docs: align cli args and README

docs: leann help
docs: boolean flags
2025-08-15 12:03:11 -07:00 · 2025-08-15 11:51:38 -07:00 · 2025-08-15 11:50:46 -07:00 · 2025-08-15 01:01:51 -07:00 · 2025-08-14 14:29:57 -07:00 · 2025-08-14 14:25:50 -07:00
41 changed files with 4069 additions and 6230 deletions
--- a/.gitattributes
+++ b/.gitattributes
@@ -0,0 +1 @@
+paper_plot/data/big_graph_degree_data.npz filter=lfs diff=lfs merge=lfs -text
--- a/.github/workflows/build-reusable.yml
+++ b/.github/workflows/build-reusable.yml
@@ -87,7 +87,7 @@ jobs:
    runs-on: ${{ matrix.os }}

    steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
        with:
          ref: ${{ inputs.ref }}
          submodules: recursive
@@ -98,23 +98,21 @@ jobs:
          python-version: ${{ matrix.python }}

      - name: Install uv
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v4

      - name: Install system dependencies (Ubuntu)
        if: runner.os == 'Linux'
        run: |
          sudo apt-get update
          sudo apt-get install -y libomp-dev libboost-all-dev protobuf-compiler libzmq3-dev \
-            pkg-config libabsl-dev libaio-dev libprotobuf-dev \
-            patchelf
+            pkg-config libopenblas-dev patchelf libabsl-dev libaio-dev libprotobuf-dev

          # Install Intel MKL for DiskANN
          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" >> $GITHUB_ENV
+          echo "LD_LIBRARY_PATH=/opt/intel/oneapi/mkl/latest/lib/intel64:$LD_LIBRARY_PATH" >> $GITHUB_ENV

      - name: Install system dependencies (macOS)
        if: runner.os == 'macOS'
@@ -306,53 +304,3 @@ jobs:
        with:
          name: packages-${{ matrix.os }}-py${{ matrix.python }}
          path: packages/*/dist/
-
-
-  arch-smoke:
-    name: Arch Linux smoke test (install & import)
-    needs: build
-    runs-on: ubuntu-latest
-    container:
-      image: archlinux:latest
-
-    steps:
-      - name: Prepare system
-        run: |
-          pacman -Syu --noconfirm
-          pacman -S --noconfirm python python-pip gcc git zlib openssl
-
-      - name: Download ALL wheel artifacts from this run
-        uses: actions/download-artifact@v5
-        with:
-          # Don't specify name, download all artifacts
-          path: ./wheels
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v6
-
-      - name: Create virtual environment and install wheels
-        run: |
-          uv venv
-          source .venv/bin/activate || source .venv/Scripts/activate
-          uv pip install --find-links wheels leann-core
-          uv pip install --find-links wheels leann-backend-hnsw
-          uv pip install --find-links wheels leann-backend-diskann
-          uv pip install --find-links wheels leann
-
-      - name: Import & tiny runtime check
-        env:
-          OMP_NUM_THREADS: 1
-          MKL_NUM_THREADS: 1
-        run: |
-          source .venv/bin/activate || source .venv/Scripts/activate
-          python - <<'PY'
-          import leann
-          import leann_backend_hnsw as h
-          import leann_backend_diskann as d
-          from leann import LeannBuilder, LeannSearcher
-          b = LeannBuilder(backend_name="hnsw")
-          b.add_text("hello arch")
-          b.build_index("arch_demo.leann")
-          s = LeannSearcher("arch_demo.leann")
-          print("search:", s.search("hello", top_k=1))
-          PY
--- a/.github/workflows/link-check.yml
+++ b/.github/workflows/link-check.yml
@@ -14,6 +14,6 @@ jobs:
      - uses: actions/checkout@v4
      - uses: lycheeverse/lychee-action@v2
        with:
-          args: --no-progress --insecure --user-agent 'curl/7.68.0' README.md docs/ apps/ examples/ benchmarks/
+          args: --no-progress --insecure README.md docs/ apps/ examples/ benchmarks/
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
--- a/.gitignore
+++ b/.gitignore
@@ -18,7 +18,6 @@ demo/experiment_results/**/*.json
 *.eml
 *.emlx
 *.json
-!.vscode/*.json
 *.sh
 *.txt
 !CMakeLists.txt
@@ -93,6 +92,3 @@ packages/leann-backend-diskann/third_party/DiskANN/_deps/
 batchtest.py
 tests/__pytest_cache__/
 tests/__pycache__/
-paru-bin/
-
-benchmarks/data/
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -13,5 +13,4 @@ repos:
    rev: v0.12.7  # Fixed version to match pyproject.toml
    hooks:
      - id: ruff
-        args: [--fix, --exit-non-zero-on-fix]
      - id: ruff-format
--- a/.vscode/extensions.json
+++ b/.vscode/extensions.json
@@ -1,5 +0,0 @@
-{
-    "recommendations": [
-        "charliermarsh.ruff",
-    ]
-}
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -1,22 +0,0 @@
-{
-  "python.defaultInterpreterPath": ".venv/bin/python",
-  "python.terminal.activateEnvironment": true,
-  "[python]": {
-    "editor.defaultFormatter": "charliermarsh.ruff",
-    "editor.formatOnSave": true,
-    "editor.codeActionsOnSave": {
-      "source.organizeImports": "explicit",
-      "source.fixAll": "explicit"
-    },
-    "editor.insertSpaces": true,
-    "editor.tabSize": 4
-  },
-  "ruff.enable": true,
-  "files.watcherExclude": {
-    "**/.venv/**": true,
-    "**/__pycache__/**": true,
-    "**/*.egg-info/**": true,
-    "**/build/**": true,
-    "**/dist/**": true
-  }
-}
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 <p align="center">
  <img src="https://img.shields.io/badge/Python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue.svg" alt="Python Versions">
  <img src="https://github.com/yichuan-w/LEANN/actions/workflows/build-and-publish.yml/badge.svg" alt="CI Status">
-  <img src="https://img.shields.io/badge/Platform-Ubuntu%20%26%20Arch%20%26%20WSL%20%7C%20macOS%20(ARM64%2FIntel)-lightgrey" alt="Platform">
+  <img src="https://img.shields.io/badge/Platform-Ubuntu%20%7C%20macOS%20(ARM64%2FIntel)-lightgrey" alt="Platform">
  <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="MIT License">
  <img src="https://img.shields.io/badge/MCP-Native%20Integration-blue" alt="MCP Integration">
 </p>
@@ -31,7 +31,7 @@ LEANN achieves this through *graph-based selective recomputation* with *high-deg
  <img src="assets/effects.png" alt="LEANN vs Traditional Vector DB Storage Comparison" width="70%">
 </p>

-> **The numbers speak for themselves:** Index 60 million text chunks in just 6GB instead of 201GB. From emails to browser history, everything fits on your laptop. [See detailed benchmarks for different applications below ↓](#-storage-comparison)
+> **The numbers speak for themselves:** Index 60 million text chunks in just 6GB instead of 201GB. From emails to browser history, everything fits on your laptop. [See detailed benchmarks for different applications below ↓](#storage-comparison)


 🔒 **Privacy:** Your data never leaves your laptop. No OpenAI, no cloud, no "terms of service".
@@ -70,8 +70,8 @@ uv venv
 source .venv/bin/activate
 uv pip install leann
 ```
-<!--
-> Low-resource? See “Low-resource setups” in the [Configuration Guide](docs/configuration-guide.md#low-resource-setups). -->
+
+> Low-resource? See “Low-resource setups” in the [Configuration Guide](docs/configuration-guide.md#low-resource-setups).

 <details>
 <summary>
@@ -87,60 +87,15 @@ git submodule update --init --recursive
 ```

 **macOS:**
-
-Note: DiskANN requires MacOS 13.3 or later.
-
 ```bash
-brew install libomp boost protobuf zeromq pkgconf
-uv sync --extra diskann
+brew install llvm libomp boost protobuf zeromq pkgconf
+CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ uv sync
 ```

-**Linux (Ubuntu/Debian):**
-
-Note: On Ubuntu 20.04, you may need to build a newer Abseil and pin Protobuf (e.g., v3.20.x) for building DiskANN. See [Issue #30](https://github.com/yichuan-w/LEANN/issues/30) for a step-by-step note.
-
-You can manually install [Intel oneAPI MKL](https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl.html) instead of `libmkl-full-dev` for DiskANN. You can also use `libopenblas-dev` for building HNSW only, by removing `--extra diskann` in the command below.
-
+**Linux:**
 ```bash
-sudo apt-get update && sudo apt-get install -y \
-  libomp-dev libboost-all-dev protobuf-compiler libzmq3-dev \
-  pkg-config libabsl-dev libaio-dev libprotobuf-dev \
-  libmkl-full-dev
-
-uv sync --extra diskann
-```
-
-**Linux (Arch Linux):**
-
-```bash
-sudo pacman -Syu && sudo pacman -S --needed base-devel cmake pkgconf git gcc \
-  boost boost-libs protobuf abseil-cpp libaio zeromq
-
-# For MKL in DiskANN
-sudo pacman -S --needed base-devel git
-git clone https://aur.archlinux.org/paru-bin.git
-cd paru-bin && makepkg -si
-paru -S intel-oneapi-mkl intel-oneapi-compiler
-source /opt/intel/oneapi/setvars.sh
-
-uv sync --extra diskann
-```
-
-**Linux (RHEL / CentOS Stream / Oracle / Rocky / AlmaLinux):**
-
-See [Issue #50](https://github.com/yichuan-w/LEANN/issues/50) for more details.
-
-```bash
-sudo dnf groupinstall -y "Development Tools"
-sudo dnf install -y libomp-devel boost-devel protobuf-compiler protobuf-devel \
-  abseil-cpp-devel libaio-devel zeromq-devel pkgconf-pkg-config
-
-# For MKL in DiskANN
-sudo dnf install -y intel-oneapi-mkl intel-oneapi-mkl-devel \
-  intel-oneapi-openmp || sudo dnf install -y intel-oneapi-compiler
-source /opt/intel/oneapi/setvars.sh
-
-uv sync --extra diskann
+sudo apt-get install libomp-dev libboost-all-dev protobuf-compiler libabsl-dev libmkl-full-dev libaio-dev libzmq3-dev
+uv sync
 ```

 </details>
@@ -176,8 +131,6 @@ response = chat.ask("How much storage does LEANN save?", top_k=1)

 LEANN supports RAG on various data sources including documents (`.pdf`, `.txt`, `.md`), Apple Mail, Google Search History, WeChat, and more.

-
-
 ### Generation Model Setup

 LEANN supports multiple LLM providers for text generation (OpenAI API, HuggingFace, Ollama).
@@ -296,12 +249,6 @@ python -m apps.document_rag --data-dir "~/Documents/Papers" --chunk-size 1024

 # Filter only markdown and Python files with smaller chunks
 python -m apps.document_rag --data-dir "./docs" --chunk-size 256 --file-types .md .py
-
-# Enable AST-aware chunking for code files
-python -m apps.document_rag --enable-code-chunking --data-dir "./my_project"
-
-# Or use the specialized code RAG for better code understanding
-python -m apps.code_rag --repo-dir "./my_codebase" --query "How does authentication work?"
 ```

 </details>
@@ -476,34 +423,24 @@ Once the index is built, you can ask questions like:

 ### 🚀 Claude Code Integration: Transform Your Development Workflow!

-<details>
-<summary><strong>NEW!! AST‑Aware Code Chunking</strong></summary>
-
-LEANN features intelligent code chunking that preserves semantic boundaries (functions, classes, methods) for Python, Java, C#, and TypeScript, improving code understanding compared to text-based chunking.
-
-📖 Read the [AST Chunking Guide →](docs/ast_chunking_guide.md)
-
-</details>
-
 **The future of code assistance is here.** Transform your development workflow with LEANN's native MCP integration for Claude Code. Index your entire codebase and get intelligent code assistance directly in your IDE.

 **Key features:**
- 🔍 **Semantic code search** across your entire project, fully local index and lightweight
- 🧠 **AST-aware chunking** preserves code structure (functions, classes)
+- 🔍 **Semantic code search** across your entire project
 - 📚 **Context-aware assistance** for debugging and development
 - 🚀 **Zero-config setup** with automatic language detection

 ```bash
 # Install LEANN globally for MCP integration
-uv tool install leann-core --with leann
-claude mcp add --scope user leann-server -- leann_mcp
+uv tool install leann-core
+
 # Setup is automatic - just start using Claude Code!
 ```
 Try our fully agentic pipeline with auto query rewriting, semantic search planning, and more:

 ![LEANN MCP Integration](assets/mcp_leann.png)

-**🔥 Ready to supercharge your coding?** [Complete Setup Guide →](packages/leann-mcp/README.md)
+**Ready to supercharge your coding?** [Complete Setup Guide →](packages/leann-mcp/README.md)

 ## 🖥️ Command Line Interface

@@ -520,8 +457,7 @@ leann --help
 **To make it globally available:**
 ```bash
 # Install the LEANN CLI globally using uv tool
-uv tool install leann-core --with leann
-
+uv tool install leann-core

 # Now you can use leann from anywhere without activating venv
 leann --help
@@ -545,15 +481,11 @@ leann ask my-docs --interactive

 # List all your indexes
 leann list
-
-# Remove an index
-leann remove my-docs
 ```

 **Key CLI features:**
 - Auto-detects document formats (PDF, TXT, MD, DOCX, PPTX + code files)
- **🧠 AST-aware chunking** for Python, Java, C#, TypeScript files
- Smart text chunking with overlap for all other content
+- Smart text chunking with overlap
 - Multiple LLM providers (Ollama, OpenAI, HuggingFace)
 - Organized index storage in `.leann/indexes/` (project-local)
 - Support for advanced search parameters
@@ -561,7 +493,7 @@ leann remove my-docs
 <details>
 <summary><strong>📋 Click to expand: Complete CLI Reference</strong></summary>

-You can use `leann --help`, or `leann build --help`, `leann search --help`, `leann ask --help`, `leann list --help`, `leann remove --help` to get the complete CLI reference.
+You can use `leann --help`, or `leann build --help`, `leann search --help`, `leann ask --help` to get the complete CLI reference.

 **Build Command:**
 ```bash
@@ -599,31 +531,6 @@ Options:
  --top-k N                  Retrieval count (default: 20)
 ```

-**List Command:**
-```bash
-leann list
-
-# Lists all indexes across all projects with status indicators:
-# ✅ - Index is complete and ready to use
-# ❌ - Index is incomplete or corrupted
-# 📁 - CLI-created index (in .leann/indexes/)
-# 📄 - App-created index (*.leann.meta.json files)
-```
-
-**Remove Command:**
-```bash
-leann remove INDEX_NAME [OPTIONS]
-
-Options:
-  --force, -f    Force removal without confirmation
-
-# Smart removal: automatically finds and safely removes indexes
-# - Shows all matching indexes across projects
-# - Requires confirmation for cross-project removal
-# - Interactive selection when multiple matches found
-# - Supports both CLI and app-created indexes
-```
-
 </details>

 ## 🏗️ Architecture & How It Works
@@ -665,7 +572,6 @@ Options:
 ```bash
 uv pip install -e ".[dev]"  # Install dev dependencies
 python benchmarks/run_evaluation.py    # Will auto-download evaluation data and run benchmarks
-python benchmarks/run_evaluation.py benchmarks/data/indices/rpj_wiki/rpj_wiki --num-queries 2000    # After downloading data, you can run the benchmark with our biggest index
 ```

 The evaluation script downloads data automatically on first run. The last three results were tested with partial personal data, and you can reproduce them with your own data!
@@ -705,9 +611,6 @@ MIT License - see [LICENSE](LICENSE) for details.

 Core Contributors: [Yichuan Wang](https://yichuan-w.github.io/) & [Zhifei Li](https://github.com/andylizf).

-Active Contributors: [Gabriel Dehan](https://github.com/gabriel-dehan)
-
-
 We welcome more contributors! Feel free to open issues or submit PRs.

 This work is done at [**Berkeley Sky Computing Lab**](https://sky.cs.berkeley.edu/).
--- a/apps/base_rag_example.py
+++ b/apps/base_rag_example.py
@@ -10,7 +10,7 @@ from typing import Any

 import dotenv
 from leann.api import LeannBuilder, LeannChat
-from leann.registry import register_project_directory
+from llama_index.core.node_parser import SentenceSplitter

 dotenv.load_dotenv()

@@ -108,38 +108,6 @@ class BaseRAGExample(ABC):
            help="Thinking budget for reasoning models (low/medium/high). Supported by GPT-Oss:20b and other reasoning models.",
        )

-        # AST Chunking parameters
-        ast_group = parser.add_argument_group("AST Chunking Parameters")
-        ast_group.add_argument(
-            "--use-ast-chunking",
-            action="store_true",
-            help="Enable AST-aware chunking for code files (requires astchunk)",
-        )
-        ast_group.add_argument(
-            "--ast-chunk-size",
-            type=int,
-            default=512,
-            help="Maximum characters per AST chunk (default: 512)",
-        )
-        ast_group.add_argument(
-            "--ast-chunk-overlap",
-            type=int,
-            default=64,
-            help="Overlap between AST chunks (default: 64)",
-        )
-        ast_group.add_argument(
-            "--code-file-extensions",
-            nargs="+",
-            default=None,
-            help="Additional code file extensions to process with AST chunking (e.g., .py .java .cs .ts)",
-        )
-        ast_group.add_argument(
-            "--ast-fallback-traditional",
-            action="store_true",
-            default=True,
-            help="Fall back to traditional chunking if AST chunking fails (default: True)",
-        )
-
        # Search parameters
        search_group = parser.add_argument_group("Search Parameters")
        search_group.add_argument(
@@ -246,11 +214,6 @@ class BaseRAGExample(ABC):
        builder.build_index(index_path)
        print(f"Index saved to: {index_path}")

-        # Register project directory so leann list can discover this index
-        # The index is saved as args.index_dir/index_name.leann
-        # We want to register the current working directory where the app is run
-        register_project_directory(Path.cwd())
-
        return index_path

    async def run_interactive_chat(self, args, index_path: str):
@@ -299,6 +262,7 @@ class BaseRAGExample(ABC):
        chat = LeannChat(
            index_path,
            llm_config=self.get_llm_config(args),
+            system_prompt=f"You are a helpful assistant that answers questions about {self.name} data.",
            complexity=args.search_complexity,
        )

@@ -340,3 +304,21 @@ class BaseRAGExample(ABC):
            await self.run_single_query(args, index_path, args.query)
        else:
            await self.run_interactive_chat(args, index_path)
+
+
+def create_text_chunks(documents, chunk_size=256, chunk_overlap=25) -> list[str]:
+    """Helper function to create text chunks from documents."""
+    node_parser = SentenceSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        separator=" ",
+        paragraph_separator="\n\n",
+    )
+
+    all_texts = []
+    for doc in documents:
+        nodes = node_parser.get_nodes_from_documents([doc])
+        if nodes:
+            all_texts.extend(node.get_content() for node in nodes)
+
+    return all_texts
--- a/apps/chunking/init.py
+++ b/apps/chunking/init.py
@@ -1,22 +0,0 @@
-"""
-Chunking utilities for LEANN RAG applications.
-Provides AST-aware and traditional text chunking functionality.
-"""
-
-from .utils import (
-    CODE_EXTENSIONS,
-    create_ast_chunks,
-    create_text_chunks,
-    create_traditional_chunks,
-    detect_code_files,
-    get_language_from_extension,
-)
-
-__all__ = [
-    "CODE_EXTENSIONS",
-    "create_ast_chunks",
-    "create_text_chunks",
-    "create_traditional_chunks",
-    "detect_code_files",
-    "get_language_from_extension",
-]
--- a/apps/chunking/utils.py
+++ b/apps/chunking/utils.py
@@ -1,320 +0,0 @@
-"""
-Enhanced chunking utilities with AST-aware code chunking support.
-Provides unified interface for both traditional and AST-based text chunking.
-"""
-
-import logging
-from pathlib import Path
-from typing import Optional
-
-from llama_index.core.node_parser import SentenceSplitter
-
-logger = logging.getLogger(__name__)
-
-# Code file extensions supported by astchunk
-CODE_EXTENSIONS = {
-    ".py": "python",
-    ".java": "java",
-    ".cs": "csharp",
-    ".ts": "typescript",
-    ".tsx": "typescript",
-    ".js": "typescript",
-    ".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)
-    """
-    if code_extensions is None:
-        code_extensions = CODE_EXTENSIONS
-
-    code_docs = []
-    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", "")
-
-        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)
-            else:
-                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)
-
-    logger.info(f"Detected {len(code_docs)} code files and {len(text_docs)} text files")
-    return code_docs, text_docs
-
-
-def get_language_from_extension(file_path: str) -> Optional[str]:
-    """Get the programming language from file extension."""
-    ext = Path(file_path).suffix.lower()
-    return CODE_EXTENSIONS.get(ext)
-
-
-def create_ast_chunks(
-    documents,
-    max_chunk_size: int = 512,
-    chunk_overlap: int = 64,
-    metadata_template: str = "default",
-) -> list[str]:
-    """
-    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
-    """
-    try:
-        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 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,
-                "metadata_template": metadata_template,
-                "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", ""),
-                "creation_date": doc.metadata.get("creation_date", ""),
-                "last_modified_date": doc.metadata.get("last_modified_date", ""),
-            }
-            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
-                elif isinstance(chunk, dict) and "text" in chunk:
-                    chunk_text = chunk["text"]
-                elif isinstance(chunk, str):
-                    chunk_text = chunk
-                else:
-                    # Try to convert to string
-                    chunk_text = str(chunk)
-
-                if chunk_text and chunk_text.strip():
-                    all_chunks.append(chunk_text.strip())
-
-            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)
-
-    return all_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
-    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:
-        chunk_overlap = chunk_size // 2
-
-    node_parser = SentenceSplitter(
-        chunk_size=chunk_size,
-        chunk_overlap=chunk_overlap,
-        separator=" ",
-        paragraph_separator="\n\n",
-    )
-
-    all_texts = []
-    for doc in documents:
-        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")
-        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())
-
-    return all_texts
-
-
-def create_text_chunks(
-    documents,
-    chunk_size: int = 256,
-    chunk_overlap: int = 128,
-    use_ast_chunking: bool = False,
-    ast_chunk_size: int = 512,
-    ast_chunk_overlap: int = 64,
-    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
-    """
-    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",
-            ".cs": "c_sharp",
-            ".ts": "typescript",
-            ".tsx": "typescript",
-        }
-        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(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(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")
-    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)}")
-    return all_chunks
--- a/apps/code_rag.py
+++ b/apps/code_rag.py
@@ -1,211 +0,0 @@
-"""
-Code RAG example using AST-aware chunking for optimal code understanding.
-Specialized for code repositories with automatic language detection and
-optimized chunking parameters.
-"""
-
-import sys
-from pathlib import Path
-
-# Add parent directory to path for imports
-sys.path.insert(0, str(Path(__file__).parent))
-
-from base_rag_example import BaseRAGExample
-from chunking import CODE_EXTENSIONS, create_text_chunks
-from llama_index.core import SimpleDirectoryReader
-
-
-class CodeRAG(BaseRAGExample):
-    """Specialized RAG example for code repositories with AST-aware chunking."""
-
-    def __init__(self):
-        super().__init__(
-            name="Code",
-            description="Process and query code repositories with AST-aware chunking",
-            default_index_name="code_index",
-        )
-        # Override defaults for code-specific usage
-        self.embedding_model_default = "facebook/contriever"  # Good for code
-        self.max_items_default = -1  # Process all code files by default
-
-    def _add_specific_arguments(self, parser):
-        """Add code-specific arguments."""
-        code_group = parser.add_argument_group("Code Repository Parameters")
-
-        code_group.add_argument(
-            "--repo-dir",
-            type=str,
-            default=".",
-            help="Code repository directory to index (default: current directory)",
-        )
-        code_group.add_argument(
-            "--include-extensions",
-            nargs="+",
-            default=list(CODE_EXTENSIONS.keys()),
-            help="File extensions to include (default: supported code extensions)",
-        )
-        code_group.add_argument(
-            "--exclude-dirs",
-            nargs="+",
-            default=[
-                ".git",
-                "__pycache__",
-                "node_modules",
-                "venv",
-                ".venv",
-                "build",
-                "dist",
-                "target",
-            ],
-            help="Directories to exclude from indexing",
-        )
-        code_group.add_argument(
-            "--max-file-size",
-            type=int,
-            default=1000000,  # 1MB
-            help="Maximum file size in bytes to process (default: 1MB)",
-        )
-        code_group.add_argument(
-            "--include-comments",
-            action="store_true",
-            help="Include comments in chunking (useful for documentation)",
-        )
-        code_group.add_argument(
-            "--preserve-imports",
-            action="store_true",
-            default=True,
-            help="Try to preserve import statements in chunks (default: True)",
-        )
-
-    async def load_data(self, args) -> list[str]:
-        """Load code files and convert to AST-aware chunks."""
-        print(f"🔍 Scanning code repository: {args.repo_dir}")
-        print(f"📁 Including extensions: {args.include_extensions}")
-        print(f"🚫 Excluding directories: {args.exclude_dirs}")
-
-        # Check if repository directory exists
-        repo_path = Path(args.repo_dir)
-        if not repo_path.exists():
-            raise ValueError(f"Repository directory not found: {args.repo_dir}")
-
-        # Load code files with filtering
-        reader_kwargs = {
-            "recursive": True,
-            "encoding": "utf-8",
-            "required_exts": args.include_extensions,
-            "exclude_hidden": True,
-        }
-
-        # Create exclusion filter
-        def file_filter(file_path: str) -> bool:
-            """Filter out unwanted files and directories."""
-            path = Path(file_path)
-
-            # Check file size
-            try:
-                if path.stat().st_size > args.max_file_size:
-                    print(f"⚠️ Skipping large file: {path.name} ({path.stat().st_size} bytes)")
-                    return False
-            except Exception:
-                return False
-
-            # Check if in excluded directory
-            for exclude_dir in args.exclude_dirs:
-                if exclude_dir in path.parts:
-                    return False
-
-            return True
-
-        try:
-            # Load documents with file filtering
-            documents = SimpleDirectoryReader(
-                args.repo_dir,
-                file_extractor=None,  # Use default extractors
-                **reader_kwargs,
-            ).load_data(show_progress=True)
-
-            # Apply custom filtering
-            filtered_docs = []
-            for doc in documents:
-                file_path = doc.metadata.get("file_path", "")
-                if file_filter(file_path):
-                    filtered_docs.append(doc)
-
-            documents = filtered_docs
-
-        except Exception as e:
-            print(f"❌ Error loading code files: {e}")
-            return []
-
-        if not documents:
-            print(
-                f"❌ No code files found in {args.repo_dir} with extensions {args.include_extensions}"
-            )
-            return []
-
-        print(f"✅ Loaded {len(documents)} code files")
-
-        # Show breakdown by language/extension
-        ext_counts = {}
-        for doc in documents:
-            file_path = doc.metadata.get("file_path", "")
-            if file_path:
-                ext = Path(file_path).suffix.lower()
-                ext_counts[ext] = ext_counts.get(ext, 0) + 1
-
-        print("📊 Files by extension:")
-        for ext, count in sorted(ext_counts.items()):
-            print(f"   {ext}: {count} files")
-
-        # Use AST-aware chunking by default for code
-        print(
-            f"🧠 Using AST-aware chunking (chunk_size: {args.ast_chunk_size}, overlap: {args.ast_chunk_overlap})"
-        )
-
-        all_texts = create_text_chunks(
-            documents,
-            chunk_size=256,  # Fallback for non-code files
-            chunk_overlap=64,
-            use_ast_chunking=True,  # Always use AST for code RAG
-            ast_chunk_size=args.ast_chunk_size,
-            ast_chunk_overlap=args.ast_chunk_overlap,
-            code_file_extensions=args.include_extensions,
-            ast_fallback_traditional=True,
-        )
-
-        # Apply max_items limit if specified
-        if args.max_items > 0 and len(all_texts) > args.max_items:
-            print(f"⏳ Limiting to {args.max_items} chunks (from {len(all_texts)})")
-            all_texts = all_texts[: args.max_items]
-
-        print(f"✅ Generated {len(all_texts)} code chunks")
-        return all_texts
-
-
-if __name__ == "__main__":
-    import asyncio
-
-    # Example queries for code RAG
-    print("\n💻 Code RAG Example")
-    print("=" * 50)
-    print("\nExample queries you can try:")
-    print("- 'How does the embedding computation work?'")
-    print("- 'What are the main classes in this codebase?'")
-    print("- 'Show me the search implementation'")
-    print("- 'How is error handling implemented?'")
-    print("- 'What design patterns are used?'")
-    print("- 'Explain the chunking logic'")
-    print("\n🚀 Features:")
-    print("- ✅ AST-aware chunking preserves code structure")
-    print("- ✅ Automatic language detection")
-    print("- ✅ Smart filtering of large files and common excludes")
-    print("- ✅ Optimized for code understanding")
-    print("\nUsage examples:")
-    print("  python -m apps.code_rag --repo-dir ./my_project")
-    print(
-        "  python -m apps.code_rag --include-extensions .py .js --query 'How does authentication work?'"
-    )
-    print("\nOr run without --query for interactive mode\n")
-
-    rag = CodeRAG()
-    asyncio.run(rag.run())
--- a/apps/document_rag.py
+++ b/apps/document_rag.py
@@ -9,8 +9,7 @@ from pathlib import Path
 # Add parent directory to path for imports
 sys.path.insert(0, str(Path(__file__).parent))

-from base_rag_example import BaseRAGExample
-from chunking import create_text_chunks
+from base_rag_example import BaseRAGExample, create_text_chunks
 from llama_index.core import SimpleDirectoryReader


@@ -45,11 +44,6 @@ class DocumentRAG(BaseRAGExample):
        doc_group.add_argument(
            "--chunk-overlap", type=int, default=128, help="Text chunk overlap (default: 128)"
        )
-        doc_group.add_argument(
-            "--enable-code-chunking",
-            action="store_true",
-            help="Enable AST-aware chunking for code files in the data directory",
-        )

    async def load_data(self, args) -> list[str]:
        """Load documents and convert to text chunks."""
@@ -82,22 +76,9 @@ class DocumentRAG(BaseRAGExample):

        print(f"Loaded {len(documents)} documents")

-        # Determine chunking strategy
-        use_ast = args.enable_code_chunking or getattr(args, "use_ast_chunking", False)
-
-        if use_ast:
-            print("Using AST-aware chunking for code files")
-
-        # Convert to text chunks with optional AST support
+        # Convert to text chunks
        all_texts = create_text_chunks(
-            documents,
-            chunk_size=args.chunk_size,
-            chunk_overlap=args.chunk_overlap,
-            use_ast_chunking=use_ast,
-            ast_chunk_size=getattr(args, "ast_chunk_size", 512),
-            ast_chunk_overlap=getattr(args, "ast_chunk_overlap", 64),
-            code_file_extensions=getattr(args, "code_file_extensions", None),
-            ast_fallback_traditional=getattr(args, "ast_fallback_traditional", True),
+            documents, chunk_size=args.chunk_size, chunk_overlap=args.chunk_overlap
        )

        # Apply max_items limit if specified
@@ -121,10 +102,6 @@ if __name__ == "__main__":
    print(
        "- 'What is the problem of developing pan gu model Huawei meets? (盘古大模型开发中遇到什么问题?)'"
    )
-    print("\n🚀 NEW: Code-aware chunking available!")
-    print("- Use --enable-code-chunking to enable AST-aware chunking for code files")
-    print("- Supports Python, Java, C#, TypeScript files")
-    print("- Better semantic understanding of code structure")
    print("\nOr run without --query for interactive mode\n")

    rag = DocumentRAG()
--- a/benchmarks/data/.gitattributes
+++ b/benchmarks/data/.gitattributes
@@ -0,0 +1,82 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+ground_truth/dpr/id_map.json filter=lfs diff=lfs merge=lfs -text
+indices/dpr/dpr_diskann.passages.idx filter=lfs diff=lfs merge=lfs -text
+indices/dpr/dpr_diskann.passages.jsonl filter=lfs diff=lfs merge=lfs -text
+indices/dpr/dpr_diskann_disk.index filter=lfs diff=lfs merge=lfs -text
+indices/dpr/leann.labels.map filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/leann.labels.map filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.index filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.0.idx filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.0.jsonl filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.1.idx filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.1.jsonl filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.2.idx filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.2.jsonl filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.3.idx filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.3.jsonl filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.4.idx filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.4.jsonl filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.5.idx filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.5.jsonl filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.6.idx filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.6.jsonl filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.7.idx filter=lfs diff=lfs merge=lfs -text
+indices/rpj_wiki/rpj_wiki.passages.7.jsonl filter=lfs diff=lfs merge=lfs -text
--- a/benchmarks/data/README.md
+++ b/benchmarks/data/README.md
@@ -1,44 +0,0 @@
---
-license: mit
---
-
-# LEANN-RAG Evaluation Data
-
-This repository contains the necessary data to run the recall evaluation scripts for the [LEANN-RAG](https://huggingface.co/LEANN-RAG) project.
-
-## Dataset Components
-
-This dataset is structured into three main parts:
-
-1.  **Pre-built LEANN Indices**:
-    *   `dpr/`: A pre-built index for the DPR dataset.
-    *   `rpj_wiki/`: A pre-built index for the RPJ-Wiki dataset.
-    These indices were created using the `leann-core` library and are required by the `LeannSearcher`.
-
-2.  **Ground Truth Data**:
-    *   `ground_truth/`: Contains the ground truth files (`flat_results_nq_k3.json`) for both the DPR and RPJ-Wiki datasets. These files map queries to the original passage IDs from the Natural Questions benchmark, evaluated using the Contriever model.
-
-3.  **Queries**:
-    *   `queries/`: Contains the `nq_open.jsonl` file with the Natural Questions queries used for the evaluation.
-
-## Usage
-
-To use this data, you can download it locally using the `huggingface-hub` library. First, install the library:
-
-```bash
-pip install huggingface-hub
-```
-
-Then, you can download the entire dataset to a local directory (e.g., `data/`) with the following Python script:
-
-```python
-from huggingface_hub import snapshot_download
-
-snapshot_download(
-    repo_id="LEANN-RAG/leann-rag-evaluation-data",
-    repo_type="dataset",
-    local_dir="data"
-)
-```
-
-This will download all the necessary files into a local `data` folder, preserving the repository structure. The evaluation scripts in the main [LEANN-RAG Space](https://huggingface.co/LEANN-RAG) are configured to work with this data structure.
--- a/benchmarks/run_evaluation.py
+++ b/benchmarks/run_evaluation.py
@@ -12,7 +12,7 @@ import time
 from pathlib import Path

 import numpy as np
-from leann.api import LeannBuilder, LeannChat, LeannSearcher
+from leann.api import LeannBuilder, LeannSearcher


 def download_data_if_needed(data_root: Path, download_embeddings: bool = False):
@@ -197,25 +197,6 @@ def main():
    parser.add_argument(
        "--ef-search", type=int, default=120, help="The 'efSearch' parameter for HNSW."
    )
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        default=0,
-        help="Batch size for HNSW batched search (0 disables batching)",
-    )
-    parser.add_argument(
-        "--llm-type",
-        type=str,
-        choices=["ollama", "hf", "openai", "gemini", "simulated"],
-        default="ollama",
-        help="LLM backend type to optionally query during evaluation (default: ollama)",
-    )
-    parser.add_argument(
-        "--llm-model",
-        type=str,
-        default="qwen3:1.7b",
-        help="LLM model identifier for the chosen backend (default: qwen3:1.7b)",
-    )
    args = parser.parse_args()

    # --- Path Configuration ---
@@ -337,24 +318,9 @@ def main():

        for i in range(num_eval_queries):
            start_time = time.time()
-            new_results = searcher.search(
-                queries[i],
-                top_k=args.top_k,
-                complexity=args.ef_search,
-                batch_size=args.batch_size,
-            )
+            new_results = searcher.search(queries[i], top_k=args.top_k, ef=args.ef_search)
            search_times.append(time.time() - start_time)

-            # Optional: also call the LLM with configurable backend/model (does not affect recall)
-            llm_config = {"type": args.llm_type, "model": args.llm_model}
-            chat = LeannChat(args.index_path, llm_config=llm_config, searcher=searcher)
-            answer = chat.ask(
-                queries[i],
-                top_k=args.top_k,
-                complexity=args.ef_search,
-                batch_size=args.batch_size,
-            )
-            print(f"Answer: {answer}")
            # Correct Recall Calculation: Based on TEXT content
            new_texts = {result.text for result in new_results}

--- a/benchmarks/simple_mac_tpt_test.py
+++ b/benchmarks/simple_mac_tpt_test.py
@@ -20,7 +20,7 @@ except ImportError:

@dataclass
 class BenchmarkConfig:
-    model_path: str = "facebook/contriever-msmarco"
+    model_path: str = "facebook/contriever"
    batch_sizes: list[int] = None
    seq_length: int = 256
    num_runs: int = 5
@@ -34,7 +34,7 @@ class BenchmarkConfig:

    def __post_init__(self):
        if self.batch_sizes is None:
-            self.batch_sizes = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]
+            self.batch_sizes = [1, 2, 4, 8, 16, 32, 64]


 class MLXBenchmark:
@@ -179,16 +179,10 @@ class Benchmark:

    def _run_inference(self, input_ids: torch.Tensor) -> float:
        attention_mask = torch.ones_like(input_ids)
-        # print shape of input_ids and attention_mask
-        print(f"input_ids shape: {input_ids.shape}")
-        print(f"attention_mask shape: {attention_mask.shape}")
+
        start_time = time.time()
        with torch.no_grad():
            self.model(input_ids=input_ids, attention_mask=attention_mask)
-        if torch.cuda.is_available():
-            torch.cuda.synchronize()
-        if torch.backends.mps.is_available():
-            torch.mps.synchronize()
        end_time = time.time()

        return end_time - start_time
--- a/docs/ast_chunking_guide.md
+++ b/docs/ast_chunking_guide.md
@@ -1,128 +0,0 @@
-# AST-Aware Code chunking guide
-
-## Overview
-
-This guide covers best practices for using AST-aware code chunking in LEANN. AST chunking provides better semantic understanding of code structure compared to traditional text-based chunking.
-
-## Quick Start
-
-### Basic Usage
-
-```bash
-# Enable AST chunking for mixed content (code + docs)
-python -m apps.document_rag --enable-code-chunking --data-dir ./my_project
-
-# Specialized code repository indexing
-python -m apps.code_rag --repo-dir ./my_codebase
-
-# Global CLI with AST support
-leann build my-code-index --docs ./src --use-ast-chunking
-```
-
-### Installation
-
-```bash
-# Install LEANN with AST chunking support
-uv pip install -e "."
-```
-
-## Best Practices
-
-### When to Use AST Chunking
-
-✅ **Recommended for:**
- Code repositories with multiple languages
- Mixed documentation and code content
- Complex codebases with deep function/class hierarchies
- When working with Claude Code for code assistance
-
-❌ **Not recommended for:**
- Pure text documents
- Very large files (>1MB)
- Languages not supported by tree-sitter
-
-### Optimal Configuration
-
-```bash
-# Recommended settings for most codebases
-python -m apps.code_rag \
-    --repo-dir ./src \
-    --ast-chunk-size 768 \
-    --ast-chunk-overlap 96 \
-    --exclude-dirs .git __pycache__ node_modules build dist
-```
-
-### Supported Languages
-
-| Extension | Language | Status |
-|-----------|----------|--------|
-| `.py` | Python | ✅ Full support |
-| `.java` | Java | ✅ Full support |
-| `.cs` | C# | ✅ Full support |
-| `.ts`, `.tsx` | TypeScript | ✅ Full support |
-| `.js`, `.jsx` | JavaScript | ✅ Via TypeScript parser |
-
-## Integration Examples
-
-### Document RAG with Code Support
-
-```python
-# Enable code chunking in document RAG
-python -m apps.document_rag \
-    --enable-code-chunking \
-    --data-dir ./project \
-    --query "How does authentication work in the codebase?"
-```
-
-### Claude Code Integration
-
-When using with Claude Code MCP server, AST chunking provides better context for:
- Code completion and suggestions
- Bug analysis and debugging
- Architecture understanding
- Refactoring assistance
-
-## Troubleshooting
-
-### Common Issues
-
-1. **Fallback to Traditional Chunking**
-   - Normal behavior for unsupported languages
-   - Check logs for specific language support
-
-2. **Performance with Large Files**
-   - Adjust `--max-file-size` parameter
-   - Use `--exclude-dirs` to skip unnecessary directories
-
-3. **Quality Issues**
-   - Try different `--ast-chunk-size` values (512, 768, 1024)
-   - Adjust overlap for better context preservation
-
-### Debug Mode
-
-```bash
-export LEANN_LOG_LEVEL=DEBUG
-python -m apps.code_rag --repo-dir ./my_code
-```
-
-## Migration from Traditional Chunking
-
-Existing workflows continue to work without changes. To enable AST chunking:
-
-```bash
-# Before
-python -m apps.document_rag --chunk-size 256
-
-# After (maintains traditional chunking for non-code files)
-python -m apps.document_rag --enable-code-chunking --chunk-size 256 --ast-chunk-size 768
-```
-
-## References
-
- [astchunk GitHub Repository](https://github.com/yilinjz/astchunk)
- [LEANN MCP Integration](../packages/leann-mcp/README.md)
- [Research Paper](https://arxiv.org/html/2506.15655v1)
-
---
-
-**Note**: AST chunking maintains full backward compatibility while enhancing code understanding capabilities.
--- a/docs/features.md
+++ b/docs/features.md
@@ -3,7 +3,6 @@
 ## 🔥 Core Features

 - **🔄 Real-time Embeddings** - Eliminate heavy embedding storage with dynamic computation using optimized ZMQ servers and highly optimized search paradigm (overlapping and batching) with highly optimized embedding engine
- **🧠 AST-Aware Code Chunking** - Intelligent code chunking that preserves semantic boundaries (functions, classes, methods) for Python, Java, C#, and TypeScript files
 - **📈 Scalable Architecture** - Handles millions of documents on consumer hardware; the larger your dataset, the more LEANN can save
 - **🎯 Graph Pruning** - Advanced techniques to minimize the storage overhead of vector search to a limited footprint
 - **🏗️ Pluggable Backends** - HNSW/FAISS (default), with optional DiskANN for large-scale deployments
--- a/packages/leann-backend-diskann/leann_backend_diskann/diskann_embedding_server.py
+++ b/packages/leann-backend-diskann/leann_backend_diskann/diskann_embedding_server.py
@@ -83,7 +83,9 @@ def create_diskann_embedding_server(

    logger.info(f"Loading PassageManager with metadata_file_path: {passages_file}")
    passages = PassageManager(meta["passage_sources"], metadata_file_path=passages_file)
-    logger.info(f"Loaded PassageManager with {len(passages)} passages from metadata")
+    logger.info(
+        f"Loaded PassageManager with {len(passages.global_offset_map)} passages from metadata"
+    )

    # Import protobuf after ensuring the path is correct
    try:
--- a/packages/leann-backend-diskann/pyproject.toml
+++ b/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 = "0.2.9"
+dependencies = ["leann-core==0.2.9", "numpy", "protobuf>=3.19.0"]

 [tool.scikit-build]
 # Key: simplified CMake path
--- a/packages/leann-backend-diskann/third_party/DiskANN
+++ b/packages/leann-backend-diskann/third_party/DiskANN
--- a/packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py
+++ b/packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py
@@ -1,7 +1,6 @@
 import logging
 import os
 import shutil
-import time
 from pathlib import Path
 from typing import Any, Literal, Optional

@@ -237,7 +236,6 @@ class HNSWSearcher(BaseSearcher):
        distances = np.empty((batch_size_query, top_k), dtype=np.float32)
        labels = np.empty((batch_size_query, top_k), dtype=np.int64)

-        search_time = time.time()
        self._index.search(
            query.shape[0],
            faiss.swig_ptr(query),
@@ -246,8 +244,7 @@ class HNSWSearcher(BaseSearcher):
            faiss.swig_ptr(labels),
            params,
        )
-        search_time = time.time() - search_time
-        logger.info(f"  Search time in HNSWSearcher.search() backend: {search_time} seconds")
+
        string_labels = [[str(int_label) for int_label in batch_labels] for batch_labels in labels]

        return {"labels": string_labels, "distances": distances}
--- a/packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_embedding_server.py
+++ b/packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_embedding_server.py
@@ -90,7 +90,9 @@ def create_hnsw_embedding_server(
        embedding_dim: int = int(meta.get("dimensions", 0))
    except Exception:
        embedding_dim = 0
-    logger.info(f"Loaded PassageManager with {len(passages)} passages from metadata")
+    logger.info(
+        f"Loaded PassageManager with {len(passages.global_offset_map)} passages from metadata"
+    )

    # (legacy ZMQ thread removed; using shutdown-capable server only)

--- a/packages/leann-backend-hnsw/pyproject.toml
+++ b/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 = "0.2.9"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
 dependencies = [
-    "leann-core==0.3.2",
+    "leann-core==0.2.9",
    "numpy",
    "pyzmq>=23.0.0",
    "msgpack>=1.0.0",
--- a/packages/leann-core/pyproject.toml
+++ b/packages/leann-core/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "leann-core"
-version = "0.3.2"
+version = "0.2.9"
 description = "Core API and plugin system for LEANN"
 readme = "README.md"
 requires-python = ">=3.9"
--- a/packages/leann-core/src/leann/api.py
+++ b/packages/leann-core/src/leann/api.py
@@ -46,7 +46,6 @@ def compute_embeddings(
            - "sentence-transformers": Use sentence-transformers library (default)
            - "mlx": Use MLX backend for Apple Silicon
            - "openai": Use OpenAI embedding API
-            - "gemini": Use Google Gemini embedding API
        use_server: Whether to use embedding server (True for search, False for build)

    Returns:
@@ -119,12 +118,9 @@ class PassageManager:
    def __init__(
        self, passage_sources: list[dict[str, Any]], metadata_file_path: Optional[str] = None
    ):
-        self.offset_maps: dict[str, dict[str, int]] = {}
-        self.passage_files: dict[str, str] = {}
-        # Avoid materializing a single gigantic global map to reduce memory
-        # footprint on very large corpora (e.g., 60M+ passages). Instead, keep
-        # per-shard maps and do a lightweight per-shard lookup on demand.
-        self._total_count: int = 0
+        self.offset_maps = {}
+        self.passage_files = {}
+        self.global_offset_map = {}  # Combined map for fast lookup

        # Derive index base name for standard sibling fallbacks, e.g., <index_name>.passages.*
        index_name_base = None
@@ -145,25 +141,12 @@ class PassageManager:
                default_name: Optional[str],
                source_dict: dict[str, Any],
            ) -> list[Path]:
-                """
-                Build an ordered list of candidate paths. For relative paths specified in
-                metadata, prefer resolution relative to the metadata file directory first,
-                then fall back to CWD-based resolution, and finally to conventional
-                sibling defaults (e.g., <index_base>.passages.idx / .jsonl).
-                """
                candidates: list[Path] = []
-                # 1) Primary path
+                # 1) Primary as-is (absolute or relative)
                if primary:
                    p = Path(primary)
-                    if p.is_absolute():
-                        candidates.append(p)
-                    else:
-                        # Prefer metadata-relative resolution for relative paths
-                        if metadata_file_path:
-                            candidates.append(Path(metadata_file_path).parent / p)
-                        # Also consider CWD-relative as a fallback for legacy layouts
-                        candidates.append(Path.cwd() / p)
-                # 2) metadata-relative explicit relative key (if present)
+                    candidates.append(p if p.is_absolute() else (Path.cwd() / p))
+                # 2) metadata-relative explicit relative key
                if metadata_file_path and source_dict.get(relative_key):
                    candidates.append(Path(metadata_file_path).parent / source_dict[relative_key])
                # 3) metadata-relative standard sibling filename
@@ -193,28 +176,23 @@ class PassageManager:
                raise FileNotFoundError(f"Passage index file not found: {index_file}")

            with open(index_file, "rb") as f:
-                offset_map: dict[str, int] = pickle.load(f)
+                offset_map = pickle.load(f)
                self.offset_maps[passage_file] = offset_map
                self.passage_files[passage_file] = passage_file
-                self._total_count += len(offset_map)
+
+                # Build global map for O(1) lookup
+                for passage_id, offset in offset_map.items():
+                    self.global_offset_map[passage_id] = (passage_file, offset)

    def get_passage(self, passage_id: str) -> dict[str, Any]:
-        # Fast path: check each shard map (there are typically few shards).
-        # This avoids building a massive combined dict while keeping lookups
-        # bounded by the number of shards.
-        for passage_file, offset_map in self.offset_maps.items():
-            try:
-                offset = offset_map[passage_id]
-                with open(passage_file, encoding="utf-8") as f:
-                    f.seek(offset)
-                    return json.loads(f.readline())
-            except KeyError:
-                continue
+        if passage_id in self.global_offset_map:
+            passage_file, offset = self.global_offset_map[passage_id]
+            # Lazy file opening - only open when needed
+            with open(passage_file, encoding="utf-8") as f:
+                f.seek(offset)
+                return json.loads(f.readline())
        raise KeyError(f"Passage ID not found: {passage_id}")

-    def __len__(self) -> int:
-        return self._total_count
-

 class LeannBuilder:
    def __init__(
@@ -328,23 +306,6 @@ class LeannBuilder:
    def build_index(self, index_path: str):
        if not self.chunks:
            raise ValueError("No chunks added.")
-
-        # Filter out invalid/empty text chunks early to keep passage and embedding counts aligned
-        valid_chunks: list[dict[str, Any]] = []
-        skipped = 0
-        for chunk in self.chunks:
-            text = chunk.get("text", "")
-            if isinstance(text, str) and text.strip():
-                valid_chunks.append(chunk)
-            else:
-                skipped += 1
-        if skipped > 0:
-            print(
-                f"Warning: Skipping {skipped} empty/invalid text chunk(s). Processing {len(valid_chunks)} valid chunks"
-            )
-            self.chunks = valid_chunks
-            if not self.chunks:
-                raise ValueError("All provided chunks are empty or invalid. Nothing to index.")
        if self.dimensions is None:
            self.dimensions = len(
                compute_embeddings(
@@ -578,8 +539,6 @@ class LeannSearcher:
        self.passage_manager = PassageManager(
            self.meta_data.get("passage_sources", []), metadata_file_path=self.meta_path_str
        )
-        # Preserve backend name for conditional parameter forwarding
-        self.backend_name = backend_name
        backend_factory = BACKEND_REGISTRY.get(backend_name)
        if backend_factory is None:
            raise ValueError(f"Backend '{backend_name}' not found.")
@@ -599,7 +558,6 @@ class LeannSearcher:
        recompute_embeddings: bool = True,
        pruning_strategy: Literal["global", "local", "proportional"] = "global",
        expected_zmq_port: int = 5557,
-        batch_size: int = 0,
        **kwargs,
    ) -> list[SearchResult]:
        logger.info("🔍 LeannSearcher.search() called:")
@@ -608,9 +566,7 @@ class LeannSearcher:
        logger.info(f"  Additional kwargs: {kwargs}")

        # Smart top_k detection and adjustment
-        # Use PassageManager length (sum of shard sizes) to avoid
-        # depending on a massive combined map
-        total_docs = len(self.passage_manager)
+        total_docs = len(self.passage_manager.global_offset_map)
        original_top_k = top_k
        if top_k > total_docs:
            top_k = total_docs
@@ -639,33 +595,23 @@ class LeannSearcher:
            use_server_if_available=recompute_embeddings,
            zmq_port=zmq_port,
        )
-        logger.info(f"  Generated embedding shape: {query_embedding.shape}")
-        embedding_time = time.time() - start_time
-        logger.info(f"  Embedding time: {embedding_time} seconds")
+        # logger.info(f"  Generated embedding shape: {query_embedding.shape}")
+        time.time() - start_time
+        # logger.info(f"  Embedding time: {embedding_time} seconds")

        start_time = time.time()
-        backend_search_kwargs: dict[str, Any] = {
-            "complexity": complexity,
-            "beam_width": beam_width,
-            "prune_ratio": prune_ratio,
-            "recompute_embeddings": recompute_embeddings,
-            "pruning_strategy": pruning_strategy,
-            "zmq_port": zmq_port,
-        }
-        # Only HNSW supports batching; forward conditionally
-        if self.backend_name == "hnsw":
-            backend_search_kwargs["batch_size"] = batch_size
-
-        # Merge any extra kwargs last
-        backend_search_kwargs.update(kwargs)
-
        results = self.backend_impl.search(
            query_embedding,
            top_k,
-            **backend_search_kwargs,
+            complexity=complexity,
+            beam_width=beam_width,
+            prune_ratio=prune_ratio,
+            recompute_embeddings=recompute_embeddings,
+            pruning_strategy=pruning_strategy,
+            zmq_port=zmq_port,
+            **kwargs,
        )
-        search_time = time.time() - start_time
-        logger.info(f"  Search time in search() LEANN searcher: {search_time} seconds")
+        # logger.info(f"  Search time: {search_time} seconds")
        logger.info(f"  Backend returned: labels={len(results.get('labels', [[]])[0])} results")

        enriched_results = []
@@ -716,9 +662,8 @@ class LeannSearcher:
        This method should be called after you're done using the searcher,
        especially in test environments or batch processing scenarios.
        """
-        backend = getattr(self.backend_impl, "embedding_server_manager", None)
-        if backend is not None:
-            backend.stop_server()
+        if hasattr(self.backend_impl, "embedding_server_manager"):
+            self.backend_impl.embedding_server_manager.stop_server()

    # Enable automatic cleanup patterns
    def __enter__(self):
@@ -744,15 +689,9 @@ class LeannChat:
        index_path: str,
        llm_config: Optional[dict[str, Any]] = None,
        enable_warmup: bool = False,
-        searcher: Optional[LeannSearcher] = None,
        **kwargs,
    ):
-        if searcher is None:
-            self.searcher = LeannSearcher(index_path, enable_warmup=enable_warmup, **kwargs)
-            self._owns_searcher = True
-        else:
-            self.searcher = searcher
-            self._owns_searcher = False
+        self.searcher = LeannSearcher(index_path, enable_warmup=enable_warmup, **kwargs)
        self.llm = get_llm(llm_config)

    def ask(
@@ -766,7 +705,6 @@ class LeannChat:
        pruning_strategy: Literal["global", "local", "proportional"] = "global",
        llm_kwargs: Optional[dict[str, Any]] = None,
        expected_zmq_port: int = 5557,
-        batch_size: int = 0,
        **search_kwargs,
    ):
        if llm_kwargs is None:
@@ -781,11 +719,10 @@ class LeannChat:
            recompute_embeddings=recompute_embeddings,
            pruning_strategy=pruning_strategy,
            expected_zmq_port=expected_zmq_port,
-            batch_size=batch_size,
            **search_kwargs,
        )
        search_time = time.time() - search_time
-        logger.info(f"  Search time: {search_time} seconds")
+        # logger.info(f"  Search time: {search_time} seconds")
        context = "\n\n".join([r.text for r in results])
        prompt = (
            "Here is some retrieved context that might help answer your question:\n\n"
@@ -821,9 +758,7 @@ class LeannChat:
        This method should be called after you're done using the chat interface,
        especially in test environments or batch processing scenarios.
        """
-        # Only stop the embedding server if this LeannChat instance created the searcher.
-        # When a shared searcher is passed in, avoid shutting down the server to enable reuse.
-        if getattr(self, "_owns_searcher", False) and hasattr(self.searcher, "cleanup"):
+        if hasattr(self.searcher, "cleanup"):
            self.searcher.cleanup()

    # Enable automatic cleanup patterns
--- a/packages/leann-core/src/leann/chat.py
+++ b/packages/leann-core/src/leann/chat.py
@@ -680,60 +680,6 @@ class HFChat(LLMInterface):
        return response.strip()


-class GeminiChat(LLMInterface):
-    """LLM interface for Google Gemini models."""
-
-    def __init__(self, model: str = "gemini-2.5-flash", api_key: Optional[str] = None):
-        self.model = model
-        self.api_key = api_key or os.getenv("GEMINI_API_KEY")
-
-        if not self.api_key:
-            raise ValueError(
-                "Gemini API key is required. Set GEMINI_API_KEY environment variable or pass api_key parameter."
-            )
-
-        logger.info(f"Initializing Gemini Chat with model='{model}'")
-
-        try:
-            import google.genai as genai
-
-            self.client = genai.Client(api_key=self.api_key)
-        except ImportError:
-            raise ImportError(
-                "The 'google-genai' library is required for Gemini models. Please install it with 'uv pip install google-genai'."
-            )
-
-    def ask(self, prompt: str, **kwargs) -> str:
-        logger.info(f"Sending request to Gemini with model {self.model}")
-
-        try:
-            from google.genai.types import GenerateContentConfig
-
-            generation_config = GenerateContentConfig(
-                temperature=kwargs.get("temperature", 0.7),
-                max_output_tokens=kwargs.get("max_tokens", 1000),
-            )
-
-            # Handle top_p parameter
-            if "top_p" in kwargs:
-                generation_config.top_p = kwargs["top_p"]
-
-            response = self.client.models.generate_content(
-                model=self.model,
-                contents=prompt,
-                config=generation_config,
-            )
-            # Handle potential None response text
-            response_text = response.text
-            if response_text is None:
-                logger.warning("Gemini returned None response text")
-                return ""
-            return response_text.strip()
-        except Exception as e:
-            logger.error(f"Error communicating with Gemini: {e}")
-            return f"Error: Could not get a response from Gemini. Details: {e}"
-
-
 class OpenAIChat(LLMInterface):
    """LLM interface for OpenAI models."""

@@ -847,8 +793,6 @@ def get_llm(llm_config: Optional[dict[str, Any]] = None) -> LLMInterface:
        return HFChat(model_name=model or "deepseek-ai/deepseek-llm-7b-chat")
    elif llm_type == "openai":
        return OpenAIChat(model=model or "gpt-4o", api_key=llm_config.get("api_key"))
-    elif llm_type == "gemini":
-        return GeminiChat(model=model or "gemini-2.5-flash", api_key=llm_config.get("api_key"))
    elif llm_type == "simulated":
        return SimulatedChat()
    else:
--- a/packages/leann-core/src/leann/cli.py
+++ b/packages/leann-core/src/leann/cli.py
@@ -1,15 +1,13 @@
 import argparse
 import asyncio
-import sys
 from pathlib import Path
-from typing import Any, Optional, Union
+from typing import Union

 from llama_index.core import SimpleDirectoryReader
 from llama_index.core.node_parser import SentenceSplitter
 from tqdm import tqdm

 from .api import LeannBuilder, LeannChat, LeannSearcher
-from .registry import register_project_directory


 def extract_pdf_text_with_pymupdf(file_path: str) -> str:
@@ -86,7 +84,6 @@ Examples:
  leann search my-docs "query"                                           # Search in my-docs index
  leann ask my-docs "question"                                           # Ask my-docs index
  leann list                                                             # List all stored indexes
-  leann remove my-docs                                                   # Remove an index (local first, then global)
            """,
        )

@@ -151,59 +148,6 @@ Examples:
            type=str,
            help="Comma-separated list of file extensions to include (e.g., '.txt,.pdf,.pptx'). If not specified, uses default supported types.",
        )
-        build_parser.add_argument(
-            "--include-hidden",
-            action=argparse.BooleanOptionalAction,
-            default=False,
-            help="Include hidden files and directories (paths starting with '.') during indexing (default: false)",
-        )
-        build_parser.add_argument(
-            "--doc-chunk-size",
-            type=int,
-            default=256,
-            help="Document chunk size in tokens/characters (default: 256)",
-        )
-        build_parser.add_argument(
-            "--doc-chunk-overlap",
-            type=int,
-            default=128,
-            help="Document chunk overlap (default: 128)",
-        )
-        build_parser.add_argument(
-            "--code-chunk-size",
-            type=int,
-            default=512,
-            help="Code chunk size in tokens/lines (default: 512)",
-        )
-        build_parser.add_argument(
-            "--code-chunk-overlap",
-            type=int,
-            default=50,
-            help="Code chunk overlap (default: 50)",
-        )
-        build_parser.add_argument(
-            "--use-ast-chunking",
-            action="store_true",
-            help="Enable AST-aware chunking for code files (requires astchunk)",
-        )
-        build_parser.add_argument(
-            "--ast-chunk-size",
-            type=int,
-            default=768,
-            help="AST chunk size in characters (default: 768)",
-        )
-        build_parser.add_argument(
-            "--ast-chunk-overlap",
-            type=int,
-            default=96,
-            help="AST chunk overlap in characters (default: 96)",
-        )
-        build_parser.add_argument(
-            "--ast-fallback-traditional",
-            action="store_true",
-            default=True,
-            help="Fall back to traditional chunking if AST chunking fails (default: True)",
-        )

        # Search command
        search_parser = subparsers.add_parser("search", help="Search documents")
@@ -230,11 +174,6 @@ Examples:
            default="global",
            help="Pruning strategy (default: global)",
        )
-        search_parser.add_argument(
-            "--non-interactive",
-            action="store_true",
-            help="Non-interactive mode: automatically select index without prompting",
-        )

        # Ask command
        ask_parser = subparsers.add_parser("ask", help="Ask questions")
@@ -282,18 +221,35 @@ Examples:
        # List command
        subparsers.add_parser("list", help="List all indexes")

-        # Remove command
-        remove_parser = subparsers.add_parser("remove", help="Remove an index")
-        remove_parser.add_argument("index_name", help="Index name to remove")
-        remove_parser.add_argument(
-            "--force", "-f", action="store_true", help="Force removal without confirmation"
-        )
-
        return parser

    def register_project_dir(self):
        """Register current project directory in global registry"""
-        register_project_directory()
+        global_registry = Path.home() / ".leann" / "projects.json"
+        global_registry.parent.mkdir(exist_ok=True)
+
+        current_dir = str(Path.cwd())
+
+        # Load existing registry
+        projects = []
+        if global_registry.exists():
+            try:
+                import json
+
+                with open(global_registry) as f:
+                    projects = json.load(f)
+            except Exception:
+                projects = []
+
+        # Add current directory if not already present
+        if current_dir not in projects:
+            projects.append(current_dir)
+
+        # Save registry
+        import json
+
+        with open(global_registry, "w") as f:
+            json.dump(projects, f, indent=2)

    def _build_gitignore_parser(self, docs_dir: str):
        """Build gitignore parser using gitignore-parser library."""
@@ -353,6 +309,8 @@ Examples:
            return False

    def list_indexes(self):
+        print("Stored LEANN indexes:")
+
        # Get all project directories with .leann
        global_registry = Path.home() / ".leann" / "projects.json"
        all_projects = []
@@ -378,486 +336,58 @@ Examples:
        if (current_path / ".leann" / "indexes").exists() and current_path not in valid_projects:
            valid_projects.append(current_path)

-        # Separate current and other projects
-        other_projects = []
-
-        for project_path in valid_projects:
-            if project_path != current_path:
-                other_projects.append(project_path)
-
-        print("📚 LEANN Indexes")
-        print("=" * 50)
+        if not valid_projects:
+            print(
+                "No indexes found. Use 'leann build <name> --docs <dir> [<dir2> ...]' to create one."
+            )
+            return

        total_indexes = 0
-        current_indexes_count = 0
+        current_dir = Path.cwd()

-        # Show current project first (most important)
-        print("\n🏠 Current Project")
-        print(f"   {current_path}")
-        print("   " + "─" * 45)
-
-        current_indexes = self._discover_indexes_in_project(current_path)
-        if current_indexes:
-            for idx in current_indexes:
-                total_indexes += 1
-                current_indexes_count += 1
-                type_icon = "📁" if idx["type"] == "cli" else "📄"
-                print(f"   {current_indexes_count}. {type_icon} {idx['name']} {idx['status']}")
-                if idx["size_mb"] > 0:
-                    print(f"      📦 Size: {idx['size_mb']:.1f} MB")
-        else:
-            print("   📭 No indexes in current project")
-
-        # Show other projects (reference information)
-        if other_projects:
-            print("\n\n🗂️  Other Projects")
-            print("   " + "─" * 45)
-
-            for project_path in other_projects:
-                project_indexes = self._discover_indexes_in_project(project_path)
-                if not project_indexes:
-                    continue
-
-                print(f"\n   📂 {project_path.name}")
-                print(f"      {project_path}")
-
-                for idx in project_indexes:
-                    total_indexes += 1
-                    type_icon = "📁" if idx["type"] == "cli" else "📄"
-                    print(f"      • {type_icon} {idx['name']} {idx['status']}")
-                    if idx["size_mb"] > 0:
-                        print(f"        📦 {idx['size_mb']:.1f} MB")
-
-        # Summary and usage info
-        print("\n" + "=" * 50)
-        if total_indexes == 0:
-            print("💡 Get started:")
-            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
-            )
-            print(f"📊 Total: {total_indexes} indexes across {projects_count} projects")
-
-            if current_indexes_count > 0:
-                print("\n💫 Quick start (current project):")
-                # Get first index from current project for example
-                current_indexes_dir = current_path / ".leann" / "indexes"
-                if current_indexes_dir.exists():
-                    current_index_dirs = [d for d in current_indexes_dir.iterdir() if d.is_dir()]
-                    if current_index_dirs:
-                        example_name = current_index_dirs[0].name
-                        print(f'   leann search {example_name} "your query"')
-                        print(f"   leann ask {example_name} --interactive")
-            else:
-                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)"""
-        indexes = []
-
-        # 1. CLI format: .leann/indexes/index_name/
-        cli_indexes_dir = project_path / ".leann" / "indexes"
-        if cli_indexes_dir.exists():
-            for index_dir in cli_indexes_dir.iterdir():
-                if index_dir.is_dir():
-                    meta_file = index_dir / "documents.leann.meta.json"
-                    status = "✅" if meta_file.exists() else "❌"
-
-                    size_mb = 0
-                    if meta_file.exists():
-                        try:
-                            size_mb = sum(
-                                f.stat().st_size for f in index_dir.iterdir() if f.is_file()
-                            ) / (1024 * 1024)
-                        except (OSError, PermissionError):
-                            pass
-
-                    indexes.append(
-                        {
-                            "name": index_dir.name,
-                            "type": "cli",
-                            "status": status,
-                            "size_mb": size_mb,
-                            "path": index_dir,
-                        }
-                    )
-
-        # 2. Apps format: *.leann.meta.json files anywhere in the project
-        cli_indexes_dir = project_path / ".leann" / "indexes"
-        for meta_file in project_path.rglob("*.leann.meta.json"):
-            if meta_file.is_file():
-                # Skip CLI-built indexes (which store meta under .leann/indexes/<name>/)
-                try:
-                    if cli_indexes_dir.exists() and cli_indexes_dir in meta_file.parents:
-                        continue
-                except Exception:
-                    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
-                file_base = meta_file.name.replace(".leann.meta.json", "")
-
-                # Apps indexes are considered complete if the .leann.meta.json file exists
-                status = "✅"
-
-                # Calculate total size of all related files (use file base)
-                size_mb = 0
-                try:
-                    index_dir = meta_file.parent
-                    for related_file in index_dir.glob(f"{file_base}.leann*"):
-                        size_mb += related_file.stat().st_size / (1024 * 1024)
-                except (OSError, PermissionError):
-                    pass
-
-                indexes.append(
-                    {
-                        "name": display_name,
-                        "type": "app",
-                        "status": status,
-                        "size_mb": size_mb,
-                        "path": meta_file,
-                    }
-                )
-
-        return indexes
-
-    def remove_index(self, index_name: str, force: bool = False):
-        """Safely remove an index - always show all matches for transparency"""
-
-        # Always do a comprehensive search for safety
-        print(f"🔍 Searching for all indexes named '{index_name}'...")
-        all_matches = self._find_all_matching_indexes(index_name)
-
-        if not all_matches:
-            print(f"❌ Index '{index_name}' not found in any project.")
-            return False
-
-        if len(all_matches) == 1:
-            return self._remove_single_match(all_matches[0], index_name, force)
-        else:
-            return self._remove_from_multiple_matches(all_matches, index_name, force)
-
-    def _find_all_matching_indexes(self, index_name: str):
-        """Find all indexes with the given name across all projects"""
-        matches = []
-
-        # Get all registered projects
-        global_registry = Path.home() / ".leann" / "projects.json"
-        all_projects = []
-
-        if global_registry.exists():
-            try:
-                import json
-
-                with open(global_registry) as f:
-                    all_projects = json.load(f)
-            except Exception:
-                pass
-
-        # Always include current project
-        current_path = Path.cwd()
-        if str(current_path) not in all_projects:
-            all_projects.append(str(current_path))
-
-        # Search across all projects
-        for project_dir in all_projects:
-            project_path = Path(project_dir)
-            if not project_path.exists():
+        for project_path in valid_projects:
+            indexes_dir = project_path / ".leann" / "indexes"
+            if not indexes_dir.exists():
                continue

-            # 1) CLI-format index under .leann/indexes/<name>
-            index_dir = project_path / ".leann" / "indexes" / index_name
-            if index_dir.exists():
-                is_current = project_path == current_path
-                matches.append(
-                    {
-                        "project_path": project_path,
-                        "index_dir": index_dir,
-                        "is_current": is_current,
-                        "kind": "cli",
-                    }
-                )
+            index_dirs = [d for d in indexes_dir.iterdir() if d.is_dir()]
+            if not index_dirs:
+                continue

-            # 2) App-format indexes
-            # We support two ways of addressing apps:
-            #   a) by the file base (e.g., `pdf_documents`)
-            #   b) by the parent directory name (e.g., `new_txt`)
-            seen_app_meta = set()
-
-            # 2a) by file base
-            for meta_file in project_path.rglob(f"{index_name}.leann.meta.json"):
-                if meta_file.is_file():
-                    # Skip CLI-built indexes' meta under .leann/indexes
-                    try:
-                        cli_indexes_dir = project_path / ".leann" / "indexes"
-                        if cli_indexes_dir.exists() and cli_indexes_dir in meta_file.parents:
-                            continue
-                    except Exception:
-                        pass
-                    is_current = project_path == current_path
-                    key = (str(project_path), str(meta_file))
-                    if key in seen_app_meta:
-                        continue
-                    seen_app_meta.add(key)
-                    matches.append(
-                        {
-                            "project_path": project_path,
-                            "files_dir": meta_file.parent,
-                            "meta_file": meta_file,
-                            "is_current": is_current,
-                            "kind": "app",
-                            "display_name": meta_file.parent.name,
-                            "file_base": meta_file.name.replace(".leann.meta.json", ""),
-                        }
-                    )
-
-            # 2b) by parent directory name
-            for meta_file in project_path.rglob("*.leann.meta.json"):
-                if meta_file.is_file() and meta_file.parent.name == index_name:
-                    # Skip CLI-built indexes' meta under .leann/indexes
-                    try:
-                        cli_indexes_dir = project_path / ".leann" / "indexes"
-                        if cli_indexes_dir.exists() and cli_indexes_dir in meta_file.parents:
-                            continue
-                    except Exception:
-                        pass
-                    is_current = project_path == current_path
-                    key = (str(project_path), str(meta_file))
-                    if key in seen_app_meta:
-                        continue
-                    seen_app_meta.add(key)
-                    matches.append(
-                        {
-                            "project_path": project_path,
-                            "files_dir": meta_file.parent,
-                            "meta_file": meta_file,
-                            "is_current": is_current,
-                            "kind": "app",
-                            "display_name": meta_file.parent.name,
-                            "file_base": meta_file.name.replace(".leann.meta.json", ""),
-                        }
-                    )
-
-        # Sort: current project first, then by project name
-        matches.sort(key=lambda x: (not x["is_current"], x["project_path"].name))
-        return matches
-
-    def _remove_single_match(self, match, index_name: str, force: bool):
-        """Handle removal when only one match is found"""
-        project_path = match["project_path"]
-        is_current = match["is_current"]
-        kind = match.get("kind", "cli")
-
-        if is_current:
-            location_info = "current project"
-            emoji = "🏠"
-        else:
-            location_info = f"other project '{project_path.name}'"
-            emoji = "📂"
-
-        print(f"✅ Found 1 index named '{index_name}':")
-        print(f"   {emoji} Location: {location_info}")
-        if kind == "cli":
-            print(f"   📍 Path: {project_path / '.leann' / 'indexes' / index_name}")
-        else:
-            print(f"   📍 Meta: {match['meta_file']}")
-
-        if not force:
-            if not is_current:
-                print("\n⚠️  CROSS-PROJECT REMOVAL!")
-                print("   This will delete the index from another project.")
-
-            response = input(f"   ❓ Confirm removal from {location_info}? (y/N): ").strip().lower()
-            if response not in ["y", "yes"]:
-                print("   ❌ Removal cancelled.")
-                return False
-
-        if kind == "cli":
-            return self._delete_index_directory(
-                match["index_dir"],
-                index_name,
-                project_path if not is_current else None,
-                is_app=False,
-            )
-        else:
-            return self._delete_index_directory(
-                match["files_dir"],
-                match.get("display_name", index_name),
-                project_path if not is_current else None,
-                is_app=True,
-                meta_file=match.get("meta_file"),
-                app_file_base=match.get("file_base"),
-            )
-
-    def _remove_from_multiple_matches(self, matches, index_name: str, force: bool):
-        """Handle removal when multiple matches are found"""
-
-        print(f"⚠️  Found {len(matches)} indexes named '{index_name}':")
-        print("   " + "─" * 50)
-
-        for i, match in enumerate(matches, 1):
-            project_path = match["project_path"]
-            is_current = match["is_current"]
-            kind = match.get("kind", "cli")
-
-            if is_current:
-                print(f"   {i}. 🏠 Current project ({'CLI' if kind == 'cli' else 'APP'})")
+            # Show project header
+            if project_path == current_dir:
+                print(f"\n📁 Current project ({project_path}):")
            else:
-                print(f"   {i}. 📂 {project_path.name} ({'CLI' if kind == 'cli' else 'APP'})")
+                print(f"\n📂 {project_path}:")

-            # Show path details
-            if kind == "cli":
-                print(f"      📍 {project_path / '.leann' / 'indexes' / index_name}")
-            else:
-                print(f"      📍 {match['meta_file']}")
+            for index_dir in index_dirs:
+                total_indexes += 1
+                index_name = index_dir.name
+                meta_file = index_dir / "documents.leann.meta.json"
+                status = "✓" if meta_file.exists() else "✗"

-            # Show size info
-            try:
-                if kind == "cli":
-                    size_mb = sum(
-                        f.stat().st_size for f in match["index_dir"].iterdir() if f.is_file()
-                    ) / (1024 * 1024)
-                else:
-                    file_base = match.get("file_base")
-                    size_mb = 0.0
-                    if file_base:
-                        size_mb = sum(
-                            f.stat().st_size
-                            for f in match["files_dir"].glob(f"{file_base}.leann*")
-                            if f.is_file()
-                        ) / (1024 * 1024)
-                print(f"      📦 Size: {size_mb:.1f} MB")
-            except (OSError, PermissionError):
-                pass
-
-        print("   " + "─" * 50)
-
-        if force:
-            print("   ❌ Multiple matches found, but --force specified.")
-            print("   Please run without --force to choose which one to remove.")
-            return False
-
-        try:
-            choice = input(
-                f"   ❓ Which one to remove? (1-{len(matches)}, or 'c' to cancel): "
-            ).strip()
-            if choice.lower() == "c":
-                print("   ❌ Removal cancelled.")
-                return False
-
-            choice_idx = int(choice) - 1
-            if 0 <= choice_idx < len(matches):
-                selected_match = matches[choice_idx]
-                project_path = selected_match["project_path"]
-                is_current = selected_match["is_current"]
-                kind = selected_match.get("kind", "cli")
-
-                location = "current project" if is_current else f"'{project_path.name}' project"
-                print(f"   🎯 Selected: Remove from {location}")
-
-                # Final confirmation for safety
-                confirm = input(
-                    f"   ❓ FINAL CONFIRMATION - Type '{index_name}' to proceed: "
-                ).strip()
-                if confirm != index_name:
-                    print("   ❌ Confirmation failed. Removal cancelled.")
-                    return False
-
-                if kind == "cli":
-                    return self._delete_index_directory(
-                        selected_match["index_dir"],
-                        index_name,
-                        project_path if not is_current else None,
-                        is_app=False,
+                print(f"  {total_indexes}. {index_name} [{status}]")
+                if status == "✓":
+                    size_mb = sum(f.stat().st_size for f in index_dir.iterdir() if f.is_file()) / (
+                        1024 * 1024
                    )
-                else:
-                    return self._delete_index_directory(
-                        selected_match["files_dir"],
-                        selected_match.get("display_name", index_name),
-                        project_path if not is_current else None,
-                        is_app=True,
-                        meta_file=selected_match.get("meta_file"),
-                        app_file_base=selected_match.get("file_base"),
-                    )
-            else:
-                print("   ❌ Invalid choice. Removal cancelled.")
-                return False
+                    print(f"     Size: {size_mb:.1f} MB")

-        except (ValueError, KeyboardInterrupt):
-            print("\n   ❌ Invalid input. Removal cancelled.")
-            return False
+        if total_indexes > 0:
+            print(f"\nTotal: {total_indexes} indexes across {len(valid_projects)} projects")
+            print("\nUsage (current project only):")

-    def _delete_index_directory(
-        self,
-        index_dir: Path,
-        index_display_name: str,
-        project_path: Optional[Path] = None,
-        is_app: bool = False,
-        meta_file: Optional[Path] = None,
-        app_file_base: Optional[str] = None,
-    ):
-        """Delete a CLI index directory or APP index files safely."""
-        try:
-            if is_app:
-                removed = 0
-                errors = 0
-                # Delete only files that belong to this app index (based on file base)
-                pattern_base = app_file_base or ""
-                for f in index_dir.glob(f"{pattern_base}.leann*"):
-                    try:
-                        f.unlink()
-                        removed += 1
-                    except Exception:
-                        errors += 1
-                # Best-effort: also remove the meta file if specified and still exists
-                if meta_file and meta_file.exists():
-                    try:
-                        meta_file.unlink()
-                        removed += 1
-                    except Exception:
-                        errors += 1
-
-                if removed > 0 and errors == 0:
-                    if project_path:
-                        print(
-                            f"✅ App index '{index_display_name}' removed from {project_path.name}"
-                        )
-                    else:
-                        print(f"✅ App index '{index_display_name}' removed successfully")
-                    return True
-                elif removed > 0 and errors > 0:
-                    print(
-                        f"⚠️  App index '{index_display_name}' partially removed (some files couldn't be deleted)"
-                    )
-                    return True
-                else:
-                    print(
-                        f"❌ No files found to remove for app index '{index_display_name}' in {index_dir}"
-                    )
-                    return False
-            else:
-                import shutil
-
-                shutil.rmtree(index_dir)
-
-                if project_path:
-                    print(f"✅ Index '{index_display_name}' removed from {project_path.name}")
-                else:
-                    print(f"✅ Index '{index_display_name}' removed successfully")
-                return True
-        except Exception as e:
-            print(f"❌ Error removing index '{index_display_name}': {e}")
-            return False
+            # Show example from current project
+            current_indexes_dir = current_dir / ".leann" / "indexes"
+            if current_indexes_dir.exists():
+                current_index_dirs = [d for d in current_indexes_dir.iterdir() if d.is_dir()]
+                if current_index_dirs:
+                    example_name = current_index_dirs[0].name
+                    print(f'  leann search {example_name} "your query"')
+                    print(f"  leann ask {example_name} --interactive")

    def load_documents(
-        self,
-        docs_paths: Union[str, list],
-        custom_file_types: Union[str, None] = None,
-        include_hidden: bool = False,
-        args: Optional[dict[str, Any]] = None,
+        self, docs_paths: Union[str, list], custom_file_types: Union[str, None] = None
    ):
        # Handle both single path (string) and multiple paths (list) for backward compatibility
        if isinstance(docs_paths, str):
@@ -901,10 +431,6 @@ Examples:

        all_documents = []

-        # Helper to detect hidden path components
-        def _path_has_hidden_segment(p: Path) -> bool:
-            return any(part.startswith(".") and part not in [".", ".."] for part in p.parts)
-
        # First, process individual files if any
        if files:
            print(f"\n🔄 Processing {len(files)} individual file{'s' if len(files) > 1 else ''}...")
@@ -917,12 +443,8 @@ Examples:

                files_by_dir = defaultdict(list)
                for file_path in files:
-                    file_path_obj = Path(file_path)
-                    if not include_hidden and _path_has_hidden_segment(file_path_obj):
-                        print(f"  ⚠️  Skipping hidden file: {file_path}")
-                        continue
-                    parent_dir = str(file_path_obj.parent)
-                    files_by_dir[parent_dir].append(str(file_path_obj))
+                    parent_dir = str(Path(file_path).parent)
+                    files_by_dir[parent_dir].append(file_path)

                # Load files from each parent directory
                for parent_dir, file_list in files_by_dir.items():
@@ -933,7 +455,6 @@ Examples:
                        file_docs = SimpleDirectoryReader(
                            parent_dir,
                            input_files=file_list,
-                            # exclude_hidden only affects directory scans; input_files are explicit
                            filename_as_id=True,
                        ).load_data()
                        all_documents.extend(file_docs)
@@ -1032,8 +553,6 @@ Examples:
                    # Check if file matches any exclude pattern
                    try:
                        relative_path = file_path.relative_to(docs_path)
-                        if not include_hidden and _path_has_hidden_segment(relative_path):
-                            continue
                        if self._should_exclude_file(relative_path, gitignore_matches):
                            continue
                    except ValueError:
@@ -1061,7 +580,6 @@ Examples:
                        try:
                            default_docs = SimpleDirectoryReader(
                                str(file_path.parent),
-                                exclude_hidden=not include_hidden,
                                filename_as_id=True,
                                required_exts=[file_path.suffix],
                            ).load_data()
@@ -1090,7 +608,6 @@ Examples:
                    encoding="utf-8",
                    required_exts=code_extensions,
                    file_extractor={},  # Use default extractors
-                    exclude_hidden=not include_hidden,
                    filename_as_id=True,
                ).load_data(show_progress=True)

@@ -1163,50 +680,18 @@ Examples:
        }

        print("start chunking documents")
+        # Add progress bar for document chunking
+        for doc in tqdm(documents, desc="Chunking documents", unit="doc"):
+            # Check if this is a code file based on source path
+            source_path = doc.metadata.get("source", "")
+            is_code_file = any(source_path.endswith(ext) for ext in code_file_exts)

-        # Check if AST chunking is requested
-        use_ast = getattr(args, "use_ast_chunking", False)
+            # Use appropriate parser based on file type
+            parser = self.code_parser if is_code_file else self.node_parser
+            nodes = parser.get_nodes_from_documents([doc])

-        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
-
-                # Use enhanced chunking with AST support
-                all_texts = create_text_chunks(
-                    documents,
-                    chunk_size=self.node_parser.chunk_size,
-                    chunk_overlap=self.node_parser.chunk_overlap,
-                    use_ast_chunking=True,
-                    ast_chunk_size=getattr(args, "ast_chunk_size", 768),
-                    ast_chunk_overlap=getattr(args, "ast_chunk_overlap", 96),
-                    code_file_extensions=None,  # Use defaults
-                    ast_fallback_traditional=getattr(args, "ast_fallback_traditional", True),
-                )
-
-            except ImportError as e:
-                print(f"⚠️  AST chunking not available ({e}), falling back to traditional chunking")
-                use_ast = False
-
-        if not use_ast:
-            # Use traditional chunking logic
-            for doc in tqdm(documents, desc="Chunking documents", unit="doc"):
-                # Check if this is a code file based on source path
-                source_path = doc.metadata.get("source", "")
-                is_code_file = any(source_path.endswith(ext) for ext in code_file_exts)
-
-                # Use appropriate parser based on file type
-                parser = self.code_parser if is_code_file else self.node_parser
-                nodes = parser.get_nodes_from_documents([doc])
-
-                for node in nodes:
-                    all_texts.append(node.get_content())
+            for node in nodes:
+                all_texts.append(node.get_content())

        print(f"Loaded {len(documents)} documents, {len(all_texts)} chunks")
        return all_texts
@@ -1241,40 +726,7 @@ Examples:
            print(f"Index '{index_name}' already exists. Use --force to rebuild.")
            return

-        # Configure chunking based on CLI args before loading documents
-        # Guard against invalid configurations
-        doc_chunk_size = max(1, int(args.doc_chunk_size))
-        doc_chunk_overlap = max(0, int(args.doc_chunk_overlap))
-        if doc_chunk_overlap >= doc_chunk_size:
-            print(
-                f"⚠️  Adjusting doc chunk overlap from {doc_chunk_overlap} to {doc_chunk_size - 1} (must be < chunk size)"
-            )
-            doc_chunk_overlap = doc_chunk_size - 1
-
-        code_chunk_size = max(1, int(args.code_chunk_size))
-        code_chunk_overlap = max(0, int(args.code_chunk_overlap))
-        if code_chunk_overlap >= code_chunk_size:
-            print(
-                f"⚠️  Adjusting code chunk overlap from {code_chunk_overlap} to {code_chunk_size - 1} (must be < chunk size)"
-            )
-            code_chunk_overlap = code_chunk_size - 1
-
-        self.node_parser = SentenceSplitter(
-            chunk_size=doc_chunk_size,
-            chunk_overlap=doc_chunk_overlap,
-            separator=" ",
-            paragraph_separator="\n\n",
-        )
-        self.code_parser = SentenceSplitter(
-            chunk_size=code_chunk_size,
-            chunk_overlap=code_chunk_overlap,
-            separator="\n",
-            paragraph_separator="\n\n",
-        )
-
-        all_texts = self.load_documents(
-            docs_paths, args.file_types, include_hidden=args.include_hidden, args=args
-        )
+        all_texts = self.load_documents(docs_paths, args.file_types)
        if not all_texts:
            print("No documents found")
            return
@@ -1306,101 +758,13 @@ Examples:
    async def search_documents(self, args):
        index_name = args.index_name
        query = args.query
-
-        # First try to find the index in current project
        index_path = self.get_index_path(index_name)
-        if self.index_exists(index_name):
-            # Found in current project, use it
-            pass
-        else:
-            # Search across all registered projects (like list_indexes does)
-            all_matches = self._find_all_matching_indexes(index_name)
-            if not all_matches:
-                print(
-                    f"Index '{index_name}' not found. Use 'leann build {index_name} --docs <dir> [<dir2> ...]' to create it."
-                )
-                return
-            elif len(all_matches) == 1:
-                # Found exactly one match, use it
-                match = all_matches[0]
-                if match["kind"] == "cli":
-                    index_path = str(match["index_dir"] / "documents.leann")
-                else:
-                    # App format: use the meta file to construct the path
-                    meta_file = match["meta_file"]
-                    file_base = match["file_base"]
-                    index_path = str(meta_file.parent / f"{file_base}.leann")

-                project_info = (
-                    "current project"
-                    if match["is_current"]
-                    else f"project '{match['project_path'].name}'"
-                )
-                print(f"Using index '{index_name}' from {project_info}")
-            else:
-                # Multiple matches found
-                if args.non_interactive:
-                    # Non-interactive mode: automatically select the best match
-                    # Priority: current project first, then first available
-                    current_matches = [m for m in all_matches if m["is_current"]]
-                    if current_matches:
-                        match = current_matches[0]
-                        location_desc = "current project"
-                    else:
-                        match = all_matches[0]
-                        location_desc = f"project '{match['project_path'].name}'"
-
-                    if match["kind"] == "cli":
-                        index_path = str(match["index_dir"] / "documents.leann")
-                    else:
-                        meta_file = match["meta_file"]
-                        file_base = match["file_base"]
-                        index_path = str(meta_file.parent / f"{file_base}.leann")
-
-                    print(
-                        f"Found {len(all_matches)} indexes named '{index_name}', using index from {location_desc}"
-                    )
-                else:
-                    # Interactive mode: ask user to choose
-                    print(f"Found {len(all_matches)} indexes named '{index_name}':")
-                    for i, match in enumerate(all_matches, 1):
-                        project_path = match["project_path"]
-                        is_current = match["is_current"]
-                        kind = match.get("kind", "cli")
-
-                        if is_current:
-                            print(
-                                f"   {i}. 🏠 Current project ({'CLI' if kind == 'cli' else 'APP'})"
-                            )
-                        else:
-                            print(
-                                f"   {i}. 📂 {project_path.name} ({'CLI' if kind == 'cli' else 'APP'})"
-                            )
-
-                    try:
-                        choice = input(f"Which index to search? (1-{len(all_matches)}): ").strip()
-                        choice_idx = int(choice) - 1
-                        if 0 <= choice_idx < len(all_matches):
-                            match = all_matches[choice_idx]
-                            if match["kind"] == "cli":
-                                index_path = str(match["index_dir"] / "documents.leann")
-                            else:
-                                meta_file = match["meta_file"]
-                                file_base = match["file_base"]
-                                index_path = str(meta_file.parent / f"{file_base}.leann")
-
-                            project_info = (
-                                "current project"
-                                if match["is_current"]
-                                else f"project '{match['project_path'].name}'"
-                            )
-                            print(f"Using index '{index_name}' from {project_info}")
-                        else:
-                            print("Invalid choice. Aborting search.")
-                            return
-                    except (ValueError, KeyboardInterrupt):
-                        print("Invalid input. Aborting search.")
-                        return
+        if not self.index_exists(index_name):
+            print(
+                f"Index '{index_name}' not found. Use 'leann build {index_name} --docs <dir> [<dir2> ...]' to create it."
+            )
+            return

        searcher = LeannSearcher(index_path=index_path)
        results = searcher.search(
@@ -1499,8 +863,6 @@ Examples:

        if args.command == "list":
            self.list_indexes()
-        elif args.command == "remove":
-            self.remove_index(args.index_name, args.force)
        elif args.command == "build":
            await self.build_index(args)
        elif args.command == "search":
@@ -1512,15 +874,10 @@ Examples:


 def main():
-    import logging
-
    import dotenv

    dotenv.load_dotenv()

-    # Set clean logging for CLI usage
-    logging.getLogger().setLevel(logging.WARNING)  # Only show warnings and errors
-
    cli = LeannCLI()
    asyncio.run(cli.run())

--- a/packages/leann-core/src/leann/embedding_compute.py
+++ b/packages/leann-core/src/leann/embedding_compute.py
@@ -6,7 +6,6 @@ Preserves all optimization parameters to ensure performance

 import logging
 import os
-import time
 from typing import Any

 import numpy as np
@@ -29,8 +28,6 @@ def compute_embeddings(
    is_build: bool = False,
    batch_size: int = 32,
    adaptive_optimization: bool = True,
-    manual_tokenize: bool = False,
-    max_length: int = 512,
 ) -> np.ndarray:
    """
    Unified embedding computation entry point
@@ -53,8 +50,6 @@ def compute_embeddings(
            is_build=is_build,
            batch_size=batch_size,
            adaptive_optimization=adaptive_optimization,
-            manual_tokenize=manual_tokenize,
-            max_length=max_length,
        )
    elif mode == "openai":
        return compute_embeddings_openai(texts, model_name)
@@ -62,8 +57,6 @@ def compute_embeddings(
        return compute_embeddings_mlx(texts, model_name)
    elif mode == "ollama":
        return compute_embeddings_ollama(texts, model_name, is_build=is_build)
-    elif mode == "gemini":
-        return compute_embeddings_gemini(texts, model_name, is_build=is_build)
    else:
        raise ValueError(f"Unsupported embedding mode: {mode}")

@@ -76,8 +69,6 @@ def compute_embeddings_sentence_transformers(
    batch_size: int = 32,
    is_build: bool = False,
    adaptive_optimization: bool = True,
-    manual_tokenize: bool = False,
-    max_length: int = 512,
 ) -> np.ndarray:
    """
    Compute embeddings using SentenceTransformer with model caching and adaptive optimization
@@ -221,130 +212,20 @@ def compute_embeddings_sentence_transformers(
        logger.info(f"Model cached: {cache_key}")

    # Compute embeddings with optimized inference mode
-    logger.info(
-        f"Starting embedding computation... (batch_size: {batch_size}, manual_tokenize={manual_tokenize})"
-    )
+    logger.info(f"Starting embedding computation... (batch_size: {batch_size})")

-    start_time = time.time()
-    if not manual_tokenize:
-        # Use SentenceTransformer's optimized encode path (default)
-        with torch.inference_mode():
-            embeddings = model.encode(
-                texts,
-                batch_size=batch_size,
-                show_progress_bar=is_build,  # Don't show progress bar in server environment
-                convert_to_numpy=True,
-                normalize_embeddings=False,
-                device=device,
-            )
-        # Synchronize if CUDA to measure accurate wall time
-        try:
-            if torch.cuda.is_available():
-                torch.cuda.synchronize()
-        except Exception:
-            pass
-    else:
-        # Manual tokenization + forward pass using HF AutoTokenizer/AutoModel
-        try:
-            from transformers import AutoModel, AutoTokenizer  # type: ignore
-        except Exception as e:
-            raise ImportError(f"transformers is required for manual_tokenize=True: {e}")
+    # Use torch.inference_mode for optimal performance
+    with torch.inference_mode():
+        embeddings = model.encode(
+            texts,
+            batch_size=batch_size,
+            show_progress_bar=is_build,  # Don't show progress bar in server environment
+            convert_to_numpy=True,
+            normalize_embeddings=False,
+            device=device,
+        )

-        # Cache tokenizer and model
-        tok_cache_key = f"hf_tokenizer_{model_name}"
-        mdl_cache_key = f"hf_model_{model_name}_{device}_{use_fp16}"
-        if tok_cache_key in _model_cache and mdl_cache_key in _model_cache:
-            hf_tokenizer = _model_cache[tok_cache_key]
-            hf_model = _model_cache[mdl_cache_key]
-            logger.info("Using cached HF tokenizer/model for manual path")
-        else:
-            logger.info("Loading HF tokenizer/model for manual tokenization path")
-            hf_tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
-            torch_dtype = torch.float16 if (use_fp16 and device == "cuda") else torch.float32
-            hf_model = AutoModel.from_pretrained(model_name, torch_dtype=torch_dtype)
-            hf_model.to(device)
-            hf_model.eval()
-            # Optional compile on supported devices
-            if device in ["cuda", "mps"]:
-                try:
-                    hf_model = torch.compile(hf_model, mode="reduce-overhead", dynamic=True)  # type: ignore
-                except Exception:
-                    pass
-            _model_cache[tok_cache_key] = hf_tokenizer
-            _model_cache[mdl_cache_key] = hf_model
-
-        all_embeddings: list[np.ndarray] = []
-        # Progress bar when building or for large inputs
-        show_progress = is_build or len(texts) > 32
-        try:
-            if show_progress:
-                from tqdm import tqdm  # type: ignore
-
-                batch_iter = tqdm(
-                    range(0, len(texts), batch_size),
-                    desc="Embedding (manual)",
-                    unit="batch",
-                )
-            else:
-                batch_iter = range(0, len(texts), batch_size)
-        except Exception:
-            batch_iter = range(0, len(texts), batch_size)
-
-        start_time_manual = time.time()
-        with torch.inference_mode():
-            for start_index in batch_iter:
-                end_index = min(start_index + batch_size, len(texts))
-                batch_texts = texts[start_index:end_index]
-                tokenize_start_time = time.time()
-                inputs = hf_tokenizer(
-                    batch_texts,
-                    padding=True,
-                    truncation=True,
-                    max_length=max_length,
-                    return_tensors="pt",
-                )
-                tokenize_end_time = time.time()
-                logger.info(
-                    f"Tokenize time taken: {tokenize_end_time - tokenize_start_time} seconds"
-                )
-                # Print shapes of all input tensors for debugging
-                for k, v in inputs.items():
-                    print(f"inputs[{k!r}] shape: {getattr(v, 'shape', type(v))}")
-                to_device_start_time = time.time()
-                inputs = {k: v.to(device) for k, v in inputs.items()}
-                to_device_end_time = time.time()
-                logger.info(
-                    f"To device time taken: {to_device_end_time - to_device_start_time} seconds"
-                )
-                forward_start_time = time.time()
-                outputs = hf_model(**inputs)
-                forward_end_time = time.time()
-                logger.info(f"Forward time taken: {forward_end_time - forward_start_time} seconds")
-                last_hidden_state = outputs.last_hidden_state  # (B, L, H)
-                attention_mask = inputs.get("attention_mask")
-                if attention_mask is None:
-                    # Fallback: assume all tokens are valid
-                    pooled = last_hidden_state.mean(dim=1)
-                else:
-                    mask = attention_mask.unsqueeze(-1).to(last_hidden_state.dtype)
-                    masked = last_hidden_state * mask
-                    lengths = mask.sum(dim=1).clamp(min=1)
-                    pooled = masked.sum(dim=1) / lengths
-                # Move to CPU float32
-                batch_embeddings = pooled.detach().to("cpu").float().numpy()
-                all_embeddings.append(batch_embeddings)
-
-        embeddings = np.vstack(all_embeddings).astype(np.float32, copy=False)
-        try:
-            if torch.cuda.is_available():
-                torch.cuda.synchronize()
-        except Exception:
-            pass
-        end_time = time.time()
-        logger.info(f"Manual tokenize time taken: {end_time - start_time_manual} seconds")
-    end_time = time.time()
    logger.info(f"Generated {len(embeddings)} embeddings, dimension: {embeddings.shape[1]}")
-    logger.info(f"Time taken: {end_time - start_time} seconds")

    # Validate results
    if np.isnan(embeddings).any() or np.isinf(embeddings).any():
@@ -363,16 +244,6 @@ def compute_embeddings_openai(texts: list[str], model_name: str) -> np.ndarray:
    except ImportError as e:
        raise ImportError(f"OpenAI package not installed: {e}")

-    # Validate input list
-    if not texts:
-        raise ValueError("Cannot compute embeddings for empty text list")
-    # Extra validation: abort early if any item is empty/whitespace
-    invalid_count = sum(1 for t in texts if not isinstance(t, str) or not t.strip())
-    if invalid_count > 0:
-        raise ValueError(
-            f"Found {invalid_count} empty/invalid text(s) in input. Upstream should filter before calling OpenAI."
-        )
-
    api_key = os.getenv("OPENAI_API_KEY")
    if not api_key:
        raise RuntimeError("OPENAI_API_KEY environment variable not set")
@@ -392,16 +263,8 @@ def compute_embeddings_openai(texts: list[str], model_name: str) -> np.ndarray:
    print(f"len of texts: {len(texts)}")

    # OpenAI has limits on batch size and input length
-    max_batch_size = 800  # Conservative batch size because the token limit is 300K
+    max_batch_size = 1000  # Conservative batch size
    all_embeddings = []
-    # get the avg len of texts
-    avg_len = sum(len(text) for text in texts) / len(texts)
-    print(f"avg len of texts: {avg_len}")
-    # if avg len is less than 1000, use the max batch size
-    if avg_len > 300:
-        max_batch_size = 500
-
-    # if avg len is less than 1000, use the max batch size

    try:
        from tqdm import tqdm
@@ -787,83 +650,3 @@ def compute_embeddings_ollama(
    logger.info(f"Generated {len(embeddings)} embeddings, dimension: {embeddings.shape[1]}")

    return embeddings
-
-
-def compute_embeddings_gemini(
-    texts: list[str], model_name: str = "text-embedding-004", is_build: bool = False
-) -> np.ndarray:
-    """
-    Compute embeddings using Google Gemini API.
-
-    Args:
-        texts: List of texts to compute embeddings for
-        model_name: Gemini model name (default: "text-embedding-004")
-        is_build: Whether this is a build operation (shows progress bar)
-
-    Returns:
-        Embeddings array, shape: (len(texts), embedding_dim)
-    """
-    try:
-        import os
-
-        import google.genai as genai
-    except ImportError as e:
-        raise ImportError(f"Google GenAI package not installed: {e}")
-
-    api_key = os.getenv("GEMINI_API_KEY")
-    if not api_key:
-        raise RuntimeError("GEMINI_API_KEY environment variable not set")
-
-    # Cache Gemini client
-    cache_key = "gemini_client"
-    if cache_key in _model_cache:
-        client = _model_cache[cache_key]
-    else:
-        client = genai.Client(api_key=api_key)
-        _model_cache[cache_key] = client
-        logger.info("Gemini client cached")
-
-    logger.info(
-        f"Computing embeddings for {len(texts)} texts using Gemini API, model: '{model_name}'"
-    )
-
-    # Gemini supports batch embedding
-    max_batch_size = 100  # Conservative batch size for Gemini
-    all_embeddings = []
-
-    try:
-        from tqdm import tqdm
-
-        total_batches = (len(texts) + max_batch_size - 1) // max_batch_size
-        batch_range = range(0, len(texts), max_batch_size)
-        batch_iterator = tqdm(
-            batch_range, desc="Computing embeddings", unit="batch", total=total_batches
-        )
-    except ImportError:
-        # Fallback when tqdm is not available
-        batch_iterator = range(0, len(texts), max_batch_size)
-
-    for i in batch_iterator:
-        batch_texts = texts[i : i + max_batch_size]
-
-        try:
-            # Use the embed_content method from the new Google GenAI SDK
-            response = client.models.embed_content(
-                model=model_name,
-                contents=batch_texts,
-                config=genai.types.EmbedContentConfig(
-                    task_type="RETRIEVAL_DOCUMENT"  # For document embedding
-                ),
-            )
-
-            # Extract embeddings from response
-            for embedding_data in response.embeddings:
-                all_embeddings.append(embedding_data.values)
-        except Exception as e:
-            logger.error(f"Batch {i} failed: {e}")
-            raise
-
-    embeddings = np.array(all_embeddings, dtype=np.float32)
-    logger.info(f"Generated {len(embeddings)} embeddings, dimension: {embeddings.shape[1]}")
-
-    return embeddings
--- a/packages/leann-core/src/leann/embedding_server_manager.py
+++ b/packages/leann-core/src/leann/embedding_server_manager.py
@@ -192,7 +192,6 @@ class EmbeddingServerManager:
            stderr_target = None  # Direct to console for visible logs

        # Start embedding server subprocess
-        logger.info(f"Starting server process with command: {' '.join(command)}")
        self.server_process = subprocess.Popen(
            command,
            cwd=project_root,
--- a/packages/leann-core/src/leann/mcp.py
+++ b/packages/leann-core/src/leann/mcp.py
@@ -64,6 +64,19 @@ def handle_request(request):
                            "required": ["index_name", "query"],
                        },
                    },
+                    {
+                        "name": "leann_status",
+                        "description": "📊 Check the health and stats of your code indexes - like a medical checkup for your codebase knowledge!",
+                        "inputSchema": {
+                            "type": "object",
+                            "properties": {
+                                "index_name": {
+                                    "type": "string",
+                                    "description": "Optional: Name of specific index to check. If not provided, shows status of all indexes.",
+                                }
+                            },
+                        },
+                    },
                    {
                        "name": "leann_list",
                        "description": "📋 Show all your indexed codebases - your personal code library! Use this to see what's available for search.",
@@ -94,7 +107,7 @@ def handle_request(request):
                        },
                    }

-                # Build simplified command with non-interactive flag for MCP compatibility
+                # Build simplified command
                cmd = [
                    "leann",
                    "search",
@@ -102,10 +115,18 @@ def handle_request(request):
                    args["query"],
                    f"--top-k={args.get('top_k', 5)}",
                    f"--complexity={args.get('complexity', 32)}",
-                    "--non-interactive",
                ]
                result = subprocess.run(cmd, capture_output=True, text=True)

+            elif tool_name == "leann_status":
+                if args.get("index_name"):
+                    # Check specific index status - for now, we'll use leann list and filter
+                    result = subprocess.run(["leann", "list"], capture_output=True, text=True)
+                    # We could enhance this to show more detailed status per index
+                else:
+                    # Show all indexes status
+                    result = subprocess.run(["leann", "list"], capture_output=True, text=True)
+
            elif tool_name == "leann_list":
                result = subprocess.run(["leann", "list"], capture_output=True, text=True)

--- a/packages/leann-core/src/leann/registry.py
+++ b/packages/leann-core/src/leann/registry.py
@@ -2,17 +2,11 @@

 import importlib
 import importlib.metadata
-import json
-import logging
-from pathlib import Path
-from typing import TYPE_CHECKING, Optional, Union
+from typing import TYPE_CHECKING

 if TYPE_CHECKING:
    from leann.interface import LeannBackendFactoryInterface

-# Set up logger for this module
-logger = logging.getLogger(__name__)
-
 BACKEND_REGISTRY: dict[str, "LeannBackendFactoryInterface"] = {}


@@ -20,7 +14,7 @@ def register_backend(name: str):
    """A decorator to register a new backend class."""

    def decorator(cls):
-        logger.debug(f"Registering backend '{name}'")
+        print(f"INFO: Registering backend '{name}'")
        BACKEND_REGISTRY[name] = cls
        return cls

@@ -45,54 +39,3 @@ def autodiscover_backends():
            # print(f"WARN: Could not import backend module '{backend_module_name}': {e}")
            pass
    # print("INFO: Backend auto-discovery finished.")
-
-
-def register_project_directory(project_dir: Optional[Union[str, Path]] = None):
-    """
-    Register a project directory in the global LEANN registry.
-
-    This allows `leann list` to discover indexes created by apps or other tools.
-
-    Args:
-        project_dir: Directory to register. If None, uses current working directory.
-    """
-    if project_dir is None:
-        project_dir = Path.cwd()
-    else:
-        project_dir = Path(project_dir)
-
-    # Only register directories that have some kind of LEANN content
-    # Either .leann/indexes/ (CLI format) or *.leann.meta.json files (apps format)
-    has_cli_indexes = (project_dir / ".leann" / "indexes").exists()
-    has_app_indexes = any(project_dir.rglob("*.leann.meta.json"))
-
-    if not (has_cli_indexes or has_app_indexes):
-        # Don't register if there are no LEANN indexes
-        return
-
-    global_registry = Path.home() / ".leann" / "projects.json"
-    global_registry.parent.mkdir(exist_ok=True)
-
-    project_str = str(project_dir.resolve())
-
-    # Load existing registry
-    projects = []
-    if global_registry.exists():
-        try:
-            with open(global_registry) as f:
-                projects = json.load(f)
-        except Exception:
-            logger.debug("Could not load existing project registry")
-            projects = []
-
-    # Add project if not already present
-    if project_str not in projects:
-        projects.append(project_str)
-
-        # Save updated registry
-        try:
-            with open(global_registry, "w") as f:
-                json.dump(projects, f, indent=2)
-            logger.debug(f"Registered project directory: {project_str}")
-        except Exception as e:
-            logger.warning(f"Could not save project registry: {e}")
--- a/packages/leann-mcp/README.md
+++ b/packages/leann-mcp/README.md
@@ -13,20 +13,10 @@ This installs the `leann` CLI into an isolated tool environment and includes bot

 ## 🚀 Quick Setup

-Add the LEANN MCP server to Claude Code. Choose the scope based on how widely you want it available. Below is the command to install it globally; if you prefer a local install, skip this step:
+Add the LEANN MCP server to Claude Code:

 ```bash
-# Global (recommended): available in all projects for your user
-claude mcp add --scope user leann-server -- leann_mcp
-```
-
- `leann-server`: the display name of the MCP server in Claude Code (you can change it).
- `leann_mcp`: the Python entry point installed with LEANN that starts the MCP server.
-
-Verify it is registered globally:
-
-```bash
-claude mcp list | cat
+claude mcp add leann-server -- leann_mcp
 ```

 ## 🛠️ Available Tools
@@ -35,36 +25,27 @@ Once connected, you'll have access to these powerful semantic search tools in Cl

 - **`leann_list`** - List all available indexes across your projects
 - **`leann_search`** - Perform semantic searches across code and documents
-
+- **`leann_ask`** - Ask natural language questions and get AI-powered answers from your codebase

 ## 🎯 Quick Start Example

 ```bash
-# Add locally if you did not add it globally (current folder only; default if --scope is omitted)
-claude mcp add leann-server -- leann_mcp
-
 # Build an index for your project (change to your actual path)
-# See the advanced examples below for more ways to configure indexing
-# Set the index name (replace 'my-project' with your own)
-leann build my-project --docs $(git ls-files)
+leann build my-project --docs ./

 # Start Claude Code
 claude
 ```

-## 🚀 Advanced Usage Examples to build the index
+## 🚀 Advanced Usage Examples

 ### Index Entire Git Repository
 ```bash
-# Index all tracked files in your Git repository.
-# Note: submodules are currently skipped; we can add them back if needed.
+# Index all tracked files in your git repository, note right now we will skip submodules, but we can add it back easily if you want
 leann build my-repo --docs $(git ls-files) --embedding-mode sentence-transformers --embedding-model all-MiniLM-L6-v2 --backend hnsw

-# Index only tracked Python files from Git.
+# Index only specific file types from git
 leann build my-python-code --docs $(git ls-files "*.py") --embedding-mode sentence-transformers --embedding-model all-MiniLM-L6-v2 --backend hnsw
-
-# If you encounter empty requests caused by empty files (e.g., __init__.py), exclude zero-byte files. Thanks @ww2283 for pointing [that](https://github.com/yichuan-w/LEANN/issues/48) out
-leann build leann-prospec-lig --docs $(find ./src -name "*.py" -not -empty) --embedding-mode openai --embedding-model text-embedding-3-small
 ```

 ### Multiple Directories and Files
@@ -92,7 +73,7 @@ leann build docs-and-configs --docs $(git ls-files "*.md" "*.yml" "*.yaml" "*.js
 ```


-## **Try this in Claude Code:**
+**Try this in Claude Code:**
 ```
 Help me understand this codebase. List available indexes and search for authentication patterns.
 ```
@@ -101,7 +82,6 @@ Help me understand this codebase. List available indexes and search for authenti
  <img src="../../assets/claude_code_leann.png" alt="LEANN in Claude Code" width="80%">
 </p>

-If you see a prompt asking whether to proceed with LEANN, you can now use it in your chat!

 ## 🧠 How It Works

@@ -137,11 +117,3 @@ To remove LEANN
 ```
 uv pip uninstall leann leann-backend-hnsw leann-core
 ```
-
-To globally remove LEANN (for version update)
-```
-uv tool list | cat
-uv tool uninstall leann-core
-command -v leann || echo "leann gone"
-command -v leann_mcp || echo "leann_mcp gone"
-```
--- a/packages/leann/pyproject.toml
+++ b/packages/leann/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "leann"
-version = "0.3.2"
+version = "0.2.9"
 description = "LEANN - The smallest vector index in the world. RAG Everything with LEANN!"
 readme = "README.md"
 requires-python = ">=3.9"
--- a/packages/wechat-exporter/init.py
+++ b/packages/wechat-exporter/init.py
@@ -1 +0,0 @@
-__all__ = []
--- a/packages/wechat-exporter/main.py
+++ b/packages/wechat-exporter/main.py
@@ -136,9 +136,5 @@ def export_sqlite(
    connection.commit()


-def main():
-    app()
-
-
 if __name__ == "__main__":
-    main()
+    app()
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,10 +10,11 @@ requires-python = ">=3.9"
 dependencies = [
    "leann-core",
    "leann-backend-hnsw",
-    "typer>=0.12.3",
    "numpy>=1.26.0",
    "torch",
    "tqdm",
+    "flask",
+    "flask_compress",
    "datasets>=2.15.0",
    "evaluate",
    "colorama",
@@ -46,13 +47,6 @@ dependencies = [
    "pathspec>=0.12.1",
    "nbconvert>=7.16.6",
    "gitignore-parser>=0.1.12",
-    # AST-aware code chunking dependencies
-    "astchunk>=0.1.0",
-    "tree-sitter>=0.20.0",
-    "tree-sitter-python>=0.20.0",
-    "tree-sitter-java>=0.20.0",
-    "tree-sitter-c-sharp>=0.20.0",
-    "tree-sitter-typescript>=0.20.0",
 ]

 [project.optional-dependencies]
@@ -71,7 +65,9 @@ test = [
    "pytest>=7.0",
    "pytest-timeout>=2.0",
    "llama-index-core>=0.12.0",
+    "llama-index-readers-file>=0.4.0",
    "python-dotenv>=1.0.0",
+    "sentence-transformers>=2.2.0",
 ]

 diskann = [
@@ -88,11 +84,6 @@ documents = [

 [tool.setuptools]
 py-modules = []
-packages = ["wechat_exporter"]
-package-dir = { "wechat_exporter" = "packages/wechat-exporter" }
-
-[project.scripts]
-wechat-exporter = "wechat_exporter.main:main"


 [tool.uv.sources]
@@ -103,8 +94,13 @@ leann-backend-hnsw = { path = "packages/leann-backend-hnsw", editable = true }
 [tool.ruff]
 target-version = "py39"
 line-length = 100
-extend-exclude = ["third_party"]
-
+extend-exclude = [
+    "third_party",
+    "*.egg-info",
+    "__pycache__",
+    ".git",
+    ".venv",
+]

 [tool.ruff.lint]
 select = [
@@ -127,12 +123,21 @@ ignore = [
    "RUF012", # mutable class attributes should be annotated with typing.ClassVar
 ]

+[tool.ruff.lint.per-file-ignores]
+"test/**/*.py" = ["E402"]      # module level import not at top of file (common in tests)
+"examples/**/*.py" = ["E402"]  # module level import not at top of file (common in examples)
+
 [tool.ruff.format]
 quote-style = "double"
 indent-style = "space"
 skip-magic-trailing-comma = false
 line-ending = "auto"

+[dependency-groups]
+dev = [
+    "ruff>=0.12.4",
+]
+
 [tool.lychee]
 accept = ["200", "403", "429", "503"]
 timeout = 20
--- a/tests/test_astchunk_integration.py
+++ b/tests/test_astchunk_integration.py
@@ -1,397 +0,0 @@
-"""
-Test suite for astchunk integration with LEANN.
-Tests AST-aware chunking functionality, language detection, and fallback mechanisms.
-"""
-
-import os
-import subprocess
-import sys
-import tempfile
-from pathlib import Path
-from unittest.mock import patch
-
-import pytest
-
-# Add apps directory to path for imports
-sys.path.insert(0, str(Path(__file__).parent.parent / "apps"))
-
-from typing import Optional
-
-from chunking import (
-    create_ast_chunks,
-    create_text_chunks,
-    create_traditional_chunks,
-    detect_code_files,
-    get_language_from_extension,
-)
-
-
-class MockDocument:
-    """Mock LlamaIndex Document for testing."""
-
-    def __init__(self, content: str, file_path: str = "", metadata: Optional[dict] = None):
-        self.content = content
-        self.metadata = metadata or {}
-        if file_path:
-            self.metadata["file_path"] = file_path
-
-    def get_content(self) -> str:
-        return self.content
-
-
-class TestCodeFileDetection:
-    """Test code file detection and language mapping."""
-
-    def test_detect_code_files_python(self):
-        """Test detection of Python files."""
-        docs = [
-            MockDocument("print('hello')", "/path/to/file.py"),
-            MockDocument("This is text", "/path/to/file.txt"),
-        ]
-
-        code_docs, text_docs = detect_code_files(docs)
-
-        assert len(code_docs) == 1
-        assert len(text_docs) == 1
-        assert code_docs[0].metadata["language"] == "python"
-        assert code_docs[0].metadata["is_code"] is True
-        assert text_docs[0].metadata["is_code"] is False
-
-    def test_detect_code_files_multiple_languages(self):
-        """Test detection of multiple programming languages."""
-        docs = [
-            MockDocument("def func():", "/path/to/script.py"),
-            MockDocument("public class Test {}", "/path/to/Test.java"),
-            MockDocument("interface ITest {}", "/path/to/test.ts"),
-            MockDocument("using System;", "/path/to/Program.cs"),
-            MockDocument("Regular text content", "/path/to/document.txt"),
-        ]
-
-        code_docs, text_docs = detect_code_files(docs)
-
-        assert len(code_docs) == 4
-        assert len(text_docs) == 1
-
-        languages = [doc.metadata["language"] for doc in code_docs]
-        assert "python" in languages
-        assert "java" in languages
-        assert "typescript" in languages
-        assert "csharp" in languages
-
-    def test_detect_code_files_no_file_path(self):
-        """Test handling of documents without file paths."""
-        docs = [
-            MockDocument("some content"),
-            MockDocument("other content", metadata={"some_key": "value"}),
-        ]
-
-        code_docs, text_docs = detect_code_files(docs)
-
-        assert len(code_docs) == 0
-        assert len(text_docs) == 2
-        for doc in text_docs:
-            assert doc.metadata["is_code"] is False
-
-    def test_get_language_from_extension(self):
-        """Test language detection from file extensions."""
-        assert get_language_from_extension("test.py") == "python"
-        assert get_language_from_extension("Test.java") == "java"
-        assert get_language_from_extension("component.tsx") == "typescript"
-        assert get_language_from_extension("Program.cs") == "csharp"
-        assert get_language_from_extension("document.txt") is None
-        assert get_language_from_extension("") is None
-
-
-class TestChunkingFunctions:
-    """Test various chunking functionality."""
-
-    def test_create_traditional_chunks(self):
-        """Test traditional text chunking."""
-        docs = [
-            MockDocument(
-                "This is a test document. It has multiple sentences. We want to test chunking."
-            )
-        ]
-
-        chunks = create_traditional_chunks(docs, chunk_size=50, chunk_overlap=10)
-
-        assert len(chunks) > 0
-        assert all(isinstance(chunk, str) for chunk in chunks)
-        assert all(len(chunk.strip()) > 0 for chunk in chunks)
-
-    def test_create_traditional_chunks_empty_docs(self):
-        """Test traditional chunking with empty documents."""
-        chunks = create_traditional_chunks([], chunk_size=50, chunk_overlap=10)
-        assert chunks == []
-
-    @pytest.mark.skipif(
-        os.environ.get("CI") == "true",
-        reason="Skip astchunk tests in CI - dependency may not be available",
-    )
-    def test_create_ast_chunks_with_astchunk_available(self):
-        """Test AST chunking when astchunk is available."""
-        python_code = '''
-def hello_world():
-    """Print hello world message."""
-    print("Hello, World!")
-
-def add_numbers(a, b):
-    """Add two numbers and return the result."""
-    return a + b
-
-class Calculator:
-    """A simple calculator class."""
-
-    def __init__(self):
-        self.history = []
-
-    def add(self, a, b):
-        result = a + b
-        self.history.append(f"{a} + {b} = {result}")
-        return result
-'''
-
-        docs = [MockDocument(python_code, "/test/calculator.py", {"language": "python"})]
-
-        try:
-            chunks = create_ast_chunks(docs, max_chunk_size=200, chunk_overlap=50)
-
-            # Should have multiple chunks due to different functions/classes
-            assert len(chunks) > 0
-            assert all(isinstance(chunk, str) for chunk in chunks)
-            assert all(len(chunk.strip()) > 0 for chunk in chunks)
-
-            # Check that code structure is somewhat preserved
-            combined_content = " ".join(chunks)
-            assert "def hello_world" in combined_content
-            assert "class Calculator" in combined_content
-
-        except ImportError:
-            # astchunk not available, should fall back to traditional chunking
-            chunks = create_ast_chunks(docs, max_chunk_size=200, chunk_overlap=50)
-            assert len(chunks) > 0  # Should still get chunks from fallback
-
-    def test_create_ast_chunks_fallback_to_traditional(self):
-        """Test AST chunking falls back to traditional when astchunk is not available."""
-        docs = [MockDocument("def test(): pass", "/test/script.py", {"language": "python"})]
-
-        # Mock astchunk import to fail
-        with patch("chunking.create_ast_chunks"):
-            # First call (actual test) should import astchunk and potentially fail
-            # Let's call the actual function to test the import error handling
-            chunks = create_ast_chunks(docs)
-
-            # Should return some chunks (either from astchunk or fallback)
-            assert isinstance(chunks, list)
-
-    def test_create_text_chunks_traditional_mode(self):
-        """Test text chunking in traditional mode."""
-        docs = [
-            MockDocument("def test(): pass", "/test/script.py"),
-            MockDocument("This is regular text.", "/test/doc.txt"),
-        ]
-
-        chunks = create_text_chunks(docs, use_ast_chunking=False, chunk_size=50, chunk_overlap=10)
-
-        assert len(chunks) > 0
-        assert all(isinstance(chunk, str) for chunk in chunks)
-
-    def test_create_text_chunks_ast_mode(self):
-        """Test text chunking in AST mode."""
-        docs = [
-            MockDocument("def test(): pass", "/test/script.py"),
-            MockDocument("This is regular text.", "/test/doc.txt"),
-        ]
-
-        chunks = create_text_chunks(
-            docs,
-            use_ast_chunking=True,
-            ast_chunk_size=100,
-            ast_chunk_overlap=20,
-            chunk_size=50,
-            chunk_overlap=10,
-        )
-
-        assert len(chunks) > 0
-        assert all(isinstance(chunk, str) for chunk in chunks)
-
-    def test_create_text_chunks_custom_extensions(self):
-        """Test text chunking with custom code file extensions."""
-        docs = [
-            MockDocument("function test() {}", "/test/script.js"),  # Not in default extensions
-            MockDocument("Regular text", "/test/doc.txt"),
-        ]
-
-        # First without custom extensions - should treat .js as text
-        chunks_without = create_text_chunks(docs, use_ast_chunking=True, code_file_extensions=None)
-
-        # Then with custom extensions - should treat .js as code
-        chunks_with = create_text_chunks(
-            docs, use_ast_chunking=True, code_file_extensions=[".js", ".jsx"]
-        )
-
-        # Both should return chunks
-        assert len(chunks_without) > 0
-        assert len(chunks_with) > 0
-
-
-class TestIntegrationWithDocumentRAG:
-    """Integration tests with the document RAG system."""
-
-    @pytest.fixture
-    def temp_code_dir(self):
-        """Create a temporary directory with sample code files."""
-        with tempfile.TemporaryDirectory() as temp_dir:
-            temp_path = Path(temp_dir)
-
-            # Create sample Python file
-            python_file = temp_path / "example.py"
-            python_file.write_text('''
-def fibonacci(n):
-    """Calculate fibonacci number."""
-    if n <= 1:
-        return n
-    return fibonacci(n-1) + fibonacci(n-2)
-
-class MathUtils:
-    @staticmethod
-    def factorial(n):
-        if n <= 1:
-            return 1
-        return n * MathUtils.factorial(n-1)
-''')
-
-            # Create sample text file
-            text_file = temp_path / "readme.txt"
-            text_file.write_text("This is a sample text file for testing purposes.")
-
-            yield temp_path
-
-    @pytest.mark.skipif(
-        os.environ.get("CI") == "true",
-        reason="Skip integration tests in CI to avoid dependency issues",
-    )
-    def test_document_rag_with_ast_chunking(self, temp_code_dir):
-        """Test document RAG with AST chunking enabled."""
-        with tempfile.TemporaryDirectory() as index_dir:
-            cmd = [
-                sys.executable,
-                "apps/document_rag.py",
-                "--llm",
-                "simulated",
-                "--embedding-model",
-                "facebook/contriever",
-                "--embedding-mode",
-                "sentence-transformers",
-                "--index-dir",
-                index_dir,
-                "--data-dir",
-                str(temp_code_dir),
-                "--enable-code-chunking",
-                "--query",
-                "How does the fibonacci function work?",
-            ]
-
-            env = os.environ.copy()
-            env["HF_HUB_DISABLE_SYMLINKS"] = "1"
-            env["TOKENIZERS_PARALLELISM"] = "false"
-
-            try:
-                result = subprocess.run(
-                    cmd,
-                    capture_output=True,
-                    text=True,
-                    timeout=300,  # 5 minutes
-                    env=env,
-                )
-
-                # Should succeed even if astchunk is not available (fallback)
-                assert result.returncode == 0, f"Command failed: {result.stderr}"
-
-                output = result.stdout + result.stderr
-                assert "Index saved to" in output or "Using existing index" in output
-
-            except subprocess.TimeoutExpired:
-                pytest.skip("Test timed out - likely due to model download in CI")
-
-    @pytest.mark.skipif(
-        os.environ.get("CI") == "true",
-        reason="Skip integration tests in CI to avoid dependency issues",
-    )
-    def test_code_rag_application(self, temp_code_dir):
-        """Test the specialized code RAG application."""
-        with tempfile.TemporaryDirectory() as index_dir:
-            cmd = [
-                sys.executable,
-                "apps/code_rag.py",
-                "--llm",
-                "simulated",
-                "--embedding-model",
-                "facebook/contriever",
-                "--index-dir",
-                index_dir,
-                "--repo-dir",
-                str(temp_code_dir),
-                "--query",
-                "What classes are defined in this code?",
-            ]
-
-            env = os.environ.copy()
-            env["HF_HUB_DISABLE_SYMLINKS"] = "1"
-            env["TOKENIZERS_PARALLELISM"] = "false"
-
-            try:
-                result = subprocess.run(cmd, capture_output=True, text=True, timeout=300, env=env)
-
-                # Should succeed
-                assert result.returncode == 0, f"Command failed: {result.stderr}"
-
-                output = result.stdout + result.stderr
-                assert "Using AST-aware chunking" in output or "traditional chunking" in output
-
-            except subprocess.TimeoutExpired:
-                pytest.skip("Test timed out - likely due to model download in CI")
-
-
-class TestErrorHandling:
-    """Test error handling and edge cases."""
-
-    def test_text_chunking_empty_documents(self):
-        """Test text chunking with empty document list."""
-        chunks = create_text_chunks([])
-        assert chunks == []
-
-    def test_text_chunking_invalid_parameters(self):
-        """Test text chunking with invalid parameters."""
-        docs = [MockDocument("test content")]
-
-        # Should handle negative chunk sizes gracefully
-        chunks = create_text_chunks(
-            docs, chunk_size=0, chunk_overlap=0, ast_chunk_size=0, ast_chunk_overlap=0
-        )
-
-        # Should still return some result
-        assert isinstance(chunks, list)
-
-    def test_create_ast_chunks_no_language(self):
-        """Test AST chunking with documents missing language metadata."""
-        docs = [MockDocument("def test(): pass", "/test/script.py")]  # No language set
-
-        chunks = create_ast_chunks(docs)
-
-        # Should fall back to traditional chunking
-        assert isinstance(chunks, list)
-        assert len(chunks) >= 0  # May be empty if fallback also fails
-
-    def test_create_ast_chunks_empty_content(self):
-        """Test AST chunking with empty content."""
-        docs = [MockDocument("", "/test/script.py", {"language": "python"})]
-
-        chunks = create_ast_chunks(docs)
-
-        # Should handle empty content gracefully
-        assert isinstance(chunks, list)
-
-
-if __name__ == "__main__":
-    pytest.main([__file__, "-v"])
--- a/tests/test_document_rag.py
+++ b/tests/test_document_rag.py
@@ -57,51 +57,6 @@ def test_document_rag_simulated(test_data_dir):
        assert "This is a simulated answer" in output


-@pytest.mark.skipif(
-    os.environ.get("CI") == "true",
-    reason="Skip AST chunking tests in CI to avoid dependency issues",
-)
-def test_document_rag_with_ast_chunking(test_data_dir):
-    """Test document_rag with AST-aware chunking enabled."""
-    with tempfile.TemporaryDirectory() as temp_dir:
-        # Use a subdirectory that doesn't exist yet to force index creation
-        index_dir = Path(temp_dir) / "test_ast_index"
-        cmd = [
-            sys.executable,
-            "apps/document_rag.py",
-            "--llm",
-            "simulated",
-            "--embedding-model",
-            "facebook/contriever",
-            "--embedding-mode",
-            "sentence-transformers",
-            "--index-dir",
-            str(index_dir),
-            "--data-dir",
-            str(test_data_dir),
-            "--enable-code-chunking",  # Enable AST chunking
-            "--query",
-            "What is Pride and Prejudice about?",
-        ]
-
-        env = os.environ.copy()
-        env["HF_HUB_DISABLE_SYMLINKS"] = "1"
-        env["TOKENIZERS_PARALLELISM"] = "false"
-
-        result = subprocess.run(cmd, capture_output=True, text=True, timeout=600, env=env)
-
-        # Check return code
-        assert result.returncode == 0, f"Command failed: {result.stderr}"
-
-        # Verify output
-        output = result.stdout + result.stderr
-        assert "Index saved to" in output or "Using existing index" in output
-        assert "This is a simulated answer" in output
-
-        # Should mention AST chunking if code files are present
-        # (might not be relevant for the test data, but command should succeed)
-
-
@pytest.mark.skipif(not os.environ.get("OPENAI_API_KEY"), reason="OpenAI API key not available")
@pytest.mark.skipif(
    os.environ.get("CI") == "true", reason="Skip OpenAI tests in CI to avoid API costs"
--- a/uv.lock
+++ b/uv.lock
Author	SHA1	Message	Date
Andy Lee	3de0a94efc	docs: align cli args and README	2025-08-15 12:03:11 -07:00
Andy Lee	58c12e3eed	docs: leann help	2025-08-15 11:51:38 -07:00
Andy Lee	92739c7899	docs: boolean flags	2025-08-15 11:50:46 -07:00
Andy Lee	6709afe38b	fix: hang on warnings	2025-08-15 01:01:51 -07:00
Andy Lee	ded0701504	core: auto-cleanup for LeannSearcher/LeannChat (__enter__/__exit__/__del__); ensure server terminate/kill robustness; benchmarks: use searcher.cleanup(); docs: suggest uv run	2025-08-14 14:29:57 -07:00
Andy Lee	e3518a31ed	docs: diskann recompute	2025-08-14 14:25:50 -07:00
Andy Lee	d5f6ca61ed	benchmarks: unify HNSW & DiskANN into one clean script; isolate groups, fixed ports, warm-up, param complexity	2025-08-14 13:47:53 -07:00
Andy Lee	b13b52e78c	benchmarks: fix and extend HNSW+DiskANN recompute vs no-recompute; docs: add fresh numbers and DiskANN notes	2025-08-14 12:18:07 -07:00
Andy Lee	79ca32e87b	docs: a real example on recompute	2025-08-14 11:56:10 -07:00
Andy Lee	16f4572fe7	cli: use argparse.BooleanOptionalAction for paired flags (--recompute/--compact) across build/search/ask	2025-08-14 11:22:47 -07:00
Andy Lee	2bd557d1cf	hnsw: move pruned/no-recompute assertion into backend; api: drop global assertion; docs: will adjust after benchmarking	2025-08-14 11:08:34 -07:00
Andy Lee	3e162fb177	chore: remove	2025-08-14 01:24:21 -07:00
Andy Lee	b988f0ab5b	cli: unify flags to --recompute/--no-recompute for build/search/ask; docs: update references	2025-08-14 01:20:14 -07:00
Andy Lee	43cb500ed8	merge: finalize compat resolution (delegate to PassageManager; keep relative hints in meta); resolve conflicts	2025-08-14 01:09:39 -07:00
Andy Lee	0361725323	reader: non-destructive portability (relative hints + fallback); fix comments; sky: refine yaml	2025-08-14 01:05:01 -07:00
Andy Lee	3f81861cba	feat: auto compact for hnsw when recompute	2025-08-14 00:09:57 -07:00
Andy Lee	fa2a775867	docs+sky: simplify SkyPilot flow (auto-build on launch, rsync copy-back); clarify HNSW auto non-compact when no-recompute	2025-08-13 14:30:14 -07:00
Andy Lee	737dfc960c	hnsw: auto-disable compact when --no-recompute is used; docs: expand SkyPilot with -e overrides and copy-back example	2025-08-13 14:25:16 -07:00
Andy Lee	c994635af6	sky: expand leann-build.yaml with configurable params and flags (backend, recompute, compact, embedding options)	2025-08-13 14:18:48 -07:00
Andy Lee	23b80647c5	docs: dedupe recomputation guidance; keep single Low-resource setups section	2025-08-13 14:10:10 -07:00
Andy Lee	50121972ee	cli: add --no-recompute and --no-recompute-embeddings flags; docs: clarify HNSW requires --no-compact when disabling recompute	2025-08-13 14:09:05 -07:00
Andy Lee	07e5f10204	docs: consolidate low-resource guidance into config guide; README points to it	2025-08-13 14:08:23 -07:00
Andy Lee	58711bff7e	docs: add low-resource note in README; point to config guide; suggest OpenAI embeddings, SkyPilot remote build, and --no-recompute	2025-08-13 14:06:22 -07:00
Andy Lee	a69464eb16	docs: add SkyPilot template and instructions for running embeddings/index build on cloud GPU	2025-08-13 14:01:32 -07:00
				`@@ -0,0 +1 @@`
				`paper_plot/data/big_graph_degree_data.npz filter=lfs diff=lfs merge=lfs -text`