diff --git a/src/core/request-body-search.js b/src/core/request-body-search.js
index 7031494..87a9232 100644
--- a/src/core/request-body-search.js
+++ b/src/core/request-body-search.js
@@ -884,7 +884,11 @@ class RequestBodySearch {
     toJSON() {
         const dsl = recursiveToJSON(this._body);
 
-        if (!isEmpty(this._knn)) dsl.knn = this._knn;
+        if (!isEmpty(this._knn))
+            dsl.knn =
+                this._knn.length == 1
+                    ? recMerge(this._knn)
+                    : this._knn.map(knn => recursiveToJSON(knn));
 
         if (!isEmpty(this._aggs)) dsl.aggs = recMerge(this._aggs);
 
diff --git a/test/core-test/knn.test.js b/test/core-test/knn.test.js
index 6f717a0..25f5719 100644
--- a/test/core-test/knn.test.js
+++ b/test/core-test/knn.test.js
@@ -43,6 +43,15 @@ test('knn filter method adds queries correctly', t => {
     t.deepEqual(json.filter, [query.toJSON()]);
 });
 
+test('knn filter method adds queries as array correctly', t => {
+    const knn = new KNN('my_field', 5, 10).queryVector([1, 2, 3]);
+    const query1 = new TermQuery('field1', 'value1');
+    const query2 = new TermQuery('field2', 'value2');
+    knn.filter([query1, query2]);
+    const json = knn.toJSON();
+    t.deepEqual(json.filter, [query1.toJSON(), query2.toJSON()]);
+});
+
 test('knn boost method sets correctly', t => {
     const boostValue = 1.5;
     const knn = new KNN('my_field', 5, 10)
@@ -85,3 +94,37 @@ test('knn toJSON throws error if neither query_vector nor query_vector_builder i
         'either query_vector_builder or query_vector must be provided'
     );
 });
+
+test('knn throws error when first queryVector and then queryVectorBuilder are set', t => {
+    const knn = new KNN('my_field', 5, 10).queryVector([1, 2, 3]);
+    const error = t.throws(() => {
+        knn.queryVectorBuilder('model_123', 'Sample model text');
+    });
+    t.is(
+        error.message,
+        'cannot provide both query_vector_builder and query_vector'
+    );
+});
+
+test('knn throws error when first queryVectorBuilder and then queryVector are set', t => {
+    const knn = new KNN('my_field', 5, 10).queryVectorBuilder(
+        'model_123',
+        'Sample model text'
+    );
+    const error = t.throws(() => {
+        knn.queryVector([1, 2, 3]);
+    });
+    t.is(
+        error.message,
+        'cannot provide both query_vector_builder and query_vector'
+    );
+});
+
+test('knn filter throws TypeError if non-Query type is passed', t => {
+    const knn = new KNN('my_field', 5, 10).queryVector([1, 2, 3]);
+    const error = t.throws(() => {
+        knn.filter('not_a_query');
+    }, TypeError);
+
+    t.is(error.message, 'Argument must be an instance of Query');
+});
diff --git a/test/core-test/request-body-search.test.js b/test/core-test/request-body-search.test.js
index 47b1191..2327606 100644
--- a/test/core-test/request-body-search.test.js
+++ b/test/core-test/request-body-search.test.js
@@ -16,7 +16,8 @@ import {
     Highlight,
     Rescore,
     InnerHits,
-    RuntimeField
+    RuntimeField,
+    KNN
 } from '../../src';
 import { illegalParamType, makeSetsOptionMacro } from '../_macros';
 
@@ -71,6 +72,11 @@ const innerHits = new InnerHits()
     .name('last_tweets')
     .size(5)
     .sort(new Sort('date', 'desc'));
+const kNNVectorBuilder = new KNN('my_field', 5, 10)
+    .similarity(0.6)
+    .filter(new TermQuery('field', 'value'))
+    .queryVectorBuilder('model_123', 'Sample model text');
+const kNNVector = new KNN('my_field', 5, 10).queryVector([1, 2, 3]);
 
 const instance = new RequestBodySearch();
 
@@ -83,9 +89,11 @@ test(illegalParamType, instance, 'scriptFields', 'Object');
 test(illegalParamType, instance, 'highlight', 'Highlight');
 test(illegalParamType, instance, 'rescore', 'Rescore');
 test(illegalParamType, instance, 'postFilter', 'Query');
+test(illegalParamType, instance, 'kNN', 'KNN');
 test(setsOption, 'query', { param: searchQry });
 test(setsOption, 'aggregation', { param: aggA, keyName: 'aggs' });
 test(setsOption, 'agg', { param: aggA, keyName: 'aggs' });
+test(setsOption, 'kNN', { param: kNNVectorBuilder, keyName: 'knn' });
 test(setsOption, 'suggest', { param: suggest });
 test(setsOption, 'suggestText', {
     param: 'suggest-text',
@@ -347,3 +355,83 @@ test('sets multiple indices_boost', t => {
     };
     t.deepEqual(value, expected);
 });
+
+test('kNN setup query vector builder', t => {
+    const value = new RequestBodySearch().kNN(kNNVectorBuilder).toJSON();
+    const expected = {
+        knn: {
+            field: 'my_field',
+            k: 5,
+            filter: [
+                {
+                    term: {
+                        field: 'value'
+                    }
+                }
+            ],
+            num_candidates: 10,
+            query_vector_builder: {
+                text_embeddings: {
+                    model_id: 'model_123',
+                    model_text: 'Sample model text'
+                }
+            },
+            similarity: 0.6
+        }
+    };
+
+    t.deepEqual(value, expected);
+});
+
+test('kNN setup query vector', t => {
+    const value = new RequestBodySearch().kNN(kNNVector).toJSON();
+    const expected = {
+        knn: {
+            field: 'my_field',
+            k: 5,
+            filter: [],
+            num_candidates: 10,
+            query_vector: [1, 2, 3]
+        }
+    };
+
+    t.deepEqual(value, expected);
+});
+
+test('kNN setup query vector array', t => {
+    const value = new RequestBodySearch()
+        .kNN([kNNVector, kNNVectorBuilder])
+        .toJSON();
+    const expected = {
+        knn: [
+            {
+                field: 'my_field',
+                k: 5,
+                filter: [],
+                num_candidates: 10,
+                query_vector: [1, 2, 3]
+            },
+            {
+                field: 'my_field',
+                filter: [
+                    {
+                        term: {
+                            field: 'value'
+                        }
+                    }
+                ],
+                k: 5,
+                num_candidates: 10,
+                query_vector_builder: {
+                    text_embeddings: {
+                        model_id: 'model_123',
+                        model_text: 'Sample model text'
+                    }
+                },
+                similarity: 0.6
+            }
+        ]
+    };
+
+    t.deepEqual(value, expected);
+});