diff --git a/CouchbaseLite.xcodeproj/project.pbxproj b/CouchbaseLite.xcodeproj/project.pbxproj
index a3ad64f60..ef62e5511 100644
--- a/CouchbaseLite.xcodeproj/project.pbxproj
+++ b/CouchbaseLite.xcodeproj/project.pbxproj
@@ -382,12 +382,12 @@
 		406F8DEB2C26901A000223FC /* QueryIndex.swift in Sources */ = {isa = PBXBuildFile; fileRef = 406F8DEA2C26901A000223FC /* QueryIndex.swift */; };
 		406F8DEC2C26901A000223FC /* QueryIndex.swift in Sources */ = {isa = PBXBuildFile; fileRef = 406F8DEA2C26901A000223FC /* QueryIndex.swift */; };
 		406F8DFB2C27C303000223FC /* IndexUpdater.swift in Sources */ = {isa = PBXBuildFile; fileRef = 406F8DF92C27C302000223FC /* IndexUpdater.swift */; };
-		406F8DFF2C27F0A9000223FC /* LazyVectorIndexTest.swift in Sources */ = {isa = PBXBuildFile; fileRef = 406F8DFC2C27F097000223FC /* LazyVectorIndexTest.swift */; };
-		406F8E002C27F0AB000223FC /* LazyVectorIndexTest.swift in Sources */ = {isa = PBXBuildFile; fileRef = 406F8DFC2C27F097000223FC /* LazyVectorIndexTest.swift */; };
+		406F8DFF2C27F0A9000223FC /* VectorLazyIndexTest.swift in Sources */ = {isa = PBXBuildFile; fileRef = 406F8DFC2C27F097000223FC /* VectorLazyIndexTest.swift */; };
+		406F8E002C27F0AB000223FC /* VectorLazyIndexTest.swift in Sources */ = {isa = PBXBuildFile; fileRef = 406F8DFC2C27F097000223FC /* VectorLazyIndexTest.swift */; };
 		40AA72952C28938D007FB1E0 /* VectorSearchTest.m in Sources */ = {isa = PBXBuildFile; fileRef = AE006DE62B7BB98B00884E2B /* VectorSearchTest.m */; };
 		40AA72962C28938E007FB1E0 /* VectorSearchTest.m in Sources */ = {isa = PBXBuildFile; fileRef = AE006DE62B7BB98B00884E2B /* VectorSearchTest.m */; };
-		40AA729D2C28B1A3007FB1E0 /* LazyVectorIndexTest.m in Sources */ = {isa = PBXBuildFile; fileRef = 40AA72972C28B1A3007FB1E0 /* LazyVectorIndexTest.m */; };
-		40AA729E2C28B1A3007FB1E0 /* LazyVectorIndexTest.m in Sources */ = {isa = PBXBuildFile; fileRef = 40AA72972C28B1A3007FB1E0 /* LazyVectorIndexTest.m */; };
+		40AA729D2C28B1A3007FB1E0 /* VectorLazyIndexTest.m in Sources */ = {isa = PBXBuildFile; fileRef = 40AA72972C28B1A3007FB1E0 /* VectorLazyIndexTest.m */; };
+		40AA729E2C28B1A3007FB1E0 /* VectorLazyIndexTest.m in Sources */ = {isa = PBXBuildFile; fileRef = 40AA72972C28B1A3007FB1E0 /* VectorLazyIndexTest.m */; };
 		40C5FD5B2B9947B3004BFD3B /* CBLVectorIndexTypes.h in Headers */ = {isa = PBXBuildFile; fileRef = 40C5FD5A2B9946E6004BFD3B /* CBLVectorIndexTypes.h */; settings = {ATTRIBUTES = (Public, ); }; };
 		40C5FD5C2B9947B9004BFD3B /* CBLVectorIndexTypes.h in Headers */ = {isa = PBXBuildFile; fileRef = 40C5FD5A2B9946E6004BFD3B /* CBLVectorIndexTypes.h */; settings = {ATTRIBUTES = (Private, ); }; };
 		40EF690C2B7757CF00F0CB50 /* PrivacyInfo.xcprivacy in Resources */ = {isa = PBXBuildFile; fileRef = 40EF690A2B77564000F0CB50 /* PrivacyInfo.xcprivacy */; };
@@ -2287,13 +2287,13 @@
 		4017E4642BED6E5400A438EE /* CBLContextManager.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; path = CBLContextManager.m; sourceTree = "<group>"; };
 		406F8DEA2C26901A000223FC /* QueryIndex.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = QueryIndex.swift; sourceTree = "<group>"; };
 		406F8DF92C27C302000223FC /* IndexUpdater.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = IndexUpdater.swift; sourceTree = "<group>"; };
-		406F8DFC2C27F097000223FC /* LazyVectorIndexTest.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LazyVectorIndexTest.swift; sourceTree = "<group>"; };
+		406F8DFC2C27F097000223FC /* VectorLazyIndexTest.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = VectorLazyIndexTest.swift; sourceTree = "<group>"; };
 		40A789282BE2C7D100CA43A1 /* CBL_EE.exp */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.exports; path = CBL_EE.exp; sourceTree = "<group>"; };
 		40A789292BE2C7D100CA43A1 /* CBL.exp */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.exports; path = CBL.exp; sourceTree = "<group>"; };
 		40A7892B2BE2C7D100CA43A1 /* CBL_EE.txt */ = {isa = PBXFileReference; lastKnownFileType = text; path = CBL_EE.txt; sourceTree = "<group>"; };
 		40A7892C2BE2C7D100CA43A1 /* CBL.txt */ = {isa = PBXFileReference; lastKnownFileType = text; path = CBL.txt; sourceTree = "<group>"; };
 		40A7892D2BE2C7D100CA43A1 /* generate_exports.sh */ = {isa = PBXFileReference; lastKnownFileType = text.script.sh; path = generate_exports.sh; sourceTree = "<group>"; };
-		40AA72972C28B1A3007FB1E0 /* LazyVectorIndexTest.m */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.objc; path = LazyVectorIndexTest.m; sourceTree = "<group>"; };
+		40AA72972C28B1A3007FB1E0 /* VectorLazyIndexTest.m */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.objc; path = VectorLazyIndexTest.m; sourceTree = "<group>"; };
 		40AA72A12C28B1F2007FB1E0 /* VectorSearchTest.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = VectorSearchTest.h; sourceTree = "<group>"; };
 		40C5FD5A2B9946E6004BFD3B /* CBLVectorIndexTypes.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = CBLVectorIndexTypes.h; sourceTree = "<group>"; };
 		40E905462B5B6D9D00EDF483 /* CouchbaseLiteSwift.private.modulemap */ = {isa = PBXFileReference; lastKnownFileType = "sourcecode.module-map"; path = CouchbaseLiteSwift.private.modulemap; sourceTree = "<group>"; };
@@ -3079,7 +3079,7 @@
 				934608EE247F35CC00CF2F27 /* URLEndpointListenerTest.swift */,
 				1A13DD3328B8809300BC1084 /* URLEndpointListenerTest+Collection.swift */,
 				AE5803A32B99C67D001A1BE3 /* VectorSearchTest.swift */,
-				406F8DFC2C27F097000223FC /* LazyVectorIndexTest.swift */,
+				406F8DFC2C27F097000223FC /* VectorLazyIndexTest.swift */,
 				1AA91DC522B0356000BF0BDE /* CustomLogger.swift */,
 				93249D81246B99FD000A8A6E /* iOS */,
 				939B79241E679017009A70EF /* Info.plist */,
@@ -4147,7 +4147,7 @@
 				1AA3D77222AB06E10098E16B /* CustomLogger.m */,
 				40AA72A12C28B1F2007FB1E0 /* VectorSearchTest.h */,
 				AE006DE62B7BB98B00884E2B /* VectorSearchTest.m */,
-				40AA72972C28B1A3007FB1E0 /* LazyVectorIndexTest.m */,
+				40AA72972C28B1A3007FB1E0 /* VectorLazyIndexTest.m */,
 				93DECF3E200DBE5800F44953 /* Support */,
 				936483AA1E4431C6008D08B3 /* iOS */,
 				275FF5FA1E3FBD3B005F90DD /* Performance */,
@@ -6267,7 +6267,7 @@
 				93BB1C9E246BB2BF004FFA00 /* DatabaseTest.swift in Sources */,
 				93BB1C9C246BB2BB004FFA00 /* CBLTestCase.swift in Sources */,
 				93BB1CB8246BB2F4004FFA00 /* ReplicatorTest+CustomConflict.swift in Sources */,
-				406F8E002C27F0AB000223FC /* LazyVectorIndexTest.swift in Sources */,
+				406F8E002C27F0AB000223FC /* VectorLazyIndexTest.swift in Sources */,
 			);
 			runOnlyForDeploymentPostprocessing = 0;
 		};
@@ -6694,7 +6694,7 @@
 				1A621D7A2887DCFD0017F905 /* QueryTest+Collection.m in Sources */,
 				93EB25CC21CDD12A0006FB88 /* PredictiveQueryTest.m in Sources */,
 				1AF555D422948BD90077DF6D /* QueryTest+Main.m in Sources */,
-				40AA729D2C28B1A3007FB1E0 /* LazyVectorIndexTest.m in Sources */,
+				40AA729D2C28B1A3007FB1E0 /* VectorLazyIndexTest.m in Sources */,
 				9343F13D207D61EC00F19A89 /* MigrationTest.m in Sources */,
 				9343F13E207D61EC00F19A89 /* DocumentTest.m in Sources */,
 				9343F140207D61EC00F19A89 /* DictionaryTest.m in Sources */,
@@ -6748,7 +6748,7 @@
 				1AA6744C227924130018CC6D /* QueryTest+Meta.m in Sources */,
 				934EF8322460D07B0053A47C /* TLSIdentityTest.m in Sources */,
 				9369A6AF207DD105009B5B83 /* DatabaseEncryptionTest.m in Sources */,
-				40AA729E2C28B1A3007FB1E0 /* LazyVectorIndexTest.m in Sources */,
+				40AA729E2C28B1A3007FB1E0 /* VectorLazyIndexTest.m in Sources */,
 				9343F176207D633300F19A89 /* DocumentTest.m in Sources */,
 				1A13DD4228B882A800BC1084 /* URLEndpointListenerTest+Main.m in Sources */,
 				9388CBAE21BD9187005CA66D /* DocumentExpirationTest.m in Sources */,
@@ -6810,7 +6810,7 @@
 				9343F198207D636300F19A89 /* CBLTestCase.swift in Sources */,
 				93C50EB021BDFC7B00C7E980 /* DocumentExpirationTest.swift in Sources */,
 				9369A6B7207DEB60009B5B83 /* DatabaseEncryptionTest.swift in Sources */,
-				406F8DFF2C27F0A9000223FC /* LazyVectorIndexTest.swift in Sources */,
+				406F8DFF2C27F0A9000223FC /* VectorLazyIndexTest.swift in Sources */,
 			);
 			runOnlyForDeploymentPostprocessing = 0;
 		};
diff --git a/Objective-C/Tests/LazyVectorIndexTest.m b/Objective-C/Tests/VectorLazyIndexTest.m
similarity index 98%
rename from Objective-C/Tests/LazyVectorIndexTest.m
rename to Objective-C/Tests/VectorLazyIndexTest.m
index 2b0576c02..115b43787 100644
--- a/Objective-C/Tests/LazyVectorIndexTest.m
+++ b/Objective-C/Tests/VectorLazyIndexTest.m
@@ -28,11 +28,11 @@
 
 #define LAZY_VECTOR_INDEX_CONFIG(E, D, C) [self lazyVectorIndexConfigWithExpression: (E) dimensions: (D) centroids: (C)]
 
-@interface LazyVectorIndexTest : VectorSearchTest
+@interface VectorLazyIndexTest : VectorSearchTest
 
 @end
 
-@implementation LazyVectorIndexTest
+@implementation VectorLazyIndexTest
 
 - (CBLQueryIndex*) wordsIndex {
     CBLQueryIndex* index = [self.wordsCollection indexWithName: kWordsIndexName error: nil];
@@ -208,7 +208,7 @@ - (void) testGetExistingVectorIndex {
  * 3. Create an SQL++ query:
  *     - SELECT word
  *       FROM _default.words
- *       WHERE vector_match(words_index, < dinner vector >)
+ *       WHERE vector_match(words_index, <dinner vector>)
  * 4. Execute the query and check that 0 results are returned.
  * 5. Update the documents:
  *     - Create _default.words.word301 with the content from _default.extwords.word1
@@ -261,7 +261,7 @@ - (void) testLazyVectorIndexNotAutoUpdatedChangedDocs {
  * 6. Create an SQL++ query:
  *    - SELECT word
  *      FROM _default.words
- *      WHERE vector_match(words_index, < dinner vector >, 300)
+ *      WHERE vector_match(words_index, <dinner vector>) LIMIT 300
  * 7. Execute the query and check that 1 results are returned.
  * 8. Check that the word gotten from the query result is the same as the word in Step 5.
  * 9. Delete _default.words.word1 doc.
@@ -318,7 +318,7 @@ - (void) testLazyVectorIndexAutoUpdateDeletedDocs {
  * 7. Create an SQL++ query:
  *    - SELECT word
  *      FROM _default.words
- *      WHERE vector_match(words_index, < dinner vector >, 300)
+ *      WHERE vector_match(words_index, <dinner vector>) LIMIT 300
  * 8. Execute the query and check that 1 results are returned.
  * 9. Check that the word gotten from the query result is the same as the word in Step 5.
  * 10. Purge _default.words.word1 doc.
@@ -827,7 +827,7 @@ - (void) testIndexUpdaterGettingValues {
  * 7. Execute a vector search query.
  *     - SELECT word
  *       FROM _default.words
- *       WHERE vector_match(words_index, < dinner vector >, 300)
+ *       WHERE vector_match(words_index, <dinner vector>) LIMIT 300
  * 8. Check that there are 10 words returned.
  * 9. Check that the word is in the word set from the step 5.
  */
@@ -884,8 +884,7 @@ - (void) testIndexUpdaterSetFloatArrayVectors {
  * 5. With the IndexUpdater object, call setVector() with a float array as [1.0]
  * 6. Check that the setVector throws CouchbaseLiteException with the InvalidParameter error.
  */
-// CBL-5814
-- (void) _testIndexUpdaterSetInvalidVectorDimensions {
+- (void) testIndexUpdaterSetInvalidVectorDimensions {
     [self createWordsIndexWithConfig: LAZY_VECTOR_INDEX_CONFIG(@"word", 300, 8)];
     
     NSError* error;
@@ -1091,7 +1090,7 @@ - (void) testIndexUpdaterCaughtUp {
  * 7. Execute a vector search query.
  *     - SELECT word
  *       FROM _default.words
- *       WHERE vector_match(words_index, < dinner vector >, 300)
+ *       WHERE vector_match(words_index, <dinner vector>) LIMIT 300
  * 8. Check that there are 0 words returned.
  */
 
@@ -1247,10 +1246,9 @@ - (void) testIndexUpdaterIndexOutOfBounds {
  *     - Convert the vector result which is an array object to a platform's float array.
  *     - Call setVector() with the platform's float array at the index..
  * 8. Call finish() and check that the finish() is successfully called.
- * 9. Call finish() again and check that a CouchbaseLiteException with the code Unsupported is thrown.
+ * 9. Call finish() again and check that a CouchbaseLiteException with the code NotOpen is thrown.
  */
-// CBL-5843
-- (void) _testIndexUpdaterCallFinishTwice {
+- (void) testIndexUpdaterCallFinishTwice {
     [self createWordsIndexWithConfig: LAZY_VECTOR_INDEX_CONFIG(@"word", 300, 8)];
     
     NSError* error;
@@ -1264,7 +1262,7 @@ - (void) _testIndexUpdaterCallFinishTwice {
     Assert([updater setVector: vector atIndex: 0 error: &error]);
     Assert([updater finishWithError: &error]);
     
-    [self expectError: CBLErrorDomain code: CBLErrorUnsupported in: ^BOOL(NSError** err) {
+    [self expectError: CBLErrorDomain code: CBLErrorNotOpen in: ^BOOL(NSError** err) {
         return [updater finishWithError: err];
     }];
 }
diff --git a/Objective-C/Tests/VectorSearchTest.m b/Objective-C/Tests/VectorSearchTest.m
index 653f0d292..bd577efcb 100644
--- a/Objective-C/Tests/VectorSearchTest.m
+++ b/Objective-C/Tests/VectorSearchTest.m
@@ -217,8 +217,8 @@ - (CBLQueryResultSet*) executeWordsQueryNoTrainingCheckWithLimit: (NSNumber*)lim
  *     2. Get and check the following property values:
  *         - encoding: 8-Bit Scalar Quantizer Encoding
  *         - metric: Euclidean Distance
- *         - minTrainingSize: 25 * centroids
- *         - maxTrainingSize: 256 * centroids
+ *         - minTrainingSize: 0
+ *         - maxTrainingSize: 0
  *     3. To check the encoding type, platform code will have to expose some internal
  *        property to the tests for verification.
  */
@@ -226,8 +226,9 @@ - (void) testVectorIndexConfigurationDefaultValue {
     CBLVectorIndexConfiguration* config = VECTOR_INDEX_CONFIG(@"vector", 300, 8);
     AssertEqualObjects(config.encoding, [CBLVectorEncoding scalarQuantizerWithType: kCBLSQ8]);
     AssertEqual(config.metric, kCBLDistanceMetricEuclidean);
-    AssertEqual(config.minTrainingSize, 25 * config.centroids);
-    AssertEqual(config.maxTrainingSize, 256 * config.centroids);
+    AssertEqual(config.minTrainingSize, 0);
+    AssertEqual(config.maxTrainingSize, 0);
+    AssertEqual(config.numProbes, 0);
 }
 
 /**
@@ -354,7 +355,7 @@ - (void) testCentroidsValidation {
  *     6. Create an SQL++ query:
  *         - SELECT meta().id, word
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 20)
+ *           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
  *     7. Check the explain() result of the query to ensure that the "words_index" is used.
  *     8. Execute the query and check that 20 results are returned.
  *     9. Verify that the index was trained by checking that the “Untrained index; queries may be slow” 
@@ -385,7 +386,7 @@ - (void) testCreateVectorIndex {
  *     5. Create an SQL++ query:
  *         - SELECT meta().id, word
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 350)
+ *           WHERE vector_match(words_index, <dinner vector>) LIMIT 350
  *     6. Check the explain() result of the query to ensure that the "words_index" is used.
  *     7. Execute the query and check that 300 results are returned.
  *     8. Verify that the index was trained by checking that the “Untrained index; queries may be slow” 
@@ -454,7 +455,7 @@ - (void) testUpdateVectorIndex {
  *     6. Create an SQL++ query.
  *         - SELECT meta().id, word
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 350)
+ *           WHERE vector_match(words_index, <dinner vector>) LIMIT 350
  *     7. Execute the query and check that 296 results are returned, and the results
  *        do not include document word1, word2, word3, and word4.
  *     8. Verify that the index was trained by checking that the “Untrained index; queries may be slow” 
@@ -528,7 +529,7 @@ - (void) testCreateVectorIndexWithInvalidVectors {
  *     6. Create an SQL++ query:
  *         - SELECT meta().id, word
  *           FROM _default.words
- *           WHERE vector_match(words_pred_index, <dinner vector>, 350)
+ *           WHERE vector_match(words_pred_index, <dinner vector>) LIMIT 350
  *     7. Check the explain() result of the query to ensure that the "words_pred_index" is used.
  *     8. Execute the query and check that 300 results are returned.
  *     9. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -605,7 +606,7 @@ - (void) testCreateVectorIndexUsingPredictionModel {
  *     7. Create an SQL++ query.
  *         - SELECT meta().id, word
  *           FROM _default.words
- *           WHERE vector_match(words_pred_index, <dinner vector>, 350)
+ *           WHERE vector_match(words_pred_index, <dinner vector>) LIMIT 350
  *     8. Check the explain() result of the query to ensure that the "words_predi_index" is used.
  *     9. Execute the query and check that 296 results are returned and the results
  *        do not include word1, word2, word3, and word4.
@@ -679,7 +680,7 @@ - (void) testCreateVectorIndexUsingPredictionModelWithInvalidVectors {
  *     5. Create an SQL++ query
  *         - SELECT meta().id, word
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 20)
+ *           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
  *     6. Check the explain() result of the query to ensure that the "words_index" is used.
  *     7. Execute the query and check that 20 results are returned.
  *     8. Verify that the index was trained by checking that the “Untrained index; queries may be slow” 
@@ -734,7 +735,7 @@ - (void) testCreateVectorIndexWithSQ {
  *     5. Create an SQL++ query.
  *         - SELECT meta().id, word
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 20)
+ *           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
  *     6. Check the explain() result of the query to ensure that the "words_index" is used.
  *     7. Execute the query and check that 20 results are returned.
  *     8. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -751,8 +752,6 @@ - (void) testCreateVectorIndexWithNoneEncoding {
 }
 
 /**
- * FAILED : https://issues.couchbase.com/browse/CBL-5538
- *
  * 12. TestCreateVectorIndexWithPQ
  * Description
  *     Using the PQ Encoding, test that the vector index can be created and used. The
@@ -769,7 +768,7 @@ - (void) testCreateVectorIndexWithNoneEncoding {
  *     5. Create an SQL++ query.
  *         - SELECT meta().id, word
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 20)
+ *           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
  *     6. Check the explain() result of the query to ensure that the "words_index" is used.
  *     7. Execute the query and check that 20 results are returned.
  *     8. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -852,7 +851,7 @@ - (void) testSubquantizersValidation {
  *     5. Create an SQL++ query.
  *         - SELECT meta().id, word
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 20)
+ *           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
  *     5. Check the explain() result of the query to ensure that the "words_index" is used.
  *     6. Execute the query and check that 20 results are returned.
  *     7. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -884,11 +883,9 @@ - (void) testeCreateVectorIndexWithFixedTrainingSize {
  *         - minTrainingSize: 1 and maxTrainingSize: 100
  *     3. Check that the index is created without an error returned.
  *     4. Delete the "words_index"
- *     5. Repeat Step 2 with the following cases:
- *         - minTrainingSize = 0 and maxTrainingSize 0
- *         - minTrainingSize = 0 and maxTrainingSize 100
- *         - minTrainingSize = 10 and maxTrainingSize 9
- *     6. Check that an invalid argument exception was thrown for all cases in step 4.
+ *     5. Repeat Step 2 with the following case:
+ *         - minTrainingSize = 10 and maxTrainingSize = 9
+ *     6. Check that an invalid argument exception was thrown.
  */
 - (void) testValidateMinMaxTrainingSize {
     CBLVectorIndexConfiguration* config = VECTOR_INDEX_CONFIG(@"vector", 300, 8);
@@ -896,24 +893,13 @@ - (void) testValidateMinMaxTrainingSize {
     config.maxTrainingSize = 100;
     [self createWordsIndexWithConfig: config];
     
-    NSArray<NSArray<NSNumber *> *> *trainingSizes = @[
-        @[@0, @0],
-        @[@0, @100],
-        @[@10, @9]
-    ];
-    
+    [self deleteWordsIndex];
+    config.minTrainingSize = 10;
+    config.maxTrainingSize = 9;
     CBLCollection* collection = self.wordsCollection;
-    for (size_t i = 0; i < trainingSizes.count; i++) {
-        config.minTrainingSize = trainingSizes[i][0].unsignedIntValue;
-        config.maxTrainingSize = trainingSizes[i][1].unsignedIntValue;
-        
-        // Check if exception thrown for wrong values
-        [self expectException: NSInvalidArgumentException in:^{
-            [collection createIndexWithName: kWordsIndexName config: config error: nil];
-        }];
-        
-        [self deleteWordsIndex];
-    }
+    [self expectException: NSInvalidArgumentException in:^{
+        [collection createIndexWithName: kWordsIndexName config: config error: nil];
+    }];
 }
 
 /**
@@ -933,7 +919,7 @@ - (void) testValidateMinMaxTrainingSize {
  *     5. Create an SQL++ query.
  *         - SELECT meta().id, word
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 20)
+ *           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
  *     6. Check the explain() result of the query to ensure that the "words_index" is used.
  *     7. Execute the query and check that 20 results are returned.
  *     8. Verify that the index was not trained by checking that the “Untrained index;
@@ -967,7 +953,7 @@ - (void) testQueryUntrainedVectorIndex {
  *     5. Create an SQL++ query.
  *         - SELECT meta().id, word,vector_distance(words_index)
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 20)
+ *           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
  *     6. Check the explain() result of the query to ensure that the "words_index" is used.
  *     7. Execute the query and check that 20 results are returned and the vector
  *       distance value is in between 0 – 1.0 inclusively.
@@ -1005,7 +991,7 @@ - (void) testCreateVectorIndexWithCosineDistance {
  *     5. Create an SQL++ query.
  *         - SELECT meta().id, word, vector_distance(words_index)
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 20)
+ *           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
  *     6. Check the explain() result of the query to ensure that the "words_index" is used.
  *     7. Execute the query and check that 20 results are returned and the
  *        distance value is more than zero.
@@ -1070,7 +1056,7 @@ - (void) testCreateVectorIndexWithExistingName {
  *     5. Create an SQL++ query.
  *         - SELECT meta().id, word
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 20)
+ *           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
  *     6. Check the explain() result of the query to ensure that the "words_index" is used.
  *     7. Execute the query and check that 20 results are returned.
  *     8. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -1106,7 +1092,7 @@ - (void) testDeleteVectorIndex {
  *     2. Create an SQL++ query.
  *         - SELECT meta().id, word
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 20)
+ *           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
  *     3. Check that a CouchbaseLiteException is returned as the index doesn’t exist.
  */
 - (void) testVectorMatchOnNonExistingIndex {
@@ -1163,7 +1149,7 @@ - (void) testVectorMatchDefaultLimit {
  *     4. Create an SQL++ query.
  *         - SELECT meta().id, word
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, <limit>)
+ *           WHERE vector_match(words_index, <dinner vector>) LIMIT <limit>
  *         - limit: 1 and 10000
  *     5. Check that the query can be created without an error.
  *     6. Repeat step 4 with the limit: -1, 0, and 10001
@@ -1205,7 +1191,7 @@ - (void) testVectorMatchLimitBoundary {
  *     5. Create an SQL++ query.
  *         - SELECT word, catid
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 300) AND catid = 'cat1'
+ *           WHERE vector_match(words_index, <dinner vector>) AND catid = 'cat1' LIMIT 300
  *     6. Check that the query can be created without an error.
  *     7. Check the explain() result of the query to ensure that the "words_index" is used.
  *     8. Execute the query and check that the number of results returned is 50
@@ -1245,7 +1231,7 @@ - (void) testVectorMatchWithAndExpression {
  *     5. Create an SQL++ query.
  *         - SELECT word, catid
  *           FROM _default.words
- *           WHERE (vector_match(words_index, <dinner vector>, 300) AND word is valued) AND catid = 'cat1'
+ *           WHERE (vector_match(words_index, <dinner vector>) AND word is valued) AND catid = 'cat1'  LIMIT 300
  *     6. Check that the query can be created without an error.
  *     7. Check the explain() result of the query to ensure that the "words_index" is used.
  *     8. Execute the query and check that the number of results returned is 50
@@ -1284,7 +1270,7 @@ - (void) testVectorMatchWithMultipleAndExpression {
  *     4. Create an SQL++ query.
  *         - SELECT word, catid
  *           FROM _default.words
- *           WHERE vector_match(words_index, <dinner vector>, 20) OR catid = 1
+ *           WHERE vector_match(words_index, <dinner vector>) OR catid = 1 LIMIT 20
  *     5. Check that a CouchbaseLiteException is returned when creating the query.
  */
 - (void) testInvalidVectorMatchWithOrExpression {
@@ -1319,7 +1305,7 @@ - (void) testInvalidVectorMatchWithOrExpression {
  *      7. Create an SQL++ query:
  *          - SELECT meta().id, word, vector_distance(words_index)
  *            FROM _default.words
- *            WHERE vector_match(words_index, < dinner vector >, 20)
+ *            WHERE vector_match(words_index, <dinner vector>) LIMIT 20
  *      8. Execute the query and check that 20 results are returned.
  *      9. Check that the result also contains doc id = word49.
  */
@@ -1338,6 +1324,45 @@ - (void) testIndexVectorInBase64 {
     AssertNotNil(wordMap[@"word49"]);
 }
 
+/** 
+ * 28. TestNumProbes
+ *
+ * Description
+ *      Test that the numProces specified is effective.
+ * Steps
+ *      1. Copy database words_db.
+ *      2. Create a vector index named "words_index" in _default.words collection.
+ *          - expression: "vector"
+ *          - dimensions: 300
+ *          - centroids : 8
+ *          - numProbes: 5
+ *      3. Check that the index is created without an error returned.
+ *      4. Create an SQL++ query:
+ *          - SELECT meta().id, word
+ *            FROM _default.words
+ *            WHERE vector_match(words_index, <dinner vector>) LIMIT 300
+ *      5. Execute the query and check that 20 results are returned.
+ *      6. Repeat step 2 - 6 but change the numProbes to 1.
+ *      7. Verify the number of results returned in Step 5 is larger than Step 6.
+*/
+- (void) testNumProbes {
+    CBLVectorIndexConfiguration* config = VECTOR_INDEX_CONFIG(@"vector", 300, 8);
+    config.numProbes = 5;
+    [self createWordsIndexWithConfig: config];
+    
+    CBLQueryResultSet* rs = [self executeWordsQueryWithLimit: @300];
+    NSUInteger numResultsFor5Probes = [[rs allObjects] count];
+    Assert(numResultsFor5Probes > 0);
+    
+    config.numProbes = 1;
+    [self createWordsIndexWithConfig: config];
+    rs = [self executeWordsQueryWithLimit: @300];
+    NSUInteger numResultsFor1Probes = [[rs allObjects] count];
+    Assert(numResultsFor1Probes > 0);
+    
+    Assert(numResultsFor5Probes > numResultsFor1Probes);
+}
+
 /*
  Private methods tests
  */
diff --git a/Swift/Tests/LazyVectorIndexTest.swift b/Swift/Tests/VectorLazyIndexTest.swift
similarity index 99%
rename from Swift/Tests/LazyVectorIndexTest.swift
rename to Swift/Tests/VectorLazyIndexTest.swift
index 812b5abcb..7559ae209 100644
--- a/Swift/Tests/LazyVectorIndexTest.swift
+++ b/Swift/Tests/VectorLazyIndexTest.swift
@@ -39,7 +39,7 @@ import XCTest
  * - Test 6 TestGetIndexOnClosedDatabase is done in CollectionTest.testUseCollectionAPIWhenDatabaseIsClosed()
  * - Test 7 testInvalidCollection) is done in CollectionTest.testUseCollectionAPIOnDeletedCollection()
  */
-class LazyVectorIndexTest: VectorSearchTest {
+class VectorLazyIndexTest: VectorSearchTest {
     func wordsIndex() throws -> QueryIndex {
         let index = try wordsCollection.index(withName: wordsIndexName)
         XCTAssertNotNil(index)
@@ -622,7 +622,7 @@ class LazyVectorIndexTest: VectorSearchTest {
     /// 7. Execute a vector search query.
     ///     - SELECT word
     ///       FROM _default.words
-    ///       WHERE vector_match(words_index, < dinner vector >, 300)
+    ///       WHERE vector_match(words_index, <dinner vector>) LIMIT 300
     /// 8. Check that there are 10 words returned.
     /// 9. Check that the word is in the word set from the step 5.
     func testIndexUpdaterSetFloatArrayVectors() throws {
diff --git a/Swift/Tests/VectorSearchTest.swift b/Swift/Tests/VectorSearchTest.swift
index 057684446..167031438 100644
--- a/Swift/Tests/VectorSearchTest.swift
+++ b/Swift/Tests/VectorSearchTest.swift
@@ -180,16 +180,17 @@ class VectorSearchTest: CBLTestCase {
     ///     2. Get and check the following property values:
     ///         - encoding: 8-Bit Scalar Quantizer Encoding
     ///         - metric: Euclidean Distance
-    ///         - minTrainingSize: 25 * centroids
-    ///         - maxTrainingSize: 256 * centroids
+    ///         - minTrainingSize: 0
+    ///         - maxTrainingSize: 0
     ///     3. To check the encoding type, platform code will have to expose some internal
     ///        property to the tests for verification.
     func testVectorIndexConfigurationDefaultValue() throws {
         let config = VectorIndexConfiguration(expression: "vector", dimensions: 300, centroids: 20)
         XCTAssertEqual(config.encoding, .scalarQuantizer(type: VectorIndexConfiguration.defaultEncoding));
         XCTAssertEqual(config.metric, VectorIndexConfiguration.defaultDistanceMetric)
-        XCTAssertEqual(config.minTrainingSize, 25 * config.centroids)
-        XCTAssertEqual(config.maxTrainingSize, 256 * config.centroids)
+        XCTAssertEqual(config.minTrainingSize, 0)
+        XCTAssertEqual(config.maxTrainingSize, 0)
+        XCTAssertEqual(config.numProbes, 0)
     }
     
     /// 2. TestVectorIndexConfigurationSettersAndGetters
@@ -309,7 +310,7 @@ class VectorSearchTest: CBLTestCase {
     ///     6. Create an SQL++ query:
     ///         - SELECT meta().id, word
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 20)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
     ///     7. Check the explain() result of the query to ensure that the "words_index" is used.
     ///     8. Execute the query and check that 20 results are returned.
     ///     9. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -339,7 +340,7 @@ class VectorSearchTest: CBLTestCase {
     ///     5. Create an SQL++ query:
     ///         - SELECT meta().id, word
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 350)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 350
     ///     6. Check the explain() result of the query to ensure that the "words_index" is used.
     ///     7. Execute the query and check that 300 results are returned.
     ///     8. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -410,7 +411,7 @@ class VectorSearchTest: CBLTestCase {
     ///     6. Create an SQL++ query.
     ///         - SELECT meta().id, word
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 350)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 350
     ///     7. Execute the query and check that 296 results are returned, and the results
     ///        do not include document word1, word2, word3, and word4.
     ///     8. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -479,7 +480,7 @@ class VectorSearchTest: CBLTestCase {
     ///     6. Create an SQL++ query:
     ///         - SELECT meta().id, word
     ///           FROM _default.words
-    ///           WHERE vector_match(words_pred_index, <dinner vector>, 350)
+    ///           WHERE vector_match(words_pred_index, <dinner vector>) LIMIT 350
     ///     7. Check the explain() result of the query to ensure that the "words_pred_index" is used.
     ///     8. Execute the query and check that 300 results are returned.
     ///     9. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -560,7 +561,7 @@ class VectorSearchTest: CBLTestCase {
     ///     7. Create an SQL++ query.
     ///         - SELECT meta().id, word
     ///           FROM _default.words
-    ///           WHERE vector_match(words_pred_index, <dinner vector>, 350)
+    ///           WHERE vector_match(words_pred_index, <dinner vector>) LIMIT 350
     ///     8. Check the explain() result of the query to ensure that the "words_predi_index" is used.
     ///     9. Execute the query and check that 296 results are returned and the results
     ///        do not include word1, word2, word3, and word4.
@@ -630,7 +631,7 @@ class VectorSearchTest: CBLTestCase {
     ///     5. Create an SQL++ query
     ///         - SELECT meta().id, word
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 20)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
     ///     6. Check the explain() result of the query to ensure that the "words_index" is used.
     ///     7. Execute the query and check that 20 results are returned.
     ///     8. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -683,7 +684,7 @@ class VectorSearchTest: CBLTestCase {
     ///     5. Create an SQL++ query.
     ///         - SELECT meta().id, word
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 20)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
     ///     6. Check the explain() result of the query to ensure that the "words_index" is used.
     ///     7. Execute the query and check that 20 results are returned.
     ///     8. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -717,7 +718,7 @@ class VectorSearchTest: CBLTestCase {
     ///     5. Create an SQL++ query.
     ///         - SELECT meta().id, word
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 20)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
     ///     6. Check the explain() result of the query to ensure that the "words_index" is used.
     ///     7. Execute the query and check that 20 results are returned.
     ///     8. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -803,7 +804,7 @@ class VectorSearchTest: CBLTestCase {
     ///     5. Create an SQL++ query.
     ///         - SELECT meta().id, word
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 20)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
     ///     5. Check the explain() result of the query to ensure that the "words_index" is used.
     ///     6. Execute the query and check that 20 results are returned.
     ///     7. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -833,25 +834,20 @@ class VectorSearchTest: CBLTestCase {
     ///         - minTrainingSize: 1 and maxTrainingSize: 100
     ///     3. Check that the index is created without an error returned.
     ///     4. Delete the "words_index"
-    ///     5. Repeat Step 2 with the following cases:
-    ///         - minTrainingSize = 0 and maxTrainingSize 0
-    ///         - minTrainingSize = 0 and maxTrainingSize 100
-    ///         - minTrainingSize = 10 and maxTrainingSize 9
-    ///     6. Check that an invalid argument exception was thrown for all cases in step 4.
+    ///     5. Repeat Step 2 with the following case:
+    ///         - minTrainingSize = 10 and maxTrainingSize = 9
+    ///     6. Check that an invalid argument exception was thrown.
     func testValidateMinMaxTrainingSize() throws {
         var config = VectorIndexConfiguration(expression: "vector", dimensions: 300, centroids: 20)
         config.minTrainingSize = 1
         config.maxTrainingSize = 100
         try createWordsIndex(config: config)
         
-        let trainingSizes: [[UInt32]] = [[0, 0], [0, 100], [10, 9]]
-        for size in trainingSizes {
-            try deleteWordsIndex()
-            config.minTrainingSize = size[0]
-            config.maxTrainingSize = size[1]
-            expectExcepion(exception: .invalidArgumentException) {
-                try? self.createWordsIndex(config: config)
-            }
+        try deleteWordsIndex()
+        config.minTrainingSize = 10
+        config.maxTrainingSize = 9
+        expectExcepion(exception: .invalidArgumentException) {
+            try? self.createWordsIndex(config: config)
         }
     }
     
@@ -871,7 +867,7 @@ class VectorSearchTest: CBLTestCase {
     ///     5. Create an SQL++ query.
     ///         - SELECT meta().id, word
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 20)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
     ///     6. Check the explain() result of the query to ensure that the "words_index" is used.
     ///     7. Execute the query and check that 20 results are returned.
     ///     8. Verify that the index was not trained by checking that the “Untrained index;
@@ -904,7 +900,7 @@ class VectorSearchTest: CBLTestCase {
     ///     5. Create an SQL++ query.
     ///         - SELECT meta().id, word,vector_distance(words_index)
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 20)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
     ///     6. Check the explain() result of the query to ensure that the "words_index" is used.
     ///     7. Execute the query and check that 20 results are returned and the vector
     ///       distance value is in between 0 – 1.0 inclusively.
@@ -940,7 +936,7 @@ class VectorSearchTest: CBLTestCase {
     ///     5. Create an SQL++ query.
     ///         - SELECT meta().id, word, vector_distance(words_index)
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 20)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
     ///     6. Check the explain() result of the query to ensure that the "words_index" is used.
     ///     7. Execute the query and check that 20 results are returned and the
     ///        distance value is more than zero.
@@ -1002,7 +998,7 @@ class VectorSearchTest: CBLTestCase {
     ///     5. Create an SQL++ query.
     ///         - SELECT meta().id, word
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 20)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
     ///     6. Check the explain() result of the query to ensure that the "words_index" is used.
     ///     7. Execute the query and check that 20 results are returned.
     ///     8. Verify that the index was trained by checking that the “Untrained index; queries may be slow”
@@ -1037,7 +1033,7 @@ class VectorSearchTest: CBLTestCase {
     ///     2. Create an SQL++ query.
     ///         - SELECT meta().id, word
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 20)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
     ///     3. Check that a CouchbaseLiteException is returned as the index doesn’t exist.
     func testVectorMatchOnNonExistingIndex() throws {
         self.expectError(domain: CBLError.domain, code: CBLError.missingIndex) {
@@ -1089,7 +1085,7 @@ class VectorSearchTest: CBLTestCase {
     ///     4. Create an SQL++ query.
     ///         - SELECT meta().id, word
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, <limit>)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT <limit>
     ///         - limit: 1 and 10000
     ///     5. Check that the query can be created without an error.
     ///     6. Repeat step 4 with the limit: -1, 0, and 10001
@@ -1125,7 +1121,7 @@ class VectorSearchTest: CBLTestCase {
     ///     5. Create an SQL++ query.
     ///         - SELECT word, catid
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 300) AND catid = 'cat1'
+    ///           WHERE vector_match(words_index, <dinner vector>) AND catid = 'cat1' LIMIT 300
     ///     6. Check that the query can be created without an error.
     ///     7. Check the explain() result of the query to ensure that the "words_index" is used.
     ///     8. Execute the query and check that the number of results returned is 50
@@ -1159,7 +1155,7 @@ class VectorSearchTest: CBLTestCase {
     ///     5. Create an SQL++ query.
     ///         - SELECT word, catid
     ///           FROM _default.words
-    ///           WHERE (vector_match(words_index, <dinner vector>, 300) AND word is valued) AND catid = 'cat1'
+    ///           WHERE (vector_match(words_index, <dinner vector>) AND word is valued) AND catid = 'cat1' LIMIT 300
     ///     6. Check that the query can be created without an error.
     ///     7. Check the explain() result of the query to ensure that the "words_index" is used.
     ///     8. Execute the query and check that the number of results returned is 50
@@ -1192,7 +1188,7 @@ class VectorSearchTest: CBLTestCase {
     ///     4. Create an SQL++ query.
     ///         - SELECT word, catid
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, <dinner vector>, 20) OR catid = 1
+    ///           WHERE vector_match(words_index, <dinner vector>) OR catid = 1 LIMIT 20
     ///     5. Check that a CouchbaseLiteException is returned when creating the query.
     func testInvalidVectorMatchWithOrExpression() throws {
         let config = VectorIndexConfiguration(expression: "vector", dimensions: 300, centroids: 8)
@@ -1220,7 +1216,7 @@ class VectorSearchTest: CBLTestCase {
     ///     7. Create an SQL++ query:
     ///         - SELECT meta().id, word, vector_distance(words_index)
     ///           FROM _default.words
-    ///           WHERE vector_match(words_index, < dinner vector >, 20)
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 20
     ///     8. Execute the query and check that 20 results are returned.
     ///     9. Check that the result also contains doc id = word49.
     func testIndexVectorInBase64() throws {
@@ -1236,4 +1232,39 @@ class VectorSearchTest: CBLTestCase {
         XCTAssertEqual(wordMap.count, 20)
         XCTAssertNotNil(wordMap["word49"])
     }
+    
+    /// 28. TestNumProbes
+    /// Description
+    ///     Test that the numProces specified is effective.
+    /// Steps
+    ///     1. Copy database words_db.
+    ///     2. Create a vector index named "words_index" in _default.words collection.
+    ///         - expression: "vector"
+    ///         - dimensions: 300
+    ///         - centroids : 8
+    ///         - numProbes: 5
+    ///     3. Check that the index is created without an error returned.
+    ///     4. Create an SQL++ query:
+    ///         - SELECT meta().id, word
+    ///           FROM _default.words
+    ///           WHERE vector_match(words_index, <dinner vector>) LIMIT 300
+    ///     5. Execute the query and check that 20 results are returned.
+    ///     6. Repeat step 2 - 6 but change the numProbes to 1.
+    ///     7. Verify the number of results returned in Step 5 is larger than Step 6.
+    func testNumProbes() throws {
+        var config = VectorIndexConfiguration(expression: "vector", dimensions: 300, centroids: 8)
+        config.numProbes = 5
+        try createWordsIndex(config: config)
+        var rs = try executeWordsQuery(limit: 300)
+        let numResultsFor5Probes = rs.allResults().count
+        XCTAssert(numResultsFor5Probes > 0)
+        
+        config.numProbes = 1;
+        try createWordsIndex(config: config)
+        rs = try executeWordsQuery(limit: 300)
+        let numResultsFor1Probes = rs.allResults().count
+        XCTAssert(numResultsFor1Probes > 0)
+        
+        XCTAssert(numResultsFor5Probes > numResultsFor1Probes)
+    }
 }
diff --git a/Tests/Extensions/version.txt b/Tests/Extensions/version.txt
index 5d46c9b2a..645ad1271 100644
--- a/Tests/Extensions/version.txt
+++ b/Tests/Extensions/version.txt
@@ -1 +1 @@
-1.0.0-43
+1.0.0-52
diff --git a/vendor/couchbase-lite-core b/vendor/couchbase-lite-core
index 61cfa302d..1b03b5456 160000
--- a/vendor/couchbase-lite-core
+++ b/vendor/couchbase-lite-core
@@ -1 +1 @@
-Subproject commit 61cfa302d61b8baef2c2cfeeb79c24653e1603a0
+Subproject commit 1b03b5456c508d431aa4ddfff82bda18f2fd392a