HNSW index: update Flatbuffers generated model files

Author: greenrobot-team

objectbox-java/src/main/java/io/objectbox/model/HnswDistanceType.java

@@ -32,8 +32,29 @@ private HnswDistanceType() { }
    * The default; typically "Euclidean squared" internally.
    */
   public static final short Euclidean = 1;
+  /**
+   * Cosine similarity compares two vectors irrespective of their magnitude (compares the angle of two vectors).
+   * Often used for document or semantic similarity.
+   * Value range: 0.0 - 2.0 (0.0: same direction, 1.0: orthogonal, 2.0: opposite direction)
+   */
+  public static final short Cosine = 2;
+  /**
+   * For normalized vectors (vector length == 1.0), the dot product is equivalent to the cosine similarity.
+   * Because of this, the dot product is often preferred as it performs better.
+   * Value range (normalized vectors): 0.0 - 2.0 (0.0: same direction, 1.0: orthogonal, 2.0: opposite direction)
+   */
+  public static final short DotProduct = 3;
+  /**
+   * A custom dot product similarity measure that does not require the vectors to be normalized.
+   * Note: this is no replacement for cosine similarity (like DotProduct for normalized vectors is).
+   * The non-linear conversion provides a high precision over the entire float range (for the raw dot product).
+   * The higher the dot product, the lower the distance is (the nearer the vectors are).
+   * The more negative the dot product, the higher the distance is (the farther the vectors are).
+   * Value range: 0.0 - 2.0 (nonlinear; 0.0: nearest, 1.0: orthogonal, 2.0: farthest)
+   */
+  public static final short DotProductNonNormalized = 10;
 
-  public static final String[] names = { "Unknown", "Euclidean", };
+  public static final String[] names = { "Unknown", "Euclidean", "Cosine", "DotProduct", "", "", "", "", "", "", "DotProductNonNormalized", };
 
   public static String name(int e) { return names[e]; }
 }

objectbox-java/src/main/java/io/objectbox/model/HnswParams.java

@@ -71,7 +71,7 @@ public final class HnswParams extends Table {
   public long indexingSearchCount() { int o = __offset(8); return o != 0 ? (long)bb.getInt(o + bb_pos) & 0xFFFFFFFFL : 0L; }
   public long flags() { int o = __offset(10); return o != 0 ? (long)bb.getInt(o + bb_pos) & 0xFFFFFFFFL : 0L; }
   /**
-   * The distance type used for the HNSW index; for now only "Euclidean" is supported.
+   * The distance type used for the HNSW index; if none is given, the default Euclidean is used.
    * Changing this value causes re-indexing.
    */
   public int distanceType() { int o = __offset(12); return o != 0 ? bb.getShort(o + bb_pos) & 0xFFFF : 0; }