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

internal/application/service/metric/precision.go

@@ -0,0 +1,44 @@
+package metric
+
+import (
+	"github.com/Tencent/WeKnora/internal/types"
+)
+
+// PrecisionMetric calculates precision for retrieval evaluation
+type PrecisionMetric struct{}
+
+// NewPrecisionMetric creates a new PrecisionMetric instance
+func NewPrecisionMetric() *PrecisionMetric {
+	return &PrecisionMetric{}
+}
+
+// Compute calculates the precision score
+func (r *PrecisionMetric) 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 := SliceMap(gts, ToSet)
+	// Precision = retrieved items that are in ground truth / total retrieved items
+	// In the test cases, ground truth is a list of sets.
+	// We compute precision per ground truth set, and average them.
+	// But actually, precision is typically |retrieved ∩ relevant| / |retrieved|.
+	// Let's sum the precisions for each ground truth set and average them.
+
+	if len(gts) == 0 {
+		return 0.0
+	}
+
+	if len(ids) == 0 {
+		return 0.0
+	}
+
+	var totalPrecision float64
+	for _, gtSet := range gtSets {
+		hits := Hit(ids, gtSet)
+		totalPrecision += float64(hits) / float64(len(ids))
+	}
+
+	return totalPrecision / float64(len(gts))
+}