Feat: [cog-1089] Define pydantic models for descriptive graph metrics and input metrics

cognee/modules/data/models/MetricData.py

@@ -0,0 +1,27 @@
+from datetime import datetime, timezone
+
+from sqlalchemy import Column, DateTime, Float, Integer, ARRAY, UUID
+
+from cognee.infrastructure.databases.relational import Base
+from uuid import uuid4
+
+
+class GraphMetricData(Base):
+    __tablename__ = "graph_metric_table"
+
+    # 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_selfloops = Column(Integer, nullable=True)
+    diameter = Column(Integer, nullable=True)
+    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))