chore: release v0.1.2

fix: download all artifacts in release workflow
feat: auto-update leann-core dependencies during release
2025-07-25 01:53:29 +00:00 · 2025-07-24 17:45:46 -07:00 · 2025-07-24 17:22:41 -07:00 · 2025-07-24 17:20:58 -07:00 · 2025-07-24 17:04:47 -07:00 · 2025-07-24 17:00:21 -07:00
24 changed files with 2345 additions and 1043 deletions
--- a/.github/workflows/build-and-publish.yml
+++ b/.github/workflows/build-and-publish.yml
@@ -0,0 +1,262 @@
 name: CI - Build Multi-Platform Packages
 on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]
  workflow_dispatch:
    inputs:
      publish:
        description: 'Publish to PyPI (only use for emergency fixes)'
        required: true
        default: 'false'
        type: choice
        options:
          - 'false'
          - 'test'
          - 'prod'
 jobs:
  # Build pure Python package: leann-core
  build-core:
    name: Build leann-core
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - name: Install uv
        uses: astral-sh/setup-uv@v4
      - name: Install build dependencies
        run: |
          uv pip install --system build twine
      - name: Build package
        run: |
          cd packages/leann-core
          uv build
      - name: Upload artifacts
        uses: actions/upload-artifact@v4
        with:
          name: leann-core-dist
          path: packages/leann-core/dist/
  # Build binary package: leann-backend-hnsw (default backend)
  build-hnsw:
    name: Build leann-backend-hnsw
    strategy:
      matrix:
        os: [ubuntu-latest, macos-latest]
        python-version: ['3.9', '3.10', '3.11', '3.12']
    runs-on: ${{ matrix.os }}
    steps:
      - uses: actions/checkout@v4
        with:
          submodules: recursive
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: ${{ matrix.python-version }}
      - name: Install uv
        uses: astral-sh/setup-uv@v4
      - name: Install system dependencies (Ubuntu)
        if: runner.os == 'Linux'
        run: |
          sudo apt-get update
          sudo apt-get install -y libomp-dev libboost-all-dev libzmq3-dev \
            pkg-config libopenblas-dev patchelf
      - name: Install system dependencies (macOS)
        if: runner.os == 'macOS'
        run: |
          brew install libomp boost zeromq
      - name: Install build dependencies
        run: |
          uv pip install --system scikit-build-core numpy swig
          uv pip install --system auditwheel delocate
      - name: Build wheel
        run: |
          cd packages/leann-backend-hnsw
          uv build --wheel --python python
      - name: Repair wheel (Linux)
        if: runner.os == 'Linux'
        run: |
          cd packages/leann-backend-hnsw
          auditwheel repair dist/*.whl -w dist_repaired
          rm -rf dist
          mv dist_repaired dist
      - name: Repair wheel (macOS)
        if: runner.os == 'macOS'
        run: |
          cd packages/leann-backend-hnsw
          delocate-wheel -w dist_repaired -v dist/*.whl
          rm -rf dist
          mv dist_repaired dist
      - name: Upload artifacts
        uses: actions/upload-artifact@v4
        with:
          name: hnsw-${{ matrix.os }}-py${{ matrix.python-version }}
          path: packages/leann-backend-hnsw/dist/
  # Build binary package: leann-backend-diskann (multi-platform)
  build-diskann:
    name: Build leann-backend-diskann
    strategy:
      matrix:
        os: [ubuntu-latest, macos-latest]
        python-version: ['3.9', '3.10', '3.11', '3.12']
    runs-on: ${{ matrix.os }}
    steps:
      - uses: actions/checkout@v4
        with:
          submodules: recursive
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: ${{ matrix.python-version }}
      - 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 libaio-dev libzmq3-dev \
            protobuf-compiler libprotobuf-dev libabsl-dev patchelf
          # Install Intel MKL using Intel's installer
          wget 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" >> $GITHUB_ENV
      - name: Install system dependencies (macOS)
        if: runner.os == 'macOS'
        run: |
          brew install libomp boost zeromq protobuf
          # MKL is not available on Homebrew, but DiskANN can work without it
      - name: Install build dependencies
        run: |
          uv pip install --system scikit-build-core numpy Cython pybind11
          if [[ "$RUNNER_OS" == "Linux" ]]; then
            uv pip install --system auditwheel
          else
            uv pip install --system delocate
          fi
      - name: Build wheel
        run: |
          cd packages/leann-backend-diskann
          uv build --wheel --python python
      - name: Repair wheel (Linux)
        if: runner.os == 'Linux'
        run: |
          cd packages/leann-backend-diskann
          auditwheel repair dist/*.whl -w dist_repaired
          rm -rf dist
          mv dist_repaired dist
      - name: Repair wheel (macOS)
        if: runner.os == 'macOS'
        run: |
          cd packages/leann-backend-diskann
          delocate-wheel -w dist_repaired -v dist/*.whl
          rm -rf dist
          mv dist_repaired dist
      - name: Upload artifacts
        uses: actions/upload-artifact@v4
        with:
          name: diskann-${{ matrix.os }}-py${{ matrix.python-version }}
          path: packages/leann-backend-diskann/dist/
  # Build meta-package: leann (build last)
  build-meta:
    name: Build leann meta-package
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - name: Install uv
        uses: astral-sh/setup-uv@v4
      - name: Install build dependencies
        run: |
          uv pip install --system build
      - name: Build package
        run: |
          cd packages/leann
          uv build
      - name: Upload artifacts
        uses: actions/upload-artifact@v4
        with:
          name: leann-meta-dist
          path: packages/leann/dist/
  # Publish to PyPI (only for emergency fixes or manual triggers)
  publish:
    name: Publish to PyPI (Emergency)
    needs: [build-core, build-hnsw, build-diskann, build-meta]
    runs-on: ubuntu-latest
    if: github.event_name == 'workflow_dispatch' && github.event.inputs.publish != 'false'
    steps:
      - name: Download all artifacts
        uses: actions/download-artifact@v4
        with:
          path: dist
      - name: Flatten directory structure
        run: |
          mkdir -p all_wheels
          find dist -name "*.whl" -exec cp {} all_wheels/ \;
          find dist -name "*.tar.gz" -exec cp {} all_wheels/ \;
      - name: Show what will be published
        run: |
          echo "📦 Packages to be published:"
          ls -la all_wheels/
      - name: Publish to Test PyPI
        if: github.event.inputs.publish == 'test'
        uses: pypa/gh-action-pypi-publish@release/v1
        with:
          password: ${{ secrets.TEST_PYPI_API_TOKEN }}
          repository-url: https://test.pypi.org/legacy/
          packages-dir: all_wheels/
          skip-existing: true
      - name: Publish to PyPI
        if: github.event.inputs.publish == 'prod'
        uses: pypa/gh-action-pypi-publish@release/v1
        with:
          password: ${{ secrets.PYPI_API_TOKEN }}
          packages-dir: all_wheels/
          skip-existing: true 
--- a/.github/workflows/release-manual.yml
+++ b/.github/workflows/release-manual.yml
@@ -0,0 +1,206 @@
 name: Manual Release
 on:
  workflow_dispatch:
    inputs:
      version:
        description: 'Version to release (e.g., 0.1.1)'
        required: true
        type: string
      test_pypi:
        description: 'Test on TestPyPI first'
        required: false
        type: boolean
        default: true
 jobs:
  validate-and-release:
    runs-on: ubuntu-latest
    permissions:
      contents: write
      actions: read
    steps:
      - uses: actions/checkout@v4
        with:
          token: ${{ secrets.GITHUB_TOKEN }}
      - name: Check CI status
        run: |
          echo "ℹ️  This workflow will download build artifacts from the latest CI run."
          echo "   CI must have completed successfully on the current commit."
          echo ""
      - name: Validate version format
        run: |
          if ! [[ "${{ inputs.version }}" =~ ^[0-9]+\.[0-9]+\.[0-9]+$ ]]; then
            echo "❌ Invalid version format. Use semantic versioning (e.g., 0.1.1)"
            exit 1
          fi
          echo "✅ Version format valid: ${{ inputs.version }}"
      - name: Check if version already exists
        run: |
          if git tag | grep -q "^v${{ inputs.version }}$"; then
            echo "❌ Version v${{ inputs.version }} already exists!"
            exit 1
          fi
          echo "✅ Version is new"
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.13'
      - name: Install uv
        run: |
          curl -LsSf https://astral.sh/uv/install.sh | sh
          echo "$HOME/.cargo/bin" >> $GITHUB_PATH
      - name: Update versions
        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 }}"
      - name: Get CI run ID
        id: get-ci-run
        run: |
          # Get the latest successful CI run on the previous commit (before version bump)
          COMMIT_SHA=$(git rev-parse HEAD~1)
          RUN_ID=$(gh run list \
            --workflow="CI - Build Multi-Platform Packages" \
            --status=success \
            --commit=$COMMIT_SHA \
            --json databaseId \
            --jq '.[0].databaseId')
          if [ -z "$RUN_ID" ]; then
            echo "❌ No successful CI run found for commit $COMMIT_SHA"
            echo ""
            echo "This usually means:"
            echo "1. CI hasn't run on the latest commit yet"
            echo "2. CI failed on the latest commit"
            echo ""
            echo "Please ensure CI passes on main branch before releasing."
            exit 1
          fi
          echo "✅ Found CI run: $RUN_ID"
          echo "run-id=$RUN_ID" >> $GITHUB_OUTPUT
        env:
          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      - name: Download artifacts from CI run
        run: |
          echo "📦 Downloading artifacts from CI run ${{ steps.get-ci-run.outputs.run-id }}..."
          # Download all artifacts (not just wheels-*)
          gh run download ${{ steps.get-ci-run.outputs.run-id }} \
            --dir ./dist-downloads
          # Consolidate all wheels into packages/*/dist/
          mkdir -p packages/leann-core/dist
          mkdir -p packages/leann-backend-hnsw/dist
          mkdir -p packages/leann-backend-diskann/dist
          mkdir -p packages/leann/dist
          find ./dist-downloads -name "*.whl" -exec cp {} ./packages/ \;
          # Move wheels to correct package directories
          for wheel in packages/*.whl; do
            if [[ $wheel == *"leann_core"* ]]; then
              mv "$wheel" packages/leann-core/dist/
            elif [[ $wheel == *"leann_backend_hnsw"* ]]; then
              mv "$wheel" packages/leann-backend-hnsw/dist/
            elif [[ $wheel == *"leann_backend_diskann"* ]]; then
              mv "$wheel" packages/leann-backend-diskann/dist/
            elif [[ $wheel == *"leann-"* ]] && [[ $wheel != *"backend"* ]] && [[ $wheel != *"core"* ]]; then
              mv "$wheel" packages/leann/dist/
            fi
          done
          # List downloaded wheels
          echo "✅ Downloaded wheels:"
          find packages/*/dist -name "*.whl" -type f | sort
        env:
          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      - name: Test on TestPyPI (optional)
        if: inputs.test_pypi
        continue-on-error: true
        env:
          TWINE_USERNAME: __token__
          TWINE_PASSWORD: ${{ secrets.TEST_PYPI_API_TOKEN }}
        run: |
          if [ -z "$TWINE_PASSWORD" ]; then
            echo "⚠️  TEST_PYPI_API_TOKEN not configured, skipping TestPyPI upload"
            echo "   To enable TestPyPI testing, add TEST_PYPI_API_TOKEN to repository secrets"
            exit 0
          fi
          pip install twine
          echo "📦 Uploading to TestPyPI..."
          twine upload --repository testpypi packages/*/dist/* --verbose || {
            echo "⚠️  TestPyPI upload failed, but continuing with release"
            echo "   This is optional and won't block the release"
            exit 0
          }
          echo "✅ Test upload successful!"
          echo "📋 Check packages at: https://test.pypi.org/user/your-username/"
          echo ""
          echo "To test installation:"
          echo "pip install -i https://test.pypi.org/simple/ leann"
      - 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!"
            echo "   Please add PYPI_API_TOKEN to repository secrets"
            exit 1
          fi
          pip install twine
          echo "📦 Publishing to PyPI..."
          # Collect all wheels in one place
          mkdir -p all_wheels
          find packages/*/dist -name "*.whl" -exec cp {} all_wheels/ \;
          find packages/*/dist -name "*.tar.gz" -exec cp {} all_wheels/ \;
          echo "📋 Packages to publish:"
          ls -la all_wheels/
          # Upload to PyPI
          twine upload all_wheels/* --skip-existing --verbose
          echo "✅ Published to PyPI!"
          echo "🎉 Check packages at: https://pypi.org/project/leann/"
      - name: Create and push tag
        run: |
          git tag "v${{ inputs.version }}"
          git push origin main
          git push origin "v${{ inputs.version }}"
          echo "✅ Tag v${{ inputs.version }} created and pushed"
      - name: Create GitHub Release
        uses: softprops/action-gh-release@v1
        with:
          tag_name: v${{ inputs.version }}
          name: Release v${{ inputs.version }}
          body: |
            ## 🚀 Release v${{ inputs.version }}
            ### What's Changed
            See the [full changelog](https://github.com/${{ github.repository }}/compare/...v${{ inputs.version }})
            ### Installation
            ```bash
            pip install leann==${{ inputs.version }}
            ```
--- a/.gitignore
+++ b/.gitignore
@@ -12,7 +12,6 @@ outputs/
 *.idx 
 *.map
 .history/
 scripts/
 lm_eval.egg-info/
 demo/experiment_results/**/*.json
 *.jsonl
--- a/README.md
+++ b/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.
@@ -37,7 +37,7 @@ 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
 ```bash
 git clone git@github.com:yichuan-w/LEANN.git leann
@@ -47,36 +47,30 @@ git submodule update --init --recursive
 **macOS:**
 ```bash
-brew install llvm libomp boost protobuf zeromq
+brew install llvm libomp boost protobuf zeromq pkgconf
 export CC=$(brew --prefix llvm)/bin/clang
 export CXX=$(brew --prefix llvm)/bin/clang++
 # Install with HNSW backend (default, recommended for most users)
-uv sync
+# Install uv first if you don't have it:
-
+# curl -LsSf https://astral.sh/uv/install.sh | sh
-# Or add DiskANN backend if you want to test more options
+# See: https://docs.astral.sh/uv/getting-started/installation/#installation-methods
-uv sync --extra diskann
+CC=$(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 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 (Recommended for full privacy):**
 > *You can skip this installation if you only want to use OpenAI API for generation.*
-*macOS:*
+**macOS:**
 First, [download Ollama for macOS](https://ollama.com/download/mac).
@@ -85,7 +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
@@ -97,9 +91,10 @@ ollama serve &
 ollama pull llama3.2:1b
 ```
-## Dead Simple API
+## Quick Start in 30s
-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)
 ```python
 from leann.api import LeannBuilder, LeannSearcher, LeannChat
@@ -130,24 +125,22 @@ response = chat.ask(
 )
 ```
-**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)
+The example below asks a question about summarizing two papers (uses default data in `examples/data`):
 Above we showed the Python API, while this CLI script demonstrates the same concepts while directly processing PDFs and documents, and even any directory that stores your personal files!
 The following scripts use Ollama `qwen3:8b` by default, so you need `ollama pull qwen3:8b` first. For other models: `--llm openai --model gpt-4o` (requires `OPENAI_API_KEY` environment variable) or `--llm hf --model Qwen/Qwen3-4B`.
 ```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
@@ -155,14 +148,13 @@ python ./examples/main_cli_example.py
-**Works with any text format** - research papers, personal notes, presentations. Built with LlamaIndex for document parsing.
+### 📧 Your Personal Email Secretary: RAG on Apple Mail!
-### Search Your Entire Life
+**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
+python examples/mail_reader_leann.py --query "What's the food I ordered by doordash or Uber eat mostly?"
 # "What's the number of class recommend to take per semester for incoming EECS students?"
 ```
-**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>
@@ -195,12 +187,11 @@ 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!
 ```bash
-python examples/google_history_reader_leann.py
+python examples/google_history_reader_leann.py --query "Tell me my browser history about machine learning?"
 # "Tell me my browser history about machine learning system stuff?"
 ```
-**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>
@@ -249,13 +240,13 @@ Once the index is built, you can ask questions like:
 </details>
-### WeChat Detective
+### 💬 WeChat Detective: Unlock Your Golden Memories!
 ```bash
-python examples/wechat_history_reader_leann.py
+python examples/wechat_history_reader_leann.py --query "Show me all group chats about weekend plans"
 # "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>
@@ -266,7 +257,13 @@ 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>
@@ -403,11 +400,11 @@ Same dataset, same hardware, same embedding model. LEANN just works better.
 ### 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)
+| System                | DPR (2.1M chunks) | RPJ-wiki (60M chunks) | Chat history (400K messages) | Apple emails (780K messages chunks) |Google Search History (38K entries)
 |-----------------------|------------------|------------------------|-----------------------------|------------------------------|------------------------------|
-| Traditional Vector DB(FAISS) | 3.8 GB           | 201 GB                 | 1.8G                     | 305.8 MB                     |130.4 MB                     |
+| Traditional Vector DB(FAISS) | 3.8 GB           | 201 GB                 | 1.8G                     | 2.4G                     |130.4 MB                     |
-| **LEANN**             | **324 MB**       | **6 GB**               | **64 MB**                 | **14.8 MB**                  |**6.4MB**                  |
+| **LEANN**             | **324 MB**       | **6 GB**               | **64 MB**                 | **79 MB**                  |**6.4MB**                  |
-| **Reduction**         | **91% smaller**  | **97% smaller**        | **97% smaller**             | **95% smaller**              |**95% smaller**              |
+| **Reduction**         | **91% smaller**  | **97% smaller**        | **97% smaller**             | **97% smaller**              |**95% smaller**              |
 <!-- ### Memory Usage Comparison
--- a/demo.ipynb
+++ b/demo.ipynb
@@ -1,37 +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",
+    "# 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",
    "# 1. Build the index (no embeddings stored!)\n",
    "builder = LeannBuilder(backend_name=\"hnsw\")\n",
-    "builder.add_text(\"C# is a powerful programming language\")\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 very popular\")\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\")\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",
    "# 2. Search with real-time embeddings\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",
    "# 3. Chat with LEANN using retrieved results\n",
    "llm_config = {\n",
-    "    \"type\": \"ollama\",\n",
+    "    \"type\": \"hf\",\n",
-    "    \"model\": \"llama3.2:1b\"\n",
+    "    \"model\": \"Qwen/Qwen3-0.6B\",\n",
    "}\n",
    "\n",
    "chat = LeannChat(index_path=\"knowledge.leann\", llm_config=llm_config)\n",
    "response = chat.ask(\n",
-    "    \"Compare the two retrieved programming languages and say which one is more popular today.\",\n",
+    "    \"Compare the two retrieved programming languages and tell me their advantages.\",\n",
    "    top_k=2,\n",
-    ")"
+    "    llm_kwargs={\"max_tokens\": 128}\n",
    ")\n",
    "response"
   ]
  }
 ],
--- a/docs/RELEASE.md
+++ b/docs/RELEASE.md
@@ -0,0 +1,100 @@
 # Release Guide
 ## 📋 Prerequisites
 Before releasing, ensure:
 1. ✅ All code changes are committed and pushed
 2. ✅ CI has passed on the latest commit (check [Actions](https://github.com/yichuan-w/LEANN/actions/workflows/ci.yml))
 3. ✅ You have determined the new version number
 ### Required: PyPI Configuration
 To enable PyPI publishing:
 1. Get a PyPI API token from https://pypi.org/manage/account/token/
 2. Add it to repository secrets: Settings → Secrets → Actions → New repository secret
   - Name: `PYPI_API_TOKEN`
   - Value: Your PyPI token (starts with `pypi-`)
 ### Optional: TestPyPI Configuration
 To enable TestPyPI testing (recommended but not required):
 1. Get a TestPyPI API token from https://test.pypi.org/manage/account/token/
 2. Add it to repository secrets: Settings → Secrets → Actions → New repository secret
   - Name: `TEST_PYPI_API_TOKEN`
   - Value: Your TestPyPI token (starts with `pypi-`)
 **Note**: TestPyPI testing is optional. If not configured, the release will skip TestPyPI and proceed.
 ## 🚀 Recommended: Manual Release Workflow
 ### Via GitHub UI (Most Reliable)
 1. **Verify CI Status**: Check that the latest commit has a green checkmark ✅
 2. Go to [Actions → Manual Release](https://github.com/yichuan-w/LEANN/actions/workflows/release-manual.yml)
 3. Click "Run workflow"
 4. Enter version (e.g., `0.1.1`)
 5. Toggle "Test on TestPyPI first" if desired
 6. Click "Run workflow"
 **What happens:**
 - ✅ Downloads pre-built packages from CI (no rebuild needed!)
 - ✅ Updates all package versions
 - ✅ Optionally tests on TestPyPI
 - ✅ **Publishes directly to PyPI**
 - ✅ Creates tag and GitHub release
 ### Via Command Line
 ```bash
 gh workflow run release-manual.yml -f version=0.1.1 -f test_pypi=true
 ```
 ## ⚡ Quick Release (One-Line)
 For experienced users who want the fastest path:
 ```bash
 ./scripts/release.sh 0.1.1
 ```
 This script will:
 1. Update all package versions
 2. Commit and push changes
 3. Create GitHub release
 4. **Manual Release workflow will automatically publish to PyPI**
 ⚠️ **Note**: If CI fails, you'll need to manually fix and re-tag
 ## Manual Testing Before Release
 For testing specific packages locally (especially DiskANN on macOS):
 ```bash
 # Build specific package locally
 ./scripts/build_and_test.sh diskann  # or hnsw, core, meta, all
 # Test installation in a clean environment
 python -m venv test_env
 source test_env/bin/activate
 pip install packages/*/dist/*.whl
 # Upload to Test PyPI (optional)
 ./scripts/upload_to_pypi.sh test
 # Upload to Production PyPI (use with caution)
 ./scripts/upload_to_pypi.sh prod
 ```
 ## First-time setup
 1. Install GitHub CLI:
   ```bash
   brew install gh
   gh auth login
   ```
 2. Set PyPI token in GitHub:
   ```bash
   gh secret set PYPI_API_TOKEN
   # Paste your PyPI token when prompted
   ``` 
--- a/examples/mail_reader_leann.py
+++ b/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"):
    """
@@ -77,7 +77,7 @@ def create_leann_index_from_multiple_sources(messages_dirs: List[Path], index_pa
        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=128)
+        text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=25)
        # Convert Documents to text strings and chunk them
        all_texts = []
@@ -158,7 +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 = []
@@ -218,11 +218,10 @@ 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")
@@ -233,7 +232,7 @@ async def main():
    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_debug",
+    parser.add_argument('--index-dir', type=str, default="./mail_index_index_file",
                       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)')
@@ -253,6 +252,9 @@ 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))
--- a/examples/wechat_history_reader_leann.py
+++ b/examples/wechat_history_reader_leann.py
@@ -78,7 +78,7 @@ def create_leann_index_from_multiple_wechat_exports(
        )
        # Create text splitter with 256 chunk size
-        text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=128)
+        text_splitter = SentenceSplitter(chunk_size=192, chunk_overlap=64)
        # Convert Documents to text strings and chunk them
        all_texts = []
--- a/packages/leann-backend-diskann/pyproject.toml
+++ b/packages/leann-backend-diskann/pyproject.toml
@@ -4,8 +4,8 @@ build-backend = "scikit_build_core.build"
 [project]
 name = "leann-backend-diskann"
-version = "0.1.0"
+version = "0.1.2"
-dependencies = ["leann-core==0.1.0", "numpy"]
+dependencies = ["leann-core==0.1.2", "numpy"]
 [tool.scikit-build]
 # Key: simplified CMake path
--- a/packages/leann-backend-diskann/third_party/DiskANN
+++ b/packages/leann-backend-diskann/third_party/DiskANN
--- a/packages/leann-backend-hnsw/pyproject.toml
+++ b/packages/leann-backend-hnsw/pyproject.toml
@@ -6,9 +6,14 @@ build-backend = "scikit_build_core.build"
 [project]
 name = "leann-backend-hnsw"
-version = "0.1.0"
+version = "0.1.2"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
-dependencies = ["leann-core==0.1.0", "numpy"]
+dependencies = [
    "leann-core==0.1.2", 
    "numpy",
    "pyzmq>=23.0.0",
    "msgpack>=1.0.0",
 ]
 [tool.scikit-build]
 wheel.packages = ["leann_backend_hnsw"]
--- a/packages/leann-core/pyproject.toml
+++ b/packages/leann-core/pyproject.toml
@@ -4,15 +4,23 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "leann-core"
-version = "0.1.0"
+version = "0.1.2"
-description = "Core API and plugin system for Leann."
+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",
    "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]
--- a/packages/leann-core/src/leann/api.py
+++ b/packages/leann-core/src/leann/api.py
@@ -142,7 +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,
--- a/packages/leann-core/src/leann/chat.py
+++ b/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"
+            # Check if the model exists remotely and get available tags
-            error_msg += "\nTo download a new model: ollama pull <model_name>"
+            model_exists_remotely, available_tags = check_ollama_model_exists_remotely(model_name)
-            error_msg += "\nBrowse models: https://ollama.com/library"
+            
            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":
@@ -397,7 +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}'")
@@ -408,7 +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(
@@ -417,54 +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
+        print('kwargs in HF: ', kwargs)
-        if "max_tokens" in kwargs:
+        # Check if this is a Qwen model and add /no_think by default
-            # Prefer user-provided max_new_tokens if both are present
+        is_qwen_model = "qwen" in self.model.config._name_or_path.lower()
-            kwargs.setdefault("max_new_tokens", kwargs["max_tokens"])
+        
-            # Remove the unsupported key to avoid errors in Transformers
+        # For Qwen models, automatically add /no_think to the prompt
-            kwargs.pop("max_tokens")
+        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
+        # Tokenize input
-        if "temperature" in kwargs and kwargs["temperature"] == 0.0:
+        inputs = self.tokenizer(
-            # Remove unsupported zero temperature and use deterministic generation
+            formatted_prompt, 
-            kwargs.pop("temperature")
+            return_tensors="pt", 
-            kwargs.setdefault("do_sample", False)
+            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
+        # Set generation parameters
-        params = {"max_length": 500, "num_return_sequences": 1, **kwargs}
+        generation_config = {
-        logger.info(f"Generating text with Hugging Face model with params: {params}")
+            "max_new_tokens": kwargs.get("max_tokens", kwargs.get("max_new_tokens", 512)),
-        results = self.pipeline(prompt, **params)
+            "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
+        logger.info(f"Generating with HuggingFace model, config: {generation_config}")
-        if isinstance(results, list) and len(results) > 0:
+        
-            generated_text = (
+        # Generate
-                results[0].get("generated_text", "")
+        with torch.no_grad():
-                if isinstance(results[0], dict)
+            outputs = self.model.generate(
-                else str(results[0])
+                **inputs,
                **generation_config
            )
        else:
            generated_text = str(results)
-        # Extract only the newly generated portion by removing the original prompt
+        # Decode response
-        if isinstance(generated_text, str) and generated_text.startswith(prompt):
+        generated_tokens = outputs[0][inputs["input_ids"].shape[1]:]
-            response = generated_text[len(prompt) :].strip()
+        response = self.tokenizer.decode(generated_tokens, skip_special_tokens=True)
-        else:
+        
-            # Fallback: return the full response if prompt removal fails
+        return response.strip()
            response = str(generated_text)
        return response
 class OpenAIChat(LLMInterface):
--- a/packages/leann-core/src/leann/embedding_compute.py
+++ b/packages/leann-core/src/leann/embedding_compute.py
@@ -101,7 +101,7 @@ def compute_embeddings_sentence_transformers(
        if device == "mps":
            batch_size = 128  # MPS optimal batch size from benchmark
            if model_name == "Qwen/Qwen3-Embedding-0.6B":
-                batch_size = 64
+                batch_size = 32
        elif device == "cuda":
            batch_size = 256  # CUDA optimal batch size
        # Keep original batch_size for CPU
--- a/packages/leann/README.md
+++ b/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 
--- a/packages/leann/init.py
+++ b/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"]
--- a/packages/leann/pyproject.toml
+++ b/packages/leann/pyproject.toml
@@ -0,0 +1,42 @@
 [build-system]
 requires = ["setuptools>=61.0"]
 build-backend = "setuptools.build_meta"
 [project]
 name = "leann"
 version = "0.1.2"
 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" 
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -33,8 +33,8 @@ 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>=0.26.3; sys_platform == 'darwin'",
-    "mlx-lm>=0.26.0",
+    "mlx-lm>=0.26.0; sys_platform == 'darwin'",
    "psutil>=5.8.0",
 ]
--- a/scripts/build_and_test.sh
+++ b/scripts/build_and_test.sh
@@ -0,0 +1,87 @@
 #!/bin/bash
 # Manual build and test script for local testing
 PACKAGE=${1:-"all"}  # Default to all packages
 echo "Building package: $PACKAGE"
 # Ensure we're in a virtual environment
 if [ -z "$VIRTUAL_ENV" ]; then
    echo "Error: Please activate a virtual environment first"
    echo "Run: source .venv/bin/activate (or .venv/bin/activate.fish for fish shell)"
    exit 1
 fi
 # Install build tools
 uv pip install build twine delocate auditwheel scikit-build-core cmake pybind11 numpy
 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
            # For macOS
            for wheel in dist/*.whl; do
                if [[ -f "$wheel" ]]; then
                    delocate-wheel -w dist_repaired -v "$wheel"
                fi
            done
            if [[ -d dist_repaired ]]; then
                rm -rf dist/*.whl
                mv dist_repaired/*.whl dist/
                rmdir dist_repaired
            fi
        else
            # For Linux
            for wheel in dist/*.whl; do
                if [[ -f "$wheel" ]]; then
                    auditwheel repair "$wheel" -w dist_repaired
                fi
            done
            if [[ -d dist_repaired ]]; then
                rm -rf dist/*.whl
                mv dist_repaired/*.whl dist/
                rmdir dist_repaired
            fi
        fi
    fi
    echo "Built wheels in $package_dir/dist/"
    ls -la dist/
    cd - > /dev/null
 }
 # Build specific package or all
 if [ "$PACKAGE" == "diskann" ]; then
    build_package "packages/leann-backend-diskann"
 elif [ "$PACKAGE" == "hnsw" ]; then
    build_package "packages/leann-backend-hnsw"
 elif [ "$PACKAGE" == "core" ]; then
    build_package "packages/leann-core"
 elif [ "$PACKAGE" == "meta" ]; then
    build_package "packages/leann"
 elif [ "$PACKAGE" == "all" ]; then
    build_package "packages/leann-core"
    build_package "packages/leann-backend-hnsw"
    build_package "packages/leann-backend-diskann"
    build_package "packages/leann"
 else
    echo "Unknown package: $PACKAGE"
    echo "Usage: $0 [diskann|hnsw|core|meta|all]"
    exit 1
 fi
 echo -e "\nBuild complete! Test with:"
 echo "uv pip install packages/*/dist/*.whl" 
--- a/scripts/bump_version.sh
+++ b/scripts/bump_version.sh
@@ -0,0 +1,31 @@
 #!/bin/bash
 if [ $# -eq 0 ]; then
    echo "Usage: $0 <new_version>"
    exit 1
 fi
 NEW_VERSION=$1
 # Get the directory where the script is located
 SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
 PROJECT_ROOT="$( cd "$SCRIPT_DIR/.." && pwd )"
 # Update all pyproject.toml files
 echo "Updating versions in $PROJECT_ROOT/packages/"
 # Use different sed syntax for macOS vs Linux
 if [[ "$OSTYPE" == "darwin"* ]]; then
    # Update version fields
    find "$PROJECT_ROOT/packages" -name "pyproject.toml" -exec sed -i '' "s/version = \".*\"/version = \"$NEW_VERSION\"/" {} \;
    # Update leann-core dependencies
    find "$PROJECT_ROOT/packages" -name "pyproject.toml" -exec sed -i '' "s/leann-core==[0-9.]*/leann-core==$NEW_VERSION/" {} \;
 else
    # Update version fields
    find "$PROJECT_ROOT/packages" -name "pyproject.toml" -exec sed -i "s/version = \".*\"/version = \"$NEW_VERSION\"/" {} \;
    # Update leann-core dependencies
    find "$PROJECT_ROOT/packages" -name "pyproject.toml" -exec sed -i "s/leann-core==[0-9.]*/leann-core==$NEW_VERSION/" {} \;
 fi
 echo "✅ Version updated to $NEW_VERSION"
 echo "✅ Dependencies updated to use leann-core==$NEW_VERSION" 
--- a/scripts/release.sh
+++ b/scripts/release.sh
@@ -0,0 +1,18 @@
 #!/bin/bash
 if [ $# -eq 0 ]; then
    echo "Usage: $0 <version>"
    echo "Example: $0 0.1.1"
    exit 1
 fi
 VERSION=$1
 # Update version
 ./scripts/bump_version.sh $VERSION
 # Commit and push
 git add . && git commit -m "chore: bump version to $VERSION" && git push
 # Create release (triggers CI)
 gh release create v$VERSION --generate-notes 
--- a/scripts/upload_to_pypi.sh
+++ b/scripts/upload_to_pypi.sh
@@ -0,0 +1,30 @@
 #!/bin/bash
 # Manual upload script for testing
 TARGET=${1:-"test"}  # Default to test pypi
 if [ "$TARGET" != "test" ] && [ "$TARGET" != "prod" ]; then
    echo "Usage: $0 [test|prod]"
    exit 1
 fi
 # Check for built packages
 if ! ls packages/*/dist/*.whl >/dev/null 2>&1; then
    echo "No built packages found. Run ./scripts/build_and_test.sh first"
    exit 1
 fi
 if [ "$TARGET" == "test" ]; then
    echo "Uploading to Test PyPI..."
    twine upload --repository testpypi packages/*/dist/*
 else
    echo "Uploading to PyPI..."
    echo "Are you sure? (y/N)"
    read -r response
    if [ "$response" == "y" ]; then
        twine upload packages/*/dist/*
    else
        echo "Cancelled"
    fi
 fi 
--- a/uv.lock
+++ b/uv.lock
Author	SHA1	Message	Date
GitHub Actions	cea1f6f87c	chore: release v0.1.2	2025-07-25 01:53:29 +00:00
Andy Lee	6c0e39372b	fix: download all artifacts in release workflow	2025-07-24 17:45:46 -07:00
Andy Lee	2bec67d2b6	feat: auto-update leann-core dependencies during release - Enhanced bump_version.sh to automatically update leann-core dependency versions - Script now updates both package versions and their leann-core dependencies - This ensures version consistency across all packages during release No more manual dependency version updates needed!	2025-07-24 17:22:41 -07:00
Andy Lee	133e715832	fix: resolve CI issues and consolidate workflows - Fix version dependencies: update backend packages to depend on leann-core==0.1.1 - Remove duplicate ci.yml workflow (keeping build-and-publish.yml as main CI) - Update release-manual.yml to reference correct CI workflow name This fixes the dependency resolution error and eliminates duplicate builds.	2025-07-24 17:20:58 -07:00
Andy Lee	95cf2f16e2	refactor: consolidate release and publish into single workflow - Manual Release workflow now directly publishes to PyPI after downloading CI artifacts - No more duplicate builds - reuses artifacts from CI - build-and-publish.yml renamed to 'CI - Build Multi-Platform Packages' - Publishing in CI workflow only for emergency manual triggers - Updated RELEASE.md to reflect the new streamlined process This fixes the issue where releases would trigger redundant builds.	2025-07-24 17:04:47 -07:00
Andy Lee	47a4c153eb	fix: enable PyPI publish on tag push - Manual Release workflow creates tags but build-and-publish.yml only published on 'release' events - Now build-and-publish.yml will also publish when v* tags are pushed - This fixes the issue where manual releases didn't trigger PyPI uploads	2025-07-24 17:00:21 -07:00
GitHub Actions	faf5ae3533	chore: release v0.1.1	2025-07-24 23:36:23 +00:00
Andy Lee	a44dccecac	fix: make TestPyPI upload optional and non-blocking - Add continue-on-error to TestPyPI step - Check if TEST_PYPI_API_TOKEN exists before attempting upload - Add graceful failure handling with clear messages - Update docs to explain TestPyPI token configuration - Clarify that TestPyPI testing is optional Now the release won't fail if TestPyPI is not configured or upload fails	2025-07-24 16:02:07 -07:00
yichuan520030910320	9cf9358b9c	Merge branch 'main' of https://github.com/yichuan-w/LEANN	2025-07-24 14:40:39 -07:00
yichuan520030910320	de252fef31	[chat] update 30s example	2025-07-24 14:40:33 -07:00
Andy Lee	9076bc27b8	fix: resolve CI run detection issues in release workflow - Add 'actions: read' permission to access workflow runs - Use workflow name instead of filename for gh run list - Look for CI run on HEAD~1 (before version bump commit) - Improve error messages for better debugging Fixes HTTP 403 error when trying to find successful CI runs	2025-07-24 14:27:26 -07:00
Andy Lee	50686c0819	refactor: use CI artifacts in release workflow instead of rebuilding - Download pre-built wheels from successful CI runs - Avoids duplicate builds and ensures consistency - CI artifacts are already tested across all platforms - Faster release process (no build time) - Updates release documentation to reflect new flow This ensures the released packages are exactly what was tested in CI.	2025-07-24 14:24:03 -07:00
Andy Lee	1614203786	fix: make bump_version.sh work on both macOS and Linux - macOS uses sed -i '' while Linux uses sed -i - Add OS detection to use correct syntax - Ensures script works in CI (Linux) and local dev (macOS)	2025-07-24 14:13:31 -07:00
Andy Lee	3d4c75a56c	fix: add missing scripts directory to git - Remove scripts/ from .gitignore - Add build_and_test.sh for local testing - Add bump_version.sh for version updates (used by CI) - Add release.sh and upload_to_pypi.sh for publishing - Fixes CI error: ./scripts/bump_version.sh: No such file or directory	2025-07-24 14:13:14 -07:00
Andy Lee	2684ee71dc	fix: ensure uv build uses correct Python version in CI - Add --python python flag to uv build commands - This ensures wheels are built with the correct Python version (cp313 for Python 3.13, etc) - Fixes issue where Python 3.13 CI was building cp311 wheels - Also adds Python version verification before build	2025-07-24 13:44:02 -07:00
Andy Lee	1d321953ba	ci: update all GitHub Actions to latest versions - Update actions/upload-artifact from v3 to v4 (v3 deprecated April 2024) - Update actions/setup-python from v4 to v5 (latest version) - Add Python 3.12 and 3.13 to CI test matrix - Ensure compatibility with latest Python versions and GitHub Actions	2025-07-24 13:36:21 -07:00
Andy Lee	b3cb251369	ci: add Python 3.12 and 3.13 to test matrix - Add Python 3.12 and 3.13 to CI test matrix - Ensure compatibility with latest Python versions - Python 3.12 is stable, 3.13 was released in Oct 2024	2025-07-24 13:32:29 -07:00
Andy Lee	0a17d2c9d8	feat: implement comprehensive CI/CD pipeline with two-stage release - Add ci.yml for continuous integration on every commit - Test builds on Ubuntu/macOS with Python 3.9/3.10/3.11 - Ensure code quality before any release - Add release-manual.yml for controlled releases - Manual trigger prevents accidental releases - Version validation and tag creation - Optional TestPyPI testing before production - Only creates tag after validation passes - Keep build-and-publish.yml for automated PyPI deployment - Triggered by new tags (separation of concerns) - Handles multi-platform wheel building - Allows retry if PyPI upload fails - Update RELEASE.md with clear prerequisites and workflow This setup ensures: 1. Every commit is tested (CI) 2. Releases are deliberate (manual trigger) 3. Failed CI won't create broken tags 4. PyPI publish can be retried independently	2025-07-24 13:29:21 -07:00
Andy Lee	e3defbca84	fix: add minimal CI dependencies for HNSW and DiskANN backends - HNSW (Ubuntu): add libopenblas-dev for BLAS requirements - DiskANN (Ubuntu): keep MKL, remove redundant pkg-config (HNSW already has it) - DiskANN (macOS): add protobuf for build requirements - Both: ensure patchelf for auditwheel on Linux This avoids OpenBLAS/MKL conflicts by using them in separate jobs	2025-07-24 01:06:57 -07:00
Andy Lee	e407f63977	chore: fix uv build	2025-07-24 00:51:57 -07:00
Andy Lee	7add391b2c	chore: build and package	2025-07-24 00:47:46 -07:00
yichuan520030910320	efd6373b32	[chat] update huggingface chat and make qwen no thinking	2025-07-24 00:11:42 -07:00
yichuan520030910320	d502fa24b0	[installation] update install for linux	2025-07-24 02:17:17 +00:00
yichuan520030910320	258a9a5c7f	[misc]test link again	2025-07-23 18:29:32 -07:00
yichuan520030910320	5d41ac6115	test link	2025-07-23 18:28:22 -07:00
yichuan520030910320	2a0fdb49b8	test link	2025-07-23 18:27:08 -07:00
yichuan520030910320	9d1b7231b6	fix broken link	2025-07-23 18:25:22 -07:00
yichuan520030910320	ed3095b478	fix broken link	2025-07-23 18:24:17 -07:00
yichuan520030910320	88eca75917	fix readme	2025-07-23 18:22:10 -07:00
yichuan520030910320	42de27e16a	Merge branch 'main' of https://github.com/yichuan-w/LEANN	2025-07-23 18:17:19 -07:00
yichuan520030910320	c083bda5b7	fix several bug	2025-07-23 18:17:11 -07:00
Andy Lee	e86da38726	fix: ollama hint for similar models	2025-07-23 15:45:10 -07:00
yichuan520030910320	99076e38bc	update install	2025-07-23 14:55:34 -07:00
yichuan520030910320	9698c1a02c	fix readme	2025-07-23 14:52:01 -07:00
yichuan520030910320	851f0f04c3	fix some para	2025-07-23 01:46:34 -07:00
yichuan520030910320	ae16d9d888	fix readme	2025-07-23 00:44:43 -07:00
yichuan520030910320	6e1af2eb0c	fix readme	2025-07-23 00:43:46 -07:00
yichuan520030910320	7695dd0d50	fix readme	2025-07-23 00:42:17 -07:00
yichuan520030910320	c2065473ad	fix readme	2025-07-23 00:30:42 -07:00
yichuan520030910320	5f3870564d	Merge branch 'main' of https://github.com/yichuan-w/LEANN	2025-07-23 00:09:30 -07:00
yichuan520030910320	c214b2e33e	fix readme	2025-07-23 00:09:24 -07:00
Andy Lee	2420c5fd35	chore: update sentence-transformer to prevent MixIn not found error	2025-07-22 23:27:25 -07:00
yichuan520030910320	f48f526f0a	fix readme	2025-07-22 23:21:15 -07:00
yichuan520030910320	5dd74982ba	fix readme	2025-07-22 23:14:31 -07:00
Andy Lee	e07aaf52a7	docs: align	2025-07-22 22:37:27 -07:00
Andy Lee	30e5f12616	docs: quick start	2025-07-22 22:33:04 -07:00
Andy Lee	594427bf87	docs: demo	2025-07-22 22:32:18 -07:00