feature: solve edge embedding duplicates in edge collection + retriever optimization (#1151)

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## Description
feature: solve edge embedding duplicates in edge collection + retriever
optimization

## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin.

---------

Co-authored-by: Vasilije <8619304+Vasilije1990@users.noreply.github.com>

cognee/modules/engine/utils/generate_edge_id.py

@@ -0,0 +1,5 @@
+from uuid import NAMESPACE_OID, uuid5
+
+
+def generate_edge_id(edge_id: str) -> str:
+    return uuid5(NAMESPACE_OID, edge_id.lower().replace(" ", "_").replace("'", ""))

cognee/modules/graph/cognee_graph/CogneeGraph.py

@@ -170,28 +170,19 @@ class CogneeGraph(CogneeAbstractGraph):
 
             for edge in self.edges:
                 relationship_type = edge.attributes.get("relationship_type")
-                if relationship_type and relationship_type in embedding_map:
-                    edge.attributes["vector_distance"] = embedding_map[relationship_type]
+                distance = embedding_map.get(relationship_type, None)
+                if distance is not None:
+                    edge.attributes["vector_distance"] = distance
 
         except Exception as ex:
             logger.error(f"Error mapping vector distances to edges: {str(ex)}")
             raise ex
 
     async def calculate_top_triplet_importances(self, k: int) -> List:
-        min_heap = []
+        def score(edge):
+            n1 = edge.node1.attributes.get("vector_distance", 1)
+            n2 = edge.node2.attributes.get("vector_distance", 1)
+            e = edge.attributes.get("vector_distance", 1)
+            return n1 + n2 + e
 
-        for i, edge in enumerate(self.edges):
-            source_node = self.get_node(edge.node1.id)
-            target_node = self.get_node(edge.node2.id)
-
-            source_distance = source_node.attributes.get("vector_distance", 1) if source_node else 1
-            target_distance = target_node.attributes.get("vector_distance", 1) if target_node else 1
-            edge_distance = edge.attributes.get("vector_distance", 1)
-
-            total_distance = source_distance + target_distance + edge_distance
-
-            heapq.heappush(min_heap, (-total_distance, i, edge))
-            if len(min_heap) > k:
-                heapq.heappop(min_heap)
-
-        return [edge for _, _, edge in sorted(min_heap)]
+        return heapq.nsmallest(k, self.edges, key=score)

cognee/tasks/storage/index_graph_edges.py

@@ -1,3 +1,4 @@
+from cognee.modules.engine.utils.generate_edge_id import generate_edge_id
 from cognee.shared.logging_utils import get_logger, ERROR
 from collections import Counter
 
@@ -49,7 +50,9 @@ async def index_graph_edges(batch_size: int = 1024):
     )
 
     for text, count in edge_types.items():
-        edge = EdgeType(relationship_name=text, number_of_edges=count)
+        edge = EdgeType(
+            id=generate_edge_id(edge_id=text), relationship_name=text, number_of_edges=count
+        )
         data_point_type = type(edge)
 
         for field_name in edge.metadata["index_fields"]: