fix(vertex): pass required vectors arg in list and similarity search (#5627)

mem0/vector_stores/vertex_ai_vector_search.py

@@ -487,7 +487,7 @@ class GoogleMatchingEngine(VectorStoreBase):
             # Use a large top_k if none specified
             search_limit = top_k if top_k is not None else 10000
 
-            results = self.search(query=zero_vector, top_k=search_limit, filters=filters)
+            results = self.search(query="", vectors=zero_vector, top_k=search_limit, filters=filters)
 
             logger.debug("Found %d results", len(results))
             return [results]  # Wrap in extra array to match interface
@@ -620,7 +620,7 @@ class GoogleMatchingEngine(VectorStoreBase):
         logger.debug("Filter: %s", filter)
 
         embedding = self.embedder.embed_query(query)
-        results = self.search(query=embedding, top_k=k, filters=filter)
+        results = self.search(query=query, vectors=embedding, top_k=k, filters=filter)
 
         docs_and_scores = [
             (Document(page_content=result.payload.get("text", ""), metadata=result.payload), result.score)

tests/vector_stores/test_vertex_ai_vector_search.py

@@ -116,6 +116,32 @@ def test_search_vectors(vector_store, mock_vertex_ai):
     assert results[0].payload == {"user_id": "test_user"}
 
 
+def test_list_does_not_raise_type_error(vector_store, mock_vertex_ai):
+    """list() must call search() with the required positional `vectors` arg."""
+    mock_vertex_ai["endpoint"].find_neighbors.return_value = [[]]
+
+    results = vector_store.list(filters={"user_id": "test_user"}, top_k=5)
+
+    mock_vertex_ai["endpoint"].find_neighbors.assert_called_once()
+    queries = mock_vertex_ai["endpoint"].find_neighbors.call_args[1]["queries"]
+    assert queries == [[0.0] * 768]
+    assert results == [[]]
+
+
+def test_similarity_search_with_score_passes_embedding(vector_store, mock_vertex_ai):
+    """similarity_search_with_score() must pass the embedding as `vectors`."""
+    embedding = [0.1, 0.2, 0.3]
+    vector_store.embedder = Mock()
+    vector_store.embedder.embed_query.return_value = embedding
+    mock_vertex_ai["endpoint"].find_neighbors.return_value = [[]]
+
+    vector_store.similarity_search_with_score(query="hello", k=3)
+
+    mock_vertex_ai["endpoint"].find_neighbors.assert_called_once()
+    queries = mock_vertex_ai["endpoint"].find_neighbors.call_args[1]["queries"]
+    assert queries == [embedding]
+
+
 def test_delete(vector_store, mock_vertex_ai):
     """Test deleting vectors"""
     vector_id = "test-id"