1;        // All embedded binary datas
  repeated int32 dimensions = 2;    // Shape [batch_size, embedding_dim]
  repeated uint32 missing_ids = 3;  // Missing node ids
-}
+}
--- a/packages/leann-backend-hnsw/CMakeLists.txt
+++ b/packages/leann-backend-hnsw/CMakeLists.txt
@@ -52,4 +52,4 @@ 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)

-add_subdirectory(third_party/faiss)
+add_subdirectory(third_party/faiss)
--- a/packages/leann-backend-hnsw/leann_backend_hnsw/convert_to_csr.py
+++ b/packages/leann-backend-hnsw/leann_backend_hnsw/convert_to_csr.py
@@ -72,7 +72,11 @@ def read_vector_raw(f, element_fmt_char):
 def read_numpy_vector(f, np_dtype, struct_fmt_char):
    """Reads a vector into a NumPy array."""
    count = -1  # Initialize count for robust error handling
-    print(f"  Reading vector (dtype={np_dtype}, fmt='{struct_fmt_char}')... ", end="", flush=True)
+    print(
+        f"  Reading vector (dtype={np_dtype}, fmt='{struct_fmt_char}')... ",
+        end="",
+        flush=True,
+    )
    try:
        count, data_bytes = read_vector_raw(f, struct_fmt_char)
        print(f"Count={count}, Bytes={len(data_bytes)}")
@@ -647,7 +651,10 @@ def convert_hnsw_graph_to_csr(input_filename, output_filename, prune_embeddings=
        print(f"Error: Input file not found: {input_filename}", file=sys.stderr)
        return False
    except MemoryError as e:
-        print(f"\nFatal MemoryError during conversion: {e}. Insufficient RAM.", file=sys.stderr)
+        print(
+            f"\nFatal MemoryError during conversion: {e}. Insufficient RAM.",
+            file=sys.stderr,
+        )
        # Clean up potentially partially written output file?
        try:
            os.remove(output_filename)
--- a/packages/leann-backend-hnsw/pyproject.toml
+++ b/packages/leann-backend-hnsw/pyproject.toml
@@ -9,7 +9,7 @@ name = "leann-backend-hnsw"
 version = "0.1.14"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
 dependencies = [
-    "leann-core==0.1.14", 
+    "leann-core==0.1.14",
    "numpy",
    "pyzmq>=23.0.0",
    "msgpack>=1.0.0",
@@ -24,4 +24,4 @@ build.tool-args = ["-j8"]

 # CMake definitions to optimize compilation
 [tool.scikit-build.cmake.define]
-CMAKE_BUILD_PARALLEL_LEVEL = "8"
+CMAKE_BUILD_PARALLEL_LEVEL = "8"
--- a/packages/leann-core/pyproject.toml
+++ b/packages/leann-core/pyproject.toml
@@ -46,4 +46,4 @@ colab = [
 leann = "leann.cli:main"

 [tool.setuptools.packages.find]
-where = ["src"]
+where = ["src"]
--- a/packages/leann-core/src/leann/chat.py
+++ b/packages/leann-core/src/leann/chat.py
@@ -245,7 +245,11 @@ def search_hf_models_fuzzy(query: str, limit: int = 10) -> list[str]:

        # HF Hub's search is already fuzzy! It handles typos and partial matches
        models = list_models(
-            search=query, filter="text-generation", sort="downloads", direction=-1, limit=limit
+            search=query,
+            filter="text-generation",
+            sort="downloads",
+            direction=-1,
+            limit=limit,
        )

        model_names = [model.id if hasattr(model, "id") else str(model) for model in models]
@@ -582,7 +586,11 @@ class HFChat(LLMInterface):

        # Tokenize input
        inputs = self.tokenizer(
-            formatted_prompt, return_tensors="pt", padding=True, truncation=True, max_length=2048
+            formatted_prompt,
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+            max_length=2048,
        )

        # Move inputs to device
--- a/packages/leann/README.md
+++ b/packages/leann/README.md
@@ -37,4 +37,4 @@ For full documentation, visit [https://leann.readthedocs.io](https://leann.readt

 ## License

-MIT License 
+MIT License
--- a/packages/leann/pyproject.toml
+++ b/packages/leann/pyproject.toml
@@ -39,4 +39,4 @@ diskann = [
 Homepage = "https://github.com/yourusername/leann"
 Documentation = "https://leann.readthedocs.io"
 Repository = "https://github.com/yourusername/leann"
-Issues = "https://github.com/yourusername/leann/issues" 
+Issues = "https://github.com/yourusername/leann/issues"
--- a/packages/wechat-exporter/main.py
+++ b/packages/wechat-exporter/main.py
@@ -1,6 +1,6 @@
 import json
 import sqlite3
-import xml.etree.ElementTree as ET
+import xml.etree.ElementTree as ElementTree
 from pathlib import Path
 from typing import Annotated

@@ -26,7 +26,7 @@ def get_safe_path(s: str) -> str:
 def process_history(history: str):
    if history.startswith("<?xml") or history.startswith("<msg>"):
        try:
-            root = ET.fromstring(history)
+            root = ElementTree.fromstring(history)
            title = root.find(".//title").text if root.find(".//title") is not None else None
            quoted = (
                root.find(".//refermsg/content").text
@@ -52,7 +52,8 @@ def get_message(history: dict | str):

 def export_chathistory(user_id: str):
    res = requests.get(
-        "http://localhost:48065/wechat/chatlog", params={"userId": user_id, "count": 100000}
+        "http://localhost:48065/wechat/chatlog",
+        params={"userId": user_id, "count": 100000},
    ).json()
    for i in range(len(res["chatLogs"])):
        res["chatLogs"][i]["content"] = process_history(res["chatLogs"][i]["content"])
@@ -116,7 +117,8 @@ def export_sqlite(
    all_users = requests.get("http://localhost:48065/wechat/allcontacts").json()
    for user in tqdm(all_users):
        cursor.execute(
-            "INSERT OR IGNORE INTO users (id, name) VALUES (?, ?)", (user["arg"], user["title"])
+            "INSERT OR IGNORE INTO users (id, name) VALUES (?, ?)",
+            (user["arg"], user["title"]),
        )
        usr_chatlog = export_chathistory(user["arg"])
        for msg in usr_chatlog:
--- a/scripts/build_and_test.sh
+++ b/scripts/build_and_test.sh
@@ -19,16 +19,16 @@ uv pip install build twine delocate auditwheel scikit-build-core cmake pybind11
 build_package() {
    local package_dir=$1
    local package_name=$(basename $package_dir)
-    
+
    echo "Building $package_name..."
    cd $package_dir
-    
+
    # Clean previous builds
    rm -rf dist/ build/ _skbuild/
-    
+
    # Build directly with pip wheel (avoids sdist issues)
    pip wheel . --no-deps -w dist
-    
+
    # Repair wheel for binary packages
    if [[ "$package_name" != "leann-core" ]] && [[ "$package_name" != "leann" ]]; then
        if [[ "$OSTYPE" == "darwin"* ]]; then
@@ -57,7 +57,7 @@ build_package() {
            fi
        fi
    fi
-    
+
    echo "Built wheels in $package_dir/dist/"
    ls -la dist/
    cd - > /dev/null
@@ -84,4 +84,4 @@ else
 fi

 echo -e "\nBuild complete! Test with:"
-echo "uv pip install packages/*/dist/*.whl" 
+echo "uv pip install packages/*/dist/*.whl"
--- a/scripts/bump_version.sh
+++ b/scripts/bump_version.sh
@@ -28,4 +28,4 @@ else
 fi

 echo "✅ Version updated to $NEW_VERSION"
-echo "✅ Dependencies updated to use leann-core==$NEW_VERSION" 
+echo "✅ Dependencies updated to use leann-core==$NEW_VERSION"
--- a/scripts/release.sh
+++ b/scripts/release.sh
@@ -15,4 +15,4 @@ VERSION=$1
 git add . && git commit -m "chore: bump version to $VERSION" && git push

 # Create release (triggers CI)
-gh release create v$VERSION --generate-notes 
+gh release create v$VERSION --generate-notes
--- a/scripts/upload_to_pypi.sh
+++ b/scripts/upload_to_pypi.sh
@@ -27,4 +27,4 @@ else
    else
        echo "Cancelled"
    fi
-fi 
+fi
--- a/test/micro_tpt.py
+++ b/test/micro_tpt.py
@@ -58,7 +58,8 @@ class GraphWrapper:
            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
+                    input_ids=self.static_input,
+                    attention_mask=self.static_attention_mask,
                )
            self.use_cuda_graph = True
        else:
@@ -82,7 +83,10 @@ class GraphWrapper:
    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)
+                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:
        if self.use_cuda_graph:
@@ -261,7 +265,10 @@ class Benchmark:
                                # 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
+                                    in_features,
+                                    out_features,
+                                    bias=bias,
+                                    has_fp16_weights=False,
                                )

                                # Copy weights and bias
@@ -350,8 +357,6 @@ class Benchmark:
                # Try xformers if available (only on CUDA)
                if torch.cuda.is_available():
                    try:
-                        from xformers.ops import memory_efficient_attention  # noqa: F401
-
                        if hasattr(model, "enable_xformers_memory_efficient_attention"):
                            model.enable_xformers_memory_efficient_attention()
                            print("- Enabled xformers memory efficient attention")
@@ -427,7 +432,11 @@ class Benchmark:
            else "cpu"
        )
        return torch.randint(
-            0, 1000, (batch_size, self.config.seq_length), device=device, dtype=torch.long
+            0,
+            1000,
+            (batch_size, self.config.seq_length),
+            device=device,
+            dtype=torch.long,
        )

    def _run_inference(
--- a/test/sanity_checks/README.md
+++ b/test/sanity_checks/README.md
@@ -7,7 +7,7 @@ This directory contains comprehensive sanity checks for the Leann system, ensuri
 ### `test_distance_functions.py`
 Tests all supported distance functions across DiskANN backend:
 - ✅ **MIPS** (Maximum Inner Product Search)
- ✅ **L2** (Euclidean Distance) 
+- ✅ **L2** (Euclidean Distance)
 - ✅ **Cosine** (Cosine Similarity)

 ```bash
@@ -27,7 +27,7 @@ uv run python tests/sanity_checks/test_l2_verification.py
 ### `test_sanity_check.py`
 Comprehensive end-to-end verification including:
 - Distance function testing
- Embedding model compatibility  
+- Embedding model compatibility
 - Search result correctness validation
 - Backend integration testing

@@ -64,7 +64,7 @@ When all tests pass, you should see:
 ```
 📊 测试结果总结:
  mips      : ✅ 通过
-  l2        : ✅ 通过  
+  l2        : ✅ 通过
  cosine    : ✅ 通过

 🎉 测试完成!
@@ -98,7 +98,7 @@ pkill -f "embedding_server"

 ### Typical Timing (3 documents, consumer hardware):
 - **Index Building**: 2-5 seconds per distance function
- **Search Query**: 50-200ms 
+- **Search Query**: 50-200ms
 - **Recompute Mode**: 5-15 seconds (higher accuracy)

 ### Memory Usage:
@@ -117,4 +117,4 @@ These tests are designed to be run in automated environments:
    uv run python tests/sanity_checks/test_l2_verification.py
 ```

-The tests are deterministic and should produce consistent results across different platforms.
+The tests are deterministic and should produce consistent results across different platforms.
--- a/test/sanity_checks/benchmark_embeddings.py
+++ b/test/sanity_checks/benchmark_embeddings.py
@@ -115,7 +115,13 @@ def main():
    # --- Plotting ---
    print("\n--- Generating Plot ---")
    plt.figure(figsize=(10, 6))
-    plt.plot(BATCH_SIZES, results_torch, marker="o", linestyle="-", label=f"PyTorch ({device})")
+    plt.plot(
+        BATCH_SIZES,
+        results_torch,
+        marker="o",
+        linestyle="-",
+        label=f"PyTorch ({device})",
+    )
    plt.plot(BATCH_SIZES, results_mlx, marker="s", linestyle="-", label="MLX")

    plt.title(f"Embedding Performance: MLX vs PyTorch\nModel: {MODEL_NAME_TORCH}")
--- a/test/simple_mac_tpt_test.py
+++ b/test/simple_mac_tpt_test.py
@@ -170,7 +170,11 @@ class Benchmark:

    def _create_random_batch(self, batch_size: int) -> torch.Tensor:
        return torch.randint(
-            0, 1000, (batch_size, self.config.seq_length), device=self.device, dtype=torch.long
+            0,
+            1000,
+            (batch_size, self.config.seq_length),
+            device=self.device,
+            dtype=torch.long,
        )

    def _run_inference(self, input_ids: torch.Tensor) -> float:
@@ -256,7 +260,11 @@ def run_mlx_benchmark():
    """Run MLX-specific benchmark"""
    if not MLX_AVAILABLE:
        print("MLX not available, skipping MLX benchmark")
-        return {"max_throughput": 0.0, "avg_throughput": 0.0, "error": "MLX not available"}
+        return {
+            "max_throughput": 0.0,
+            "avg_throughput": 0.0,
+            "error": "MLX not available",
+        }

    config = BenchmarkConfig(model_path="mlx-community/all-MiniLM-L6-v2-4bit", use_mlx=True)

@@ -265,7 +273,11 @@ def run_mlx_benchmark():
        results = benchmark.run()

        if not results:
-            return {"max_throughput": 0.0, "avg_throughput": 0.0, "error": "No valid results"}
+            return {
+                "max_throughput": 0.0,
+                "avg_throughput": 0.0,
+                "error": "No valid results",
+            }

        max_throughput = max(results[batch_size]["throughput"] for batch_size in results)
        avg_throughput = np.mean([results[batch_size]["throughput"] for batch_size in results])