feat: expose the vector rerank method in the CypherEngine API

crates/lance-graph-python/src/graph.rs

@@ -1056,6 +1056,50 @@ impl CypherEngine {
         }
     }
 
+    /// Execute Cypher query with vector reranking
+    ///
+    /// Convenience method combining graph traversal and vector similarity ranking.
+    /// See CypherQuery.execute_with_vector_rerank for detailed documentation.
+    ///
+    /// Parameters
+    /// ----------
+    /// query : str
+    ///     Cypher query string
+    /// vector_search : VectorSearch
+    ///     Vector search configuration
+    ///
+    /// Returns
+    /// -------
+    /// pyarrow.Table
+    ///     Results sorted by vector similarity
+    fn execute_with_vector_rerank(
+        &self,
+        py: Python,
+        query: &str,
+        vector_search: &VectorSearch,
+    ) -> PyResult<PyObject> {
+        // Parse query and execute with cached catalog/context
+        let cypher_query = RustCypherQuery::new(query)
+            .map_err(graph_error_to_pyerr)?
+            .with_config(self.config.clone());
+
+        let catalog = self.catalog.clone();
+        let context = self.context.as_ref().clone();
+        let vs = vector_search.inner.clone();
+
+        // Execute query to get candidates, then apply vector reranking
+        let result_batch = RT
+            .block_on(Some(py), async move {
+                let candidates = cypher_query
+                    .execute_with_catalog_and_context(catalog, context)
+                    .await?;
+                vs.search(&candidates).await
+            })?
+            .map_err(graph_error_to_pyerr)?;
+
+        record_batch_to_python_table(py, &result_batch)
+    }
+
     fn __repr__(&self) -> String {
         format!(
             "CypherEngine(nodes={}, relationships={})",

python/python/tests/test_vector_search.py

@@ -317,3 +317,86 @@ def test_vector_search_different_query_vectors(vector_env):
         .search(table)
     )
     assert results3.to_pydict()["name"][0] == "Doc4"
+
+
+def test_cypher_engine_execute_with_vector_rerank(vector_env):
+    """Test CypherEngine.execute_with_vector_rerank basic functionality."""
+    from lance_graph import CypherEngine
+
+    config, datasets, _ = vector_env
+    engine = CypherEngine(config, datasets)
+
+    results = engine.execute_with_vector_rerank(
+        "MATCH (d:Document) WHERE d.category = 'tech' RETURN d.id, d.name, d.embedding",
+        VectorSearch("d.embedding")
+        .query_vector([1.0, 0.0, 0.0])
+        .metric(DistanceMetric.L2)
+        .top_k(2),
+    )
+
+    data = results.to_pydict()
+    assert len(data["d.name"]) == 2
+    assert data["d.name"][0] == "Doc1"
+
+
+def test_cypher_engine_vs_cypher_query_vector_rerank_equivalence(vector_env):
+    """Test that CypherEngine produces same results as CypherQuery for vector rerank."""
+    from lance_graph import CypherEngine
+
+    config, datasets, _ = vector_env
+
+    query_text = (
+        "MATCH (d:Document) WHERE d.category = 'tech' RETURN d.id, d.name, d.embedding"
+    )
+    vector_search = (
+        VectorSearch("d.embedding")
+        .query_vector([1.0, 0.0, 0.0])
+        .metric(DistanceMetric.L2)
+        .top_k(2)
+    )
+
+    # Execute with CypherQuery
+    query = CypherQuery(query_text).with_config(config)
+    result_query = query.execute_with_vector_rerank(datasets, vector_search)
+
+    # Execute with CypherEngine
+    engine = CypherEngine(config, datasets)
+    result_engine = engine.execute_with_vector_rerank(query_text, vector_search)
+
+    # Results should be identical
+    assert result_query.to_pydict() == result_engine.to_pydict()
+
+
+def test_cypher_engine_vector_rerank_multiple_queries(vector_env):
+    """Test that CypherEngine efficiently handles multiple vector rerank queries."""
+    from lance_graph import CypherEngine
+
+    config, datasets, _ = vector_env
+    engine = CypherEngine(config, datasets)
+
+    # Execute multiple different queries using the same cached engine
+    results1 = engine.execute_with_vector_rerank(
+        "MATCH (d:Document) RETURN d.id, d.name, d.embedding",
+        VectorSearch("d.embedding")
+        .query_vector([1.0, 0.0, 0.0])
+        .metric(DistanceMetric.L2)
+        .top_k(2),
+    )
+
+    results2 = engine.execute_with_vector_rerank(
+        "MATCH (d:Document) WHERE d.category = 'science' "
+        "RETURN d.id, d.name, d.embedding",
+        VectorSearch("d.embedding")
+        .query_vector([0.0, 1.0, 0.0])
+        .metric(DistanceMetric.Cosine)
+        .top_k(1),
+    )
+
+    data1 = results1.to_pydict()
+    data2 = results2.to_pydict()
+
+    assert len(data1["d.name"]) == 2
+    assert data1["d.name"][0] == "Doc1"
+
+    assert len(data2["d.name"]) == 1
+    assert data2["d.name"][0] == "Doc3"