fix(langchain): align similarity_search() signature with Sequence protocol and issue spec

- Change query_vector type annotation from list[float] to Sequence[float]
  (matches existing code comment that explicitly documents numpy.ndarray support
  and aligns with CoordinodeClient.vector_search() signature)
- Change default k from 10 to 5 (matches issue #20 acceptance criteria)
- Strengthen test_similarity_search_returns_results: replace non-negativity
  check with full ascending-order assertion on adjacent distances
- Use store.structured_query(param_map=...) in test_upsert_relations_idempotent
  instead of store._client.cypher() — keeps integration test stable vs internal API

Author: polaz

langchain-coordinode/langchain_coordinode/graph.py

@@ -5,6 +5,7 @@
 import hashlib
 import json
 import re
+from collections.abc import Sequence
 from typing import Any
 
 from langchain_community.graphs.graph_store import GraphStore
@@ -196,8 +197,8 @@ def query(
 
     def similarity_search(
         self,
-        query_vector: list[float],
-        k: int = 10,
+        query_vector: Sequence[float],
+        k: int = 5,
         label: str = "Chunk",
         property: str = "embedding",
     ) -> list[dict[str, Any]]:

tests/integration/adapters/test_langchain.py

@@ -164,7 +164,7 @@ def test_similarity_search_returns_results(graph, unique_tag):
         assert len(results) >= 1
         assert all("id" in r and "node" in r and "distance" in r for r in results)
         assert any(r["id"] == seeded_internal_id for r in results)
-        assert results[0]["distance"] >= 0.0
+        assert all(results[i]["distance"] <= results[i + 1]["distance"] for i in range(len(results) - 1))
     finally:
         graph.query("MATCH (n:LCSim {id: $id}) DELETE n", params={"id": f"lcsim-{unique_tag}"})
 

tests/integration/adapters/test_llama_index.py

@@ -82,9 +82,9 @@ def test_upsert_relations_idempotent(store, tag):
     store.upsert_relations([rel])
     store.upsert_relations([rel])  # second call must not duplicate
 
-    rows = store._client.cypher(
+    rows = store.structured_query(
         "MATCH (a {id: $src})-[r:LI_IDEMP_REL]->(b {id: $dst}) RETURN count(r) AS cnt",
-        params={"src": src.id, "dst": dst.id},
+        param_map={"src": src.id, "dst": dst.id},
     )
     assert rows[0]["cnt"] == 1, f"expected exactly 1 edge after double upsert, got: {rows}"