feat: metrics in neo4j adapter [COG-1082]

cognee/api/v1/cognify/cognify_v2.py

@@ -165,7 +165,6 @@ async def get_default_tasks(
                 task_config={"batch_size": 10},
             ),
             Task(add_data_points, task_config={"batch_size": 10}),
-            Task(store_descriptive_metrics, include_optional=True),
         ]
     except Exception as error:
         send_telemetry("cognee.cognify DEFAULT TASKS CREATION ERRORED", user.id)

cognee/infrastructure/databases/graph/neo4j_driver/adapter.py

@@ -531,16 +531,144 @@ async def get_filtered_graph_data(self, attribute_filters):
 
         return (nodes, edges)
 
+    async def graph_exists(self, graph_name="myGraph"):
+        query = "CALL gds.graph.list() YIELD graphName RETURN collect(graphName) AS graphNames;"
+        result = await self.query(query)
+        graph_names = result[0]["graphNames"] if result else []
+        return graph_name in graph_names
+
+    async def project_entire_graph(self, graph_name="myGraph"):
+        """
+        Projects all node labels and all relationship types into an in-memory GDS graph.
+        """
+        if await self.graph_exists(graph_name):
+            return
+
+        node_labels_query = "CALL db.labels() YIELD label RETURN collect(label) AS labels;"
+        node_labels_result = await self.query(node_labels_query)
+        node_labels = node_labels_result[0]["labels"] if node_labels_result else []
+
+        relationship_types_query = "CALL db.relationshipTypes() YIELD relationshipType RETURN collect(relationshipType) AS relationships;"
+        relationship_types_result = await self.query(relationship_types_query)
+        relationship_types = (
+            relationship_types_result[0]["relationships"] if relationship_types_result else []
+        )
+
+        if not node_labels or not relationship_types:
+            raise ValueError("No node labels or relationship types found in the database.")
+
+        node_labels_str = "[" + ", ".join(f"'{label}'" for label in node_labels) + "]"
+        relationship_types_str = "[" + ", ".join(f"'{rel}'" for rel in relationship_types) + "]"
+
+        query = f"""
+        CALL gds.graph.project(
+            '{graph_name}',
+            {node_labels_str},
+            {relationship_types_str}
+        ) YIELD graphName;
+        """
+
+        await self.query(query)
+
+    async def drop_graph(self, graph_name="myGraph"):
+        if await self.graph_exists(graph_name):
+            drop_query = f"CALL gds.graph.drop('{graph_name}');"
+            await self.query(drop_query)
+
     async def get_graph_metrics(self, include_optional=False):
-        return {
-            "num_nodes": -1,
-            "num_edges": -1,
-            "mean_degree": -1,
-            "edge_density": -1,
-            "num_connected_components": -1,
-            "sizes_of_connected_components": -1,
-            "num_selfloops": -1,
-            "diameter": -1,
-            "avg_shortest_path_length": -1,
-            "avg_clustering": -1,
+        nodes, edges = await self.get_model_independent_graph_data()
+        graph_name = "myGraph"
+        await self.drop_graph(graph_name)
+        await self.project_entire_graph(graph_name)
+
+        async def _get_edge_density():
+            query = """
+            MATCH (n)
+            WITH count(n) AS num_nodes
+            MATCH ()-[r]->()
+            WITH num_nodes, count(r) AS num_edges
+            RETURN CASE
+                WHEN num_nodes < 2 THEN 0
+                ELSE num_edges * 1.0 / (num_nodes * (num_nodes - 1))
+            END AS edge_density;
+            """
+            result = await self.query(query)
+            return result[0]["edge_density"] if result else 0
+
+        async def _get_num_connected_components():
+            await self.drop_graph(graph_name)
+            await self.project_entire_graph(graph_name)
+
+            query = f"""
+            CALL gds.wcc.stream('{graph_name}')
+            YIELD componentId
+            RETURN count(DISTINCT componentId) AS num_connected_components;
+            """
+
+            result = await self.query(query)
+            return result[0]["num_connected_components"] if result else 0
+
+        async def _get_size_of_connected_components():
+            await self.drop_graph(graph_name)
+            await self.project_entire_graph(graph_name)
+
+            query = f"""
+            CALL gds.wcc.stream('{graph_name}')
+            YIELD componentId
+            RETURN componentId, count(*) AS size
+            ORDER BY size DESC;
+            """
+
+            result = await self.query(query)
+            return [record["size"] for record in result] if result else []
+
+        async def _count_self_loops():
+            query = """
+            MATCH (n)-[r]->(n)
+            RETURN count(r) AS self_loop_count;
+            """
+            result = await self.query(query)
+            return result[0]["self_loop_count"] if result else 0
+
+        async def _get_diameter():
+            logging.warning("Diameter calculation is not implemented for neo4j.")
+            return -1
+
+        async def _get_avg_shortest_path_length():
+            logging.warning(
+                "Average shortest path length calculation is not implemented for neo4j."
+            )
+            return -1
+
+        async def _get_avg_clustering():
+            logging.warning("Average clustering calculation is not implemented for neo4j.")
+            return -1
+
+        num_nodes = len(nodes[0]["nodes"])
+        num_edges = len(edges[0]["elements"])
+
+        mandatory_metrics = {
+            "num_nodes": num_nodes,
+            "num_edges": num_edges,
+            "mean_degree": (2 * num_edges) / num_nodes if num_nodes != 0 else None,
+            "edge_density": await _get_edge_density(),
+            "num_connected_components": await _get_num_connected_components(),
+            "sizes_of_connected_components": await _get_size_of_connected_components(),
         }
+
+        if include_optional:
+            optional_metrics = {
+                "num_selfloops": await _count_self_loops(),
+                "diameter": await _get_diameter(),
+                "avg_shortest_path_length": await _get_avg_shortest_path_length(),
+                "avg_clustering": await _get_avg_clustering(),
+            }
+        else:
+            optional_metrics = {
+                "num_selfloops": -1,
+                "diameter": -1,
+                "avg_shortest_path_length": -1,
+                "avg_clustering": -1,
+            }
+
+        return mandatory_metrics | optional_metrics

cognee/modules/data/models/GraphMetrics.py

@@ -24,5 +24,5 @@ class GraphMetrics(Base):
     avg_shortest_path_length = Column(Float, nullable=True)
     avg_clustering = Column(Float, nullable=True)
 
-    created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
-    updated_at = Column(DateTime(timezone=True), onupdate=lambda: datetime.now(timezone.utc))
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+    updated_at = Column(DateTime(timezone=True), onupdate=func.now())