embed: add distance correlation test for library embed models (#12796)

integration/embed_test.go

@@ -14,6 +14,10 @@ import (
 
 func dotProduct[V float32 | float64](v1, v2 []V) V {
 	var result V = 0
+	if len(v1) != len(v2) {
+		return result
+	}
+
 	for i := 0; i < len(v1); i++ {
 		result += v1[i] * v2[i]
 	}
@@ -29,9 +33,115 @@ func magnitude[V float32 | float64](v []V) V {
 }
 
 func cosineSimilarity[V float32 | float64](v1, v2 []V) V {
+	mag1 := magnitude(v1)
+	mag2 := magnitude(v2)
+
+	if mag1 == 0 || mag2 == 0 {
+		return 0
+	}
+
 	return dotProduct(v1, v2) / (magnitude(v1) * magnitude(v2))
 }
 
+func euclideanDistance[V float32 | float64](v1, v2 []V) V {
+	if len(v1) != len(v2) {
+		return V(math.Inf(1))
+	}
+
+	var sum V = 0
+	for i := 0; i < len(v1); i++ {
+		diff := v1[i] - v2[i]
+		sum += diff * diff
+	}
+
+	return V(math.Sqrt(float64(sum)))
+}
+
+func manhattanDistance[V float32 | float64](v1, v2 []V) V {
+	if len(v1) != len(v2) {
+		return V(math.Inf(1))
+	}
+
+	var sum V = 0
+	for i := 0; i < len(v1); i++ {
+		sum += V(math.Abs(float64(v1[i] - v2[i])))
+	}
+
+	return sum
+}
+
+func TestEmbedCosineDistanceCorrelation(t *testing.T) {
+	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
+	defer cancel()
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
+
+	for _, model := range libraryEmbedModels {
+		t.Run(model, func(t *testing.T) {
+			testCases := []struct {
+				a string
+				b string
+				c string
+			}{
+				{"cat", "kitten", "dog"},
+				{"king", "queen", "baron"},
+				{"paris", "london", "vancouver"},
+				{"The cat is sleeping on the sofa", "A feline is sleeping on the couch", "Quantum physics is complex"},
+				{"I love programming in python", "Coding in python brings me joy", "Pizza is delicious"},
+				{"Machine learning is fascinating", "Artificial intelligence is amazing", "I need to buy groceries"},
+				{"The quick brown fox jumps over the lazy dog", "A fast brown fox leaps over a sleepy dog", "The weather is warm and sunny today"},
+			}
+
+			for _, tc := range testCases {
+				testEmbed := make(map[string][]float32)
+				strs := []string{tc.a, tc.b, tc.c}
+
+				req := api.EmbedRequest{
+					Model:     model,
+					Input:     strs,
+					KeepAlive: &api.Duration{Duration: 10 * time.Second},
+				}
+
+				resp, err := embedTestHelper(ctx, client, t, req)
+				if err != nil {
+					t.Fatal(err)
+				}
+
+				for cnt, v := range resp.Embeddings {
+					testEmbed[strs[cnt]] = v
+				}
+
+				// Calculate cosine similarities
+				cosAB := cosineSimilarity(testEmbed[tc.a], testEmbed[tc.b])
+				cosAC := cosineSimilarity(testEmbed[tc.a], testEmbed[tc.c])
+
+				// Calculate distances
+				distAB := euclideanDistance(testEmbed[tc.a], testEmbed[tc.b])
+				distAC := euclideanDistance(testEmbed[tc.a], testEmbed[tc.c])
+
+				manhattanAB := manhattanDistance(testEmbed[tc.a], testEmbed[tc.b])
+				manhattanAC := manhattanDistance(testEmbed[tc.a], testEmbed[tc.c])
+
+				// Consistency check: if cosAB > cosAC, then distances should be smaller
+				if cosAB > cosAC {
+					if distAB >= distAC {
+						t.Errorf("Euclidean distance inconsistency (%s) for %s-%s-%s: cosAB=%f > cosAC=%f but distAB=%f >= distAC=%f",
+							model, tc.a, tc.b, tc.c, cosAB, cosAC, distAB, distAC)
+					}
+
+					if manhattanAB >= manhattanAC {
+						t.Errorf("Manhattan distance inconsistency (%s) for %s-%s-%s: cosAB=%f > cosAC=%f but manhattanAB=%f >= manhattanAC=%f",
+							model, tc.a, tc.b, tc.c, cosAB, cosAC, manhattanAB, manhattanAC)
+					}
+				} else {
+					t.Errorf("Cosine Similarity inconsistency (%s): cosinSim(%s, %s) < cosinSim(%s, %s)",
+						model, tc.a, tc.b, tc.a, tc.c)
+				}
+			}
+		})
+	}
+}
+
 func TestAllMiniLMEmbeddings(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
 	defer cancel()

integration/utils_test.go

@@ -248,12 +248,14 @@ var (
 		"zephyr",
 	}
 	libraryEmbedModels = []string{
+		"qwen3-embedding",
+		"embeddinggemma",
+		"nomic-embed-text",
 		"all-minilm",
 		"bge-large",
 		"bge-m3",
 		"granite-embedding",
 		"mxbai-embed-large",
-		"nomic-embed-text",
 		"paraphrase-multilingual",
 		"snowflake-arctic-embed",
 		"snowflake-arctic-embed2",