simplify: Make templates more concise and user-friendly

.gitignore

@@ -101,4 +101,3 @@ CLAUDE.local.md
 .claude/*.local.*
 .claude/local/*
 benchmarks/data/
-test_add/*

.gitmodules

@@ -16,4 +16,5 @@
 	url = https://github.com/zeromq/libzmq.git
 [submodule "packages/astchunk-leann"]
 	path = packages/astchunk-leann
-	url = https://github.com/yichuan-w/astchunk-leann.git
+	url = git@github.com:yichuan-w/astchunk-leann.git
+	branch = main

examples/dynamic_add_leann_no_recompute.py

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

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

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

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

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

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

@@ -5,7 +5,6 @@ with the correct, original embedding logic from the user's reference code.
 
 import json
 import logging
-import os
 import pickle
 import re
 import subprocess
@@ -20,7 +19,6 @@ import numpy as np
 from leann.interface import LeannBackendSearcherInterface
 
 from .chat import get_llm
-from .embedding_server_manager import EmbeddingServerManager
 from .interface import LeannBackendFactoryInterface
 from .metadata_filter import MetadataFilterEngine
 from .registry import BACKEND_REGISTRY
@@ -120,20 +118,6 @@ class SearchResult:
     metadata: dict[str, Any] = field(default_factory=dict)
 
 
-@dataclass
-class IncrementalAddContext:
-    """Prepared context for safe incremental add operations on an index."""
-
-    index_path: str
-    passages_file: Path
-    offsets_file: Path
-    vector_index_file: Path
-    embedding_model: str
-    embedding_mode: str
-    distance_metric: str
-    prepared_texts: Optional[list[str]] = None
-
-
 class PassageManager:
     def __init__(
         self, passage_sources: list[dict[str, Any]], metadata_file_path: Optional[str] = None
@@ -492,7 +476,9 @@ class LeannBuilder:
             is_compact = self.backend_kwargs.get("is_compact", True)
             is_recompute = self.backend_kwargs.get("is_recompute", True)
             meta_data["is_compact"] = is_compact
-            meta_data["is_pruned"] = is_recompute  # Pruned only if compact and recompute
+            meta_data["is_pruned"] = (
+                is_compact and is_recompute
+            )  # Pruned only if compact and recompute
         with open(leann_meta_path, "w", encoding="utf-8") as f:
             json.dump(meta_data, f, indent=2)
 
@@ -1032,405 +1018,8 @@ class LeannChat:
         except Exception:
             pass
 
-
-# ------------------------------
-# Incremental Add Utilities (HNSW no-recompute only)
-# ------------------------------
-
-
-def _resolve_index_paths(index_path: str) -> tuple[Path, Path, Path]:
-    """Given base index path (without extension), return (passages.jsonl, passages.idx, vector.index).
-
-    For HNSW, vector index file is typically <stem>.index (e.g., documents.index) even when base is
-    'documents.leann'. We prefer an existing <stem>.index, otherwise fall back to <name>.index.
-    """
-    base = Path(index_path)
-    passages_file = base.parent / f"{base.name}.passages.jsonl"
-    offsets_file = base.parent / f"{base.name}.passages.idx"
-    candidate_name_index = base.parent / f"{base.name}.index"
-    candidate_stem_index = base.parent / f"{base.stem}.index"
-    vector_index_file = (
-        candidate_stem_index if candidate_stem_index.exists() else candidate_name_index
-    )
-    return passages_file, offsets_file, vector_index_file
-
-
-def _read_meta_file(index_path: str) -> dict[str, Any]:
-    meta_path = Path(f"{index_path}.meta.json")
-    if not meta_path.exists():
-        raise FileNotFoundError(f"Leann metadata file not found: {meta_path}")
-    with open(meta_path, encoding="utf-8") as f:
-        return json.load(f)
-
-
-def _load_offset_map_pickle(offsets_file: Path) -> dict[str, int]:
-    if not offsets_file.exists():
-        return {}
-    with open(offsets_file, "rb") as f:
-        return pickle.load(f)
-
-
-def _append_passages_and_update_offsets(
-    passages_file: Path, offsets_file: Path, new_texts: list[str]
-) -> list[str]:
-    """Append new texts to passages file, update offset map, and return assigned string IDs.
-
-    IDs are assigned as incrementing integers based on existing keys in the offset map.
-    """
-    offset_map = _load_offset_map_pickle(offsets_file)
-    # Compute next numeric id
-    numeric_ids = [int(x) for x in offset_map.keys() if str(x).isdigit()]
-    next_id_num = (max(numeric_ids) + 1) if numeric_ids else 0
-    assigned_ids: list[str] = []
-
-    with open(passages_file, "a", encoding="utf-8") as f:
-        for text in new_texts:
-            offset = f.tell()
-            str_id = str(next_id_num)
-            json.dump({"id": str_id, "text": text, "metadata": {}}, f, ensure_ascii=False)
-            f.write("\n")
-            offset_map[str_id] = offset
-            assigned_ids.append(str_id)
-            next_id_num += 1
-
-    with open(offsets_file, "wb") as f:
-        pickle.dump(offset_map, f)
-
-    return assigned_ids
-
-
-def incremental_add_texts(
-    index_path: str,
-    texts: list[str],
-    *,
-    embedding_model: Optional[str] = None,
-    embedding_mode: Optional[str] = None,
-    ef_construction: Optional[int] = None,
-    recompute: bool = False,
-) -> int:
-    """Incrementally add text chunks to an existing HNSW index built with no-recompute.
-
-    - Validates backend is HNSW and index is non-compact (no-recompute path)
-    - Appends passages and offsets
-    - Computes embeddings and appends to the HNSW vector index
-
-    Returns number of added chunks.
-    """
-    if not texts:
-        return 0
-
-    meta = _read_meta_file(index_path)
-    if meta.get("backend_name") != "hnsw":
-        raise RuntimeError("Incremental add is currently supported only for HNSW backend")
-    if meta.get("is_compact", True):
-        raise RuntimeError(
-            "Index is compact/pruned. Rebuild base with is_recompute=False and is_compact=False for incremental add."
-        )
-
-    passages_file, offsets_file, vector_index_file = _resolve_index_paths(index_path)
-    if not vector_index_file.exists():
-        raise FileNotFoundError(
-            f"Vector index file missing: {vector_index_file}. Build base first with LeannBuilder."
-        )
-
-    # Resolve embedding config from meta if not provided
-    model_name = embedding_model or meta.get("embedding_model", "facebook/contriever")
-    mode_name = embedding_mode or meta.get("embedding_mode", "sentence-transformers")
-
-    # Append passages and update offsets
-    assigned_ids = _append_passages_and_update_offsets(passages_file, offsets_file, texts)
-
-    # Compute embeddings
-    # Embedding computation path
-    esm = None
-    port = None
-    if recompute:
-        # Determine distance metric early for server config
-        distance_metric = meta.get("backend_kwargs", {}).get("distance_metric", "mips").lower()
-        # Start embedding server and compute via ZMQ for consistency with recompute semantics
-        passages_source_file = f"{index_path}.meta.json"
-        esm = EmbeddingServerManager(
-            backend_module_name="leann_backend_hnsw.hnsw_embedding_server",
-        )
-        started, port = esm.start_server(
-            port=5557,
-            model_name=model_name,
-            embedding_mode=mode_name,
-            passages_file=passages_source_file,
-            distance_metric=distance_metric,
-            enable_warmup=False,
-        )
-        if not started:
-            raise RuntimeError("Failed to start embedding server for recompute add")
-        embeddings = compute_embeddings_via_server(texts, model_name, port)
-    else:
-        embeddings = compute_embeddings(
-            texts,
-            model_name=model_name,
-            mode=mode_name,
-            use_server=False,
-            is_build=True,
-        )
-
-    # Normalize for cosine if needed
-    if "distance_metric" not in locals():
-        distance_metric = meta.get("backend_kwargs", {}).get("distance_metric", "mips").lower()
-    if distance_metric == "cosine":
-        norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
-        norms[norms == 0] = 1
-        embeddings = embeddings / norms
-
-    # Append via backend helper (supports ef_construction/recompute plumbing)
-    try:
-        from leann_backend_hnsw.hnsw_backend import add_vectors as hnsw_add_vectors  # type: ignore
-    except Exception as e:
-        raise RuntimeError(
-            "Failed to import HNSW backend add helper. Ensure HNSW backend is installed."
-        ) from e
-
-    # Propagate ZMQ port to FAISS add path when recompute is True
-    if recompute and port is not None:
-        os.environ["LEANN_ZMQ_PORT"] = str(port)
-
-    hnsw_add_vectors(
-        str(vector_index_file),
-        embeddings,
-        ef_construction=ef_construction,
-        recompute=recompute,
-    )
-
-    # Stop server after add when recompute path used
-    if esm is not None:
+    def __del__(self):
         try:
-            esm.stop_server()
+            self.cleanup()
         except Exception:
             pass
-
-    # Sanity: ids length should match embeddings rows
-    if len(assigned_ids) != embeddings.shape[0]:
-        warnings.warn(
-            f"Assigned {len(assigned_ids)} IDs but computed {embeddings.shape[0]} embeddings.",
-            UserWarning,
-            stacklevel=2,
-        )
-
-    return len(assigned_ids)
-
-
-def create_incremental_add_context(
-    index_path: str,
-    *,
-    # Optional embedding choices; if None will use meta
-    embedding_model: Optional[str] = None,
-    embedding_mode: Optional[str] = None,
-    # Optional data-to-text preparation in context
-    data_dir: Optional[str] = None,
-    required_exts: Optional[list[str]] = None,
-    chunk_size: int = 256,
-    chunk_overlap: int = 128,
-    max_items: int = -1,
-) -> IncrementalAddContext:
-    """Validate index and prepare context for repeated incremental adds.
-
-    Additionally, if data_dir is provided, this function will load documents,
-    chunk them to texts with the specified parameters, and store them in ctx.prepared_texts.
-    """
-    meta = _read_meta_file(index_path)
-    if meta.get("backend_name") != "hnsw":
-        raise RuntimeError("Incremental add is currently supported only for HNSW backend")
-    if meta.get("is_compact", True):
-        raise RuntimeError(
-            "Index is compact/pruned. Rebuild base with is_recompute=False and is_compact=False for incremental add."
-        )
-
-    passages_file, offsets_file, vector_index_file = _resolve_index_paths(index_path)
-    if not vector_index_file.exists():
-        raise FileNotFoundError(
-            f"Vector index file missing: {vector_index_file}. Build base first with LeannBuilder."
-        )
-
-    model_name = embedding_model or meta.get("embedding_model", "facebook/contriever")
-    mode_name = embedding_mode or meta.get("embedding_mode", "sentence-transformers")
-    distance_metric = meta.get("backend_kwargs", {}).get("distance_metric", "mips").lower()
-
-    prepared_texts: Optional[list[str]] = None
-    if data_dir is not None:
-        try:
-            from llama_index.core import SimpleDirectoryReader  # type: ignore
-            from llama_index.core.node_parser import SentenceSplitter  # type: ignore
-        except Exception as e:
-            raise RuntimeError(
-                "llama-index-core is required when using data_dir in create_incremental_add_context"
-            ) from e
-
-        reader_kwargs: dict[str, Any] = {"recursive": True, "encoding": "utf-8"}
-        if required_exts:
-            reader_kwargs["required_exts"] = required_exts
-        documents = SimpleDirectoryReader(data_dir, **reader_kwargs).load_data(show_progress=True)
-        if documents:
-            splitter = SentenceSplitter(
-                chunk_size=chunk_size,
-                chunk_overlap=chunk_overlap,
-                separator=" ",
-                paragraph_separator="\n\n",
-            )
-            prepared_texts = []
-            for doc in documents:
-                try:
-                    nodes = splitter.get_nodes_from_documents([doc])
-                    if nodes:
-                        prepared_texts.extend([node.get_content() for node in nodes])
-                except Exception:
-                    content = doc.get_content()
-                    if content and content.strip():
-                        prepared_texts.append(content.strip())
-            if max_items > 0 and len(prepared_texts) > max_items:
-                prepared_texts = prepared_texts[:max_items]
-
-    return IncrementalAddContext(
-        index_path=index_path,
-        passages_file=passages_file,
-        offsets_file=offsets_file,
-        vector_index_file=vector_index_file,
-        embedding_model=model_name,
-        embedding_mode=mode_name,
-        distance_metric=distance_metric,
-        prepared_texts=prepared_texts,
-    )
-
-
-def incremental_add_texts_with_context(
-    ctx: IncrementalAddContext,
-    texts: list[str],
-    *,
-    ef_construction: Optional[int] = None,
-    recompute: bool = False,
-) -> int:
-    """Incrementally add texts using a prepared context (no repeated validation).
-
-    For non-compact HNSW, ef_construction (efConstruction) can be overridden during insertion.
-    """
-    if not texts:
-        return 0
-
-    # Append passages & offsets
-    _append_passages_and_update_offsets(ctx.passages_file, ctx.offsets_file, texts)
-
-    # Compute embeddings
-    # Embedding computation path
-    esm = None
-    port = None
-    if recompute:
-        passages_source_file = f"{ctx.index_path}.meta.json"
-        esm = EmbeddingServerManager(
-            backend_module_name="leann_backend_hnsw.hnsw_embedding_server",
-        )
-        started, port = esm.start_server(
-            port=5557,
-            model_name=ctx.embedding_model,
-            embedding_mode=ctx.embedding_mode,
-            passages_file=passages_source_file,
-            distance_metric=ctx.distance_metric,
-            enable_warmup=False,
-        )
-        if not started:
-            raise RuntimeError("Failed to start embedding server for recompute add")
-        embeddings = compute_embeddings_via_server(texts, ctx.embedding_model, port)
-    else:
-        embeddings = compute_embeddings(
-            texts,
-            model_name=ctx.embedding_model,
-            mode=ctx.embedding_mode,
-            use_server=False,
-            is_build=True,
-        )
-
-    # Normalize for cosine if needed
-    if ctx.distance_metric == "cosine":
-        norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
-        norms[norms == 0] = 1
-        embeddings = embeddings / norms
-
-    # Append via backend helper (supports ef_construction/recompute plumbing)
-    try:
-        from leann_backend_hnsw.hnsw_backend import add_vectors as hnsw_add_vectors  # type: ignore
-    except Exception as e:
-        raise RuntimeError(
-            "Failed to import HNSW backend add helper. Ensure HNSW backend is installed."
-        ) from e
-
-    if recompute and port is not None:
-        os.environ["LEANN_ZMQ_PORT"] = str(port)
-
-    hnsw_add_vectors(
-        str(ctx.vector_index_file),
-        embeddings,
-        ef_construction=ef_construction,
-        recompute=recompute,
-    )
-
-    # Stop server after add when recompute path used
-    if esm is not None:
-        try:
-            esm.stop_server()
-        except Exception:
-            pass
-
-    return embeddings.shape[0]
-
-
-def incremental_add_directory(
-    index_path: str,
-    data_dir: str,
-    *,
-    chunk_size: int = 256,
-    chunk_overlap: int = 128,
-    required_exts: Optional[list[str]] = None,
-    max_items: int = -1,
-    embedding_model: Optional[str] = None,
-    embedding_mode: Optional[str] = None,
-) -> int:
-    """Load documents from a directory, chunk them, and incrementally add to an index.
-
-    Chunking uses LlamaIndex SentenceSplitter for simplicity and avoids external app dependencies.
-    """
-    try:
-        from llama_index.core import SimpleDirectoryReader  # type: ignore
-        from llama_index.core.node_parser import SentenceSplitter  # type: ignore
-    except Exception as e:
-        raise RuntimeError("llama-index-core is required for incremental_add_directory") from e
-
-    reader_kwargs: dict[str, Any] = {"recursive": True, "encoding": "utf-8"}
-    if required_exts:
-        reader_kwargs["required_exts"] = required_exts
-    documents = SimpleDirectoryReader(data_dir, **reader_kwargs).load_data(show_progress=True)
-    if not documents:
-        return 0
-
-    # Traditional text chunking
-    splitter = SentenceSplitter(
-        chunk_size=chunk_size,
-        chunk_overlap=chunk_overlap,
-        separator=" ",
-        paragraph_separator="\n\n",
-    )
-    all_texts: list[str] = []
-    for doc in documents:
-        try:
-            nodes = splitter.get_nodes_from_documents([doc])
-            if nodes:
-                all_texts.extend([node.get_content() for node in nodes])
-        except Exception:
-            content = doc.get_content()
-            if content and content.strip():
-                all_texts.append(content.strip())
-
-    if max_items > 0 and len(all_texts) > max_items:
-        all_texts = all_texts[:max_items]
-
-    return incremental_add_texts(
-        index_path,
-        all_texts,
-        embedding_model=embedding_model,
-        embedding_mode=embedding_mode,
-    )