[cli] fix # 81

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,50 +0,0 @@
-name: Bug Report
-description: Report a bug in LEANN
-labels: ["bug"]
-
-body:
-  - type: textarea
-    id: description
-    attributes:
-      label: What happened?
-      description: A clear description of the bug
-    validations:
-      required: true
-
-  - type: textarea
-    id: reproduce
-    attributes:
-      label: How to reproduce
-      placeholder: |
-        1. Install with...
-        2. Run command...
-        3. See error
-    validations:
-      required: true
-
-  - type: textarea
-    id: error
-    attributes:
-      label: Error message
-      description: Paste any error messages
-      render: shell
-
-  - type: input
-    id: version
-    attributes:
-      label: LEANN Version
-      placeholder: "0.1.0"
-    validations:
-      required: true
-
-  - type: dropdown
-    id: os
-    attributes:
-      label: Operating System
-      options:
-        - macOS
-        - Linux
-        - Windows
-        - Docker
-    validations:
-      required: true

.github/ISSUE_TEMPLATE/config.yml

@@ -1,8 +0,0 @@
-blank_issues_enabled: true
-contact_links:
-  - name: Documentation
-    url: https://github.com/LEANN-RAG/LEANN-RAG/tree/main/docs
-    about: Read the docs first
-  - name: Discussions
-    url: https://github.com/LEANN-RAG/LEANN-RAG/discussions
-    about: Ask questions and share ideas

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -1,27 +0,0 @@
-name: Feature Request
-description: Suggest a new feature for LEANN
-labels: ["enhancement"]
-
-body:
-  - type: textarea
-    id: problem
-    attributes:
-      label: What problem does this solve?
-      description: Describe the problem or need
-    validations:
-      required: true
-
-  - type: textarea
-    id: solution
-    attributes:
-      label: Proposed solution
-      description: How would you like this to work?
-    validations:
-      required: true
-
-  - type: textarea
-    id: example
-    attributes:
-      label: Example usage
-      description: Show how the API might look
-      render: python

.github/pull_request_template.md

@@ -1,13 +0,0 @@
-## What does this PR do?
-
-<!-- Brief description of your changes -->
-
-## Related Issues
-
-Fixes #
-
-## Checklist
-
-- [ ] Tests pass (`uv run pytest`)
-- [ ] Code formatted (`ruff format` and `ruff check`)
-- [ ] Pre-commit hooks pass (`pre-commit run --all-files`)

.github/workflows/build-reusable.yml

@@ -54,17 +54,6 @@ jobs:
             python: '3.12'
           - os: ubuntu-22.04
             python: '3.13'
-          # ARM64 Linux builds
-          - os: ubuntu-24.04-arm
-            python: '3.9'
-          - os: ubuntu-24.04-arm
-            python: '3.10'
-          - os: ubuntu-24.04-arm
-            python: '3.11'
-          - os: ubuntu-24.04-arm
-            python: '3.12'
-          - os: ubuntu-24.04-arm
-            python: '3.13'
           - os: macos-14
             python: '3.9'
           - os: macos-14
@@ -119,46 +108,13 @@ jobs:
             pkg-config libabsl-dev libaio-dev libprotobuf-dev \
             patchelf
 
-          # Debug: Show system information
+          # Install Intel MKL for DiskANN
-          echo "🔍 System Information:"
-          echo "Architecture: $(uname -m)"
-          echo "OS: $(uname -a)"
-          echo "CPU info: $(lscpu | head -5)"
-
-          # Install math library based on architecture
-          ARCH=$(uname -m)
-          echo "🔍 Setting up math library for architecture: $ARCH"
-
-          if [[ "$ARCH" == "x86_64" ]]; then
-            # Install Intel MKL for DiskANN on x86_64
-            echo "📦 Installing Intel MKL for x86_64..."
           wget -q https://registrationcenter-download.intel.com/akdlm/IRC_NAS/79153e0f-74d7-45af-b8c2-258941adf58a/intel-onemkl-2025.0.0.940.sh
           sudo sh intel-onemkl-2025.0.0.940.sh -a --components intel.oneapi.lin.mkl.devel --action install --eula accept -s
           source /opt/intel/oneapi/setvars.sh
           echo "MKLROOT=/opt/intel/oneapi/mkl/latest" >> $GITHUB_ENV
           echo "LD_LIBRARY_PATH=/opt/intel/oneapi/compiler/latest/linux/compiler/lib/intel64_lin" >> $GITHUB_ENV
           echo "LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/intel/oneapi/mkl/latest/lib/intel64" >> $GITHUB_ENV
-            echo "✅ Intel MKL installed for x86_64"
-
-            # Debug: Check MKL installation
-            echo "🔍 MKL Installation Check:"
-            ls -la /opt/intel/oneapi/mkl/latest/ || echo "MKL directory not found"
-            ls -la /opt/intel/oneapi/mkl/latest/lib/ || echo "MKL lib directory not found"
-
-          elif [[ "$ARCH" == "aarch64" ]]; then
-            # Use OpenBLAS for ARM64 (MKL installer not compatible with ARM64)
-            echo "📦 Installing OpenBLAS for ARM64..."
-            sudo apt-get install -y libopenblas-dev liblapack-dev liblapacke-dev
-            echo "✅ OpenBLAS installed for ARM64"
-
-            # Debug: Check OpenBLAS installation
-            echo "🔍 OpenBLAS Installation Check:"
-            dpkg -l | grep openblas || echo "OpenBLAS package not found"
-            ls -la /usr/lib/aarch64-linux-gnu/openblas/ || echo "OpenBLAS directory not found"
-          fi
-
-          # Debug: Show final library paths
-          echo "🔍 Final LD_LIBRARY_PATH: $LD_LIBRARY_PATH"
 
       - name: Install system dependencies (macOS)
         if: runner.os == 'macOS'

.gitignore

@@ -22,7 +22,6 @@ demo/experiment_results/**/*.json
 *.sh
 *.txt
 !CMakeLists.txt
-!llms.txt
 latency_breakdown*.json
 experiment_results/eval_results/diskann/*.json
 aws/

.gitmodules

@@ -14,7 +14,3 @@
 [submodule "packages/leann-backend-hnsw/third_party/libzmq"]
 	path = packages/leann-backend-hnsw/third_party/libzmq
 	url = https://github.com/zeromq/libzmq.git
-[submodule "packages/astchunk-leann"]
-	path = packages/astchunk-leann
-	url = git@github.com:yichuan-w/astchunk-leann.git
-	branch = main

README.md

@@ -656,19 +656,6 @@ results = searcher.search(
 
 📖 **[Complete Metadata filtering guide →](docs/metadata_filtering.md)**
 
-### 🔍 Grep Search
-
-For exact text matching instead of semantic search, use the `use_grep` parameter:
-
-```python
-# Exact text search
-results = searcher.search("banana‑crocodile", use_grep=True, top_k=1)
-```
-
-**Use cases**: Finding specific code patterns, error messages, function names, or exact phrases where semantic similarity isn't needed.
-
-📖 **[Complete grep search guide →](docs/grep_search.md)**
-
 ## 🏗️ Architecture & How It Works
 
 <p align="center">

apps/chunking/__init__.py

@@ -1,38 +1,16 @@
-"""Unified chunking utilities facade.
+"""
-
+Chunking utilities for LEANN RAG applications.
-This module re-exports the packaged utilities from `leann.chunking_utils` so
+Provides AST-aware and traditional text chunking functionality.
-that both repo apps (importing `chunking`) and installed wheels share one
-single implementation. When running from the repo without installation, it
-adds the `packages/leann-core/src` directory to `sys.path` as a fallback.
 """
 
-import sys
+from .utils import (
-from pathlib import Path
-
-try:
-    from leann.chunking_utils import (
     CODE_EXTENSIONS,
     create_ast_chunks,
     create_text_chunks,
     create_traditional_chunks,
     detect_code_files,
     get_language_from_extension,
-    )
+)
-except Exception:  # pragma: no cover - best-effort fallback for dev environment
-    repo_root = Path(__file__).resolve().parents[2]
-    leann_src = repo_root / "packages" / "leann-core" / "src"
-    if leann_src.exists():
-        sys.path.insert(0, str(leann_src))
-        from leann.chunking_utils import (
-            CODE_EXTENSIONS,
-            create_ast_chunks,
-            create_text_chunks,
-            create_traditional_chunks,
-            detect_code_files,
-            get_language_from_extension,
-        )
-    else:
-        raise
 
 __all__ = [
     "CODE_EXTENSIONS",

apps/chunking/utils.py

@@ -1,6 +1,6 @@
 """
 Enhanced chunking utilities with AST-aware code chunking support.
-Packaged within leann-core so installed wheels can import it reliably.
+Provides unified interface for both traditional and AST-based text chunking.
 """
 
 import logging
@@ -22,9 +22,30 @@ CODE_EXTENSIONS = {
     ".jsx": "typescript",
 }
 
+# Default chunk parameters for different content types
+DEFAULT_CHUNK_PARAMS = {
+    "code": {
+        "max_chunk_size": 512,
+        "chunk_overlap": 64,
+    },
+    "text": {
+        "chunk_size": 256,
+        "chunk_overlap": 128,
+    },
+}
+
 
 def detect_code_files(documents, code_extensions=None) -> tuple[list, list]:
-    """Separate documents into code files and regular text files."""
+    """
+    Separate documents into code files and regular text files.
+
+    Args:
+        documents: List of LlamaIndex Document objects
+        code_extensions: Dict mapping file extensions to languages (defaults to CODE_EXTENSIONS)
+
+    Returns:
+        Tuple of (code_documents, text_documents)
+    """
     if code_extensions is None:
         code_extensions = CODE_EXTENSIONS
 
@@ -32,10 +53,16 @@ def detect_code_files(documents, code_extensions=None) -> tuple[list, list]:
     text_docs = []
 
     for doc in documents:
-        file_path = doc.metadata.get("file_path", "") or doc.metadata.get("file_name", "")
+        # Get file path from metadata
+        file_path = doc.metadata.get("file_path", "")
+        if not file_path:
+            # Fallback to file_name
+            file_path = doc.metadata.get("file_name", "")
+
         if file_path:
             file_ext = Path(file_path).suffix.lower()
             if file_ext in code_extensions:
+                # Add language info to metadata
                 doc.metadata["language"] = code_extensions[file_ext]
                 doc.metadata["is_code"] = True
                 code_docs.append(doc)
@@ -43,6 +70,7 @@ def detect_code_files(documents, code_extensions=None) -> tuple[list, list]:
                 doc.metadata["is_code"] = False
                 text_docs.append(doc)
         else:
+            # If no file path, treat as text
             doc.metadata["is_code"] = False
             text_docs.append(doc)
 
@@ -51,7 +79,7 @@ def detect_code_files(documents, code_extensions=None) -> tuple[list, list]:
 
 
 def get_language_from_extension(file_path: str) -> Optional[str]:
-    """Return language string from a filename/extension using CODE_EXTENSIONS."""
+    """Get the programming language from file extension."""
     ext = Path(file_path).suffix.lower()
     return CODE_EXTENSIONS.get(ext)
 
@@ -62,26 +90,40 @@ def create_ast_chunks(
     chunk_overlap: int = 64,
     metadata_template: str = "default",
 ) -> list[str]:
-    """Create AST-aware chunks from code documents using astchunk.
+    """
+    Create AST-aware chunks from code documents using astchunk.
 
-    Falls back to traditional chunking if astchunk is unavailable.
+    Args:
+        documents: List of code documents
+        max_chunk_size: Maximum characters per chunk
+        chunk_overlap: Number of AST nodes to overlap between chunks
+        metadata_template: Template for chunk metadata
+
+    Returns:
+        List of text chunks with preserved code structure
     """
     try:
-        from astchunk import ASTChunkBuilder  # optional dependency
+        from astchunk import ASTChunkBuilder
     except ImportError as e:
         logger.error(f"astchunk not available: {e}")
         logger.info("Falling back to traditional chunking for code files")
         return create_traditional_chunks(documents, max_chunk_size, chunk_overlap)
 
     all_chunks = []
+
     for doc in documents:
+        # Get language from metadata (set by detect_code_files)
         language = doc.metadata.get("language")
         if not language:
-            logger.warning("No language detected; falling back to traditional chunking")
+            logger.warning(
-            all_chunks.extend(create_traditional_chunks([doc], max_chunk_size, chunk_overlap))
+                "No language detected for document, falling back to traditional chunking"
+            )
+            traditional_chunks = create_traditional_chunks([doc], max_chunk_size, chunk_overlap)
+            all_chunks.extend(traditional_chunks)
             continue
 
         try:
+            # Configure astchunk
             configs = {
                 "max_chunk_size": max_chunk_size,
                 "language": language,
@@ -89,6 +131,7 @@ def create_ast_chunks(
                 "chunk_overlap": chunk_overlap if chunk_overlap > 0 else 0,
             }
 
+            # Add repository-level metadata if available
             repo_metadata = {
                 "file_path": doc.metadata.get("file_path", ""),
                 "file_name": doc.metadata.get("file_name", ""),
@@ -97,13 +140,17 @@ def create_ast_chunks(
             }
             configs["repo_level_metadata"] = repo_metadata
 
+            # Create chunk builder and process
             chunk_builder = ASTChunkBuilder(**configs)
             code_content = doc.get_content()
+
             if not code_content or not code_content.strip():
                 logger.warning("Empty code content, skipping")
                 continue
 
             chunks = chunk_builder.chunkify(code_content)
+
+            # Extract text content from chunks
             for chunk in chunks:
                 if hasattr(chunk, "text"):
                     chunk_text = chunk.text
@@ -112,6 +159,7 @@ def create_ast_chunks(
                 elif isinstance(chunk, str):
                     chunk_text = chunk
                 else:
+                    # Try to convert to string
                     chunk_text = str(chunk)
 
                 if chunk_text and chunk_text.strip():
@@ -120,10 +168,12 @@ def create_ast_chunks(
             logger.info(
                 f"Created {len(chunks)} AST chunks from {language} file: {doc.metadata.get('file_name', 'unknown')}"
             )
+
         except Exception as e:
             logger.warning(f"AST chunking failed for {language} file: {e}")
             logger.info("Falling back to traditional chunking")
-            all_chunks.extend(create_traditional_chunks([doc], max_chunk_size, chunk_overlap))
+            traditional_chunks = create_traditional_chunks([doc], max_chunk_size, chunk_overlap)
+            all_chunks.extend(traditional_chunks)
 
     return all_chunks
 
@@ -131,10 +181,23 @@ def create_ast_chunks(
 def create_traditional_chunks(
     documents, chunk_size: int = 256, chunk_overlap: int = 128
 ) -> list[str]:
-    """Create traditional text chunks using LlamaIndex SentenceSplitter."""
+    """
+    Create traditional text chunks using LlamaIndex SentenceSplitter.
+
+    Args:
+        documents: List of documents to chunk
+        chunk_size: Size of each chunk in characters
+        chunk_overlap: Overlap between chunks
+
+    Returns:
+        List of text chunks
+    """
+    # Handle invalid chunk_size values
     if chunk_size <= 0:
         logger.warning(f"Invalid chunk_size={chunk_size}, using default value of 256")
         chunk_size = 256
+
+    # Ensure chunk_overlap is not negative and not larger than chunk_size
     if chunk_overlap < 0:
         chunk_overlap = 0
     if chunk_overlap >= chunk_size:
@@ -152,9 +215,12 @@ def create_traditional_chunks(
         try:
             nodes = node_parser.get_nodes_from_documents([doc])
             if nodes:
-                all_texts.extend(node.get_content() for node in nodes)
+                chunk_texts = [node.get_content() for node in nodes]
+                all_texts.extend(chunk_texts)
+                logger.debug(f"Created {len(chunk_texts)} traditional chunks from document")
         except Exception as e:
             logger.error(f"Traditional chunking failed for document: {e}")
+            # As last resort, add the raw content
             content = doc.get_content()
             if content and content.strip():
                 all_texts.append(content.strip())
@@ -172,13 +238,32 @@ def create_text_chunks(
     code_file_extensions: Optional[list[str]] = None,
     ast_fallback_traditional: bool = True,
 ) -> list[str]:
-    """Create text chunks from documents with optional AST support for code files."""
+    """
+    Create text chunks from documents with optional AST support for code files.
+
+    Args:
+        documents: List of LlamaIndex Document objects
+        chunk_size: Size for traditional text chunks
+        chunk_overlap: Overlap for traditional text chunks
+        use_ast_chunking: Whether to use AST chunking for code files
+        ast_chunk_size: Size for AST chunks
+        ast_chunk_overlap: Overlap for AST chunks
+        code_file_extensions: Custom list of code file extensions
+        ast_fallback_traditional: Fall back to traditional chunking on AST errors
+
+    Returns:
+        List of text chunks
+    """
     if not documents:
         logger.warning("No documents provided for chunking")
         return []
 
+    # Create a local copy of supported extensions for this function call
     local_code_extensions = CODE_EXTENSIONS.copy()
+
+    # Update supported extensions if provided
     if code_file_extensions:
+        # Map extensions to languages (simplified mapping)
         ext_mapping = {
             ".py": "python",
             ".java": "java",
@@ -188,32 +273,47 @@ def create_text_chunks(
         }
         for ext in code_file_extensions:
             if ext.lower() not in local_code_extensions:
+                # Try to guess language from extension
                 if ext.lower() in ext_mapping:
                     local_code_extensions[ext.lower()] = ext_mapping[ext.lower()]
                 else:
                     logger.warning(f"Unsupported extension {ext}, will use traditional chunking")
 
     all_chunks = []
+
     if use_ast_chunking:
+        # Separate code and text documents using local extensions
         code_docs, text_docs = detect_code_files(documents, local_code_extensions)
+
+        # Process code files with AST chunking
         if code_docs:
+            logger.info(f"Processing {len(code_docs)} code files with AST chunking")
             try:
-                all_chunks.extend(
+                ast_chunks = create_ast_chunks(
-                    create_ast_chunks(
                     code_docs, max_chunk_size=ast_chunk_size, chunk_overlap=ast_chunk_overlap
                 )
-                )
+                all_chunks.extend(ast_chunks)
+                logger.info(f"Created {len(ast_chunks)} AST chunks from code files")
             except Exception as e:
                 logger.error(f"AST chunking failed: {e}")
                 if ast_fallback_traditional:
-                    all_chunks.extend(
+                    logger.info("Falling back to traditional chunking for code files")
-                        create_traditional_chunks(code_docs, chunk_size, chunk_overlap)
+                    traditional_code_chunks = create_traditional_chunks(
+                        code_docs, chunk_size, chunk_overlap
                     )
+                    all_chunks.extend(traditional_code_chunks)
                 else:
                     raise
+
+        # Process text files with traditional chunking
         if text_docs:
-            all_chunks.extend(create_traditional_chunks(text_docs, chunk_size, chunk_overlap))
+            logger.info(f"Processing {len(text_docs)} text files with traditional chunking")
+            text_chunks = create_traditional_chunks(text_docs, chunk_size, chunk_overlap)
+            all_chunks.extend(text_chunks)
+            logger.info(f"Created {len(text_chunks)} traditional chunks from text files")
     else:
+        # Use traditional chunking for all files
+        logger.info(f"Processing {len(documents)} documents with traditional chunking")
         all_chunks = create_traditional_chunks(documents, chunk_size, chunk_overlap)
 
     logger.info(f"Total chunks created: {len(all_chunks)}")

apps/history_data/history.py

@@ -74,7 +74,7 @@ class ChromeHistoryReader(BaseReader):
                 if count >= max_count and max_count > 0:
                     break
 
-                last_visit, url, title, visit_count, typed_count, _hidden = row
+                last_visit, url, title, visit_count, typed_count, hidden = row
 
                 # Create document content with metadata embedded in text
                 doc_content = f"""

docs/ast_chunking_guide.md

@@ -26,21 +26,6 @@ leann build my-code-index --docs ./src --use-ast-chunking
 uv pip install -e "."
 ```
 
-#### For normal users (PyPI install)
-- Use `pip install leann` or `uv pip install leann`.
-- `astchunk` is pulled automatically from PyPI as a dependency; no extra steps.
-
-#### For developers (from source, editable)
-```bash
-git clone https://github.com/yichuan-w/LEANN.git leann
-cd leann
-git submodule update --init --recursive
-uv sync
-```
-- This repo vendors `astchunk` as a git submodule at `packages/astchunk-leann` (our fork).
-- `[tool.uv.sources]` maps the `astchunk` package to that path in editable mode.
-- You can edit code under `packages/astchunk-leann` and Python will use your changes immediately (no separate `pip install astchunk` needed).
-
 ## Best Practices
 
 ### When to Use AST Chunking

docs/grep_search.md

@@ -1,149 +0,0 @@
-# LEANN Grep Search Usage Guide
-
-## Overview
-
-LEANN's grep search functionality provides exact text matching for finding specific code patterns, error messages, function names, or exact phrases in your indexed documents.
-
-## Basic Usage
-
-### Simple Grep Search
-
-```python
-from leann.api import LeannSearcher
-
-searcher = LeannSearcher("your_index_path")
-
-# Exact text search
-results = searcher.search("def authenticate_user", use_grep=True, top_k=5)
-
-for result in results:
-    print(f"Score: {result.score}")
-    print(f"Text: {result.text[:100]}...")
-    print("-" * 40)
-```
-
-### Comparison: Semantic vs Grep Search
-
-```python
-# Semantic search - finds conceptually similar content
-semantic_results = searcher.search("machine learning algorithms", top_k=3)
-
-# Grep search - finds exact text matches
-grep_results = searcher.search("def train_model", use_grep=True, top_k=3)
-```
-
-## When to Use Grep Search
-
-### Use Cases
-
-- **Code Search**: Finding specific function definitions, class names, or variable references
-- **Error Debugging**: Locating exact error messages or stack traces
-- **Documentation**: Finding specific API endpoints or exact terminology
-
-### Examples
-
-```python
-# Find function definitions
-functions = searcher.search("def __init__", use_grep=True)
-
-# Find import statements
-imports = searcher.search("from sklearn import", use_grep=True)
-
-# Find specific error types
-errors = searcher.search("FileNotFoundError", use_grep=True)
-
-# Find TODO comments
-todos = searcher.search("TODO:", use_grep=True)
-
-# Find configuration entries
-configs = searcher.search("server_port=", use_grep=True)
-```
-
-## Technical Details
-
-### How It Works
-
-1. **File Location**: Grep search operates on the raw text stored in `.jsonl` files
-2. **Command Execution**: Uses the system `grep` command with case-insensitive search
-3. **Result Processing**: Parses JSON lines and extracts text and metadata
-4. **Scoring**: Simple frequency-based scoring based on query term occurrences
-
-### Search Process
-
-```
-Query: "def train_model"
-  ↓
-grep -i -n "def train_model" documents.leann.passages.jsonl
-  ↓
-Parse matching JSON lines
-  ↓
-Calculate scores based on term frequency
-  ↓
-Return top_k results
-```
-
-### Scoring Algorithm
-
-```python
-# Term frequency in document
-score = text.lower().count(query.lower())
-```
-
-Results are ranked by score (highest first), with higher scores indicating more occurrences of the search term.
-
-## Error Handling
-
-### Common Issues
-
-#### Grep Command Not Found
-```
-RuntimeError: grep command not found. Please install grep or use semantic search.
-```
-
-**Solution**: Install grep on your system:
-- **Ubuntu/Debian**: `sudo apt-get install grep`
-- **macOS**: grep is pre-installed
-- **Windows**: Use WSL or install grep via Git Bash/MSYS2
-
-#### No Results Found
-```python
-# Check if your query exists in the raw data
-results = searcher.search("your_query", use_grep=True)
-if not results:
-    print("No exact matches found. Try:")
-    print("1. Check spelling and case")
-    print("2. Use partial terms")
-    print("3. Switch to semantic search")
-```
-
-## Complete Example
-
-```python
-#!/usr/bin/env python3
-"""
-Grep Search Example
-Demonstrates grep search for exact text matching.
-"""
-
-from leann.api import LeannSearcher
-
-def demonstrate_grep_search():
-    # Initialize searcher
-    searcher = LeannSearcher("my_index")
-
-    print("=== Function Search ===")
-    functions = searcher.search("def __init__", use_grep=True, top_k=5)
-    for i, result in enumerate(functions, 1):
-        print(f"{i}. Score: {result.score}")
-        print(f"   Preview: {result.text[:60]}...")
-        print()
-
-    print("=== Error Search ===")
-    errors = searcher.search("FileNotFoundError", use_grep=True, top_k=3)
-    for result in errors:
-        print(f"Content: {result.text.strip()}")
-        print("-" * 40)
-
-if __name__ == "__main__":
-    demonstrate_grep_search()
-```

examples/grep_search_example.py

@@ -1,35 +0,0 @@
-"""
-Grep Search Example
-
-Shows how to use grep-based text search instead of semantic search.
-Useful when you need exact text matches rather than meaning-based results.
-"""
-
-from leann import LeannSearcher
-
-# Load your index
-searcher = LeannSearcher("my-documents.leann")
-
-# Regular semantic search
-print("=== Semantic Search ===")
-results = searcher.search("machine learning algorithms", top_k=3)
-for result in results:
-    print(f"Score: {result.score:.3f}")
-    print(f"Text: {result.text[:80]}...")
-    print()
-
-# Grep-based search for exact text matches
-print("=== Grep Search ===")
-results = searcher.search("def train_model", top_k=3, use_grep=True)
-for result in results:
-    print(f"Score: {result.score}")
-    print(f"Text: {result.text[:80]}...")
-    print()
-
-# Find specific error messages
-error_results = searcher.search("FileNotFoundError", use_grep=True)
-print(f"Found {len(error_results)} files mentioning FileNotFoundError")
-
-# Search for function definitions
-func_results = searcher.search("class SearchResult", use_grep=True, top_k=5)
-print(f"Found {len(func_results)} class definitions")

llms.txt

@@ -1,28 +0,0 @@
-# llms.txt — LEANN MCP and Agent Integration
-product: LEANN
-homepage: https://github.com/yichuan-w/LEANN
-contact: https://github.com/yichuan-w/LEANN/issues
-
-# Installation
-install: uv tool install leann-core --with leann
-
-# MCP Server Entry Point
-mcp.server: leann_mcp
-mcp.protocol_version: 2024-11-05
-
-# Tools
-mcp.tools: leann_list, leann_search
-
-mcp.tool.leann_list.description: List available LEANN indexes
-mcp.tool.leann_list.input: {}
-
-mcp.tool.leann_search.description: Semantic search across a named LEANN index
-mcp.tool.leann_search.input.index_name: string, required
-mcp.tool.leann_search.input.query: string, required
-mcp.tool.leann_search.input.top_k: integer, optional, default=5, min=1, max=20
-mcp.tool.leann_search.input.complexity: integer, optional, default=32, min=16, max=128
-
-# Notes
-note: Build indexes with `leann build <name> --docs <files...>` before searching.
-example.add: claude mcp add --scope user leann-server -- leann_mcp
-example.verify: claude mcp list | cat

packages/leann-backend-diskann/pyproject.toml

@@ -4,8 +4,8 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-diskann"
-version = "0.3.4"
+version = "0.3.2"
-dependencies = ["leann-core==0.3.4", "numpy", "protobuf>=3.19.0"]
+dependencies = ["leann-core==0.3.2", "numpy", "protobuf>=3.19.0"]
 
 [tool.scikit-build]
 # Key: simplified CMake path

packages/leann-backend-hnsw/CMakeLists.txt

@@ -49,28 +49,9 @@ set(BUILD_TESTING OFF CACHE BOOL "" FORCE)
 set(FAISS_ENABLE_C_API OFF CACHE BOOL "" FORCE)
 set(FAISS_OPT_LEVEL "generic" CACHE STRING "" FORCE)
 
-# Disable x86-specific SIMD optimizations (important for ARM64 compatibility)
+# Disable additional SIMD versions to speed up compilation
 set(FAISS_ENABLE_AVX2 OFF CACHE BOOL "" FORCE)
 set(FAISS_ENABLE_AVX512 OFF CACHE BOOL "" FORCE)
-set(FAISS_ENABLE_SSE4_1 OFF CACHE BOOL "" FORCE)
-
-# ARM64-specific configuration
-if(CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64|arm64")
-    message(STATUS "Configuring Faiss for ARM64 architecture")
-
-    if(CMAKE_SYSTEM_NAME STREQUAL "Linux")
-        # Use SVE optimization level for ARM64 Linux (as seen in Faiss conda build)
-        set(FAISS_OPT_LEVEL "sve" CACHE STRING "" FORCE)
-        message(STATUS "Setting FAISS_OPT_LEVEL to 'sve' for ARM64 Linux")
-    else()
-        # Use generic optimization for other ARM64 platforms (like macOS)
-        set(FAISS_OPT_LEVEL "generic" CACHE STRING "" FORCE)
-        message(STATUS "Setting FAISS_OPT_LEVEL to 'generic' for ARM64 ${CMAKE_SYSTEM_NAME}")
-    endif()
-
-    # ARM64 compatibility: Faiss submodule has been modified to fix x86 header inclusion
-    message(STATUS "Using ARM64-compatible Faiss submodule")
-endif()
 
 # Additional optimization options from INSTALL.md
 set(CMAKE_BUILD_TYPE "Release" CACHE STRING "" FORCE)

packages/leann-backend-hnsw/pyproject.toml

@@ -6,10 +6,10 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-hnsw"
-version = "0.3.4"
+version = "0.3.2"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
 dependencies = [
-    "leann-core==0.3.4",
+    "leann-core==0.3.2",
     "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 = "0.3.4"
+version = "0.3.2"
 description = "Core API and plugin system for LEANN"
 readme = "README.md"
 requires-python = ">=3.9"

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

@@ -6,8 +6,6 @@ with the correct, original embedding logic from the user's reference code.
 import json
 import logging
 import pickle
-import re
-import subprocess
 import time
 import warnings
 from dataclasses import dataclass, field
@@ -655,7 +653,6 @@ class LeannSearcher:
         expected_zmq_port: int = 5557,
         metadata_filters: Optional[dict[str, dict[str, Union[str, int, float, bool, list]]]] = None,
         batch_size: int = 0,
-        use_grep: bool = False,
         **kwargs,
     ) -> list[SearchResult]:
         """
@@ -682,10 +679,6 @@ class LeannSearcher:
         Returns:
             List of SearchResult objects with text, metadata, and similarity scores
         """
-        # Handle grep search
-        if use_grep:
-            return self._grep_search(query, top_k)
-
         logger.info("🔍 LeannSearcher.search() called:")
         logger.info(f"  Query: '{query}'")
         logger.info(f"  Top_k: {top_k}")
@@ -802,96 +795,9 @@ class LeannSearcher:
         logger.info(f"  {GREEN}✓ Final enriched results: {len(enriched_results)} passages{RESET}")
         return enriched_results
 
-    def _find_jsonl_file(self) -> Optional[str]:
-        """Find the .jsonl file containing raw passages for grep search"""
-        index_path = Path(self.meta_path_str).parent
-        potential_files = [
-            index_path / "documents.leann.passages.jsonl",
-            index_path.parent / "documents.leann.passages.jsonl",
-        ]
-
-        for file_path in potential_files:
-            if file_path.exists():
-                return str(file_path)
-        return None
-
-    def _grep_search(self, query: str, top_k: int = 5) -> list[SearchResult]:
-        """Perform grep-based search on raw passages"""
-        jsonl_file = self._find_jsonl_file()
-        if not jsonl_file:
-            raise FileNotFoundError("No .jsonl passages file found for grep search")
-
-        try:
-            cmd = ["grep", "-i", "-n", query, jsonl_file]
-            result = subprocess.run(cmd, capture_output=True, text=True, check=False)
-
-            if result.returncode == 1:
-                return []
-            elif result.returncode != 0:
-                raise RuntimeError(f"Grep failed: {result.stderr}")
-
-            matches = []
-            for line in result.stdout.strip().split("\n"):
-                if not line:
-                    continue
-                parts = line.split(":", 1)
-                if len(parts) != 2:
-                    continue
-
-                try:
-                    data = json.loads(parts[1])
-                    text = data.get("text", "")
-                    score = text.lower().count(query.lower())
-
-                    matches.append(
-                        SearchResult(
-                            id=data.get("id", parts[0]),
-                            text=text,
-                            metadata=data.get("metadata", {}),
-                            score=float(score),
-                        )
-                    )
-                except json.JSONDecodeError:
-                    continue
-
-            matches.sort(key=lambda x: x.score, reverse=True)
-            return matches[:top_k]
-
-        except FileNotFoundError:
-            raise RuntimeError(
-                "grep command not found. Please install grep or use semantic search."
-            )
-
-    def _python_regex_search(self, query: str, top_k: int = 5) -> list[SearchResult]:
-        """Fallback regex search"""
-        jsonl_file = self._find_jsonl_file()
-        if not jsonl_file:
-            raise FileNotFoundError("No .jsonl file found")
-
-        pattern = re.compile(re.escape(query), re.IGNORECASE)
-        matches = []
-
-        with open(jsonl_file, encoding="utf-8") as f:
-            for line_num, line in enumerate(f, 1):
-                if pattern.search(line):
-                    try:
-                        data = json.loads(line.strip())
-                        matches.append(
-                            SearchResult(
-                                id=data.get("id", str(line_num)),
-                                text=data.get("text", ""),
-                                metadata=data.get("metadata", {}),
-                                score=float(len(pattern.findall(data.get("text", "")))),
-                            )
-                        )
-                    except json.JSONDecodeError:
-                        continue
-
-        matches.sort(key=lambda x: x.score, reverse=True)
-        return matches[:top_k]
-
     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.
         """
@@ -947,7 +853,6 @@ class LeannChat:
         expected_zmq_port: int = 5557,
         metadata_filters: Optional[dict[str, dict[str, Union[str, int, float, bool, list]]]] = None,
         batch_size: int = 0,
-        use_grep: bool = False,
         **search_kwargs,
     ):
         if llm_kwargs is None:

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

@@ -1,5 +1,6 @@
 import argparse
 import asyncio
+import sys
 from pathlib import Path
 from typing import Any, Optional, Union
 
@@ -1215,8 +1216,13 @@ Examples:
         if use_ast:
             print("🧠 Using AST-aware chunking for code files")
             try:
-                # Import enhanced chunking utilities from packaged module
+                # Import enhanced chunking utilities
-                from .chunking_utils import create_text_chunks
+                # Add apps directory to path to import chunking utilities
+                apps_dir = Path(__file__).parent.parent.parent.parent.parent / "apps"
+                if apps_dir.exists():
+                    sys.path.insert(0, str(apps_dir))
+
+                from chunking import create_text_chunks
 
                 # Use enhanced chunking with AST support
                 all_texts = create_text_chunks(
@@ -1231,9 +1237,7 @@ Examples:
                 )
 
             except ImportError as e:
-                print(
+                print(f"⚠️  AST chunking not available ({e}), falling back to traditional chunking")
-                    f"⚠️  AST chunking utilities not available in package ({e}), falling back to traditional chunking"
-                )
                 use_ast = False
 
         if not use_ast:

packages/leann-mcp/README.md

@@ -2,8 +2,6 @@
 
 Transform your development workflow with intelligent code assistance using LEANN's semantic search directly in Claude Code.
 
-For agent-facing discovery details, see `llms.txt` in the repository root.
-
 ## Prerequisites
 
 Install LEANN globally for MCP integration (with default backend):

packages/leann/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann"
-version = "0.3.4"
+version = "0.3.2"
 description = "LEANN - The smallest vector index in the world. RAG Everything with LEANN!"
 readme = "README.md"
 requires-python = ">=3.9"

pyproject.toml

@@ -99,7 +99,6 @@ wechat-exporter = "wechat_exporter.main:main"
 leann-core = { path = "packages/leann-core", editable = true }
 leann-backend-diskann = { path = "packages/leann-backend-diskann", editable = true }
 leann-backend-hnsw = { path = "packages/leann-backend-hnsw", editable = true }
-astchunk = { path = "packages/astchunk-leann", editable = true }
 
 [tool.ruff]
 target-version = "py39"

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.9"
 resolution-markers = [
     "python_full_version >= '3.12'",
@@ -201,7 +201,7 @@ wheels = [
 [[package]]
 name = "astchunk"
 version = "0.1.0"
-source = { editable = "packages/astchunk-leann" }
+source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
@@ -214,31 +214,10 @@ dependencies = [
     { name = "tree-sitter-python" },
     { name = "tree-sitter-typescript" },
 ]
-
+sdist = { url = "https://files.pythonhosted.org/packages/db/2a/7a35e2fac7d550265ae2ee40651425083b37555f921d1a1b77c3f525e0df/astchunk-0.1.0.tar.gz", hash = "sha256:f4dff0ef8b3b3bcfeac363384db1e153f74d4c825dc2e35864abfab027713be4", size = 18093, upload-time = "2025-06-19T04:37:25.34Z" }
-[package.metadata]
+wheels = [
-requires-dist = [
+    { url = "https://files.pythonhosted.org/packages/be/84/5433ab0e933b572750cb16fd7edf3d6c7902b069461a22ec670042752a4d/astchunk-0.1.0-py3-none-any.whl", hash = "sha256:33ada9fc3620807fdda5846fa1948af463f281a60e0d43d4f3782b6dbb416d24", size = 15396, upload-time = "2025-06-19T04:37:23.87Z" },
-    { name = "black", marker = "extra == 'dev'", specifier = ">=22.0.0" },
-    { name = "flake8", marker = "extra == 'dev'", specifier = ">=5.0.0" },
-    { name = "isort", marker = "extra == 'dev'", specifier = ">=5.10.0" },
-    { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.0.0" },
-    { name = "myst-parser", marker = "extra == 'docs'", specifier = ">=0.18.0" },
-    { name = "numpy", specifier = ">=1.20.0" },
-    { name = "pre-commit", marker = "extra == 'dev'", specifier = ">=2.20.0" },
-    { name = "pyrsistent", specifier = ">=0.18.0" },
-    { name = "pytest", marker = "extra == 'dev'", specifier = ">=7.0.0" },
-    { name = "pytest", marker = "extra == 'test'", specifier = ">=7.0.0" },
-    { name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=4.0.0" },
-    { name = "pytest-cov", marker = "extra == 'test'", specifier = ">=4.0.0" },
-    { name = "pytest-xdist", marker = "extra == 'test'", specifier = ">=2.5.0" },
-    { name = "sphinx", marker = "extra == 'docs'", specifier = ">=5.0.0" },
-    { name = "sphinx-rtd-theme", marker = "extra == 'docs'", specifier = ">=1.0.0" },
-    { name = "tree-sitter", specifier = ">=0.20.0" },
-    { name = "tree-sitter-c-sharp", specifier = ">=0.20.0" },
-    { name = "tree-sitter-java", specifier = ">=0.20.0" },
-    { name = "tree-sitter-python", specifier = ">=0.20.0" },
-    { name = "tree-sitter-typescript", specifier = ">=0.20.0" },
 ]
-provides-extras = ["dev", "docs", "test"]
 
 [[package]]
 name = "asttokens"
@@ -1585,7 +1564,7 @@ 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, upload-time = "2025-04-27T15:29:01.736Z" }
 wheels = [
@@ -2138,7 +2117,7 @@ wheels = [
 
 [[package]]
 name = "leann-backend-diskann"
-version = "0.3.3"
+version = "0.3.2"
 source = { editable = "packages/leann-backend-diskann" }
 dependencies = [
     { name = "leann-core" },
@@ -2150,14 +2129,14 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "leann-core", specifier = "==0.3.3" },
+    { name = "leann-core", specifier = "==0.3.2" },
     { name = "numpy" },
     { name = "protobuf", specifier = ">=3.19.0" },
 ]
 
 [[package]]
 name = "leann-backend-hnsw"
-version = "0.3.3"
+version = "0.3.2"
 source = { editable = "packages/leann-backend-hnsw" }
 dependencies = [
     { name = "leann-core" },
@@ -2170,7 +2149,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "leann-core", specifier = "==0.3.3" },
+    { name = "leann-core", specifier = "==0.3.2" },
     { name = "msgpack", specifier = ">=1.0.0" },
     { name = "numpy" },
     { name = "pyzmq", specifier = ">=23.0.0" },
@@ -2178,7 +2157,7 @@ requires-dist = [
 
 [[package]]
 name = "leann-core"
-version = "0.3.3"
+version = "0.3.2"
 source = { editable = "packages/leann-core" }
 dependencies = [
     { name = "accelerate" },
@@ -2318,7 +2297,7 @@ test = [
 
 [package.metadata]
 requires-dist = [
-    { name = "astchunk", editable = "packages/astchunk-leann" },
+    { name = "astchunk", specifier = ">=0.1.0" },
     { name = "beautifulsoup4", marker = "extra == 'documents'", specifier = ">=4.13.0" },
     { name = "black", marker = "extra == 'dev'", specifier = ">=23.0" },
     { name = "boto3" },