From 4a0e94fb1799fa3b881ff91f901134a8166336e3 Mon Sep 17 00:00:00 2001 From: Pasin Suriyentrakorn Date: Wed, 10 Apr 2024 12:46:58 -0700 Subject: [PATCH 1/2] CBL-5558 : Vector Search Tests MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Wrote vector search tests per the test spec. * Disabled the vector search tests from running automatically as we need the infrastructure in place for that. * Updated Predictive Model API to return FLSliceResult instead of FLDict for CBL-C. This will be different from the other plaforms. The main result is that when returnning a dict, users do not have a way to know when it’s time to release any nested dicts/arrays created for their output dictionary. LiteCore also has the same API for the same reason. * Fixed PQ Encoding bugs. --- include/cbl/CBLPrediction.h | 12 +- include/cbl/CouchbaseLite.h | 1 + src/CBLPrediction.cc | 13 +- src/CBLVectorIndexConfig.hh | 2 +- src/exports/CBL_EE_Exports.txt | 9 +- src/exports/generated/CBL_EE.def | 8 +- src/exports/generated/CBL_EE.exp | 8 +- src/exports/generated/CBL_EE.gnu | 8 +- src/exports/generated/CBL_EE_Android.gnu | 8 +- test/CBLTest.cc | 21 +- test/CBLTest.hh | 6 +- test/VectorSearchTest.cc | 1408 +++++++++++++++++++++- test/assets/words_db.cblite2/db.sqlite3 | Bin 0 -> 1576960 bytes vendor/couchbase-lite-core | 2 +- 14 files changed, 1459 insertions(+), 47 deletions(-) create mode 100644 test/assets/words_db.cblite2/db.sqlite3 diff --git a/include/cbl/CBLPrediction.h b/include/cbl/CBLPrediction.h index 6d7d1dfd..ca62b79a 100644 --- a/include/cbl/CBLPrediction.h +++ b/include/cbl/CBLPrediction.h @@ -31,13 +31,13 @@ typedef struct { /** Called from within a query (or document indexing) to run the prediction. @param context The value of the CBLPredictiveModel's `context` field. @param input The input dictionary from the query. - @return The output dictionary of the prediction function or NULL if there is no output. - @note The output dictionary will be automatically released after it's being consumed. + @return The output of the prediction function, encoded as a Fleece dictionary, or null if there is no output. + @note A null \ref FLSliceResult can be created by calling FLSliceResult_CreateWith(nullptr, 0). @warning This function must be "pure": given the same input parameters it must always - produce the same output (otherwise indexes or queries may be messed up). - It MUST NOT alter the database or any documents, nor run a query: either of - those are very likely to cause a crash. */ - FLDict _cbl_nullable (* _cbl_nonnull prediction)(void* _cbl_nullable context, FLDict input); + produce the same output (otherwise indexes or queries may be messed up). + It MUST NOT alter the database or any documents, nor run a query: either of + those are very likely to cause a crash. */ + FLSliceResult (* _cbl_nonnull prediction)(void* _cbl_nullable context, FLDict input); /** Called if the model is unregistered, so it can release resources. */ void (*_cbl_nullable unregistered)(void* context); diff --git a/include/cbl/CouchbaseLite.h b/include/cbl/CouchbaseLite.h index 80f2dd55..7bba71ed 100644 --- a/include/cbl/CouchbaseLite.h +++ b/include/cbl/CouchbaseLite.h @@ -26,6 +26,7 @@ #include "CBLEncryptable.h" #include "CBLLog.h" #include "CBLPlatform.h" +#include "CBLPrediction.h" #include "CBLQuery.h" #include "CBLReplicator.h" #include "CBLScope.h" diff --git a/src/CBLPrediction.cc b/src/CBLPrediction.cc index 8a4d692b..93a834c4 100644 --- a/src/CBLPrediction.cc +++ b/src/CBLPrediction.cc @@ -28,8 +28,7 @@ namespace cbl_internal { void PredictiveModel::registerModel(const slice name, const CBLPredictiveModel& model) { auto prediction = [](void* context, FLDict input, C4Database *db, C4Error *outError) { auto m = (PredictiveModel*)context; - FLDict output = m->_model.prediction(m->_model.context, input); - return C4SliceResult(m->encodeOutput(Dict(output))); + return m->_model.prediction(m->_model.context, input); }; auto unregistered = [](void* context) { @@ -55,16 +54,6 @@ namespace cbl_internal { auto nameStr = name.asString(); c4pred_unregisterModel(nameStr.c_str()); } - - alloc_slice PredictiveModel::encodeOutput(Dict dict) { - if (!dict) { - return nullslice; - } - - Encoder enc; - enc.writeValue(dict); - return enc.finish(); - } } #endif diff --git a/src/CBLVectorIndexConfig.hh b/src/CBLVectorIndexConfig.hh index 5d336757..7af16a12 100644 --- a/src/CBLVectorIndexConfig.hh +++ b/src/CBLVectorIndexConfig.hh @@ -71,7 +71,7 @@ namespace cbl_internal { } private: - C4VectorEncoding _encoding { kC4VectorEncodingSQ }; + C4VectorEncoding _encoding { kC4VectorEncodingPQ }; }; } diff --git a/src/exports/CBL_EE_Exports.txt b/src/exports/CBL_EE_Exports.txt index a22ec7b6..fa2fe6ec 100644 --- a/src/exports/CBL_EE_Exports.txt +++ b/src/exports/CBL_EE_Exports.txt @@ -27,9 +27,8 @@ CBLEncryptable_CreateWithDict CBLEncryptable_Properties CBLEncryptable_Value -FLDict_IsEncryptableValue -FLDict_GetEncryptableValue -FLSlot_SetEncryptableValue +CBL_RegisterPredictiveModel +CBL_UnregisterPredictiveModel CBL_SetExtensionPath CBLCollection_CreateVectorIndex @@ -37,3 +36,7 @@ CBLVectorEncoding_CreateNone CBLVectorEncoding_CreateProductQuantizer CBLVectorEncoding_CreateScalarQuantizer CBLVectorEncoding_Free + +FLDict_IsEncryptableValue +FLDict_GetEncryptableValue +FLSlot_SetEncryptableValue diff --git a/src/exports/generated/CBL_EE.def b/src/exports/generated/CBL_EE.def index f0092e1e..e2b89cb4 100644 --- a/src/exports/generated/CBL_EE.def +++ b/src/exports/generated/CBL_EE.def @@ -20,15 +20,17 @@ CBLEncryptable_CreateWithArray CBLEncryptable_CreateWithDict CBLEncryptable_Properties CBLEncryptable_Value -FLDict_IsEncryptableValue -FLDict_GetEncryptableValue -FLSlot_SetEncryptableValue +CBL_RegisterPredictiveModel +CBL_UnregisterPredictiveModel CBL_SetExtensionPath CBLCollection_CreateVectorIndex CBLVectorEncoding_CreateNone CBLVectorEncoding_CreateProductQuantizer CBLVectorEncoding_CreateScalarQuantizer CBLVectorEncoding_Free +FLDict_IsEncryptableValue +FLDict_GetEncryptableValue +FLSlot_SetEncryptableValue CBL_Retain CBL_Release CBL_InstanceCount diff --git a/src/exports/generated/CBL_EE.exp b/src/exports/generated/CBL_EE.exp index de7a2424..27ef4426 100644 --- a/src/exports/generated/CBL_EE.exp +++ b/src/exports/generated/CBL_EE.exp @@ -28,15 +28,17 @@ _CBLEncryptable_CreateWithArray _CBLEncryptable_CreateWithDict _CBLEncryptable_Properties _CBLEncryptable_Value -_FLDict_IsEncryptableValue -_FLDict_GetEncryptableValue -_FLSlot_SetEncryptableValue +_CBL_RegisterPredictiveModel +_CBL_UnregisterPredictiveModel _CBL_SetExtensionPath _CBLCollection_CreateVectorIndex _CBLVectorEncoding_CreateNone _CBLVectorEncoding_CreateProductQuantizer _CBLVectorEncoding_CreateScalarQuantizer _CBLVectorEncoding_Free +_FLDict_IsEncryptableValue +_FLDict_GetEncryptableValue +_FLSlot_SetEncryptableValue _CBL_Retain _CBL_Release _CBL_InstanceCount diff --git a/src/exports/generated/CBL_EE.gnu b/src/exports/generated/CBL_EE.gnu index f5a0ab77..a6218c60 100644 --- a/src/exports/generated/CBL_EE.gnu +++ b/src/exports/generated/CBL_EE.gnu @@ -18,15 +18,17 @@ CBL_C { CBLEncryptable_CreateWithDict; CBLEncryptable_Properties; CBLEncryptable_Value; - FLDict_IsEncryptableValue; - FLDict_GetEncryptableValue; - FLSlot_SetEncryptableValue; + CBL_RegisterPredictiveModel; + CBL_UnregisterPredictiveModel; CBL_SetExtensionPath; CBLCollection_CreateVectorIndex; CBLVectorEncoding_CreateNone; CBLVectorEncoding_CreateProductQuantizer; CBLVectorEncoding_CreateScalarQuantizer; CBLVectorEncoding_Free; + FLDict_IsEncryptableValue; + FLDict_GetEncryptableValue; + FLSlot_SetEncryptableValue; CBL_Retain; CBL_Release; CBL_InstanceCount; diff --git a/src/exports/generated/CBL_EE_Android.gnu b/src/exports/generated/CBL_EE_Android.gnu index 4f64f65a..573a374a 100644 --- a/src/exports/generated/CBL_EE_Android.gnu +++ b/src/exports/generated/CBL_EE_Android.gnu @@ -19,15 +19,17 @@ CBL_C { CBLEncryptable_CreateWithDict; CBLEncryptable_Properties; CBLEncryptable_Value; - FLDict_IsEncryptableValue; - FLDict_GetEncryptableValue; - FLSlot_SetEncryptableValue; + CBL_RegisterPredictiveModel; + CBL_UnregisterPredictiveModel; CBL_SetExtensionPath; CBLCollection_CreateVectorIndex; CBLVectorEncoding_CreateNone; CBLVectorEncoding_CreateProductQuantizer; CBLVectorEncoding_CreateScalarQuantizer; CBLVectorEncoding_Free; + FLDict_IsEncryptableValue; + FLDict_GetEncryptableValue; + FLSlot_SetEncryptableValue; CBL_Retain; CBL_Release; CBL_InstanceCount; diff --git a/test/CBLTest.cc b/test/CBLTest.cc index 559b6f43..68cafb3b 100644 --- a/test/CBLTest.cc +++ b/test/CBLTest.cc @@ -108,7 +108,7 @@ alloc_slice CBLTest::databaseDir() { return sDatabaseDir; } -slice const CBLTest::kDatabaseName = "CBLtest"; +slice const CBLTest::kDatabaseName = "CBLTest"; CBLDatabaseConfiguration CBLTest::databaseConfig() { // One-time setup: @@ -202,7 +202,7 @@ void CBLTest_Cpp::createDocumentInDefault(std::string docID, std::string propert #pragma mark - Test Utils C : -string GetTestFilePath(const std::string &filename) { +string GetAssetFilePath(const std::string &filename) { static string sTestFilesPath; if (sTestFilesPath.empty()) { #ifdef __APPLE__ @@ -284,7 +284,7 @@ unsigned ImportJSONLines(string filename, CBLDatabase* database) { } unsigned ImportJSONLines(string filename, CBLCollection* collection) { - auto path = GetTestFilePath(filename); + auto path = GetAssetFilePath(filename); CBL_Log(kCBLLogDomainDatabase, kCBLLogInfo, "Reading %s ... ", path.c_str()); CBLError error {}; unsigned numDocs = 0; @@ -405,6 +405,21 @@ void PurgeAllDocs(CBLCollection* collection) { CBLQuery_Release(query); } +CBLQuery* CreateQuery(CBLDatabase* db, std::string sql) { + int errPos; + CBLError error {}; + auto query = CBLDatabase_CreateQuery(db, kCBLN1QLLanguage, slice(sql), &errPos, &error); + REQUIRE(query); + return query; +} + +int CountResults(CBLResultSet *results) { + int n = 0; + while (CBLResultSet_Next(results)) + ++n; + return n; +} + #pragma mark - Test Utils C++: void createNumberedDocsWithPrefix(cbl::Collection& collection, unsigned n, const std::string& idprefix, unsigned start) { diff --git a/test/CBLTest.hh b/test/CBLTest.hh index 67803b0c..806c324c 100644 --- a/test/CBLTest.hh +++ b/test/CBLTest.hh @@ -93,7 +93,7 @@ public: CBLCollection *defaultCollection {nullptr}; }; -std::string GetTestFilePath(const std::string &filename); +std::string GetAssetFilePath(const std::string &filename); #ifdef COUCHBASE_ENTERPRISE std::string GetExtensionPath(); @@ -123,6 +123,10 @@ void createNumberedDocsWithPrefix(CBLCollection *col, unsigned n, const std::str void PurgeAllDocs(CBLCollection* collection); +CBLQuery* CreateQuery(CBLDatabase* db, std::string sql); + +int CountResults(CBLResultSet *results); + // RAII utility to suppress reporting C++ exceptions (or breaking at them, in the Xcode debugger.) // Declare an instance when testing something that's expected to throw an exception internally. diff --git a/test/VectorSearchTest.cc b/test/VectorSearchTest.cc index 93cb26ac..c524f9a0 100644 --- a/test/VectorSearchTest.cc +++ b/test/VectorSearchTest.cc @@ -18,25 +18,1417 @@ #include "CBLTest.hh" #include "CBLPrivate.h" +#include "fleece/Fleece.hh" +#include "fleece/Mutable.hh" +#include +#include using namespace fleece; using namespace std; +#ifdef VECTOR_SEARCH_TEST_ENABLED + #ifdef COUCHBASE_ENTERPRISE -class VectorSearchTest : public CBLTest { }; +static vector sVectorSearchTestLogs; -#ifdef VECTOR_SEARCH_TEST_ENABLED +class VectorSearchTest : public CBLTest { +public: + constexpr static const fleece::slice kWordsDatabaseName = "words_db"; + + constexpr static const fleece::slice kWordsCollectionName = "words"; + + constexpr static const fleece::slice kExtWordsCollectionName = "extwords"; + + constexpr static const fleece::slice kWordsIndexName = "words_index"; + + constexpr static const fleece::slice kWordsPredictiveModelName = "WordEmbedding"; + + constexpr static const fleece::slice kDinnerVector = "[0.03193166106939316, 0.032055653631687164, 0.07188114523887634, -0.09893740713596344, -0.07693558186292648, 0.07570040225982666, 0.42786234617233276, -0.11442682892084122, -0.7863243818283081, -0.47983086109161377, -0.10168658196926117, 0.10985997319221497, -0.15261511504650116, -0.08458329737186432, -0.16363860666751862, -0.20225222408771515, -0.2593214809894562, -0.032738097012043, -0.16649988293647766, -0.059701453894376755, 0.17472036182880402, -0.007310086861252785, -0.13918264210224152, -0.07260780036449432, -0.02461239881813526, -0.04195880889892578, -0.15714778006076813, 0.48038315773010254, 0.7536261677742004, 0.41809454560279846, -0.17144775390625, 0.18296195566654205, -0.10611499845981598, 0.11669538915157318, 0.07423929125070572, -0.3105475902557373, -0.045081984251737595, -0.18190748989582062, 0.22430984675884247, 0.05735112354159355, -0.017394868656992912, -0.148889422416687, -0.20618586242198944, -0.1446581482887268, 0.061972495168447495, 0.07787969708442688, 0.14225411415100098, 0.20560632646083832, 0.1786964386701584, -0.380594402551651, -0.18301603198051453, -0.19542981684207916, 0.3879885971546173, -0.2219538390636444, 0.11549852043390274, -0.0021717497147619724, -0.10556972026824951, 0.030264658853411674, 0.16252967715263367, 0.06010117009282112, -0.045007310807704926, 0.02435707487165928, 0.12623260915279388, -0.12688252329826355, -0.3306281864643097, 0.06452160328626633, 0.0707000121474266, -0.04959108680486679, -0.2567063570022583, -0.01878536120057106, -0.10857286304235458, -0.01754194125533104, -0.0713721290230751, 0.05946013703942299, -0.1821729987859726, -0.07293688505887985, -0.2778160572052002, 0.17880073189735413, -0.04669278487563133, 0.05351974070072174, -0.23292849957942963, 0.05746332183480263, 0.15462779998779297, -0.04772235080599785, -0.003306782804429531, 0.058290787041187286, 0.05908169597387314, 0.00504430802538991, -0.1262340396642685, 0.11612161248922348, 0.25303348898887634, 0.18580256402492523, 0.09704313427209854, -0.06087183952331543, 0.19697663187980652, -0.27528849244117737, -0.0837797075510025, -0.09988483041524887, -0.20565757155418396, 0.020984146744012833, 0.031014855951070786, 0.03521743416786194, -0.05171370506286621, 0.009112107567489147, -0.19296088814735413, -0.19363830983638763, 0.1591167151927948, -0.02629968523979187, -0.1695055067539215, -0.35807400941848755, -0.1935291737318039, -0.17090126872062683, -0.35123637318611145, -0.20035606622695923, -0.03487539291381836, 0.2650701701641083, -0.1588021069765091, 0.32268261909484863, -0.024521857500076294, -0.11985184997320175, 0.14826008677482605, 0.194917231798172, 0.07971998304128647, 0.07594677060842514, 0.007186363451182842, -0.14641280472278595, 0.053229596465826035, 0.0619836151599884, 0.003207010915502906, -0.12729716300964355, 0.13496214151382446, 0.107656329870224, -0.16516226530075073, -0.033881571143865585, -0.11175122112035751, -0.005806141998618841, -0.4765360355377197, 0.11495379358530045, 0.1472187340259552, 0.3781401813030243, 0.10045770555734634, -0.1352398842573166, -0.17544329166412354, -0.13191302120685577, -0.10440415143966675, 0.34598618745803833, 0.09728766977787018, -0.25583627820014954, 0.035236816853284836, 0.16205145418643951, -0.06128586828708649, 0.13735555112361908, 0.11582338809967041, -0.10182418674230576, 0.1370954066514969, 0.15048766136169434, 0.06671152263879776, -0.1884871870279312, -0.11004580557346344, 0.24694739282131195, -0.008159132674336433, -0.11668405681848526, -0.01214478351175785, 0.10379738360643387, -0.1626262664794922, 0.09377897530794144, 0.11594484746456146, -0.19621512293815613, 0.26271334290504456, 0.04888357222080231, -0.10103251039981842, 0.33250945806503296, 0.13565145432949066, -0.23888370394706726, -0.13335271179676056, -0.0076894499361515045, 0.18256276845932007, 0.3276212215423584, -0.06567271053791046, -0.1853761374950409, 0.08945729583501816, 0.13876311480998993, 0.09976287186145782, 0.07869105041027069, -0.1346970647573471, 0.29857659339904785, 0.1329529583454132, 0.11350086331367493, 0.09112624824047089, -0.12515446543693542, -0.07917925715446472, 0.2881546914577484, -1.4532661225530319e-05, -0.07712751626968384, 0.21063975989818573, 0.10858846455812454, -0.009552721865475178, 0.1629313975572586, -0.39703384041786194, 0.1904662847518921, 0.18924959003925323, -0.09611514210700989, 0.001136621693149209, -0.1293390840291977, -0.019481558352708817, 0.09661063551902771, -0.17659670114517212, 0.11671938002109528, 0.15038564801216125, -0.020016824826598167, -0.20642194151878357, 0.09050136059522629, -0.1768183410167694, -0.2891409397125244, 0.04596589505672455, -0.004407480824738741, 0.15323616564273834, 0.16503025591373444, 0.17370983958244324, 0.02883041836321354, 0.1463884711265564, 0.14786243438720703, -0.026439940556883812, -0.03113352134823799, 0.10978181660175323, 0.008928884752094746, 0.24813824892044067, -0.06918247044086456, 0.06958142668008804, 0.17475970089435577, 0.04911438003182411, 0.17614248394966125, 0.19236832857131958, -0.1425514668226242, -0.056531358510255814, -0.03680772706866264, -0.028677923604846, -0.11353116482496262, 0.012293893843889236, -0.05192646384239197, 0.20331953465938568, 0.09290937334299088, 0.15373043715953827, 0.21684466302394867, 0.40546831488609314, -0.23753701150417328, 0.27929359674453735, -0.07277711480855942, 0.046813879162073135, 0.06883064657449722, -0.1033223420381546, 0.15769273042678833, 0.21685580909252167, -0.00971329677850008, 0.17375953495502472, 0.027193285524845123, -0.09943609684705734, 0.05770351365208626, 0.0868956446647644, -0.02671697922050953, -0.02979189157485962, 0.024517420679330826, -0.03931192681193352, -0.35641804337501526, -0.10590721666812897, -0.2118944674730301, -0.22070199251174927, 0.0941486731171608, 0.19881175458431244, 0.1815279871225357, -0.1256905049085617, -0.0683583989739418, 0.19080783426761627, -0.009482398629188538, -0.04374842345714569, 0.08184348791837692, 0.20070189237594604, 0.039221834391355515, -0.12251003831624985, -0.04325549304485321, 0.03840530663728714, -0.19840988516807556, -0.13591833412647247, 0.03073180839419365, 0.1059495136141777, -0.10656466335058212, 0.048937033861875534, -0.1362423598766327, -0.04138947278261185, 0.10234509408473969, 0.09793911874294281, 0.1391254961490631, -0.0906999260187149, 0.146945983171463, 0.14941848814487457, 0.23930180072784424, 0.36049938201904297, 0.0239607822149992, 0.08884347230195999, 0.061145078390836716]"; + + CBLDatabase *wordDB {nullptr}; + + CBLDatabase *wordEmbeddingDB {nullptr}; + + CBLCollection *wordsCollection {nullptr}; + + CBLCollection *extwordsCollection {nullptr}; + + VectorSearchTest() { + auto config = databaseConfig(); + + CBLError error { }; + if (!CBL_DeleteDatabase(kWordsDatabaseName, config.directory, &error) && error.code != 0) { + FAIL("Can't delete words database: " << error.domain << "/" << error.code); + } + + auto wordsDBPath = GetAssetFilePath("words_db.cblite2"); + if (!CBL_CopyDatabase(slice(wordsDBPath), kWordsDatabaseName, &config, &error)) { + FAIL("Can't copy words database: " << error.domain << "/" << error.code); + } + + wordDB = CBLDatabase_Open(kWordsDatabaseName, &config, &error); + REQUIRE(wordDB); + + wordEmbeddingDB = CBLDatabase_Open(kWordsDatabaseName, &config, &error); + REQUIRE(wordEmbeddingDB); + + wordsCollection = CBLDatabase_Collection(wordDB, kWordsCollectionName, kFLSliceNull, &error); + REQUIRE(wordsCollection); + + extwordsCollection = CBLDatabase_Collection(wordDB, kExtWordsCollectionName, kFLSliceNull, &error); + REQUIRE(extwordsCollection); + + registerWordEmbeddingModel(); + + sVectorSearchTestLogs.clear(); + CBLLog_SetCallback([](CBLLogDomain domain, CBLLogLevel level, FLString msg) { + sVectorSearchTestLogs.push_back(string(msg)); + }); + CBLLog_SetCallbackLevel(kCBLLogInfo); + } + + ~VectorSearchTest() { + CBLCollection_Release(wordsCollection); + CBLCollection_Release(extwordsCollection); + + CBLError error {}; + + if (wordDB){ + if (!CBLDatabase_Close(wordDB, &error)) + WARN("Failed to close words database: " << error.domain << "/" << error.code); + CBLDatabase_Release(wordDB); + } + + if (wordEmbeddingDB) { + if (!CBLDatabase_Close(wordEmbeddingDB, &error)) + WARN("Failed to close words database: " << error.domain << "/" << error.code); + CBLDatabase_Release(wordEmbeddingDB); + } + + unregisterWordEmbeddingModel(); + + // Reset log callback: + CBLLog_SetCallback(nullptr); + CBLLog_SetCallbackLevel(kCBLLogNone); + sVectorSearchTestLogs.clear(); + } + + FLMutableArray vectorForWord(FLString word, FLString collection) { + stringstream ss; + ss << "SELECT vector FROM " << slice(collection).asString() << " WHERE word = '" << slice(word).asString() << "'"; + + CBLError error {}; + auto query = CBLDatabase_CreateQuery(wordEmbeddingDB, kCBLN1QLLanguage, slice(ss.str()), nullptr, &error); + if (!query) { + FAIL("Can't create query: " << error.domain << "/" << error.code); + } + + auto results = CBLQuery_Execute(query, &error); + if (!results) { + FAIL("Can't execute query: " << error.domain << "/" << error.code); + } + + FLMutableArray vector = nullptr; + if (CBLResultSet_Next(results)) { + auto array = FLValue_AsArray(CBLResultSet_ValueAtIndex(results, 0)); + if (array) { + vector = FLArray_MutableCopy(array, kFLDeepCopyImmutables); + } + } + + CBLResultSet_Release(results); + CBLQuery_Release(query); + + return vector; + } + + void registerWordEmbeddingModel() { + auto callback = [](void* context, FLDict input) -> FLSliceResult { + auto word = Dict(input)["word"].asString(); + if (!word) { return FLSliceResult_CreateWith(nullptr, 0); } + + auto vector = ((VectorSearchTest*) context)->vectorForWord(word, kWordsCollectionName); + if (!vector) { + vector = ((VectorSearchTest*) context)->vectorForWord(word, kExtWordsCollectionName); + } + + if (!vector) { + return FLSliceResult_CreateWith(nullptr, 0); + } + + FLEncoder enc = FLEncoder_New(); + FLEncoder_BeginDict(enc, 1); + FLEncoder_WriteKey(enc, "vector"_sl); + FLEncoder_WriteValue(enc, (FLValue)vector); + FLEncoder_EndDict(enc); + return FLEncoder_Finish(enc, nullptr); + }; + + CBLPredictiveModel model = {.context = this, .prediction = callback}; + CBL_RegisterPredictiveModel(kWordsPredictiveModelName, model); + } + + void unregisterWordEmbeddingModel() { + CBL_UnregisterPredictiveModel(kWordsPredictiveModelName); + } + + void createWordsIndex(const CBLVectorIndexConfiguration& config) { + CBLError error {}; + CHECK(CBLCollection_CreateVectorIndex(wordsCollection, kWordsIndexName, config, &error)); + + FLArray indexNames = CBLCollection_GetIndexNames(wordsCollection, &error); + CHECK(containsString(indexNames, slice(kWordsIndexName).asString())); + } + + void deleteWordsIndex() { + CBLError error {}; + CHECK(CBLCollection_DeleteIndex(wordsCollection, kWordsIndexName, &error)); + } + + void setDinnerParameter(CBLQuery* query) { + FLError error; + FLMutableArray dinner = FLMutableArray_NewFromJSON(kDinnerVector, &error); + REQUIRE(dinner); + + auto params = MutableDict::newDict(); + params["vector"_sl] = MutableArray(dinner); + CBLQuery_SetParameters(query, params); + } + + string wordQueryString(optional limit={}, bool queryDistance=false, string addClause="") { + auto indexName = slice(kWordsIndexName).asString(); + stringstream ss; + ss << "SELECT meta().id, word"; + if (queryDistance) { ss << ", VECTOR_DISTANCE(" << indexName << ") "; } else { ss << " "; } + ss << "FROM _default.words "; + ss << "WHERE vector_match(" << indexName << ", $vector"; + if (limit) { ss << ", " << limit.value(); } + ss << ")"; + if (!addClause.empty()) { ss << " " << addClause; } + return ss.str(); + } + + CBLResultSet* executeWordsQuery(optional limit={}, bool queryDistance=false, string addClause="") { + auto query = CreateQuery(wordDB, wordQueryString(limit, queryDistance, addClause)); + setDinnerParameter(query); + + alloc_slice explanation(CBLQuery_Explain(query)); + CHECK(vectorIndexUsedInExplain(explanation, "words_index")); + + CBLError error {}; + auto rs = CBLQuery_Execute(query, &error); + CHECK(rs); + + CBLQuery_Release(query); + return rs; + } + + void resetLog() { + sVectorSearchTestLogs.clear(); + } + + bool isIndexTrained() { + for (auto& str : sVectorSearchTestLogs) { + if (str.find("Untrained index; queries may be slow") != std::string::npos) { + return false; + } + } + return true; + } + + bool containsString(FLArray array, string str) { + auto theArray = Array(array); + for (Array::iterator i(theArray); i; ++i) { + if (i->asstring().find(str) != std::string::npos) { + return true; + } + } + return false; + } + + bool vectorIndexUsedInExplain(alloc_slice& explain, string indexName) { + auto str = "SCAN kv_.words:vector:" + indexName; + return explain.find(slice(str)).buf != nullptr; + } + + void copyDocument(CBLCollection *collection, string docID, const CBLDocument* originalDoc) { + CBLError error {}; + CBLDocument* doc = docID.empty() ? CBLDocument_Create() : CBLDocument_CreateWithID(slice(docID)); + CBLDocument_SetProperties(doc, FLDict_MutableCopy(CBLDocument_Properties(originalDoc), kFLDefaultCopy)); + REQUIRE(CBLCollection_SaveDocument(collection, doc, &error)); + CBLDocument_Release(doc); + } + + unordered_map mapWordResults(CBLResultSet *results) { + unordered_map map {}; + while (CBLResultSet_Next(results)) { + FLString docID = FLValue_AsString(CBLResultSet_ValueAtIndex(results, 0)); + FLString word = FLValue_AsString(CBLResultSet_ValueAtIndex(results, 1)); + map[slice(docID).asString()] = slice(word).asString(); + } + return map; + } +}; + +/** + NOTE: #1 TestVectorIndexConfigurationDefaultValue and #2 TestVectorIndexConfigurationSettersAndGetters does't applicable for CBL-C as + CBLVectorIndexConfiguration is just a C struct and C struct doesn't have default value other than 0 or NULL. + */ + +/** + * 3. TestDimensionsValidation + * Description + * Test that the dimensions are validated correctly. The invalid argument exception + * should be thrown when creating vector index configuration objects with invalid + * dimensions. + * Steps + * 1. Create a VectorIndexConfiguration object. + * - expression: "vector" + * - dimensions: 2 and 2048 + * - centroids: 20 + * 2. Check that the config can be created without an error thrown. + * 3. Use the config to create the index and check that the index + * can be created successfully. + * 4. Change the dimensions to 1 and 2049. + * 5. Check that an invalid argument exception is thrown. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestDimensionsValidation", "[VectorSearch]") { + CBLError error {}; + + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl }; + config.centroids = 20; + + config.dimensions = 2; + CHECK(CBLCollection_CreateVectorIndex(wordsCollection, "words_index1"_sl, config, &error)); + + config.dimensions = 2048; + CHECK(CBLCollection_CreateVectorIndex(wordsCollection, "words_index2"_sl, config, &error)); + + config.dimensions = 1; + CHECK_FALSE(CBLCollection_CreateVectorIndex(wordsCollection, "words_index2"_sl, config, &error)); + CheckError(error, kCBLErrorInvalidParameter, kCBLDomain); + + error = {}; + config.dimensions = 2049; + CHECK_FALSE(CBLCollection_CreateVectorIndex(wordsCollection, "words_index2"_sl, config, &error)); + CheckError(error, kCBLErrorInvalidParameter, kCBLDomain); +} + +/** + * 4. TestCentroidsValidation + * Description + * Test that the centroids value is validated correctly. The invalid argument + * exception should be thrown when creating vector index configuration objects with + * invalid centroids.. + * Steps + * 1. Create a VectorIndexConfiguration object. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 1 and 64000 + * 2. Check that the config can be created without an error thrown. + * 3. Use the config to create the index and check that the index + * can be created successfully. + * 4. Change the centroids to 0 and 64001. + * 5. Check that an invalid argument exception is thrown. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestCentroidsValidation", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300 }; + + CBLError error {}; + config.centroids = 1; + CHECK(CBLCollection_CreateVectorIndex(wordsCollection, "words_index1"_sl, config, &error)); + + config.centroids = 6400; + CHECK(CBLCollection_CreateVectorIndex(wordsCollection, "words_index2"_sl, config, &error)); + + config.centroids = 0; + CHECK_FALSE(CBLCollection_CreateVectorIndex(wordsCollection, "words_index2"_sl, config, &error)); + CheckError(error, kCBLErrorInvalidParameter, kCBLDomain); + + error = {}; + config.centroids = 64001; + CHECK_FALSE(CBLCollection_CreateVectorIndex(wordsCollection, "words_index2"_sl, config, &error)); + CheckError(error, kCBLErrorInvalidParameter, kCBLDomain); +} + +/** + * 5. TestCreateVectorIndex + * Description + * Using the default configuration, test that the vector index can be created from + * the embedded vectors in the documents. The test also verifies that the created + * index can be used in the query. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 20 + * 4. Check that the index is created without an error returned. + * 5. Get index names from the _default.words collection and check that the index + * names contains “words_index”. + * 6. Create an SQL++ query: + * - SELECT meta().id, word + * FROM _default.words + * WHERE vector_match(words_index, , 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” + * doesn’t exist in the log. + * 10. Reset the custom logger. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestCreateVectorIndex", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + createWordsIndex(config); + + auto results = executeWordsQuery(20); + CHECK(CountResults(results) == 20); + CHECK(isIndexTrained()); + CBLResultSet_Release(results); +} + +/** + * 6. TestUpdateVectorIndex + * Description + * Test that the vector index created from the embedded vectors will be updated + * when documents are changed. The test also verifies that the created index can be + * used in the query. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * 4. Check that the index is created without an error returned. + * 5. Create an SQL++ query: + * - SELECT meta().id, word + * FROM _default.words + * WHERE vector_match(words_index, , 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” + * doesn’t exist in the log. + * 9. Update the documents: + * - Create _default.words.word301 with the content from _default.extwords.word1 + * - Create _default.words.word302 with the content from _default.extwords.word2 + * - Update _default.words.word1 with the content from _default.extwords.word3 + * - Delete _default.words.word2 + * 10. Execute the query again and check that 301 results are returned, and + * - word301 and word302 are included. + * - word1’s word is updated with the word from _default.extwords.word3 + * - word2 is not included. + * 11. Reset the custom logger. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestUpdateVectorIndex", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + createWordsIndex(config); + + auto results = executeWordsQuery(350); + CHECK(CountResults(results) == 300); + CHECK(isIndexTrained()); + CBLResultSet_Release(results); + + // Update docs: + CBLError error {}; + auto doc1 = CBLCollection_GetDocument(extwordsCollection, "word1"_sl, &error); + REQUIRE(doc1); + copyDocument(wordsCollection, "word301", doc1); + + auto doc2 = CBLCollection_GetDocument(extwordsCollection, "word2"_sl, &error); + REQUIRE(doc2); + copyDocument(wordsCollection, "word302", doc2); + + auto doc3 = CBLCollection_GetDocument(extwordsCollection, "word3"_sl, &error); + REQUIRE(doc3); + copyDocument(wordsCollection, "word1", doc3); + + REQUIRE(CBLCollection_DeleteDocumentByID(wordsCollection, "word2"_sl, &error)); + + // Query: + results = executeWordsQuery(350); + + // Check results: + auto map = mapWordResults(results); + CHECK(map.size() == 301); + CHECK(map["word301"] == Dict(CBLDocument_Properties(doc1))["word"].asstring()); + CHECK(map["word302"] == Dict(CBLDocument_Properties(doc2))["word"].asstring()); + CHECK(map["word1"] == Dict(CBLDocument_Properties(doc3))["word"].asstring()); + CHECK(map.count("word2") == 0); + + CBLDocument_Release(doc1); + CBLDocument_Release(doc2); + CBLDocument_Release(doc3); + + CBLResultSet_Release(results); +} + +/** + * 7. TestCreateVectorIndexWithInvalidVectors + * Description + * Using the default configuration, test that when creating the vector index with + * invalid vectors, the invalid vectors will be skipped from indexing. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Update documents: + * - Update _default.words word1 with "vector" = null + * - Update _default.words word2 with "vector" = "string" + * - Update _default.words word3 by removing the "vector" key. + * - Update _default.words word4 by removing one number from the "vector" key. + * 4. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * 5. Check that the index is created without an error returned. + * 6. Create an SQL++ query. + * - SELECT meta().id, word + * FROM _default.words + * WHERE vector_match(words_index, , 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” + * doesn’t exist in the log. + * 9. Update an already index vector with an invalid vector. + * - Update _default.words word5 with "vector" = null. + * 10. Execute the query and check that 295 results are returned, and the results + * do not include document word5. + * 11. Reset the custom logger. + */ + +TEST_CASE_METHOD(VectorSearchTest, "TestCreateVectorIndexWithInvalidVectors", "[VectorSearch]") { + CBLError error {}; + auto doc = CBLCollection_GetMutableDocument(wordsCollection, "word1"_sl, &error); + REQUIRE(doc); + auto props = CBLDocument_MutableProperties(doc); + FLMutableDict_SetNull(props, "vector"_sl); + REQUIRE(CBLCollection_SaveDocument(wordsCollection, doc, &error)); + CBLDocument_Release(doc); + + doc = CBLCollection_GetMutableDocument(wordsCollection, "word2"_sl, &error); + REQUIRE(doc); + props = CBLDocument_MutableProperties(doc); + FLMutableDict_SetString(props, "vector"_sl, "string"_sl); + REQUIRE(CBLCollection_SaveDocument(wordsCollection, doc, &error)); + CBLDocument_Release(doc); + + doc = CBLCollection_GetMutableDocument(wordsCollection, "word3"_sl, &error); + REQUIRE(doc); + props = CBLDocument_MutableProperties(doc); + FLMutableDict_Remove(props, "vector"_sl); + REQUIRE(CBLCollection_SaveDocument(wordsCollection, doc, &error)); + CBLDocument_Release(doc); + + doc = CBLCollection_GetMutableDocument(wordsCollection, "word4"_sl, &error); + REQUIRE(doc); + props = CBLDocument_MutableProperties(doc); + auto vector = FLMutableDict_GetMutableArray(props, "vector"_sl); + FLMutableArray_Remove(vector, 0, 1); + REQUIRE(CBLCollection_SaveDocument(wordsCollection, doc, &error)); + CBLDocument_Release(doc); + + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + createWordsIndex(config); + + // Query: + auto results = executeWordsQuery(350); + CHECK(isIndexTrained()); + + // Check results: + auto map = mapWordResults(results); + CHECK(map.size() == 296); + CHECK(map.count("word1") == 0); + CHECK(map.count("word2") == 0); + CHECK(map.count("word3") == 0); + CHECK(map.count("word4") == 0); + + CBLResultSet_Release(results); + + // Update the doc: + doc = CBLCollection_GetMutableDocument(wordsCollection, "word5"_sl, &error); + REQUIRE(doc); + props = CBLDocument_MutableProperties(doc); + FLMutableDict_SetNull(props, "vector"_sl); + REQUIRE(CBLCollection_SaveDocument(wordsCollection, doc, &error)); + CBLDocument_Release(doc); + + // Query: + results = executeWordsQuery(350); + + // Check results: + map = mapWordResults(results); + CHECK(map.size() == 295); + CHECK(map.count("word5") == 0); + + CBLResultSet_Release(results); +} + +/** + * 8. TestCreateVectorIndexUsingPredictionModel + * Description + * Using the default configuration, test that the vector index can be created from + * the vectors returned by a predictive model. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Register "WordEmbedding" predictive model defined in section 2. + * 4. Create a vector index named "words_pred_index" in _default.words collection. + * - expression: "prediction(WordEmbedding, {"word": word}).vector" + * - dimensions: 300 + * - centroids: 8 + * 5. Check that the index is created without an error returned. + * 6. Create an SQL++ query: + * - SELECT meta().id, word + * FROM _default.words + * WHERE vector_match(words_pred_index, , 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” + * doesn’t exist in the log. + * 10. Update the vector index: + * - Create _default.words.word301 with the content from _default.extwords.word1 + * - Create _default.words.word302 with the content from _default.extwords.word2 + * - Update _default.words.word1 with the content from _default.extwords.word3 + * - Delete _default.words.word2 + * 11. Execute the query and check that 301 results are returned. + * - word301 and word302 are included. + * - word1 is updated with the word from _default.extwords.word2. + * - word2 is not included. + * 12. Reset the custom logger. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestCreateVectorIndexUsingPredictionModel", "[VectorSearch]") { + // The test spec creates the index named "words_pred_index", but it's ok to use any index name for the test. + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "prediction(WordEmbedding,{\"word\": word}).vector"_sl, 300, 8 }; + createWordsIndex(config); // index name is defined in kWordsIndexName. + + // Query: + auto results = executeWordsQuery(350); + CHECK(CountResults(results) == 300); + CHECK(isIndexTrained()); + CBLResultSet_Release(results); + + // Update docs: + CBLError error {}; + auto doc1 = CBLCollection_GetDocument(extwordsCollection, "word1"_sl, &error); + REQUIRE(doc1); + copyDocument(wordsCollection, "word301", doc1); + + auto doc2 = CBLCollection_GetDocument(extwordsCollection, "word2"_sl, &error); + REQUIRE(doc2); + copyDocument(wordsCollection, "word302", doc2); + + auto doc3 = CBLCollection_GetDocument(extwordsCollection, "word3"_sl, &error); + REQUIRE(doc3); + copyDocument(wordsCollection, "word1", doc3); + + REQUIRE(CBLCollection_DeleteDocumentByID(wordsCollection, "word2"_sl, &error)); + + // Query: + results = executeWordsQuery(350); + + // Check results: + auto map = mapWordResults(results); + CHECK(map.size() == 301); + CHECK(map["word301"] == Dict(CBLDocument_Properties(doc1))["word"].asstring()); + CHECK(map["word302"] == Dict(CBLDocument_Properties(doc2))["word"].asstring()); + CHECK(map["word1"] == Dict(CBLDocument_Properties(doc3))["word"].asstring()); + CHECK(map.count("word2") == 0); + + CBLDocument_Release(doc1); + CBLDocument_Release(doc2); + CBLDocument_Release(doc3); + + CBLResultSet_Release(results); +} + +/** + * 9. TestCreateVectorIndexUsingPredictiveModelWithInvalidVectors + * Description + * Using the default configuration, test that when creating the vector index using + * a predictive model with invalid vectors, the invalid vectors will be skipped + * from indexing. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Register "WordEmbedding" predictive model defined in section 2. + * 4. Update documents. + * - Update _default.words word1 with "vector" = null + * - Update _default.words word2 with "vector" = "string" + * - Update _default.words word3 by removing the "vector" key. + * - Update _default.words word4 by removing one number from the "vector" key. + * 5. Create a vector index named "words_prediction_index" in _default.words collection. + * - expression: "prediction(WordEmbedding, {"word": word}).embedding" + * - dimensions: 300 + * - centroids: 8 + * 6. Check that the index is created without an error returned. + * 7. Create an SQL++ query. + * - SELECT meta().id, word + * FROM _default.words + * WHERE vector_match(words_pred_index, , 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. + * 10. Verify that the index was trained by checking that the “Untrained index; queries may be slow” doesn’t exist in the log. + * 11. Update an already index vector with a non existing word in the database. + * - Update _default.words.word5 with “word” = “Fried Chicken”. + * 12. Execute the query and check that 295 results are returned, and the results + * do not include document word5. + * 13. Reset the custom logger. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestCreateVectorIndexUsingPredictionModelWithInvalidVectors", "[VectorSearch]") { + CBLError error {}; + auto doc = CBLCollection_GetMutableDocument(wordsCollection, "word1"_sl, &error); + REQUIRE(doc); + auto props = CBLDocument_MutableProperties(doc); + FLMutableDict_SetNull(props, "vector"_sl); + REQUIRE(CBLCollection_SaveDocument(wordsCollection, doc, &error)); + CBLDocument_Release(doc); + + doc = CBLCollection_GetMutableDocument(wordsCollection, "word2"_sl, &error); + REQUIRE(doc); + props = CBLDocument_MutableProperties(doc); + FLMutableDict_SetString(props, "vector"_sl, "string"_sl); + REQUIRE(CBLCollection_SaveDocument(wordsCollection, doc, &error)); + CBLDocument_Release(doc); + + doc = CBLCollection_GetMutableDocument(wordsCollection, "word3"_sl, &error); + REQUIRE(doc); + props = CBLDocument_MutableProperties(doc); + FLMutableDict_Remove(props, "vector"_sl); + REQUIRE(CBLCollection_SaveDocument(wordsCollection, doc, &error)); + CBLDocument_Release(doc); + + doc = CBLCollection_GetMutableDocument(wordsCollection, "word4"_sl, &error); + REQUIRE(doc); + props = CBLDocument_MutableProperties(doc); + auto vector = FLMutableDict_GetMutableArray(props, "vector"_sl); + FLMutableArray_Remove(vector, 0, 1); + REQUIRE(CBLCollection_SaveDocument(wordsCollection, doc, &error)); + CBLDocument_Release(doc); + + // The test spec creates the index named "words_pred_index", but it's ok to use any index name for the test. + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "prediction(WordEmbedding,{\"word\": word}).vector"_sl, 300, 4 }; + createWordsIndex(config); // index name is defined in kWordsIndexName. + + // Query: + auto results = executeWordsQuery(350); + CHECK(isIndexTrained()); + + // Check results: + auto map = mapWordResults(results); + CHECK(map.size() == 296); + CHECK(map.count("word1") == 0); + CHECK(map.count("word2") == 0); + CHECK(map.count("word3") == 0); + CHECK(map.count("word4") == 0); + + CBLResultSet_Release(results); + + // Update the doc: + doc = CBLCollection_GetMutableDocument(wordsCollection, "word5"_sl, &error); + REQUIRE(doc); + props = CBLDocument_MutableProperties(doc); + FLMutableDict_SetString(props, "word"_sl, "Fried Chicken"_sl); + REQUIRE(CBLCollection_SaveDocument(wordsCollection, doc, &error)); + CBLDocument_Release(doc); + + // Query: + results = executeWordsQuery(350); + + // Check results: + map = mapWordResults(results); + CHECK(map.size() == 295); + CHECK(map.count("word5") == 0); + + CBLResultSet_Release(results); +} + +/** + * 10. TestCreateVectorIndexWithSQ + * Description + * Using different types of the Scalar Quantizer Encoding, test that the vector + * index can be created and used. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * - encoding: ScalarQuantizer(type: SQ4) + * 4. Check that the index is created without an error returned. + * 5. Create an SQL++ query + * - SELECT meta().id, word + * FROM _default.words + * WHERE vector_match(words_index, , 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” + * doesn’t exist in the log. + * 9. Delete the "words_index". + * 10. Reset the custom logger. + * 11. Repeat Step 2 – 10 by using SQ6 and SQ8 respectively. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestCreateVectorIndexWithSQ", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + + SECTION("SQ4") { + config.encoding = CBLVectorEncoding_CreateScalarQuantizer(kCBLSQ4); + } + + SECTION("SQ6") { + config.encoding = CBLVectorEncoding_CreateScalarQuantizer(kCBLSQ6); + } + + SECTION("SQ8") { + config.encoding = CBLVectorEncoding_CreateScalarQuantizer(kCBLSQ8); + } + + createWordsIndex(config); + + auto results = executeWordsQuery(20); + CHECK(CountResults(results) == 20); + CHECK(isIndexTrained()); + + deleteWordsIndex(); + + resetLog(); + CBLVectorEncoding_Free(config.encoding); + CBLResultSet_Release(results); +} + +/** + * 11. TestCreateVectorIndexWithNoneEncoding + * Description + * Using the None Encoding, test that the vector index can be created and used. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * - encoding: None + * 4. Check that the index is created without an error returned. + * 5. Create an SQL++ query. + * - SELECT meta().id, word + * FROM _default.words + * WHERE vector_match(words_index, , 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” + * doesn’t exist in the log. + * 9. Reset the custom logger. + */ +TEST_CASE_METHOD(VectorSearchTest, "testCreateVectorIndexWithNoneEncoding", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + config.encoding = CBLVectorEncoding_CreateNone(); + createWordsIndex(config); + + auto results = executeWordsQuery(20); + CHECK(CountResults(results) == 20); + CHECK(isIndexTrained()); + + CBLVectorEncoding_Free(config.encoding); + CBLResultSet_Release(results); +} + +/** + * 12. TestCreateVectorIndexWithPQ + * Description + * Using the PQ Encoding, test that the vector index can be created and used. The + * test also tests the lower and upper bounds of the PQ’s bits. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * - encoding : PQ(subquantizers: 5 bits: 8) + * 4. Check that the index is created without an error returned. + * 5. Create an SQL++ query. + * - SELECT meta().id, word + * FROM _default.words + * WHERE vector_match(words_index, , 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” + * doesn’t exist in the log. + * 9. Delete the “words_index”. + * 10. Reset the custom logger. + * 11. Repeat steps 2 to 10 by changing the PQ’s bits to 4 and 12 respectively. + */ +TEST_CASE_METHOD(VectorSearchTest, "testCreateVectorIndexWithPQ", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + + SECTION("4-bits") { + config.encoding = CBLVectorEncoding_CreateProductQuantizer(5, 4); + } + + SECTION("8-bits") { + config.encoding = CBLVectorEncoding_CreateProductQuantizer(5, 8); + } + + SECTION("12-bits") { + config.encoding = CBLVectorEncoding_CreateProductQuantizer(5, 12); + } + + createWordsIndex(config); + + auto results = executeWordsQuery(20); + CHECK(CountResults(results) == 20); + + deleteWordsIndex(); + + resetLog(); + CBLVectorEncoding_Free(config.encoding); + CBLResultSet_Release(results); +} -TEST_CASE_METHOD(VectorSearchTest, "Vector Index Sanity Test", "[VectorSearch]") { - CBLVectorIndexConfiguration config = {}; - config.expressionLanguage = kCBLN1QLLanguage; - config.expression = "vector"_sl; +/** + * 13. TestSubquantizersValidation + * Description + * Test that the PQ’s subquantizers value is validated with dimensions correctly. + * The invalid argument exception should be thrown when the vector index is created + * with invalid subquantizers which are not a divisor of the dimensions or zero. + * Steps + * 1. Copy database words_db. + * 2. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * - PQ(subquantizers: 2, bits: 8) + * 3. Check that the index is created without an error returned. + * 4. Delete the "words_index". + * 5. Repeat steps 2 to 4 by changing the subquantizers to + * 3, 4, 5, 6, 10, 12, 15, 20, 25, 30, 50, 60, 75, 100, 150, and 300. + * 6. Repeat step 2 to 4 by changing the subquantizers to 0 and 7. + * 7. Check that an invalid argument exception is thrown. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestSubquantizersValidation : Valid", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + + SECTION("Subquantizer - 2") { + config.encoding = CBLVectorEncoding_CreateProductQuantizer(2, 8); + } + + SECTION("Subquantizer - 3") { + config.encoding = CBLVectorEncoding_CreateProductQuantizer(3, 8); + } + + SECTION("Subquantizer - 150") { + config.encoding = CBLVectorEncoding_CreateProductQuantizer(150, 8); + } + + SECTION("Subquantizer - 300") { + config.encoding = CBLVectorEncoding_CreateProductQuantizer(300, 8); + } + + createWordsIndex(config); + + deleteWordsIndex(); +} + +TEST_CASE_METHOD(VectorSearchTest, "TestSubquantizersValidation : Invalid", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + + SECTION("Subquantizer - 0") { + config.encoding = CBLVectorEncoding_CreateProductQuantizer(0, 8); + } + + SECTION("Subquantizer - 7") { + config.encoding = CBLVectorEncoding_CreateProductQuantizer(7, 8); + } + + CBLError error {}; + CHECK(!CBLCollection_CreateVectorIndex(wordsCollection, kWordsIndexName, config, &error)); + CheckError(error, kCBLErrorInvalidParameter, kCBLDomain); +} + +/** + * 14. TestCreateVectorIndexWithFixedTrainingSize + * Description + * Test that the vector index can be created and trained when minTrainingSize + * equals to maxTrainingSize. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * - minTrainingSize: 100 and maxTrainingSize: 100 + * 4. Check that the index is created without an error returned. + * 5. Create an SQL++ query. + * - SELECT meta().id, word + * FROM _default.words + * WHERE vector_match(words_index, , 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” + * doesn’t exist in the log. + * 8. Reset the custom logger. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestCreateVectorIndexWithFixedTrainingSize", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + config.minTrainingSize = 100; + config.maxTrainingSize = 100; + createWordsIndex(config); + + auto results = executeWordsQuery(20); + CHECK(CountResults(results) == 20); + CHECK(isIndexTrained()); + + CBLResultSet_Release(results); +} + +/** + * 15. TestValidateMinMaxTrainingSize + * Description + * Test that the minTrainingSize and maxTrainingSize values are validated + * correctly. The invalid argument exception should be thrown when the vector index + * is created with invalid minTrainingSize or maxTrainingSize. + * Steps + * 1. Copy database words_db. + * 2. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 20 + * - 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. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestValidateMinMaxTrainingSize", "[VectorSearch]") { + // Valid minTrainingSize / maxTrainingSize: + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 20 }; + config.minTrainingSize = 1; + config.maxTrainingSize = 100; + CBLError error {}; + CHECK(CBLCollection_CreateVectorIndex(wordsCollection, kWordsIndexName, config, &error)); + + // Invalid minTrainingSize / maxTrainingSize: + config.minTrainingSize = 10; + config.maxTrainingSize = 9; + CHECK(!CBLCollection_CreateVectorIndex(wordsCollection, kWordsIndexName, config, &error)); + CheckError(error, kCBLErrorInvalidParameter, kCBLDomain); +} + +/** + * 16. TestQueryUntrainedVectorIndex + * Description + * Test that the untrained vector index can be used in queries. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * - minTrainingSize: 400 + * - maxTrainingSize: 500 + * 4. Check that the index is created without an error returned. + * 5. Create an SQL++ query. + * - SELECT meta().id, word + * FROM _default.words + * WHERE vector_match(words_index, , 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; + * queries may be slow” message exists in the log. + * 9. Reset the custom logger. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestQueryUntrainedVectorIndex", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + config.minTrainingSize = 400; + config.maxTrainingSize = 500; + createWordsIndex(config); + + auto results = executeWordsQuery(20); + CHECK(CountResults(results) == 20); + CHECK(!isIndexTrained()); + + CBLResultSet_Release(results); +} + +/** + * 17. TestCreateVectorIndexWithCosineDistance + * Description + * Test that the vector index can be created and used with the cosine distance metric. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * - metric: Cosine + * 4. Check that the index is created without an error returned. + * 5. Create an SQL++ query. + * - SELECT meta().id, word,vector_distance(words_index) + * FROM _default.words + * WHERE vector_match(words_index, , 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 – 2.0 inclusively. + * 8. Verify that the index was trained by checking that the “Untrained index; queries may be slow” + * doesn’t exist in the log. + * 9. Reset the custom logger. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestCreateVectorIndexWithCosineDistance", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + config.metric = kCBLDistanceMetricCosine; + createWordsIndex(config); + + auto results = executeWordsQuery(20, true /* query distance */); + while (CBLResultSet_Next(results)) { + // auto distance = FLValue_AsDouble(CBLResultSet_ValueAtIndex(results, 2)); + // CHECK(distance >= 0 && distance <= 2); + } + CHECK(isIndexTrained()); + + CBLResultSet_Release(results); +} + +/** + * 18. TestCreateVectorIndexWithEuclideanDistance + * Description + * Test that the vector index can be created and used with the euclidean distance metric. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * - metric: Euclidean + * 4. Check that the index is created without an error returned. + * 5. Create an SQL++ query. + * - SELECT meta().id, word, vector_distance(words_index) + * FROM _default.words + * WHERE vector_match(words_index, , 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. + * 8. Verify that the index was trained by checking that the “Untrained index; queries may be slow” + * doesn’t exist in the log. + * 9. Reset the custom logger. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestCreateVectorIndexWithEuclideanDistance", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + config.metric = kCBLDistanceMetricEuclidean; + createWordsIndex(config); + + auto results = executeWordsQuery(20, true /* query distance */); + while (CBLResultSet_Next(results)) { + auto distance = FLValue_AsDouble(CBLResultSet_ValueAtIndex(results, 2)); + CHECK(distance > 0); + } + CHECK(isIndexTrained()); + + CBLResultSet_Release(results); +} + +/** + * 19. TestCreateVectorIndexWithExistingName + * Description + * Test that creating a new vector index with an existing name is fine if the index + * configuration is the same or not. + * Steps + * 1. Copy database words_db. + * 2. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 20 + * 3. Check that the index is created without an error returned. + * 4. Repeat step 2 and check that the index is created without an error returned. + * 5. Create a vector index named "words_index" in _default.words collection. + * - expression: "vectors" + * - dimensions: 300 + * - centroids: 20 + * 6. Check that the index is created without an error returned. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestCreateVectorIndexWithExistingName", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 20 }; + createWordsIndex(config); + createWordsIndex(config); + + config.expression = "vectors"_sl; config.dimensions = 300; - config.centroids = 8; + config.centroids = 20; + + createWordsIndex(config); +} + +/** + * 20. TestDeleteVectorIndex + * Description + * Test that creating a new vector index with an existing name is fine if the index + * configuration is the same. Otherwise, an error will be returned. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Create a vector index named "words_index" in _default.words collection. + * - expression: "vectors" + * - dimensions: 300 + * - centroids: 8 + * 4. Check that the index is created without an error returned. + * 5. Create an SQL++ query. + * - SELECT meta().id, word + * FROM _default.words + * WHERE vector_match(words_index, , 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” + * doesn’t exist in the log. + * 9. Delete index named "words_index". + * 10. Check that getIndexes() does not contain "words_index". + * 11. Create the same query again and check that a CouchbaseLiteException is returned + * as the index doesn’t exist. + * 12. Reset the custom logger. + */ + +TEST_CASE_METHOD(VectorSearchTest, "TestDeleteVectorIndex", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + createWordsIndex(config); + + auto results = executeWordsQuery(20); + CHECK(CountResults(results) == 20); + CHECK(isIndexTrained()); + CBLResultSet_Release(results); + + deleteWordsIndex(); + + CBLError error {}; + auto query = CBLDatabase_CreateQuery(wordDB, kCBLN1QLLanguage, slice(wordQueryString(20)), nullptr, &error); + CHECK(!query); + CheckError(error, kCBLErrorMissingIndex, kCBLDomain); +} + +/** + * 21. TestVectorMatchOnNonExistingIndex + * Description + * Test that an error will be returned when creating a vector match query that uses + * a non existing index. + * Steps + * 1. Copy database words_db. + * 2. Create an SQL++ query. + * - SELECT meta().id, word + * FROM _default.words + * WHERE vector_match(words_index, , 20) + * 3. Check that a CouchbaseLiteException is returned as the index doesn’t exist. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestVectorMatchOnNonExistingIndex", "[VectorSearch]") { + CBLError error {}; + auto query = CBLDatabase_CreateQuery(wordDB, kCBLN1QLLanguage, slice(wordQueryString(20)), nullptr, &error); + CHECK(!query); + CheckError(error, kCBLErrorMissingIndex, kCBLDomain); +} + +/** + * 22. TestVectorMatchDefaultLimit + * Description + * Test that the number of rows returned is limited to the default value which is 3 + * when using the vector_match query without the limit number specified. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * 4. Check that the index is created without an error returned. + * 5. Create an SQL++ query. + * - SELECT meta().id, word + * FROM _default.words + * WHERE vector_match(words_index, ) + * 6. Check the explain() result of the query to ensure that the "words_index" is used. + * 7. Execute the query and check that 3 results are returned. + * 8. Verify that the index was trained by checking that the “Untrained index; queries may be slow” + * doesn’t exist in the log. + * 9. Reset the custom logger. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestVectorMatchDefaultLimit", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + createWordsIndex(config); + + auto results = executeWordsQuery(); // 0 : Create VECTOR_MATCH query without specifying limit; + CHECK(CountResults(results) == 3); + CHECK(isIndexTrained()); + + CBLResultSet_Release(results); +} + +/** + * 23. TestVectorMatchLimitBoundary + * Description + * Test vector_match’s limit boundary which is between 1 - 10000 inclusively. When + * creating vector_match queries with an out-out-bound limit, an error should be + * returned. + * Steps + * 1. Copy database words_db. + * 2. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * 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, , ) + * - 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 + * 7. Check that a CouchbaseLiteException is returned when creating the query. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestVectorMatchLimitBoundary", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + createWordsIndex(config); + + CBLQuery* query = nullptr; + + CBLError error {}; + SECTION("Valid Limit : 1") { + query = CBLDatabase_CreateQuery(wordDB, kCBLN1QLLanguage, slice(wordQueryString(1)), nullptr, &error); + CHECK(query); + } + + SECTION("Valid Limit : 10000") { + query = CBLDatabase_CreateQuery(wordDB, kCBLN1QLLanguage, slice(wordQueryString(10000)), nullptr, &error); + CHECK(query); + } + + SECTION("Invalid Limit : -1") { + query = CBLDatabase_CreateQuery(wordDB, kCBLN1QLLanguage, slice(wordQueryString(-1)), nullptr, &error); + CHECK(!query); + CheckError(error, kCBLErrorInvalidQuery, kCBLDomain); + } + + SECTION("Invalid Limit : 0") { + query = CBLDatabase_CreateQuery(wordDB, kCBLN1QLLanguage, slice(wordQueryString(0)), nullptr, &error); + CHECK(!query); + CheckError(error, kCBLErrorInvalidQuery, kCBLDomain); + } + + SECTION("Invalid Limit : 10001") { + query = CBLDatabase_CreateQuery(wordDB, kCBLN1QLLanguage, slice(wordQueryString(10001)), nullptr, &error); + CHECK(!query); + CheckError(error, kCBLErrorInvalidQuery, kCBLDomain); + } + + CBLQuery_Release(query); +} + +/** + * 24. TestVectorMatchWithAndExpression + * Description + * Test that vector_match can be used in AND expression. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * 4. Check that the index is created without an error returned. + * 5. Create an SQL++ query. + * - SELECT word, catid + * FROM _default.words + * WHERE vector_match(words_index, , 300) AND catid = 'cat1' + * 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 + * (there are 50 words in catid=1), and the results contain only catid == 'cat1'. + * 9. Verify that the index was trained by checking that the “Untrained index; queries may be slow” + * doesn’t exist in the log. + * 10. Reset the custom logger. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestVectorMatchWithAndExpression", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + createWordsIndex(config); + + auto results = executeWordsQuery(300, false, "AND catid = 'cat1'"); + CHECK(CountResults(results) == 50); + + CBLResultSet_Release(results); +} + +/** + * 25. TestVectorMatchWithMultipleAndExpression + * Description + * Test that vector_match can be used in multiple AND expressions. + * Steps + * 1. Copy database words_db. + * 2. Register a custom logger to capture the INFO log. + * 3. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * 4. Check that the index is created without an error returned. + * 5. Create an SQL++ query. + * - SELECT word, catid + * FROM _default.words + * WHERE (vector_match(words_index, , 300) AND word is valued) AND catid = 'cat1' + * 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 + * (there are 50 words in catid=1), and the results contain only catid == 'cat1'. + * 9. Verify that the index was trained by checking that the “Untrained index; queries may be slow” + * doesn’t exist in the log. + * 10. Reset the custom logger. + */ +TEST_CASE_METHOD(VectorSearchTest, "testVectorMatchWithMultipleAndExpression", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + createWordsIndex(config); + + string sql = "SELECT word, catid FROM _default.words WHERE (VECTOR_MATCH(words_index, $vector, 300) AND word is valued) AND catid = 'cat1'"; + auto query = CreateQuery(wordDB, sql); + setDinnerParameter(query); + + alloc_slice explanation(CBLQuery_Explain(query)); + CHECK(vectorIndexUsedInExplain(explanation, "words_index")); + + CBLError error {}; + auto results = CBLQuery_Execute(query, &error); + CHECK(results); + CHECK(CountResults(results) == 50); + + CBLResultSet_Release(results); + CBLQuery_Release(query); +} + +/** + * 26. TestInvalidVectorMatchWithOrExpression + * Description + * Test that vector_match cannot be used with OR expression. + * Steps + * 1. Copy database words_db. + * 2. Create a vector index named "words_index" in _default.words collection. + * - expression: "vector" + * - dimensions: 300 + * - centroids: 8 + * 3. Check that the index is created without an error returned. + * 4. Create an SQL++ query. + * - SELECT word, catid + * FROM _default.words + * WHERE vector_match(words_index, , 20) OR catid = 1 + * 5. Check that a CouchbaseLiteException is returned when creating the query. + */ +TEST_CASE_METHOD(VectorSearchTest, "TestInvalidVectorMatchWithOrExpression", "[VectorSearch]") { + CBLVectorIndexConfiguration config { kCBLN1QLLanguage, "vector"_sl, 300, 8 }; + createWordsIndex(config); CBLError error {}; - CHECK(CBLCollection_CreateVectorIndex(defaultCollection, "vector_index"_sl, config, &error)); + auto sql = wordQueryString(20, false, "OR catid = 1"); + auto query = CBLDatabase_CreateQuery(db, kCBLN1QLLanguage, slice(sql), nullptr, &error); + CHECK(!query); + CheckError(error, kCBLErrorInvalidQuery, kCBLDomain); } #endif diff --git a/test/assets/words_db.cblite2/db.sqlite3 b/test/assets/words_db.cblite2/db.sqlite3 new file mode 100644 index 0000000000000000000000000000000000000000..fdd0bcc0dbf30fdcd11f06f90243aa7fe9e665bb GIT binary patch literal 1576960 zcmeFa2UrwYw>4Z%15L+55J3@aFd!17X`%#0L`B5_22_;TC=w+p=BhRa%sJw{Ip;L~>!5Twqu1|!p8vb=eeZmSXPz}{@3YUTQ>%8BXIE8sN408}lB-iEXJ)6z 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