fix: recompute args in searcher

- Test script to reproduce slow search performance issue
- Generates ~90K chunks (~180MB) similar to user's dataset
- Tests search performance with different complexity values (8, 16, 32, 64)
- Demonstrates that complexity=16-32 achieves ~2s search time
- Validates the performance analysis findings

.github/workflows/link-check.yml

@@ -14,6 +14,6 @@ jobs:
       - uses: actions/checkout@v4
       - uses: lycheeverse/lychee-action@v2
         with:
-          args: --no-progress --insecure --user-agent 'curl/7.68.0' --exclude '.*api\.star-history\.com.*' --accept 200,201,202,203,204,205,206,207,208,226,300,301,302,303,304,305,306,307,308,503 README.md docs/ apps/ examples/ benchmarks/
+          args: --no-progress --insecure --user-agent 'curl/7.68.0' README.md docs/ apps/ examples/ benchmarks/
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.lycheeignore

@@ -1,4 +0,0 @@
-# Exclude star-history API from link checking
-# This service is intermittently unavailable (503 errors)
-# but the link still works when the service is up
-.*api\.star-history\.com.*

ISSUE_159_CONCLUSION.md

@@ -0,0 +1,110 @@
+# Issue #159 Performance Analysis - Conclusion
+
+## Problem Summary
+User reported search times of 15-30 seconds instead of the ~2 seconds mentioned in the paper.
+
+**Configuration:**
+- GPU: 4090×1
+- Embedding Model: BAAI/bge-large-zh-v1.5 (~300M parameters)
+- Data Size: 180MB text (~90K chunks)
+- Backend: HNSW
+- beam_width: 10
+- Other parameters: Default values
+
+## Root Cause Analysis
+
+### 1. **Search Complexity Parameter**
+The **default `complexity` parameter is 64**, which is too high for achieving ~2 second search times with this configuration.
+
+**Test Results (Reproduced):**
+- **Complexity 64 (default)**: **36.17 seconds** ❌
+- **Complexity 32**: **2.49 seconds** ✅
+- **Complexity 16**: **2.24 seconds** ✅ (Close to paper's ~2 seconds)
+- **Complexity 8**: **1.67 seconds** ✅
+
+### 2. **beam_width Parameter**
+The `beam_width` parameter is **mainly for DiskANN backend**, not HNSW. Setting it to 10 has minimal/no effect on HNSW search performance.
+
+### 3. **Embedding Model Size**
+The paper uses a smaller embedding model (~100M parameters), while the user is using `BAAI/bge-large-zh-v1.5` (~300M parameters). This contributes to slower embedding computation during search, but the main bottleneck is the search complexity parameter.
+
+## Solution
+
+### **Recommended Fix: Reduce Search Complexity**
+
+To achieve search times close to ~2 seconds, use:
+
+```python
+from leann.api import LeannSearcher
+
+searcher = LeannSearcher(INDEX_PATH)
+results = searcher.search(
+    query="your query",
+    top_k=10,
+    complexity=16,  # or complexity=32 for slightly better accuracy
+    # beam_width parameter doesn't affect HNSW, can be ignored
+)
+```
+
+Or via CLI:
+```bash
+leann search your-index "your query" --complexity 16
+```
+
+### **Alternative Solutions**
+
+1. **Use DiskANN Backend** (Recommended by maintainer)
+   - DiskANN is faster for large datasets
+   - Better performance scaling
+   - `beam_width` parameter is relevant here
+   ```python
+   builder = LeannBuilder(backend_name="diskann")
+   ```
+
+2. **Use Smaller Embedding Model**
+   - Switch to a smaller model (~100M parameters) like the paper
+   - Faster embedding computation
+   - Example: `BAAI/bge-base-zh-v1.5` instead of `bge-large-zh-v1.5`
+
+3. **Disable Recomputation** (Trade storage for speed)
+   - Use `--no-recompute` flag
+   - Stores all embeddings (much larger storage)
+   - Faster search (no embedding recomputation)
+   ```bash
+   leann build your-index --no-recompute --no-compact
+   leann search your-index "query" --no-recompute
+   ```
+
+## Performance Comparison
+
+| Complexity | Search Time | Accuracy | Recommendation |
+|------------|-------------|----------|---------------|
+| 64 (default) | ~36s | Highest | ❌ Too slow |
+| 32 | ~2.5s | High | ✅ Good balance |
+| 16 | ~2.2s | Good | ✅ **Recommended** (matches paper) |
+| 8 | ~1.7s | Lower | ⚠️ May sacrifice accuracy |
+
+## Key Takeaways
+
+1. **The default `complexity=64` is optimized for accuracy, not speed**
+2. **For ~2 second search times, use `complexity=16` or `complexity=32`**
+3. **`beam_width` parameter is for DiskANN, not HNSW**
+4. **The paper's ~2 second results likely used:**
+   - Smaller embedding model (~100M params)
+   - Lower complexity (16-32)
+   - Possibly DiskANN backend
+
+## Verification
+
+The issue has been reproduced and verified. The test script `test_issue_159.py` demonstrates:
+- Default complexity (64) results in ~36 second search times
+- Reducing complexity to 16-32 achieves ~2 second search times
+- This matches the user's reported issue and provides a clear solution
+
+## Next Steps
+
+1. ✅ Issue reproduced and root cause identified
+2. ✅ Solution provided (reduce complexity parameter)
+3. ⏳ User should test with `complexity=16` or `complexity=32`
+4. ⏳ Consider updating documentation to clarify complexity parameter trade-offs
+

apps/slack_data/slack_mcp_reader.py

@@ -7,7 +7,6 @@ for indexing in LEANN. It supports various Slack MCP server implementations and
 flexible message processing options.
 """
 
-import ast
 import asyncio
 import json
 import logging
@@ -147,16 +146,16 @@ class SlackMCPReader:
                     match = re.search(r"'error':\s*(\{[^}]+\})", str(e))
                     if match:
                         try:
-                            error_dict = ast.literal_eval(match.group(1))
+                            error_dict = eval(match.group(1))
-                        except (ValueError, SyntaxError):
+                        except (ValueError, SyntaxError, NameError):
                             pass
                     else:
                         # Try alternative format
                         match = re.search(r"Failed to fetch messages:\s*(\{[^}]+\})", str(e))
                         if match:
                             try:
-                                error_dict = ast.literal_eval(match.group(1))
+                                error_dict = eval(match.group(1))
-                            except (ValueError, SyntaxError):
+                            except (ValueError, SyntaxError, NameError):
                                 pass
 
                 if self._is_cache_sync_error(error_dict):

issue_159.py

@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+"""
+Test script to reproduce issue #159: Slow search performance
+Configuration:
+- GPU: 4090×1
+- embedding_model: BAAI/bge-large-zh-v1.5
+- data size: 180M text (~90K chunks)
+- beam_width: 10 (though this is mainly for DiskANN, not HNSW)
+- backend: hnsw
+"""
+
+import os
+import time
+from pathlib import Path
+
+from leann.api import LeannBuilder, LeannSearcher, SearchResult
+
+os.environ["LEANN_LOG_LEVEL"] = "DEBUG"
+
+# Configuration matching the issue
+INDEX_PATH = "./test_issue_159.leann"
+EMBEDDING_MODEL = "BAAI/bge-large-zh-v1.5"
+BACKEND_NAME = "hnsw"
+
+
+def generate_test_data(num_chunks=90000, chunk_size=2000):
+    """Generate test data similar to 180MB text (~90K chunks)"""
+    # Each chunk is approximately 2000 characters
+    # 90K chunks * 2000 chars ≈ 180MB
+    chunks = []
+    base_text = (
+        "这是一个测试文档。LEANN是一个创新的向量数据库，通过图基选择性重计算实现97%的存储节省。"
+    )
+
+    for i in range(num_chunks):
+        chunk = f"{base_text} 文档编号: {i}. " * (chunk_size // len(base_text) + 1)
+        chunks.append(chunk[:chunk_size])
+
+    return chunks
+
+
+def test_search_performance():
+    """Test search performance with different configurations"""
+    print("=" * 80)
+    print("Testing LEANN Search Performance (Issue #159)")
+    print("=" * 80)
+
+    meta_path = Path(f"{INDEX_PATH}.meta.json")
+    if meta_path.exists():
+        print(f"\n✓ Index already exists at {INDEX_PATH}")
+        print("  Skipping build phase. Delete the index to rebuild.")
+    else:
+        print("\n📦 Building index...")
+        print(f"  Backend: {BACKEND_NAME}")
+        print(f"  Embedding Model: {EMBEDDING_MODEL}")
+        print("  Generating test data (~90K chunks, ~180MB)...")
+
+        chunks = generate_test_data(num_chunks=90000)
+        print(f"  Generated {len(chunks)} chunks")
+        print(f"  Total text size: {sum(len(c) for c in chunks) / (1024 * 1024):.2f} MB")
+
+        builder = LeannBuilder(
+            backend_name=BACKEND_NAME,
+            embedding_model=EMBEDDING_MODEL,
+        )
+
+        print("  Adding chunks to builder...")
+        start_time = time.time()
+        for i, chunk in enumerate(chunks):
+            builder.add_text(chunk)
+            if (i + 1) % 10000 == 0:
+                print(f"    Added {i + 1}/{len(chunks)} chunks...")
+
+        print("  Building index...")
+        build_start = time.time()
+        builder.build_index(INDEX_PATH)
+        build_time = time.time() - build_start
+        print(f"  ✓ Index built in {build_time:.2f} seconds")
+
+    # Test search with different complexity values
+    print("\n🔍 Testing search performance...")
+    searcher = LeannSearcher(INDEX_PATH)
+
+    test_query = "LEANN向量数据库存储优化"
+
+    # Test with default complexity (64)
+    print("\n  Test 1: Default complexity (64) `1 ")
+    print(f"    Query: '{test_query}'")
+    start_time = time.time()
+    results: list[SearchResult] = searcher.search(test_query, top_k=10, complexity=64)
+    search_time = time.time() - start_time
+    print(f"    ✓ Search completed in {search_time:.2f} seconds")
+    print(f"    Results: {len(results)} items")
+
+    # Test with default complexity (64)
+    print("\n  Test 1: Default complexity (64)")
+    print(f"    Query: '{test_query}'")
+    start_time = time.time()
+    results = searcher.search(test_query, top_k=10, complexity=64)
+    search_time = time.time() - start_time
+    print(f"    ✓ Search completed in {search_time:.2f} seconds")
+    print(f"    Results: {len(results)} items")
+
+    # Test with lower complexity (32)
+    print("\n  Test 2: Lower complexity (32)")
+    print(f"    Query: '{test_query}'")
+    start_time = time.time()
+    results = searcher.search(test_query, top_k=10, complexity=32)
+    search_time = time.time() - start_time
+    print(f"    ✓ Search completed in {search_time:.2f} seconds")
+    print(f"    Results: {len(results)} items")
+
+    # Test with even lower complexity (16)
+    print("\n  Test 3: Lower complexity (16)")
+    print(f"    Query: '{test_query}'")
+    start_time = time.time()
+    results = searcher.search(test_query, top_k=10, complexity=16)
+    search_time = time.time() - start_time
+    print(f"    ✓ Search completed in {search_time:.2f} seconds")
+    print(f"    Results: {len(results)} items")
+
+    # Test with minimal complexity (8)
+    print("\n  Test 4: Minimal complexity (8)")
+    print(f"    Query: '{test_query}'")
+    start_time = time.time()
+    results = searcher.search(test_query, top_k=10, complexity=8)
+    search_time = time.time() - start_time
+    print(f"    ✓ Search completed in {search_time:.2f} seconds")
+    print(f"    Results: {len(results)} items")
+
+    print("\n" + "=" * 80)
+    print("Performance Analysis:")
+    print("=" * 80)
+    print("\nKey Findings:")
+    print("1. beam_width parameter is mainly for DiskANN backend, not HNSW")
+    print("2. For HNSW, the main parameter affecting search speed is 'complexity'")
+    print("3. Lower complexity values (16-32) should provide faster search")
+    print("4. The paper mentions ~2 seconds, which likely uses:")
+    print("   - Smaller embedding model (~100M params vs 300M for bge-large)")
+    print("   - Lower complexity (16-32)")
+    print("   - Possibly DiskANN backend for better performance")
+    print("\nRecommendations:")
+    print("- Try complexity=16 or complexity=32 for faster search")
+    print("- Consider using DiskANN backend for better performance on large datasets")
+    print("- Or use a smaller embedding model if speed is critical")
+
+
+if __name__ == "__main__":
+    test_search_performance()

packages/leann-backend-diskann/pyproject.toml

@@ -4,8 +4,8 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-diskann"
-version = "0.3.5"
+version = "0.3.4"
-dependencies = ["leann-core==0.3.5", "numpy", "protobuf>=3.19.0"]
+dependencies = ["leann-core==0.3.4", "numpy", "protobuf>=3.19.0"]
 
 [tool.scikit-build]
 # Key: simplified CMake path

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

@@ -143,8 +143,6 @@ def create_hnsw_embedding_server(
             pass
         return str(nid)
 
-    # (legacy ZMQ thread removed; using shutdown-capable server only)
-
     def zmq_server_thread_with_shutdown(shutdown_event):
         """ZMQ server thread that respects shutdown signal.
 
@@ -158,225 +156,238 @@ def create_hnsw_embedding_server(
         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)
 
-        # Track last request type/length for shape-correct fallbacks
+        last_request_type = "unknown"
-        last_request_type = "unknown"  # 'text' | 'distance' | 'embedding' | 'unknown'
         last_request_length = 0
 
+        def _build_safe_fallback():
+            if last_request_type == "distance":
+                large_distance = 1e9
+                fallback_len = max(0, int(last_request_length))
+                return [[large_distance] * fallback_len]
+            if last_request_type == "embedding":
+                bsz = max(0, int(last_request_length))
+                dim = max(0, int(embedding_dim))
+                if dim > 0:
+                    return [[bsz, dim], [0.0] * (bsz * dim)]
+                return [[0, 0], []]
+            if last_request_type == "text":
+                return []
+            return [[0, int(embedding_dim) if embedding_dim > 0 else 0], []]
+
+        def _handle_text_embedding(request: list[str]) -> None:
+            nonlocal last_request_type, last_request_length
+
+            e2e_start = time.time()
+            last_request_type = "text"
+            last_request_length = len(request)
+            embeddings = compute_embeddings(
+                request,
+                model_name,
+                mode=embedding_mode,
+                provider_options=PROVIDER_OPTIONS,
+            )
+            rep_socket.send(msgpack.packb(embeddings.tolist()))
+            e2e_end = time.time()
+            logger.info(f"⏱️  Direct text embedding E2E time: {e2e_end - e2e_start:.6f}s")
+
+        def _handle_distance_request(request: list[Any]) -> None:
+            nonlocal last_request_type, last_request_length
+
+            e2e_start = time.time()
+            node_ids = request[0]
+            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)}")
+
+            texts: list[str] = []
+            found_indices: list[int] = []
+            for idx, nid in enumerate(node_ids):
+                try:
+                    passage_id = _map_node_id(nid)
+                    passage_data = passages.get_passage(passage_id)
+                    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 {passage_id}")
+                except KeyError:
+                    logger.error(f"Passage ID {nid} not found")
+                except Exception as exc:
+                    logger.error(f"Exception looking up passage ID {nid}: {exc}")
+
+            large_distance = 1e9
+            response_distances = [large_distance] * len(node_ids)
+
+            if texts:
+                try:
+                    embeddings = compute_embeddings(
+                        texts,
+                        model_name,
+                        mode=embedding_mode,
+                        provider_options=PROVIDER_OPTIONS,
+                    )
+                    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:
+                        partial = -np.dot(embeddings, query_vector)
+
+                    for pos, dval in zip(found_indices, partial.flatten().tolist()):
+                        response_distances[pos] = float(dval)
+                except Exception as exc:
+                    logger.error(f"Distance computation error, using sentinels: {exc}")
+
+            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")
+
+        def _handle_embedding_by_id(request: Any) -> None:
+            nonlocal last_request_type, last_request_length
+
+            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 = []
+
+            e2e_start = time.time()
+            last_request_type = "embedding"
+            last_request_length = len(node_ids)
+            logger.info(f"ZMQ received {len(node_ids)} node IDs for embedding fetch")
+
+            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])
+
+            texts: list[str] = []
+            found_indices: list[int] = []
+            for idx, nid in enumerate(node_ids):
+                try:
+                    passage_id = _map_node_id(nid)
+                    passage_data = passages.get_passage(passage_id)
+                    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 {passage_id}")
+                except KeyError:
+                    logger.error(f"Passage with ID {nid} not found")
+                except Exception as exc:
+                    logger.error(f"Exception looking up passage ID {nid}: {exc}")
+
+            if texts:
+                try:
+                    embeddings = compute_embeddings(
+                        texts,
+                        model_name,
+                        mode=embedding_mode,
+                        provider_options=PROVIDER_OPTIONS,
+                    )
+                    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]
+                        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 exc:
+                    logger.error(f"Embedding computation error, returning zeros: {exc}")
+
+            response_payload = [dims, flat_data]
+            rep_socket.send(msgpack.packb(response_payload, use_single_float=True))
+            e2e_end = time.time()
+            logger.info(f"⏱️ Fallback Embed by Id E2E time: {e2e_end - e2e_start:.6f}s")
+
         try:
             while not shutdown_event.is_set():
                 try:
-                    e2e_start = time.time()
                     logger.debug("🔍 Waiting for ZMQ message...")
                     request_bytes = rep_socket.recv()
+                except zmq.Again:
+                    continue
 
-                    # Rest of the processing logic (same as original)
+                try:
                     request = msgpack.unpackb(request_bytes)
+                except Exception as exc:
+                    if shutdown_event.is_set():
+                        logger.info("Shutdown in progress, ignoring ZMQ error")
+                        break
+                    logger.error(f"Error unpacking ZMQ message: {exc}")
+                    try:
+                        safe = _build_safe_fallback()
+                        rep_socket.send(msgpack.packb(safe, use_single_float=True))
+                    except Exception:
+                        pass
+                    continue
 
-                    if len(request) == 1 and request[0] == "__QUERY_MODEL__":
+                try:
-                        response_bytes = msgpack.packb([model_name])
+                    # Model query
-                        rep_socket.send(response_bytes)
-                        continue
-
-                    # Handle direct text embedding request
                     if (
+                        isinstance(request, list)
+                        and len(request) == 1
+                        and request[0] == "__QUERY_MODEL__"
+                    ):
+                        rep_socket.send(msgpack.packb([model_name]))
+                    # Direct text embedding
+                    elif (
                         isinstance(request, list)
                         and request
                         and all(isinstance(item, str) for item in request)
                     ):
-                        last_request_type = "text"
+                        _handle_text_embedding(request)
-                        last_request_length = len(request)
+                    # Distance calculation: [[ids], [query_vector]]
-                        embeddings = compute_embeddings(
+                    elif (
-                            request,
-                            model_name,
-                            mode=embedding_mode,
-                            provider_options=PROVIDER_OPTIONS,
-                        )
-                        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 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_distance_request(request)
-                        # Handle nested [[ids]] shape defensively
+                    # Embedding-by-id fallback
-                        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_id = _map_node_id(nid)
-                                passage_data = passages.get_passage(passage_id)
-                                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 {passage_id}")
-                            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,
-                                    provider_options=PROVIDER_OPTIONS,
-                                )
-                                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")
-
-                    # 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])
-
-                    # Collect texts for found ids
-                    texts: list[str] = []
-                    found_indices: list[int] = []
-                    for idx, nid in enumerate(node_ids):
-                        try:
-                            passage_id = _map_node_id(nid)
-                            passage_data = passages.get_passage(passage_id)
-                            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 {passage_id}")
-                        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:
-                            embeddings = compute_embeddings(
-                                texts,
-                                model_name,
-                                mode=embedding_mode,
-                                provider_options=PROVIDER_OPTIONS,
-                            )
-                            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]
-                                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"Embedding computation error, returning zeros: {e}")
-
-                    response_payload = [dims, flat_data]
-                    response_bytes = msgpack.packb(response_payload, use_single_float=True)
-
-                    rep_socket.send(response_bytes)
-                    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}")
-                        # 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:
+                        _handle_embedding_by_id(request)
+                except Exception as exc:
+                    if shutdown_event.is_set():
                         logger.info("Shutdown in progress, ignoring ZMQ error")
                         break
+                    logger.error(f"Error in ZMQ server loop: {exc}")
+                    try:
+                        safe = _build_safe_fallback()
+                        rep_socket.send(msgpack.packb(safe, use_single_float=True))
+                    except Exception:
+                        pass
         finally:
             try:
                 rep_socket.close(0)

packages/leann-backend-hnsw/pyproject.toml

@@ -6,10 +6,10 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-hnsw"
-version = "0.3.5"
+version = "0.3.4"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
 dependencies = [
-    "leann-core==0.3.5",
+    "leann-core==0.3.4",
     "numpy",
     "pyzmq>=23.0.0",
     "msgpack>=1.0.0",

packages/leann-core/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann-core"
-version = "0.3.5"
+version = "0.3.4"
 description = "Core API and plugin system for LEANN"
 readme = "README.md"
 requires-python = ">=3.9"

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

@@ -864,7 +864,13 @@ class LeannBuilder:
 
 
 class LeannSearcher:
-    def __init__(self, index_path: str, enable_warmup: bool = False, **backend_kwargs):
+    def __init__(
+        self,
+        index_path: str,
+        enable_warmup: bool = True,
+        recompute_embeddings: bool = True,
+        **backend_kwargs,
+    ):
         # Fix path resolution for Colab and other environments
         if not Path(index_path).is_absolute():
             index_path = str(Path(index_path).resolve())
@@ -895,14 +901,32 @@ class LeannSearcher:
         backend_factory = BACKEND_REGISTRY.get(backend_name)
         if backend_factory is None:
             raise ValueError(f"Backend '{backend_name}' not found.")
+
+        # Global recompute flag for this searcher (explicit knob, default True)
+        self.recompute_embeddings: bool = bool(recompute_embeddings)
+
+        # Warmup flag: keep using the existing enable_warmup parameter,
+        # but default it to True so cold-start happens earlier.
+        self._warmup: bool = bool(enable_warmup)
+
         final_kwargs = {**self.meta_data.get("backend_kwargs", {}), **backend_kwargs}
-        final_kwargs["enable_warmup"] = enable_warmup
+        final_kwargs["enable_warmup"] = self._warmup
         if self.embedding_options:
             final_kwargs.setdefault("embedding_options", self.embedding_options)
         self.backend_impl: LeannBackendSearcherInterface = backend_factory.searcher(
             index_path, **final_kwargs
         )
 
+        # Optional one-shot warmup at construction time to hide cold-start latency.
+        if self._warmup:
+            try:
+                _ = self.backend_impl.compute_query_embedding(
+                    "__LEANN_WARMUP__",
+                    use_server_if_available=self.recompute_embeddings,
+                )
+            except Exception as exc:
+                logger.warning(f"Warmup embedding failed (ignored): {exc}")
+
     def search(
         self,
         query: str,
@@ -910,7 +934,7 @@ class LeannSearcher:
         complexity: int = 64,
         beam_width: int = 1,
         prune_ratio: float = 0.0,
-        recompute_embeddings: bool = True,
+        recompute_embeddings: Optional[bool] = None,
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
         expected_zmq_port: int = 5557,
         metadata_filters: Optional[dict[str, dict[str, Union[str, int, float, bool, list]]]] = None,
@@ -927,7 +951,8 @@ class LeannSearcher:
             complexity: Search complexity/candidate list size, higher = more accurate but slower
             beam_width: Number of parallel search paths/IO requests per iteration
             prune_ratio: Ratio of neighbors to prune via approximate distance (0.0-1.0)
-            recompute_embeddings: Whether to fetch fresh embeddings from server vs use stored codes
+            recompute_embeddings: (Deprecated) Per-call override for recompute mode.
+                Configure this at LeannSearcher(..., recompute_embeddings=...) instead.
             pruning_strategy: Candidate selection strategy - "global" (default), "local", or "proportional"
             expected_zmq_port: ZMQ port for embedding server communication
             metadata_filters: Optional filters to apply to search results based on metadata.
@@ -966,8 +991,19 @@ class LeannSearcher:
 
         zmq_port = None
 
+        # Resolve effective recompute flag for this search.
+        if recompute_embeddings is not None:
+            logger.warning(
+                "LeannSearcher.search(..., recompute_embeddings=...) is deprecated and "
+                "will be removed in a future version. Configure recompute at "
+                "LeannSearcher(..., recompute_embeddings=...) instead."
+            )
+            effective_recompute = bool(recompute_embeddings)
+        else:
+            effective_recompute = self.recompute_embeddings
+
         start_time = time.time()
-        if recompute_embeddings:
+        if effective_recompute:
             zmq_port = self.backend_impl._ensure_server_running(
                 self.meta_path_str,
                 port=expected_zmq_port,
@@ -981,7 +1017,7 @@ class LeannSearcher:
 
         query_embedding = self.backend_impl.compute_query_embedding(
             query,
-            use_server_if_available=recompute_embeddings,
+            use_server_if_available=effective_recompute,
             zmq_port=zmq_port,
         )
         logger.info(f"  Generated embedding shape: {query_embedding.shape}")
@@ -993,7 +1029,7 @@ class LeannSearcher:
             "complexity": complexity,
             "beam_width": beam_width,
             "prune_ratio": prune_ratio,
-            "recompute_embeddings": recompute_embeddings,
+            "recompute_embeddings": effective_recompute,
             "pruning_strategy": pruning_strategy,
             "zmq_port": zmq_port,
         }

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

@@ -215,9 +215,14 @@ def compute_embeddings(
         Normalized embeddings array, shape: (len(texts), embedding_dim)
     """
     provider_options = provider_options or {}
+    wrapper_start_time = time.time()
+    logger.debug(
+        f"[compute_embeddings] entry: mode={mode}, model='{model_name}', text_count={len(texts)}"
+    )
 
     if mode == "sentence-transformers":
-        return compute_embeddings_sentence_transformers(
+        inner_start_time = time.time()
+        result = compute_embeddings_sentence_transformers(
             texts,
             model_name,
             is_build=is_build,
@@ -226,6 +231,14 @@ def compute_embeddings(
             manual_tokenize=manual_tokenize,
             max_length=max_length,
         )
+        inner_end_time = time.time()
+        wrapper_end_time = time.time()
+        logger.debug(
+            "[compute_embeddings] sentence-transformers timings: "
+            f"inner={inner_end_time - inner_start_time:.6f}s, "
+            f"wrapper_total={wrapper_end_time - wrapper_start_time:.6f}s"
+        )
+        return result
     elif mode == "openai":
         return compute_embeddings_openai(
             texts,
@@ -271,6 +284,7 @@ def compute_embeddings_sentence_transformers(
         is_build: Whether this is a build operation (shows progress bar)
         adaptive_optimization: Whether to use adaptive optimization based on batch size
     """
+    outer_start_time = time.time()
     # Handle empty input
     if not texts:
         raise ValueError("Cannot compute embeddings for empty text list")
@@ -301,7 +315,14 @@ def compute_embeddings_sentence_transformers(
     # Create cache key
     cache_key = f"sentence_transformers_{model_name}_{device}_{use_fp16}_optimized"
 
+    pre_model_init_end_time = time.time()
+    logger.debug(
+        "compute_embeddings_sentence_transformers pre-model-init time "
+        f"(device/batch selection etc.): {pre_model_init_end_time - outer_start_time:.6f}s"
+    )
+
     # Check if model is already cached
+    start_time = time.time()
     if cache_key in _model_cache:
         logger.info(f"Using cached optimized model: {model_name}")
         model = _model_cache[cache_key]
@@ -441,10 +462,13 @@ def compute_embeddings_sentence_transformers(
         _model_cache[cache_key] = model
         logger.info(f"Model cached: {cache_key}")
 
-    # Compute embeddings with optimized inference mode
+        end_time = time.time()
-    logger.info(
+
-        f"Starting embedding computation... (batch_size: {batch_size}, manual_tokenize={manual_tokenize})"
+        # Compute embeddings with optimized inference mode
-    )
+        logger.info(
+            f"Starting embedding computation... (batch_size: {batch_size}, manual_tokenize={manual_tokenize})"
+        )
+        logger.info(f"start sentence transformers {model} takes {end_time - start_time}")
 
     start_time = time.time()
     if not manual_tokenize:
@@ -465,32 +489,46 @@ def compute_embeddings_sentence_transformers(
         except Exception:
             pass
     else:
-        # Manual tokenization + forward pass using HF AutoTokenizer/AutoModel
+        # Manual tokenization + forward pass using HF AutoTokenizer/AutoModel.
+        # This path is reserved for an aggressively optimized FP pipeline
+        # (no quantization), mainly for experimentation.
         try:
             from transformers import AutoModel, AutoTokenizer  # type: ignore
         except Exception as e:
             raise ImportError(f"transformers is required for manual_tokenize=True: {e}")
 
-        # Cache tokenizer and model
         tok_cache_key = f"hf_tokenizer_{model_name}"
-        mdl_cache_key = f"hf_model_{model_name}_{device}_{use_fp16}"
+        mdl_cache_key = f"hf_model_{model_name}_{device}_{use_fp16}_fp"
+
         if tok_cache_key in _model_cache and mdl_cache_key in _model_cache:
             hf_tokenizer = _model_cache[tok_cache_key]
             hf_model = _model_cache[mdl_cache_key]
-            logger.info("Using cached HF tokenizer/model for manual path")
+            logger.info("Using cached HF tokenizer/model for manual FP path")
         else:
-            logger.info("Loading HF tokenizer/model for manual tokenization path")
+            logger.info("Loading HF tokenizer/model for manual FP path")
             hf_tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+
             torch_dtype = torch.float16 if (use_fp16 and device == "cuda") else torch.float32
-            hf_model = AutoModel.from_pretrained(model_name, torch_dtype=torch_dtype)
+            hf_model = AutoModel.from_pretrained(
+                model_name,
+                torch_dtype=torch_dtype,
+            )
             hf_model.to(device)
+
             hf_model.eval()
             # Optional compile on supported devices
             if device in ["cuda", "mps"]:
                 try:
-                    hf_model = torch.compile(hf_model, mode="reduce-overhead", dynamic=True)  # type: ignore
+                    hf_model = torch.compile(  # type: ignore
-                except Exception:
+                        hf_model, mode="reduce-overhead", dynamic=True
-                    pass
+                    )
+                    logger.info(
+                        f"Applied torch.compile to HF model for {model_name} "
+                        f"(device={device}, dtype={torch_dtype})"
+                    )
+                except Exception as exc:
+                    logger.warning(f"torch.compile optimization failed: {exc}")
+
             _model_cache[tok_cache_key] = hf_tokenizer
             _model_cache[mdl_cache_key] = hf_model
 
@@ -516,7 +554,6 @@ def compute_embeddings_sentence_transformers(
             for start_index in batch_iter:
                 end_index = min(start_index + batch_size, len(texts))
                 batch_texts = texts[start_index:end_index]
-                tokenize_start_time = time.time()
                 inputs = hf_tokenizer(
                     batch_texts,
                     padding=True,
@@ -524,34 +561,17 @@ def compute_embeddings_sentence_transformers(
                     max_length=max_length,
                     return_tensors="pt",
                 )
-                tokenize_end_time = time.time()
-                logger.info(
-                    f"Tokenize time taken: {tokenize_end_time - tokenize_start_time} seconds"
-                )
-                # Print shapes of all input tensors for debugging
-                for k, v in inputs.items():
-                    print(f"inputs[{k!r}] shape: {getattr(v, 'shape', type(v))}")
-                to_device_start_time = time.time()
                 inputs = {k: v.to(device) for k, v in inputs.items()}
-                to_device_end_time = time.time()
-                logger.info(
-                    f"To device time taken: {to_device_end_time - to_device_start_time} seconds"
-                )
-                forward_start_time = time.time()
                 outputs = hf_model(**inputs)
-                forward_end_time = time.time()
-                logger.info(f"Forward time taken: {forward_end_time - forward_start_time} seconds")
                 last_hidden_state = outputs.last_hidden_state  # (B, L, H)
                 attention_mask = inputs.get("attention_mask")
                 if attention_mask is None:
-                    # Fallback: assume all tokens are valid
                     pooled = last_hidden_state.mean(dim=1)
                 else:
                     mask = attention_mask.unsqueeze(-1).to(last_hidden_state.dtype)
                     masked = last_hidden_state * mask
                     lengths = mask.sum(dim=1).clamp(min=1)
                     pooled = masked.sum(dim=1) / lengths
-                # Move to CPU float32
                 batch_embeddings = pooled.detach().to("cpu").float().numpy()
                 all_embeddings.append(batch_embeddings)
 
@@ -571,6 +591,12 @@ def compute_embeddings_sentence_transformers(
     if np.isnan(embeddings).any() or np.isinf(embeddings).any():
         raise RuntimeError(f"Detected NaN or Inf values in embeddings, model: {model_name}")
 
+    outer_end_time = time.time()
+    logger.debug(
+        "compute_embeddings_sentence_transformers total time "
+        f"(function entry -> return): {outer_end_time - outer_start_time:.6f}s"
+    )
+
     return embeddings
 
 

packages/leann/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann"
-version = "0.3.5"
+version = "0.3.4"
 description = "LEANN - The smallest vector index in the world. RAG Everything with LEANN!"
 readme = "README.md"
 requires-python = ">=3.9"