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

internal/application/service/metric/map.go

@@ -0,0 +1,70 @@
+package metric
+
+import (
+	"github.com/Tencent/WeKnora/internal/types"
+)
+
+// MAPMetric calculates Mean Average Precision for retrieval evaluation
+type MAPMetric struct{}
+
+// NewMAPMetric creates a new MAPMetric instance
+func NewMAPMetric() *MAPMetric {
+	return &MAPMetric{}
+}
+
+// Compute calculates the Mean Average Precision score
+func (m *MAPMetric) Compute(metricInput *types.MetricInput) float64 {
+	// Convert ground truth to sets for efficient lookup
+	gts := metricInput.RetrievalGT
+	ids := metricInput.RetrievalIDs
+
+	// Create sets of relevant document IDs for each query
+	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 apSum float64 // Sum of average precision for all queries
+
+	// Calculate average precision for each query
+	for _, gtSet := range gtSets {
+		// Mark which predicted documents are relevant
+		predHits := make([]bool, len(ids))
+		for i, predID := range ids {
+			if _, ok := gtSet[predID]; ok {
+				predHits[i] = true
+			} else {
+				predHits[i] = false
+			}
+		}
+
+		var (
+			ap       float64 // Average precision for current query
+			hitCount int     // Number of relevant documents found
+		)
+
+		// Calculate precision at each rank position
+		for k := 0; k < len(predHits); k++ {
+			if predHits[k] {
+				hitCount++
+				// Precision at k: relevant docs found up to k / k
+				ap += float64(hitCount) / float64(k+1)
+			}
+		}
+		// Normalize by number of relevant documents
+		if hitCount > 0 {
+			ap /= float64(hitCount)
+		}
+		apSum += ap
+	}
+
+	// Handle case with no ground truth
+	if len(gtSets) == 0 {
+		return 0
+	}
+	// Return mean of average precision across all queries
+	return apSum / float64(len(gtSets))
+}