docs: update README to use proper module imports for apps

- Change from 'python apps/xxx.py' to 'python -m apps.xxx'
- More professional and pythonic module calling
- Ensures proper module resolution and imports
- Better separation between apps/ (production tools) and examples/ (demos)

README.md

@@ -170,8 +170,6 @@ ollama pull llama3.2:1b
 
 LEANN provides flexible parameters for embedding models, search strategies, and data processing to fit your specific needs.
 
-📚 **Need configuration best practices?** Check our [Configuration Guide](docs/configuration-guide.md) for detailed optimization tips, model selection advice, and solutions to common issues like slow embeddings or poor search quality.
-
 <details>
 <summary><strong>📋 Click to expand: Common Parameters (Available in All Examples)</strong></summary>
 
@@ -516,7 +514,7 @@ 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:** DiskANN or HNSW - pick what works for your data size.
 
 ## Benchmarks
 
@@ -536,7 +534,8 @@ Options:
 
 ```bash
 uv pip install -e ".[dev]"  # Install dev dependencies
-python benchmarks/run_evaluation.py    # Will auto-download evaluation data and run benchmarks
+python benchmarks/run_evaluation.py data/indices/dpr/dpr_diskann      # DPR dataset
+python benchmarks/run_evaluation.py data/indices/rpj_wiki/rpj_wiki.index  # Wikipedia
 ```
 
 The evaluation script downloads data automatically on first run. The last three results were tested with partial personal data, and you can reproduce them with your own data!

apps/document_rag.py

@@ -99,9 +99,7 @@ if __name__ == "__main__":
     print("- 'What are the main techniques LEANN uses?'")
     print("- 'What is the technique DLPM?'")
     print("- 'Who does Elizabeth Bennet marry?'")
-    print(
+    print("- 'What is the problem of developing pan gu model? (盘古大模型开发中遇到什么问题?)'")
-        "- 'What is the problem of developing pan gu model Huawei meets? (盘古大模型开发中遇到什么问题?)'"
-    )
     print("\nOr run without --query for interactive mode\n")
 
     rag = DocumentRAG()

docs/configuration-guide.md

@@ -1,236 +0,0 @@
-# LEANN Configuration Guide
-
-This guide helps you optimize LEANN for different use cases and understand the trade-offs between various configuration options.
-
-## Getting Started: Simple is Better
-
-When first trying LEANN, start with a small dataset to quickly validate your approach:
-
-**For document RAG**: The default `data/` directory works perfectly - includes 2 AI research papers, Pride and Prejudice literature, and a technical report
-```bash
-python -m apps.document_rag --query "What techniques does LEANN use?"
-```
-
-**For other data sources**: Limit the dataset size for quick testing
-```bash
-# WeChat: Test with recent messages only
-python -m apps.wechat_rag --max-items 100 --query "What did we discuss about the project timeline?"
-
-# Browser history: Last few days
-python -m apps.browser_rag --max-items 500 --query "Find documentation about vector databases"
-
-# Email: Recent inbox
-python -m apps.email_rag --max-items 200 --query "Who sent updates about the deployment status?"
-```
-
-Once validated, scale up gradually:
-- 100 documents → 1,000 → 10,000 → full dataset (`--max-items -1`)
-- This helps identify issues early before committing to long processing times
-
-## Embedding Model Selection: Understanding the Trade-offs
-
-Based on our experience developing LEANN, embedding models fall into three categories:
-
-### Small Models (< 100M parameters)
-**Example**: `sentence-transformers/all-MiniLM-L6-v2` (22M params)
-- **Pros**: Lightweight, fast for both indexing and inference
-- **Cons**: Lower semantic understanding, may miss nuanced relationships
-- **Use when**: Speed is critical, handling simple queries, interactive mode, or just experimenting with LEANN. If time is not a constraint, consider using a larger/better embedding model
-
-### Medium Models (100M-500M parameters)
-**Example**: `facebook/contriever` (110M params), `BAAI/bge-base-en-v1.5` (110M params)
-- **Pros**: Balanced performance, good multilingual support, reasonable speed
-- **Cons**: Requires more compute than small models
-- **Use when**: Need quality results without extreme compute requirements, general-purpose RAG applications
-
-### Large Models (500M+ parameters)
-**Example**: `Qwen/Qwen3-Embedding-0.6B` (600M params), `intfloat/multilingual-e5-large` (560M params)
-- **Pros**: Best semantic understanding, captures complex relationships, excellent multilingual support. **Qwen3-Embedding-0.6B achieves nearly OpenAI API performance!**
-- **Cons**: Slower inference, longer index build times
-- **Use when**: Quality is paramount and you have sufficient compute resources. **Highly recommended** for production use
-
-### Quick Start: OpenAI Embeddings (Fastest Setup)
-
-For immediate testing without local model downloads:
-```bash
-# Set OpenAI embeddings (requires OPENAI_API_KEY)
---embedding-mode openai --embedding-model text-embedding-3-small
-```
-
-<details>
-<summary><strong>Cloud vs Local Trade-offs</strong></summary>
-
-**OpenAI Embeddings** (`text-embedding-3-small/large`)
-- **Pros**: No local compute needed, consistently fast, high quality
-- **Cons**: Requires API key, costs money, data leaves your system, [known limitations with certain languages](https://yichuan-w.github.io/blog/lessons_learned_in_dev_leann/)
-- **When to use**: Prototyping, non-sensitive data, need immediate results
-
-**Local Embeddings**
-- **Pros**: Complete privacy, no ongoing costs, full control, can sometimes outperform OpenAI embeddings
-- **Cons**: Slower than cloud APIs, requires local compute resources
-- **When to use**: Production systems, sensitive data, cost-sensitive applications
-
-</details>
-
-## Index Selection: Matching Your Scale
-
-### HNSW (Hierarchical Navigable Small World)
-**Best for**: Small to medium datasets (< 10M vectors) - **Default and recommended for extreme low storage**
-- Full recomputation required
-- High memory usage during build phase
-- Excellent recall (95%+)
-
-```bash
-# Optimal for most use cases
---backend-name hnsw --graph-degree 32 --build-complexity 64
-```
-
-### 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
-
-```bash
-# For billion-scale deployments
---backend-name diskann --graph-degree 64 --build-complexity 128
-```
-
-## LLM Selection: Engine and Model Comparison
-
-### LLM Engines
-
-**OpenAI** (`--llm openai`)
-- **Pros**: Best quality, consistent performance, no local resources needed
-- **Cons**: Costs money ($0.15-2.5 per million tokens), requires internet, data privacy concerns
-- **Models**: `gpt-4o-mini` (fast, cheap), `gpt-4o` (best quality), `o3-mini` (reasoning, not so expensive)
-- **Note**: Our current default, but we recommend switching to Ollama for most use cases
-
-**Ollama** (`--llm ollama`)
-- **Pros**: Fully local, free, privacy-preserving, good model variety
-- **Cons**: Requires local GPU/CPU resources, slower than cloud APIs, need to install extra [ollama app](https://github.com/ollama/ollama?tab=readme-ov-file#ollama) and pre-download models by `ollama pull`
-- **Models**: `qwen3:0.6b` (ultra-fast), `qwen3:1.7b` (balanced), `qwen3:4b` (good quality), `qwen3:7b` (high quality), `deepseek-r1:1.5b` (reasoning)
-
-**HuggingFace** (`--llm hf`)
-- **Pros**: Free tier available, huge model selection, direct model loading (vs Ollama's server-based approach)
-- **Cons**: More complex initial setup
-- **Models**: `Qwen/Qwen3-1.7B-FP8`
-
-## Parameter Tuning Guide
-
-### Search Complexity Parameters
-
-**`--build-complexity`** (index building)
-- Controls thoroughness during index construction
-- Higher = better recall but slower build
-- Recommendations:
-  - 32: Quick prototyping
-  - 64: Balanced (default)
-  - 128: Production systems
-  - 256: Maximum quality
-
-**`--search-complexity`** (query time)
-- Controls search thoroughness
-- Higher = better results but slower
-- Recommendations:
-  - 16: Fast/Interactive search
-  - 32: High quality with diversity
-  - 64+: Maximum accuracy
-
-### Top-K Selection
-
-**`--top-k`** (number of retrieved chunks)
-- More chunks = better context but slower LLM processing
-- Should be always smaller than `--search-complexity`
-- Guidelines:
-  - 10-20: General questions (default: 20)
-  - 30+: Complex multi-hop reasoning requiring comprehensive context
-
-**Trade-off formula**:
-- Retrieval time ∝ log(n) × search_complexity
-- LLM processing time ∝ top_k × chunk_size
-- Total context = top_k × chunk_size tokens
-
-### Graph Degree (HNSW/DiskANN)
-
-**`--graph-degree`**
-- Number of connections per node in the graph
-- Higher = better recall but more memory
-- HNSW: 16-32 (default: 32)
-- DiskANN: 32-128 (default: 64)
-
-
-## Performance Optimization Checklist
-
-### If Embedding is Too Slow
-
-1. **Switch to smaller model**:
-   ```bash
-   # From large model
-   --embedding-model Qwen/Qwen3-Embedding-0.6B
-   # To small model
-   --embedding-model sentence-transformers/all-MiniLM-L6-v2
-   ```
-
-2. **Limit dataset size for testing**:
-   ```bash
-   --max-items 1000  # Process first 1k items only
-   ```
-
-3. **Use MLX on Apple Silicon** (optional optimization):
-   ```bash
-   --embedding-mode mlx --embedding-model mlx-community/multilingual-e5-base-mlx
-   ```
-
-### If Search Quality is Poor
-
-1. **Increase retrieval count**:
-   ```bash
-   --top-k 30  # Retrieve more candidates
-   ```
-
-2. **Upgrade embedding model**:
-   ```bash
-   # For English
-   --embedding-model BAAI/bge-base-en-v1.5
-   # For multilingual
-   --embedding-model intfloat/multilingual-e5-large
-   ```
-
-## Understanding the Trade-offs
-
-Every configuration choice involves trade-offs:
-
-| Factor | Small/Fast | Large/Quality |
-|--------|------------|---------------|
-| Embedding Model | `all-MiniLM-L6-v2` | `Qwen/Qwen3-Embedding-0.6B` |
-| Chunk Size | 512 tokens | 128 tokens |
-| Index Type | HNSW | DiskANN |
-| LLM | `qwen3:1.7b` | `gpt-4o` |
-
-The key is finding the right balance for your specific use case. Start small and simple, measure performance, then scale up only where needed.
-
-## Deep Dive: Critical Configuration Decisions
-
-### When to Disable Recomputation
-
-LEANN's recomputation feature provides exact distance calculations but can be disabled for extreme QPS requirements:
-
-```bash
---no-recompute  # Disable selective recomputation
-```
-
-**Trade-offs**:
-- **With recomputation** (default): Exact distances, best quality, higher latency, minimal storage (only stores metadata, recomputes embeddings on-demand)
-- **Without recomputation**: Must store full embeddings, significantly higher memory and storage usage (10-100x more), but faster search
-
-**Disable when**:
-- You have abundant storage and memory
-- Need extremely low latency (< 100ms)
-- Running a read-heavy workload where storage cost is acceptable
-
-## 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)

docs/features.md

@@ -5,7 +5,7 @@
 - **🔄 Real-time Embeddings** - Eliminate heavy embedding storage with dynamic computation using optimized ZMQ servers and highly optimized search paradigm (overlapping and batching) with highly optimized embedding engine
 - **📈 Scalable Architecture** - Handles millions of documents on consumer hardware; the larger your dataset, the more LEANN can save
 - **🎯 Graph Pruning** - Advanced techniques to minimize the storage overhead of vector search to a limited footprint
-- **🏗️ Pluggable Backends** - HNSW/FAISS (default), with optional DiskANN for large-scale deployments
+- **🏗️ Pluggable Backends** - DiskANN, HNSW/FAISS with unified API
 
 ## 🛠️ Technical Highlights
 - **🔄 Recompute Mode** - Highest accuracy scenarios while eliminating vector storage overhead

docs/roadmap.md

@@ -2,8 +2,8 @@
 
 ## 🎯 Q2 2025
 
-- [X] HNSW backend integration
 - [X] DiskANN backend with MIPS/L2/Cosine support
+- [X] HNSW backend integration
 - [X] Real-time embedding pipeline
 - [X] Memory-efficient graph pruning
 

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

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

packages/leann-backend-diskann/pyproject.toml

@@ -4,8 +4,8 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-diskann"
-version = "0.2.0"
+version = "0.1.16"
-dependencies = ["leann-core==0.2.0", "numpy", "protobuf>=3.19.0"]
+dependencies = ["leann-core==0.1.16", "numpy", "protobuf>=3.19.0"]
 
 [tool.scikit-build]
 # Key: simplified CMake path

packages/leann-backend-hnsw/pyproject.toml

@@ -6,10 +6,10 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-hnsw"
-version = "0.2.0"
+version = "0.1.16"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
 dependencies = [
-    "leann-core==0.2.0",
+    "leann-core==0.1.16",
     "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 = "0.2.0"
+version = "0.1.16"
 description = "Core API and plugin system for LEANN"
 readme = "README.md"
 requires-python = ">=3.9"

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

@@ -636,10 +636,7 @@ class LeannChat:
             "Please provide the best answer you can based on this context and your knowledge."
         )
 
-        ask_time = time.time()
         ans = self.llm.ask(prompt, **llm_kwargs)
-        ask_time = time.time() - ask_time
-        logger.info(f"  Ask time: {ask_time} seconds")
         return ans
 
     def start_interactive(self):

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

@@ -358,11 +358,7 @@ def validate_model_and_suggest(model_name: str, llm_type: str) -> str | None:
                 error_msg += f"\n\nModel '{model_name}' was not found in Ollama's library."
 
                 if suggestions:
-                    error_msg += (
+                    error_msg += "\n\nDid you mean one of these installed models?\n"
-                        "\n\nDid you mean one of these installed models?\n"
-                        + "\nTry to use ollama pull to install the model you need\n"
-                    )
-
                     for i, suggestion in enumerate(suggestions, 1):
                         error_msg += f"  {i}. {suggestion}\n"
                 else:
@@ -546,41 +542,14 @@ class HFChat(LLMInterface):
             self.device = "cpu"
             logger.info("No GPU detected. Using CPU.")
 
-        # Load tokenizer and model with timeout protection
+        # Load tokenizer and model
-        try:
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-            import signal
+        self.model = AutoModelForCausalLM.from_pretrained(
-
+            model_name,
-            def timeout_handler(signum, frame):
+            torch_dtype=torch.float16 if self.device != "cpu" else torch.float32,
-                raise TimeoutError("Model download/loading timed out")
+            device_map="auto" if self.device != "cpu" else None,
-
+            trust_remote_code=True,
-            # Set timeout for model loading (60 seconds)
+        )
-            old_handler = signal.signal(signal.SIGALRM, timeout_handler)
-            signal.alarm(60)
-
-            try:
-                logger.info(f"Loading tokenizer for {model_name}...")
-                self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-                logger.info(f"Loading model {model_name}...")
-                self.model = AutoModelForCausalLM.from_pretrained(
-                    model_name,
-                    torch_dtype=torch.float16 if self.device != "cpu" else torch.float32,
-                    device_map="auto" if self.device != "cpu" else None,
-                    trust_remote_code=True,
-                )
-                logger.info(f"Successfully loaded {model_name}")
-            finally:
-                signal.alarm(0)  # Cancel the alarm
-                signal.signal(signal.SIGALRM, old_handler)  # Restore old handler
-
-        except TimeoutError:
-            logger.error(f"Model loading timed out for {model_name}")
-            raise RuntimeError(
-                f"Model loading timed out for {model_name}. Please check your internet connection or try a smaller model."
-            )
-        except Exception as e:
-            logger.error(f"Failed to load model {model_name}: {e}")
-            raise
 
         # Move model to device if not using device_map
         if self.device != "cpu" and "device_map" not in str(self.model):

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

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

packages/leann/README.md

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

packages/leann/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann"
-version = "0.2.0"
+version = "0.1.16"
 description = "LEANN - The smallest vector index in the world. RAG Everything with LEANN!"
 readme = "README.md"
 requires-python = ">=3.9"
@@ -24,15 +24,16 @@ classifiers = [
     "Programming Language :: Python :: 3.12",
 ]
 
-# Default installation: core + hnsw + diskann
+# Default installation: core + hnsw
 dependencies = [
     "leann-core>=0.1.0",
     "leann-backend-hnsw>=0.1.0",
-    "leann-backend-diskann>=0.1.0",
 ]
 
 [project.optional-dependencies]
-# All backends now included by default
+diskann = [
+    "leann-backend-diskann>=0.1.0",
+]
 
 [project.urls]
 Repository = "https://github.com/yichuan-w/LEANN"

uv.lock

@@ -1650,7 +1650,7 @@ name = "importlib-metadata"
 version = "8.7.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "zipp", marker = "python_full_version < '3.10'" },
+    { name = "zipp" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/76/66/650a33bd90f786193e4de4b3ad86ea60b53c89b669a5c7be931fac31cdb0/importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000", size = 56641 }
 wheels = [
@@ -2155,7 +2155,7 @@ wheels = [
 
 [[package]]
 name = "leann-backend-diskann"
-version = "0.2.0"
+version = "0.1.15"
 source = { editable = "packages/leann-backend-diskann" }
 dependencies = [
     { name = "leann-core" },
@@ -2167,14 +2167,14 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "leann-core", specifier = "==0.2.0" },
+    { name = "leann-core", specifier = "==0.1.15" },
     { name = "numpy" },
     { name = "protobuf", specifier = ">=3.19.0" },
 ]
 
 [[package]]
 name = "leann-backend-hnsw"
-version = "0.2.0"
+version = "0.1.15"
 source = { editable = "packages/leann-backend-hnsw" }
 dependencies = [
     { name = "leann-core" },
@@ -2187,7 +2187,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "leann-core", specifier = "==0.2.0" },
+    { name = "leann-core", specifier = "==0.1.15" },
     { name = "msgpack", specifier = ">=1.0.0" },
     { name = "numpy" },
     { name = "pyzmq", specifier = ">=23.0.0" },
@@ -2195,7 +2195,7 @@ requires-dist = [
 
 [[package]]
 name = "leann-core"
-version = "0.2.0"
+version = "0.1.15"
 source = { editable = "packages/leann-core" }
 dependencies = [
     { name = "accelerate" },