unstructured2graph: parameterize vector index name and dimension

integrations/lightrag-memgraph/src/lightrag_memgraph/__init__.py

@@ -6,7 +6,12 @@
 
 from .core import MemgraphLightRAGWrapper
 from .docstatus_impl import MemgraphDocStatusStorage
-from .embeddings import build_memgraph_sentence_embed, memgraph_sentence_embed
+from .embeddings import (
+    DEFAULT_EMBEDDING_DIM,
+    DEFAULT_MODEL_NAME,
+    build_memgraph_sentence_embed,
+    memgraph_sentence_embed,
+)
 from .kv_impl import MemgraphKVStorage
 from .registry import register_memgraph_storage
 from .vector_impl import MemgraphVectorStorage
@@ -125,6 +130,8 @@ async def _wrapped(
 _patch_anthropic()
 
 __all__ = [
+    "DEFAULT_EMBEDDING_DIM",
+    "DEFAULT_MODEL_NAME",
     "MemgraphDocStatusStorage",
     "MemgraphKVStorage",
     "MemgraphLightRAGWrapper",

integrations/lightrag-memgraph/tests/test_import.py

@@ -29,3 +29,13 @@ def test_wrapper_with_custom_params():
     )
     assert wrapper.log_level == "DEBUG"
     assert wrapper.full_memgraph_persistence is False
+
+
+def test_embedding_defaults_are_exported_from_top_level():
+    """DEFAULT_EMBEDDING_DIM/DEFAULT_MODEL_NAME must be reachable without
+    digging into the embeddings submodule, so downstream packages can treat
+    them as the single source of truth for the default embedding dimension."""
+    from lightrag_memgraph import DEFAULT_EMBEDDING_DIM, DEFAULT_MODEL_NAME, embeddings
+
+    assert DEFAULT_EMBEDDING_DIM == embeddings.DEFAULT_EMBEDDING_DIM
+    assert DEFAULT_MODEL_NAME == embeddings.DEFAULT_MODEL_NAME

unstructured2graph/src/unstructured2graph/memgraph.py

@@ -1,6 +1,7 @@
 import logging
 import time
 
+from lightrag_memgraph import DEFAULT_EMBEDDING_DIM
 from memgraph_toolbox.api.memgraph import Memgraph
 
 logger = logging.getLogger(__name__)
@@ -127,10 +128,17 @@ def create_label_index(memgraph: Memgraph, label: str):
         logger.warning(f"Could not create label index on :{label}: {e}")
 
 
-def create_vector_search_index(memgraph: Memgraph, label: str, property: str):
+def create_vector_search_index(
+    memgraph: Memgraph,
+    label: str,
+    property: str,
+    dimension: int = DEFAULT_EMBEDDING_DIM,
+    index_name: str = "vs_name",
+):
     try:
         memgraph.query(
-            f"CREATE VECTOR INDEX vs_name ON :{label}({property}) WITH CONFIG {{'dimension': 384, 'capacity': 10000}};"
+            f"CREATE VECTOR INDEX {index_name} ON :{label}({property}) "
+            f"WITH CONFIG {{'dimension': {dimension}, 'capacity': 10000}};"
         )
     except Exception as e:
         logger.warning(f"Error creating vector search index: {e}")

unstructured2graph/tests/test_memgraph.py

@@ -2,7 +2,33 @@
 
 from unittest.mock import MagicMock
 
-from unstructured2graph.memgraph import create_nodes_from_list, create_unique_constraint
+from lightrag_memgraph import DEFAULT_EMBEDDING_DIM
+from unstructured2graph.memgraph import create_nodes_from_list, create_unique_constraint, create_vector_search_index
+
+
+def test_create_vector_search_index_defaults_match_embedding_dim():
+    """Default dimension must track lightrag_memgraph's own default, not a
+    separately-maintained literal."""
+    memgraph = MagicMock()
+
+    create_vector_search_index(memgraph, "Chunk", "embedding")
+
+    query = memgraph.query.call_args[0][0]
+    assert "CREATE VECTOR INDEX vs_name ON :Chunk(embedding)" in query
+    assert f"'dimension': {DEFAULT_EMBEDDING_DIM}" in query
+
+
+def test_create_vector_search_index_accepts_custom_dimension_and_name():
+    """A caller using a different embedding model must be able to pass a
+    matching dimension and a distinct index name (e.g. for a second index
+    on another label)."""
+    memgraph = MagicMock()
+
+    create_vector_search_index(memgraph, "Entity", "embedding", dimension=768, index_name="entity_vs")
+
+    query = memgraph.query.call_args[0][0]
+    assert "CREATE VECTOR INDEX entity_vs ON :Entity(embedding)" in query
+    assert "'dimension': 768" in query
 
 
 def test_create_nodes_from_list_defaults_to_create():