fix: preserve integer indices in empty SparseEmbedding.from_dict results

fastembed/sparse/sparse_embedding_base.py

@@ -26,7 +26,7 @@ def as_dict(self) -> dict[int, float]:
     @classmethod
     def from_dict(cls, data: dict[int, float]) -> "SparseEmbedding":
         if len(data) == 0:
-            return cls(values=np.array([]), indices=np.array([]))
+            return cls(values=np.array([]), indices=np.array([], dtype=np.int64))
         indices, values = zip(*data.items())
         return cls(values=np.array(values), indices=np.array(indices))
 

tests/test_sparse_embedding_base.py

@@ -0,0 +1,74 @@
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from fastembed.sparse.bm25 import Bm25
+from fastembed.sparse.sparse_embedding_base import SparseEmbedding
+
+
+def test_from_dict_empty_indices_support_numpy_indexing() -> None:
+    embedding = SparseEmbedding.from_dict({})
+
+    assert embedding.indices.dtype == np.int64
+    assert embedding.indices.shape == (0,)
+    assert embedding.values.shape == (0,)
+    assert embedding.as_dict() == {}
+
+    dense = np.arange(5, dtype=float)
+    expected = dense.copy()
+    dense[embedding.indices] = embedding.values
+
+    np.testing.assert_array_equal(dense, expected)
+
+
+def test_from_dict_preserves_index_value_pairs_and_roundtrip() -> None:
+    data = {3: 1.5, 0: 2.0, 4: -0.25}
+
+    embedding = SparseEmbedding.from_dict(data)
+
+    assert np.issubdtype(embedding.indices.dtype, np.integer)
+    np.testing.assert_array_equal(embedding.indices, [3, 0, 4])
+    np.testing.assert_array_equal(embedding.values, [1.5, 2.0, -0.25])
+    assert embedding.as_dict() == data
+
+
+@pytest.mark.parametrize("empty_first", [True, False])
+def test_concatenating_empty_embedding_preserves_integer_indices(empty_first: bool) -> None:
+    empty = SparseEmbedding.from_dict({})
+    nonempty = SparseEmbedding.from_dict({3: 1.5, 0: 2.0, 4: -0.25})
+    embeddings = [empty, nonempty] if empty_first else [nonempty, empty]
+
+    indices = np.concatenate([embedding.indices for embedding in embeddings])
+    values = np.concatenate([embedding.values for embedding in embeddings])
+
+    assert np.issubdtype(indices.dtype, np.integer)
+    dense = np.zeros(5)
+    dense[indices] = values
+
+    np.testing.assert_array_equal(dense, [2.0, 0.0, 0.0, 1.5, -0.25])
+
+
+@pytest.mark.parametrize("document", ["", "the and", "!!!"])
+def test_bm25_empty_documents_have_integer_indices(document: str, tmp_path: Path) -> None:
+    model = Bm25(
+        "Qdrant/bm25",
+        cache_dir=str(tmp_path),
+        specific_model_path=str(tmp_path),
+        disable_stemmer=True,
+        stopwords={"the", "and"},
+        local_files_only=True,
+    )
+
+    embeddings = list(model.embed([document]))
+
+    assert len(embeddings) == 1
+    embedding = embeddings[0]
+    assert embedding.indices.dtype == np.int64
+    assert embedding.indices.shape == (0,)
+    assert embedding.values.shape == (0,)
+
+    dense = np.zeros(5)
+    dense[embedding.indices] = embedding.values
+
+    np.testing.assert_array_equal(dense, np.zeros(5))