WeKnora deployment source with the reviewed wiki identity, summary and recovery fixes

internal/application/service/metric/mrr.go

@@ -0,0 +1,48 @@
+package metric
+
+import (
+	"github.com/Tencent/WeKnora/internal/types"
+)
+
+// MRRMetric calculates Mean Reciprocal Rank for retrieval evaluation
+type MRRMetric struct{}
+
+// NewMRRMetric creates a new MRRMetric instance
+func NewMRRMetric() *MRRMetric {
+	return &MRRMetric{}
+}
+
+// Compute calculates the Mean Reciprocal Rank score
+func (m *MRRMetric) Compute(metricInput *types.MetricInput) float64 {
+	// Get ground truth and predicted IDs
+	gts := metricInput.RetrievalGT
+	ids := metricInput.RetrievalIDs
+
+	// Convert ground truth to sets for efficient lookup
+	gtSets := make([]map[int]struct{}, len(gts))
+	for i, gt := range gts {
+		gtSets[i] = make(map[int]struct{})
+		for _, docID := range gt {
+			gtSets[i][docID] = struct{}{}
+		}
+	}
+
+	var sumRR float64 // Sum of reciprocal ranks
+	// Calculate reciprocal rank for each query
+	for _, gtSet := range gtSets {
+		// Find first relevant document in results
+		for i, predID := range ids {
+			if _, ok := gtSet[predID]; ok {
+				// Reciprocal rank is 1/position (1-based)
+				sumRR += 1.0 / float64(i+1)
+				break // Only consider first relevant document
+			}
+		}
+	}
+	// Handle case with no ground truth
+	if len(gtSets) == 0 {
+		return 0
+	}
+	// Return mean of reciprocal ranks
+	return sumRR / float64(len(gtSets))
+}