Merge branch 'main' into dynamic-add

* feat: Add GitHub PR and issue templates for better contributor experience

* simplify: Make templates more concise and user-friendly

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,50 @@
+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

@@ -0,0 +1,8 @@
+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

@@ -0,0 +1,27 @@
+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

@@ -0,0 +1,13 @@
+## 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`)

.gitignore

@@ -22,6 +22,7 @@ demo/experiment_results/**/*.json
 *.sh
 *.txt
 !CMakeLists.txt
+!llms.txt
 latency_breakdown*.json
 experiment_results/eval_results/diskann/*.json
 aws/
@@ -100,3 +101,4 @@ CLAUDE.local.md
 .claude/*.local.*
 .claude/local/*
 benchmarks/data/
+test_add/*

.gitmodules

@@ -14,3 +14,6 @@
 [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 = https://github.com/yichuan-w/astchunk-leann.git

README.md

@@ -656,6 +656,19 @@ 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

@@ -1,16 +1,38 @@
-"""
-Chunking utilities for LEANN RAG applications.
-Provides AST-aware and traditional text chunking functionality.
+"""Unified chunking utilities facade.
+
+This module re-exports the packaged utilities from `leann.chunking_utils` so
+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.
 """
 
-from .utils import (
-    CODE_EXTENSIONS,
-    create_ast_chunks,
-    create_text_chunks,
-    create_traditional_chunks,
-    detect_code_files,
-    get_language_from_extension,
-)
+import sys
+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/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

@@ -26,6 +26,21 @@ 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

@@ -0,0 +1,149 @@
+# 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/dynamic_add_leann_no_recompute.py

@@ -0,0 +1,380 @@
+"""
+Dynamic add example for LEANN using HNSW backend without recompute.
+
+- Builds a base index from a directory of documents
+- Incrementally adds new documents without recomputing stored embeddings
+
+Defaults:
+- Base data: /Users/yichuan/Desktop/code/LEANN/leann/data
+- Incremental data: /Users/yichuan/Desktop/code/LEANN/leann/test_add
+- Index path: <index_dir>/documents.leann
+
+Usage examples:
+  uv run python examples/dynamic_add_leann_no_recompute.py --build-base \
+    --base-dir /Users/yichuan/Desktop/code/LEANN/leann/data \
+    --index-dir ./test_doc_files
+
+  uv run python examples/dynamic_add_leann_no_recompute.py --add-incremental \
+    --add-dir /Users/yichuan/Desktop/code/LEANN/leann/test_add \
+    --index-dir ./test_doc_files
+
+Quick recompute test (both true):
+  # Recompute build
+  uv run python examples/dynamic_add_leann_no_recompute.py --build-base \
+    --recompute-build --ef-construction 200 \
+    --base-dir /Users/yichuan/Desktop/code/LEANN/leann/data \
+    --index-dir ./test_doc_files --index-name documents.leann
+
+  # Recompute add
+  uv run python examples/dynamic_add_leann_no_recompute.py --add-incremental \
+    --recompute-add --ef-construction 32 \
+    --add-dir /Users/yichuan/Desktop/code/LEANN/leann/test_add \
+    --index-dir ./test_doc_files --index-name documents.leann
+"""
+
+import argparse
+import json
+import pickle
+import sys
+from pathlib import Path
+from typing import Any, Optional
+
+# Ensure we can import from the local packages and apps folders
+ROOT = Path(__file__).resolve().parents[1]
+CORE_SRC = ROOT / "packages" / "leann-core" / "src"
+HNSW_PKG_DIR = ROOT / "packages" / "leann-backend-hnsw"
+APPS_DIR = ROOT / "apps"
+
+
+# Prefer the installed backend if available (it contains the compiled extension)
+def _prefer_installed(pkg_name: str) -> bool:
+    try:
+        import importlib
+        import importlib.util
+
+        spec = importlib.util.find_spec(pkg_name)
+        if spec and spec.origin and "site-packages" in spec.origin:
+            # ensure the faiss shim/extension is importable from the installed package
+            importlib.import_module(f"{pkg_name}.faiss")
+            return True
+    except Exception:
+        pass
+    return False
+
+
+# Prepend paths, but only add the repo backend if the installed one is not present
+paths_to_prepend = [CORE_SRC, APPS_DIR]
+if not _prefer_installed("leann_backend_hnsw"):
+    paths_to_prepend.insert(1, HNSW_PKG_DIR)
+
+for p in paths_to_prepend:
+    p_str = str(p)
+    if p_str not in sys.path:
+        sys.path.insert(0, p_str)
+
+# Defer non-stdlib imports until after sys.path setup within functions (avoid E402)
+
+
+def _load_documents(data_dir: str, required_exts: Optional[list[str]] = None) -> list[Any]:
+    from llama_index.core import SimpleDirectoryReader  # type: ignore
+
+    reader_kwargs: dict[str, Any] = {"recursive": True, "encoding": "utf-8"}
+    if required_exts:
+        reader_kwargs["required_exts"] = required_exts
+    documents = SimpleDirectoryReader(data_dir, **reader_kwargs).load_data(show_progress=True)
+    return documents
+
+
+def _ensure_index_dir(index_dir: Path) -> None:
+    index_dir.mkdir(parents=True, exist_ok=True)
+
+
+def _index_files(index_path: Path) -> tuple[Path, Path, Path]:
+    """Return (passages.jsonl, passages.idx, index.index) paths for a given index base path.
+
+    Note: HNSWBackend writes the FAISS index using the stem (without .leann),
+    i.e., for base 'documents.leann' the file is 'documents.index'. We prefer the
+    existing file among candidates.
+    """
+    passages_file = index_path.parent / f"{index_path.name}.passages.jsonl"
+    offsets_file = index_path.parent / f"{index_path.name}.passages.idx"
+    candidate_name_index = index_path.parent / f"{index_path.name}.index"
+    candidate_stem_index = index_path.parent / f"{index_path.stem}.index"
+    index_file = candidate_stem_index if candidate_stem_index.exists() else candidate_name_index
+    return passages_file, offsets_file, index_file
+
+
+def _read_meta(index_path: Path) -> dict[str, Any]:
+    meta_path = index_path.parent / f"{index_path.name}.meta.json"
+    if not meta_path.exists():
+        raise FileNotFoundError(f"Metadata file not found: {meta_path}")
+    with open(meta_path, encoding="utf-8") as f:
+        return json.load(f)
+
+
+def _autodetect_index_base(index_dir: Path) -> Optional[Path]:
+    """If exactly one *.leann.meta.json exists, return its base path (without .meta.json)."""
+    candidates = list(index_dir.glob("*.leann.meta.json"))
+    if len(candidates) == 1:
+        meta = candidates[0]
+        base = meta.with_suffix("")  # remove .json
+        base = base.with_suffix("")  # remove .meta
+        return base
+    return None
+
+
+def _load_offset_map(offsets_file: Path) -> dict[str, int]:
+    if not offsets_file.exists():
+        return {}
+    with open(offsets_file, "rb") as f:
+        return pickle.load(f)
+
+
+def _next_numeric_id(existing_ids: list[str]) -> int:
+    numeric_ids = [int(x) for x in existing_ids if x.isdigit()]
+    if not numeric_ids:
+        return 0
+    return max(numeric_ids) + 1
+
+
+def build_base_index(
+    base_dir: str,
+    index_dir: str,
+    index_name: str,
+    embedding_model: str,
+    embedding_mode: str,
+    chunk_size: int,
+    chunk_overlap: int,
+    file_types: Optional[list[str]] = None,
+    max_items: int = -1,
+    ef_construction: Optional[int] = None,
+    recompute_build: bool = False,
+) -> str:
+    print(f"Building base index from: {base_dir}")
+    documents = _load_documents(base_dir, required_exts=file_types)
+    if not documents:
+        raise ValueError(f"No documents found in base_dir: {base_dir}")
+
+    from chunking import create_text_chunks
+
+    texts = create_text_chunks(
+        documents,
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        use_ast_chunking=False,
+    )
+    if max_items > 0 and len(texts) > max_items:
+        texts = texts[:max_items]
+        print(f"Limiting to {max_items} chunks")
+
+    index_dir_path = Path(index_dir)
+    _ensure_index_dir(index_dir_path)
+    index_path = index_dir_path / index_name
+
+    print("Creating HNSW index (non-compact)...")
+    from leann.api import LeannBuilder
+    from leann.registry import register_project_directory
+
+    builder = LeannBuilder(
+        backend_name="hnsw",
+        embedding_model=embedding_model,
+        embedding_mode=embedding_mode,
+        is_recompute=recompute_build,
+        is_compact=False,
+        efConstruction=(ef_construction if ef_construction is not None else 200),
+    )
+    for t in texts:
+        builder.add_text(t)
+    builder.build_index(str(index_path))
+
+    # Register for discovery
+    register_project_directory(Path.cwd())
+
+    print(f"Base index built at: {index_path}")
+    return str(index_path)
+
+
+def add_incremental(
+    add_dir: str,
+    index_dir: str,
+    index_name: Optional[str] = None,
+    embedding_model: Optional[str] = None,
+    embedding_mode: Optional[str] = None,
+    chunk_size: int = 256,
+    chunk_overlap: int = 128,
+    file_types: Optional[list[str]] = None,
+    max_items: int = -1,
+    ef_construction: Optional[int] = None,
+    recompute_add: bool = False,
+) -> str:
+    print(f"Adding incremental data from: {add_dir}")
+    index_dir_path = Path(index_dir)
+    index_path = index_dir_path / (index_name or "documents.leann")
+
+    # If specified base doesn't exist, try to auto-detect an existing base
+    try:
+        _read_meta(index_path)
+    except FileNotFoundError:
+        auto_base = _autodetect_index_base(index_dir_path)
+        if auto_base is not None:
+            print(f"Auto-detected index base: {auto_base.name}")
+            index_path = auto_base
+            _read_meta(index_path)
+        else:
+            raise FileNotFoundError(
+                f"No index metadata found for base '{index_path.name}'. Build base first with --build-base "
+                f"or provide --index-name to match an existing index (e.g., 'test_doc_files.leann')."
+            )
+
+    # Prepare validated context from core (checks backend/no-recompute and resolves embedding defaults)
+    from leann.api import create_incremental_add_context, incremental_add_texts_with_context
+
+    ctx = create_incremental_add_context(
+        str(index_path),
+        embedding_model=embedding_model,
+        embedding_mode=embedding_mode,
+        data_dir=add_dir,
+        required_exts=file_types,
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        max_items=max_items,
+    )
+
+    # Use prepared texts from context to perform the add
+    prepared_texts = ctx.prepared_texts or []
+    if not prepared_texts:
+        print("No new chunks to add.")
+        return str(index_path)
+
+    added = incremental_add_texts_with_context(
+        ctx,
+        prepared_texts,
+        ef_construction=ef_construction,
+        recompute=recompute_add,
+    )
+
+    print(f"Incremental add completed. Added {added} chunks. Index: {index_path}")
+    return str(index_path)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Dynamic add to LEANN HNSW index without recompute",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+
+    parser.add_argument("--build-base", action="store_true", help="Build base index")
+    parser.add_argument("--add-incremental", action="store_true", help="Add incremental data")
+
+    parser.add_argument(
+        "--base-dir",
+        type=str,
+        default="/Users/yichuan/Desktop/code/LEANN/leann/data",
+        help="Base data directory",
+    )
+    parser.add_argument(
+        "--add-dir",
+        type=str,
+        default="/Users/yichuan/Desktop/code/LEANN/leann/test_add",
+        help="Incremental data directory",
+    )
+    parser.add_argument(
+        "--index-dir",
+        type=str,
+        default="./test_doc_files",
+        help="Directory containing the index",
+    )
+    parser.add_argument(
+        "--index-name",
+        type=str,
+        default="documents.leann",
+        help=(
+            "Index base file name. If you built via document_rag.py, use 'test_doc_files.leann'. "
+            "Default: documents.leann"
+        ),
+    )
+
+    parser.add_argument(
+        "--embedding-model",
+        type=str,
+        default="facebook/contriever",
+        help="Embedding model name",
+    )
+    parser.add_argument(
+        "--embedding-mode",
+        type=str,
+        default="sentence-transformers",
+        choices=["sentence-transformers", "openai", "mlx", "ollama"],
+        help="Embedding backend mode",
+    )
+
+    parser.add_argument("--chunk-size", type=int, default=256)
+    parser.add_argument("--chunk-overlap", type=int, default=128)
+    parser.add_argument("--file-types", nargs="+", default=None)
+    parser.add_argument("--max-items", type=int, default=-1)
+    parser.add_argument("--ef-construction", type=int, default=32)
+    parser.add_argument(
+        "--recompute-add", action="store_true", help="Enable recompute-mode add (non-compact only)"
+    )
+    parser.add_argument(
+        "--recompute-build",
+        action="store_true",
+        help="Enable recompute-mode base build (non-compact only)",
+    )
+
+    args = parser.parse_args()
+
+    if not args.build_base and not args.add_incremental:
+        print("Nothing to do. Use --build-base and/or --add-incremental.")
+        return
+
+    index_path_str: Optional[str] = None
+
+    if args.build_base:
+        index_path_str = build_base_index(
+            base_dir=args.base_dir,
+            index_dir=args.index_dir,
+            index_name=args.index_name,
+            embedding_model=args.embedding_model,
+            embedding_mode=args.embedding_mode,
+            chunk_size=args.chunk_size,
+            chunk_overlap=args.chunk_overlap,
+            file_types=args.file_types,
+            max_items=args.max_items,
+            ef_construction=args.ef_construction,
+            recompute_build=args.recompute_build,
+        )
+
+    if args.add_incremental:
+        index_path_str = add_incremental(
+            add_dir=args.add_dir,
+            index_dir=args.index_dir,
+            index_name=args.index_name,
+            embedding_model=args.embedding_model,
+            embedding_mode=args.embedding_mode,
+            chunk_size=args.chunk_size,
+            chunk_overlap=args.chunk_overlap,
+            file_types=args.file_types,
+            max_items=args.max_items,
+            ef_construction=args.ef_construction,
+            recompute_add=args.recompute_add,
+        )
+
+    # Optional: quick test query using searcher
+    if index_path_str:
+        try:
+            from leann.api import LeannSearcher
+
+            searcher = LeannSearcher(index_path_str)
+            query = "what is LEANN?"
+            if args.add_incremental:
+                query = "what is the multi vector search and how it works?"
+            results = searcher.search(query, top_k=5)
+            if results:
+                print(f"Sample result: {results[0].text[:80]}...")
+        except Exception:
+            pass
+
+
+if __name__ == "__main__":
+    main()

examples/grep_search_example.py

@@ -0,0 +1,35 @@
+"""
+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

@@ -0,0 +1,28 @@
+# 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 = "0.3.2"
-dependencies = ["leann-core==0.3.2", "numpy", "protobuf>=3.19.0"]
+version = "0.3.4"
+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_backend.py

@@ -15,6 +15,7 @@ from leann.registry import register_backend
 from leann.searcher_base import BaseSearcher
 
 from .convert_to_csr import convert_hnsw_graph_to_csr
+from .prune_index import prune_embeddings_preserve_graph_inplace
 
 logger = logging.getLogger(__name__)
 
@@ -90,8 +91,16 @@ class HNSWBuilder(LeannBackendBuilderInterface):
         index_file = index_dir / f"{index_prefix}.index"
         faiss.write_index(index, str(index_file))
 
-        if self.is_compact:
-            self._convert_to_csr(index_file)
+        if self.is_recompute:
+            if self.is_compact:
+                self._convert_to_csr(index_file)
+            else:
+                # Non-compact format: prune only embeddings, keep original graph
+                ok = prune_embeddings_preserve_graph_inplace(str(index_file))
+                if not ok:
+                    raise RuntimeError(
+                        "Pruning embeddings while preserving graph failed for non-compact index"
+                    )
 
     def _convert_to_csr(self, index_file: Path):
         """Convert built index to CSR format"""
@@ -148,7 +157,13 @@ class HNSWSearcher(BaseSearcher):
             self.is_pruned
         )  # In C++ code, it's called is_recompute, but it's only for loading IIUC.
 
-        self._index = faiss.read_index(str(index_file), faiss.IO_FLAG_MMAP, hnsw_config)
+        # If pruned (recompute mode), explicitly skip storage to avoid reading
+        # the pruned section. Still allow MMAP for graph.
+        io_flags = faiss.IO_FLAG_MMAP
+        if self.is_pruned:
+            io_flags |= faiss.IO_FLAG_SKIP_STORAGE
+
+        self._index = faiss.read_index(str(index_file), io_flags, hnsw_config)
 
     def search(
         self,
@@ -251,3 +266,55 @@ class HNSWSearcher(BaseSearcher):
         string_labels = [[str(int_label) for int_label in batch_labels] for batch_labels in labels]
 
         return {"labels": string_labels, "distances": distances}
+
+
+# ---------- Helper API for incremental add (Python-level) ----------
+def add_vectors(
+    index_file_path: str,
+    embeddings: np.ndarray,
+    *,
+    ef_construction: Optional[int] = None,
+    recompute: bool = False,
+) -> None:
+    """Append vectors to an existing non-compact HNSW index.
+
+    Args:
+        index_file_path: Path to the HNSW .index file
+        embeddings: float32 numpy array (N, D)
+        ef_construction: Optional override for efConstruction during insertion
+        recompute: Reserved for future use to control insertion-time recompute behaviors
+    """
+    from . import faiss  # type: ignore
+
+    if embeddings.dtype != np.float32:
+        embeddings = embeddings.astype(np.float32)
+    if not embeddings.flags.c_contiguous:
+        embeddings = np.ascontiguousarray(embeddings, dtype=np.float32)
+
+    # Load index normally to ensure storage is present; toggle is_recompute on the object
+    index = faiss.read_index(str(index_file_path), faiss.IO_FLAG_MMAP)
+
+    # Best-effort: explicitly set flag on the object if the binding exposes it
+    try:
+        index.is_recompute = bool(recompute)
+    except Exception:
+        pass
+    try:
+        if ef_construction is not None:
+            index.hnsw.efConstruction = int(ef_construction)
+    except Exception:
+        # Best-effort; ignore if backend doesn't expose setter
+        pass
+
+    # For non-compact HNSW, calling add directly is sufficient. When is_recompute is set
+    # (via config or attribute), FAISS will run the insertion/search path accordingly.
+    # To strictly follow per-point insert semantics in recompute mode, add one-by-one.
+    if recompute:
+        # Insert row by row
+        n = embeddings.shape[0]
+        for i in range(n):
+            row = embeddings[i : i + 1]
+            index.add(1, faiss.swig_ptr(row))
+    else:
+        index.add(embeddings.shape[0], faiss.swig_ptr(embeddings))
+    faiss.write_index(index, str(index_file_path))

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

@@ -0,0 +1,149 @@
+import os
+import struct
+from pathlib import Path
+
+from .convert_to_csr import (
+    EXPECTED_HNSW_FOURCCS,
+    NULL_INDEX_FOURCC,
+    read_struct,
+    read_vector_raw,
+)
+
+
+def _write_vector_raw(f_out, count: int, data_bytes: bytes) -> None:
+    """Write a vector in the same binary layout as read_vector_raw reads: <Q count> + raw bytes."""
+    f_out.write(struct.pack("<Q", count))
+    if count > 0 and data_bytes:
+        f_out.write(data_bytes)
+
+
+def prune_embeddings_preserve_graph(input_filename: str, output_filename: str) -> bool:
+    """
+    Copy an original (non-compact) HNSW index file while pruning the trailing embedding storage.
+    Preserves the graph structure and metadata exactly; only writes a NULL storage marker instead of
+    the original storage fourcc and payload.
+
+    Returns True on success.
+    """
+    print(f"Pruning embeddings from {input_filename} to {output_filename}")
+    print("--------------------------------")
+    # running in mode is-recompute=True and is-compact=False
+    in_path = Path(input_filename)
+    out_path = Path(output_filename)
+
+    try:
+        with open(in_path, "rb") as f_in, open(out_path, "wb") as f_out:
+            # Header
+            index_fourcc = read_struct(f_in, "<I")
+            if index_fourcc not in EXPECTED_HNSW_FOURCCS:
+                # Still proceed, but this is unexpected
+                pass
+            f_out.write(struct.pack("<I", index_fourcc))
+
+            d = read_struct(f_in, "<i")
+            ntotal_hdr = read_struct(f_in, "<q")
+            dummy1 = read_struct(f_in, "<q")
+            dummy2 = read_struct(f_in, "<q")
+            is_trained = read_struct(f_in, "?")
+            metric_type = read_struct(f_in, "<i")
+            f_out.write(struct.pack("<i", d))
+            f_out.write(struct.pack("<q", ntotal_hdr))
+            f_out.write(struct.pack("<q", dummy1))
+            f_out.write(struct.pack("<q", dummy2))
+            f_out.write(struct.pack("<?", is_trained))
+            f_out.write(struct.pack("<i", metric_type))
+
+            if metric_type > 1:
+                metric_arg = read_struct(f_in, "<f")
+                f_out.write(struct.pack("<f", metric_arg))
+
+            # Vectors: assign_probas (double), cum_nneighbor_per_level (int32), levels (int32)
+            cnt, data = read_vector_raw(f_in, "d")
+            _write_vector_raw(f_out, cnt, data)
+
+            cnt, data = read_vector_raw(f_in, "i")
+            _write_vector_raw(f_out, cnt, data)
+
+            cnt, data = read_vector_raw(f_in, "i")
+            _write_vector_raw(f_out, cnt, data)
+
+            # Probe potential extra alignment/flag byte present in some original formats
+            probe = f_in.read(1)
+            if probe:
+                if probe == b"\x00":
+                    # Preserve this unexpected 0x00 byte
+                    f_out.write(probe)
+                else:
+                    # Likely part of the next vector; rewind
+                    f_in.seek(-1, os.SEEK_CUR)
+
+            # Offsets (uint64) and neighbors (int32)
+            cnt, data = read_vector_raw(f_in, "Q")
+            _write_vector_raw(f_out, cnt, data)
+
+            cnt, data = read_vector_raw(f_in, "i")
+            _write_vector_raw(f_out, cnt, data)
+
+            # Scalar params
+            entry_point = read_struct(f_in, "<i")
+            max_level = read_struct(f_in, "<i")
+            ef_construction = read_struct(f_in, "<i")
+            ef_search = read_struct(f_in, "<i")
+            dummy_upper_beam = read_struct(f_in, "<i")
+            f_out.write(struct.pack("<i", entry_point))
+            f_out.write(struct.pack("<i", max_level))
+            f_out.write(struct.pack("<i", ef_construction))
+            f_out.write(struct.pack("<i", ef_search))
+            f_out.write(struct.pack("<i", dummy_upper_beam))
+
+            # Storage fourcc (if present) — write NULL marker and drop any remaining data
+            try:
+                read_struct(f_in, "<I")
+                # Regardless of original, write NULL
+                f_out.write(struct.pack("<I", NULL_INDEX_FOURCC))
+                # Discard the rest of the file (embedding payload)
+                # (Do not copy anything else)
+            except EOFError:
+                # No storage section; nothing else to write
+                pass
+
+        return True
+    except Exception:
+        # Best-effort cleanup
+        try:
+            if out_path.exists():
+                out_path.unlink()
+        except OSError:
+            pass
+        return False
+
+
+def prune_embeddings_preserve_graph_inplace(index_file_path: str) -> bool:
+    """
+    Convenience wrapper: write pruned file to a temporary path next to the
+    original, then atomically replace on success.
+    """
+    print(f"Pruning embeddings from {index_file_path} to {index_file_path}")
+    print("--------------------------------")
+    # running in mode is-recompute=True and is-compact=False
+    src = Path(index_file_path)
+    tmp = src.with_suffix(".pruned.tmp")
+    ok = prune_embeddings_preserve_graph(str(src), str(tmp))
+    if not ok:
+        if tmp.exists():
+            try:
+                tmp.unlink()
+            except OSError:
+                pass
+        return False
+    try:
+        os.replace(str(tmp), str(src))
+    except Exception:
+        # Rollback on failure
+        try:
+            if tmp.exists():
+                tmp.unlink()
+        except OSError:
+            pass
+        return False
+    return True

packages/leann-backend-hnsw/pyproject.toml

@@ -6,10 +6,10 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-hnsw"
-version = "0.3.2"
+version = "0.3.4"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
 dependencies = [
-    "leann-core==0.3.2",
+    "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 = "0.3.2"
+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

@@ -5,7 +5,10 @@ with the correct, original embedding logic from the user's reference code.
 
 import json
 import logging
+import os
 import pickle
+import re
+import subprocess
 import time
 import warnings
 from dataclasses import dataclass, field
@@ -17,6 +20,7 @@ import numpy as np
 from leann.interface import LeannBackendSearcherInterface
 
 from .chat import get_llm
+from .embedding_server_manager import EmbeddingServerManager
 from .interface import LeannBackendFactoryInterface
 from .metadata_filter import MetadataFilterEngine
 from .registry import BACKEND_REGISTRY
@@ -116,6 +120,20 @@ class SearchResult:
     metadata: dict[str, Any] = field(default_factory=dict)
 
 
+@dataclass
+class IncrementalAddContext:
+    """Prepared context for safe incremental add operations on an index."""
+
+    index_path: str
+    passages_file: Path
+    offsets_file: Path
+    vector_index_file: Path
+    embedding_model: str
+    embedding_mode: str
+    distance_metric: str
+    prepared_texts: Optional[list[str]] = None
+
+
 class PassageManager:
     def __init__(
         self, passage_sources: list[dict[str, Any]], metadata_file_path: Optional[str] = None
@@ -474,9 +492,7 @@ class LeannBuilder:
             is_compact = self.backend_kwargs.get("is_compact", True)
             is_recompute = self.backend_kwargs.get("is_recompute", True)
             meta_data["is_compact"] = is_compact
-            meta_data["is_pruned"] = (
-                is_compact and is_recompute
-            )  # Pruned only if compact and recompute
+            meta_data["is_pruned"] = is_recompute  # Pruned only if compact and recompute
         with open(leann_meta_path, "w", encoding="utf-8") as f:
             json.dump(meta_data, f, indent=2)
 
@@ -653,6 +669,7 @@ 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]:
         """
@@ -679,6 +696,10 @@ 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}")
@@ -795,9 +816,96 @@ 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.
         """
@@ -853,6 +961,7 @@ 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:
@@ -923,8 +1032,405 @@ class LeannChat:
         except Exception:
             pass
 
-    def __del__(self):
+
+# ------------------------------
+# Incremental Add Utilities (HNSW no-recompute only)
+# ------------------------------
+
+
+def _resolve_index_paths(index_path: str) -> tuple[Path, Path, Path]:
+    """Given base index path (without extension), return (passages.jsonl, passages.idx, vector.index).
+
+    For HNSW, vector index file is typically <stem>.index (e.g., documents.index) even when base is
+    'documents.leann'. We prefer an existing <stem>.index, otherwise fall back to <name>.index.
+    """
+    base = Path(index_path)
+    passages_file = base.parent / f"{base.name}.passages.jsonl"
+    offsets_file = base.parent / f"{base.name}.passages.idx"
+    candidate_name_index = base.parent / f"{base.name}.index"
+    candidate_stem_index = base.parent / f"{base.stem}.index"
+    vector_index_file = (
+        candidate_stem_index if candidate_stem_index.exists() else candidate_name_index
+    )
+    return passages_file, offsets_file, vector_index_file
+
+
+def _read_meta_file(index_path: str) -> dict[str, Any]:
+    meta_path = Path(f"{index_path}.meta.json")
+    if not meta_path.exists():
+        raise FileNotFoundError(f"Leann metadata file not found: {meta_path}")
+    with open(meta_path, encoding="utf-8") as f:
+        return json.load(f)
+
+
+def _load_offset_map_pickle(offsets_file: Path) -> dict[str, int]:
+    if not offsets_file.exists():
+        return {}
+    with open(offsets_file, "rb") as f:
+        return pickle.load(f)
+
+
+def _append_passages_and_update_offsets(
+    passages_file: Path, offsets_file: Path, new_texts: list[str]
+) -> list[str]:
+    """Append new texts to passages file, update offset map, and return assigned string IDs.
+
+    IDs are assigned as incrementing integers based on existing keys in the offset map.
+    """
+    offset_map = _load_offset_map_pickle(offsets_file)
+    # Compute next numeric id
+    numeric_ids = [int(x) for x in offset_map.keys() if str(x).isdigit()]
+    next_id_num = (max(numeric_ids) + 1) if numeric_ids else 0
+    assigned_ids: list[str] = []
+
+    with open(passages_file, "a", encoding="utf-8") as f:
+        for text in new_texts:
+            offset = f.tell()
+            str_id = str(next_id_num)
+            json.dump({"id": str_id, "text": text, "metadata": {}}, f, ensure_ascii=False)
+            f.write("\n")
+            offset_map[str_id] = offset
+            assigned_ids.append(str_id)
+            next_id_num += 1
+
+    with open(offsets_file, "wb") as f:
+        pickle.dump(offset_map, f)
+
+    return assigned_ids
+
+
+def incremental_add_texts(
+    index_path: str,
+    texts: list[str],
+    *,
+    embedding_model: Optional[str] = None,
+    embedding_mode: Optional[str] = None,
+    ef_construction: Optional[int] = None,
+    recompute: bool = False,
+) -> int:
+    """Incrementally add text chunks to an existing HNSW index built with no-recompute.
+
+    - Validates backend is HNSW and index is non-compact (no-recompute path)
+    - Appends passages and offsets
+    - Computes embeddings and appends to the HNSW vector index
+
+    Returns number of added chunks.
+    """
+    if not texts:
+        return 0
+
+    meta = _read_meta_file(index_path)
+    if meta.get("backend_name") != "hnsw":
+        raise RuntimeError("Incremental add is currently supported only for HNSW backend")
+    if meta.get("is_compact", True):
+        raise RuntimeError(
+            "Index is compact/pruned. Rebuild base with is_recompute=False and is_compact=False for incremental add."
+        )
+
+    passages_file, offsets_file, vector_index_file = _resolve_index_paths(index_path)
+    if not vector_index_file.exists():
+        raise FileNotFoundError(
+            f"Vector index file missing: {vector_index_file}. Build base first with LeannBuilder."
+        )
+
+    # Resolve embedding config from meta if not provided
+    model_name = embedding_model or meta.get("embedding_model", "facebook/contriever")
+    mode_name = embedding_mode or meta.get("embedding_mode", "sentence-transformers")
+
+    # Append passages and update offsets
+    assigned_ids = _append_passages_and_update_offsets(passages_file, offsets_file, texts)
+
+    # Compute embeddings
+    # Embedding computation path
+    esm = None
+    port = None
+    if recompute:
+        # Determine distance metric early for server config
+        distance_metric = meta.get("backend_kwargs", {}).get("distance_metric", "mips").lower()
+        # Start embedding server and compute via ZMQ for consistency with recompute semantics
+        passages_source_file = f"{index_path}.meta.json"
+        esm = EmbeddingServerManager(
+            backend_module_name="leann_backend_hnsw.hnsw_embedding_server",
+        )
+        started, port = esm.start_server(
+            port=5557,
+            model_name=model_name,
+            embedding_mode=mode_name,
+            passages_file=passages_source_file,
+            distance_metric=distance_metric,
+            enable_warmup=False,
+        )
+        if not started:
+            raise RuntimeError("Failed to start embedding server for recompute add")
+        embeddings = compute_embeddings_via_server(texts, model_name, port)
+    else:
+        embeddings = compute_embeddings(
+            texts,
+            model_name=model_name,
+            mode=mode_name,
+            use_server=False,
+            is_build=True,
+        )
+
+    # Normalize for cosine if needed
+    if "distance_metric" not in locals():
+        distance_metric = meta.get("backend_kwargs", {}).get("distance_metric", "mips").lower()
+    if distance_metric == "cosine":
+        norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
+        norms[norms == 0] = 1
+        embeddings = embeddings / norms
+
+    # Append via backend helper (supports ef_construction/recompute plumbing)
+    try:
+        from leann_backend_hnsw.hnsw_backend import add_vectors as hnsw_add_vectors  # type: ignore
+    except Exception as e:
+        raise RuntimeError(
+            "Failed to import HNSW backend add helper. Ensure HNSW backend is installed."
+        ) from e
+
+    # Propagate ZMQ port to FAISS add path when recompute is True
+    if recompute and port is not None:
+        os.environ["LEANN_ZMQ_PORT"] = str(port)
+
+    hnsw_add_vectors(
+        str(vector_index_file),
+        embeddings,
+        ef_construction=ef_construction,
+        recompute=recompute,
+    )
+
+    # Stop server after add when recompute path used
+    if esm is not None:
         try:
-            self.cleanup()
+            esm.stop_server()
         except Exception:
             pass
+
+    # Sanity: ids length should match embeddings rows
+    if len(assigned_ids) != embeddings.shape[0]:
+        warnings.warn(
+            f"Assigned {len(assigned_ids)} IDs but computed {embeddings.shape[0]} embeddings.",
+            UserWarning,
+            stacklevel=2,
+        )
+
+    return len(assigned_ids)
+
+
+def create_incremental_add_context(
+    index_path: str,
+    *,
+    # Optional embedding choices; if None will use meta
+    embedding_model: Optional[str] = None,
+    embedding_mode: Optional[str] = None,
+    # Optional data-to-text preparation in context
+    data_dir: Optional[str] = None,
+    required_exts: Optional[list[str]] = None,
+    chunk_size: int = 256,
+    chunk_overlap: int = 128,
+    max_items: int = -1,
+) -> IncrementalAddContext:
+    """Validate index and prepare context for repeated incremental adds.
+
+    Additionally, if data_dir is provided, this function will load documents,
+    chunk them to texts with the specified parameters, and store them in ctx.prepared_texts.
+    """
+    meta = _read_meta_file(index_path)
+    if meta.get("backend_name") != "hnsw":
+        raise RuntimeError("Incremental add is currently supported only for HNSW backend")
+    if meta.get("is_compact", True):
+        raise RuntimeError(
+            "Index is compact/pruned. Rebuild base with is_recompute=False and is_compact=False for incremental add."
+        )
+
+    passages_file, offsets_file, vector_index_file = _resolve_index_paths(index_path)
+    if not vector_index_file.exists():
+        raise FileNotFoundError(
+            f"Vector index file missing: {vector_index_file}. Build base first with LeannBuilder."
+        )
+
+    model_name = embedding_model or meta.get("embedding_model", "facebook/contriever")
+    mode_name = embedding_mode or meta.get("embedding_mode", "sentence-transformers")
+    distance_metric = meta.get("backend_kwargs", {}).get("distance_metric", "mips").lower()
+
+    prepared_texts: Optional[list[str]] = None
+    if data_dir is not None:
+        try:
+            from llama_index.core import SimpleDirectoryReader  # type: ignore
+            from llama_index.core.node_parser import SentenceSplitter  # type: ignore
+        except Exception as e:
+            raise RuntimeError(
+                "llama-index-core is required when using data_dir in create_incremental_add_context"
+            ) from e
+
+        reader_kwargs: dict[str, Any] = {"recursive": True, "encoding": "utf-8"}
+        if required_exts:
+            reader_kwargs["required_exts"] = required_exts
+        documents = SimpleDirectoryReader(data_dir, **reader_kwargs).load_data(show_progress=True)
+        if documents:
+            splitter = SentenceSplitter(
+                chunk_size=chunk_size,
+                chunk_overlap=chunk_overlap,
+                separator=" ",
+                paragraph_separator="\n\n",
+            )
+            prepared_texts = []
+            for doc in documents:
+                try:
+                    nodes = splitter.get_nodes_from_documents([doc])
+                    if nodes:
+                        prepared_texts.extend([node.get_content() for node in nodes])
+                except Exception:
+                    content = doc.get_content()
+                    if content and content.strip():
+                        prepared_texts.append(content.strip())
+            if max_items > 0 and len(prepared_texts) > max_items:
+                prepared_texts = prepared_texts[:max_items]
+
+    return IncrementalAddContext(
+        index_path=index_path,
+        passages_file=passages_file,
+        offsets_file=offsets_file,
+        vector_index_file=vector_index_file,
+        embedding_model=model_name,
+        embedding_mode=mode_name,
+        distance_metric=distance_metric,
+        prepared_texts=prepared_texts,
+    )
+
+
+def incremental_add_texts_with_context(
+    ctx: IncrementalAddContext,
+    texts: list[str],
+    *,
+    ef_construction: Optional[int] = None,
+    recompute: bool = False,
+) -> int:
+    """Incrementally add texts using a prepared context (no repeated validation).
+
+    For non-compact HNSW, ef_construction (efConstruction) can be overridden during insertion.
+    """
+    if not texts:
+        return 0
+
+    # Append passages & offsets
+    _append_passages_and_update_offsets(ctx.passages_file, ctx.offsets_file, texts)
+
+    # Compute embeddings
+    # Embedding computation path
+    esm = None
+    port = None
+    if recompute:
+        passages_source_file = f"{ctx.index_path}.meta.json"
+        esm = EmbeddingServerManager(
+            backend_module_name="leann_backend_hnsw.hnsw_embedding_server",
+        )
+        started, port = esm.start_server(
+            port=5557,
+            model_name=ctx.embedding_model,
+            embedding_mode=ctx.embedding_mode,
+            passages_file=passages_source_file,
+            distance_metric=ctx.distance_metric,
+            enable_warmup=False,
+        )
+        if not started:
+            raise RuntimeError("Failed to start embedding server for recompute add")
+        embeddings = compute_embeddings_via_server(texts, ctx.embedding_model, port)
+    else:
+        embeddings = compute_embeddings(
+            texts,
+            model_name=ctx.embedding_model,
+            mode=ctx.embedding_mode,
+            use_server=False,
+            is_build=True,
+        )
+
+    # Normalize for cosine if needed
+    if ctx.distance_metric == "cosine":
+        norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
+        norms[norms == 0] = 1
+        embeddings = embeddings / norms
+
+    # Append via backend helper (supports ef_construction/recompute plumbing)
+    try:
+        from leann_backend_hnsw.hnsw_backend import add_vectors as hnsw_add_vectors  # type: ignore
+    except Exception as e:
+        raise RuntimeError(
+            "Failed to import HNSW backend add helper. Ensure HNSW backend is installed."
+        ) from e
+
+    if recompute and port is not None:
+        os.environ["LEANN_ZMQ_PORT"] = str(port)
+
+    hnsw_add_vectors(
+        str(ctx.vector_index_file),
+        embeddings,
+        ef_construction=ef_construction,
+        recompute=recompute,
+    )
+
+    # Stop server after add when recompute path used
+    if esm is not None:
+        try:
+            esm.stop_server()
+        except Exception:
+            pass
+
+    return embeddings.shape[0]
+
+
+def incremental_add_directory(
+    index_path: str,
+    data_dir: str,
+    *,
+    chunk_size: int = 256,
+    chunk_overlap: int = 128,
+    required_exts: Optional[list[str]] = None,
+    max_items: int = -1,
+    embedding_model: Optional[str] = None,
+    embedding_mode: Optional[str] = None,
+) -> int:
+    """Load documents from a directory, chunk them, and incrementally add to an index.
+
+    Chunking uses LlamaIndex SentenceSplitter for simplicity and avoids external app dependencies.
+    """
+    try:
+        from llama_index.core import SimpleDirectoryReader  # type: ignore
+        from llama_index.core.node_parser import SentenceSplitter  # type: ignore
+    except Exception as e:
+        raise RuntimeError("llama-index-core is required for incremental_add_directory") from e
+
+    reader_kwargs: dict[str, Any] = {"recursive": True, "encoding": "utf-8"}
+    if required_exts:
+        reader_kwargs["required_exts"] = required_exts
+    documents = SimpleDirectoryReader(data_dir, **reader_kwargs).load_data(show_progress=True)
+    if not documents:
+        return 0
+
+    # Traditional text chunking
+    splitter = SentenceSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        separator=" ",
+        paragraph_separator="\n\n",
+    )
+    all_texts: list[str] = []
+    for doc in documents:
+        try:
+            nodes = splitter.get_nodes_from_documents([doc])
+            if nodes:
+                all_texts.extend([node.get_content() for node in nodes])
+        except Exception:
+            content = doc.get_content()
+            if content and content.strip():
+                all_texts.append(content.strip())
+
+    if max_items > 0 and len(all_texts) > max_items:
+        all_texts = all_texts[:max_items]
+
+    return incremental_add_texts(
+        index_path,
+        all_texts,
+        embedding_model=embedding_model,
+        embedding_mode=embedding_mode,
+    )

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

@@ -1,6 +1,6 @@
 """
 Enhanced chunking utilities with AST-aware code chunking support.
-Provides unified interface for both traditional and AST-based text chunking.
+Packaged within leann-core so installed wheels can import it reliably.
 """
 
 import logging
@@ -22,30 +22,9 @@ 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.
-
-    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)
-    """
+    """Separate documents into code files and regular text files."""
     if code_extensions is None:
         code_extensions = CODE_EXTENSIONS
 
@@ -53,16 +32,10 @@ def detect_code_files(documents, code_extensions=None) -> tuple[list, list]:
     text_docs = []
 
     for doc in documents:
-        # 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", "")
-
+        file_path = doc.metadata.get("file_path", "") or 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)
@@ -70,7 +43,6 @@ 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)
 
@@ -79,7 +51,7 @@ def detect_code_files(documents, code_extensions=None) -> tuple[list, list]:
 
 
 def get_language_from_extension(file_path: str) -> Optional[str]:
-    """Get the programming language from file extension."""
+    """Return language string from a filename/extension using CODE_EXTENSIONS."""
     ext = Path(file_path).suffix.lower()
     return CODE_EXTENSIONS.get(ext)
 
@@ -90,40 +62,26 @@ 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.
 
-    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
+    Falls back to traditional chunking if astchunk is unavailable.
     """
     try:
-        from astchunk import ASTChunkBuilder
+        from astchunk import ASTChunkBuilder  # optional dependency
     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 for document, falling back to traditional chunking"
-            )
-            traditional_chunks = create_traditional_chunks([doc], max_chunk_size, chunk_overlap)
-            all_chunks.extend(traditional_chunks)
+            logger.warning("No language detected; falling back to traditional chunking")
+            all_chunks.extend(create_traditional_chunks([doc], max_chunk_size, chunk_overlap))
             continue
 
         try:
-            # Configure astchunk
             configs = {
                 "max_chunk_size": max_chunk_size,
                 "language": language,
@@ -131,7 +89,6 @@ 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", ""),
@@ -140,17 +97,13 @@ 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
@@ -159,7 +112,6 @@ 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():
@@ -168,12 +120,10 @@ 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")
-            traditional_chunks = create_traditional_chunks([doc], max_chunk_size, chunk_overlap)
-            all_chunks.extend(traditional_chunks)
+            all_chunks.extend(create_traditional_chunks([doc], max_chunk_size, chunk_overlap))
 
     return all_chunks
 
@@ -181,23 +131,10 @@ 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.
-
-    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
+    """Create traditional text chunks using LlamaIndex SentenceSplitter."""
     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:
@@ -215,12 +152,9 @@ def create_traditional_chunks(
         try:
             nodes = node_parser.get_nodes_from_documents([doc])
             if 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")
+                all_texts.extend(node.get_content() for node in nodes)
         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())
@@ -238,32 +172,13 @@ 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.
-
-    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
-    """
+    """Create text chunks from documents with optional AST support for code files."""
     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",
@@ -273,47 +188,32 @@ 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:
-                ast_chunks = create_ast_chunks(
-                    code_docs, max_chunk_size=ast_chunk_size, chunk_overlap=ast_chunk_overlap
+                all_chunks.extend(
+                    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:
-                    logger.info("Falling back to traditional chunking for code files")
-                    traditional_code_chunks = create_traditional_chunks(
-                        code_docs, chunk_size, chunk_overlap
+                    all_chunks.extend(
+                        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:
-            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")
+            all_chunks.extend(create_traditional_chunks(text_docs, chunk_size, chunk_overlap))
     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)}")

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

@@ -1,6 +1,5 @@
 import argparse
 import asyncio
-import sys
 from pathlib import Path
 from typing import Any, Optional, Union
 
@@ -322,9 +321,17 @@ Examples:
 
         return basic_matches
 
-    def _should_exclude_file(self, relative_path: Path, gitignore_matches) -> bool:
-        """Check if a file should be excluded using gitignore parser."""
-        return gitignore_matches(str(relative_path))
+    def _should_exclude_file(self, file_path: Path, gitignore_matches) -> bool:
+        """Check if a file should be excluded using gitignore parser.
+
+        Always match against absolute, posix-style paths for consistency with
+        gitignore_parser expectations.
+        """
+        try:
+            absolute_path = file_path.resolve()
+        except Exception:
+            absolute_path = Path(str(file_path))
+        return gitignore_matches(absolute_path.as_posix())
 
     def _is_git_submodule(self, path: Path) -> bool:
         """Check if a path is a git submodule."""
@@ -396,7 +403,9 @@ Examples:
         print(f"   {current_path}")
         print("   " + "─" * 45)
 
-        current_indexes = self._discover_indexes_in_project(current_path)
+        current_indexes = self._discover_indexes_in_project(
+            current_path, exclude_dirs=other_projects
+        )
         if current_indexes:
             for idx in current_indexes:
                 total_indexes += 1
@@ -435,9 +444,14 @@ Examples:
             print("   leann build my-docs --docs ./documents")
         else:
             # Count only projects that have at least one discoverable index
-            projects_count = sum(
-                1 for p in valid_projects if len(self._discover_indexes_in_project(p)) > 0
-            )
+            projects_count = 0
+            for p in valid_projects:
+                if p == current_path:
+                    discovered = self._discover_indexes_in_project(p, exclude_dirs=other_projects)
+                else:
+                    discovered = self._discover_indexes_in_project(p)
+                if len(discovered) > 0:
+                    projects_count += 1
             print(f"📊 Total: {total_indexes} indexes across {projects_count} projects")
 
             if current_indexes_count > 0:
@@ -454,9 +468,22 @@ Examples:
                 print("\n💡 Create your first index:")
                 print("   leann build my-docs --docs ./documents")
 
-    def _discover_indexes_in_project(self, project_path: Path):
-        """Discover all indexes in a project directory (both CLI and apps formats)"""
+    def _discover_indexes_in_project(
+        self, project_path: Path, exclude_dirs: Optional[list[Path]] = None
+    ):
+        """Discover all indexes in a project directory (both CLI and apps formats)
+
+        exclude_dirs: when provided, skip any APP-format index files that are
+        located under these directories. This prevents duplicates when the
+        current project is a parent directory of other registered projects.
+        """
         indexes = []
+        exclude_dirs = exclude_dirs or []
+        # normalize to resolved paths once for comparison
+        try:
+            exclude_dirs_resolved = [p.resolve() for p in exclude_dirs]
+        except Exception:
+            exclude_dirs_resolved = exclude_dirs
 
         # 1. CLI format: .leann/indexes/index_name/
         cli_indexes_dir = project_path / ".leann" / "indexes"
@@ -495,6 +522,17 @@ Examples:
                         continue
                 except Exception:
                     pass
+                # Skip meta files that live under excluded directories
+                try:
+                    meta_parent_resolved = meta_file.parent.resolve()
+                    if any(
+                        meta_parent_resolved.is_relative_to(ex_dir)
+                        for ex_dir in exclude_dirs_resolved
+                    ):
+                        continue
+                except Exception:
+                    # best effort; if resolve or comparison fails, do not exclude
+                    pass
                 # Use the parent directory name as the app index display name
                 display_name = meta_file.parent.name
                 # Extract file base used to store files
@@ -1022,7 +1060,8 @@ Examples:
 
             # Try to use better PDF parsers first, but only if PDFs are requested
             documents = []
-            docs_path = Path(docs_dir)
+            # Use resolved absolute paths to avoid mismatches (symlinks, relative vs absolute)
+            docs_path = Path(docs_dir).resolve()
 
             # Check if we should process PDFs
             should_process_pdfs = custom_file_types is None or ".pdf" in custom_file_types
@@ -1031,10 +1070,15 @@ Examples:
                 for file_path in docs_path.rglob("*.pdf"):
                     # Check if file matches any exclude pattern
                     try:
+                        # Ensure both paths are resolved before computing relativity
+                        file_path_resolved = file_path.resolve()
+                        # Determine directory scope using the non-resolved path to avoid
+                        # misclassifying symlinked entries as outside the docs directory
                         relative_path = file_path.relative_to(docs_path)
                         if not include_hidden and _path_has_hidden_segment(relative_path):
                             continue
-                        if self._should_exclude_file(relative_path, gitignore_matches):
+                        # Use absolute path for gitignore matching
+                        if self._should_exclude_file(file_path_resolved, gitignore_matches):
                             continue
                     except ValueError:
                         # Skip files that can't be made relative to docs_path
@@ -1077,10 +1121,11 @@ Examples:
                 ) -> bool:
                     """Return True if file should be included (not excluded)"""
                     try:
-                        docs_path_obj = Path(docs_dir)
-                        file_path_obj = Path(file_path)
-                        relative_path = file_path_obj.relative_to(docs_path_obj)
-                        return not self._should_exclude_file(relative_path, gitignore_matches)
+                        docs_path_obj = Path(docs_dir).resolve()
+                        file_path_obj = Path(file_path).resolve()
+                        # Use absolute path for gitignore matching
+                        _ = file_path_obj.relative_to(docs_path_obj)  # validate scope
+                        return not self._should_exclude_file(file_path_obj, gitignore_matches)
                     except (ValueError, OSError):
                         return True  # Include files that can't be processed
 
@@ -1170,13 +1215,8 @@ Examples:
         if use_ast:
             print("🧠 Using AST-aware chunking for code files")
             try:
-                # Import enhanced chunking utilities
-                # 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
+                # Import enhanced chunking utilities from packaged module
+                from .chunking_utils import create_text_chunks
 
                 # Use enhanced chunking with AST support
                 all_texts = create_text_chunks(
@@ -1191,7 +1231,9 @@ Examples:
                 )
 
             except ImportError as e:
-                print(f"⚠️  AST chunking not available ({e}), falling back to traditional chunking")
+                print(
+                    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

@@ -2,6 +2,8 @@
 
 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 = "0.3.2"
+version = "0.3.4"
 description = "LEANN - The smallest vector index in the world. RAG Everything with LEANN!"
 readme = "README.md"
 requires-python = ">=3.9"

pyproject.toml

@@ -99,6 +99,7 @@ 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 = 3
+revision = 2
 requires-python = ">=3.9"
 resolution-markers = [
     "python_full_version >= '3.12'",
@@ -201,7 +201,7 @@ wheels = [
 [[package]]
 name = "astchunk"
 version = "0.1.0"
-source = { registry = "https://pypi.org/simple" }
+source = { editable = "packages/astchunk-leann" }
 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.*'" },
@@ -214,10 +214,31 @@ 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" }
-wheels = [
-    { 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" },
+
+[package.metadata]
+requires-dist = [
+    { 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"
@@ -1564,7 +1585,7 @@ name = "importlib-metadata"
 version = "8.7.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "zipp" },
+    { name = "zipp", marker = "python_full_version < '3.10'" },
 ]
 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 = [
@@ -2117,7 +2138,7 @@ wheels = [
 
 [[package]]
 name = "leann-backend-diskann"
-version = "0.3.2"
+version = "0.3.3"
 source = { editable = "packages/leann-backend-diskann" }
 dependencies = [
     { name = "leann-core" },
@@ -2129,14 +2150,14 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "leann-core", specifier = "==0.3.2" },
+    { name = "leann-core", specifier = "==0.3.3" },
     { name = "numpy" },
     { name = "protobuf", specifier = ">=3.19.0" },
 ]
 
 [[package]]
 name = "leann-backend-hnsw"
-version = "0.3.2"
+version = "0.3.3"
 source = { editable = "packages/leann-backend-hnsw" }
 dependencies = [
     { name = "leann-core" },
@@ -2149,7 +2170,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "leann-core", specifier = "==0.3.2" },
+    { name = "leann-core", specifier = "==0.3.3" },
     { name = "msgpack", specifier = ">=1.0.0" },
     { name = "numpy" },
     { name = "pyzmq", specifier = ">=23.0.0" },
@@ -2157,7 +2178,7 @@ requires-dist = [
 
 [[package]]
 name = "leann-core"
-version = "0.3.2"
+version = "0.3.3"
 source = { editable = "packages/leann-core" }
 dependencies = [
     { name = "accelerate" },
@@ -2297,7 +2318,7 @@ test = [
 
 [package.metadata]
 requires-dist = [
-    { name = "astchunk", specifier = ">=0.1.0" },
+    { name = "astchunk", editable = "packages/astchunk-leann" },
     { name = "beautifulsoup4", marker = "extra == 'documents'", specifier = ">=4.13.0" },
     { name = "black", marker = "extra == 'dev'", specifier = ">=23.0" },
     { name = "boto3" },