style: apply ruff format

* docs: add SkyPilot template and instructions for running embeddings/index build on cloud GPU

* docs: add low-resource note in README; point to config guide; suggest OpenAI embeddings, SkyPilot remote build, and --no-recompute

* docs: consolidate low-resource guidance into config guide; README points to it

* cli: add --no-recompute and --no-recompute-embeddings flags; docs: clarify HNSW requires --no-compact when disabling recompute

* docs: dedupe recomputation guidance; keep single Low-resource setups section

* sky: expand leann-build.yaml with configurable params and flags (backend, recompute, compact, embedding options)

* hnsw: auto-disable compact when --no-recompute is used; docs: expand SkyPilot with -e overrides and copy-back example

* docs+sky: simplify SkyPilot flow (auto-build on launch, rsync copy-back); clarify HNSW auto non-compact when no-recompute

* feat: auto compact for hnsw when recompute

* reader: non-destructive portability (relative hints + fallback); fix comments; sky: refine yaml

* cli: unify flags to --recompute/--no-recompute for build/search/ask; docs: update references

* chore: remove

* hnsw: move pruned/no-recompute assertion into backend; api: drop global assertion; docs: will adjust after benchmarking

* cli: use argparse.BooleanOptionalAction for paired flags (--recompute/--compact) across build/search/ask

* docs: a real example on recompute

* benchmarks: fix and extend HNSW+DiskANN recompute vs no-recompute; docs: add fresh numbers and DiskANN notes

* benchmarks: unify HNSW & DiskANN into one clean script; isolate groups, fixed ports, warm-up, param complexity

* docs: diskann recompute

* core: auto-cleanup for LeannSearcher/LeannChat (__enter__/__exit__/__del__); ensure server terminate/kill robustness; benchmarks: use searcher.cleanup(); docs: suggest uv run

* fix: hang on warnings

* docs: boolean flags

* docs: leann help

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

@@ -5,6 +5,7 @@ on:
     branches: [ main ]
   pull_request:
     branches: [ main ]
+  workflow_dispatch:
 
 jobs:
   build:

.github/workflows/build-reusable.yml

@@ -277,19 +277,16 @@ jobs:
 
       - name: Run tests with pytest
         env:
-          CI: true  # Mark as CI environment to skip memory-intensive tests
+          CI: true
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           HF_HUB_DISABLE_SYMLINKS: 1
           TOKENIZERS_PARALLELISM: false
-          PYTORCH_ENABLE_MPS_FALLBACK: 0  # Disable MPS on macOS CI to avoid memory issues
-          OMP_NUM_THREADS: 1  # Disable OpenMP parallelism to avoid libomp crashes
-          MKL_NUM_THREADS: 1  # Single thread for MKL operations
+          PYTORCH_ENABLE_MPS_FALLBACK: 0
+          OMP_NUM_THREADS: 1
+          MKL_NUM_THREADS: 1
         run: |
-          # Activate virtual environment
           source .venv/bin/activate || source .venv/Scripts/activate
-
-          # Run tests
-          pytest -v tests/
+          pytest tests/ -v --tb=short
 
       - name: Run sanity checks (optional)
         run: |

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v5.0.0
     hooks:
       - id: trailing-whitespace
       - id: end-of-file-fixer
@@ -10,7 +10,7 @@ repos:
       - id: debug-statements
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.2.1
+    rev: v0.12.7  # Fixed version to match pyproject.toml
     hooks:
       - id: ruff
       - id: ruff-format

README.md

@@ -31,7 +31,7 @@ LEANN achieves this through *graph-based selective recomputation* with *high-deg
   <img src="assets/effects.png" alt="LEANN vs Traditional Vector DB Storage Comparison" width="70%">
 </p>
 
-> **The numbers speak for themselves:** Index 60 million text chunks in just 6GB instead of 201GB. From emails to browser history, everything fits on your laptop. [See detailed benchmarks for different applications below ↓](#storage-comparison)
+> **The numbers speak for themselves:** Index 60 million text chunks in just 6GB instead of 201GB. From emails to browser history, everything fits on your laptop. [See detailed benchmarks for different applications below ↓](#-storage-comparison)
 
 
 🔒 **Privacy:** Your data never leaves your laptop. No OpenAI, no cloud, no "terms of service".
@@ -70,8 +70,8 @@ uv venv
 source .venv/bin/activate
 uv pip install leann
 ```
-
-> Low-resource? See “Low-resource setups” in the [Configuration Guide](docs/configuration-guide.md#low-resource-setups).
+<!--
+> Low-resource? See “Low-resource setups” in the [Configuration Guide](docs/configuration-guide.md#low-resource-setups). -->
 
 <details>
 <summary>
@@ -186,34 +186,34 @@ All RAG examples share these common parameters. **Interactive mode** is availabl
 
 ```bash
 # Core Parameters (General preprocessing for all examples)
---index-dir DIR          # Directory to store the index (default: current directory)
---query "YOUR QUESTION"  # Single query mode. Omit for interactive chat (type 'quit' to exit), and now you can play with your index interactively
---max-items N           # Limit data preprocessing (default: -1, process all data)
---force-rebuild         # Force rebuild index even if it exists
+--index-dir DIR              # Directory to store the index (default: current directory)
+--query "YOUR QUESTION"      # Single query mode. Omit for interactive chat (type 'quit' to exit), and now you can play with your index interactively
+--max-items N                # Limit data preprocessing (default: -1, process all data)
+--force-rebuild              # Force rebuild index even if it exists
 
 # Embedding Parameters
---embedding-model MODEL  # e.g., facebook/contriever, text-embedding-3-small, nomic-embed-text,mlx-community/Qwen3-Embedding-0.6B-8bit or nomic-embed-text
---embedding-mode MODE    # sentence-transformers, openai, mlx, or ollama
+--embedding-model MODEL      # e.g., facebook/contriever, text-embedding-3-small, mlx-community/Qwen3-Embedding-0.6B-8bit or nomic-embed-text
+--embedding-mode MODE        # sentence-transformers, openai, mlx, or ollama
 
 # LLM Parameters (Text generation models)
---llm TYPE              # LLM backend: openai, ollama, or hf (default: openai)
---llm-model MODEL       # Model name (default: gpt-4o) e.g., gpt-4o-mini, llama3.2:1b, Qwen/Qwen2.5-1.5B-Instruct
---thinking-budget LEVEL # Thinking budget for reasoning models: low/medium/high (supported by o3, o3-mini, GPT-Oss:20b, and other reasoning models)
+--llm TYPE                   # LLM backend: openai, ollama, or hf (default: openai)
+--llm-model MODEL            # Model name (default: gpt-4o) e.g., gpt-4o-mini, llama3.2:1b, Qwen/Qwen2.5-1.5B-Instruct
+--thinking-budget LEVEL      # Thinking budget for reasoning models: low/medium/high (supported by o3, o3-mini, GPT-Oss:20b, and other reasoning models)
 
 # Search Parameters
---top-k N               # Number of results to retrieve (default: 20)
---search-complexity N   # Search complexity for graph traversal (default: 32)
+--top-k N                    # Number of results to retrieve (default: 20)
+--search-complexity N        # Search complexity for graph traversal (default: 32)
 
 # Chunking Parameters
---chunk-size N          # Size of text chunks (default varies by source: 256 for most, 192 for WeChat)
---chunk-overlap N       # Overlap between chunks (default varies: 25-128 depending on source)
+--chunk-size N               # Size of text chunks (default varies by source: 256 for most, 192 for WeChat)
+--chunk-overlap N            # Overlap between chunks (default varies: 25-128 depending on source)
 
 # Index Building Parameters
---backend-name NAME     # Backend to use: hnsw or diskann (default: hnsw)
---graph-degree N        # Graph degree for index construction (default: 32)
---build-complexity N    # Build complexity for index construction (default: 64)
---no-compact           # Disable compact index storage (compact storage IS enabled to save storage by default)
---no-recompute         # Disable embedding recomputation (recomputation IS enabled to save storage by default)
+--backend-name NAME          # Backend to use: hnsw or diskann (default: hnsw)
+--graph-degree N             # Graph degree for index construction (default: 32)
+--build-complexity N         # Build complexity for index construction (default: 64)
+--compact / --no-compact     # Use compact storage (default: true). Must be `no-compact` for `no-recompute` build.
+--recompute / --no-recompute # Enable/disable embedding recomputation (default: enabled). Should not do a `no-recompute` search in a `recompute` build.
 ```
 
 </details>
@@ -457,7 +457,7 @@ leann --help
 **To make it globally available:**
 ```bash
 # Install the LEANN CLI globally using uv tool
-uv tool install leann
+uv tool install leann-core
 
 # Now you can use leann from anywhere without activating venv
 leann --help
@@ -484,27 +484,29 @@ leann list
 ```
 
 **Key CLI features:**
-- Auto-detects document formats (PDF, TXT, MD, DOCX)
+- Auto-detects document formats (PDF, TXT, MD, DOCX, PPTX + code files)
 - Smart text chunking with overlap
 - Multiple LLM providers (Ollama, OpenAI, HuggingFace)
-- Organized index storage in `~/.leann/indexes/`
+- Organized index storage in `.leann/indexes/` (project-local)
 - Support for advanced search parameters
 
 <details>
 <summary><strong>📋 Click to expand: Complete CLI Reference</strong></summary>
 
+You can use `leann --help`, or `leann build --help`, `leann search --help`, `leann ask --help` to get the complete CLI reference.
+
 **Build Command:**
 ```bash
-leann build INDEX_NAME --docs DIRECTORY [OPTIONS]
+leann build INDEX_NAME --docs DIRECTORY|FILE [DIRECTORY|FILE ...] [OPTIONS]
 
 Options:
   --backend {hnsw,diskann}     Backend to use (default: hnsw)
   --embedding-model MODEL      Embedding model (default: facebook/contriever)
-  --graph-degree N            Graph degree (default: 32)
-  --complexity N              Build complexity (default: 64)
-  --force                     Force rebuild existing index
-  --compact                   Use compact storage (default: true)
-  --recompute                 Enable recomputation (default: true)
+  --graph-degree N             Graph degree (default: 32)
+  --complexity N               Build complexity (default: 64)
+  --force                      Force rebuild existing index
+  --compact / --no-compact     Use compact storage (default: true). Must be `no-compact` for `no-recompute` build.
+  --recompute / --no-recompute Enable recomputation (default: true)
 ```
 
 **Search Command:**
@@ -512,9 +514,9 @@ Options:
 leann search INDEX_NAME QUERY [OPTIONS]
 
 Options:
-  --top-k N                   Number of results (default: 5)
-  --complexity N              Search complexity (default: 64)
-  --recompute-embeddings      Use recomputation for highest accuracy
+  --top-k N                     Number of results (default: 5)
+  --complexity N                Search complexity (default: 64)
+  --recompute / --no-recompute  Enable/disable embedding recomputation (default: enabled). Should not do a `no-recompute` search in a `recompute` build.
   --pruning-strategy {global,local,proportional}
 ```
 
@@ -545,12 +547,16 @@ Options:
 - **Dynamic batching:** Efficiently batch embedding computations for GPU utilization
 - **Two-level search:** Smart graph traversal that prioritizes promising nodes
 
-**Backends:** HNSW (default) for most use cases, with optional DiskANN support for billion-scale datasets.
+**Backends:**
+- **HNSW** (default): Ideal for most datasets with maximum storage savings through full recomputation
+- **DiskANN**: Advanced option with superior search performance, using PQ-based graph traversal with real-time reranking for the best speed-accuracy trade-off
 
 ## Benchmarks
 
+**[DiskANN vs HNSW Performance Comparison →](benchmarks/diskann_vs_hnsw_speed_comparison.py)** - Compare search performance between both backends
+
+**[Simple Example: Compare LEANN vs FAISS →](benchmarks/compare_faiss_vs_leann.py)** - See storage savings in action
 
-**[Simple Example: Compare LEANN vs FAISS →](benchmarks/compare_faiss_vs_leann.py)**
 ### 📊 Storage Comparison
 
 | System | DPR (2.1M) | Wiki (60M) | Chat (400K) | Email (780K) | Browser (38K) |

apps/base_rag_example.py

@@ -69,14 +69,14 @@ class BaseRAGExample(ABC):
             "--embedding-model",
             type=str,
             default=embedding_model_default,
-            help=f"Embedding model to use (default: {embedding_model_default})",
+            help=f"Embedding model to use (default: {embedding_model_default}), we provide facebook/contriever, text-embedding-3-small,mlx-community/Qwen3-Embedding-0.6B-8bit or nomic-embed-text",
         )
         embedding_group.add_argument(
             "--embedding-mode",
             type=str,
             default="sentence-transformers",
             choices=["sentence-transformers", "openai", "mlx", "ollama"],
-            help="Embedding backend mode (default: sentence-transformers)",
+            help="Embedding backend mode (default: sentence-transformers), we provide sentence-transformers, openai, mlx, or ollama",
         )
 
         # LLM parameters
@@ -86,13 +86,13 @@ class BaseRAGExample(ABC):
             type=str,
             default="openai",
             choices=["openai", "ollama", "hf", "simulated"],
-            help="LLM backend to use (default: openai)",
+            help="LLM backend: openai, ollama, or hf (default: openai)",
         )
         llm_group.add_argument(
             "--llm-model",
             type=str,
             default=None,
-            help="LLM model name (default: gpt-4o for openai, llama3.2:1b for ollama)",
+            help="Model name (default: gpt-4o) e.g., gpt-4o-mini, llama3.2:1b, Qwen/Qwen2.5-1.5B-Instruct",
         )
         llm_group.add_argument(
             "--llm-host",
@@ -178,6 +178,9 @@ class BaseRAGExample(ABC):
             config["host"] = args.llm_host
         elif args.llm == "hf":
             config["model"] = args.llm_model or "Qwen/Qwen2.5-1.5B-Instruct"
+        elif args.llm == "simulated":
+            # Simulated LLM doesn't need additional configuration
+            pass
 
         return config
 

benchmarks/README.md

@@ -1,9 +1,24 @@
-# 🧪 Leann Sanity Checks
+# 🧪 LEANN Benchmarks & Testing
 
-This directory contains comprehensive sanity checks for the Leann system, ensuring all components work correctly across different configurations.
+This directory contains performance benchmarks and comprehensive tests for the LEANN system, including backend comparisons and sanity checks across different configurations.
 
 ## 📁 Test Files
 
+### `diskann_vs_hnsw_speed_comparison.py`
+Performance comparison between DiskANN and HNSW backends:
+- ✅ **Search latency** comparison with both backends using recompute
+- ✅ **Index size** and **build time** measurements
+- ✅ **Score validity** testing (ensures no -inf scores)
+- ✅ **Configurable dataset sizes** for different scales
+
+```bash
+# Quick comparison with 500 docs, 10 queries
+python benchmarks/diskann_vs_hnsw_speed_comparison.py
+
+# Large-scale comparison with 2000 docs, 20 queries
+python benchmarks/diskann_vs_hnsw_speed_comparison.py 2000 20
+```
+
 ### `test_distance_functions.py`
 Tests all supported distance functions across DiskANN backend:
 - ✅ **MIPS** (Maximum Inner Product Search)

benchmarks/benchmark_no_recompute.py

@@ -0,0 +1,148 @@
+import argparse
+import os
+import time
+from pathlib import Path
+
+from leann import LeannBuilder, LeannSearcher
+
+
+def _meta_exists(index_path: str) -> bool:
+    p = Path(index_path)
+    return (p.parent / f"{p.stem}.meta.json").exists()
+
+
+def ensure_index(index_path: str, backend_name: str, num_docs: int, is_recompute: bool) -> None:
+    # if _meta_exists(index_path):
+    #     return
+    kwargs = {}
+    if backend_name == "hnsw":
+        kwargs["is_compact"] = is_recompute
+    builder = LeannBuilder(
+        backend_name=backend_name,
+        embedding_model=os.getenv("LEANN_EMBED_MODEL", "facebook/contriever"),
+        embedding_mode=os.getenv("LEANN_EMBED_MODE", "sentence-transformers"),
+        graph_degree=32,
+        complexity=64,
+        is_recompute=is_recompute,
+        num_threads=4,
+        **kwargs,
+    )
+    for i in range(num_docs):
+        builder.add_text(
+            f"This is a test document number {i}. It contains some repeated text for benchmarking."
+        )
+    builder.build_index(index_path)
+
+
+def _bench_group(
+    index_path: str,
+    recompute: bool,
+    query: str,
+    repeats: int,
+    complexity: int = 32,
+    top_k: int = 10,
+) -> float:
+    # Independent searcher per group; fixed port when recompute
+    searcher = LeannSearcher(index_path=index_path)
+
+    # Warm-up once
+    _ = searcher.search(
+        query,
+        top_k=top_k,
+        complexity=complexity,
+        recompute_embeddings=recompute,
+    )
+
+    def _once() -> float:
+        t0 = time.time()
+        _ = searcher.search(
+            query,
+            top_k=top_k,
+            complexity=complexity,
+            recompute_embeddings=recompute,
+        )
+        return time.time() - t0
+
+    if repeats <= 1:
+        t = _once()
+    else:
+        vals = [_once() for _ in range(repeats)]
+        vals.sort()
+        t = vals[len(vals) // 2]
+
+    searcher.cleanup()
+    return t
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--num-docs", type=int, default=5000)
+    parser.add_argument("--repeats", type=int, default=3)
+    parser.add_argument("--complexity", type=int, default=32)
+    args = parser.parse_args()
+
+    base = Path.cwd() / ".leann" / "indexes" / f"bench_n{args.num_docs}"
+    base.parent.mkdir(parents=True, exist_ok=True)
+    # ---------- Build HNSW variants ----------
+    hnsw_r = str(base / f"hnsw_recompute_n{args.num_docs}.leann")
+    hnsw_nr = str(base / f"hnsw_norecompute_n{args.num_docs}.leann")
+    ensure_index(hnsw_r, "hnsw", args.num_docs, True)
+    ensure_index(hnsw_nr, "hnsw", args.num_docs, False)
+
+    # ---------- Build DiskANN variants ----------
+    diskann_r = str(base / "diskann_r.leann")
+    diskann_nr = str(base / "diskann_nr.leann")
+    ensure_index(diskann_r, "diskann", args.num_docs, True)
+    ensure_index(diskann_nr, "diskann", args.num_docs, False)
+
+    # ---------- Helpers ----------
+    def _size_for(prefix: str) -> int:
+        p = Path(prefix)
+        base_dir = p.parent
+        stem = p.stem
+        total = 0
+        for f in base_dir.iterdir():
+            if f.is_file() and f.name.startswith(stem):
+                total += f.stat().st_size
+        return total
+
+    # ---------- HNSW benchmark ----------
+    t_hnsw_r = _bench_group(
+        hnsw_r, True, "test document number 42", repeats=args.repeats, complexity=args.complexity
+    )
+    t_hnsw_nr = _bench_group(
+        hnsw_nr, False, "test document number 42", repeats=args.repeats, complexity=args.complexity
+    )
+    size_hnsw_r = _size_for(hnsw_r)
+    size_hnsw_nr = _size_for(hnsw_nr)
+
+    print("Benchmark results (HNSW):")
+    print(f"  recompute=True:  search_time={t_hnsw_r:.3f}s, size={size_hnsw_r / 1024 / 1024:.1f}MB")
+    print(
+        f"  recompute=False: search_time={t_hnsw_nr:.3f}s, size={size_hnsw_nr / 1024 / 1024:.1f}MB"
+    )
+    print("  Expectation: no-recompute should be faster but larger on disk.")
+
+    # ---------- DiskANN benchmark ----------
+    t_diskann_r = _bench_group(
+        diskann_r, True, "DiskANN R test doc 123", repeats=args.repeats, complexity=args.complexity
+    )
+    t_diskann_nr = _bench_group(
+        diskann_nr,
+        False,
+        "DiskANN NR test doc 123",
+        repeats=args.repeats,
+        complexity=args.complexity,
+    )
+    size_diskann_r = _size_for(diskann_r)
+    size_diskann_nr = _size_for(diskann_nr)
+
+    print("\nBenchmark results (DiskANN):")
+    print(f"  build(recompute=True, partition): size={size_diskann_r / 1024 / 1024:.1f}MB")
+    print(f"  build(recompute=False):          size={size_diskann_nr / 1024 / 1024:.1f}MB")
+    print(f"  search recompute=True (final rerank): {t_diskann_r:.3f}s")
+    print(f"  search recompute=False (PQ only):     {t_diskann_nr:.3f}s")
+
+
+if __name__ == "__main__":
+    main()

benchmarks/diskann_vs_hnsw_speed_comparison.py

@@ -0,0 +1,286 @@
+#!/usr/bin/env python3
+"""
+DiskANN vs HNSW Search Performance Comparison
+
+This benchmark compares search performance between DiskANN and HNSW backends:
+- DiskANN: With graph partitioning enabled (is_recompute=True)
+- HNSW: With recompute enabled (is_recompute=True)
+- Tests performance across different dataset sizes
+- Measures search latency, recall, and index size
+"""
+
+import gc
+import multiprocessing as mp
+import tempfile
+import time
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+# Prefer 'fork' start method to avoid POSIX semaphore leaks on macOS
+try:
+    mp.set_start_method("fork", force=True)
+except Exception:
+    pass
+
+
+def create_test_texts(n_docs: int) -> list[str]:
+    """Create synthetic test documents for benchmarking."""
+    np.random.seed(42)
+    topics = [
+        "machine learning and artificial intelligence",
+        "natural language processing and text analysis",
+        "computer vision and image recognition",
+        "data science and statistical analysis",
+        "deep learning and neural networks",
+        "information retrieval and search engines",
+        "database systems and data management",
+        "software engineering and programming",
+        "cybersecurity and network protection",
+        "cloud computing and distributed systems",
+    ]
+
+    texts = []
+    for i in range(n_docs):
+        topic = topics[i % len(topics)]
+        variation = np.random.randint(1, 100)
+        text = (
+            f"This is document {i} about {topic}. Content variation {variation}. "
+            f"Additional information about {topic} with details and examples. "
+            f"Technical discussion of {topic} including implementation aspects."
+        )
+        texts.append(text)
+
+    return texts
+
+
+def benchmark_backend(
+    backend_name: str, texts: list[str], test_queries: list[str], backend_kwargs: dict[str, Any]
+) -> dict[str, float]:
+    """Benchmark a specific backend with the given configuration."""
+    from leann.api import LeannBuilder, LeannSearcher
+
+    print(f"\n🔧 Testing {backend_name.upper()} backend...")
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        index_path = str(Path(temp_dir) / f"benchmark_{backend_name}.leann")
+
+        # Build index
+        print(f"📦 Building {backend_name} index with {len(texts)} documents...")
+        start_time = time.time()
+
+        builder = LeannBuilder(
+            backend_name=backend_name,
+            embedding_model="facebook/contriever",
+            embedding_mode="sentence-transformers",
+            **backend_kwargs,
+        )
+
+        for text in texts:
+            builder.add_text(text)
+
+        builder.build_index(index_path)
+        build_time = time.time() - start_time
+
+        # Measure index size
+        index_dir = Path(index_path).parent
+        index_files = list(index_dir.glob(f"{Path(index_path).stem}.*"))
+        total_size = sum(f.stat().st_size for f in index_files if f.is_file())
+        size_mb = total_size / (1024 * 1024)
+
+        print(f"   ✅ Build completed in {build_time:.2f}s, index size: {size_mb:.1f}MB")
+
+        # Search benchmark
+        print("🔍 Running search benchmark...")
+        searcher = LeannSearcher(index_path)
+
+        search_times = []
+        all_results = []
+
+        for query in test_queries:
+            start_time = time.time()
+            results = searcher.search(query, top_k=5)
+            search_time = time.time() - start_time
+            search_times.append(search_time)
+            all_results.append(results)
+
+        avg_search_time = np.mean(search_times) * 1000  # Convert to ms
+        print(f"   ✅ Average search time: {avg_search_time:.1f}ms")
+
+        # Check for valid scores (detect -inf issues)
+        all_scores = [
+            result.score
+            for results in all_results
+            for result in results
+            if result.score is not None
+        ]
+        valid_scores = [
+            score for score in all_scores if score != float("-inf") and score != float("inf")
+        ]
+        score_validity_rate = len(valid_scores) / len(all_scores) if all_scores else 0
+
+        # Clean up (ensure embedding server shutdown and object GC)
+        try:
+            if hasattr(searcher, "cleanup"):
+                searcher.cleanup()
+            del searcher
+            del builder
+            gc.collect()
+        except Exception as e:
+            print(f"⚠️  Warning: Resource cleanup error: {e}")
+
+        return {
+            "build_time": build_time,
+            "avg_search_time_ms": avg_search_time,
+            "index_size_mb": size_mb,
+            "score_validity_rate": score_validity_rate,
+        }
+
+
+def run_comparison(n_docs: int = 500, n_queries: int = 10):
+    """Run performance comparison between DiskANN and HNSW."""
+    print("🚀 Starting DiskANN vs HNSW Performance Comparison")
+    print(f"📊 Dataset: {n_docs} documents, {n_queries} test queries")
+
+    # Create test data
+    texts = create_test_texts(n_docs)
+    test_queries = [
+        "machine learning algorithms",
+        "natural language processing",
+        "computer vision techniques",
+        "data analysis methods",
+        "neural network architectures",
+        "database query optimization",
+        "software development practices",
+        "security vulnerabilities",
+        "cloud infrastructure",
+        "distributed computing",
+    ][:n_queries]
+
+    # HNSW benchmark
+    hnsw_results = benchmark_backend(
+        backend_name="hnsw",
+        texts=texts,
+        test_queries=test_queries,
+        backend_kwargs={
+            "is_recompute": True,  # Enable recompute for fair comparison
+            "M": 16,
+            "efConstruction": 200,
+        },
+    )
+
+    # DiskANN benchmark
+    diskann_results = benchmark_backend(
+        backend_name="diskann",
+        texts=texts,
+        test_queries=test_queries,
+        backend_kwargs={
+            "is_recompute": True,  # Enable graph partitioning
+            "num_neighbors": 32,
+            "search_list_size": 50,
+        },
+    )
+
+    # Performance comparison
+    print("\n📈 Performance Comparison Results")
+    print(f"{'=' * 60}")
+    print(f"{'Metric':<25} {'HNSW':<15} {'DiskANN':<15} {'Speedup':<10}")
+    print(f"{'-' * 60}")
+
+    # Build time comparison
+    build_speedup = hnsw_results["build_time"] / diskann_results["build_time"]
+    print(
+        f"{'Build Time (s)':<25} {hnsw_results['build_time']:<15.2f} {diskann_results['build_time']:<15.2f} {build_speedup:<10.2f}x"
+    )
+
+    # Search time comparison
+    search_speedup = hnsw_results["avg_search_time_ms"] / diskann_results["avg_search_time_ms"]
+    print(
+        f"{'Search Time (ms)':<25} {hnsw_results['avg_search_time_ms']:<15.1f} {diskann_results['avg_search_time_ms']:<15.1f} {search_speedup:<10.2f}x"
+    )
+
+    # Index size comparison
+    size_ratio = diskann_results["index_size_mb"] / hnsw_results["index_size_mb"]
+    print(
+        f"{'Index Size (MB)':<25} {hnsw_results['index_size_mb']:<15.1f} {diskann_results['index_size_mb']:<15.1f} {size_ratio:<10.2f}x"
+    )
+
+    # Score validity
+    print(
+        f"{'Score Validity (%)':<25} {hnsw_results['score_validity_rate'] * 100:<15.1f} {diskann_results['score_validity_rate'] * 100:<15.1f}"
+    )
+
+    print(f"{'=' * 60}")
+    print("\n🎯 Summary:")
+    if search_speedup > 1:
+        print(f"   DiskANN is {search_speedup:.2f}x faster than HNSW for search")
+    else:
+        print(f"   HNSW is {1 / search_speedup:.2f}x faster than DiskANN for search")
+
+    if size_ratio > 1:
+        print(f"   DiskANN uses {size_ratio:.2f}x more storage than HNSW")
+    else:
+        print(f"   DiskANN uses {1 / size_ratio:.2f}x less storage than HNSW")
+
+    print(
+        f"   Both backends achieved {min(hnsw_results['score_validity_rate'], diskann_results['score_validity_rate']) * 100:.1f}% score validity"
+    )
+
+
+if __name__ == "__main__":
+    import sys
+
+    try:
+        # Handle help request
+        if len(sys.argv) > 1 and sys.argv[1] in ["-h", "--help", "help"]:
+            print("DiskANN vs HNSW Performance Comparison")
+            print("=" * 50)
+            print(f"Usage: python {sys.argv[0]} [n_docs] [n_queries]")
+            print()
+            print("Arguments:")
+            print("  n_docs      Number of documents to index (default: 500)")
+            print("  n_queries   Number of test queries to run (default: 10)")
+            print()
+            print("Examples:")
+            print("  python benchmarks/diskann_vs_hnsw_speed_comparison.py")
+            print("  python benchmarks/diskann_vs_hnsw_speed_comparison.py 1000")
+            print("  python benchmarks/diskann_vs_hnsw_speed_comparison.py 2000 20")
+            sys.exit(0)
+
+        # Parse command line arguments
+        n_docs = int(sys.argv[1]) if len(sys.argv) > 1 else 500
+        n_queries = int(sys.argv[2]) if len(sys.argv) > 2 else 10
+
+        print("DiskANN vs HNSW Performance Comparison")
+        print("=" * 50)
+        print(f"Dataset: {n_docs} documents, {n_queries} queries")
+        print()
+
+        run_comparison(n_docs=n_docs, n_queries=n_queries)
+
+    except KeyboardInterrupt:
+        print("\n⚠️  Benchmark interrupted by user")
+        sys.exit(130)
+    except Exception as e:
+        print(f"\n❌ Benchmark failed: {e}")
+        sys.exit(1)
+    finally:
+        # Ensure clean exit (forceful to prevent rare hangs from atexit/threads)
+        try:
+            gc.collect()
+            print("\n🧹 Cleanup completed")
+            # Flush stdio to ensure message is visible before hard-exit
+            try:
+                import sys as _sys
+
+                _sys.stdout.flush()
+                _sys.stderr.flush()
+            except Exception:
+                pass
+        except Exception:
+            pass
+        # Use os._exit to bypass atexit handlers that may hang in rare cases
+        import os as _os
+
+        _os._exit(0)

docs/configuration-guide.md

@@ -52,7 +52,7 @@ Based on our experience developing LEANN, embedding models fall into three categ
 ### Quick Start: Cloud and Local Embedding Options
 
 **OpenAI Embeddings (Fastest Setup)**
-For immediate testing without local model downloads:
+For immediate testing without local model downloads(also if you [do not have GPU](https://github.com/yichuan-w/LEANN/issues/43) and do not care that much about your document leak, you should use this, we compute the embedding and recompute using openai API):
 ```bash
 # Set OpenAI embeddings (requires OPENAI_API_KEY)
 --embedding-mode openai --embedding-model text-embedding-3-small
@@ -97,16 +97,24 @@ ollama pull nomic-embed-text
 ```
 
 ### DiskANN
-**Best for**: Large datasets (> 10M vectors, 10GB+ index size) - **⚠️ Beta version, still in active development**
-- Uses Product Quantization (PQ) for coarse filtering during graph traversal
-- Novel approach: stores only PQ codes, performs rerank with exact computation in final step
-- Implements a corner case of double-queue: prunes all neighbors and recomputes at the end
+**Best for**: Large datasets, especially when you want `recompute=True`.
+
+**Key advantages:**
+- **Faster search** on large datasets (3x+ speedup vs HNSW in many cases)
+- **Smart storage**: `recompute=True` enables automatic graph partitioning for smaller indexes
+- **Better scaling**: Designed for 100k+ documents
+
+**Recompute behavior:**
+- `recompute=True` (recommended): Pure PQ traversal + final reranking - faster and enables partitioning
+- `recompute=False`: PQ + partial real distances during traversal - slower but higher accuracy
 
 ```bash
-# For billion-scale deployments
---backend-name diskann --graph-degree 64 --build-complexity 128
+# Recommended for most use cases
+--backend-name diskann --graph-degree 32 --build-complexity 64
 ```
 
+**Performance Benchmark**: Run `uv run benchmarks/diskann_vs_hnsw_speed_comparison.py` to compare DiskANN and HNSW on your system.
+
 ## LLM Selection: Engine and Model Comparison
 
 ### LLM Engines
@@ -277,23 +285,30 @@ leann build my-index \
 
 # Search with OpenAI embeddings (recompute at query time)
 leann search my-index "your query" \
-  --recompute-embeddings
+  --recompute
 ```
 
 ### 2) Run remote builds with SkyPilot (cloud GPU)
 
-Offload embedding generation and index building to a GPU VM using SkyPilot. A template is provided at `sky/leann-build.yaml`.
+Offload embedding generation and index building to a GPU VM using [SkyPilot](https://skypilot.readthedocs.io/en/latest/). A template is provided at `sky/leann-build.yaml`.
 
 ```bash
 # One-time: install and configure SkyPilot
 pip install skypilot
+
+# Launch with defaults (L4:1) and mount ./data to ~/leann-data; the build runs automatically
 sky launch -c leann-gpu sky/leann-build.yaml
 
-# Build remotely (template installs uv + leann CLI)
-sky exec leann-gpu -- "leann build my-index --docs ~/leann-data --backend hnsw --complexity 64 --graph-degree 32"
-```
+# Override parameters via -e key=value (optional)
+sky launch -c leann-gpu sky/leann-build.yaml \
+  -e index_name=my-index \
+  -e backend=hnsw \
+  -e embedding_mode=sentence-transformers \
+  -e embedding_model=Qwen/Qwen3-Embedding-0.6B
 
-Details: see “Running Builds on SkyPilot (Optional)” below.
+# Copy the built index back to your local .leann (use rsync)
+rsync -Pavz leann-gpu:~/.leann/indexes/my-index ./.leann/indexes/
+```
 
 ### 3) Disable recomputation to trade storage for speed
 
@@ -307,35 +322,63 @@ leann build my-index --no-recompute --no-compact
 leann search my-index "your query" --no-recompute
 ```
 
-Trade-offs: lower query-time latency, but significantly higher storage usage.
+When to use:
+- Extreme low latency requirements (high QPS, interactive assistants)
+- Read-heavy workloads where storage is cheaper than latency
+- No always-available GPU
 
-## Running Builds on SkyPilot (Optional)
+Constraints:
+- HNSW: when `--no-recompute` is set, LEANN automatically disables compact mode during build
+- DiskANN: supported; `--no-recompute` skips selective recompute during search
 
-You can offload embedding generation and index building to a cloud GPU VM using SkyPilot, without changing any LEANN code. This is useful when your local machine lacks a GPU or you want faster throughput.
-
-### Quick Start
-
-1) Install SkyPilot by following their docs (`pip install skypilot`), then configure cloud credentials.
-
-2) Use the provided SkyPilot template:
+Storage impact:
+- Storing N embeddings of dimension D with float32 requires approximately N × D × 4 bytes
+- Example: 1,000,000 chunks × 768 dims × 4 bytes ≈ 2.86 GB (plus graph/metadata)
 
+Converting an existing index (rebuild required):
 ```bash
-sky launch -c leann-gpu sky/leann-build.yaml
+# Rebuild in-place (ensure you still have original docs or can regenerate chunks)
+leann build my-index --force --no-recompute --no-compact
 ```
 
-3) On the remote, either put your data under the mounted path or adjust `file_mounts` in `sky/leann-build.yaml`. Then run the LEANN build:
+Python API usage:
+```python
+from leann import LeannSearcher
 
-```bash
-sky exec leann-gpu -- "leann build my-index --docs ~/leann-data --backend hnsw --complexity 64 --graph-degree 32"
+searcher = LeannSearcher("/path/to/my-index.leann")
+results = searcher.search("your query", top_k=10, recompute_embeddings=False)
 ```
 
-Notes:
-- The template installs `uv` and the `leann` CLI globally on the remote instance.
-- Change the `accelerators` and `cloud` settings in `sky/leann-build.yaml` to match your budget/availability (e.g., `A10G:1`, `A100:1`, or CPU-only if you prefer).
-- You can also build with `diskann` by switching `--backend diskann`.
+Trade-offs:
+- Lower latency and fewer network hops at query time
+- Significantly higher storage (10–100× vs selective recomputation)
+- Slightly larger memory footprint during build and search
+
+Quick benchmark results (`benchmarks/benchmark_no_recompute.py` with 5k texts, complexity=32):
+
+- HNSW
+
+  ```text
+  recompute=True:  search_time=0.818s, size=1.1MB
+  recompute=False: search_time=0.012s, size=16.6MB
+  ```
+
+- DiskANN
+
+  ```text
+  recompute=True:  search_time=0.041s, size=5.9MB
+  recompute=False: search_time=0.013s, size=24.6MB
+  ```
+
+Conclusion:
+- **HNSW**: `no-recompute` is significantly faster (no embedding recomputation) but requires much more storage (stores all embeddings)
+- **DiskANN**: `no-recompute` uses PQ + partial real distances during traversal (slower but higher accuracy), while `recompute=True` uses pure PQ traversal + final reranking (faster traversal, enables build-time partitioning for smaller storage)
+
+
 
 ## Further Reading
 
 - [Lessons Learned Developing LEANN](https://yichuan-w.github.io/blog/lessons_learned_in_dev_leann/)
 - [LEANN Technical Paper](https://arxiv.org/abs/2506.08276)
 - [DiskANN Original Paper](https://papers.nips.cc/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf)
+- [SSD-based Graph Partitioning](https://github.com/SonglinLife/SSD_BASED_PLAN)

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

@@ -1 +1,7 @@
 from . import diskann_backend as diskann_backend
+from . import graph_partition
+
+# Export main classes and functions
+from .graph_partition import GraphPartitioner, partition_graph
+
+__all__ = ["GraphPartitioner", "diskann_backend", "graph_partition", "partition_graph"]

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

@@ -22,6 +22,11 @@ logger = logging.getLogger(__name__)
 @contextlib.contextmanager
 def suppress_cpp_output_if_needed():
     """Suppress C++ stdout/stderr based on LEANN_LOG_LEVEL"""
+    # In CI we avoid fiddling with low-level file descriptors to prevent aborts
+    if os.getenv("CI") == "true":
+        yield
+        return
+
     log_level = os.getenv("LEANN_LOG_LEVEL", "WARNING").upper()
 
     # Only suppress if log level is WARNING or higher (ERROR, CRITICAL)
@@ -137,6 +142,71 @@ class DiskannBuilder(LeannBackendBuilderInterface):
     def __init__(self, **kwargs):
         self.build_params = kwargs
 
+    def _safe_cleanup_after_partition(self, index_dir: Path, index_prefix: str):
+        """
+        Safely cleanup files after partition.
+        In partition mode, C++ doesn't read _disk.index content,
+        so we can delete it if all derived files exist.
+        """
+        disk_index_file = index_dir / f"{index_prefix}_disk.index"
+        beam_search_file = index_dir / f"{index_prefix}_disk_beam_search.index"
+
+        # Required files that C++ partition mode needs
+        # Note: C++ generates these with _disk.index suffix
+        disk_suffix = "_disk.index"
+        required_files = [
+            f"{index_prefix}{disk_suffix}_medoids.bin",  # Critical: assert fails if missing
+            # Note: _centroids.bin is not created in single-shot build - C++ handles this automatically
+            f"{index_prefix}_pq_pivots.bin",  # PQ table
+            f"{index_prefix}_pq_compressed.bin",  # PQ compressed vectors
+        ]
+
+        # Check if all required files exist
+        missing_files = []
+        for filename in required_files:
+            file_path = index_dir / filename
+            if not file_path.exists():
+                missing_files.append(filename)
+
+        if missing_files:
+            logger.warning(
+                f"Cannot safely delete _disk.index - missing required files: {missing_files}"
+            )
+            logger.info("Keeping all original files for safety")
+            return
+
+        # Calculate space savings
+        space_saved = 0
+        files_to_delete = []
+
+        if disk_index_file.exists():
+            space_saved += disk_index_file.stat().st_size
+            files_to_delete.append(disk_index_file)
+
+        if beam_search_file.exists():
+            space_saved += beam_search_file.stat().st_size
+            files_to_delete.append(beam_search_file)
+
+        # Safe to delete!
+        for file_to_delete in files_to_delete:
+            try:
+                os.remove(file_to_delete)
+                logger.info(f"✅ Safely deleted: {file_to_delete.name}")
+            except Exception as e:
+                logger.warning(f"Failed to delete {file_to_delete.name}: {e}")
+
+        if space_saved > 0:
+            space_saved_mb = space_saved / (1024 * 1024)
+            logger.info(f"💾 Space saved: {space_saved_mb:.1f} MB")
+
+            # Show what files are kept
+            logger.info("📁 Kept essential files for partition mode:")
+            for filename in required_files:
+                file_path = index_dir / filename
+                if file_path.exists():
+                    size_mb = file_path.stat().st_size / (1024 * 1024)
+                    logger.info(f"  - {filename} ({size_mb:.1f} MB)")
+
     def build(self, data: np.ndarray, ids: list[str], index_path: str, **kwargs):
         path = Path(index_path)
         index_dir = path.parent
@@ -151,6 +221,17 @@ class DiskannBuilder(LeannBackendBuilderInterface):
         _write_vectors_to_bin(data, index_dir / data_filename)
 
         build_kwargs = {**self.build_params, **kwargs}
+
+        # Extract is_recompute from nested backend_kwargs if needed
+        is_recompute = build_kwargs.get("is_recompute", False)
+        if not is_recompute and "backend_kwargs" in build_kwargs:
+            is_recompute = build_kwargs["backend_kwargs"].get("is_recompute", False)
+
+        # Flatten all backend_kwargs parameters to top level for compatibility
+        if "backend_kwargs" in build_kwargs:
+            nested_params = build_kwargs.pop("backend_kwargs")
+            build_kwargs.update(nested_params)
+
         metric_enum = _get_diskann_metrics().get(
             build_kwargs.get("distance_metric", "mips").lower()
         )
@@ -185,6 +266,30 @@ class DiskannBuilder(LeannBackendBuilderInterface):
                     build_kwargs.get("pq_disk_bytes", 0),
                     "",
                 )
+
+            # Auto-partition if is_recompute is enabled
+            if build_kwargs.get("is_recompute", False):
+                logger.info("is_recompute=True, starting automatic graph partitioning...")
+                from .graph_partition import partition_graph
+
+                # Partition the index using absolute paths
+                # Convert to absolute paths to avoid issues with working directory changes
+                absolute_index_dir = Path(index_dir).resolve()
+                absolute_index_prefix_path = str(absolute_index_dir / index_prefix)
+                disk_graph_path, partition_bin_path = partition_graph(
+                    index_prefix_path=absolute_index_prefix_path,
+                    output_dir=str(absolute_index_dir),
+                    partition_prefix=index_prefix,
+                )
+
+                # Safe cleanup: In partition mode, C++ doesn't read _disk.index content
+                # but still needs the derived files (_medoids.bin, _centroids.bin, etc.)
+                self._safe_cleanup_after_partition(index_dir, index_prefix)
+
+                logger.info("✅ Graph partitioning completed successfully!")
+                logger.info(f"  - Disk graph: {disk_graph_path}")
+                logger.info(f"  - Partition file: {partition_bin_path}")
+
         finally:
             temp_data_file = index_dir / data_filename
             if temp_data_file.exists():
@@ -213,7 +318,26 @@ class DiskannSearcher(BaseSearcher):
 
             # For DiskANN, we need to reinitialize the index when zmq_port changes
             # Store the initialization parameters for later use
-            full_index_prefix = str(self.index_dir / self.index_path.stem)
+            # Note: C++ load method expects the BASE path (without _disk.index suffix)
+            # C++ internally constructs: index_prefix + "_disk.index"
+            index_name = self.index_path.stem  # "simple_test.leann" -> "simple_test"
+            diskann_index_prefix = str(self.index_dir / index_name)  # /path/to/simple_test
+            full_index_prefix = diskann_index_prefix  # /path/to/simple_test (base path)
+
+            # Auto-detect partition files and set partition_prefix
+            partition_graph_file = self.index_dir / f"{index_name}_disk_graph.index"
+            partition_bin_file = self.index_dir / f"{index_name}_partition.bin"
+
+            partition_prefix = ""
+            if partition_graph_file.exists() and partition_bin_file.exists():
+                # C++ expects full path prefix, not just filename
+                partition_prefix = str(self.index_dir / index_name)  # /path/to/simple_test
+                logger.info(
+                    f"✅ Detected partition files, using partition_prefix='{partition_prefix}'"
+                )
+            else:
+                logger.debug("No partition files detected, using standard index files")
+
             self._init_params = {
                 "metric_enum": metric_enum,
                 "full_index_prefix": full_index_prefix,
@@ -221,8 +345,14 @@ class DiskannSearcher(BaseSearcher):
                 "num_nodes_to_cache": kwargs.get("num_nodes_to_cache", 0),
                 "cache_mechanism": 1,
                 "pq_prefix": "",
-                "partition_prefix": "",
+                "partition_prefix": partition_prefix,
             }
+
+            # Log partition configuration for debugging
+            if partition_prefix:
+                logger.info(
+                    f"✅ Detected partition files, using partition_prefix='{partition_prefix}'"
+                )
             self._diskannpy = diskannpy
             self._current_zmq_port = None
             self._index = None
@@ -311,9 +441,14 @@ class DiskannSearcher(BaseSearcher):
         else:  # "global"
             use_global_pruning = True
 
-        # Perform search with suppressed C++ output based on log level
-        use_deferred_fetch = kwargs.get("USE_DEFERRED_FETCH", True)
-        recompute_neighors = False
+        # Strategy:
+        # - Traversal always uses PQ distances
+        # - If recompute_embeddings=True, do a single final rerank via deferred fetch
+        #   (fetch embeddings for the final candidate set only)
+        # - Do not recompute neighbor distances along the path
+        use_deferred_fetch = True if recompute_embeddings else False
+        recompute_neighors = False  # Expected typo. For backward compatibility.
+
         with suppress_cpp_output_if_needed():
             labels, distances = self._index.batch_search(
                 query,

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

@@ -81,7 +81,8 @@ def create_diskann_embedding_server(
     with open(passages_file) as f:
         meta = json.load(f)
 
-    passages = PassageManager(meta["passage_sources"])
+    logger.info(f"Loading PassageManager with metadata_file_path: {passages_file}")
+    passages = PassageManager(meta["passage_sources"], metadata_file_path=passages_file)
     logger.info(
         f"Loaded PassageManager with {len(passages.global_offset_map)} passages from metadata"
     )
@@ -102,8 +103,9 @@ def create_diskann_embedding_server(
         socket.bind(f"tcp://*:{zmq_port}")
         logger.info(f"DiskANN ZMQ REP server listening on port {zmq_port}")
 
-        socket.setsockopt(zmq.RCVTIMEO, 300000)
-        socket.setsockopt(zmq.SNDTIMEO, 300000)
+        socket.setsockopt(zmq.RCVTIMEO, 1000)
+        socket.setsockopt(zmq.SNDTIMEO, 1000)
+        socket.setsockopt(zmq.LINGER, 0)
 
         while True:
             try:
@@ -220,30 +222,217 @@ def create_diskann_embedding_server(
                 traceback.print_exc()
                 raise
 
-    zmq_thread = threading.Thread(target=zmq_server_thread, daemon=True)
+    def zmq_server_thread_with_shutdown(shutdown_event):
+        """ZMQ server thread that respects shutdown signal.
+
+        This creates its own REP socket, binds to zmq_port, and periodically
+        checks shutdown_event using recv timeouts to exit cleanly.
+        """
+        logger.info("DiskANN ZMQ server thread started with shutdown support")
+
+        context = zmq.Context()
+        rep_socket = context.socket(zmq.REP)
+        rep_socket.bind(f"tcp://*:{zmq_port}")
+        logger.info(f"DiskANN ZMQ REP server listening on port {zmq_port}")
+
+        # Set receive timeout so we can check shutdown_event periodically
+        rep_socket.setsockopt(zmq.RCVTIMEO, 1000)  # 1 second timeout
+        rep_socket.setsockopt(zmq.SNDTIMEO, 1000)
+        rep_socket.setsockopt(zmq.LINGER, 0)
+
+        try:
+            while not shutdown_event.is_set():
+                try:
+                    e2e_start = time.time()
+                    # REP socket receives single-part messages
+                    message = rep_socket.recv()
+
+                    # Check for empty messages - REP socket requires response to every request
+                    if not message:
+                        logger.warning("Received empty message, sending empty response")
+                        rep_socket.send(b"")
+                        continue
+
+                    # Try protobuf first (same logic as original)
+                    texts = []
+                    is_text_request = False
+
+                    try:
+                        req_proto = embedding_pb2.NodeEmbeddingRequest()
+                        req_proto.ParseFromString(message)
+                        node_ids = list(req_proto.node_ids)
+
+                        # Look up texts by node IDs
+                        for nid in node_ids:
+                            try:
+                                passage_data = passages.get_passage(str(nid))
+                                txt = passage_data["text"]
+                                if not txt:
+                                    raise RuntimeError(f"FATAL: Empty text for passage ID {nid}")
+                                texts.append(txt)
+                            except KeyError:
+                                raise RuntimeError(f"FATAL: Passage with ID {nid} not found")
+
+                        logger.info(f"ZMQ received protobuf request for {len(node_ids)} node IDs")
+                    except Exception:
+                        # Fallback to msgpack for text requests
+                        try:
+                            import msgpack
+
+                            request = msgpack.unpackb(message)
+                            if isinstance(request, list) and all(
+                                isinstance(item, str) for item in request
+                            ):
+                                texts = request
+                                is_text_request = True
+                                logger.info(
+                                    f"ZMQ received msgpack text request for {len(texts)} texts"
+                                )
+                            else:
+                                raise ValueError("Not a valid msgpack text request")
+                        except Exception:
+                            logger.error("Both protobuf and msgpack parsing failed!")
+                            # Send error response
+                            resp_proto = embedding_pb2.NodeEmbeddingResponse()
+                            rep_socket.send(resp_proto.SerializeToString())
+                            continue
+
+                    # Process the request
+                    embeddings = compute_embeddings(texts, model_name, mode=embedding_mode)
+                    logger.info(f"Computed embeddings shape: {embeddings.shape}")
+
+                    # Validation
+                    if np.isnan(embeddings).any() or np.isinf(embeddings).any():
+                        logger.error("NaN or Inf detected in embeddings!")
+                        # Send error response
+                        if is_text_request:
+                            import msgpack
+
+                            response_data = msgpack.packb([])
+                        else:
+                            resp_proto = embedding_pb2.NodeEmbeddingResponse()
+                            response_data = resp_proto.SerializeToString()
+                        rep_socket.send(response_data)
+                        continue
+
+                    # Prepare response based on request type
+                    if is_text_request:
+                        # For direct text requests, return msgpack
+                        import msgpack
+
+                        response_data = msgpack.packb(embeddings.tolist())
+                    else:
+                        # For protobuf requests, return protobuf
+                        resp_proto = embedding_pb2.NodeEmbeddingResponse()
+                        hidden_contiguous = np.ascontiguousarray(embeddings, dtype=np.float32)
+
+                        resp_proto.embeddings_data = hidden_contiguous.tobytes()
+                        resp_proto.dimensions.append(hidden_contiguous.shape[0])
+                        resp_proto.dimensions.append(hidden_contiguous.shape[1])
+
+                        response_data = resp_proto.SerializeToString()
+
+                    # Send response back to the client
+                    rep_socket.send(response_data)
+
+                    e2e_end = time.time()
+                    logger.info(f"⏱️  ZMQ E2E time: {e2e_end - e2e_start:.6f}s")
+
+                except zmq.Again:
+                    # Timeout - check shutdown_event and continue
+                    continue
+                except Exception as e:
+                    if not shutdown_event.is_set():
+                        logger.error(f"Error in ZMQ server loop: {e}")
+                        try:
+                            # Send error response for REP socket
+                            resp_proto = embedding_pb2.NodeEmbeddingResponse()
+                            rep_socket.send(resp_proto.SerializeToString())
+                        except Exception:
+                            pass
+                    else:
+                        logger.info("Shutdown in progress, ignoring ZMQ error")
+                        break
+        finally:
+            try:
+                rep_socket.close(0)
+            except Exception:
+                pass
+            try:
+                context.term()
+            except Exception:
+                pass
+
+        logger.info("DiskANN ZMQ server thread exiting gracefully")
+
+    # Add shutdown coordination
+    shutdown_event = threading.Event()
+
+    def shutdown_zmq_server():
+        """Gracefully shutdown ZMQ server."""
+        logger.info("Initiating graceful shutdown...")
+        shutdown_event.set()
+
+        if zmq_thread.is_alive():
+            logger.info("Waiting for ZMQ thread to finish...")
+            zmq_thread.join(timeout=5)
+            if zmq_thread.is_alive():
+                logger.warning("ZMQ thread did not finish in time")
+
+        # Clean up ZMQ resources
+        try:
+            # Note: socket and context are cleaned up by thread exit
+            logger.info("ZMQ resources cleaned up")
+        except Exception as e:
+            logger.warning(f"Error cleaning ZMQ resources: {e}")
+
+        # Clean up other resources
+        try:
+            import gc
+
+            gc.collect()
+            logger.info("Additional resources cleaned up")
+        except Exception as e:
+            logger.warning(f"Error cleaning additional resources: {e}")
+
+        logger.info("Graceful shutdown completed")
+        sys.exit(0)
+
+    # Register signal handlers within this function scope
+    import signal
+
+    def signal_handler(sig, frame):
+        logger.info(f"Received signal {sig}, shutting down gracefully...")
+        shutdown_zmq_server()
+
+    signal.signal(signal.SIGTERM, signal_handler)
+    signal.signal(signal.SIGINT, signal_handler)
+
+    # Start ZMQ thread (NOT daemon!)
+    zmq_thread = threading.Thread(
+        target=lambda: zmq_server_thread_with_shutdown(shutdown_event),
+        daemon=False,  # Not daemon - we want to wait for it
+    )
     zmq_thread.start()
     logger.info(f"Started DiskANN ZMQ server thread on port {zmq_port}")
 
     # Keep the main thread alive
     try:
-        while True:
-            time.sleep(1)
+        while not shutdown_event.is_set():
+            time.sleep(0.1)  # Check shutdown more frequently
     except KeyboardInterrupt:
         logger.info("DiskANN Server shutting down...")
+        shutdown_zmq_server()
         return
 
+    # If we reach here, shutdown was triggered by signal
+    logger.info("Main loop exited, process should be shutting down")
+
 
 if __name__ == "__main__":
-    import signal
     import sys
 
-    def signal_handler(sig, frame):
-        logger.info(f"Received signal {sig}, shutting down gracefully...")
-        sys.exit(0)
-
-    # Register signal handlers for graceful shutdown
-    signal.signal(signal.SIGTERM, signal_handler)
-    signal.signal(signal.SIGINT, signal_handler)
+    # Signal handlers are now registered within create_diskann_embedding_server
 
     parser = argparse.ArgumentParser(description="DiskANN Embedding service")
     parser.add_argument("--zmq-port", type=int, default=5555, help="ZMQ port to run on")

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

@@ -0,0 +1,299 @@
+#!/usr/bin/env python3
+"""
+Graph Partition Module for LEANN DiskANN Backend
+
+This module provides Python bindings for the graph partition functionality
+of DiskANN, allowing users to partition disk-based indices for better
+performance.
+"""
+
+import os
+import shutil
+import subprocess
+import tempfile
+from pathlib import Path
+from typing import Optional
+
+
+class GraphPartitioner:
+    """
+    A Python interface for DiskANN's graph partition functionality.
+
+    This class provides methods to partition disk-based indices for improved
+    search performance and memory efficiency.
+    """
+
+    def __init__(self, build_type: str = "release"):
+        """
+        Initialize the GraphPartitioner.
+
+        Args:
+            build_type: Build type for the executables ("debug" or "release")
+        """
+        self.build_type = build_type
+        self._ensure_executables()
+
+    def _get_executable_path(self, name: str) -> str:
+        """Get the path to a graph partition executable."""
+        # Get the directory where this Python module is located
+        module_dir = Path(__file__).parent
+        # Navigate to the graph_partition directory
+        graph_partition_dir = module_dir.parent / "third_party" / "DiskANN" / "graph_partition"
+        executable_path = graph_partition_dir / "build" / self.build_type / "graph_partition" / name
+
+        if not executable_path.exists():
+            raise FileNotFoundError(f"Executable {name} not found at {executable_path}")
+
+        return str(executable_path)
+
+    def _ensure_executables(self):
+        """Ensure that the required executables are built."""
+        try:
+            self._get_executable_path("partitioner")
+            self._get_executable_path("index_relayout")
+        except FileNotFoundError:
+            # Try to build the executables automatically
+            print("Executables not found, attempting to build them...")
+            self._build_executables()
+
+    def _build_executables(self):
+        """Build the required executables."""
+        graph_partition_dir = (
+            Path(__file__).parent.parent / "third_party" / "DiskANN" / "graph_partition"
+        )
+        original_dir = os.getcwd()
+
+        try:
+            os.chdir(graph_partition_dir)
+
+            # Clean any existing build
+            if (graph_partition_dir / "build").exists():
+                shutil.rmtree(graph_partition_dir / "build")
+
+            # Run the build script
+            cmd = ["./build.sh", self.build_type, "split_graph", "/tmp/dummy"]
+            subprocess.run(cmd, capture_output=True, text=True, cwd=graph_partition_dir)
+
+            # Check if executables were created
+            partitioner_path = self._get_executable_path("partitioner")
+            relayout_path = self._get_executable_path("index_relayout")
+
+            print(f"✅ Built partitioner: {partitioner_path}")
+            print(f"✅ Built index_relayout: {relayout_path}")
+
+        except Exception as e:
+            raise RuntimeError(f"Failed to build executables: {e}")
+        finally:
+            os.chdir(original_dir)
+
+    def partition_graph(
+        self,
+        index_prefix_path: str,
+        output_dir: Optional[str] = None,
+        partition_prefix: Optional[str] = None,
+        **kwargs,
+    ) -> tuple[str, str]:
+        """
+        Partition a disk-based index for improved performance.
+
+        Args:
+            index_prefix_path: Path to the index prefix (e.g., "/path/to/index")
+            output_dir: Output directory for results (defaults to parent of index_prefix_path)
+            partition_prefix: Prefix for output files (defaults to basename of index_prefix_path)
+            **kwargs: Additional parameters for graph partitioning:
+                - gp_times: Number of LDG partition iterations (default: 10)
+                - lock_nums: Number of lock nodes (default: 10)
+                - cut: Cut adjacency list degree (default: 100)
+                - scale_factor: Scale factor (default: 1)
+                - data_type: Data type (default: "float")
+                - thread_nums: Number of threads (default: 10)
+
+        Returns:
+            Tuple of (disk_graph_index_path, partition_bin_path)
+
+        Raises:
+            RuntimeError: If the partitioning process fails
+        """
+        # Set default parameters
+        params = {
+            "gp_times": 10,
+            "lock_nums": 10,
+            "cut": 100,
+            "scale_factor": 1,
+            "data_type": "float",
+            "thread_nums": 10,
+            **kwargs,
+        }
+
+        # Determine output directory
+        if output_dir is None:
+            output_dir = str(Path(index_prefix_path).parent)
+
+        # Create output directory if it doesn't exist
+        Path(output_dir).mkdir(parents=True, exist_ok=True)
+
+        # Determine partition prefix
+        if partition_prefix is None:
+            partition_prefix = Path(index_prefix_path).name
+
+        # Get executable paths
+        partitioner_path = self._get_executable_path("partitioner")
+        relayout_path = self._get_executable_path("index_relayout")
+
+        # Create temporary directory for processing
+        with tempfile.TemporaryDirectory() as temp_dir:
+            # Change to the graph_partition directory for temporary files
+            graph_partition_dir = (
+                Path(__file__).parent.parent / "third_party" / "DiskANN" / "graph_partition"
+            )
+            original_dir = os.getcwd()
+
+            try:
+                os.chdir(graph_partition_dir)
+
+                # Create temporary data directory
+                temp_data_dir = Path(temp_dir) / "data"
+                temp_data_dir.mkdir(parents=True, exist_ok=True)
+
+                # Set up paths for temporary files
+                graph_path = temp_data_dir / "starling" / "_M_R_L_B" / "GRAPH"
+                graph_gp_path = (
+                    graph_path
+                    / f"GP_TIMES_{params['gp_times']}_LOCK_{params['lock_nums']}_GP_USE_FREQ0_CUT{params['cut']}_SCALE{params['scale_factor']}"
+                )
+                graph_gp_path.mkdir(parents=True, exist_ok=True)
+
+                # Find input index file
+                old_index_file = f"{index_prefix_path}_disk_beam_search.index"
+                if not os.path.exists(old_index_file):
+                    old_index_file = f"{index_prefix_path}_disk.index"
+
+                if not os.path.exists(old_index_file):
+                    raise RuntimeError(f"Index file not found: {old_index_file}")
+
+                # Run partitioner
+                gp_file_path = graph_gp_path / "_part.bin"
+                partitioner_cmd = [
+                    partitioner_path,
+                    "--index_file",
+                    old_index_file,
+                    "--data_type",
+                    params["data_type"],
+                    "--gp_file",
+                    str(gp_file_path),
+                    "-T",
+                    str(params["thread_nums"]),
+                    "--ldg_times",
+                    str(params["gp_times"]),
+                    "--scale",
+                    str(params["scale_factor"]),
+                    "--mode",
+                    "1",
+                ]
+
+                print(f"Running partitioner: {' '.join(partitioner_cmd)}")
+                result = subprocess.run(
+                    partitioner_cmd, capture_output=True, text=True, cwd=graph_partition_dir
+                )
+
+                if result.returncode != 0:
+                    raise RuntimeError(
+                        f"Partitioner failed with return code {result.returncode}.\n"
+                        f"stdout: {result.stdout}\n"
+                        f"stderr: {result.stderr}"
+                    )
+
+                # Run relayout
+                part_tmp_index = graph_gp_path / "_part_tmp.index"
+                relayout_cmd = [
+                    relayout_path,
+                    old_index_file,
+                    str(gp_file_path),
+                    params["data_type"],
+                    "1",
+                ]
+
+                print(f"Running relayout: {' '.join(relayout_cmd)}")
+                result = subprocess.run(
+                    relayout_cmd, capture_output=True, text=True, cwd=graph_partition_dir
+                )
+
+                if result.returncode != 0:
+                    raise RuntimeError(
+                        f"Relayout failed with return code {result.returncode}.\n"
+                        f"stdout: {result.stdout}\n"
+                        f"stderr: {result.stderr}"
+                    )
+
+                # Copy results to output directory
+                disk_graph_path = Path(output_dir) / f"{partition_prefix}_disk_graph.index"
+                partition_bin_path = Path(output_dir) / f"{partition_prefix}_partition.bin"
+
+                shutil.copy2(part_tmp_index, disk_graph_path)
+                shutil.copy2(gp_file_path, partition_bin_path)
+
+                print(f"Results copied to: {output_dir}")
+                return str(disk_graph_path), str(partition_bin_path)
+
+            finally:
+                os.chdir(original_dir)
+
+    def get_partition_info(self, partition_bin_path: str) -> dict:
+        """
+        Get information about a partition file.
+
+        Args:
+            partition_bin_path: Path to the partition binary file
+
+        Returns:
+            Dictionary containing partition information
+        """
+        if not os.path.exists(partition_bin_path):
+            raise FileNotFoundError(f"Partition file not found: {partition_bin_path}")
+
+        # For now, return basic file information
+        # In the future, this could parse the binary file for detailed info
+        stat = os.stat(partition_bin_path)
+        return {
+            "file_size": stat.st_size,
+            "file_path": partition_bin_path,
+            "modified_time": stat.st_mtime,
+        }
+
+
+def partition_graph(
+    index_prefix_path: str,
+    output_dir: Optional[str] = None,
+    partition_prefix: Optional[str] = None,
+    build_type: str = "release",
+    **kwargs,
+) -> tuple[str, str]:
+    """
+    Convenience function to partition a graph index.
+
+    Args:
+        index_prefix_path: Path to the index prefix
+        output_dir: Output directory (defaults to parent of index_prefix_path)
+        partition_prefix: Prefix for output files (defaults to basename of index_prefix_path)
+        build_type: Build type for executables ("debug" or "release")
+        **kwargs: Additional parameters for graph partitioning
+
+    Returns:
+        Tuple of (disk_graph_index_path, partition_bin_path)
+    """
+    partitioner = GraphPartitioner(build_type=build_type)
+    return partitioner.partition_graph(index_prefix_path, output_dir, partition_prefix, **kwargs)
+
+
+# Example usage:
+if __name__ == "__main__":
+    # Example: partition an index
+    try:
+        disk_graph_path, partition_bin_path = partition_graph(
+            "/path/to/your/index_prefix", gp_times=10, lock_nums=10, cut=100
+        )
+        print("Partitioning completed successfully!")
+        print(f"Disk graph index: {disk_graph_path}")
+        print(f"Partition binary: {partition_bin_path}")
+    except Exception as e:
+        print(f"Partitioning failed: {e}")

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

@@ -1,5 +1,6 @@
 import argparse
 import gc  # Import garbage collector interface
+import logging
 import os
 import struct
 import sys
@@ -7,6 +8,12 @@ import time
 
 import numpy as np
 
+# Set up logging to avoid print buffer issues
+logger = logging.getLogger(__name__)
+LOG_LEVEL = os.getenv("LEANN_LOG_LEVEL", "WARNING").upper()
+log_level = getattr(logging, LOG_LEVEL, logging.WARNING)
+logger.setLevel(log_level)
+
 # --- FourCCs (add more if needed) ---
 INDEX_HNSW_FLAT_FOURCC = int.from_bytes(b"IHNf", "little")
 # Add other HNSW fourccs if you expect different storage types inside HNSW
@@ -243,6 +250,8 @@ def convert_hnsw_graph_to_csr(input_filename, output_filename, prune_embeddings=
         output_filename: Output CSR index file
         prune_embeddings: Whether to prune embedding storage (write NULL storage marker)
     """
+    # Keep prints simple; rely on CI runner to flush output as needed
+
     print(f"Starting conversion: {input_filename} -> {output_filename}")
     start_time = time.time()
     original_hnsw_data = {}

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

@@ -54,12 +54,13 @@ class HNSWBuilder(LeannBackendBuilderInterface):
         self.efConstruction = self.build_params.setdefault("efConstruction", 200)
         self.distance_metric = self.build_params.setdefault("distance_metric", "mips")
         self.dimensions = self.build_params.get("dimensions")
-        if not self.is_recompute:
-            if self.is_compact:
-                # TODO: support this case @andy
-                raise ValueError(
-                    "is_recompute is False, but is_compact is True. This is not compatible now. change is compact to False and you can use the original HNSW index."
-                )
+        if not self.is_recompute and self.is_compact:
+            # Auto-correct: non-recompute requires non-compact storage for HNSW
+            logger.warning(
+                "is_recompute=False requires non-compact HNSW. Forcing is_compact=False."
+            )
+            self.is_compact = False
+            self.build_params["is_compact"] = False
 
     def build(self, data: np.ndarray, ids: list[str], index_path: str, **kwargs):
         from . import faiss  # type: ignore
@@ -184,9 +185,11 @@ class HNSWSearcher(BaseSearcher):
         """
         from . import faiss  # type: ignore
 
-        if not recompute_embeddings:
-            if self.is_pruned:
-                raise RuntimeError("Recompute is required for pruned index.")
+        if not recompute_embeddings and self.is_pruned:
+            raise RuntimeError(
+                "Recompute is required for pruned/compact HNSW index. "
+                "Re-run search with --recompute, or rebuild with --no-recompute and --no-compact."
+            )
         if recompute_embeddings:
             if zmq_port is None:
                 raise ValueError("zmq_port must be provided if recompute_embeddings is True")

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

@@ -10,7 +10,7 @@ import sys
 import threading
 import time
 from pathlib import Path
-from typing import Union
+from typing import Optional
 
 import msgpack
 import numpy as np
@@ -34,7 +34,7 @@ if not logger.handlers:
 
 
 def create_hnsw_embedding_server(
-    passages_file: Union[str, None] = None,
+    passages_file: Optional[str] = None,
     zmq_port: int = 5555,
     model_name: str = "sentence-transformers/all-mpnet-base-v2",
     distance_metric: str = "mips",
@@ -82,84 +82,160 @@ def create_hnsw_embedding_server(
     with open(passages_file) as f:
         meta = json.load(f)
 
-    # Convert relative paths to absolute paths based on metadata file location
-    metadata_dir = Path(passages_file).parent.parent  # Go up one level from the metadata file
-    passage_sources = []
-    for source in meta["passage_sources"]:
-        source_copy = source.copy()
-        # Convert relative paths to absolute paths
-        if not Path(source_copy["path"]).is_absolute():
-            source_copy["path"] = str(metadata_dir / source_copy["path"])
-        if not Path(source_copy["index_path"]).is_absolute():
-            source_copy["index_path"] = str(metadata_dir / source_copy["index_path"])
-        passage_sources.append(source_copy)
-
-    passages = PassageManager(passage_sources)
-    # Use index dimensions from metadata for shaping fallback responses
-    embedding_dim: int = int(meta.get("dimensions", 0))
+    # Let PassageManager handle path resolution uniformly. It supports fallback order:
+    # 1) path/index_path; 2) *_relative; 3) standard siblings next to meta
+    passages = PassageManager(meta["passage_sources"], metadata_file_path=passages_file)
+    # Dimension from metadata for shaping responses
+    try:
+        embedding_dim: int = int(meta.get("dimensions", 0))
+    except Exception:
+        embedding_dim = 0
     logger.info(
         f"Loaded PassageManager with {len(passages.global_offset_map)} passages from metadata"
     )
 
-    def zmq_server_thread():
-        """ZMQ server thread"""
+    # (legacy ZMQ thread removed; using shutdown-capable server only)
+
+    def zmq_server_thread_with_shutdown(shutdown_event):
+        """ZMQ server thread that respects shutdown signal.
+
+        Creates its own REP socket bound to zmq_port and polls with timeouts
+        to allow graceful shutdown.
+        """
+        logger.info("ZMQ server thread started with shutdown support")
+
         context = zmq.Context()
-        socket = context.socket(zmq.REP)
-        socket.bind(f"tcp://*:{zmq_port}")
-        logger.info(f"HNSW ZMQ server listening on port {zmq_port}")
+        rep_socket = context.socket(zmq.REP)
+        rep_socket.bind(f"tcp://*:{zmq_port}")
+        logger.info(f"HNSW ZMQ REP server listening on port {zmq_port}")
+        rep_socket.setsockopt(zmq.RCVTIMEO, 1000)
+        # Keep sends from blocking during shutdown; fail fast and drop on close
+        rep_socket.setsockopt(zmq.SNDTIMEO, 1000)
+        rep_socket.setsockopt(zmq.LINGER, 0)
 
-        socket.setsockopt(zmq.RCVTIMEO, 300000)
-        socket.setsockopt(zmq.SNDTIMEO, 300000)
-
-        # Track last request type for safe fallback responses on exceptions
-        last_request_type = "unknown"  # one of: 'text', 'distance', 'embedding', 'unknown'
+        # Track last request type/length for shape-correct fallbacks
+        last_request_type = "unknown"  # 'text' | 'distance' | 'embedding' | 'unknown'
         last_request_length = 0
-        while True:
-            try:
-                message_bytes = socket.recv()
-                logger.debug(f"Received ZMQ request of size {len(message_bytes)} bytes")
 
-                e2e_start = time.time()
-                request_payload = msgpack.unpackb(message_bytes)
+        try:
+            while not shutdown_event.is_set():
+                try:
+                    e2e_start = time.time()
+                    logger.debug("🔍 Waiting for ZMQ message...")
+                    request_bytes = rep_socket.recv()
 
-                # Handle direct text embedding request
-                if isinstance(request_payload, list) and len(request_payload) > 0:
-                    # Check if this is a direct text request (list of strings)
-                    if all(isinstance(item, str) for item in request_payload):
+                    # Rest of the processing logic (same as original)
+                    request = msgpack.unpackb(request_bytes)
+
+                    if len(request) == 1 and request[0] == "__QUERY_MODEL__":
+                        response_bytes = msgpack.packb([model_name])
+                        rep_socket.send(response_bytes)
+                        continue
+
+                    # Handle direct text embedding request
+                    if (
+                        isinstance(request, list)
+                        and request
+                        and all(isinstance(item, str) for item in request)
+                    ):
                         last_request_type = "text"
-                        last_request_length = len(request_payload)
-                        logger.info(
-                            f"Processing direct text embedding request for {len(request_payload)} texts in {embedding_mode} mode"
-                        )
-
-                        # Use unified embedding computation (now with model caching)
-                        embeddings = compute_embeddings(
-                            request_payload, model_name, mode=embedding_mode
-                        )
-
-                        response = embeddings.tolist()
-                        socket.send(msgpack.packb(response))
+                        last_request_length = len(request)
+                        embeddings = compute_embeddings(request, model_name, mode=embedding_mode)
+                        rep_socket.send(msgpack.packb(embeddings.tolist()))
                         e2e_end = time.time()
                         logger.info(f"⏱️  Text embedding E2E time: {e2e_end - e2e_start:.6f}s")
                         continue
 
-                # Handle distance calculation requests
-                if (
-                    isinstance(request_payload, list)
-                    and len(request_payload) == 2
-                    and isinstance(request_payload[0], list)
-                    and isinstance(request_payload[1], list)
-                ):
-                    node_ids = request_payload[0]
-                    query_vector = np.array(request_payload[1], dtype=np.float32)
-                    last_request_type = "distance"
+                    # Handle distance calculation request: [[ids], [query_vector]]
+                    if (
+                        isinstance(request, list)
+                        and len(request) == 2
+                        and isinstance(request[0], list)
+                        and isinstance(request[1], list)
+                    ):
+                        node_ids = request[0]
+                        # Handle nested [[ids]] shape defensively
+                        if len(node_ids) == 1 and isinstance(node_ids[0], list):
+                            node_ids = node_ids[0]
+                        query_vector = np.array(request[1], dtype=np.float32)
+                        last_request_type = "distance"
+                        last_request_length = len(node_ids)
+
+                        logger.debug("Distance calculation request received")
+                        logger.debug(f"    Node IDs: {node_ids}")
+                        logger.debug(f"    Query vector dim: {len(query_vector)}")
+
+                        # Gather texts for found ids
+                        texts: list[str] = []
+                        found_indices: list[int] = []
+                        for idx, nid in enumerate(node_ids):
+                            try:
+                                passage_data = passages.get_passage(str(nid))
+                                txt = passage_data.get("text", "")
+                                if isinstance(txt, str) and len(txt) > 0:
+                                    texts.append(txt)
+                                    found_indices.append(idx)
+                                else:
+                                    logger.error(f"Empty text for passage ID {nid}")
+                            except KeyError:
+                                logger.error(f"Passage ID {nid} not found")
+                            except Exception as e:
+                                logger.error(f"Exception looking up passage ID {nid}: {e}")
+
+                        # Prepare full-length response with large sentinel values
+                        large_distance = 1e9
+                        response_distances = [large_distance] * len(node_ids)
+
+                        if texts:
+                            try:
+                                embeddings = compute_embeddings(
+                                    texts, model_name, mode=embedding_mode
+                                )
+                                logger.info(
+                                    f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
+                                )
+                                if distance_metric == "l2":
+                                    partial = np.sum(
+                                        np.square(embeddings - query_vector.reshape(1, -1)), axis=1
+                                    )
+                                else:  # mips or cosine
+                                    partial = -np.dot(embeddings, query_vector)
+
+                                for pos, dval in zip(found_indices, partial.flatten().tolist()):
+                                    response_distances[pos] = float(dval)
+                            except Exception as e:
+                                logger.error(f"Distance computation error, using sentinels: {e}")
+
+                        # Send response in expected shape [[distances]]
+                        rep_socket.send(msgpack.packb([response_distances], use_single_float=True))
+                        e2e_end = time.time()
+                        logger.info(f"⏱️  Distance calculation E2E time: {e2e_end - e2e_start:.6f}s")
+                        continue
+
+                    # Fallback: treat as embedding-by-id request
+                    if (
+                        isinstance(request, list)
+                        and len(request) == 1
+                        and isinstance(request[0], list)
+                    ):
+                        node_ids = request[0]
+                    elif isinstance(request, list):
+                        node_ids = request
+                    else:
+                        node_ids = []
+                    last_request_type = "embedding"
                     last_request_length = len(node_ids)
+                    logger.info(f"ZMQ received {len(node_ids)} node IDs for embedding fetch")
 
-                    logger.debug("Distance calculation request received")
-                    logger.debug(f"    Node IDs: {node_ids}")
-                    logger.debug(f"    Query vector dim: {len(query_vector)}")
+                    # Preallocate zero-filled flat data for robustness
+                    if embedding_dim <= 0:
+                        dims = [0, 0]
+                        flat_data: list[float] = []
+                    else:
+                        dims = [len(node_ids), embedding_dim]
+                        flat_data = [0.0] * (dims[0] * dims[1])
 
-                    # Get embeddings for node IDs, tolerate missing IDs
+                    # Collect texts for found ids
                     texts: list[str] = []
                     found_indices: list[int] = []
                     for idx, nid in enumerate(node_ids):
@@ -172,175 +248,151 @@ def create_hnsw_embedding_server(
                             else:
                                 logger.error(f"Empty text for passage ID {nid}")
                         except KeyError:
-                            logger.error(f"Passage ID {nid} not found")
+                            logger.error(f"Passage with ID {nid} not found")
                         except Exception as e:
                             logger.error(f"Exception looking up passage ID {nid}: {e}")
 
-                    # Prepare full-length response distances with safe fallbacks
-                    large_distance = 1e9
-                    response_distances = [large_distance] * len(node_ids)
-
                     if texts:
                         try:
-                            # Process embeddings only for found indices
                             embeddings = compute_embeddings(texts, model_name, mode=embedding_mode)
                             logger.info(
                                 f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
                             )
 
-                            # Calculate distances for found embeddings only
-                            if distance_metric == "l2":
-                                partial_distances = np.sum(
-                                    np.square(embeddings - query_vector.reshape(1, -1)), axis=1
+                            if np.isnan(embeddings).any() or np.isinf(embeddings).any():
+                                logger.error(
+                                    f"NaN or Inf detected in embeddings! Requested IDs: {node_ids[:5]}..."
                                 )
-                            else:  # mips or cosine
-                                partial_distances = -np.dot(embeddings, query_vector)
-
-                            # Place computed distances back into the full response array
-                            for pos, dval in zip(
-                                found_indices, partial_distances.flatten().tolist()
-                            ):
-                                response_distances[pos] = float(dval)
+                                dims = [0, embedding_dim]
+                                flat_data = []
+                            else:
+                                emb_f32 = np.ascontiguousarray(embeddings, dtype=np.float32)
+                                flat = emb_f32.flatten().tolist()
+                                for j, pos in enumerate(found_indices):
+                                    start = pos * embedding_dim
+                                    end = start + embedding_dim
+                                    if end <= len(flat_data):
+                                        flat_data[start:end] = flat[
+                                            j * embedding_dim : (j + 1) * embedding_dim
+                                        ]
                         except Exception as e:
-                            logger.error(
-                                f"Distance computation error, falling back to large distances: {e}"
-                            )
+                            logger.error(f"Embedding computation error, returning zeros: {e}")
 
-                    # Always reply with exactly len(node_ids) distances
-                    response_bytes = msgpack.packb([response_distances], use_single_float=True)
-                    logger.debug(
-                        f"Sending distance response with {len(response_distances)} distances (found={len(found_indices)})"
-                    )
+                    response_payload = [dims, flat_data]
+                    response_bytes = msgpack.packb(response_payload, use_single_float=True)
 
-                    socket.send(response_bytes)
+                    rep_socket.send(response_bytes)
                     e2e_end = time.time()
-                    logger.info(f"⏱️  Distance calculation E2E time: {e2e_end - e2e_start:.6f}s")
+                    logger.info(f"⏱️  ZMQ E2E time: {e2e_end - e2e_start:.6f}s")
+
+                except zmq.Again:
+                    # Timeout - check shutdown_event and continue
                     continue
-
-                # Standard embedding request (passage ID lookup)
-                if (
-                    not isinstance(request_payload, list)
-                    or len(request_payload) != 1
-                    or not isinstance(request_payload[0], list)
-                ):
-                    logger.error(
-                        f"Invalid MessagePack request format. Expected [[ids...]] or [texts...], got: {type(request_payload)}"
-                    )
-                    socket.send(msgpack.packb([[], []]))
-                    continue
-
-                node_ids = request_payload[0]
-                logger.debug(f"Request for {len(node_ids)} node embeddings")
-                last_request_type = "embedding"
-                last_request_length = len(node_ids)
-
-                # Allocate output buffer (B, D) and fill with zeros for robustness
-                if embedding_dim <= 0:
-                    logger.error("Embedding dimension unknown; cannot serve embedding request")
-                    dims = [0, 0]
-                    data = []
-                else:
-                    dims = [len(node_ids), embedding_dim]
-                    data = [0.0] * (dims[0] * dims[1])
-
-                # Look up texts by node IDs; compute embeddings where available
-                texts: list[str] = []
-                found_indices: list[int] = []
-                for idx, nid in enumerate(node_ids):
-                    try:
-                        passage_data = passages.get_passage(str(nid))
-                        txt = passage_data.get("text", "")
-                        if isinstance(txt, str) and len(txt) > 0:
-                            texts.append(txt)
-                            found_indices.append(idx)
-                        else:
-                            logger.error(f"Empty text for passage ID {nid}")
-                    except KeyError:
-                        logger.error(f"Passage with ID {nid} not found")
-                    except Exception as e:
-                        logger.error(f"Exception looking up passage ID {nid}: {e}")
-
-                if texts:
-                    try:
-                        # Process embeddings for found texts only
-                        embeddings = compute_embeddings(texts, model_name, mode=embedding_mode)
-                        logger.info(
-                            f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
-                        )
-
-                        if np.isnan(embeddings).any() or np.isinf(embeddings).any():
-                            logger.error(
-                                f"NaN or Inf detected in embeddings! Requested IDs: {node_ids[:5]}..."
-                            )
-                            dims = [0, embedding_dim]
-                            data = []
-                        else:
-                            # Copy computed embeddings into the correct positions
-                            emb_f32 = np.ascontiguousarray(embeddings, dtype=np.float32)
-                            flat = emb_f32.flatten().tolist()
-                            for j, pos in enumerate(found_indices):
-                                start = pos * embedding_dim
-                                end = start + embedding_dim
-                                data[start:end] = flat[j * embedding_dim : (j + 1) * embedding_dim]
-                    except Exception as e:
-                        logger.error(f"Embedding computation error, returning zeros: {e}")
-
-                response_payload = [dims, data]
-                response_bytes = msgpack.packb(response_payload, use_single_float=True)
-
-                socket.send(response_bytes)
-                e2e_end = time.time()
-                logger.info(f"⏱️  ZMQ E2E time: {e2e_end - e2e_start:.6f}s")
-
-            except zmq.Again:
-                logger.debug("ZMQ socket timeout, continuing to listen")
-                continue
-            except Exception as e:
-                logger.error(f"Error in ZMQ server loop: {e}")
-                import traceback
-
-                traceback.print_exc()
-                # Fallback to a safe, minimal-structure response to avoid client crashes
-                if last_request_type == "distance":
-                    # Return a vector of large distances with the expected length
-                    fallback_len = max(0, int(last_request_length))
-                    large_distance = 1e9
-                    safe_response = [[large_distance] * fallback_len]
-                elif last_request_type == "embedding":
-                    # Return an empty embedding block with known dimension if available
-                    if embedding_dim > 0:
-                        safe_response = [[0, embedding_dim], []]
+                except Exception as e:
+                    if not shutdown_event.is_set():
+                        logger.error(f"Error in ZMQ server loop: {e}")
+                        # Shape-correct fallback
+                        try:
+                            if last_request_type == "distance":
+                                large_distance = 1e9
+                                fallback_len = max(0, int(last_request_length))
+                                safe = [[large_distance] * fallback_len]
+                            elif last_request_type == "embedding":
+                                bsz = max(0, int(last_request_length))
+                                dim = max(0, int(embedding_dim))
+                                safe = (
+                                    [[bsz, dim], [0.0] * (bsz * dim)] if dim > 0 else [[0, 0], []]
+                                )
+                            elif last_request_type == "text":
+                                safe = []  # direct text embeddings expectation is a flat list
+                            else:
+                                safe = [[0, int(embedding_dim) if embedding_dim > 0 else 0], []]
+                            rep_socket.send(msgpack.packb(safe, use_single_float=True))
+                        except Exception:
+                            pass
                     else:
-                        safe_response = [[0, 0], []]
-                else:
-                    # Unknown request type: default to empty embedding structure
-                    safe_response = [[0, int(embedding_dim) if embedding_dim > 0 else 0], []]
-                socket.send(msgpack.packb(safe_response, use_single_float=True))
+                        logger.info("Shutdown in progress, ignoring ZMQ error")
+                        break
+        finally:
+            try:
+                rep_socket.close(0)
+            except Exception:
+                pass
+            try:
+                context.term()
+            except Exception:
+                pass
 
-    zmq_thread = threading.Thread(target=zmq_server_thread, daemon=True)
+        logger.info("ZMQ server thread exiting gracefully")
+
+    # Add shutdown coordination
+    shutdown_event = threading.Event()
+
+    def shutdown_zmq_server():
+        """Gracefully shutdown ZMQ server."""
+        logger.info("Initiating graceful shutdown...")
+        shutdown_event.set()
+
+        if zmq_thread.is_alive():
+            logger.info("Waiting for ZMQ thread to finish...")
+            zmq_thread.join(timeout=5)
+            if zmq_thread.is_alive():
+                logger.warning("ZMQ thread did not finish in time")
+
+        # Clean up ZMQ resources
+        try:
+            # Note: socket and context are cleaned up by thread exit
+            logger.info("ZMQ resources cleaned up")
+        except Exception as e:
+            logger.warning(f"Error cleaning ZMQ resources: {e}")
+
+        # Clean up other resources
+        try:
+            import gc
+
+            gc.collect()
+            logger.info("Additional resources cleaned up")
+        except Exception as e:
+            logger.warning(f"Error cleaning additional resources: {e}")
+
+        logger.info("Graceful shutdown completed")
+        sys.exit(0)
+
+    # Register signal handlers within this function scope
+    import signal
+
+    def signal_handler(sig, frame):
+        logger.info(f"Received signal {sig}, shutting down gracefully...")
+        shutdown_zmq_server()
+
+    signal.signal(signal.SIGTERM, signal_handler)
+    signal.signal(signal.SIGINT, signal_handler)
+
+    # Pass shutdown_event to ZMQ thread
+    zmq_thread = threading.Thread(
+        target=lambda: zmq_server_thread_with_shutdown(shutdown_event),
+        daemon=False,  # Not daemon - we want to wait for it
+    )
     zmq_thread.start()
     logger.info(f"Started HNSW ZMQ server thread on port {zmq_port}")
 
     # Keep the main thread alive
     try:
-        while True:
-            time.sleep(1)
+        while not shutdown_event.is_set():
+            time.sleep(0.1)  # Check shutdown more frequently
     except KeyboardInterrupt:
         logger.info("HNSW Server shutting down...")
+        shutdown_zmq_server()
         return
 
+    # If we reach here, shutdown was triggered by signal
+    logger.info("Main loop exited, process should be shutting down")
+
 
 if __name__ == "__main__":
-    import signal
     import sys
 
-    def signal_handler(sig, frame):
-        logger.info(f"Received signal {sig}, shutting down gracefully...")
-        sys.exit(0)
-
-    # Register signal handlers for graceful shutdown
-    signal.signal(signal.SIGTERM, signal_handler)
-    signal.signal(signal.SIGINT, signal_handler)
+    # Signal handlers are now registered within create_hnsw_embedding_server
 
     parser = argparse.ArgumentParser(description="HNSW Embedding service")
     parser.add_argument("--zmq-port", type=int, default=5555, help="ZMQ port to run on")

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

@@ -115,20 +115,62 @@ class SearchResult:
 
 
 class PassageManager:
-    def __init__(self, passage_sources: list[dict[str, Any]]):
+    def __init__(
+        self, passage_sources: list[dict[str, Any]], metadata_file_path: Optional[str] = None
+    ):
         self.offset_maps = {}
         self.passage_files = {}
         self.global_offset_map = {}  # Combined map for fast lookup
 
+        # Derive index base name for standard sibling fallbacks, e.g., <index_name>.passages.*
+        index_name_base = None
+        if metadata_file_path:
+            meta_name = Path(metadata_file_path).name
+            if meta_name.endswith(".meta.json"):
+                index_name_base = meta_name[: -len(".meta.json")]
+
         for source in passage_sources:
             assert source["type"] == "jsonl", "only jsonl is supported"
-            passage_file = source["path"]
-            index_file = source["index_path"]  # .idx file
+            passage_file = source.get("path", "")
+            index_file = source.get("index_path", "")  # .idx file
 
-            # Fix path resolution for Colab and other environments
-            if not Path(index_file).is_absolute():
-                # If relative path, try to resolve it properly
-                index_file = str(Path(index_file).resolve())
+            # Fix path resolution - relative paths should be relative to metadata file directory
+            def _resolve_candidates(
+                primary: str,
+                relative_key: str,
+                default_name: Optional[str],
+                source_dict: dict[str, Any],
+            ) -> list[Path]:
+                candidates: list[Path] = []
+                # 1) Primary as-is (absolute or relative)
+                if primary:
+                    p = Path(primary)
+                    candidates.append(p if p.is_absolute() else (Path.cwd() / p))
+                # 2) metadata-relative explicit relative key
+                if metadata_file_path and source_dict.get(relative_key):
+                    candidates.append(Path(metadata_file_path).parent / source_dict[relative_key])
+                # 3) metadata-relative standard sibling filename
+                if metadata_file_path and default_name:
+                    candidates.append(Path(metadata_file_path).parent / default_name)
+                return candidates
+
+            # Build candidate lists and pick first existing; otherwise keep last candidate for error message
+            idx_default = f"{index_name_base}.passages.idx" if index_name_base else None
+            idx_candidates = _resolve_candidates(
+                index_file, "index_path_relative", idx_default, source
+            )
+            pas_default = f"{index_name_base}.passages.jsonl" if index_name_base else None
+            pas_candidates = _resolve_candidates(passage_file, "path_relative", pas_default, source)
+
+            def _pick_existing(cands: list[Path]) -> str:
+                for c in cands:
+                    if c.exists():
+                        return str(c.resolve())
+                # Fallback to last candidate (best guess) even if not exists; will error below
+                return str(cands[-1].resolve()) if cands else ""
+
+            index_file = _pick_existing(idx_candidates)
+            passage_file = _pick_existing(pas_candidates)
 
             if not Path(index_file).exists():
                 raise FileNotFoundError(f"Passage index file not found: {index_file}")
@@ -162,6 +204,18 @@ class LeannBuilder:
         **backend_kwargs,
     ):
         self.backend_name = backend_name
+        # Normalize incompatible combinations early (for consistent metadata)
+        if backend_name == "hnsw":
+            is_recompute = backend_kwargs.get("is_recompute", True)
+            is_compact = backend_kwargs.get("is_compact", True)
+            if is_recompute is False and is_compact is True:
+                warnings.warn(
+                    "HNSW with is_recompute=False requires non-compact storage. Forcing is_compact=False.",
+                    UserWarning,
+                    stacklevel=2,
+                )
+                backend_kwargs["is_compact"] = False
+
         backend_factory: Optional[LeannBackendFactoryInterface] = BACKEND_REGISTRY.get(backend_name)
         if backend_factory is None:
             raise ValueError(f"Backend '{backend_name}' not found or not registered.")
@@ -314,8 +368,12 @@ class LeannBuilder:
             "passage_sources": [
                 {
                     "type": "jsonl",
-                    "path": str(passages_file),
-                    "index_path": str(offset_file),
+                    # Preserve existing relative file names (backward-compatible)
+                    "path": passages_file.name,
+                    "index_path": offset_file.name,
+                    # Add optional redundant relative keys for remote build portability (non-breaking)
+                    "path_relative": passages_file.name,
+                    "index_path_relative": offset_file.name,
                 }
             ],
         }
@@ -430,8 +488,12 @@ class LeannBuilder:
             "passage_sources": [
                 {
                     "type": "jsonl",
-                    "path": str(passages_file),
-                    "index_path": str(offset_file),
+                    # Preserve existing relative file names (backward-compatible)
+                    "path": passages_file.name,
+                    "index_path": offset_file.name,
+                    # Add optional redundant relative keys for remote build portability (non-breaking)
+                    "path_relative": passages_file.name,
+                    "index_path_relative": offset_file.name,
                 }
             ],
             "built_from_precomputed_embeddings": True,
@@ -473,7 +535,10 @@ class LeannSearcher:
         self.embedding_model = self.meta_data["embedding_model"]
         # Support both old and new format
         self.embedding_mode = self.meta_data.get("embedding_mode", "sentence-transformers")
-        self.passage_manager = PassageManager(self.meta_data.get("passage_sources", []))
+        # Delegate portability handling to PassageManager
+        self.passage_manager = PassageManager(
+            self.meta_data.get("passage_sources", []), metadata_file_path=self.meta_path_str
+        )
         backend_factory = BACKEND_REGISTRY.get(backend_name)
         if backend_factory is None:
             raise ValueError(f"Backend '{backend_name}' not found.")
@@ -546,13 +611,13 @@ class LeannSearcher:
             zmq_port=zmq_port,
             **kwargs,
         )
-        time.time() - start_time
         # logger.info(f"  Search time: {search_time} seconds")
         logger.info(f"  Backend returned: labels={len(results.get('labels', [[]])[0])} results")
 
         enriched_results = []
         if "labels" in results and "distances" in results:
             logger.info(f"  Processing {len(results['labels'][0])} passage IDs:")
+            # Python 3.9 does not support zip(strict=...); lengths are expected to match
             for i, (string_id, dist) in enumerate(
                 zip(results["labels"][0], results["distances"][0])
             ):
@@ -580,13 +645,43 @@ class LeannSearcher:
                     )
                 except KeyError:
                     RED = "\033[91m"
+                    RESET = "\033[0m"
                     logger.error(
                         f"   {RED}✗{RESET} [{i + 1:2d}] ID: '{string_id}' -> {RED}ERROR: Passage not found!{RESET}"
                     )
 
+        # Define color codes outside the loop for final message
+        GREEN = "\033[92m"
+        RESET = "\033[0m"
         logger.info(f"  {GREEN}✓ Final enriched results: {len(enriched_results)} passages{RESET}")
         return enriched_results
 
+    def cleanup(self):
+        """Explicitly cleanup embedding server resources.
+
+        This method should be called after you're done using the searcher,
+        especially in test environments or batch processing scenarios.
+        """
+        if hasattr(self.backend_impl, "embedding_server_manager"):
+            self.backend_impl.embedding_server_manager.stop_server()
+
+    # Enable automatic cleanup patterns
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc, tb):
+        try:
+            self.cleanup()
+        except Exception:
+            pass
+
+    def __del__(self):
+        try:
+            self.cleanup()
+        except Exception:
+            # Avoid noisy errors during interpreter shutdown
+            pass
+
 
 class LeannChat:
     def __init__(
@@ -656,3 +751,28 @@ class LeannChat:
             except (KeyboardInterrupt, EOFError):
                 print("\nGoodbye!")
                 break
+
+    def cleanup(self):
+        """Explicitly cleanup embedding server resources.
+
+        This method should be called after you're done using the chat interface,
+        especially in test environments or batch processing scenarios.
+        """
+        if hasattr(self.searcher, "cleanup"):
+            self.searcher.cleanup()
+
+    # Enable automatic cleanup patterns
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc, tb):
+        try:
+            self.cleanup()
+        except Exception:
+            pass
+
+    def __del__(self):
+        try:
+            self.cleanup()
+        except Exception:
+            pass

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

@@ -422,7 +422,6 @@ class LLMInterface(ABC):
                 top_k=10,
                 complexity=64,
                 beam_width=8,
-                USE_DEFERRED_FETCH=True,
                 skip_search_reorder=True,
                 recompute_beighbor_embeddings=True,
                 dedup_node_dis=True,
@@ -434,7 +433,6 @@ class LLMInterface(ABC):
         Supported kwargs:
             - complexity (int): Search complexity parameter (default: 32)
             - beam_width (int): Beam width for search (default: 4)
-            - USE_DEFERRED_FETCH (bool): Enable deferred fetch mode (default: False)
             - skip_search_reorder (bool): Skip search reorder step (default: False)
             - recompute_beighbor_embeddings (bool): Enable ZMQ embedding server for neighbor recomputation (default: False)
             - dedup_node_dis (bool): Deduplicate nodes by distance (default: False)

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

@@ -72,7 +72,7 @@ class LeannCLI:
     def create_parser(self) -> argparse.ArgumentParser:
         parser = argparse.ArgumentParser(
             prog="leann",
-            description="LEANN - Local Enhanced AI Navigation",
+            description="The smallest vector index in the world. RAG Everything with LEANN!",
             formatter_class=argparse.RawDescriptionHelpFormatter,
             epilog="""
 Examples:
@@ -102,9 +102,18 @@ Examples:
             help="Documents directories and/or files (default: current directory)",
         )
         build_parser.add_argument(
-            "--backend", type=str, default="hnsw", choices=["hnsw", "diskann"]
+            "--backend",
+            type=str,
+            default="hnsw",
+            choices=["hnsw", "diskann"],
+            help="Backend to use (default: hnsw)",
+        )
+        build_parser.add_argument(
+            "--embedding-model",
+            type=str,
+            default="facebook/contriever",
+            help="Embedding model (default: facebook/contriever)",
         )
-        build_parser.add_argument("--embedding-model", type=str, default="facebook/contriever")
         build_parser.add_argument(
             "--embedding-mode",
             type=str,
@@ -112,60 +121,88 @@ Examples:
             choices=["sentence-transformers", "openai", "mlx", "ollama"],
             help="Embedding backend mode (default: sentence-transformers)",
         )
-        build_parser.add_argument("--force", "-f", action="store_true", help="Force rebuild")
-        build_parser.add_argument("--graph-degree", type=int, default=32)
-        build_parser.add_argument("--complexity", type=int, default=64)
-        build_parser.add_argument("--num-threads", type=int, default=1)
-        build_parser.add_argument("--compact", action="store_true", default=True)
         build_parser.add_argument(
-            "--no-compact",
-            dest="compact",
-            action="store_false",
-            help="Disable compact index storage (store full embeddings; higher storage)",
+            "--force", "-f", action="store_true", help="Force rebuild existing index"
         )
-        build_parser.add_argument("--recompute", action="store_true", default=True)
         build_parser.add_argument(
-            "--no-recompute",
-            dest="recompute",
-            action="store_false",
-            help="Disable embedding recomputation (store full embeddings; lower query latency)",
+            "--graph-degree", type=int, default=32, help="Graph degree (default: 32)"
+        )
+        build_parser.add_argument(
+            "--complexity", type=int, default=64, help="Build complexity (default: 64)"
+        )
+        build_parser.add_argument("--num-threads", type=int, default=1)
+        build_parser.add_argument(
+            "--compact",
+            action=argparse.BooleanOptionalAction,
+            default=True,
+            help="Use compact storage (default: true). Must be `no-compact` for `no-recompute` build.",
+        )
+        build_parser.add_argument(
+            "--recompute",
+            action=argparse.BooleanOptionalAction,
+            default=True,
+            help="Enable recomputation (default: true)",
         )
         build_parser.add_argument(
             "--file-types",
             type=str,
             help="Comma-separated list of file extensions to include (e.g., '.txt,.pdf,.pptx'). If not specified, uses default supported types.",
         )
+        build_parser.add_argument(
+            "--include-hidden",
+            action=argparse.BooleanOptionalAction,
+            default=False,
+            help="Include hidden files and directories (paths starting with '.') during indexing (default: false)",
+        )
+        build_parser.add_argument(
+            "--doc-chunk-size",
+            type=int,
+            default=256,
+            help="Document chunk size in tokens/characters (default: 256)",
+        )
+        build_parser.add_argument(
+            "--doc-chunk-overlap",
+            type=int,
+            default=128,
+            help="Document chunk overlap (default: 128)",
+        )
+        build_parser.add_argument(
+            "--code-chunk-size",
+            type=int,
+            default=512,
+            help="Code chunk size in tokens/lines (default: 512)",
+        )
+        build_parser.add_argument(
+            "--code-chunk-overlap",
+            type=int,
+            default=50,
+            help="Code chunk overlap (default: 50)",
+        )
 
         # Search command
         search_parser = subparsers.add_parser("search", help="Search documents")
         search_parser.add_argument("index_name", help="Index name")
         search_parser.add_argument("query", help="Search query")
-        search_parser.add_argument("--top-k", type=int, default=5)
-        search_parser.add_argument("--complexity", type=int, default=64)
+        search_parser.add_argument(
+            "--top-k", type=int, default=5, help="Number of results (default: 5)"
+        )
+        search_parser.add_argument(
+            "--complexity", type=int, default=64, help="Search complexity (default: 64)"
+        )
         search_parser.add_argument("--beam-width", type=int, default=1)
         search_parser.add_argument("--prune-ratio", type=float, default=0.0)
         search_parser.add_argument(
-            "--recompute-embeddings",
-            action="store_true",
+            "--recompute",
+            dest="recompute_embeddings",
+            action=argparse.BooleanOptionalAction,
             default=True,
-            help="Recompute embeddings (default: True)",
-        )
-        search_parser.add_argument(
-            "--no-recompute-embeddings",
-            dest="recompute_embeddings",
-            action="store_false",
-            help="Disable embedding recomputation during search",
-        )
-        search_parser.add_argument(
-            "--no-recompute",
-            dest="recompute_embeddings",
-            action="store_false",
-            help="Alias for --no-recompute-embeddings",
+            help="Enable/disable embedding recomputation (default: enabled). Should not do a `no-recompute` search in a `recompute` build.",
         )
         search_parser.add_argument(
             "--pruning-strategy",
             choices=["global", "local", "proportional"],
             default="global",
+            help="Pruning strategy (default: global)",
         )
 
         # Ask command
@@ -176,31 +213,27 @@ Examples:
             type=str,
             default="ollama",
             choices=["simulated", "ollama", "hf", "openai"],
+            help="LLM provider (default: ollama)",
+        )
+        ask_parser.add_argument(
+            "--model", type=str, default="qwen3:8b", help="Model name (default: qwen3:8b)"
         )
-        ask_parser.add_argument("--model", type=str, default="qwen3:8b")
         ask_parser.add_argument("--host", type=str, default="http://localhost:11434")
-        ask_parser.add_argument("--interactive", "-i", action="store_true")
-        ask_parser.add_argument("--top-k", type=int, default=20)
+        ask_parser.add_argument(
+            "--interactive", "-i", action="store_true", help="Interactive chat mode"
+        )
+        ask_parser.add_argument(
+            "--top-k", type=int, default=20, help="Retrieval count (default: 20)"
+        )
         ask_parser.add_argument("--complexity", type=int, default=32)
         ask_parser.add_argument("--beam-width", type=int, default=1)
         ask_parser.add_argument("--prune-ratio", type=float, default=0.0)
         ask_parser.add_argument(
-            "--recompute-embeddings",
-            action="store_true",
+            "--recompute",
+            dest="recompute_embeddings",
+            action=argparse.BooleanOptionalAction,
             default=True,
-            help="Recompute embeddings (default: True)",
-        )
-        ask_parser.add_argument(
-            "--no-recompute-embeddings",
-            dest="recompute_embeddings",
-            action="store_false",
-            help="Disable embedding recomputation during ask",
-        )
-        ask_parser.add_argument(
-            "--no-recompute",
-            dest="recompute_embeddings",
-            action="store_false",
-            help="Alias for --no-recompute-embeddings",
+            help="Enable/disable embedding recomputation during ask (default: enabled)",
         )
         ask_parser.add_argument(
             "--pruning-strategy",
@@ -384,7 +417,10 @@ Examples:
                     print(f"  leann ask {example_name} --interactive")
 
     def load_documents(
-        self, docs_paths: Union[str, list], custom_file_types: Union[str, None] = None
+        self,
+        docs_paths: Union[str, list],
+        custom_file_types: Union[str, None] = None,
+        include_hidden: bool = False,
     ):
         # Handle both single path (string) and multiple paths (list) for backward compatibility
         if isinstance(docs_paths, str):
@@ -428,6 +464,10 @@ Examples:
 
         all_documents = []
 
+        # Helper to detect hidden path components
+        def _path_has_hidden_segment(p: Path) -> bool:
+            return any(part.startswith(".") and part not in [".", ".."] for part in p.parts)
+
         # First, process individual files if any
         if files:
             print(f"\n🔄 Processing {len(files)} individual file{'s' if len(files) > 1 else ''}...")
@@ -440,8 +480,12 @@ Examples:
 
                 files_by_dir = defaultdict(list)
                 for file_path in files:
-                    parent_dir = str(Path(file_path).parent)
-                    files_by_dir[parent_dir].append(file_path)
+                    file_path_obj = Path(file_path)
+                    if not include_hidden and _path_has_hidden_segment(file_path_obj):
+                        print(f"  ⚠️  Skipping hidden file: {file_path}")
+                        continue
+                    parent_dir = str(file_path_obj.parent)
+                    files_by_dir[parent_dir].append(str(file_path_obj))
 
                 # Load files from each parent directory
                 for parent_dir, file_list in files_by_dir.items():
@@ -452,6 +496,7 @@ Examples:
                         file_docs = SimpleDirectoryReader(
                             parent_dir,
                             input_files=file_list,
+                            # exclude_hidden only affects directory scans; input_files are explicit
                             filename_as_id=True,
                         ).load_data()
                         all_documents.extend(file_docs)
@@ -550,6 +595,8 @@ Examples:
                     # Check if file matches any exclude pattern
                     try:
                         relative_path = file_path.relative_to(docs_path)
+                        if not include_hidden and _path_has_hidden_segment(relative_path):
+                            continue
                         if self._should_exclude_file(relative_path, gitignore_matches):
                             continue
                     except ValueError:
@@ -577,6 +624,7 @@ Examples:
                         try:
                             default_docs = SimpleDirectoryReader(
                                 str(file_path.parent),
+                                exclude_hidden=not include_hidden,
                                 filename_as_id=True,
                                 required_exts=[file_path.suffix],
                             ).load_data()
@@ -605,6 +653,7 @@ Examples:
                     encoding="utf-8",
                     required_exts=code_extensions,
                     file_extractor={},  # Use default extractors
+                    exclude_hidden=not include_hidden,
                     filename_as_id=True,
                 ).load_data(show_progress=True)
 
@@ -723,7 +772,40 @@ Examples:
             print(f"Index '{index_name}' already exists. Use --force to rebuild.")
             return
 
-        all_texts = self.load_documents(docs_paths, args.file_types)
+        # Configure chunking based on CLI args before loading documents
+        # Guard against invalid configurations
+        doc_chunk_size = max(1, int(args.doc_chunk_size))
+        doc_chunk_overlap = max(0, int(args.doc_chunk_overlap))
+        if doc_chunk_overlap >= doc_chunk_size:
+            print(
+                f"⚠️  Adjusting doc chunk overlap from {doc_chunk_overlap} to {doc_chunk_size - 1} (must be < chunk size)"
+            )
+            doc_chunk_overlap = doc_chunk_size - 1
+
+        code_chunk_size = max(1, int(args.code_chunk_size))
+        code_chunk_overlap = max(0, int(args.code_chunk_overlap))
+        if code_chunk_overlap >= code_chunk_size:
+            print(
+                f"⚠️  Adjusting code chunk overlap from {code_chunk_overlap} to {code_chunk_size - 1} (must be < chunk size)"
+            )
+            code_chunk_overlap = code_chunk_size - 1
+
+        self.node_parser = SentenceSplitter(
+            chunk_size=doc_chunk_size,
+            chunk_overlap=doc_chunk_overlap,
+            separator=" ",
+            paragraph_separator="\n\n",
+        )
+        self.code_parser = SentenceSplitter(
+            chunk_size=code_chunk_size,
+            chunk_overlap=code_chunk_overlap,
+            separator="\n",
+            paragraph_separator="\n\n",
+        )
+
+        all_texts = self.load_documents(
+            docs_paths, args.file_types, include_hidden=args.include_hidden
+        )
         if not all_texts:
             print("No documents found")
             return

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

@@ -263,8 +263,16 @@ def compute_embeddings_openai(texts: list[str], model_name: str) -> np.ndarray:
     print(f"len of texts: {len(texts)}")
 
     # OpenAI has limits on batch size and input length
-    max_batch_size = 1000  # Conservative batch size
+    max_batch_size = 800  # Conservative batch size because the token limit is 300K
     all_embeddings = []
+    # get the avg len of texts
+    avg_len = sum(len(text) for text in texts) / len(texts)
+    print(f"avg len of texts: {avg_len}")
+    # if avg len is less than 1000, use the max batch size
+    if avg_len > 300:
+        max_batch_size = 500
+
+    # if avg len is less than 1000, use the max batch size
 
     try:
         from tqdm import tqdm

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

@@ -8,7 +8,7 @@ import time
 from pathlib import Path
 from typing import Optional
 
-import psutil
+# Lightweight, self-contained server manager with no cross-process inspection
 
 # Set up logging based on environment variable
 LOG_LEVEL = os.getenv("LEANN_LOG_LEVEL", "WARNING").upper()
@@ -43,130 +43,7 @@ def _check_port(port: int) -> bool:
         return s.connect_ex(("localhost", port)) == 0
 
 
-def _check_process_matches_config(
-    port: int, expected_model: str, expected_passages_file: str
-) -> bool:
-    """
-    Check if the process using the port matches our expected model and passages file.
-    Returns True if matches, False otherwise.
-    """
-    try:
-        for proc in psutil.process_iter(["pid", "cmdline"]):
-            if not _is_process_listening_on_port(proc, port):
-                continue
-
-            cmdline = proc.info["cmdline"]
-            if not cmdline:
-                continue
-
-            return _check_cmdline_matches_config(
-                cmdline, port, expected_model, expected_passages_file
-            )
-
-        logger.debug(f"No process found listening on port {port}")
-        return False
-
-    except Exception as e:
-        logger.warning(f"Could not check process on port {port}: {e}")
-        return False
-
-
-def _is_process_listening_on_port(proc, port: int) -> bool:
-    """Check if a process is listening on the given port."""
-    try:
-        connections = proc.net_connections()
-        for conn in connections:
-            if conn.laddr.port == port and conn.status == psutil.CONN_LISTEN:
-                return True
-        return False
-    except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess):
-        return False
-
-
-def _check_cmdline_matches_config(
-    cmdline: list, port: int, expected_model: str, expected_passages_file: str
-) -> bool:
-    """Check if command line matches our expected configuration."""
-    cmdline_str = " ".join(cmdline)
-    logger.debug(f"Found process on port {port}: {cmdline_str}")
-
-    # Check if it's our embedding server
-    is_embedding_server = any(
-        server_type in cmdline_str
-        for server_type in [
-            "embedding_server",
-            "leann_backend_diskann.embedding_server",
-            "leann_backend_hnsw.hnsw_embedding_server",
-        ]
-    )
-
-    if not is_embedding_server:
-        logger.debug(f"Process on port {port} is not our embedding server")
-        return False
-
-    # Check model name
-    model_matches = _check_model_in_cmdline(cmdline, expected_model)
-
-    # Check passages file if provided
-    passages_matches = _check_passages_in_cmdline(cmdline, expected_passages_file)
-
-    result = model_matches and passages_matches
-    logger.debug(
-        f"model_matches: {model_matches}, passages_matches: {passages_matches}, overall: {result}"
-    )
-    return result
-
-
-def _check_model_in_cmdline(cmdline: list, expected_model: str) -> bool:
-    """Check if the command line contains the expected model."""
-    if "--model-name" not in cmdline:
-        return False
-
-    model_idx = cmdline.index("--model-name")
-    if model_idx + 1 >= len(cmdline):
-        return False
-
-    actual_model = cmdline[model_idx + 1]
-    return actual_model == expected_model
-
-
-def _check_passages_in_cmdline(cmdline: list, expected_passages_file: str) -> bool:
-    """Check if the command line contains the expected passages file."""
-    if "--passages-file" not in cmdline:
-        return False  # Expected but not found
-
-    passages_idx = cmdline.index("--passages-file")
-    if passages_idx + 1 >= len(cmdline):
-        return False
-
-    actual_passages = cmdline[passages_idx + 1]
-    expected_path = Path(expected_passages_file).resolve()
-    actual_path = Path(actual_passages).resolve()
-    return actual_path == expected_path
-
-
-def _find_compatible_port_or_next_available(
-    start_port: int, model_name: str, passages_file: str, max_attempts: int = 100
-) -> tuple[int, bool]:
-    """
-    Find a port that either has a compatible server or is available.
-    Returns (port, is_compatible) where is_compatible indicates if we found a matching server.
-    """
-    for port in range(start_port, start_port + max_attempts):
-        if not _check_port(port):
-            # Port is available
-            return port, False
-
-        # Port is in use, check if it's compatible
-        if _check_process_matches_config(port, model_name, passages_file):
-            logger.info(f"Found compatible server on port {port}")
-            return port, True
-        else:
-            logger.info(f"Port {port} has incompatible server, trying next port...")
-
-    raise RuntimeError(
-        f"Could not find compatible or available port in range {start_port}-{start_port + max_attempts}"
-    )
+# Note: All cross-process scanning helpers removed for simplicity
 
 
 class EmbeddingServerManager:
@@ -185,7 +62,16 @@ class EmbeddingServerManager:
         self.backend_module_name = backend_module_name
         self.server_process: Optional[subprocess.Popen] = None
         self.server_port: Optional[int] = None
+        # Track last-started config for in-process reuse only
+        self._server_config: Optional[dict] = None
         self._atexit_registered = False
+        # Also register a weakref finalizer to ensure cleanup when manager is GC'ed
+        try:
+            import weakref
+
+            self._finalizer = weakref.finalize(self, self._finalize_process)
+        except Exception:
+            self._finalizer = None
 
     def start_server(
         self,
@@ -195,26 +81,24 @@ class EmbeddingServerManager:
         **kwargs,
     ) -> tuple[bool, int]:
         """Start the embedding server."""
-        passages_file = kwargs.get("passages_file")
+        # passages_file may be present in kwargs for server CLI, but we don't need it here
 
-        # Check if we have a compatible server already running
-        if self._has_compatible_running_server(model_name, passages_file):
-            logger.info("Found compatible running server!")
-            return True, port
+        # If this manager already has a live server, just reuse it
+        if self.server_process and self.server_process.poll() is None and self.server_port:
+            logger.info("Reusing in-process server")
+            return True, self.server_port
 
         # For Colab environment, use a different strategy
         if _is_colab_environment():
             logger.info("Detected Colab environment, using alternative startup strategy")
             return self._start_server_colab(port, model_name, embedding_mode, **kwargs)
 
-        # Find a compatible port or next available
-        actual_port, is_compatible = _find_compatible_port_or_next_available(
-            port, model_name, passages_file
-        )
-
-        if is_compatible:
-            logger.info(f"Found compatible server on port {actual_port}")
-            return True, actual_port
+        # Always pick a fresh available port
+        try:
+            actual_port = _get_available_port(port)
+        except RuntimeError:
+            logger.error("No available ports found")
+            return False, port
 
         # Start a new server
         return self._start_new_server(actual_port, model_name, embedding_mode, **kwargs)
@@ -247,17 +131,7 @@ class EmbeddingServerManager:
             logger.error(f"Failed to start embedding server in Colab: {e}")
             return False, actual_port
 
-    def _has_compatible_running_server(self, model_name: str, passages_file: str) -> bool:
-        """Check if we have a compatible running server."""
-        if not (self.server_process and self.server_process.poll() is None and self.server_port):
-            return False
-
-        if _check_process_matches_config(self.server_port, model_name, passages_file):
-            logger.info(f"Existing server process (PID {self.server_process.pid}) is compatible")
-            return True
-
-        logger.info("Existing server process is incompatible. Should start a new server.")
-        return False
+    # Note: No compatibility check needed; manager is per-searcher and configs are stable per instance
 
     def _start_new_server(
         self, port: int, model_name: str, embedding_mode: str, **kwargs
@@ -304,22 +178,61 @@ class EmbeddingServerManager:
         project_root = Path(__file__).parent.parent.parent.parent.parent
         logger.info(f"Command: {' '.join(command)}")
 
-        # Let server output go directly to console
-        # The server will respect LEANN_LOG_LEVEL environment variable
+        # In CI environment, redirect stdout to avoid buffer deadlock but keep stderr for debugging
+        # Embedding servers use many print statements that can fill stdout buffers
+        is_ci = os.environ.get("CI") == "true"
+        if is_ci:
+            stdout_target = subprocess.DEVNULL
+            stderr_target = None  # Keep stderr for error debugging in CI
+            logger.info(
+                "CI environment detected, redirecting embedding server stdout to DEVNULL, keeping stderr"
+            )
+        else:
+            stdout_target = None  # Direct to console for visible logs
+            stderr_target = None  # Direct to console for visible logs
+
+        # Start embedding server subprocess
         self.server_process = subprocess.Popen(
             command,
             cwd=project_root,
-            stdout=None,  # Direct to console
-            stderr=None,  # Direct to console
+            stdout=stdout_target,
+            stderr=stderr_target,
         )
         self.server_port = port
+        # Record config for in-process reuse
+        try:
+            self._server_config = {
+                "model_name": command[command.index("--model-name") + 1]
+                if "--model-name" in command
+                else "",
+                "passages_file": command[command.index("--passages-file") + 1]
+                if "--passages-file" in command
+                else "",
+                "embedding_mode": command[command.index("--embedding-mode") + 1]
+                if "--embedding-mode" in command
+                else "sentence-transformers",
+            }
+        except Exception:
+            self._server_config = {
+                "model_name": "",
+                "passages_file": "",
+                "embedding_mode": "sentence-transformers",
+            }
         logger.info(f"Server process started with PID: {self.server_process.pid}")
 
         # Register atexit callback only when we actually start a process
         if not self._atexit_registered:
-            # Use a lambda to avoid issues with bound methods
-            atexit.register(lambda: self.stop_server() if self.server_process else None)
+            # Always attempt best-effort finalize at interpreter exit
+            atexit.register(self._finalize_process)
             self._atexit_registered = True
+        # Touch finalizer so it knows there is a live process
+        if getattr(self, "_finalizer", None) is not None and not self._finalizer.alive:
+            try:
+                import weakref
+
+                self._finalizer = weakref.finalize(self, self._finalize_process)
+            except Exception:
+                pass
 
     def _wait_for_server_ready(self, port: int) -> tuple[bool, int]:
         """Wait for the server to be ready."""
@@ -344,24 +257,35 @@ class EmbeddingServerManager:
         if not self.server_process:
             return
 
-        if self.server_process.poll() is not None:
+        if self.server_process and self.server_process.poll() is not None:
             # Process already terminated
             self.server_process = None
+            self.server_port = None
+            self._server_config = None
             return
 
         logger.info(
             f"Terminating server process (PID: {self.server_process.pid}) for backend {self.backend_module_name}..."
         )
-        self.server_process.terminate()
+
+        # Use simple termination first; if the server installed signal handlers,
+        # it will exit cleanly. Otherwise escalate to kill after a short wait.
+        try:
+            self.server_process.terminate()
+        except Exception:
+            pass
 
         try:
-            self.server_process.wait(timeout=3)
-            logger.info(f"Server process {self.server_process.pid} terminated.")
+            self.server_process.wait(timeout=5)  # Give more time for graceful shutdown
+            logger.info(f"Server process {self.server_process.pid} terminated gracefully.")
         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 within 5 seconds, force killing..."
             )
-            self.server_process.kill()
+            try:
+                self.server_process.kill()
+            except Exception:
+                pass
             try:
                 self.server_process.wait(timeout=2)
                 logger.info(f"Server process {self.server_process.pid} killed successfully.")
@@ -369,15 +293,33 @@ class EmbeddingServerManager:
                 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
+        # Clean up process resources with timeout to avoid CI hang
         try:
-            self.server_process.wait()  # Ensure process is fully cleaned up
+            # Use shorter timeout in CI environments
+            is_ci = os.environ.get("CI") == "true"
+            timeout = 3 if is_ci else 10
+            self.server_process.wait(timeout=timeout)
+            logger.info(f"Server process {self.server_process.pid} cleanup completed")
+        except subprocess.TimeoutExpired:
+            logger.warning(f"Process cleanup timeout after {timeout}s, proceeding anyway")
+        except Exception as e:
+            logger.warning(f"Error during process cleanup: {e}")
+        finally:
+            self.server_process = None
+            self.server_port = None
+            self._server_config = None
+
+    def _finalize_process(self) -> None:
+        """Best-effort cleanup used by weakref.finalize/atexit."""
+        try:
+            self.stop_server()
         except Exception:
             pass
 
-        self.server_process = None
+    def _adopt_existing_server(self, *args, **kwargs) -> None:
+        # Removed: cross-process adoption no longer supported
+        return
 
     def _launch_server_process_colab(self, command: list, port: int) -> None:
         """Launch the server process with Colab-specific settings."""
@@ -393,10 +335,16 @@ class EmbeddingServerManager:
         self.server_port = port
         logger.info(f"Colab server process started with PID: {self.server_process.pid}")
 
-        # Register atexit callback
+        # Register atexit callback (unified)
         if not self._atexit_registered:
-            atexit.register(lambda: self.stop_server() if self.server_process else None)
+            atexit.register(self._finalize_process)
             self._atexit_registered = True
+        # Record config for in-process reuse is best-effort in Colab mode
+        self._server_config = {
+            "model_name": "",
+            "passages_file": "",
+            "embedding_mode": "sentence-transformers",
+        }
 
     def _wait_for_server_ready_colab(self, port: int) -> tuple[bool, int]:
         """Wait for the server to be ready with Colab-specific timeout."""

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

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Literal, Union
+from typing import Any, Literal, Optional
 
 import numpy as np
 
@@ -35,7 +35,7 @@ class LeannBackendSearcherInterface(ABC):
 
     @abstractmethod
     def _ensure_server_running(
-        self, passages_source_file: str, port: Union[int, None], **kwargs
+        self, passages_source_file: str, port: Optional[int], **kwargs
     ) -> int:
         """Ensure server is running"""
         pass
@@ -50,7 +50,7 @@ class LeannBackendSearcherInterface(ABC):
         prune_ratio: float = 0.0,
         recompute_embeddings: bool = False,
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
-        zmq_port: Union[int, None] = None,
+        zmq_port: Optional[int] = None,
         **kwargs,
     ) -> dict[str, Any]:
         """Search for nearest neighbors
@@ -76,7 +76,7 @@ class LeannBackendSearcherInterface(ABC):
         self,
         query: str,
         use_server_if_available: bool = True,
-        zmq_port: Union[int, None] = None,
+        zmq_port: Optional[int] = None,
     ) -> np.ndarray:
         """Compute embedding for a query string
 

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

@@ -64,19 +64,6 @@ def handle_request(request):
                             "required": ["index_name", "query"],
                         },
                     },
-                    {
-                        "name": "leann_status",
-                        "description": "📊 Check the health and stats of your code indexes - like a medical checkup for your codebase knowledge!",
-                        "inputSchema": {
-                            "type": "object",
-                            "properties": {
-                                "index_name": {
-                                    "type": "string",
-                                    "description": "Optional: Name of specific index to check. If not provided, shows status of all indexes.",
-                                }
-                            },
-                        },
-                    },
                     {
                         "name": "leann_list",
                         "description": "📋 Show all your indexed codebases - your personal code library! Use this to see what's available for search.",
@@ -116,18 +103,8 @@ def handle_request(request):
                     f"--top-k={args.get('top_k', 5)}",
                     f"--complexity={args.get('complexity', 32)}",
                 ]
-
                 result = subprocess.run(cmd, capture_output=True, text=True)
 
-            elif tool_name == "leann_status":
-                if args.get("index_name"):
-                    # Check specific index status - for now, we'll use leann list and filter
-                    result = subprocess.run(["leann", "list"], capture_output=True, text=True)
-                    # We could enhance this to show more detailed status per index
-                else:
-                    # Show all indexes status
-                    result = subprocess.run(["leann", "list"], capture_output=True, text=True)
-
             elif tool_name == "leann_list":
                 result = subprocess.run(["leann", "list"], capture_output=True, text=True)
 

packages/leann-mcp/README.md

@@ -13,10 +13,20 @@ This installs the `leann` CLI into an isolated tool environment and includes bot
 
 ## 🚀 Quick Setup
 
-Add the LEANN MCP server to Claude Code:
+Add the LEANN MCP server to Claude Code. Choose the scope based on how widely you want it available. Below is the command to install it globally; if you prefer a local install, skip this step:
 
 ```bash
-claude mcp add leann-server -- leann_mcp
+# Global (recommended): available in all projects for your user
+claude mcp add --scope user leann-server -- leann_mcp
+```
+
+- `leann-server`: the display name of the MCP server in Claude Code (you can change it).
+- `leann_mcp`: the Python entry point installed with LEANN that starts the MCP server.
+
+Verify it is registered globally:
+
+```bash
+claude mcp list | cat
 ```
 
 ## 🛠️ Available Tools
@@ -25,27 +35,36 @@ Once connected, you'll have access to these powerful semantic search tools in Cl
 
 - **`leann_list`** - List all available indexes across your projects
 - **`leann_search`** - Perform semantic searches across code and documents
-- **`leann_ask`** - Ask natural language questions and get AI-powered answers from your codebase
+
 
 ## 🎯 Quick Start Example
 
 ```bash
+# Add locally if you did not add it globally (current folder only; default if --scope is omitted)
+claude mcp add leann-server -- leann_mcp
+
 # Build an index for your project (change to your actual path)
-leann build my-project --docs ./
+# See the advanced examples below for more ways to configure indexing
+# Set the index name (replace 'my-project' with your own)
+leann build my-project --docs $(git ls-files)
 
 # Start Claude Code
 claude
 ```
 
-## 🚀 Advanced Usage Examples
+## 🚀 Advanced Usage Examples to build the index
 
 ### Index Entire Git Repository
 ```bash
-# Index all tracked files in your git repository, note right now we will skip submodules, but we can add it back easily if you want
+# Index all tracked files in your Git repository.
+# Note: submodules are currently skipped; we can add them back if needed.
 leann build my-repo --docs $(git ls-files) --embedding-mode sentence-transformers --embedding-model all-MiniLM-L6-v2 --backend hnsw
 
-# Index only specific file types from git
+# Index only tracked Python files from Git.
 leann build my-python-code --docs $(git ls-files "*.py") --embedding-mode sentence-transformers --embedding-model all-MiniLM-L6-v2 --backend hnsw
+
+# If you encounter empty requests caused by empty files (e.g., __init__.py), exclude zero-byte files. Thanks @ww2283 for pointing [that](https://github.com/yichuan-w/LEANN/issues/48) out
+leann build leann-prospec-lig --docs $(find ./src -name "*.py" -not -empty) --embedding-mode openai --embedding-model text-embedding-3-small
 ```
 
 ### Multiple Directories and Files
@@ -82,6 +101,7 @@ Help me understand this codebase. List available indexes and search for authenti
   <img src="../../assets/claude_code_leann.png" alt="LEANN in Claude Code" width="80%">
 </p>
 
+If you see a prompt asking whether to proceed with LEANN, you can now use it in your chat!
 
 ## 🧠 How It Works
 
@@ -117,3 +137,11 @@ To remove LEANN
 ```
 uv pip uninstall leann leann-backend-hnsw leann-core
 ```
+
+To globally remove LEANN (for version update)
+```
+uv tool list | cat
+uv tool uninstall leann-core
+command -v leann || echo "leann gone"
+command -v leann_mcp || echo "leann_mcp gone"
+```

packages/wechat-exporter/__init__.py

@@ -0,0 +1 @@
+__all__ = []

packages/wechat-exporter/main.py

@@ -136,5 +136,9 @@ def export_sqlite(
     connection.commit()
 
 
-if __name__ == "__main__":
+def main():
     app()
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -10,6 +10,7 @@ requires-python = ">=3.9"
 dependencies = [
     "leann-core",
     "leann-backend-hnsw",
+    "typer>=0.12.3",
     "numpy>=1.26.0",
     "torch",
     "tqdm",
@@ -43,6 +44,7 @@ dependencies = [
     "mlx>=0.26.3; sys_platform == 'darwin' and platform_machine == 'arm64'",
     "mlx-lm>=0.26.0; sys_platform == 'darwin' and platform_machine == 'arm64'",
     "psutil>=5.8.0",
+    "pybind11>=3.0.0",
     "pathspec>=0.12.1",
     "nbconvert>=7.16.6",
     "gitignore-parser>=0.1.12",
@@ -54,7 +56,7 @@ dev = [
     "pytest-cov>=4.0",
     "pytest-xdist>=3.0",  # For parallel test execution
     "black>=23.0",
-    "ruff>=0.1.0",
+    "ruff==0.12.7",  # Fixed version to ensure consistent formatting across all environments
     "matplotlib",
     "huggingface-hub>=0.20.0",
     "pre-commit>=3.5.0",
@@ -83,6 +85,11 @@ documents = [
 
 [tool.setuptools]
 py-modules = []
+packages = ["wechat_exporter"]
+package-dir = { "wechat_exporter" = "packages/wechat-exporter" }
+
+[project.scripts]
+wechat-exporter = "wechat_exporter.main:main"
 
 
 [tool.uv.sources]
@@ -154,7 +161,7 @@ markers = [
     "slow: marks tests as slow (deselect with '-m \"not slow\"')",
     "openai: marks tests that require OpenAI API key",
 ]
-timeout = 600
+timeout = 300  # Reduced from 600s (10min) to 300s (5min) for CI safety
 addopts = [
     "-v",
     "--tb=short",

sky/leann-build.yaml

@@ -7,7 +7,7 @@ resources:
   # cloud: aws
   disk_size: 100
 
-env:
+envs:
   # Build parameters (override with: sky launch -c leann-gpu sky/leann-build.yaml -e key=value)
   index_name: my-index
   docs: ./data
@@ -19,10 +19,12 @@ env:
   embedding_mode: sentence-transformers   # sentence-transformers | openai | mlx | ollama
   embedding_model: facebook/contriever
   # Storage/latency knobs
-  recompute: true             # true => selective recomputation; false => store full embeddings
-  compact: true               # for HNSW only: false when recompute=false
+  recompute: true             # true => selective recomputation (recommended)
+  compact: true               # for HNSW only
   # Optional pass-through
   extra_args: ""
+  # Rebuild control
+  force: true
 
 # Sync local paths to the remote VM. Adjust as needed.
 file_mounts:
@@ -35,8 +37,17 @@ setup: |
   curl -LsSf https://astral.sh/uv/install.sh | sh
   export PATH="$HOME/.local/bin:$PATH"
 
-  # Install the LEANN CLI globally on the remote machine
-  uv tool install leann
+  # Ensure modern libstdc++ for FAISS (GLIBCXX >= 3.4.30)
+  sudo apt-get update -y
+  sudo apt-get install -y libstdc++6 libgomp1
+  # Also upgrade conda's libstdc++ in base env (Skypilot images include conda)
+  if command -v conda >/dev/null 2>&1; then
+    conda install -y -n base -c conda-forge libstdcxx-ng
+  fi
+
+  # Install LEANN CLI and backends into the user environment
+  uv pip install --upgrade pip
+  uv pip install leann-core leann-backend-hnsw leann-backend-diskann
 
 run: |
   export PATH="$HOME/.local/bin:$PATH"
@@ -45,13 +56,13 @@ run: |
   if [ "${recompute}" = "false" ] || [ "${recompute}" = "0" ]; then
     recompute_flag="--no-recompute"
   fi
-  compact_flag=""
-  if [ "${compact}" = "false" ] || [ "${compact}" = "0" ]; then
-    compact_flag="--no-compact"
+  force_flag=""
+  if [ "${force}" = "true" ] || [ "${force}" = "1" ]; then
+    force_flag="--force"
   fi
 
   # Build command
-  leann build ${index_name} \
+  python -m leann.cli build ${index_name} \
     --docs ~/leann-data \
     --backend ${backend} \
     --complexity ${complexity} \
@@ -59,4 +70,7 @@ run: |
     --num-threads ${num_threads} \
     --embedding-mode ${embedding_mode} \
     --embedding-model ${embedding_model} \
-    ${recompute_flag} ${compact_flag} ${extra_args}
+    ${recompute_flag} ${force_flag} ${extra_args}
+
+  # Print where the index is stored for downstream rsync
+  echo "INDEX_OUT_DIR=~/.leann/indexes/${index_name}"

tests/README.md

@@ -6,10 +6,11 @@ This directory contains automated tests for the LEANN project using pytest.
 
 ### `test_readme_examples.py`
 Tests the examples shown in README.md:
-- The basic example code that users see first
+- The basic example code that users see first (parametrized for both HNSW and DiskANN backends)
 - Import statements work correctly
 - Different backend options (HNSW, DiskANN)
-- Different LLM configuration options
+- Different LLM configuration options (parametrized for both backends)
+- **All main README examples are tested with both HNSW and DiskANN backends using pytest parametrization**
 
 ### `test_basic.py`
 Basic functionality tests that verify:
@@ -25,6 +26,16 @@ Tests the document RAG example functionality:
 - Tests error handling with invalid parameters
 - Verifies that normalized embeddings are detected and cosine distance is used
 
+### `test_diskann_partition.py`
+Tests DiskANN graph partitioning functionality:
+- Tests DiskANN index building without partitioning (baseline)
+- Tests automatic graph partitioning with `is_recompute=True`
+- Verifies that partition files are created and large files are cleaned up for storage saving
+- Tests search functionality with partitioned indices
+- Validates medoid and max_base_norm file generation and usage
+- Includes performance comparison between DiskANN (with partition) and HNSW
+- **Note**: These tests are skipped in CI due to hardware requirements and computation time
+
 ## Running Tests
 
 ### Install test dependencies:
@@ -54,15 +65,23 @@ pytest tests/ -m "not openai"
 
 # Skip slow tests
 pytest tests/ -m "not slow"
+
+# Run DiskANN partition tests (requires local machine, not CI)
+pytest tests/test_diskann_partition.py
 ```
 
 ### Run with specific backend:
 ```bash
 # Test only HNSW backend
 pytest tests/test_basic.py::test_backend_basic[hnsw]
+pytest tests/test_readme_examples.py::test_readme_basic_example[hnsw]
 
 # Test only DiskANN backend
 pytest tests/test_basic.py::test_backend_basic[diskann]
+pytest tests/test_readme_examples.py::test_readme_basic_example[diskann]
+
+# All DiskANN tests (parametrized + specialized partition tests)
+pytest tests/ -k diskann
 ```
 
 ## CI/CD Integration

tests/test_basic.py

@@ -64,6 +64,9 @@ def test_backend_basic(backend_name):
         assert isinstance(results[0], SearchResult)
         assert "topic 2" in results[0].text or "document" in results[0].text
 
+        # Ensure cleanup to avoid hanging background servers
+        searcher.cleanup()
+
 
 @pytest.mark.skipif(
     os.environ.get("CI") == "true", reason="Skip model tests in CI to avoid MPS memory issues"
@@ -90,3 +93,5 @@ def test_large_index():
         searcher = LeannSearcher(index_path)
         results = searcher.search(["word10 word20"], top_k=10)
         assert len(results[0]) == 10
+        # Cleanup
+        searcher.cleanup()

tests/test_diskann_partition.py

@@ -0,0 +1,369 @@
+"""
+Test DiskANN graph partitioning functionality.
+
+Tests the automatic graph partitioning feature that was implemented to save
+storage space by partitioning large DiskANN indices and safely deleting
+redundant files while maintaining search functionality.
+"""
+
+import os
+import tempfile
+from pathlib import Path
+
+import pytest
+
+
+@pytest.mark.skipif(
+    os.environ.get("CI") == "true",
+    reason="Skip DiskANN partition tests in CI - requires specific hardware and large memory",
+)
+def test_diskann_without_partition():
+    """Test DiskANN index building without partition (baseline)."""
+    from leann.api import LeannBuilder, LeannSearcher
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        index_path = str(Path(temp_dir) / "test_no_partition.leann")
+
+        # Test data - enough to trigger index building
+        texts = [
+            f"Document {i} discusses topic {i % 10} with detailed analysis of subject {i // 10}."
+            for i in range(500)
+        ]
+
+        # Build without partition (is_recompute=False)
+        builder = LeannBuilder(
+            backend_name="diskann",
+            embedding_model="facebook/contriever",
+            embedding_mode="sentence-transformers",
+            num_neighbors=32,
+            search_list_size=50,
+            is_recompute=False,  # No partition
+        )
+
+        for text in texts:
+            builder.add_text(text)
+
+        builder.build_index(index_path)
+
+        # Verify index was created
+        index_dir = Path(index_path).parent
+        assert index_dir.exists()
+
+        # Check that traditional DiskANN files exist
+        index_prefix = Path(index_path).stem
+        # Core DiskANN files (beam search index may not be created for small datasets)
+        required_files = [
+            f"{index_prefix}_disk.index",
+            f"{index_prefix}_pq_compressed.bin",
+            f"{index_prefix}_pq_pivots.bin",
+        ]
+
+        # Check all generated files first for debugging
+        generated_files = [f.name for f in index_dir.glob(f"{index_prefix}*")]
+        print(f"Generated files: {generated_files}")
+
+        for required_file in required_files:
+            file_path = index_dir / required_file
+            assert file_path.exists(), f"Required file {required_file} not found"
+
+        # Ensure no partition files exist in non-partition mode
+        partition_files = [f"{index_prefix}_disk_graph.index", f"{index_prefix}_partition.bin"]
+
+        for partition_file in partition_files:
+            file_path = index_dir / partition_file
+            assert not file_path.exists(), (
+                f"Partition file {partition_file} should not exist in non-partition mode"
+            )
+
+        # Test search functionality
+        searcher = LeannSearcher(index_path)
+        results = searcher.search("topic 3 analysis", top_k=3)
+
+        assert len(results) > 0
+        assert all(result.score is not None and result.score != float("-inf") for result in results)
+
+
+@pytest.mark.skipif(
+    os.environ.get("CI") == "true",
+    reason="Skip DiskANN partition tests in CI - requires specific hardware and large memory",
+)
+def test_diskann_with_partition():
+    """Test DiskANN index building with automatic graph partitioning."""
+    from leann.api import LeannBuilder
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        index_path = str(Path(temp_dir) / "test_with_partition.leann")
+
+        # Test data - enough to trigger partitioning
+        texts = [
+            f"Document {i} explores subject {i % 15} with comprehensive coverage of area {i // 15}."
+            for i in range(500)
+        ]
+
+        # Build with partition (is_recompute=True)
+        builder = LeannBuilder(
+            backend_name="diskann",
+            embedding_model="facebook/contriever",
+            embedding_mode="sentence-transformers",
+            num_neighbors=32,
+            search_list_size=50,
+            is_recompute=True,  # Enable automatic partitioning
+        )
+
+        for text in texts:
+            builder.add_text(text)
+
+        builder.build_index(index_path)
+
+        # Verify index was created
+        index_dir = Path(index_path).parent
+        assert index_dir.exists()
+
+        # Check that partition files exist
+        index_prefix = Path(index_path).stem
+        partition_files = [
+            f"{index_prefix}_disk_graph.index",  # Partitioned graph
+            f"{index_prefix}_partition.bin",  # Partition metadata
+            f"{index_prefix}_pq_compressed.bin",
+            f"{index_prefix}_pq_pivots.bin",
+        ]
+
+        for partition_file in partition_files:
+            file_path = index_dir / partition_file
+            assert file_path.exists(), f"Expected partition file {partition_file} not found"
+
+        # Check that large files were cleaned up (storage saving goal)
+        large_files = [f"{index_prefix}_disk.index", f"{index_prefix}_disk_beam_search.index"]
+
+        for large_file in large_files:
+            file_path = index_dir / large_file
+            assert not file_path.exists(), (
+                f"Large file {large_file} should have been deleted for storage saving"
+            )
+
+        # Verify required auxiliary files for partition mode exist
+        required_files = [
+            f"{index_prefix}_disk.index_medoids.bin",
+            f"{index_prefix}_disk.index_max_base_norm.bin",
+        ]
+
+        for req_file in required_files:
+            file_path = index_dir / req_file
+            assert file_path.exists(), (
+                f"Required auxiliary file {req_file} missing for partition mode"
+            )
+
+
+@pytest.mark.skipif(
+    os.environ.get("CI") == "true",
+    reason="Skip DiskANN partition tests in CI - requires specific hardware and large memory",
+)
+def test_diskann_partition_search_functionality():
+    """Test that search works correctly with partitioned indices."""
+    from leann.api import LeannBuilder, LeannSearcher
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        index_path = str(Path(temp_dir) / "test_partition_search.leann")
+
+        # Create diverse test data
+        texts = [
+            "LEANN is a storage-efficient approximate nearest neighbor search system.",
+            "Graph partitioning helps reduce memory usage in large scale vector search.",
+            "DiskANN provides high-performance disk-based approximate nearest neighbor search.",
+            "Vector embeddings enable semantic search over unstructured text data.",
+            "Approximate nearest neighbor algorithms trade accuracy for speed and storage.",
+        ] * 100  # Repeat to get enough data
+
+        # Build with partitioning
+        builder = LeannBuilder(
+            backend_name="diskann",
+            embedding_model="facebook/contriever",
+            embedding_mode="sentence-transformers",
+            is_recompute=True,  # Enable partitioning
+        )
+
+        for text in texts:
+            builder.add_text(text)
+
+        builder.build_index(index_path)
+
+        # Test search with partitioned index
+        searcher = LeannSearcher(index_path)
+
+        # Test various queries
+        test_queries = [
+            ("vector search algorithms", 5),
+            ("LEANN storage efficiency", 3),
+            ("graph partitioning memory", 4),
+            ("approximate nearest neighbor", 7),
+        ]
+
+        for query, top_k in test_queries:
+            results = searcher.search(query, top_k=top_k)
+
+            # Verify search results
+            assert len(results) == top_k, f"Expected {top_k} results for query '{query}'"
+            assert all(result.score is not None for result in results), (
+                "All results should have scores"
+            )
+            assert all(result.score != float("-inf") for result in results), (
+                "No result should have -inf score"
+            )
+            assert all(result.text is not None for result in results), (
+                "All results should have text"
+            )
+
+            # Scores should be in descending order (higher similarity first)
+            scores = [result.score for result in results]
+            assert scores == sorted(scores, reverse=True), (
+                "Results should be sorted by score descending"
+            )
+
+
+@pytest.mark.skipif(
+    os.environ.get("CI") == "true",
+    reason="Skip DiskANN partition tests in CI - requires specific hardware and large memory",
+)
+def test_diskann_medoid_and_norm_files():
+    """Test that medoid and max_base_norm files are correctly generated and used."""
+    import struct
+
+    from leann.api import LeannBuilder, LeannSearcher
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        index_path = str(Path(temp_dir) / "test_medoid_norm.leann")
+
+        # Small but sufficient dataset
+        texts = [f"Test document {i} with content about subject {i % 10}." for i in range(200)]
+
+        builder = LeannBuilder(
+            backend_name="diskann",
+            embedding_model="facebook/contriever",
+            embedding_mode="sentence-transformers",
+            is_recompute=True,
+        )
+
+        for text in texts:
+            builder.add_text(text)
+
+        builder.build_index(index_path)
+
+        index_dir = Path(index_path).parent
+        index_prefix = Path(index_path).stem
+
+        # Test medoids file
+        medoids_file = index_dir / f"{index_prefix}_disk.index_medoids.bin"
+        assert medoids_file.exists(), "Medoids file should be generated"
+
+        # Read and validate medoids file format
+        with open(medoids_file, "rb") as f:
+            nshards = struct.unpack("<I", f.read(4))[0]
+            one_val = struct.unpack("<I", f.read(4))[0]
+            medoid_id = struct.unpack("<I", f.read(4))[0]
+
+            assert nshards == 1, "Single-shot build should have 1 shard"
+            assert one_val == 1, "Expected value should be 1"
+            assert medoid_id >= 0, "Medoid ID should be valid (not hardcoded 0)"
+
+        # Test max_base_norm file
+        norm_file = index_dir / f"{index_prefix}_disk.index_max_base_norm.bin"
+        assert norm_file.exists(), "Max base norm file should be generated"
+
+        # Read and validate norm file
+        with open(norm_file, "rb") as f:
+            npts = struct.unpack("<I", f.read(4))[0]
+            ndims = struct.unpack("<I", f.read(4))[0]
+            norm_val = struct.unpack("<f", f.read(4))[0]
+
+            assert npts == 1, "Should have 1 norm point"
+            assert ndims == 1, "Should have 1 dimension"
+            assert norm_val > 0, "Norm value should be positive"
+            assert norm_val != float("inf"), "Norm value should be finite"
+
+        # Test that search works with these files
+        searcher = LeannSearcher(index_path)
+        results = searcher.search("test subject", top_k=3)
+
+        # Verify that scores are not -inf (which indicates norm file was loaded correctly)
+        assert len(results) > 0
+        assert all(result.score != float("-inf") for result in results), (
+            "Scores should not be -inf when norm file is correct"
+        )
+
+
+@pytest.mark.skipif(
+    os.environ.get("CI") == "true",
+    reason="Skip performance comparison in CI - requires significant compute time",
+)
+def test_diskann_vs_hnsw_performance():
+    """Compare DiskANN (with partition) vs HNSW performance."""
+    import time
+
+    from leann.api import LeannBuilder, LeannSearcher
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        # Test data
+        texts = [
+            f"Performance test document {i} covering topic {i % 20} in detail." for i in range(1000)
+        ]
+        query = "performance topic test"
+
+        # Test DiskANN with partitioning
+        diskann_path = str(Path(temp_dir) / "perf_diskann.leann")
+        diskann_builder = LeannBuilder(
+            backend_name="diskann",
+            embedding_model="facebook/contriever",
+            embedding_mode="sentence-transformers",
+            is_recompute=True,
+        )
+
+        for text in texts:
+            diskann_builder.add_text(text)
+
+        start_time = time.time()
+        diskann_builder.build_index(diskann_path)
+
+        # Test HNSW
+        hnsw_path = str(Path(temp_dir) / "perf_hnsw.leann")
+        hnsw_builder = LeannBuilder(
+            backend_name="hnsw",
+            embedding_model="facebook/contriever",
+            embedding_mode="sentence-transformers",
+            is_recompute=True,
+        )
+
+        for text in texts:
+            hnsw_builder.add_text(text)
+
+        start_time = time.time()
+        hnsw_builder.build_index(hnsw_path)
+
+        # Compare search performance
+        diskann_searcher = LeannSearcher(diskann_path)
+        hnsw_searcher = LeannSearcher(hnsw_path)
+
+        # Warm up searches
+        diskann_searcher.search(query, top_k=5)
+        hnsw_searcher.search(query, top_k=5)
+
+        # Timed searches
+        start_time = time.time()
+        diskann_results = diskann_searcher.search(query, top_k=10)
+        diskann_search_time = time.time() - start_time
+
+        start_time = time.time()
+        hnsw_results = hnsw_searcher.search(query, top_k=10)
+        hnsw_search_time = time.time() - start_time
+
+        # Basic assertions
+        assert len(diskann_results) == 10
+        assert len(hnsw_results) == 10
+        assert all(r.score != float("-inf") for r in diskann_results)
+        assert all(r.score != float("-inf") for r in hnsw_results)
+
+        # Performance ratio (informational)
+        if hnsw_search_time > 0:
+            speed_ratio = hnsw_search_time / diskann_search_time
+            print(f"DiskANN search time: {diskann_search_time:.4f}s")
+            print(f"HNSW search time: {hnsw_search_time:.4f}s")
+            print(f"DiskANN is {speed_ratio:.2f}x faster than HNSW")

tests/test_document_rag.py

@@ -58,6 +58,9 @@ def test_document_rag_simulated(test_data_dir):
 
 
 @pytest.mark.skipif(not os.environ.get("OPENAI_API_KEY"), reason="OpenAI API key not available")
+@pytest.mark.skipif(
+    os.environ.get("CI") == "true", reason="Skip OpenAI tests in CI to avoid API costs"
+)
 def test_document_rag_openai(test_data_dir):
     """Test document_rag with OpenAI embeddings."""
     with tempfile.TemporaryDirectory() as temp_dir:

tests/test_readme_examples.py

@@ -10,29 +10,33 @@ from pathlib import Path
 import pytest
 
 
-def test_readme_basic_example():
-    """Test the basic example from README.md."""
+@pytest.mark.parametrize("backend_name", ["hnsw", "diskann"])
+def test_readme_basic_example(backend_name):
+    """Test the basic example from README.md with both backends."""
     # Skip on macOS CI due to MPS environment issues with all-MiniLM-L6-v2
     if os.environ.get("CI") == "true" and platform.system() == "Darwin":
         pytest.skip("Skipping on macOS CI due to MPS environment issues with all-MiniLM-L6-v2")
+    # Skip DiskANN on CI (Linux runners) due to C++ extension memory/hardware constraints
+    if os.environ.get("CI") == "true" and backend_name == "diskann":
+        pytest.skip("Skip DiskANN tests in CI due to resource constraints and instability")
 
     # This is the exact code from README (with smaller model for CI)
     from leann import LeannBuilder, LeannChat, LeannSearcher
     from leann.api import SearchResult
 
     with tempfile.TemporaryDirectory() as temp_dir:
-        INDEX_PATH = str(Path(temp_dir) / "demo.leann")
+        INDEX_PATH = str(Path(temp_dir) / f"demo_{backend_name}.leann")
 
         # Build an index
         # In CI, use a smaller model to avoid memory issues
         if os.environ.get("CI") == "true":
             builder = LeannBuilder(
-                backend_name="hnsw",
+                backend_name=backend_name,
                 embedding_model="sentence-transformers/all-MiniLM-L6-v2",  # Smaller model
                 dimensions=384,  # Smaller dimensions
             )
         else:
-            builder = LeannBuilder(backend_name="hnsw")
+            builder = LeannBuilder(backend_name=backend_name)
         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)
@@ -52,9 +56,15 @@ def test_readme_basic_example():
         # Verify search results
         assert len(results) > 0
         assert isinstance(results[0], SearchResult)
+        assert results[0].score != float("-inf"), (
+            f"should return valid scores, got {results[0].score}"
+        )
         # The second text about banana-crocodile should be more relevant
         assert "banana" in results[0].text or "crocodile" in results[0].text
 
+        # Ensure we cleanup background embedding server
+        searcher.cleanup()
+
         # Chat with your data (using simulated LLM to avoid external dependencies)
         chat = LeannChat(INDEX_PATH, llm_config={"type": "simulated"})
         response = chat.ask("How much storage does LEANN save?", top_k=1)
@@ -62,6 +72,8 @@ def test_readme_basic_example():
         # Verify chat works
         assert isinstance(response, str)
         assert len(response) > 0
+        # Cleanup chat resources
+        chat.cleanup()
 
 
 def test_readme_imports():
@@ -110,26 +122,31 @@ def test_backend_options():
         assert len(list(Path(diskann_path).parent.glob(f"{Path(diskann_path).stem}.*"))) > 0
 
 
-def test_llm_config_simulated():
-    """Test simulated LLM configuration option."""
+@pytest.mark.parametrize("backend_name", ["hnsw", "diskann"])
+def test_llm_config_simulated(backend_name):
+    """Test simulated LLM configuration option with both backends."""
     # Skip on macOS CI due to MPS environment issues with all-MiniLM-L6-v2
     if os.environ.get("CI") == "true" and platform.system() == "Darwin":
         pytest.skip("Skipping on macOS CI due to MPS environment issues with all-MiniLM-L6-v2")
 
+    # Skip DiskANN tests in CI due to hardware requirements
+    if os.environ.get("CI") == "true" and backend_name == "diskann":
+        pytest.skip("Skip DiskANN tests in CI - requires specific hardware and large memory")
+
     from leann import LeannBuilder, LeannChat
 
     with tempfile.TemporaryDirectory() as temp_dir:
         # Build a simple index
-        index_path = str(Path(temp_dir) / "test.leann")
+        index_path = str(Path(temp_dir) / f"test_{backend_name}.leann")
         # Use smaller model in CI to avoid memory issues
         if os.environ.get("CI") == "true":
             builder = LeannBuilder(
-                backend_name="hnsw",
+                backend_name=backend_name,
                 embedding_model="sentence-transformers/all-MiniLM-L6-v2",
                 dimensions=384,
             )
         else:
-            builder = LeannBuilder(backend_name="hnsw")
+            builder = LeannBuilder(backend_name=backend_name)
         builder.add_text("Test document for LLM testing")
         builder.build_index(index_path)
 

uv.lock

@@ -2223,7 +2223,7 @@ wheels = [
 
 [[package]]
 name = "leann-backend-diskann"
-version = "0.2.6"
+version = "0.2.9"
 source = { editable = "packages/leann-backend-diskann" }
 dependencies = [
     { name = "leann-core" },
@@ -2235,14 +2235,14 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "leann-core", specifier = "==0.2.6" },
+    { name = "leann-core", specifier = "==0.2.9" },
     { name = "numpy" },
     { name = "protobuf", specifier = ">=3.19.0" },
 ]
 
 [[package]]
 name = "leann-backend-hnsw"
-version = "0.2.6"
+version = "0.2.9"
 source = { editable = "packages/leann-backend-hnsw" }
 dependencies = [
     { name = "leann-core" },
@@ -2255,7 +2255,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "leann-core", specifier = "==0.2.6" },
+    { name = "leann-core", specifier = "==0.2.9" },
     { name = "msgpack", specifier = ">=1.0.0" },
     { name = "numpy" },
     { name = "pyzmq", specifier = ">=23.0.0" },
@@ -2263,7 +2263,7 @@ requires-dist = [
 
 [[package]]
 name = "leann-core"
-version = "0.2.6"
+version = "0.2.9"
 source = { editable = "packages/leann-core" }
 dependencies = [
     { name = "accelerate" },
@@ -2272,8 +2272,8 @@ dependencies = [
     { name = "llama-index-core" },
     { name = "llama-index-embeddings-huggingface" },
     { name = "llama-index-readers-file" },
-    { name = "mlx", marker = "sys_platform == 'darwin'" },
-    { name = "mlx-lm", marker = "sys_platform == 'darwin'" },
+    { name = "mlx", marker = "platform_machine == 'arm64' and sys_platform == 'darwin'" },
+    { name = "mlx-lm", marker = "platform_machine == 'arm64' and sys_platform == 'darwin'" },
     { name = "msgpack" },
     { name = "nbconvert" },
     { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
@@ -2302,8 +2302,8 @@ requires-dist = [
     { name = "llama-index-core", specifier = ">=0.12.0" },
     { name = "llama-index-embeddings-huggingface", specifier = ">=0.5.5" },
     { name = "llama-index-readers-file", specifier = ">=0.4.0" },
-    { name = "mlx", marker = "sys_platform == 'darwin'", specifier = ">=0.26.3" },
-    { name = "mlx-lm", marker = "sys_platform == 'darwin'", specifier = ">=0.26.0" },
+    { name = "mlx", marker = "platform_machine == 'arm64' and sys_platform == 'darwin'", specifier = ">=0.26.3" },
+    { name = "mlx-lm", marker = "platform_machine == 'arm64' and sys_platform == 'darwin'", specifier = ">=0.26.0" },
     { name = "msgpack", specifier = ">=1.0.0" },
     { name = "nbconvert", specifier = ">=7.0.0" },
     { name = "numpy", specifier = ">=1.20.0" },
@@ -2343,8 +2343,8 @@ dependencies = [
     { name = "llama-index-embeddings-huggingface" },
     { name = "llama-index-readers-file" },
     { name = "llama-index-vector-stores-faiss" },
-    { name = "mlx", marker = "sys_platform == 'darwin'" },
-    { name = "mlx-lm", marker = "sys_platform == 'darwin'" },
+    { name = "mlx", marker = "platform_machine == 'arm64' and sys_platform == 'darwin'" },
+    { name = "mlx-lm", marker = "platform_machine == 'arm64' and sys_platform == 'darwin'" },
     { name = "msgpack" },
     { name = "nbconvert" },
     { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
@@ -2356,6 +2356,7 @@ dependencies = [
     { name = "pdfplumber" },
     { name = "protobuf" },
     { name = "psutil" },
+    { name = "pybind11" },
     { name = "pymupdf" },
     { name = "pypdf2" },
     { name = "pypdfium2" },
@@ -2424,8 +2425,8 @@ requires-dist = [
     { name = "llama-index-readers-file", marker = "extra == 'test'", specifier = ">=0.4.0" },
     { name = "llama-index-vector-stores-faiss", specifier = ">=0.4.0" },
     { name = "matplotlib", marker = "extra == 'dev'" },
-    { name = "mlx", marker = "sys_platform == 'darwin'", specifier = ">=0.26.3" },
-    { name = "mlx-lm", marker = "sys_platform == 'darwin'", specifier = ">=0.26.0" },
+    { name = "mlx", marker = "platform_machine == 'arm64' and sys_platform == 'darwin'", specifier = ">=0.26.3" },
+    { name = "mlx-lm", marker = "platform_machine == 'arm64' and sys_platform == 'darwin'", specifier = ">=0.26.0" },
     { name = "msgpack", specifier = ">=1.1.1" },
     { name = "nbconvert", specifier = ">=7.16.6" },
     { name = "numpy", specifier = ">=1.26.0" },
@@ -2438,6 +2439,7 @@ requires-dist = [
     { name = "pre-commit", marker = "extra == 'dev'", specifier = ">=3.5.0" },
     { name = "protobuf", specifier = "==4.25.3" },
     { name = "psutil", specifier = ">=5.8.0" },
+    { name = "pybind11", specifier = ">=3.0.0" },
     { name = "pymupdf", specifier = ">=1.26.0" },
     { name = "pypdf2", specifier = ">=3.0.0" },
     { name = "pypdfium2", specifier = ">=4.30.0" },
@@ -2449,7 +2451,7 @@ requires-dist = [
     { name = "python-docx", marker = "extra == 'documents'", specifier = ">=0.8.11" },
     { name = "python-dotenv", marker = "extra == 'test'", specifier = ">=1.0.0" },
     { name = "requests", specifier = ">=2.25.0" },
-    { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.1.0" },
+    { name = "ruff", marker = "extra == 'dev'", specifier = "==0.12.7" },
     { name = "sentence-transformers", specifier = ">=2.2.0" },
     { name = "sentence-transformers", marker = "extra == 'test'", specifier = ">=2.2.0" },
     { name = "sglang" },
@@ -4358,6 +4360,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/10/15/6b30e77872012bbfe8265d42a01d5b3c17ef0ac0f2fae531ad91b6a6c02e/pyarrow-21.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:cdc4c17afda4dab2a9c0b79148a43a7f4e1094916b3e18d8975bfd6d6d52241f", size = 26227521 },
 ]
 
+[[package]]
+name = "pybind11"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ef/83/698d120e257a116f2472c710932023ad779409adf2734d2e940f34eea2c5/pybind11-3.0.0.tar.gz", hash = "sha256:c3f07bce3ada51c3e4b76badfa85df11688d12c46111f9d242bc5c9415af7862", size = 544819 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/41/9c/85f50a5476832c3efc67b6d7997808388236ae4754bf53e1749b3bc27577/pybind11-3.0.0-py3-none-any.whl", hash = "sha256:7c5cac504da5a701b5163f0e6a7ba736c713a096a5378383c5b4b064b753f607", size = 292118 },
+]
+
 [[package]]
 name = "pycparser"
 version = "2.22"
@@ -5204,27 +5215,27 @@ wheels = [
 
 [[package]]
 name = "ruff"
-version = "0.12.5"
+version = "0.12.7"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/30/cd/01015eb5034605fd98d829c5839ec2c6b4582b479707f7c1c2af861e8258/ruff-0.12.5.tar.gz", hash = "sha256:b209db6102b66f13625940b7f8c7d0f18e20039bb7f6101fbdac935c9612057e", size = 5170722 }
+sdist = { url = "https://files.pythonhosted.org/packages/a1/81/0bd3594fa0f690466e41bd033bdcdf86cba8288345ac77ad4afbe5ec743a/ruff-0.12.7.tar.gz", hash = "sha256:1fc3193f238bc2d7968772c82831a4ff69252f673be371fb49663f0068b7ec71", size = 5197814 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/d4/de/ad2f68f0798ff15dd8c0bcc2889558970d9a685b3249565a937cd820ad34/ruff-0.12.5-py3-none-linux_armv6l.whl", hash = "sha256:1de2c887e9dec6cb31fcb9948299de5b2db38144e66403b9660c9548a67abd92", size = 11819133 },
-    { url = "https://files.pythonhosted.org/packages/f8/fc/c6b65cd0e7fbe60f17e7ad619dca796aa49fbca34bb9bea5f8faf1ec2643/ruff-0.12.5-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:d1ab65e7d8152f519e7dea4de892317c9da7a108da1c56b6a3c1d5e7cf4c5e9a", size = 12501114 },
-    { url = "https://files.pythonhosted.org/packages/c5/de/c6bec1dce5ead9f9e6a946ea15e8d698c35f19edc508289d70a577921b30/ruff-0.12.5-py3-none-macosx_11_0_arm64.whl", hash = "sha256:962775ed5b27c7aa3fdc0d8f4d4433deae7659ef99ea20f783d666e77338b8cf", size = 11716873 },
-    { url = "https://files.pythonhosted.org/packages/a1/16/cf372d2ebe91e4eb5b82a2275c3acfa879e0566a7ac94d331ea37b765ac8/ruff-0.12.5-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:73b4cae449597e7195a49eb1cdca89fd9fbb16140c7579899e87f4c85bf82f73", size = 11958829 },
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