fix(langchain): guard empty query_vector via len() for Sequence compatibility

similarity_search() now uses len(query_vector) == 0 instead of truthiness
to avoid ValueError on numpy.ndarray and other Sequence types.

Author: polaz

langchain-coordinode/langchain_coordinode/graph.py

@@ -217,7 +217,10 @@ def similarity_search(
         Returns:
             List of result dicts sorted by ascending distance.
         """
-        if not query_vector:
+        # Use len() instead of truthiness check: numpy.ndarray (and other Sequence
+        # types) raise ValueError("The truth value of an array is ambiguous") when
+        # used in a boolean context. len() == 0 works for all sequence types.
+        if len(query_vector) == 0:
             return []
         results = sorted(
             self._client.vector_search(

tests/integration/adapters/test_langchain.py

@@ -152,6 +152,10 @@ def test_similarity_search_returns_results(graph, unique_tag):
             "CREATE (n:LCSim {id: $id, embedding: $vec}) RETURN n AS nid",
             params={"id": f"lcsim-{unique_tag}", "vec": vec},
         )
+        # graph.query() wraps CoordinodeClient.cypher() which returns raw dict values.
+        # CoordiNode: CREATE ... RETURN n yields the internal integer node ID directly
+        # (NOT a node object). similarity_search() also returns {"id": r.node.id, ...}
+        # where r.node.id is the same integer. Direct equality comparison is correct.
         seeded_internal_id = seed_rows[0]["nid"]
 
         results = graph.similarity_search(vec, k=5, label="LCSim", property="embedding")