format

- Fix typo: 'matrilize' -> 'materialize'
- Improve clarity and formatting of --no-recompute flag explanation
- Add code block for better readability

.github/workflows/build-reusable.yml

@@ -28,36 +28,15 @@ jobs:
         run: |
           uv run --only-group lint pre-commit run --all-files --show-diff-on-failure
 
-  type-check:
-    name: Type Check with ty
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ inputs.ref }}
-          submodules: recursive
-
-      - name: Install uv and Python
-        uses: astral-sh/setup-uv@v6
-        with:
-          python-version: '3.11'
-
-      - name: Install ty
-        run: uv tool install ty
-
-      - name: Run ty type checker
-        run: |
-          # Run ty on core packages, apps, and tests
-          ty check packages/leann-core/src apps tests
 
   build:
-    needs: [lint, type-check]
+    needs: lint
     name: Build ${{ matrix.os }} Python ${{ matrix.python }}
     strategy:
       matrix:
         include:
-          # Note: Python 3.9 dropped - uses PEP 604 union syntax (str | None)
+          - os: ubuntu-22.04
-          # which requires Python 3.10+
+            python: '3.9'
           - os: ubuntu-22.04
             python: '3.10'
           - os: ubuntu-22.04
@@ -67,6 +46,8 @@ jobs:
           - os: ubuntu-22.04
             python: '3.13'
           # ARM64 Linux builds
+          - os: ubuntu-24.04-arm
+            python: '3.9'
           - os: ubuntu-24.04-arm
             python: '3.10'
           - os: ubuntu-24.04-arm
@@ -75,6 +56,8 @@ jobs:
             python: '3.12'
           - os: ubuntu-24.04-arm
             python: '3.13'
+          - os: macos-14
+            python: '3.9'
           - os: macos-14
             python: '3.10'
           - os: macos-14
@@ -83,6 +66,8 @@ jobs:
             python: '3.12'
           - os: macos-14
             python: '3.13'
+          - os: macos-15
+            python: '3.9'
           - os: macos-15
             python: '3.10'
           - os: macos-15
@@ -91,24 +76,16 @@ jobs:
             python: '3.12'
           - os: macos-15
             python: '3.13'
-          # Intel Mac builds (x86_64) - replaces deprecated macos-13
+          - os: macos-13
-          # Note: Python 3.13 excluded - PyTorch has no wheels for macOS x86_64 + Python 3.13
+            python: '3.9'
-          # (PyTorch <=2.4.1 lacks cp313, PyTorch >=2.5.0 dropped Intel Mac support)
+          - os: macos-13
-          - os: macos-15-intel
             python: '3.10'
-          - os: macos-15-intel
+          - os: macos-13
             python: '3.11'
-          - os: macos-15-intel
+          - os: macos-13
             python: '3.12'
-          # macOS 26 (beta) - arm64
+          # Note: macos-13 + Python 3.13 excluded due to PyTorch compatibility
-          - os: macos-26
+          # (PyTorch 2.5+ supports Python 3.13 but not Intel Mac x86_64)
-            python: '3.10'
-          - os: macos-26
-            python: '3.11'
-          - os: macos-26
-            python: '3.12'
-          - os: macos-26
-            python: '3.13'
     runs-on: ${{ matrix.os }}
 
     steps:
@@ -227,16 +204,13 @@ jobs:
             # Use system clang for better compatibility
             export CC=clang
             export CXX=clang++
-            # Set deployment target based on runner
+            # Homebrew libraries on each macOS version require matching minimum version
-            # macos-15-intel runs macOS 15, so target 15.0 (system libraries require it)
+            if [[ "${{ matrix.os }}" == "macos-13" ]]; then
-            if [[ "${{ matrix.os }}" == "macos-15-intel" ]]; then
+              export MACOSX_DEPLOYMENT_TARGET=13.0
-              export MACOSX_DEPLOYMENT_TARGET=15.0
+            elif [[ "${{ matrix.os }}" == "macos-14" ]]; then
-            elif [[ "${{ matrix.os }}" == macos-14* ]]; then
               export MACOSX_DEPLOYMENT_TARGET=14.0
-            elif [[ "${{ matrix.os }}" == macos-15* ]]; then
+            elif [[ "${{ matrix.os }}" == "macos-15" ]]; then
               export MACOSX_DEPLOYMENT_TARGET=15.0
-            elif [[ "${{ matrix.os }}" == macos-26* ]]; then
-              export MACOSX_DEPLOYMENT_TARGET=26.0
             fi
             uv build --wheel --python ${{ matrix.python }} --find-links ${GITHUB_WORKSPACE}/packages/leann-core/dist
           else
@@ -250,16 +224,14 @@ jobs:
             # Use system clang for better compatibility
             export CC=clang
             export CXX=clang++
-            # Set deployment target based on runner
+            # DiskANN requires macOS 13.3+ for sgesdd_ LAPACK function
-            # macos-15-intel runs macOS 15, so target 15.0 (system libraries require it)
+            # But Homebrew libraries on each macOS version require matching minimum version
-            if [[ "${{ matrix.os }}" == "macos-15-intel" ]]; then
+            if [[ "${{ matrix.os }}" == "macos-13" ]]; then
-              export MACOSX_DEPLOYMENT_TARGET=15.0
+              export MACOSX_DEPLOYMENT_TARGET=13.3
-            elif [[ "${{ matrix.os }}" == macos-14* ]]; then
+            elif [[ "${{ matrix.os }}" == "macos-14" ]]; then
               export MACOSX_DEPLOYMENT_TARGET=14.0
-            elif [[ "${{ matrix.os }}" == macos-15* ]]; then
+            elif [[ "${{ matrix.os }}" == "macos-15" ]]; then
               export MACOSX_DEPLOYMENT_TARGET=15.0
-            elif [[ "${{ matrix.os }}" == macos-26* ]]; then
-              export MACOSX_DEPLOYMENT_TARGET=26.0
             fi
             uv build --wheel --python ${{ matrix.python }} --find-links ${GITHUB_WORKSPACE}/packages/leann-core/dist
           else
@@ -297,19 +269,16 @@ jobs:
         if: runner.os == 'macOS'
         run: |
           # Determine deployment target based on runner OS
-          # macos-15-intel runs macOS 15, so target 15.0 (system libraries require it)
+          # Must match the Homebrew libraries for each macOS version
-          if [[ "${{ matrix.os }}" == "macos-15-intel" ]]; then
+          if [[ "${{ matrix.os }}" == "macos-13" ]]; then
-            HNSW_TARGET="15.0"
+            HNSW_TARGET="13.0"
-            DISKANN_TARGET="15.0"
+            DISKANN_TARGET="13.3"
-          elif [[ "${{ matrix.os }}" == macos-14* ]]; then
+          elif [[ "${{ matrix.os }}" == "macos-14" ]]; then
             HNSW_TARGET="14.0"
             DISKANN_TARGET="14.0"
-          elif [[ "${{ matrix.os }}" == macos-15* ]]; then
+          elif [[ "${{ matrix.os }}" == "macos-15" ]]; then
             HNSW_TARGET="15.0"
             DISKANN_TARGET="15.0"
-          elif [[ "${{ matrix.os }}" == macos-26* ]]; then
-            HNSW_TARGET="26.0"
-            DISKANN_TARGET="26.0"
           fi
 
           # Repair HNSW wheel
@@ -365,15 +334,12 @@ jobs:
           PY_TAG=$($UV_PY -c "import sys; print(f'cp{sys.version_info[0]}{sys.version_info[1]}')")
 
           if [[ "$RUNNER_OS" == "macOS" ]]; then
-            # macos-15-intel runs macOS 15, so target 15.0 (system libraries require it)
+            if [[ "${{ matrix.os }}" == "macos-13" ]]; then
-            if [[ "${{ matrix.os }}" == "macos-15-intel" ]]; then
+              export MACOSX_DEPLOYMENT_TARGET=13.3
-              export MACOSX_DEPLOYMENT_TARGET=15.0
+            elif [[ "${{ matrix.os }}" == "macos-14" ]]; then
-            elif [[ "${{ matrix.os }}" == macos-14* ]]; then
               export MACOSX_DEPLOYMENT_TARGET=14.0
-            elif [[ "${{ matrix.os }}" == macos-15* ]]; then
+            elif [[ "${{ matrix.os }}" == "macos-15" ]]; then
               export MACOSX_DEPLOYMENT_TARGET=15.0
-            elif [[ "${{ matrix.os }}" == macos-26* ]]; then
-              export MACOSX_DEPLOYMENT_TARGET=26.0
             fi
           fi
 

README.md

@@ -36,7 +36,7 @@ LEANN is an innovative vector database that democratizes personal AI. Transform
 
 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 semantic 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)** ([WeChat](#-wechat-detective-unlock-your-golden-memories), [iMessage](#-imessage-history-your-personal-conversation-archive)), **[agent memory](#-chatgpt-chat-history-your-personal-ai-conversation-archive)** ([ChatGPT](#-chatgpt-chat-history-your-personal-ai-conversation-archive), [Claude](#-claude-chat-history-your-personal-ai-conversation-archive)), **[live data](#mcp-integration-rag-on-live-data-from-any-platform)** ([Slack](#slack-messages-search-your-team-conversations), [Twitter](#-twitter-bookmarks-your-personal-tweet-library)), **[codebase](#-claude-code-integration-transform-your-development-workflow)**\* , 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 semantic 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)** ([WeChat](#-wechat-detective-unlock-your-golden-memories), [iMessage](#-imessage-history-your-personal-conversation-archive)), **[agent memory](#-chatgpt-chat-history-your-personal-ai-conversation-archive)** ([ChatGPT](#-chatgpt-chat-history-your-personal-ai-conversation-archive), [Claude](#-claude-chat-history-your-personal-ai-conversation-archive)), **[live data](#mcp-integration-rag-on-live-data-from-any-platform)** ([Slack](#mcp-integration-rag-on-live-data-from-any-platform), [Twitter](#mcp-integration-rag-on-live-data-from-any-platform)), **[codebase](#-claude-code-integration-transform-your-development-workflow)**\* , or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
 
 
 \* Claude Code only supports basic `grep`-style keyword search. **LEANN** is a drop-in **semantic search MCP service fully compatible with Claude Code**, unlocking intelligent retrieval without changing your workflow. 🔥 Check out [the easy setup →](packages/leann-mcp/README.md)
@@ -392,54 +392,6 @@ python -m apps.code_rag --repo-dir "./my_codebase" --query "How does authenticat
 
 </details>
 
-### 🎨 ColQwen: Multimodal PDF Retrieval with Vision-Language Models
-
-Search through PDFs using both text and visual understanding with ColQwen2/ColPali models. Perfect for research papers, technical documents, and any PDFs with complex layouts, figures, or diagrams.
-
-> **🍎 Mac Users**: ColQwen is optimized for Apple Silicon with MPS acceleration for faster inference!
-
-```bash
-# Build index from PDFs
-python -m apps.colqwen_rag build --pdfs ./my_papers/ --index research_papers
-
-# Search with text queries
-python -m apps.colqwen_rag search research_papers "How does attention mechanism work?"
-
-# Interactive Q&A
-python -m apps.colqwen_rag ask research_papers --interactive
-```
-
-<details>
-<summary><strong>📋 Click to expand: ColQwen Setup & Usage</strong></summary>
-
-#### Prerequisites
-```bash
-# Install dependencies
-uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn
-brew install poppler  # macOS only, for PDF processing
-```
-
-#### Build Index
-```bash
-python -m apps.colqwen_rag build \
-  --pdfs ./pdf_directory/ \
-  --index my_index \
-  --model colqwen2  # or colpali
-```
-
-#### Search
-```bash
-python -m apps.colqwen_rag search my_index "your question here" --top-k 5
-```
-
-#### Models
-- **ColQwen2** (`colqwen2`): Latest vision-language model with improved performance
-- **ColPali** (`colpali`): Proven multimodal retriever
-
-For detailed usage, see the [ColQwen Guide](docs/COLQWEN_GUIDE.md).
-
-</details>
-
 ### 📧 Your Personal Email Secretary: RAG on Apple Mail!
 
 > **Note:** The examples below currently support macOS only. Windows support coming soon.

apps/base_rag_example.py

@@ -257,8 +257,8 @@ class BaseRAGExample(ABC):
         pass
 
     @abstractmethod
-    async def load_data(self, args) -> list[dict[str, Any]]:
+    async def load_data(self, args) -> list[str]:
-        """Load data from the source. Returns list of text chunks as dicts with 'text' and 'metadata' keys."""
+        """Load data from the source. Returns list of text chunks."""
         pass
 
     def get_llm_config(self, args) -> dict[str, Any]:
@@ -282,8 +282,8 @@ class BaseRAGExample(ABC):
 
         return config
 
-    async def build_index(self, args, texts: list[dict[str, Any]]) -> str:
+    async def build_index(self, args, texts: list[str]) -> str:
-        """Build LEANN index from text chunks (dicts with 'text' and 'metadata' keys)."""
+        """Build LEANN index from texts."""
         index_path = str(Path(args.index_dir) / f"{self.default_index_name}.leann")
 
         print(f"\n[Building Index] Creating {self.name} index...")
@@ -314,14 +314,8 @@ class BaseRAGExample(ABC):
         batch_size = 1000
         for i in range(0, len(texts), batch_size):
             batch = texts[i : i + batch_size]
-            for item in batch:
+            for text in batch:
-                # Handle both dict format (from create_text_chunks) and plain strings
+                builder.add_text(text)
-                if isinstance(item, dict):
-                    text = item.get("text", "")
-                    metadata = item.get("metadata")
-                    builder.add_text(text, metadata)
-                else:
-                    builder.add_text(item)
             print(f"Added {min(i + batch_size, len(texts))}/{len(texts)} texts...")
 
         print("Building index structure...")

apps/browser_rag.py

@@ -6,7 +6,6 @@ Supports Chrome browser history.
 import os
 import sys
 from pathlib import Path
-from typing import Any
 
 # Add parent directory to path for imports
 sys.path.insert(0, str(Path(__file__).parent))
@@ -86,7 +85,7 @@ class BrowserRAG(BaseRAGExample):
 
         return profiles
 
-    async def load_data(self, args) -> list[dict[str, Any]]:
+    async def load_data(self, args) -> list[str]:
         """Load browser history and convert to text chunks."""
         # Determine Chrome profiles
         if args.chrome_profile and not args.auto_find_profiles:

apps/chatgpt_rag.py

@@ -5,7 +5,6 @@ Supports ChatGPT export data from chat.html files.
 
 import sys
 from pathlib import Path
-from typing import Any
 
 # Add parent directory to path for imports
 sys.path.insert(0, str(Path(__file__).parent))
@@ -81,7 +80,7 @@ class ChatGPTRAG(BaseRAGExample):
 
         return export_files
 
-    async def load_data(self, args) -> list[dict[str, Any]]:
+    async def load_data(self, args) -> list[str]:
         """Load ChatGPT export data and convert to text chunks."""
         export_path = Path(args.export_path)
 

apps/claude_rag.py

@@ -5,7 +5,6 @@ Supports Claude export data from JSON files.
 
 import sys
 from pathlib import Path
-from typing import Any
 
 # Add parent directory to path for imports
 sys.path.insert(0, str(Path(__file__).parent))
@@ -81,7 +80,7 @@ class ClaudeRAG(BaseRAGExample):
 
         return export_files
 
-    async def load_data(self, args) -> list[dict[str, Any]]:
+    async def load_data(self, args) -> list[str]:
         """Load Claude export data and convert to text chunks."""
         export_path = Path(args.export_path)
 

apps/code_rag.py

@@ -6,7 +6,6 @@ optimized chunking parameters.
 
 import sys
 from pathlib import Path
-from typing import Any
 
 # Add parent directory to path for imports
 sys.path.insert(0, str(Path(__file__).parent))
@@ -78,7 +77,7 @@ class CodeRAG(BaseRAGExample):
             help="Try to preserve import statements in chunks (default: True)",
         )
 
-    async def load_data(self, args) -> list[dict[str, Any]]:
+    async def load_data(self, args) -> list[str]:
         """Load code files and convert to AST-aware chunks."""
         print(f"🔍 Scanning code repository: {args.repo_dir}")
         print(f"📁 Including extensions: {args.include_extensions}")
@@ -89,6 +88,14 @@ class CodeRAG(BaseRAGExample):
         if not repo_path.exists():
             raise ValueError(f"Repository directory not found: {args.repo_dir}")
 
+        # Load code files with filtering
+        reader_kwargs = {
+            "recursive": True,
+            "encoding": "utf-8",
+            "required_exts": args.include_extensions,
+            "exclude_hidden": True,
+        }
+
         # Create exclusion filter
         def file_filter(file_path: str) -> bool:
             """Filter out unwanted files and directories."""
@@ -113,11 +120,8 @@ class CodeRAG(BaseRAGExample):
             # Load documents with file filtering
             documents = SimpleDirectoryReader(
                 args.repo_dir,
-                file_extractor=None,
+                file_extractor=None,  # Use default extractors
-                recursive=True,
+                **reader_kwargs,
-                encoding="utf-8",
-                required_exts=args.include_extensions,
-                exclude_hidden=True,
             ).load_data(show_progress=True)
 
             # Apply custom filtering

apps/colqwen_rag.py

@@ -1,364 +0,0 @@
-#!/usr/bin/env python3
-"""
-ColQwen RAG - Easy-to-use multimodal PDF retrieval with ColQwen2/ColPali
-
-Usage:
-    python -m apps.colqwen_rag build --pdfs ./my_pdfs/ --index my_index
-    python -m apps.colqwen_rag search my_index "How does attention work?"
-    python -m apps.colqwen_rag ask my_index --interactive
-"""
-
-import argparse
-import os
-import sys
-from pathlib import Path
-from typing import Optional, cast
-
-# Add LEANN packages to path
-_repo_root = Path(__file__).resolve().parents[1]
-_leann_core_src = _repo_root / "packages" / "leann-core" / "src"
-_leann_hnsw_pkg = _repo_root / "packages" / "leann-backend-hnsw"
-if str(_leann_core_src) not in sys.path:
-    sys.path.append(str(_leann_core_src))
-if str(_leann_hnsw_pkg) not in sys.path:
-    sys.path.append(str(_leann_hnsw_pkg))
-
-import torch  # noqa: E402
-from colpali_engine import ColPali, ColPaliProcessor, ColQwen2, ColQwen2Processor  # noqa: E402
-from colpali_engine.utils.torch_utils import ListDataset  # noqa: E402
-from pdf2image import convert_from_path  # noqa: E402
-from PIL import Image  # noqa: E402
-from torch.utils.data import DataLoader  # noqa: E402
-from tqdm import tqdm  # noqa: E402
-
-# Import the existing multi-vector implementation
-sys.path.append(str(_repo_root / "apps" / "multimodal" / "vision-based-pdf-multi-vector"))
-from leann_multi_vector import LeannMultiVector  # noqa: E402
-
-
-class ColQwenRAG:
-    """Easy-to-use ColQwen RAG system for multimodal PDF retrieval."""
-
-    def __init__(self, model_type: str = "colpali"):
-        """
-        Initialize ColQwen RAG system.
-
-        Args:
-            model_type: "colqwen2" or "colpali"
-        """
-        self.model_type = model_type
-        self.device = self._get_device()
-        # Use float32 on MPS to avoid memory issues, float16 on CUDA, bfloat16 on CPU
-        if self.device.type == "mps":
-            self.dtype = torch.float32
-        elif self.device.type == "cuda":
-            self.dtype = torch.float16
-        else:
-            self.dtype = torch.bfloat16
-
-        print(f"🚀 Initializing {model_type.upper()} on {self.device} with {self.dtype}")
-
-        # Load model and processor with MPS-optimized settings
-        try:
-            if model_type == "colqwen2":
-                self.model_name = "vidore/colqwen2-v1.0"
-                if self.device.type == "mps":
-                    # For MPS, load on CPU first then move to avoid memory allocation issues
-                    self.model = ColQwen2.from_pretrained(
-                        self.model_name,
-                        torch_dtype=self.dtype,
-                        device_map="cpu",
-                        low_cpu_mem_usage=True,
-                    ).eval()
-                    self.model = self.model.to(self.device)
-                else:
-                    self.model = ColQwen2.from_pretrained(
-                        self.model_name,
-                        torch_dtype=self.dtype,
-                        device_map=self.device,
-                        low_cpu_mem_usage=True,
-                    ).eval()
-                self.processor = ColQwen2Processor.from_pretrained(self.model_name)
-            else:  # colpali
-                self.model_name = "vidore/colpali-v1.2"
-                if self.device.type == "mps":
-                    # For MPS, load on CPU first then move to avoid memory allocation issues
-                    self.model = ColPali.from_pretrained(
-                        self.model_name,
-                        torch_dtype=self.dtype,
-                        device_map="cpu",
-                        low_cpu_mem_usage=True,
-                    ).eval()
-                    self.model = self.model.to(self.device)
-                else:
-                    self.model = ColPali.from_pretrained(
-                        self.model_name,
-                        torch_dtype=self.dtype,
-                        device_map=self.device,
-                        low_cpu_mem_usage=True,
-                    ).eval()
-                self.processor = ColPaliProcessor.from_pretrained(self.model_name)
-        except Exception as e:
-            if "memory" in str(e).lower() or "offload" in str(e).lower():
-                print(f"⚠️  Memory constraint on {self.device}, using CPU with optimizations...")
-                self.device = torch.device("cpu")
-                self.dtype = torch.float32
-
-                if model_type == "colqwen2":
-                    self.model = ColQwen2.from_pretrained(
-                        self.model_name,
-                        torch_dtype=self.dtype,
-                        device_map="cpu",
-                        low_cpu_mem_usage=True,
-                    ).eval()
-                else:
-                    self.model = ColPali.from_pretrained(
-                        self.model_name,
-                        torch_dtype=self.dtype,
-                        device_map="cpu",
-                        low_cpu_mem_usage=True,
-                    ).eval()
-            else:
-                raise
-
-    def _get_device(self):
-        """Auto-select best available device."""
-        if torch.cuda.is_available():
-            return torch.device("cuda")
-        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
-            return torch.device("mps")
-        else:
-            return torch.device("cpu")
-
-    def build_index(self, pdf_paths: list[str], index_name: str, pages_dir: Optional[str] = None):
-        """
-        Build multimodal index from PDF files.
-
-        Args:
-            pdf_paths: List of PDF file paths
-            index_name: Name for the index
-            pages_dir: Directory to save page images (optional)
-        """
-        print(f"Building index '{index_name}' from {len(pdf_paths)} PDFs...")
-
-        # Convert PDFs to images
-        all_images = []
-        all_metadata = []
-
-        if pages_dir:
-            os.makedirs(pages_dir, exist_ok=True)
-
-        for pdf_path in tqdm(pdf_paths, desc="Converting PDFs"):
-            try:
-                images = convert_from_path(pdf_path, dpi=150)
-                pdf_name = Path(pdf_path).stem
-
-                for i, image in enumerate(images):
-                    # Save image if pages_dir specified
-                    if pages_dir:
-                        image_path = Path(pages_dir) / f"{pdf_name}_page_{i + 1}.png"
-                        image.save(image_path)
-
-                    all_images.append(image)
-                    all_metadata.append(
-                        {
-                            "pdf_path": pdf_path,
-                            "pdf_name": pdf_name,
-                            "page_number": i + 1,
-                            "image_path": str(image_path) if pages_dir else None,
-                        }
-                    )
-
-            except Exception as e:
-                print(f"❌ Error processing {pdf_path}: {e}")
-                continue
-
-        print(f"📄 Converted {len(all_images)} pages from {len(pdf_paths)} PDFs")
-        print(f"All metadata: {all_metadata}")
-
-        # Generate embeddings
-        print("🧠 Generating embeddings...")
-        embeddings = self._embed_images(all_images)
-
-        # Build LEANN index
-        print("🔍 Building LEANN index...")
-        leann_mv = LeannMultiVector(
-            index_path=index_name,
-            dim=embeddings.shape[-1],
-            embedding_model_name=self.model_type,
-        )
-
-        # Create collection and insert data
-        leann_mv.create_collection()
-        for i, (embedding, metadata) in enumerate(zip(embeddings, all_metadata)):
-            data = {
-                "doc_id": i,
-                "filepath": metadata.get("image_path", ""),
-                "colbert_vecs": embedding.numpy(),  # Convert tensor to numpy
-            }
-            leann_mv.insert(data)
-
-        # Build the index
-        leann_mv.create_index()
-        print(f"✅ Index '{index_name}' built successfully!")
-
-        return leann_mv
-
-    def search(self, index_name: str, query: str, top_k: int = 5):
-        """
-        Search the index with a text query.
-
-        Args:
-            index_name: Name of the index to search
-            query: Text query
-            top_k: Number of results to return
-        """
-        print(f"🔍 Searching '{index_name}' for: '{query}'")
-
-        # Load index
-        leann_mv = LeannMultiVector(
-            index_path=index_name,
-            dim=128,  # Will be updated when loading
-            embedding_model_name=self.model_type,
-        )
-
-        # Generate query embedding
-        query_embedding = self._embed_query(query)
-
-        # Search (returns list of (score, doc_id) tuples)
-        search_results = leann_mv.search(query_embedding.numpy(), topk=top_k)
-
-        # Display results
-        print(f"\n📋 Top {len(search_results)} results:")
-        for i, (score, doc_id) in enumerate(search_results, 1):
-            # Get metadata for this doc_id (we need to load the metadata)
-            print(f"{i}. Score: {score:.3f} | Doc ID: {doc_id}")
-
-        return search_results
-
-    def ask(self, index_name: str, interactive: bool = False):
-        """
-        Interactive Q&A with the indexed documents.
-
-        Args:
-            index_name: Name of the index to query
-            interactive: Whether to run in interactive mode
-        """
-        print(f"💬 ColQwen Chat with '{index_name}'")
-
-        if interactive:
-            print("Type 'quit' to exit, 'help' for commands")
-            while True:
-                try:
-                    query = input("\n🤔 Your question: ").strip()
-                    if query.lower() in ["quit", "exit", "q"]:
-                        break
-                    elif query.lower() == "help":
-                        print("Commands: quit/exit/q (exit), help (this message)")
-                        continue
-                    elif not query:
-                        continue
-
-                    self.search(index_name, query, top_k=3)
-
-                    # TODO: Add answer generation with Qwen-VL
-                    print("\n💡 For detailed answers, we can integrate Qwen-VL here!")
-
-                except KeyboardInterrupt:
-                    print("\n👋 Goodbye!")
-                    break
-        else:
-            query = input("🤔 Your question: ").strip()
-            if query:
-                self.search(index_name, query)
-
-    def _embed_images(self, images: list[Image.Image]) -> torch.Tensor:
-        """Generate embeddings for a list of images."""
-        dataset = ListDataset(images)
-        dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x)
-
-        embeddings = []
-        with torch.no_grad():
-            for batch in tqdm(dataloader, desc="Embedding images"):
-                batch_images = cast(list, batch)
-                batch_inputs = self.processor.process_images(batch_images).to(self.device)
-                batch_embeddings = self.model(**batch_inputs)
-                embeddings.append(batch_embeddings.cpu())
-
-        return torch.cat(embeddings, dim=0)
-
-    def _embed_query(self, query: str) -> torch.Tensor:
-        """Generate embedding for a text query."""
-        with torch.no_grad():
-            query_inputs = self.processor.process_queries([query]).to(self.device)
-            query_embedding = self.model(**query_inputs)
-            return query_embedding.cpu()
-
-
-def main():
-    parser = argparse.ArgumentParser(description="ColQwen RAG - Easy multimodal PDF retrieval")
-    subparsers = parser.add_subparsers(dest="command", help="Available commands")
-
-    # Build command
-    build_parser = subparsers.add_parser("build", help="Build index from PDFs")
-    build_parser.add_argument("--pdfs", required=True, help="Directory containing PDF files")
-    build_parser.add_argument("--index", required=True, help="Index name")
-    build_parser.add_argument(
-        "--model", choices=["colqwen2", "colpali"], default="colqwen2", help="Model to use"
-    )
-    build_parser.add_argument("--pages-dir", help="Directory to save page images")
-
-    # Search command
-    search_parser = subparsers.add_parser("search", help="Search the index")
-    search_parser.add_argument("index", help="Index name")
-    search_parser.add_argument("query", help="Search query")
-    search_parser.add_argument("--top-k", type=int, default=5, help="Number of results")
-    search_parser.add_argument(
-        "--model", choices=["colqwen2", "colpali"], default="colqwen2", help="Model to use"
-    )
-
-    # Ask command
-    ask_parser = subparsers.add_parser("ask", help="Interactive Q&A")
-    ask_parser.add_argument("index", help="Index name")
-    ask_parser.add_argument("--interactive", action="store_true", help="Interactive mode")
-    ask_parser.add_argument(
-        "--model", choices=["colqwen2", "colpali"], default="colqwen2", help="Model to use"
-    )
-
-    args = parser.parse_args()
-
-    if not args.command:
-        parser.print_help()
-        return
-
-    # Initialize ColQwen RAG
-    if args.command == "build":
-        colqwen = ColQwenRAG(args.model)
-
-        # Get PDF files
-        pdf_dir = Path(args.pdfs)
-        if pdf_dir.is_file() and pdf_dir.suffix.lower() == ".pdf":
-            pdf_paths = [str(pdf_dir)]
-        elif pdf_dir.is_dir():
-            pdf_paths = [str(p) for p in pdf_dir.glob("*.pdf")]
-        else:
-            print(f"❌ Invalid PDF path: {args.pdfs}")
-            return
-
-        if not pdf_paths:
-            print(f"❌ No PDF files found in {args.pdfs}")
-            return
-
-        colqwen.build_index(pdf_paths, args.index, args.pages_dir)
-
-    elif args.command == "search":
-        colqwen = ColQwenRAG(args.model)
-        colqwen.search(args.index, args.query, args.top_k)
-
-    elif args.command == "ask":
-        colqwen = ColQwenRAG(args.model)
-        colqwen.ask(args.index, args.interactive)
-
-
-if __name__ == "__main__":
-    main()

apps/document_rag.py

@@ -5,7 +5,6 @@ Supports PDF, TXT, MD, and other document formats.
 
 import sys
 from pathlib import Path
-from typing import Any
 
 # Add parent directory to path for imports
 sys.path.insert(0, str(Path(__file__).parent))
@@ -52,7 +51,7 @@ class DocumentRAG(BaseRAGExample):
             help="Enable AST-aware chunking for code files in the data directory",
         )
 
-    async def load_data(self, args) -> list[dict[str, Any]]:
+    async def load_data(self, args) -> list[str]:
         """Load documents and convert to text chunks."""
         print(f"Loading documents from: {args.data_dir}")
         if args.file_types:
@@ -66,12 +65,16 @@ class DocumentRAG(BaseRAGExample):
             raise ValueError(f"Data directory not found: {args.data_dir}")
 
         # Load documents
-        documents = SimpleDirectoryReader(
+        reader_kwargs = {
-            args.data_dir,
+            "recursive": True,
-            recursive=True,
+            "encoding": "utf-8",
-            encoding="utf-8",
+        }
-            required_exts=args.file_types if args.file_types else None,
+        if args.file_types:
-        ).load_data(show_progress=True)
+            reader_kwargs["required_exts"] = args.file_types
+
+        documents = SimpleDirectoryReader(args.data_dir, **reader_kwargs).load_data(
+            show_progress=True
+        )
 
         if not documents:
             print(f"No documents found in {args.data_dir} with extensions {args.file_types}")

apps/email_data/email.py

@@ -127,12 +127,11 @@ class EmlxMboxReader(MboxReader):
 
     def load_data(
         self,
-        file: Path,  # Note: for EmlxMboxReader, this is actually a directory
+        directory: Path,
         extra_info: dict | None = None,
         fs: AbstractFileSystem | None = None,
     ) -> list[Document]:
         """Parse .emlx files from directory into strings using MboxReader logic."""
-        directory = file  # Rename for clarity - this is a directory of .emlx files
         import os
         import tempfile
 

apps/email_rag.py

@@ -5,7 +5,6 @@ Supports Apple Mail on macOS.
 
 import sys
 from pathlib import Path
-from typing import Any
 
 # Add parent directory to path for imports
 sys.path.insert(0, str(Path(__file__).parent))
@@ -65,7 +64,7 @@ class EmailRAG(BaseRAGExample):
 
         return messages_dirs
 
-    async def load_data(self, args) -> list[dict[str, Any]]:
+    async def load_data(self, args) -> list[str]:
         """Load emails and convert to text chunks."""
         # Determine mail directories
         if args.mail_path:

apps/history_data/wechat_history.py

@@ -86,7 +86,7 @@ class WeChatHistoryReader(BaseReader):
                 text=True,
                 timeout=5,
             )
-            return result.returncode == 0 and bool(result.stdout.strip())
+            return result.returncode == 0 and result.stdout.strip()
         except Exception:
             return False
 
@@ -314,9 +314,7 @@ class WeChatHistoryReader(BaseReader):
 
         return concatenated_groups
 
-    def _create_concatenated_content(
+    def _create_concatenated_content(self, message_group: dict, contact_name: str) -> str:
-        self, message_group: dict, contact_name: str
-    ) -> tuple[str, str]:
         """
         Create concatenated content from a group of messages.
 

apps/image_rag.py

@@ -1,219 +0,0 @@
-#!/usr/bin/env python3
-"""
-CLIP Image RAG Application
-
-This application enables RAG (Retrieval-Augmented Generation) on images using CLIP embeddings.
-You can index a directory of images and search them using text queries.
-
-Usage:
-    python -m apps.image_rag --image-dir ./my_images/ --query "a sunset over mountains"
-    python -m apps.image_rag --image-dir ./my_images/ --interactive
-"""
-
-import argparse
-import pickle
-import tempfile
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-from PIL import Image
-from sentence_transformers import SentenceTransformer
-from tqdm import tqdm
-
-from apps.base_rag_example import BaseRAGExample
-
-
-class ImageRAG(BaseRAGExample):
-    """
-    RAG application for images using CLIP embeddings.
-
-    This class provides a complete RAG pipeline for image data, including
-    CLIP embedding generation, indexing, and text-based image search.
-    """
-
-    def __init__(self):
-        super().__init__(
-            name="Image RAG",
-            description="RAG application for images using CLIP embeddings",
-            default_index_name="image_index",
-        )
-        # Override default embedding model to use CLIP
-        self.embedding_model_default = "clip-ViT-L-14"
-        self.embedding_mode_default = "sentence-transformers"
-        self._image_data: list[dict] = []
-
-    def _add_specific_arguments(self, parser: argparse.ArgumentParser):
-        """Add image-specific arguments."""
-        image_group = parser.add_argument_group("Image Parameters")
-        image_group.add_argument(
-            "--image-dir",
-            type=str,
-            required=True,
-            help="Directory containing images to index",
-        )
-        image_group.add_argument(
-            "--image-extensions",
-            type=str,
-            nargs="+",
-            default=[".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp"],
-            help="Image file extensions to process (default: .jpg .jpeg .png .gif .bmp .webp)",
-        )
-        image_group.add_argument(
-            "--batch-size",
-            type=int,
-            default=32,
-            help="Batch size for CLIP embedding generation (default: 32)",
-        )
-
-    async def load_data(self, args) -> list[dict[str, Any]]:
-        """Load images, generate CLIP embeddings, and return text descriptions."""
-        self._image_data = self._load_images_and_embeddings(args)
-        return [entry["text"] for entry in self._image_data]
-
-    def _load_images_and_embeddings(self, args) -> list[dict]:
-        """Helper to process images and produce embeddings/metadata."""
-        image_dir = Path(args.image_dir)
-        if not image_dir.exists():
-            raise ValueError(f"Image directory does not exist: {image_dir}")
-
-        print(f"📸 Loading images from {image_dir}...")
-
-        # Find all image files
-        image_files = []
-        for ext in args.image_extensions:
-            image_files.extend(image_dir.rglob(f"*{ext}"))
-            image_files.extend(image_dir.rglob(f"*{ext.upper()}"))
-
-        if not image_files:
-            raise ValueError(
-                f"No images found in {image_dir} with extensions {args.image_extensions}"
-            )
-
-        print(f"✅ Found {len(image_files)} images")
-
-        # Limit if max_items is set
-        if args.max_items > 0:
-            image_files = image_files[: args.max_items]
-            print(f"📊 Processing {len(image_files)} images (limited by --max-items)")
-
-        # Load CLIP model
-        print("🔍 Loading CLIP model...")
-        model = SentenceTransformer(self.embedding_model_default)
-
-        # Process images and generate embeddings
-        print("🖼️  Processing images and generating embeddings...")
-        image_data = []
-        batch_images = []
-        batch_paths = []
-
-        for image_path in tqdm(image_files, desc="Processing images"):
-            try:
-                image = Image.open(image_path).convert("RGB")
-                batch_images.append(image)
-                batch_paths.append(image_path)
-
-                # Process in batches
-                if len(batch_images) >= args.batch_size:
-                    embeddings = model.encode(
-                        batch_images,
-                        convert_to_numpy=True,
-                        normalize_embeddings=True,
-                        batch_size=args.batch_size,
-                        show_progress_bar=False,
-                    )
-
-                    for img_path, embedding in zip(batch_paths, embeddings):
-                        image_data.append(
-                            {
-                                "text": f"Image: {img_path.name}\nPath: {img_path}",
-                                "metadata": {
-                                    "image_path": str(img_path),
-                                    "image_name": img_path.name,
-                                    "image_dir": str(image_dir),
-                                },
-                                "embedding": embedding.astype(np.float32),
-                            }
-                        )
-
-                    batch_images = []
-                    batch_paths = []
-
-            except Exception as e:
-                print(f"⚠️  Failed to process {image_path}: {e}")
-                continue
-
-        # Process remaining images
-        if batch_images:
-            embeddings = model.encode(
-                batch_images,
-                convert_to_numpy=True,
-                normalize_embeddings=True,
-                batch_size=len(batch_images),
-                show_progress_bar=False,
-            )
-
-            for img_path, embedding in zip(batch_paths, embeddings):
-                image_data.append(
-                    {
-                        "text": f"Image: {img_path.name}\nPath: {img_path}",
-                        "metadata": {
-                            "image_path": str(img_path),
-                            "image_name": img_path.name,
-                            "image_dir": str(image_dir),
-                        },
-                        "embedding": embedding.astype(np.float32),
-                    }
-                )
-
-        print(f"✅ Processed {len(image_data)} images")
-        return image_data
-
-    async def build_index(self, args, texts: list[dict[str, Any]]) -> str:
-        """Build index using pre-computed CLIP embeddings."""
-        from leann.api import LeannBuilder
-
-        if not self._image_data or len(self._image_data) != len(texts):
-            raise RuntimeError("No image data found. Make sure load_data() ran successfully.")
-
-        print("🔨 Building LEANN index with CLIP embeddings...")
-        builder = LeannBuilder(
-            backend_name=args.backend_name,
-            embedding_model=self.embedding_model_default,
-            embedding_mode=self.embedding_mode_default,
-            is_recompute=False,
-            distance_metric="cosine",
-            graph_degree=args.graph_degree,
-            build_complexity=args.build_complexity,
-            is_compact=not args.no_compact,
-        )
-
-        for text, data in zip(texts, self._image_data):
-            builder.add_text(text=text, metadata=data["metadata"])
-
-        ids = [str(i) for i in range(len(self._image_data))]
-        embeddings = np.array([data["embedding"] for data in self._image_data], dtype=np.float32)
-
-        with tempfile.NamedTemporaryFile(mode="wb", suffix=".pkl", delete=False) as f:
-            pickle.dump((ids, embeddings), f)
-            pkl_path = f.name
-
-        try:
-            index_path = str(Path(args.index_dir) / f"{self.default_index_name}.leann")
-            builder.build_index_from_embeddings(index_path, pkl_path)
-            print(f"✅ Index built successfully at {index_path}")
-            return index_path
-        finally:
-            Path(pkl_path).unlink()
-
-
-def main():
-    """Main entry point for the image RAG application."""
-    import asyncio
-
-    app = ImageRAG()
-    asyncio.run(app.run())
-
-
-if __name__ == "__main__":
-    main()

apps/imessage_rag.py

@@ -6,7 +6,6 @@ This example demonstrates how to build a RAG system on your iMessage conversatio
 
 import asyncio
 from pathlib import Path
-from typing import Any
 
 from leann.chunking_utils import create_text_chunks
 
@@ -57,7 +56,7 @@ class IMessageRAG(BaseRAGExample):
             help="Overlap between text chunks (default: 200)",
         )
 
-    async def load_data(self, args) -> list[dict[str, Any]]:
+    async def load_data(self, args) -> list[str]:
         """Load iMessage history and convert to text chunks."""
         print("Loading iMessage conversation history...")
 

apps/multimodal/vision-based-pdf-multi-vector/multi-vector-leann-paper-example.py

@@ -18,11 +18,10 @@ _repo_root = Path(__file__).resolve().parents[3]
 _leann_core_src = _repo_root / "packages" / "leann-core" / "src"
 _leann_hnsw_pkg = _repo_root / "packages" / "leann-backend-hnsw"
 if str(_leann_core_src) not in sys.path:
-    sys.path.insert(0, str(_leann_core_src))
+    sys.path.append(str(_leann_core_src))
 if str(_leann_hnsw_pkg) not in sys.path:
-    sys.path.insert(0, str(_leann_hnsw_pkg))
+    sys.path.append(str(_leann_hnsw_pkg))
 
-from leann_multi_vector import LeannMultiVector
 
 import torch
 from colpali_engine.models import ColPali
@@ -94,9 +93,9 @@ for batch_doc in tqdm(dataloader):
 print(ds[0].shape)
 
 # %%
-# Build HNSW index via LeannMultiVector primitives and run search
+# Build HNSW index via LeannRetriever primitives and run search
 index_path = "./indexes/colpali.leann"
-retriever = LeannMultiVector(index_path=index_path, dim=int(ds[0].shape[-1]))
+retriever = LeannRetriever(index_path=index_path, dim=int(ds[0].shape[-1]))
 retriever.create_collection()
 filepaths = [os.path.join("./pages", name) for name in page_filenames]
 for i in range(len(filepaths)):

apps/multimodal/vision-based-pdf-multi-vector/multi-vector-leann-similarity-map.py

@@ -5,7 +5,7 @@ import argparse
 import faulthandler
 import os
 import time
-from typing import Any, Optional, cast
+from typing import Any, Optional
 
 import numpy as np
 from PIL import Image
@@ -223,7 +223,7 @@ if need_to_build_index:
         # Use filenames as identifiers instead of full paths for cleaner metadata
         filepaths = [os.path.basename(fp) for fp in filepaths]
     elif USE_HF_DATASET:
-        from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset
+        from datasets import load_dataset, concatenate_datasets, DatasetDict
 
         # Determine which datasets to load
         if DATASET_NAMES is not None:
@@ -281,12 +281,12 @@ if need_to_build_index:
                 splits_to_load = DATASET_SPLITS
 
             # Load and concatenate multiple splits for this dataset
-            datasets_to_concat: list[Dataset] = []
+            datasets_to_concat = []
             for split in splits_to_load:
                 if split not in dataset_dict:
                     print(f"  Warning: Split '{split}' not found in dataset. Available splits: {list(dataset_dict.keys())}")
                     continue
-                split_dataset = cast(Dataset, dataset_dict[split])
+                split_dataset = dataset_dict[split]
                 print(f"  Loaded split '{split}': {len(split_dataset)} pages")
                 datasets_to_concat.append(split_dataset)
 

apps/multimodal/vision-based-pdf-multi-vector/vidore_v1_benchmark.py

@@ -25,9 +25,9 @@ Usage:
 import argparse
 import json
 import os
-from typing import Any, Optional, cast
+from typing import Optional
 
-from datasets import Dataset, load_dataset
+from datasets import load_dataset
 from leann_multi_vector import (
     ViDoReBenchmarkEvaluator,
     _ensure_repo_paths_importable,
@@ -151,43 +151,40 @@ def load_vidore_v1_data(
     """
     print(f"Loading dataset: {dataset_path} (split={split})")
 
-    # Load queries - cast to Dataset since we know split returns Dataset not DatasetDict
+    # Load queries
-    query_ds = cast(Dataset, load_dataset(dataset_path, "queries", split=split, revision=revision))
+    query_ds = load_dataset(dataset_path, "queries", split=split, revision=revision)
 
-    queries: dict[str, str] = {}
+    queries = {}
     for row in query_ds:
-        row_dict = cast(dict[str, Any], row)
+        query_id = f"query-{split}-{row['query-id']}"
-        query_id = f"query-{split}-{row_dict['query-id']}"
+        queries[query_id] = row["query"]
-        queries[query_id] = row_dict["query"]
 
-    # Load corpus (images) - cast to Dataset
+    # Load corpus (images)
-    corpus_ds = cast(Dataset, load_dataset(dataset_path, "corpus", split=split, revision=revision))
+    corpus_ds = load_dataset(dataset_path, "corpus", split=split, revision=revision)
 
-    corpus: dict[str, Any] = {}
+    corpus = {}
     for row in corpus_ds:
-        row_dict = cast(dict[str, Any], row)
+        corpus_id = f"corpus-{split}-{row['corpus-id']}"
-        corpus_id = f"corpus-{split}-{row_dict['corpus-id']}"
         # Extract image from the dataset row
-        if "image" in row_dict:
+        if "image" in row:
-            corpus[corpus_id] = row_dict["image"]
+            corpus[corpus_id] = row["image"]
-        elif "page_image" in row_dict:
+        elif "page_image" in row:
-            corpus[corpus_id] = row_dict["page_image"]
+            corpus[corpus_id] = row["page_image"]
         else:
             raise ValueError(
-                f"No image field found in corpus. Available fields: {list(row_dict.keys())}"
+                f"No image field found in corpus. Available fields: {list(row.keys())}"
             )
 
-    # Load qrels (relevance judgments) - cast to Dataset
+    # Load qrels (relevance judgments)
-    qrels_ds = cast(Dataset, load_dataset(dataset_path, "qrels", split=split, revision=revision))
+    qrels_ds = load_dataset(dataset_path, "qrels", split=split, revision=revision)
 
-    qrels: dict[str, dict[str, int]] = {}
+    qrels = {}
     for row in qrels_ds:
-        row_dict = cast(dict[str, Any], row)
+        query_id = f"query-{split}-{row['query-id']}"
-        query_id = f"query-{split}-{row_dict['query-id']}"
+        corpus_id = f"corpus-{split}-{row['corpus-id']}"
-        corpus_id = f"corpus-{split}-{row_dict['corpus-id']}"
         if query_id not in qrels:
             qrels[query_id] = {}
-        qrels[query_id][corpus_id] = int(row_dict["score"])
+        qrels[query_id][corpus_id] = int(row["score"])
 
     print(
         f"Loaded {len(queries)} queries, {len(corpus)} corpus items, {len(qrels)} query-relevance mappings"
@@ -237,8 +234,8 @@ def evaluate_task(
         raise ValueError(f"Unknown task: {task_name}. Available: {list(VIDORE_V1_TASKS.keys())}")
 
     task_config = VIDORE_V1_TASKS[task_name]
-    dataset_path = str(task_config["dataset_path"])
+    dataset_path = task_config["dataset_path"]
-    revision = str(task_config["revision"])
+    revision = task_config["revision"]
 
     # Load data
     corpus, queries, qrels = load_vidore_v1_data(
@@ -289,7 +286,7 @@ def evaluate_task(
     )
 
     # Search queries
-    task_prompt = cast(Optional[dict[str, str]], task_config.get("prompt"))
+    task_prompt = task_config.get("prompt")
     results = evaluator.search_queries(
         queries=queries,
         corpus_ids=corpus_ids_ordered,

apps/multimodal/vision-based-pdf-multi-vector/vidore_v2_benchmark.py

@@ -25,9 +25,9 @@ Usage:
 import argparse
 import json
 import os
-from typing import Any, Optional, cast
+from typing import Optional
 
-from datasets import Dataset, load_dataset
+from datasets import load_dataset
 from leann_multi_vector import (
     ViDoReBenchmarkEvaluator,
     _ensure_repo_paths_importable,
@@ -91,8 +91,8 @@ def load_vidore_v2_data(
     """
     print(f"Loading dataset: {dataset_path} (split={split}, language={language})")
 
-    # Load queries - cast to Dataset since we know split returns Dataset not DatasetDict
+    # Load queries
-    query_ds = cast(Dataset, load_dataset(dataset_path, "queries", split=split, revision=revision))
+    query_ds = load_dataset(dataset_path, "queries", split=split, revision=revision)
 
     # Check if dataset has language field before filtering
     has_language_field = len(query_ds) > 0 and "language" in query_ds.column_names
@@ -112,9 +112,8 @@ def load_vidore_v2_data(
             if len(query_ds_filtered) == 0:
                 # Try to get a sample to see actual language values
                 try:
-                    sample_ds = cast(
+                    sample_ds = load_dataset(
-                        Dataset,
+                        dataset_path, "queries", split=split, revision=revision
-                        load_dataset(dataset_path, "queries", split=split, revision=revision),
                     )
                     if len(sample_ds) > 0 and "language" in sample_ds.column_names:
                         sample_langs = set(sample_ds["language"])
@@ -127,40 +126,37 @@ def load_vidore_v2_data(
                 )
         query_ds = query_ds_filtered
 
-    queries: dict[str, str] = {}
+    queries = {}
     for row in query_ds:
-        row_dict = cast(dict[str, Any], row)
+        query_id = f"query-{split}-{row['query-id']}"
-        query_id = f"query-{split}-{row_dict['query-id']}"
+        queries[query_id] = row["query"]
-        queries[query_id] = row_dict["query"]
 
-    # Load corpus (images) - cast to Dataset
+    # Load corpus (images)
-    corpus_ds = cast(Dataset, load_dataset(dataset_path, "corpus", split=split, revision=revision))
+    corpus_ds = load_dataset(dataset_path, "corpus", split=split, revision=revision)
 
-    corpus: dict[str, Any] = {}
+    corpus = {}
     for row in corpus_ds:
-        row_dict = cast(dict[str, Any], row)
+        corpus_id = f"corpus-{split}-{row['corpus-id']}"
-        corpus_id = f"corpus-{split}-{row_dict['corpus-id']}"
         # Extract image from the dataset row
-        if "image" in row_dict:
+        if "image" in row:
-            corpus[corpus_id] = row_dict["image"]
+            corpus[corpus_id] = row["image"]
-        elif "page_image" in row_dict:
+        elif "page_image" in row:
-            corpus[corpus_id] = row_dict["page_image"]
+            corpus[corpus_id] = row["page_image"]
         else:
             raise ValueError(
-                f"No image field found in corpus. Available fields: {list(row_dict.keys())}"
+                f"No image field found in corpus. Available fields: {list(row.keys())}"
             )
 
-    # Load qrels (relevance judgments) - cast to Dataset
+    # Load qrels (relevance judgments)
-    qrels_ds = cast(Dataset, load_dataset(dataset_path, "qrels", split=split, revision=revision))
+    qrels_ds = load_dataset(dataset_path, "qrels", split=split, revision=revision)
 
-    qrels: dict[str, dict[str, int]] = {}
+    qrels = {}
     for row in qrels_ds:
-        row_dict = cast(dict[str, Any], row)
+        query_id = f"query-{split}-{row['query-id']}"
-        query_id = f"query-{split}-{row_dict['query-id']}"
+        corpus_id = f"corpus-{split}-{row['corpus-id']}"
-        corpus_id = f"corpus-{split}-{row_dict['corpus-id']}"
         if query_id not in qrels:
             qrels[query_id] = {}
-        qrels[query_id][corpus_id] = int(row_dict["score"])
+        qrels[query_id][corpus_id] = int(row["score"])
 
     print(
         f"Loaded {len(queries)} queries, {len(corpus)} corpus items, {len(qrels)} query-relevance mappings"
@@ -208,13 +204,13 @@ def evaluate_task(
         raise ValueError(f"Unknown task: {task_name}. Available: {list(VIDORE_V2_TASKS.keys())}")
 
     task_config = VIDORE_V2_TASKS[task_name]
-    dataset_path = str(task_config["dataset_path"])
+    dataset_path = task_config["dataset_path"]
-    revision = str(task_config["revision"])
+    revision = task_config["revision"]
 
     # Determine language
     if language is None:
         # Use first language if multiple available
-        languages = cast(Optional[list[str]], task_config.get("languages"))
+        languages = task_config.get("languages")
         if languages is None:
             # Task doesn't support language filtering (e.g., Vidore2ESGReportsHLRetrieval)
             language = None
@@ -273,7 +269,7 @@ def evaluate_task(
     )
 
     # Search queries
-    task_prompt = cast(Optional[dict[str, str]], task_config.get("prompt"))
+    task_prompt = task_config.get("prompt")
     results = evaluator.search_queries(
         queries=queries,
         corpus_ids=corpus_ids_ordered,

apps/slack_data/slack_mcp_reader.py

@@ -177,9 +177,7 @@ class SlackMCPReader:
                     break
 
         # If we get here, all retries failed or it's not a retryable error
-        if last_exception is not None:
         raise last_exception
-        raise RuntimeError("Unexpected error: no exception captured during retry loop")
 
     async def fetch_slack_messages(
         self, channel: Optional[str] = None, limit: int = 100
@@ -269,10 +267,7 @@ class SlackMCPReader:
                     messages = json.loads(content["text"])
                 except json.JSONDecodeError:
                     # If not JSON, try to parse as CSV format (Slack MCP server format)
-                    text_content = content.get("text", "")
+                    messages = self._parse_csv_messages(content["text"], channel)
-                    messages = self._parse_csv_messages(
-                        text_content if text_content else "", channel or "unknown"
-                    )
             else:
                 messages = result["content"]
         else:

apps/slack_rag.py

@@ -11,7 +11,6 @@ Usage:
 
 import argparse
 import asyncio
-from typing import Any
 
 from apps.base_rag_example import BaseRAGExample
 from apps.slack_data.slack_mcp_reader import SlackMCPReader
@@ -140,7 +139,7 @@ class SlackMCPRAG(BaseRAGExample):
             print("4. Try running the MCP server command directly to test it")
             return False
 
-    async def load_data(self, args) -> list[dict[str, Any]]:
+    async def load_data(self, args) -> list[str]:
         """Load Slack messages via MCP server."""
         print(f"Connecting to Slack MCP server: {args.mcp_server}")
 
@@ -189,8 +188,7 @@ class SlackMCPRAG(BaseRAGExample):
                 print(sample_text)
                 print("-" * 40)
 
-            # Convert strings to dict format expected by base class
+            return texts
-            return [{"text": text, "metadata": {"source": "slack"}} for text in texts]
 
         except Exception as e:
             print(f"Error loading Slack data: {e}")

apps/twitter_rag.py

@@ -11,7 +11,6 @@ Usage:
 
 import argparse
 import asyncio
-from typing import Any
 
 from apps.base_rag_example import BaseRAGExample
 from apps.twitter_data.twitter_mcp_reader import TwitterMCPReader
@@ -117,7 +116,7 @@ class TwitterMCPRAG(BaseRAGExample):
             print("5. Try running the MCP server command directly to test it")
             return False
 
-    async def load_data(self, args) -> list[dict[str, Any]]:
+    async def load_data(self, args) -> list[str]:
         """Load Twitter bookmarks via MCP server."""
         print(f"Connecting to Twitter MCP server: {args.mcp_server}")
 
@@ -157,8 +156,7 @@ class TwitterMCPRAG(BaseRAGExample):
                 print(sample_text)
                 print("-" * 50)
 
-            # Convert strings to dict format expected by base class
+            return texts
-            return [{"text": text, "metadata": {"source": "twitter"}} for text in texts]
 
         except Exception as e:
             print(f"❌ Error loading Twitter bookmarks: {e}")

apps/wechat_rag.py

@@ -6,7 +6,6 @@ Supports WeChat chat history export and search.
 import subprocess
 import sys
 from pathlib import Path
-from typing import Any
 
 # Add parent directory to path for imports
 sys.path.insert(0, str(Path(__file__).parent))
@@ -92,7 +91,7 @@ class WeChatRAG(BaseRAGExample):
             print(f"Export error: {e}")
             return False
 
-    async def load_data(self, args) -> list[dict[str, Any]]:
+    async def load_data(self, args) -> list[str]:
         """Load WeChat history and convert to text chunks."""
         # Initialize WeChat reader with export capabilities
         reader = WeChatHistoryReader()

docs/COLQWEN_GUIDE.md

@@ -1,200 +0,0 @@
-# ColQwen Integration Guide
-
-Easy-to-use multimodal PDF retrieval with ColQwen2/ColPali models.
-
-## Quick Start
-
-> **🍎 Mac Users**: ColQwen is optimized for Apple Silicon with MPS acceleration for faster inference!
-
-### 1. Install Dependencies
-```bash
-uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn
-brew install poppler  # macOS only, for PDF processing
-```
-
-### 2. Basic Usage
-```bash
-# Build index from PDFs
-python -m apps.colqwen_rag build --pdfs ./my_papers/ --index research_papers
-
-# Search with text queries
-python -m apps.colqwen_rag search research_papers "How does attention mechanism work?"
-
-# Interactive Q&A
-python -m apps.colqwen_rag ask research_papers --interactive
-```
-
-## Commands
-
-### Build Index
-```bash
-python -m apps.colqwen_rag build \
-  --pdfs ./pdf_directory/ \
-  --index my_index \
-  --model colqwen2 \
-  --pages-dir ./page_images/  # Optional: save page images
-```
-
-**Options:**
-- `--pdfs`: Directory containing PDF files (or single PDF path)
-- `--index`: Name for the index (required)
-- `--model`: `colqwen2` (default) or `colpali`
-- `--pages-dir`: Directory to save page images (optional)
-
-### Search Index
-```bash
-python -m apps.colqwen_rag search my_index "your question here" --top-k 5
-```
-
-**Options:**
-- `--top-k`: Number of results to return (default: 5)
-- `--model`: Model used for search (should match build model)
-
-### Interactive Q&A
-```bash
-python -m apps.colqwen_rag ask my_index --interactive
-```
-
-**Commands in interactive mode:**
-- Type your questions naturally
-- `help`: Show available commands
-- `quit`/`exit`/`q`: Exit interactive mode
-
-## 🧪 Test & Reproduce Results
-
-Run the reproduction test for issue #119:
-```bash
-python test_colqwen_reproduction.py
-```
-
-This will:
-1. ✅ Check dependencies
-2. 📥 Download sample PDF (Attention Is All You Need paper)
-3. 🏗️ Build test index
-4. 🔍 Run sample queries
-5. 📊 Show how to generate similarity maps
-
-## 🎨 Advanced: Similarity Maps
-
-For visual similarity analysis, use the existing advanced script:
-```bash
-cd apps/multimodal/vision-based-pdf-multi-vector/
-python multi-vector-leann-similarity-map.py
-```
-
-Edit the script to customize:
-- `QUERY`: Your question
-- `MODEL`: "colqwen2" or "colpali"
-- `USE_HF_DATASET`: Use HuggingFace dataset or local PDFs
-- `SIMILARITY_MAP`: Generate heatmaps
-- `ANSWER`: Enable Qwen-VL answer generation
-
-## 🔧 How It Works
-
-### ColQwen2 vs ColPali
-- **ColQwen2** (`vidore/colqwen2-v1.0`): Latest vision-language model
-- **ColPali** (`vidore/colpali-v1.2`): Proven multimodal retriever
-
-### Architecture
-1. **PDF → Images**: Convert PDF pages to images (150 DPI)
-2. **Vision Encoding**: Process images with ColQwen2/ColPali
-3. **Multi-Vector Index**: Build LEANN HNSW index with multiple embeddings per page
-4. **Query Processing**: Encode text queries with same model
-5. **Similarity Search**: Find most relevant pages/regions
-6. **Visual Maps**: Generate attention heatmaps (optional)
-
-### Device Support
-- **CUDA**: Best performance with GPU acceleration
-- **MPS**: Apple Silicon Mac support
-- **CPU**: Fallback for any system (slower)
-
-Auto-detection: CUDA > MPS > CPU
-
-## 📊 Performance Tips
-
-### For Best Performance:
-```bash
-# Use ColQwen2 for latest features
---model colqwen2
-
-# Save page images for reuse
---pages-dir ./cached_pages/
-
-# Adjust batch size based on GPU memory
-# (automatically handled)
-```
-
-### For Large Document Sets:
-- Process PDFs in batches
-- Use SSD storage for index files
-- Consider using CUDA if available
-
-## 🔗 Related Resources
-
-- **Fast-PLAID**: https://github.com/lightonai/fast-plaid
-- **Pylate**: https://github.com/lightonai/pylate
-- **ColBERT**: https://github.com/stanford-futuredata/ColBERT
-- **ColPali Paper**: Vision-Language Models for Document Retrieval
-- **Issue #119**: https://github.com/yichuan-w/LEANN/issues/119
-
-## 🐛 Troubleshooting
-
-### PDF Conversion Issues (macOS)
-```bash
-# Install poppler
-brew install poppler
-which pdfinfo && pdfinfo -v
-```
-
-### Memory Issues
-- Reduce batch size (automatically handled)
-- Use CPU instead of GPU: `export CUDA_VISIBLE_DEVICES=""`
-- Process fewer PDFs at once
-
-### Model Download Issues
-- Ensure internet connection for first run
-- Models are cached after first download
-- Use HuggingFace mirrors if needed
-
-### Import Errors
-```bash
-# Ensure all dependencies installed
-uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn
-
-# Check PyTorch installation
-python -c "import torch; print(torch.__version__)"
-```
-
-## 💡 Examples
-
-### Research Paper Analysis
-```bash
-# Index your research papers
-python -m apps.colqwen_rag build --pdfs ~/Papers/AI/ --index ai_papers
-
-# Ask research questions
-python -m apps.colqwen_rag search ai_papers "What are the limitations of transformer models?"
-python -m apps.colqwen_rag search ai_papers "How does BERT compare to GPT?"
-```
-
-### Document Q&A
-```bash
-# Index business documents
-python -m apps.colqwen_rag build --pdfs ~/Documents/Reports/ --index reports
-
-# Interactive analysis
-python -m apps.colqwen_rag ask reports --interactive
-```
-
-### Visual Analysis
-```bash
-# Generate similarity maps for specific queries
-cd apps/multimodal/vision-based-pdf-multi-vector/
-# Edit multi-vector-leann-similarity-map.py with your query
-python multi-vector-leann-similarity-map.py
-# Check ./figures/ for generated heatmaps
-```
-
----
-
-**🎯 This integration makes ColQwen as easy to use as other LEANN features while maintaining the full power of multimodal document understanding!**

packages/leann-core/pyproject.toml

@@ -7,7 +7,7 @@ name = "leann-core"
 version = "0.3.5"
 description = "Core API and plugin system for LEANN"
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.9"
 license = { text = "MIT" }
 
 # All required dependencies included

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

@@ -239,11 +239,11 @@ def create_ast_chunks(
 
             chunks = chunk_builder.chunkify(code_content)
             for chunk in chunks:
-                chunk_text: str | None = None
+                chunk_text = None
-                astchunk_metadata: dict[str, Any] = {}
+                astchunk_metadata = {}
 
                 if hasattr(chunk, "text"):
-                    chunk_text = str(chunk.text) if chunk.text else None
+                    chunk_text = chunk.text
                 elif isinstance(chunk, str):
                     chunk_text = chunk
                 elif isinstance(chunk, dict):

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

@@ -19,7 +19,7 @@ from .settings import (
 )
 
 
-def extract_pdf_text_with_pymupdf(file_path: str) -> str | None:
+def extract_pdf_text_with_pymupdf(file_path: str) -> str:
     """Extract text from PDF using PyMuPDF for better quality."""
     try:
         import fitz  # PyMuPDF
@@ -35,7 +35,7 @@ def extract_pdf_text_with_pymupdf(file_path: str) -> str | None:
         return None
 
 
-def extract_pdf_text_with_pdfplumber(file_path: str) -> str | None:
+def extract_pdf_text_with_pdfplumber(file_path: str) -> str:
     """Extract text from PDF using pdfplumber for better quality."""
     try:
         import pdfplumber

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

@@ -451,8 +451,7 @@ def compute_embeddings_sentence_transformers(
             # TODO: Haven't tested this yet
             torch.set_num_threads(min(8, os.cpu_count() or 4))
             try:
-                # PyTorch's ContextProp type is complex; cast for type checker
+                torch.backends.mkldnn.enabled = True
-                torch.backends.mkldnn.enabled = True  # type: ignore[assignment]
             except AttributeError:
                 pass
 

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

@@ -11,15 +11,14 @@ from pathlib import Path
 from typing import Callable, Optional
 
 # Try to import readline with fallback for Windows
-HAS_READLINE = False
-readline = None  # type: ignore[assignment]
 try:
-    import readline  # type: ignore[no-redef]
+    import readline
 
     HAS_READLINE = True
 except ImportError:
     # Windows doesn't have readline by default
-    pass
+    HAS_READLINE = False
+    readline = None
 
 
 class InteractiveSession:

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

@@ -7,7 +7,7 @@ operators for different data types including numbers, strings, booleans, and lis
 """
 
 import logging
-from typing import Any, Optional, Union
+from typing import Any, Union
 
 logger = logging.getLogger(__name__)
 
@@ -47,7 +47,7 @@ class MetadataFilterEngine:
         }
 
     def apply_filters(
-        self, search_results: list[dict[str, Any]], metadata_filters: Optional[MetadataFilters]
+        self, search_results: list[dict[str, Any]], metadata_filters: MetadataFilters
     ) -> list[dict[str, Any]]:
         """
         Apply metadata filters to a list of search results.

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

@@ -56,9 +56,7 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
         with open(meta_path, encoding="utf-8") as f:
             return json.load(f)
 
-    def _ensure_server_running(
+    def _ensure_server_running(self, passages_source_file: str, port: int, **kwargs) -> int:
-        self, passages_source_file: str, port: Optional[int], **kwargs
-    ) -> int:
         """
         Ensures the embedding server is running if recompute is needed.
         This is a helper for subclasses.
@@ -83,7 +81,7 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
         }
 
         server_started, actual_port = self.embedding_server_manager.start_server(
-            port=port if port is not None else 5557,
+            port=port,
             model_name=self.embedding_model,
             embedding_mode=self.embedding_mode,
             passages_file=passages_source_file,
@@ -100,7 +98,7 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
         self,
         query: str,
         use_server_if_available: bool = True,
-        zmq_port: Optional[int] = None,
+        zmq_port: int = 5557,
         query_template: Optional[str] = None,
     ) -> np.ndarray:
         """

packages/leann/pyproject.toml

@@ -7,7 +7,7 @@ name = "leann"
 version = "0.3.5"
 description = "LEANN - The smallest vector index in the world. RAG Everything with LEANN!"
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.9"
 license = { text = "MIT" }
 authors = [
     { name = "LEANN Team" }
@@ -18,10 +18,10 @@ classifiers = [
     "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",
-    "Programming Language :: Python :: 3.13",
 ]
 
 # Default installation: core + hnsw + diskann

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "leann-workspace"
 version = "0.1.0"
-requires-python = ">=3.10"
+requires-python = ">=3.9"
 
 dependencies = [
     "leann-core",
@@ -157,19 +157,6 @@ exclude = ["localhost", "127.0.0.1", "example.com"]
 exclude_path = [".git/", ".venv/", "__pycache__/", "third_party/"]
 scheme = ["https", "http"]
 
-[tool.ty]
-# Type checking with ty (Astral's fast Python type checker)
-# ty is 10-100x faster than mypy. See: https://docs.astral.sh/ty/
-
-[tool.ty.environment]
-python-version = "3.11"
-extra-paths = ["apps", "packages/leann-core/src"]
-
-[tool.ty.rules]
-# Disable some noisy rules that have many false positives
-possibly-missing-attribute = "ignore"
-unresolved-import = "ignore"  # Many optional dependencies
-
 [tool.pytest.ini_options]
 testpaths = ["tests"]
 python_files = ["test_*.py"]

tests/test_basic.py

@@ -91,7 +91,7 @@ def test_large_index():
         builder.build_index(index_path)
 
         searcher = LeannSearcher(index_path)
-        results = searcher.search("word10 word20", top_k=10)
+        results = searcher.search(["word10 word20"], top_k=10)
-        assert len(results) == 10
+        assert len(results[0]) == 10
         # Cleanup
         searcher.cleanup()

tests/test_cli_prompt_template.py

@@ -123,7 +123,7 @@ class TestPromptTemplateStoredInEmbeddingOptions:
         cli = LeannCLI()
 
         # Mock load_documents to return a document so builder is created
-        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])  # type: ignore[assignment]
+        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])
 
         parser = cli.create_parser()
 
@@ -175,7 +175,7 @@ class TestPromptTemplateStoredInEmbeddingOptions:
         cli = LeannCLI()
 
         # Mock load_documents to return a document so builder is created
-        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])  # type: ignore[assignment]
+        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])
 
         parser = cli.create_parser()
 
@@ -230,7 +230,7 @@ class TestPromptTemplateStoredInEmbeddingOptions:
         cli = LeannCLI()
 
         # Mock load_documents to return a document so builder is created
-        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])  # type: ignore[assignment]
+        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])
 
         parser = cli.create_parser()
 
@@ -307,7 +307,7 @@ class TestPromptTemplateStoredInEmbeddingOptions:
         cli = LeannCLI()
 
         # Mock load_documents to return a document so builder is created
-        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])  # type: ignore[assignment]
+        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])
 
         parser = cli.create_parser()
 
@@ -376,7 +376,7 @@ class TestPromptTemplateStoredInEmbeddingOptions:
         cli = LeannCLI()
 
         # Mock load_documents to return a document so builder is created
-        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])  # type: ignore[assignment]
+        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])
 
         parser = cli.create_parser()
 
@@ -432,7 +432,7 @@ class TestPromptTemplateFlowsToComputeEmbeddings:
         cli = LeannCLI()
 
         # Mock load_documents to return a simple document
-        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])  # type: ignore[assignment]
+        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])
 
         parser = cli.create_parser()
 

tests/test_prompt_template_e2e.py

@@ -67,7 +67,7 @@ def check_lmstudio_available() -> bool:
         return False
 
 
-def get_lmstudio_first_model() -> str | None:
+def get_lmstudio_first_model() -> str:
     """Get the first available model from LM Studio."""
     try:
         response = requests.get("http://localhost:1234/v1/models", timeout=5.0)
@@ -91,7 +91,6 @@ class TestPromptTemplateOpenAI:
         model_name = get_lmstudio_first_model()
         if not model_name:
             pytest.skip("No models loaded in LM Studio")
-        assert model_name is not None  # Type narrowing for type checker
 
         texts = ["artificial intelligence", "machine learning"]
         prompt_template = "search_query: "
@@ -121,7 +120,6 @@ class TestPromptTemplateOpenAI:
         model_name = get_lmstudio_first_model()
         if not model_name:
             pytest.skip("No models loaded in LM Studio")
-        assert model_name is not None  # Type narrowing for type checker
 
         text = "machine learning"
         base_url = "http://localhost:1234/v1"
@@ -273,7 +271,6 @@ class TestLMStudioSDK:
         model_name = get_lmstudio_first_model()
         if not model_name:
             pytest.skip("No models loaded in LM Studio")
-        assert model_name is not None  # Type narrowing for type checker
 
         try:
             from leann.embedding_compute import _query_lmstudio_context_limit

tests/test_prompt_template_persistence.py

@@ -581,18 +581,7 @@ class TestQueryTemplateApplicationInComputeEmbedding:
 
         # Create a concrete implementation for testing
         class TestSearcher(BaseSearcher):
-            def search(
+            def search(self, query_vectors, top_k, complexity, beam_width=1, **kwargs):
-                self,
-                query,
-                top_k,
-                complexity=64,
-                beam_width=1,
-                prune_ratio=0.0,
-                recompute_embeddings=False,
-                pruning_strategy="global",
-                zmq_port=None,
-                **kwargs,
-            ):
                 return {"labels": [], "distances": []}
 
         searcher = object.__new__(TestSearcher)
@@ -636,18 +625,7 @@ class TestQueryTemplateApplicationInComputeEmbedding:
 
         # Create a concrete implementation for testing
         class TestSearcher(BaseSearcher):
-            def search(
+            def search(self, query_vectors, top_k, complexity, beam_width=1, **kwargs):
-                self,
-                query,
-                top_k,
-                complexity=64,
-                beam_width=1,
-                prune_ratio=0.0,
-                recompute_embeddings=False,
-                pruning_strategy="global",
-                zmq_port=None,
-                **kwargs,
-            ):
                 return {"labels": [], "distances": []}
 
         searcher = object.__new__(TestSearcher)
@@ -693,18 +671,7 @@ class TestQueryTemplateApplicationInComputeEmbedding:
         from leann.searcher_base import BaseSearcher
 
         class TestSearcher(BaseSearcher):
-            def search(
+            def search(self, query_vectors, top_k, complexity, beam_width=1, **kwargs):
-                self,
-                query,
-                top_k,
-                complexity=64,
-                beam_width=1,
-                prune_ratio=0.0,
-                recompute_embeddings=False,
-                pruning_strategy="global",
-                zmq_port=None,
-                **kwargs,
-            ):
                 return {"labels": [], "distances": []}
 
         searcher = object.__new__(TestSearcher)
@@ -743,18 +710,7 @@ class TestQueryTemplateApplicationInComputeEmbedding:
         from leann.searcher_base import BaseSearcher
 
         class TestSearcher(BaseSearcher):
-            def search(
+            def search(self, query_vectors, top_k, complexity, beam_width=1, **kwargs):
-                self,
-                query,
-                top_k,
-                complexity=64,
-                beam_width=1,
-                prune_ratio=0.0,
-                recompute_embeddings=False,
-                pruning_strategy="global",
-                zmq_port=None,
-                **kwargs,
-            ):
                 return {"labels": [], "distances": []}
 
         searcher = object.__new__(TestSearcher)
@@ -818,18 +774,7 @@ class TestQueryTemplateApplicationInComputeEmbedding:
         from leann.searcher_base import BaseSearcher
 
         class TestSearcher(BaseSearcher):
-            def search(
+            def search(self, query_vectors, top_k, complexity, beam_width=1, **kwargs):
-                self,
-                query,
-                top_k,
-                complexity=64,
-                beam_width=1,
-                prune_ratio=0.0,
-                recompute_embeddings=False,
-                pruning_strategy="global",
-                zmq_port=None,
-                **kwargs,
-            ):
                 return {"labels": [], "distances": []}
 
         searcher = object.__new__(TestSearcher)

tests/test_readme_examples.py

@@ -97,17 +97,17 @@ def test_backend_options():
 
     with tempfile.TemporaryDirectory() as temp_dir:
         # Use smaller model in CI to avoid memory issues
-        is_ci = os.environ.get("CI") == "true"
+        if os.environ.get("CI") == "true":
-        embedding_model = (
+            model_args = {
-            "sentence-transformers/all-MiniLM-L6-v2" if is_ci else "facebook/contriever"
+                "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
-        )
+                "dimensions": 384,
-        dimensions = 384 if is_ci else None
+            }
+        else:
+            model_args = {}
 
         # Test HNSW backend (as shown in README)
         hnsw_path = str(Path(temp_dir) / "test_hnsw.leann")
-        builder_hnsw = LeannBuilder(
+        builder_hnsw = LeannBuilder(backend_name="hnsw", **model_args)
-            backend_name="hnsw", embedding_model=embedding_model, dimensions=dimensions
-        )
         builder_hnsw.add_text("Test document for HNSW backend")
         builder_hnsw.build_index(hnsw_path)
         assert Path(hnsw_path).parent.exists()
@@ -115,9 +115,7 @@ def test_backend_options():
 
         # Test DiskANN backend (mentioned as available option)
         diskann_path = str(Path(temp_dir) / "test_diskann.leann")
-        builder_diskann = LeannBuilder(
+        builder_diskann = LeannBuilder(backend_name="diskann", **model_args)
-            backend_name="diskann", embedding_model=embedding_model, dimensions=dimensions
-        )
         builder_diskann.add_text("Test document for DiskANN backend")
         builder_diskann.build_index(diskann_path)
         assert Path(diskann_path).parent.exists()