Expose source_model parameter for vector-enabled collections

src/main/java/io/stargate/sgv2/jsonapi/api/configuration/CommandObjectMapperHandler.java

@@ -38,7 +38,7 @@ public boolean handleUnknownProperty(
     }
     if (typeStr.endsWith("CreateCollectionCommand$Options$VectorSearchConfig")) {
       throw ErrorCodeV1.INVALID_CREATE_COLLECTION_OPTIONS.toApiException(
-          "Unrecognized field \"%s\" for `createCollection.options.vector` (known fields: \"dimension\", \"metric\", \"service\")",
+          "Unrecognized field \"%s\" for `createCollection.options.vector` (known fields: \"dimension\", \"metric\", \"service\", \"sourceModel\",)",
           propertyName);
     }
 

src/main/java/io/stargate/sgv2/jsonapi/api/model/command/impl/CreateCollectionCommand.java

@@ -95,6 +95,20 @@ public record VectorSearchConfig(
             @JsonProperty("metric")
             @JsonAlias("function") // old name
             String metric,
+        @Nullable
+            @Pattern(
+                regexp =
+                    "(ada002|bert|cohere-v3|gecko|nv-qa-4|openai-v3-large|openai-v3-small|other)",
+                message =
+                    "sourceModel options are 'ada002', 'bert', 'cohere-v3', 'gecko', 'nv-qa-4', 'openai-v3-large', 'openai-v3-small', and 'other'")
+            @Schema(
+                description =
+                    "The 'sourceModel' option configures the index with the fastest settings for a given source of embeddings vectors",
+                defaultValue = "other",
+                type = SchemaType.STRING,
+                implementation = String.class)
+            @JsonProperty("sourceModel")
+            String sourceModel,
         @Valid
             @Nullable
             @JsonInclude(JsonInclude.Include.NON_NULL)
@@ -105,9 +119,11 @@ public record VectorSearchConfig(
             @JsonProperty("service")
             VectorizeConfig vectorizeConfig) {
 
-      public VectorSearchConfig(Integer dimension, String metric, VectorizeConfig vectorizeConfig) {
+      public VectorSearchConfig(
+          Integer dimension, String metric, String sourceModel, VectorizeConfig vectorizeConfig) {
         this.dimension = dimension;
-        this.metric = metric == null ? "cosine" : metric;
+        this.metric = metric;
+        this.sourceModel = sourceModel;
         this.vectorizeConfig = vectorizeConfig;
       }
     }

src/main/java/io/stargate/sgv2/jsonapi/config/constants/DocumentConstants.java

@@ -27,9 +27,6 @@ interface Fields {
     /** Document field name that will have text value for which vectorize method in called */
     String VECTOR_EMBEDDING_TEXT_FIELD = "$vectorize";
 
-    /** Key for vector function name definition in cql index. */
-    String VECTOR_INDEX_FUNCTION_NAME = "similarity_function";
-
     /** Field name used in projection clause to get similarity score in response. */
     String VECTOR_FUNCTION_SIMILARITY_FIELD = "$similarity";
 

src/main/java/io/stargate/sgv2/jsonapi/config/constants/VectorConstants.java

@@ -1,36 +1,13 @@
 package io.stargate.sgv2.jsonapi.config.constants;
 
 import io.smallrye.config.ConfigMapping;
-import io.stargate.sgv2.jsonapi.service.schema.SimilarityFunction;
-import java.util.Map;
 
 @ConfigMapping(prefix = "stargate.jsonapi.vector")
 public interface VectorConstants {
-  /*
-  Supported Source Models and suggested function for Vector Index in Cassandra
-   */
-  Map<String, SimilarityFunction> SUPPORTED_SOURCES =
-      Map.of(
-          "ada002",
-          SimilarityFunction.DOT_PRODUCT,
-          "openai_v3_small",
-          SimilarityFunction.DOT_PRODUCT,
-          "openai_v3_large",
-          SimilarityFunction.DOT_PRODUCT,
-          "bert",
-          SimilarityFunction.DOT_PRODUCT,
-          "gecko",
-          SimilarityFunction.DOT_PRODUCT,
-          "nv_qa_4",
-          SimilarityFunction.DOT_PRODUCT,
-          "cohere_v3",
-          SimilarityFunction.DOT_PRODUCT,
-          "other",
-          SimilarityFunction.COSINE);
-
   interface VectorColumn {
     String DIMENSION = "dimension";
     String METRIC = "metric";
+    String SOURCE_MODEL = "sourceModel";
     String SERVICE = "service";
   }
 

src/main/java/io/stargate/sgv2/jsonapi/exception/ErrorCodeV1.java

@@ -141,6 +141,8 @@ public enum ErrorCodeV1 {
 
   VECTOR_SEARCH_INVALID_FUNCTION_NAME("Invalid vector search function name"),
 
+  VECTOR_SEARCH_UNRECOGNIZED_SOURCE_MODEL_NAME("Unrecognized vector search source model name"),
+
   VECTOR_SEARCH_TOO_BIG_VALUE("Vector embedding property '$vector' length too big"),
   VECTOR_SIZE_MISMATCH("Length of vector parameter different from declared '$vector' dimension"),
 

src/main/java/io/stargate/sgv2/jsonapi/service/cqldriver/executor/VectorColumnDefinition.java

@@ -5,19 +5,22 @@
 import io.stargate.sgv2.jsonapi.config.constants.DocumentConstants;
 import io.stargate.sgv2.jsonapi.config.constants.VectorConstants;
 import io.stargate.sgv2.jsonapi.service.schema.SimilarityFunction;
+import io.stargate.sgv2.jsonapi.service.schema.SourceModel;
 
 /**
  * Configuration vector column with the extra info we need for vectors
  *
  * @param fieldName Is still a string because this is also used by collections
  * @param vectorSize
  * @param similarityFunction
+ * @param sourceModel
  * @param vectorizeDefinition
  */
 public record VectorColumnDefinition(
     String fieldName,
     int vectorSize,
     SimilarityFunction similarityFunction,
+    SourceModel sourceModel,
     VectorizeDefinition vectorizeDefinition) {
 
   /**
@@ -34,11 +37,16 @@ public static VectorColumnDefinition fromJson(JsonNode jsonNode, ObjectMapper ob
     int dimension = jsonNode.get(VectorConstants.VectorColumn.DIMENSION).asInt();
     var similarityFunction =
         SimilarityFunction.fromString(jsonNode.get(VectorConstants.VectorColumn.METRIC).asText());
+    // sourceModel doesn't exist if the collection was created before supporting sourceModel; if
+    // missing, it will be an empty string and sourceModel becomes OTHER.
+    var sourceModel =
+        SourceModel.fromString(jsonNode.path(VectorConstants.VectorColumn.SOURCE_MODEL).asText());
 
     return fromJson(
         DocumentConstants.Fields.VECTOR_EMBEDDING_TEXT_FIELD,
         dimension,
         similarityFunction,
+        sourceModel,
         jsonNode,
         objectMapper);
   }
@@ -48,6 +56,7 @@ public static VectorColumnDefinition fromJson(
       String fieldName,
       int dimension,
       SimilarityFunction similarityFunction,
+      SourceModel sourceModel,
       JsonNode jsonNode,
       ObjectMapper objectMapper) {
 
@@ -58,6 +67,6 @@ public static VectorColumnDefinition fromJson(
             : VectorizeDefinition.fromJson(vectorizeServiceNode, objectMapper);
 
     return new VectorColumnDefinition(
-        fieldName, dimension, similarityFunction, vectorizeDefinition);
+        fieldName, dimension, similarityFunction, sourceModel, vectorizeDefinition);
   }
 }

src/main/java/io/stargate/sgv2/jsonapi/service/cqldriver/executor/VectorConfig.java

@@ -10,6 +10,7 @@
 import com.fasterxml.jackson.databind.ObjectMapper;
 import io.stargate.sgv2.jsonapi.config.constants.VectorConstants;
 import io.stargate.sgv2.jsonapi.service.schema.SimilarityFunction;
+import io.stargate.sgv2.jsonapi.service.schema.SourceModel;
 import java.util.*;
 import java.util.function.Function;
 import java.util.stream.Collectors;
@@ -43,6 +44,7 @@ public static VectorConfig fromColumnDefinitions(
     return new VectorConfig(vectorColumnDefinitions);
   }
 
+  /** Get table schema object from table metadata */
   public static VectorConfig from(TableMetadata tableMetadata, ObjectMapper objectMapper) {
 
     Map<String, VectorizeDefinition> vectorizeDefs =
@@ -67,22 +69,32 @@ public static VectorConfig from(TableMetadata tableMetadata, ObjectMapper object
       var indexFunction =
           columnIndex.map(
               index -> {
-                String similarityFunction =
+                String similarityFunctionStr =
                     index.getOptions().get(VectorConstants.CQLAnnIndex.SIMILARITY_FUNCTION);
-                if (similarityFunction != null) {
-                  return SimilarityFunction.fromString(similarityFunction);
-                }
+                // if similarity function is set, use it
+                SimilarityFunction similarityFunction =
+                    similarityFunctionStr != null
+                        ? SimilarityFunction.fromString(similarityFunctionStr)
+                        : null;
 
-                String sourceModel =
+                String sourceModelStr =
                     index.getOptions().get(VectorConstants.CQLAnnIndex.SOURCE_MODEL);
-                if (sourceModel != null) {
-                  return VectorConstants.SUPPORTED_SOURCES.get(sourceModel);
+                SourceModel sourceModel = null;
+                if (sourceModelStr != null) {
+                  // if similarity function is not set, use the source model to determine it
+                  similarityFunction =
+                      similarityFunction == null
+                          ? SourceModel.getSimilarityFunction(sourceModelStr)
+                          : similarityFunction;
+                  sourceModel = SourceModel.fromString(sourceModelStr);
                 }
-                return null;
+                return new AbstractMap.SimpleEntry<>(similarityFunction, sourceModel);
               });
 
       // if now index, or we could not work out the function, default
-      var similarityFunction = indexFunction.orElse(SimilarityFunction.COSINE);
+      var similarityFunction =
+          indexFunction.map(Map.Entry::getKey).orElse(SimilarityFunction.COSINE);
+      var sourModel = indexFunction.map(Map.Entry::getValue).orElse(SourceModel.OTHER);
       int dimensions = ((VectorType) column.getType()).getDimensions();
 
       // NOTE: need to keep the column name as a string in the VectorColumnDefinition
@@ -93,6 +105,7 @@ public static VectorConfig from(TableMetadata tableMetadata, ObjectMapper object
               cqlIdentifierToJsonKey(column.getName()),
               dimensions,
               similarityFunction,
+              sourModel,
               vectorizeDefs.get(column.getName().asInternal())));
     }
     return VectorConfig.fromColumnDefinitions(columnDefs);

src/main/java/io/stargate/sgv2/jsonapi/service/operation/collections/CreateCollectionOperation.java

@@ -20,6 +20,7 @@
 import io.stargate.sgv2.jsonapi.service.cqldriver.executor.QueryExecutor;
 import io.stargate.sgv2.jsonapi.service.operation.Operation;
 import io.stargate.sgv2.jsonapi.service.schema.SimilarityFunction;
+import io.stargate.sgv2.jsonapi.service.schema.SourceModel;
 import io.stargate.sgv2.jsonapi.service.schema.collections.CollectionSchemaObject;
 import io.stargate.sgv2.jsonapi.service.schema.collections.CollectionTableMatcher;
 import java.time.Duration;
@@ -41,6 +42,7 @@ public record CreateCollectionOperation(
     boolean vectorSearch,
     int vectorSize,
     String vectorFunction,
+    String sourceModel,
     String comment,
     int ddlDelayMillis,
     boolean tooManyIndexesRollbackEnabled,
@@ -60,6 +62,7 @@ public static CreateCollectionOperation withVectorSearch(
       String name,
       int vectorSize,
       String vectorFunction,
+      String sourceModel,
       String comment,
       int ddlDelayMillis,
       boolean tooManyIndexesRollbackEnabled,
@@ -73,6 +76,7 @@ public static CreateCollectionOperation withVectorSearch(
         true,
         vectorSize,
         vectorFunction,
+        sourceModel,
         comment,
         ddlDelayMillis,
         tooManyIndexesRollbackEnabled,
@@ -98,6 +102,7 @@ public static CreateCollectionOperation withoutVectorSearch(
         false,
         0,
         null,
+        null,
         comment,
         ddlDelayMillis,
         tooManyIndexesRollbackEnabled,
@@ -142,6 +147,7 @@ public Uni<Supplier<CommandResult>> execute(
             vectorSearch,
             vectorSize,
             SimilarityFunction.fromString(vectorFunction),
+            SourceModel.fromString(sourceModel),
             comment,
             objectMapper);
     // if table exists we have to choices:
@@ -492,6 +498,8 @@ public List<SimpleStatement> getIndexStatements(
           appender
               + " \"%s_query_vector_value\" ON \"%s\".\"%s\" (query_vector_value) USING 'StorageAttachedIndex' WITH OPTIONS = { 'similarity_function': '"
               + vectorFunction()
+              + "', 'source_model': '"
+              + sourceModel()
               + "'}";
       statements.add(
           SimpleStatement.newInstance(String.format(vectorSearch, table, keyspace, table)));

src/main/java/io/stargate/sgv2/jsonapi/service/operation/tables/CreateIndexAttempt.java

@@ -13,6 +13,7 @@
 import com.datastax.oss.driver.api.querybuilder.schema.CreateIndexOnTable;
 import com.datastax.oss.driver.api.querybuilder.schema.CreateIndexStart;
 import com.datastax.oss.driver.internal.querybuilder.schema.DefaultCreateIndex;
+import io.stargate.sgv2.jsonapi.config.constants.VectorConstants;
 import io.stargate.sgv2.jsonapi.service.cqldriver.executor.TableSchemaObject;
 import io.stargate.sgv2.jsonapi.service.cqldriver.override.ExtendedCreateIndex;
 import io.stargate.sgv2.jsonapi.service.operation.SchemaAttempt;
@@ -90,10 +91,11 @@ public record VectorIndexOptions(SimilarityFunction similarityFunction, String s
     public Map<String, Object> getOptions() {
       Map<String, Object> options = new HashMap<>();
       if (similarityFunction != null) {
-        options.put("similarity_function", similarityFunction.getMetric());
+        options.put(
+            VectorConstants.CQLAnnIndex.SIMILARITY_FUNCTION, similarityFunction.getMetric());
       }
       if (sourceModel != null) {
-        options.put("source_model", sourceModel);
+        options.put(VectorConstants.CQLAnnIndex.SOURCE_MODEL, sourceModel);
       }
       return options;
     }

src/main/java/io/stargate/sgv2/jsonapi/service/resolver/CreateCollectionCommandResolver.java

@@ -15,6 +15,8 @@
 import io.stargate.sgv2.jsonapi.service.cqldriver.executor.KeyspaceSchemaObject;
 import io.stargate.sgv2.jsonapi.service.operation.Operation;
 import io.stargate.sgv2.jsonapi.service.operation.collections.CreateCollectionOperation;
+import io.stargate.sgv2.jsonapi.service.schema.SimilarityFunction;
+import io.stargate.sgv2.jsonapi.service.schema.SourceModel;
 import jakarta.enterprise.context.ApplicationScoped;
 import jakarta.inject.Inject;
 
@@ -106,6 +108,7 @@ public Operation resolveKeyspaceCommand(
           command.name(),
           vector.dimension(),
           vector.metric(),
+          vector.sourceModel(),
           comment,
           operationsConfig.databaseConfig().ddlDelayMillis(),
           operationsConfig.tooManyIndexesRollbackEnabled(),
@@ -192,13 +195,35 @@ private CreateCollectionCommand.Options.VectorSearchConfig validateVectorOptions
 
     Integer vectorDimension = vector.dimension();
     VectorizeConfig service = vector.vectorizeConfig();
+    String sourceModel = vector.sourceModel();
+    String metric = vector.metric();
+
+    // decide sourceModel and metric value
+    if (sourceModel != null) {
+      if (metric == null) {
+        // (1) sourceModel is provided but metric is not - set metric to cosine or dot_product based
+        // on the map
+        metric = SourceModel.getSimilarityFunction(sourceModel).getMetric();
+      }
+      // (2) both sourceModel and metric are provided - do nothing
+    } else {
+      if (metric != null) {
+        // (3) sourceModel is not provided but metric is - set sourceModel to 'other'
+        sourceModel = SourceModel.OTHER.getName();
+      } else {
+        // (4) both sourceModel and metric are not provided - set sourceModel to 'other' and metric
+        // to 'cosine'
+        sourceModel = SourceModel.DEFAULT_SOURCE_MODEL.getName();
+        metric = SimilarityFunction.DEFAULT_SIMILARITY_FUNCTION.getMetric();
+      }
+    }
 
     if (service != null) {
       // Validate service configuration and auto populate vector dimension.
       vectorDimension = validateVectorize.validateService(service, vectorDimension);
       vector =
           new CreateCollectionCommand.Options.VectorSearchConfig(
-              vectorDimension, vector.metric(), vector.vectorizeConfig());
+              vectorDimension, metric, sourceModel, vector.vectorizeConfig());
     } else {
       // Ensure vector dimension is provided when service configuration is absent.
       if (vectorDimension == null) {
@@ -209,6 +234,9 @@ private CreateCollectionCommand.Options.VectorSearchConfig validateVectorOptions
         throw ErrorCodeV1.VECTOR_SEARCH_TOO_BIG_VALUE.toApiException(
             "%d (max %d)", vectorDimension, documentLimitsConfig.maxVectorEmbeddingLength());
       }
+      vector =
+          new CreateCollectionCommand.Options.VectorSearchConfig(
+              vectorDimension, metric, sourceModel, null);
     }
     return vector;
   }