Merge pull request #2012 from nosqlbench/driver/weaviate

Code cleanups in Azure AI Search driver adapter

nb-adapters/adapter-azure-aisearch/src/main/java/io/nosqlbench/adapter/azureaisearch/AzureAISearchAdapterUtils.java

@@ -24,7 +24,6 @@ import com.azure.search.documents.SearchDocument;
 import com.azure.search.documents.util.SearchPagedIterable;
 
 public class AzureAISearchAdapterUtils {
-
 	public static final String AZURE_AI_SEARCH = "azure_aisearch";
 
 	public static List<String> splitNames(String input) {
@@ -57,9 +56,18 @@ public class AzureAISearchAdapterUtils {
 		return masked.toString();
 	}
 
-	public String[] responseFieldToStringArray(String fieldName, SearchPagedIterable response) {
+	/**
-		return response.stream()
+	 * Prepares an integer array of the indices of keys containing the result
-				.map(searchResult -> searchResult.getDocument(SearchDocument.class).get(fieldName).toString())
+	 * vectors.
-				.toArray(String[]::new);
+	 *
+	 * @param field    field to search for the index values of the vectors.
+	 * @param response results from which we need to search for the indexes.
+	 * @return an {@code int[]} of the indexes of the vectors.
+	 */
+	public static int[] searchDocumentsResponseIdToIntArray(String field, SearchPagedIterable response) {
+		return response.stream().mapToInt(r -> {
+			SearchDocument returnObj = r.getDocument(SearchDocument.class);
+			return Integer.valueOf((String) returnObj.get(field));
+		}).toArray();
 	}
 }

nb-adapters/adapter-azure-aisearch/src/main/java/io/nosqlbench/adapter/azureaisearch/AzureAISearchOpMapper.java

@@ -68,8 +68,8 @@ public class AzureAISearchOpMapper implements OpMapper<AzureAISearchBaseOp<?>> {
 		case upload_documents -> new AzureAISearchUploadDocumentsOpDispenser(adapter, op, typeAndTarget.targetFunction);
 		case search_documents -> new AzureAISearchSearchDocumentsOpDispenser(adapter, op, typeAndTarget.targetFunction);
 
-//            default -> throw new RuntimeException("Unrecognized op type '" + typeAndTarget.enumId.name() + "' while " +
+//		default -> throw new RuntimeException(
-//                "mapping parsed op " + op);
+//				"Unrecognized op type '" + typeAndTarget.enumId.name() + "' while " + "mapping parsed op " + op);
 		};
 	}
 }

nb-adapters/adapter-azure-aisearch/src/main/java/io/nosqlbench/adapter/azureaisearch/AzureAISearchSpace.java

@@ -56,7 +56,6 @@ public class AzureAISearchSpace implements AutoCloseable {
 	private final NBConfiguration cfg;
 
 	protected SearchIndexClient searchIndexClient;
-//	protected SearchClient searchClient;
 
 	/**
 	 * Create a new {@code AzureAISearchSpace} Object which stores all stateful
@@ -78,13 +77,6 @@ public class AzureAISearchSpace implements AutoCloseable {
 		return searchIndexClient;
 	}
 
-//	public synchronized SearchClient getSearchClient() {
-//		if (searchClient == null) {
-//			createSearchClients();
-//		}
-//		return searchClient;
-//	}
-
 	private SearchIndexClient createSearchClients() {
 		String uri = cfg.get("endpoint");
 		var requiredToken = cfg.getOptional("token_file").map(Paths::get).map(tokenFilePath -> {
@@ -98,27 +90,21 @@ public class AzureAISearchSpace implements AutoCloseable {
 		}).orElseGet(() -> cfg.getOptional("token").orElseThrow(() -> new RuntimeException(
 				"You must provide either a 'token_file' or a 'token' to configure a Azure AI Search client")));
 
-		logger.info("{}: Creating new Azure AI Search Client with (masked) token/key [{}], uri/endpoint [{}]",
+		logger.info(() -> "Creating new Azure AI Search Client with (masked) token/key ["
-				this.name, AzureAISearchAdapterUtils.maskDigits(requiredToken), uri);
+				+ AzureAISearchAdapterUtils.maskDigits(requiredToken) + "], uri/endpoint [" + uri + "]");
 
 		var searchIndexClientBuilder = new SearchIndexClientBuilder().endpoint(uri);
-//		var searchClientBuilder = new SearchClientBuilder().endpoint(uri);
 		if (!requiredToken.isBlank()) {
 			searchIndexClientBuilder = searchIndexClientBuilder.credential(new AzureKeyCredential(requiredToken));
-//			searchClientBuilder = searchClientBuilder.credential(new AzureKeyCredential(requiredToken));
 		} else {
 			TokenCredential tokenCredential = new DefaultAzureCredentialBuilder().build();
 			searchIndexClientBuilder = searchIndexClientBuilder.credential(tokenCredential);
-//			searchClientBuilder = searchClientBuilder.credential(tokenCredential);
 		}
 		// Should we leave these below to leverage the SearchServiceVersion.getLatest()?
 		String apiVersion = cfg.getOptional("api_version").orElse(SearchServiceVersion.V2024_07_01.name());
 		logger.warn(
-				"Latest search service version supported by this client is '{}', but we're using '{}' version. Ignore this warning if both are same.",
+				() -> "Latest search service version supported by this client is '" + SearchServiceVersion.getLatest()
-				SearchServiceVersion.getLatest(), apiVersion);
+						+ "', but we're using '" + apiVersion + "' version. Ignore this warning if both are same.");
-		// TODO - try to find a way to get rid of placeholder
-//		this.searchClient = searchClientBuilder.serviceVersion(SearchServiceVersion.valueOf(apiVersion))
-//				.indexName("PLACEHOLDER").buildClient();
 		return searchIndexClientBuilder.serviceVersion(SearchServiceVersion.valueOf(apiVersion)).buildClient();
 	}
 

nb-adapters/adapter-azure-aisearch/src/main/java/io/nosqlbench/adapter/azureaisearch/ops/AzureAISearchBaseOp.java

@@ -31,30 +31,24 @@ public abstract class AzureAISearchBaseOp<T> implements CycleOp<Object> {
 	protected final static Logger logger = LogManager.getLogger(AzureAISearchBaseOp.class);
 
 	protected final SearchIndexClient searchIndexClient;
-//	protected final SearchClient searchClient;
 	protected final T request;
 	protected final LongFunction<Object> apiCall;
 
 	public AzureAISearchBaseOp(SearchIndexClient searchIndexClient, T requestParam) {
 		this.searchIndexClient = searchIndexClient;
-		// TODO - figure out how to do this cleanly
-//		this.searchClient = searchIndexClient.getSearchClient("PLACEHOLDER");
 		this.request = requestParam;
 		this.apiCall = this::applyOp;
 	}
 
 	public AzureAISearchBaseOp(SearchIndexClient searchIndexClient, T requestParam, LongFunction<Object> call) {
 		this.searchIndexClient = searchIndexClient;
-		// TODO - figure out how to do this cleanly
-//		this.searchClient = searchIndexClient.getSearchClient("PLACEHOLDER");
 		this.request = requestParam;
 		this.apiCall = call;
 	}
 
-	@SuppressWarnings("unchecked")
 	@Override
 	public final Object apply(long value) {
-		logger.trace("applying op: " + this);
+		logger.trace(() -> "applying op: " + this);
 
 		try {
 			Object result = applyOp(value);

nb-adapters/adapter-azure-aisearch/src/main/java/io/nosqlbench/adapter/azureaisearch/ops/AzureAISearchCreateOrUpdateIndexOp.java

@@ -28,16 +28,8 @@ public class AzureAISearchCreateOrUpdateIndexOp extends AzureAISearchBaseOp<Sear
 	public Object applyOp(long value) {
 		SearchIndex createResponse = null;
 		try {
-			if (logger.isDebugEnabled()) {
-				request.getFields().forEach((field) -> {
-					logger.debug(
-							">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>SearchIndex: Name:{}-ProfileName:{}-Type:{}-Dimension:{}",
-							field.getName(), field.getVectorSearchProfileName(), field.getType().toString(),
-							field.getVectorSearchDimensions());
-				});
-			}
 			createResponse = searchIndexClient.createOrUpdateIndex(request);
-			logger.debug("Successfully created the collection with return code of {}", createResponse.toString());
+			logger.debug("Successfully created the collection with return response: {}", createResponse.toString());
 		} catch (RuntimeException rte) {
 			throw rte;
 		}

nb-adapters/adapter-azure-aisearch/src/main/java/io/nosqlbench/adapter/azureaisearch/ops/AzureAISearchListIndexesOp.java

@@ -30,12 +30,11 @@ public class AzureAISearchListIndexesOp extends AzureAISearchBaseOp<String> {
 		try {
 			PagedIterable<SearchIndex> response = searchIndexClient.listIndexes();
 			response.forEach((index) -> {
-				logger.info("Indexes available are: Name: {},  ETag: {}", index.getName(), index.getETag());
+				logger.info(() -> "Indexes available are: Name: " + index.getName() + ",  ETag: " + index.getETag());
 				index.getFields().forEach(field -> {
-					logger.info(
+					logger.info(() -> "Field Name: " + field.getName() + ", Field isKey?: " + field.isKey()
-							"Field Name: {}, Field isKey?: {}, Field Dimension: {}, Field Vector Search Profile: {}",
+							+ ", Field Dimension: " + field.getVectorSearchDimensions()
-							field.getName(), field.isKey(), field.getVectorSearchDimensions(),
+							+ ", Field Vector Search Profile: " + field.getVectorSearchProfileName());
-							field.getVectorSearchProfileName());
 				});
 			});
 		} catch (RuntimeException rte) {

nb-adapters/adapter-azure-aisearch/src/main/java/io/nosqlbench/adapter/azureaisearch/ops/AzureAISearchSearchDocumentsOp.java

@@ -17,7 +17,6 @@ package io.nosqlbench.adapter.azureaisearch.ops;
 
 import com.azure.core.util.Context;
 import com.azure.search.documents.SearchClient;
-import com.azure.search.documents.SearchDocument;
 import com.azure.search.documents.indexes.SearchIndexClient;
 import com.azure.search.documents.models.SearchOptions;
 import com.azure.search.documents.util.SearchPagedIterable;
@@ -36,14 +35,6 @@ public class AzureAISearchSearchDocumentsOp extends AzureAISearchClientBaseOp<Se
 			searchDocsResponse = searchClient.search(null, // we've not implemented other complex searches yet here.
 					request,
 					Context.NONE);
-			if (logger.isInfoEnabled()) {
-				searchDocsResponse.forEach((r) -> {
-					SearchDocument doc = r.getDocument(SearchDocument.class);
-					logger.debug(
-							"Successfully searched the index and returned id: {}, score: {}, vector embedding: {}",
-							doc.get("id"), r.getScore(), doc.get("value"));
-				});
-			}
 		} catch (RuntimeException rte) {
 			throw rte;
 		}

nb-adapters/adapter-azure-aisearch/src/main/java/io/nosqlbench/adapter/azureaisearch/ops/AzureAISearchUploadDocumentsOp.java

@@ -33,18 +33,12 @@ public class AzureAISearchUploadDocumentsOp extends AzureAISearchClientBaseOp<Se
 	public Object applyOp(long value) {
 		IndexDocumentsResult uploadDocsResponse = null;
 		try {
-//			request.getFields().forEach((field) -> {
-//				logger.info(
-//						">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>[AzureAISearchUploadDocumentsOp] SearchIndex: Name:{}-ProfileName:{}-Type:{}-Dimension:{}",
-//						field.getName(), field.getVectorSearchProfileName(), field.getType().toString(),
-//						field.getVectorSearchDimensions());
-//			});
 			uploadDocsResponse = searchClient.uploadDocuments(List.of(request));
 			if (logger.isDebugEnabled()) {
 				uploadDocsResponse.getResults().forEach((r) -> {
-					logger.debug(
+					logger.debug(() -> "Successfully created the collection with return status code: "
-							"Successfully created the collection with return status code: {}, key: {}, succeeded?: {}, error message: {}",
+							+ r.getStatusCode() + ", key: " + r.getKey() + ", succeeded?: " + r.isSucceeded()
-							r.getStatusCode(), r.getKey(), r.isSucceeded(), r.getErrorMessage());
+							+ ", error message: " + r.getErrorMessage());
 				});
 			}
 		} catch (RuntimeException rte) {

nb-adapters/adapter-azure-aisearch/src/main/java/io/nosqlbench/adapter/azureaisearch/opsdispenser/AzureAISearchCreateOrUpdateIndexOpDispenser.java

@@ -66,7 +66,6 @@ public class AzureAISearchCreateOrUpdateIndexOpDispenser extends AzureAISearchBa
 	@Override
 	public LongFunction<SearchIndex> getParamFunc(LongFunction<SearchIndexClient> clientF, ParsedOp op,
 			LongFunction<String> targetF) {
-		logger.debug(">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>getParamFunc");
 		LongFunction<SearchIndex> ebF = l -> new SearchIndex(targetF.apply(l));
 
 		Optional<LongFunction<Map>> fieldsMapF = op.getAsOptionalFunction("fields", Map.class);
@@ -94,28 +93,16 @@ public class AzureAISearchCreateOrUpdateIndexOpDispenser extends AzureAISearchBa
 			Map<String, Object> vsMap = mapLongFunc.apply(l);
 			VectorSearch vectorSearch = new VectorSearch();
 			vsMap.forEach((vsField, vsValue) -> {
-				logger.debug(
-						">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>vsField:{}  vsValue:{}",
-						vsField, vsValue);
 				if (vsValue instanceof Map) {
 					((Map<String, Object>) vsValue).forEach((innerKey, innerValue) -> {
-						logger.debug(
-								">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>innerKey:{}  innerValue:{}",
-								innerKey, innerValue);
 						if ("compressions".equals(vsField)) {
 							List<VectorSearchCompression> vsCompList = new ArrayList<>();
 							String kind;
 							if (((Map<String, Object>) innerValue).containsKey("kind")) {
 								kind = (String) ((Map<String, Object>) innerValue).get("kind");
-								logger.debug(
-										">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>compressions>>>>kind:{}",
-										kind);
 								if (kind.equals("scalarQuantization")) {
 									ScalarQuantizationCompression sqComp = new ScalarQuantizationCompression(innerKey);
 									((Map<String, Object>) innerValue).forEach((compressKey, compressValue) -> {
-										logger.debug(
-												">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>kind:{}  compressKey:{}  compressValue:{}",
-												kind, compressKey, compressValue);
 										if (compressKey.equals("kind")) {
 											sqComp.getKind().fromString((String) compressValue);
 										}
@@ -137,15 +124,11 @@ public class AzureAISearchCreateOrUpdateIndexOpDispenser extends AzureAISearchBa
 										}
 									});
 									vsCompList.add(sqComp);
-//									vsCompList.add(buildVectorSearchCompression(bqComp, compressKey, compressValue, true));
 								} else {
 									// BinaryQuantization is assumed here
 
 									BinaryQuantizationCompression bqComp = new BinaryQuantizationCompression(innerKey);
 									((Map<String, Object>) innerValue).forEach((compressKey, compressValue) -> {
-										logger.debug(
-												">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>kind:{}  compressKey:{}  compressValue:{}",
-												kind, compressKey, compressValue);
 										if (compressKey.equals("kind")) {
 											bqComp.getKind().fromString((String) compressValue);
 										}
@@ -157,15 +140,10 @@ public class AzureAISearchCreateOrUpdateIndexOpDispenser extends AzureAISearchBa
 										}
 									});
 									vsCompList.add(bqComp);
-//									vsCompList.add(
-//											buildVectorSearchCompression(bqComp, compressKey, compressValue, false));
 								}
 							} else {
 								VectorSearchCompression vsComp = new VectorSearchCompression(innerKey);
 								((Map<String, Object>) innerValue).forEach((compressKey, compressValue) -> {
-									logger.debug(
-											">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>kind:{}  compressKey:{}  compressValue:{}",
-											null, compressKey, compressValue);
 									if (compressKey.equals("kind")) {
 										vsComp.getKind().fromString((String) compressValue);
 									}
@@ -179,33 +157,18 @@ public class AzureAISearchCreateOrUpdateIndexOpDispenser extends AzureAISearchBa
 								vsCompList.add(vsComp);
 							}
 							vectorSearch.setCompressions(vsCompList);
-							vectorSearch.getCompressions().forEach((comp) -> {
-								logger.debug(
-										">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>compressions FINAL: Name:{}",
-										comp.getCompressionName());
-							});
 						}
 						if ("algorithms".equals(vsField)) {
 							List<VectorSearchAlgorithmConfiguration> vsAlgoList = new ArrayList<>();
 							String kind;
 							if (((Map<String, Object>) innerValue).containsKey("kind")) {
 								kind = (String) ((Map<String, Object>) innerValue).get("kind");
-								logger.debug(
-										">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>algorithms>>>>kind:{}",
-										kind);
 								if("hnsw".equals(kind)) {
 									HnswAlgorithmConfiguration hnswAlgoConf = new HnswAlgorithmConfiguration(innerKey);
 									((Map<String, Object>) innerValue).forEach((hnswKey, hnswValue) -> {
-										logger.debug(
-												">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>algorithms>>>>kind:{}  hnswKey:{}  hnswValue:{}",
-												kind, hnswKey, hnswValue);
 										if ("hnswParameters".equals(hnswKey)) {
 											((Map<String, Object>) innerValue)
 													.forEach((hnswParamsKey, hnswParamsValue) -> {
-														logger.debug(
-																">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>algorithms>>>>kind:{}  hnswKey:{}  hnswValue:{}  hnswParamsKey:{} hnswParamsValue:{}",
-																kind, hnswKey, hnswValue, hnswParamsKey,
-																hnswParamsValue);
 														HnswParameters hnswParams = new HnswParameters();
 														if ("m".equals(hnswParamsKey)) {
 															hnswParams.setM(((Number) hnswParamsValue).intValue());
@@ -232,15 +195,9 @@ public class AzureAISearchCreateOrUpdateIndexOpDispenser extends AzureAISearchBa
 									ExhaustiveKnnAlgorithmConfiguration exhausKnnAlgoConf = new ExhaustiveKnnAlgorithmConfiguration(
 											innerKey);
 									((Map<String, Object>) innerValue).forEach((algoKey, algoValue) -> {
-										logger.debug(
-												">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>algorithms>>>>kind:{}  algoKey:{}  algoValue:{}",
-												kind, algoKey, algoValue);
 										if (algoKey.equals("exhaustiveKnnParameters")) {
 											ExhaustiveKnnParameters eKnnParms = new ExhaustiveKnnParameters();
 											((Map<String, Object>) algoValue).forEach((ekpKey, ekpVal) -> {
-												logger.debug(
-														">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>algorithms>>>>kind:{}  algoKey:{}  algoValue:{}  ekpKey:{}  ekpVal:{}",
-														kind, algoKey, algoValue, ekpKey, ekpVal);
 												if (ekpKey.equals("quantizedDataType")) {
 													eKnnParms.setMetric(
 															VectorSearchAlgorithmMetric.fromString((String) ekpVal));
@@ -253,45 +210,21 @@ public class AzureAISearchCreateOrUpdateIndexOpDispenser extends AzureAISearchBa
 								}
 							}
 							vectorSearch.setAlgorithms(vsAlgoList);
-							vectorSearch.getAlgorithms().forEach((algo) -> {
-								logger.debug(
-										">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>algorithms FINAL: Name:{}",
-										algo.getName());
-							});
 						}
 						if ("profiles".equals(vsField)) {
-							logger.debug(
-									">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>profiles");
 							List<VectorSearchProfile> vsProfileList = new ArrayList<>();
-							// VectorSearchProfile vsProfile = new VectorSearchProfile(innerKey, null);
 							((Map<String, Object>) vsValue).forEach((profKey, profVal) -> {
-								logger.debug(
-										">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>profiles: profKey:{}  profVal:{}",
-										profKey, profVal);
 								((Map<String, Object>) profVal).forEach((pK, pV) -> {
-									logger.debug(
-											">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>profiles: profKey:{}  profVal:{}  pK:{}  pV:{}",
-											profKey, profVal, pK, pV);
 									if ("algorithm".equals(pK)) {
 										vsProfile = new VectorSearchProfile(profKey, (String) pV);
 									}
 									if ("compression".equals(pK)) {
 										vsProfile.setCompressionName((String) pV);
 									}
-
-									logger.debug(
-											">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>profiles: Name:{}>>>AlgoName:{}>>>CompressionName:{}",
-											vsProfile.getName(), vsProfile.getAlgorithmConfigurationName(),
-											vsProfile.getCompressionName());
 								});
 								vsProfileList.add(vsProfile);
 							});
 							vectorSearch.setProfiles(vsProfileList);
-							vectorSearch.getProfiles().forEach((profile) -> {
-								logger.debug(
-										">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>VectorSearch>>>>buildVectorSearchStruct>>>>profiles FINAL: Name:{}  AlgorithmConfName:{}",
-										profile.getName(), profile.getAlgorithmConfigurationName());
-							});
 						}
 					});
 				} else {
@@ -304,48 +237,15 @@ public class AzureAISearchCreateOrUpdateIndexOpDispenser extends AzureAISearchBa
 		}).orElse(null);
 	}
 
-	@SuppressWarnings({ "unchecked", "static-access" })
-	private VectorSearchCompression buildVectorSearchCompression(VectorSearchCompression vsComp, String key, Object val,
-			boolean isSQ) {
-		if (key.equals("kind")) {
-			vsComp.getKind().fromString((String) val);
-		}
-		if (key.equals("rerankWithOriginalVectors")) {
-			vsComp.setRerankWithOriginalVectors((Boolean) val);
-		}
-		if (key.equals("defaultOversampling")) {
-			vsComp.setDefaultOversampling(((Number) val).doubleValue());
-		}
-		if (isSQ) {
-			if (key.equals("scalarQuantizationParameters")) {
-				ScalarQuantizationParameters sqParams = new ScalarQuantizationParameters();
-				((Map<String, Object>) val).forEach((sqKey, sqVal) -> {
-					if (sqKey.equals("quantizedDataType")) {
-						sqParams.setQuantizedDataType(VectorSearchCompressionTarget.fromString((String) sqVal));
-					}
-				});
-				((ScalarQuantizationCompression) vsComp).setParameters(sqParams);
-			}
-		}
-		return vsComp;
-	}
-
 	@SuppressWarnings({ "unchecked", "rawtypes" })
 	private LongFunction<List<SearchField>> buildFieldsStruct(ParsedOp op) {
-		logger.debug(">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>buildFieldsStruct");
 		Optional<LongFunction<Map>> baseFunc = op.getAsOptionalFunction("fields", Map.class);
 		return baseFunc.<LongFunction<List<SearchField>>>map(mapLongFunc -> l -> {
 			Map<String, Object> fMap = mapLongFunc.apply(l);
 			List<SearchField> fieldsList = new ArrayList<>();
 			fMap.forEach((fName, fValue) -> {
 				if (fValue instanceof Map) {
-					logger.debug(
-							">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>buildFieldsStruct>>>>fName:{}  fValue:{}",
-							fName, fValue);
 					((Map<String, Object>) fValue).forEach((innerKey, innerValue) -> {
-						logger.debug(
-								">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>buildFieldsStruct>>>>fName:{}  fValue:{}  fName:{}  fValue:{}",
-								fName, fValue, innerKey, innerValue);
 						if (innerKey.equals("type")) {
 							searchField = new SearchField(fName, SearchFieldDataType.fromString((String) innerValue));
 						}
@@ -357,8 +257,6 @@ public class AzureAISearchCreateOrUpdateIndexOpDispenser extends AzureAISearchBa
 						}
 						if (innerKey.equals("vectorSearchProfile")) {
 							searchField.setVectorSearchProfileName((String) innerValue);
-							logger.debug("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% {} %n",
-									searchField.getVectorSearchProfileName());
 						}
 						if (innerKey.equals("filterable")) {
 							searchField.setFilterable((Boolean) innerValue);
@@ -386,13 +284,6 @@ public class AzureAISearchCreateOrUpdateIndexOpDispenser extends AzureAISearchBa
 									+ fValue.getClass().getSimpleName() + " instead for the inner value");
 				}
 				fieldsList.add(searchField);
-				if (logger.isDebugEnabled()) {
-					fieldsList.forEach((field) -> {
-						logger.debug(
-								">>>>>>>>>>>>AzureAISearchCreateOrUpdateIndexOpDispenser>>>>>>>>>>>>buildFieldsStruct>>>> fields FINAL: Name:{}  VSProfileName:{}",
-								field.getName(), field.getVectorSearchProfileName());
-					});
-				}
 			});
 			return fieldsList;
 		}).orElse(null);

nb-adapters/adapter-azure-aisearch/src/main/java/io/nosqlbench/adapter/azureaisearch/opsdispenser/AzureAISearchListIndexesOpDispenser.java

@@ -24,6 +24,9 @@ import io.nosqlbench.adapter.azureaisearch.ops.AzureAISearchBaseOp;
 import io.nosqlbench.adapter.azureaisearch.ops.AzureAISearchListIndexesOp;
 import io.nosqlbench.adapters.api.templating.ParsedOp;
 
+/**
+ * Lists the indexes available.
+ */
 public class AzureAISearchListIndexesOpDispenser extends AzureAISearchBaseOpDispenser<String> {
 	public AzureAISearchListIndexesOpDispenser(AzureAISearchDriverAdapter adapter, ParsedOp op,
 			LongFunction<String> targetF) {

nb-adapters/adapter-azure-aisearch/src/main/java/io/nosqlbench/adapter/azureaisearch/opsdispenser/AzureAISearchSearchDocumentsOpDispenser.java

@@ -33,8 +33,12 @@ import io.nosqlbench.adapters.api.templating.ParsedOp;
 import io.nosqlbench.nb.api.errors.OpConfigError;
 
 /**
- * @see https://learn.microsoft.com/en-us/rest/api/searchservice/documents/search-get?view=rest-searchservice-2024-07-01&tabs=HTTP#rawvectorquery
+ * @see <a href=
- * @see https://learn.microsoft.com/en-us/azure/search/vector-search-how-to-query?tabs=query-2024-07-01%2Cfilter-2024-07-01%2Cbuiltin-portal#vector-query-request
+ *      "https://learn.microsoft.com/en-us/rest/api/searchservice/documents/search-get?view=rest-searchservice-2024-07-01&tabs=HTTP#rawvectorquery">
+ *      Search GET API<a/>
+ * @see <a href=
+ *      "https://learn.microsoft.com/en-us/azure/search/vector-search-how-to-query?tabs=query-2024-07-01%2Cfilter-2024-07-01%2Cbuiltin-portal#vector-query-request">How
+ *      to query/vector search</a>
  */
 public class AzureAISearchSearchDocumentsOpDispenser extends AzureAISearchBaseOpDispenser<SearchOptions> {
 	public AzureAISearchSearchDocumentsOpDispenser(AzureAISearchDriverAdapter adapter, ParsedOp op,

nb-adapters/adapter-azure-aisearch/src/main/resources/activities/azure_aisearch_vectors_live.yaml

@@ -37,7 +37,8 @@ scenarios:
       run tags==block:upload_documents
       errors===warn,counter
       cycles===TEMPLATE(train_cycles,TEMPLATE(trainsize,1000)) threads===TEMPLATE(train_threads,AUTO)
-      token_file===TEMPLATE(token_file) endpoint===TEMPLATE(azureaisearchhost)
+      endpoint=TEMPLATE(azureaisearchhost) token_file=TEMPLATE(token_file)
+    #token_file===TEMPLATE(token_file) endpoint===TEMPLATE(azureaisearchhost)
        
     search_documents: >-
       run tags==block:search_documents
@@ -94,6 +95,18 @@ blocks:
             facetable: false
             retrievable: true
             hidden: false
+# Caused by: com.azure.core.exception.HttpResponseException: Status code 400, "{"error":{"code":"OperationNotAllowed","message":"The request is invalid. Details: definition : The searchable field 'id' must be of type Edm.String or Collection(Edm.String) or Collection(Edm.Single).","details":[{"code":"CannotEnableFieldForSearching","message":"The searchable field 'id' must be of type Edm.String or Collection(Edm.String) or Collection(Edm.Single). Parameters: definition"}]}}"
+# Caused by: com.azure.core.exception.HttpResponseException: Status code 400, "{"error":{"code":"InvalidRequestParameter","message":"The request is invalid. Details: definition : The key field 'id' must be of type Edm.String.","details":[{"code":"InvalidKeyField","message":"The key field 'id' must be of type Edm.String. Parameters: definition"}]}}"
+# Caused by: com.azure.core.exception.HttpResponseException: Status code 400, "{"error":{"code":"InvalidRequestParameter","message":"The request is invalid. Details: definition : The key field 'id' is marked as non-retrievable. Please set the 'retrievable' property for this field to 'true' or leave it unset.","details":[{"code":"InvalidKeyField","message":"The key field 'id' is marked as non-retrievable. Please set the 'retrievable' property for this field to 'true' or leave it unset. Parameters: definition"}]}}"
+#          dummy_key:
+#            type: "Edm.Int32" # Data types - https://learn.microsoft.com/en-us/rest/api/searchservice/supported-data-types#edm-data-types-for-vector-fields
+#            key: false
+#            filterable: true
+#            sortable: true
+#            searchable: true
+#            facetable: false
+#            retrievable: true
+#            hidden: false
           value:
             type: "Collection(Edm.Single)"
             dimensions: TEMPLATE(dimensions)
@@ -139,6 +152,7 @@ blocks:
         upload_documents: "TEMPLATE(collection)"
         fields:
           id: "{row_key}"
+#          dummy_key: "{id_val}"
           value: "{train_floatlist_TEMPLATE(filetype)}"
                 
   search_documents:
@@ -165,8 +179,8 @@ blocks:
           }
         verifier: |
           // driver-specific function
-            actual_indices=io.nosqlbench.adapter.azureaisearch.AzureAISearchAdapterUtils.responseFieldToStringArray("id",result)
+          actual_indices=io.nosqlbench.adapter.azureaisearch.AzureAISearchAdapterUtils.searchDocumentsResponseIdToIntArray("id",result)
-            System.out.println("actual_indices ------>>>>: " + actual_indices);
+          // System.out.println("actual_indices ------>>>>: " + actual_indices);
           // driver-agnostic function
           relevancy.accept({relevant_indices_TEMPLATE(filetype)},actual_indices);
           // because we are "verifying" although this needs to be reorganized