fix: remove unused storage_fourcc

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

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

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

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