skip several macos test because strange issue on ci

- Set OMP_NUM_THREADS=1 to avoid OpenMP thread synchronization issues
- Set MKL_NUM_THREADS=1 for single-threaded MKL operations
- This prevents segfaults in LayerNorm on macOS CI runners
- Addresses the libomp compatibility issues with PyTorch on Apple Silicon

README.md

@@ -174,28 +174,15 @@ Ask questions directly about your personal PDFs, documents, and any directory co
   <img src="videos/paper_clear.gif" alt="LEANN Document Search Demo" width="600">
 </p>
 
-The example below asks a question about summarizing two papers (uses default data in `examples/data`) and this is the easiest example to run here:
+The example below asks a question about summarizing two papers (uses default data in `examples/data`):
 
-```bash
+```
+# Or use python directly
 source .venv/bin/activate
 python ./examples/main_cli_example.py
 ```
 
-<details>
-<summary><strong>📋 Click to expand: User Configurable Arguments</strong></summary>
 
-```bash
-# Use custom index directory
-python examples/main_cli_example.py --index-dir "./my_custom_index"
-
-# Use custom data directory
-python examples/main_cli_example.py --data-dir "./my_documents"
-
-# Ask a specific question
-python examples/main_cli_example.py --query "What are the main findings in these papers?"
-```
-
-</details>
 
 ### 📧 Your Personal Email Secretary: RAG on Apple Mail!
 
@@ -208,12 +195,12 @@ python examples/main_cli_example.py --query "What are the main findings in these
 
 **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 --query "What's the food I ordered by DoorDash or Uber Eats mostly?"
+python examples/mail_reader_leann.py --query "What's the food I ordered by doordash or Uber eat mostly?"
 ```
-**780K email chunks → 78MB storage.** 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: User Configurable Arguments</strong></summary>
+<summary><strong>📋 Click to expand: Command Examples</strong></summary>
 
 ```bash
 # Use default mail path (works for most macOS setups)
@@ -255,7 +242,7 @@ python examples/google_history_reader_leann.py --query "Tell me my browser histo
 **38K browser entries → 6MB storage.** Your browser history becomes your personal search engine.
 
 <details>
-<summary><strong>📋 Click to expand: User Configurable Arguments</strong></summary>
+<summary><strong>📋 Click to expand: Command Examples</strong></summary>
 
 ```bash
 # Use default Chrome profile (auto-finds all profiles)
@@ -332,7 +319,7 @@ Failed to find or export WeChat data. Exiting.
 </details>
 
 <details>
-<summary><strong>📋 Click to expand: User Configurable Arguments</strong></summary>
+<summary><strong>📋 Click to expand: Command Examples</strong></summary>
 
 ```bash
 # Use default settings (recommended for first run)

examples/main_cli_example.py

@@ -94,14 +94,14 @@ if __name__ == "__main__":
     parser.add_argument(
         "--llm",
         type=str,
-        default="openai",
+        default="hf",
         choices=["simulated", "ollama", "hf", "openai"],
         help="The LLM backend to use.",
     )
     parser.add_argument(
         "--model",
         type=str,
-        default="gpt-4o",
+        default="Qwen/Qwen3-0.6B",
         help="The model name to use (e.g., 'llama3:8b' for ollama, 'deepseek-ai/deepseek-llm-7b-chat' for hf, 'gpt-4o' for openai).",
     )
     parser.add_argument(

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

@@ -7,7 +7,6 @@ from pathlib import Path
 from typing import Any, Literal
 
 import numpy as np
-import psutil
 from leann.interface import (
     LeannBackendBuilderInterface,
     LeannBackendFactoryInterface,
@@ -85,43 +84,6 @@ def _write_vectors_to_bin(data: np.ndarray, file_path: Path):
         f.write(data.tobytes())
 
 
-def _calculate_smart_memory_config(data: np.ndarray) -> tuple[float, float]:
-    """
-    Calculate smart memory configuration for DiskANN based on data size and system specs.
-
-    Args:
-        data: The embedding data array
-
-    Returns:
-        tuple: (search_memory_maximum, build_memory_maximum) in GB
-    """
-    num_vectors, dim = data.shape
-
-    # Calculate embedding storage size
-    embedding_size_bytes = num_vectors * dim * 4  # float32 = 4 bytes
-    embedding_size_gb = embedding_size_bytes / (1024**3)
-
-    # search_memory_maximum: 1/10 of embedding size for optimal PQ compression
-    # This controls Product Quantization size - smaller means more compression
-    search_memory_gb = max(0.1, embedding_size_gb / 10)  # At least 100MB
-
-    # build_memory_maximum: Based on available system RAM for sharding control
-    # This controls how much memory DiskANN uses during index construction
-    available_memory_gb = psutil.virtual_memory().available / (1024**3)
-    total_memory_gb = psutil.virtual_memory().total / (1024**3)
-
-    # Use 50% of available memory, but at least 2GB and at most 75% of total
-    build_memory_gb = max(2.0, min(available_memory_gb * 0.5, total_memory_gb * 0.75))
-
-    logger.info(
-        f"Smart memory config - Data: {embedding_size_gb:.2f}GB, "
-        f"Search mem: {search_memory_gb:.2f}GB (PQ control), "
-        f"Build mem: {build_memory_gb:.2f}GB (sharding control)"
-    )
-
-    return search_memory_gb, build_memory_gb
-
-
 @register_backend("diskann")
 class DiskannBackend(LeannBackendFactoryInterface):
     @staticmethod
@@ -159,16 +121,6 @@ class DiskannBuilder(LeannBackendBuilderInterface):
                 f"Unsupported distance_metric '{build_kwargs.get('distance_metric', 'unknown')}'."
             )
 
-        # Calculate smart memory configuration if not explicitly provided
-        if (
-            "search_memory_maximum" not in build_kwargs
-            or "build_memory_maximum" not in build_kwargs
-        ):
-            smart_search_mem, smart_build_mem = _calculate_smart_memory_config(data)
-        else:
-            smart_search_mem = build_kwargs.get("search_memory_maximum", 4.0)
-            smart_build_mem = build_kwargs.get("build_memory_maximum", 8.0)
-
         try:
             from . import _diskannpy as diskannpy  # type: ignore
 
@@ -179,8 +131,8 @@ class DiskannBuilder(LeannBackendBuilderInterface):
                     index_prefix,
                     build_kwargs.get("complexity", 64),
                     build_kwargs.get("graph_degree", 32),
-                    build_kwargs.get("search_memory_maximum", smart_search_mem),
+                    build_kwargs.get("search_memory_maximum", 4.0),
-                    build_kwargs.get("build_memory_maximum", smart_build_mem),
+                    build_kwargs.get("build_memory_maximum", 8.0),
                     build_kwargs.get("num_threads", 8),
                     build_kwargs.get("pq_disk_bytes", 0),
                     "",

packages/leann-backend-diskann/pyproject.toml

@@ -4,8 +4,8 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-diskann"
-version = "0.1.16"
+version = "0.1.15"
-dependencies = ["leann-core==0.1.16", "numpy", "protobuf>=3.19.0"]
+dependencies = ["leann-core==0.1.15", "numpy", "protobuf>=3.19.0"]
 
 [tool.scikit-build]
 # Key: simplified CMake path

packages/leann-backend-hnsw/pyproject.toml

@@ -6,10 +6,10 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-hnsw"
-version = "0.1.16"
+version = "0.1.15"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
 dependencies = [
-    "leann-core==0.1.16",
+    "leann-core==0.1.15",
     "numpy",
     "pyzmq>=23.0.0",
     "msgpack>=1.0.0",

packages/leann-core/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann-core"
-version = "0.1.16"
+version = "0.1.15"
 description = "Core API and plugin system for LEANN"
 readme = "README.md"
 requires-python = ">=3.9"

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

@@ -542,41 +542,14 @@ class HFChat(LLMInterface):
             self.device = "cpu"
             logger.info("No GPU detected. Using CPU.")
 
-        # Load tokenizer and model with timeout protection
+        # Load tokenizer and model
-        try:
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-            import signal
+        self.model = AutoModelForCausalLM.from_pretrained(
-
+            model_name,
-            def timeout_handler(signum, frame):
+            torch_dtype=torch.float16 if self.device != "cpu" else torch.float32,
-                raise TimeoutError("Model download/loading timed out")
+            device_map="auto" if self.device != "cpu" else None,
-
+            trust_remote_code=True,
-            # Set timeout for model loading (60 seconds)
+        )
-            old_handler = signal.signal(signal.SIGALRM, timeout_handler)
-            signal.alarm(60)
-
-            try:
-                logger.info(f"Loading tokenizer for {model_name}...")
-                self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-                logger.info(f"Loading model {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,
-                )
-                logger.info(f"Successfully loaded {model_name}")
-            finally:
-                signal.alarm(0)  # Cancel the alarm
-                signal.signal(signal.SIGALRM, old_handler)  # Restore old handler
-
-        except TimeoutError:
-            logger.error(f"Model loading timed out for {model_name}")
-            raise RuntimeError(
-                f"Model loading timed out for {model_name}. Please check your internet connection or try a smaller model."
-            )
-        except Exception as e:
-            logger.error(f"Failed to load model {model_name}: {e}")
-            raise
 
         # Move model to device if not using device_map
         if self.device != "cpu" and "device_map" not in str(self.model):

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

@@ -354,21 +354,13 @@ class EmbeddingServerManager:
         self.server_process.terminate()
 
         try:
-            self.server_process.wait(timeout=3)
+            self.server_process.wait(timeout=5)
             logger.info(f"Server process {self.server_process.pid} terminated.")
         except subprocess.TimeoutExpired:
             logger.warning(
-                f"Server process {self.server_process.pid} did not terminate gracefully within 3 seconds, killing it."
+                f"Server process {self.server_process.pid} did not terminate gracefully, killing it."
             )
             self.server_process.kill()
-            try:
-                self.server_process.wait(timeout=2)
-                logger.info(f"Server process {self.server_process.pid} killed successfully.")
-            except subprocess.TimeoutExpired:
-                logger.error(
-                    f"Failed to kill server process {self.server_process.pid} - it may be hung"
-                )
-                # Don't hang indefinitely
 
         # Clean up process resources to prevent resource tracker warnings
         try:

packages/leann/README.md

@@ -5,8 +5,11 @@ LEANN is a revolutionary vector database that democratizes personal AI. Transfor
 ## Installation
 
 ```bash
-# Default installation (includes both HNSW and DiskANN backends)
+# Default installation (HNSW backend, recommended)
 uv pip install leann
+
+# With DiskANN backend (for large-scale deployments)
+uv pip install leann[diskann]
 ```
 
 ## Quick Start
@@ -16,8 +19,8 @@ from leann import LeannBuilder, LeannSearcher, LeannChat
 from pathlib import Path
 INDEX_PATH = str(Path("./").resolve() / "demo.leann")
 
-# Build an index (choose backend: "hnsw" or "diskann")
+# Build an index
-builder = LeannBuilder(backend_name="hnsw")  # or "diskann" for large-scale deployments
+builder = LeannBuilder(backend_name="hnsw")
 builder.add_text("LEANN saves 97% storage compared to traditional vector databases.")
 builder.add_text("Tung Tung Tung Sahur called—they need their banana‑crocodile hybrid back")
 builder.build_index(INDEX_PATH)

packages/leann/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann"
-version = "0.1.16"
+version = "0.1.15"
 description = "LEANN - The smallest vector index in the world. RAG Everything with LEANN!"
 readme = "README.md"
 requires-python = ">=3.9"
@@ -24,15 +24,16 @@ classifiers = [
     "Programming Language :: Python :: 3.12",
 ]
 
-# Default installation: core + hnsw + diskann
+# Default installation: core + hnsw
 dependencies = [
     "leann-core>=0.1.0",
     "leann-backend-hnsw>=0.1.0",
-    "leann-backend-diskann>=0.1.0",
 ]
 
 [project.optional-dependencies]
-# All backends now included by default
+diskann = [
+    "leann-backend-diskann>=0.1.0",
+]
 
 [project.urls]
 Repository = "https://github.com/yichuan-w/LEANN"