Add metrics to metric table

cognee/api/v1/cognify/cognify_v2.py

@@ -25,6 +25,7 @@
 )
 from cognee.tasks.graph import extract_graph_from_data
 from cognee.tasks.storage import add_data_points
+from cognee.tasks.storage.descriptive_metrics import store_descriptive_metrics
 from cognee.tasks.storage.index_graph_edges import index_graph_edges
 from cognee.tasks.summarization import summarize_text
 
@@ -164,6 +165,7 @@ async def get_default_tasks(
                 task_config={"batch_size": 10},
             ),
             Task(add_data_points, only_root=True, task_config={"batch_size": 10}),
+            Task(store_descriptive_metrics),
         ]
     except Exception as error:
         send_telemetry("cognee.cognify DEFAULT TASKS CREATION ERRORED", user.id)

cognee/modules/data/models/MetricData.py

@@ -11,13 +11,13 @@ class GraphMetricData(Base):
 
     # TODO: Change ID to reflect unique id of graph database
     id = Column(UUID, primary_key=True, default=uuid4)
-    num_tokens = Column(Integer)
-    num_nodes = Column(Integer)
-    num_edges = Column(Integer)
-    mean_degree = Column(Float)
-    edge_density = Column(Float)
-    num_connected_components = Column(Integer)
-    sizes_of_connected_components = Column(ARRAY(Integer))
+    num_tokens = Column(Integer, nullable=True)
+    num_nodes = Column(Integer, nullable=True)
+    num_edges = Column(Integer, nullable=True)
+    mean_degree = Column(Float, nullable=True)
+    edge_density = Column(Float, nullable=True)
+    num_connected_components = Column(Integer, nullable=True)
+    sizes_of_connected_components = Column(ARRAY(Integer), nullable=True)
     num_selfloops = Column(Integer, nullable=True)
     diameter = Column(Integer, nullable=True)
     avg_shortest_path_length = Column(Float, nullable=True)

cognee/tasks/storage/descriptive_metrics.py

@@ -0,0 +1,46 @@
+from cognee.infrastructure.engine import DataPoint
+from cognee.modules.data.processing.document_types import Document
+from cognee.infrastructure.databases.relational import get_relational_engine
+from sqlalchemy import select
+from cognee.modules.data.models import Data
+from cognee.modules.data.models.MetricData import GraphMetricData
+import uuid
+from cognee.infrastructure.databases.graph import get_graph_engine
+
+
+async def fetch_token_count(db_engine) -> int:
+    """
+    Fetches and sums token counts from the database.
+
+    Returns:
+        int: The total number of tokens across all documents.
+    """
+
+    async with db_engine.get_async_session() as session:
+        document_data_points = await session.execute(select(Data))
+        token_count_sum = sum(document.token_count for document in document_data_points.scalars())
+
+    return token_count_sum
+
+
+async def calculate_graph_metrics(graph_data):
+    nodes, edges = graph_data
+    graph_metrics = {
+        "num_nodes": len(nodes),
+        "num_edges": len(edges),
+    }
+    return graph_metrics
+
+
+async def store_descriptive_metrics(data_points: list[DataPoint]):
+    db_engine = get_relational_engine()
+    graph_engine = await get_graph_engine()
+    graph_data = await graph_engine.get_graph_data()
+
+    token_count_sum = await fetch_token_count(db_engine)
+    graph_metrics = await calculate_graph_metrics(graph_data)
+
+    table_name = "graph_metric_table"
+    metrics_dict = {"id": uuid.uuid4(), "num_tokens": token_count_sum} | graph_metrics
+
+    await db_engine.insert_data(table_name, metrics_dict)