Merge branch 'main' into main

gqlalchemy/graph_algorithms/query_builder.py

@@ -445,6 +445,29 @@ def community_detection_get(
             (weight, coloring, min_graph_shrink, community_alg_threshold, coloring_alg_threshold),
         )
 
+    def community_detection_get_subgraph(
+        self,
+        subgraph_nodes: List[Any],
+        subgraph_relationships: List[Any],
+        weight: str,
+        coloring: bool,
+        min_graph_shrink: int,
+        community_alg_threshold: float,
+        coloring_alg_threshold: float,
+    ) -> DeclarativeBase:
+        return self.call(
+            "community_detection.get_subgraph",
+            (
+                subgraph_nodes,
+                subgraph_relationships,
+                weight,
+                coloring,
+                min_graph_shrink,
+                community_alg_threshold,
+                coloring_alg_threshold,
+            ),
+        )
+
     def community_detection_online_get(self) -> DeclarativeBase:
         return self.call("community_detection_online.get")
 
@@ -854,6 +877,58 @@ def kmeans_set_clusters(
             (n_clusters, embedding_property, cluster_property, init, n_init, max_iter, tol, algorithm, random_state),
         )
 
+    def leiden_community_detection_get(
+        self, weight_property: str, gamma: float, theta: float, resolution_parameter: float, number_of_iterations: int
+    ) -> DeclarativeBase:
+        return self.call(
+            "leiden_community_detection.get",
+            (weight_property, gamma, theta, resolution_parameter, number_of_iterations),
+        )
+
+    def leiden_community_detection_get_subgraph(
+        self,
+        subgraph_nodes: List[Any],
+        subgraph_relationships: List[Any],
+        weight_property: str,
+        gamma: float,
+        theta: float,
+        resolution_parameter: float,
+        number_of_iterations: int,
+    ) -> DeclarativeBase:
+        return self.call(
+            "leiden_community_detection.get_subgraph",
+            (
+                subgraph_nodes,
+                subgraph_relationships,
+                weight_property,
+                gamma,
+                theta,
+                resolution_parameter,
+                number_of_iterations,
+            ),
+        )
+
+    def link_prediction_get_training_results(self) -> DeclarativeBase:
+        return self.call("link_prediction.get_training_results")
+
+    def link_prediction_load_model(self, path: str) -> DeclarativeBase:
+        return self.call("link_prediction.load_model", (path))
+
+    def link_prediction_predict(self, src_vertex: Any, dest_vertex: Any) -> DeclarativeBase:
+        return self.call("link_prediction.predict", (src_vertex, dest_vertex))
+
+    def link_prediction_recommend(self, src_vertex: Any, dest_vertices: List[Any], k: int) -> DeclarativeBase:
+        return self.call("link_prediction.recommend", (src_vertex, dest_vertices, k))
+
+    def link_prediction_reset_parameters(self) -> DeclarativeBase:
+        return self.call("link_prediction.reset_parameters")
+
+    def link_prediction_set_model_parameters(self, parameters: dict) -> DeclarativeBase:
+        return self.call("link_prediction.set_model_parameters", (parameters))
+
+    def link_prediction_train(self) -> DeclarativeBase:
+        return self.call("link_prediction.train")
+
     def llm_util_schema(self, output_type: str) -> DeclarativeBase:
         return self.call("llm_util.schema", (output_type))
 
@@ -1099,6 +1174,27 @@ def node2vec_online_set_word2vec_learner(
     def node2vec_online_update(self, edges: List[Any]) -> DeclarativeBase:
         return self.call("node2vec_online.update", (edges))
 
+    def node_classification_get_training_data(self) -> DeclarativeBase:
+        return self.call("node_classification.get_training_data")
+
+    def node_classification_load_model(self, num: int) -> DeclarativeBase:
+        return self.call("node_classification.load_model", (num))
+
+    def node_classification_predict(self, vertex: Any) -> DeclarativeBase:
+        return self.call("node_classification.predict", (vertex))
+
+    def node_classification_reset(self) -> DeclarativeBase:
+        return self.call("node_classification.reset")
+
+    def node_classification_save_model(self) -> DeclarativeBase:
+        return self.call("node_classification.save_model")
+
+    def node_classification_set_model_parameters(self, params: Any) -> DeclarativeBase:
+        return self.call("node_classification.set_model_parameters", (params))
+
+    def node_classification_train(self, num_epochs: int) -> DeclarativeBase:
+        return self.call("node_classification.train", (num_epochs))
+
     def node_similarity_cosine(self, node1: Any, node2: Any, mode: str = "cartesian") -> DeclarativeBase:
         return self.call("node_similarity.cosine", (node1, node2, mode))
 
@@ -1294,6 +1390,15 @@ def set_property_copyPropertyRel2Rel(
     def temporal_format(self, temporal: Any, format: str) -> DeclarativeBase:
         return self.call("temporal.format", (temporal, format))
 
+    def text_format(self, text: str, params: List[Any]) -> DeclarativeBase:
+        return self.call("text.format", (text, params))
+
+    def text_join(self, strings: List[str], delimiter: str) -> DeclarativeBase:
+        return self.call("text.join", (strings, delimiter))
+
+    def text_regexGroups(self, input: str, regex: str) -> DeclarativeBase:
+        return self.call("text.regexGroups", (input, regex))
+
     def text_search_aggregate(self, index_name: str, search_query: str, aggregation_query: str) -> DeclarativeBase:
         return self.call("text_search.aggregate", (index_name, search_query, aggregation_query))
 
@@ -1350,6 +1455,12 @@ def util_module_md5(self, values: Any) -> DeclarativeBase:
     def uuid_generator_get(self) -> DeclarativeBase:
         return self.call("uuid_generator.get")
 
+    def vector_search_search(self, index_name: str, result_set_size: int, query_vector: List[Any]) -> DeclarativeBase:
+        return self.call("vector_search.search", (index_name, result_set_size, query_vector))
+
+    def vector_search_show_index_info(self) -> DeclarativeBase:
+        return self.call("vector_search.show_index_info")
+
     def vrp_route(self, depot_node: Any, number_of_vehicles: Optional[int] = None) -> DeclarativeBase:
         return self.call("vrp.route", (depot_node, number_of_vehicles))