refactor: QdrantVectorHandler

.github/workflows/release.yml

@@ -49,7 +49,7 @@ jobs:
     - name: Semantic Release
       run: |
         bun install @conveyal/maven-semantic-release semantic-release @semantic-release/git conventional-changelog-conventionalcommits
-        bun x semantic-release --prepare @conveyal/maven-semantic-release,@semantic-release/git --publish @semantic-release/github,@conveyal/maven-semantic-release --verify-conditions @semantic-release/github,@conveyal/maven-semantic-release,@semantic-release/git --verify-release @conveyal/maven-semantic-release
+        bun x semantic-release --prepare @conveyal/maven-semantic-release --publish @semantic-release/github,@conveyal/maven-semantic-release --verify-conditions @semantic-release/github,@conveyal/maven-semantic-release --verify-release @conveyal/maven-semantic-release
       env:
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         GPG_KEY_NAME: ${{ secrets.GPG_KEY_NAME }}

.releaserc

@@ -59,15 +59,6 @@
                 ]
             }
         ],
-        "@semantic-release/release-notes-generator",
-        [
-            "@semantic-release/git",
-            {
-                "assets": [
-                    "pom.xml"
-                ],
-                "message": "chore(release): ${nextRelease.version} [skip ci]\n\n${nextRelease.notes}"
-            }
-        ]
+        "@semantic-release/release-notes-generator"
     ]
 }

README.md

@@ -49,6 +49,8 @@ spark = SparkSession.builder.config(
 
 The connector supports ingesting multiple named/unnamed, dense/sparse vectors.
 
+_Click each to expand._
+
 <details>
   <summary><b>Unnamed/Default vector</b></summary>
 
@@ -194,7 +196,6 @@ You can use the connector as a library in Databricks to ingest data into Qdrant.
 
 <img width="704" alt="Screenshot 2024-04-28 at 11 34 17 AM" src="https://github.com/qdrant/qdrant-spark/assets/46051506/0c1bd356-3fba-436a-90ce-d8ff39b02d1f">
 
-
 ## Datatype support
 
 The appropriate Spark data types are mapped to the Qdrant payload based on the provided `schema`.

pom.xml

@@ -6,7 +6,7 @@
 	<modelVersion>4.0.0</modelVersion>
 	<groupId>io.qdrant</groupId>
 	<artifactId>spark</artifactId>
-	<version>2.2.0</version>
+	<version>2.2.1</version>
 	<name>qdrant-spark</name>
 	<url>https://github.com/qdrant/qdrant-spark</url>
 	<description>An Apache Spark connector for the Qdrant vector database</description>

src/main/java/io/qdrant/spark/QdrantVectorHandler.java

@@ -9,7 +9,6 @@
 import io.qdrant.client.grpc.Points.Vectors;
 import java.util.Collections;
 import java.util.HashMap;
-import java.util.List;
 import java.util.Map;
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.types.StructType;
@@ -26,8 +25,7 @@ public static Vectors prepareVectors(
 
     // Maitaining support for the "embedding_field" and "vector_name" options
     if (!options.embeddingField.isEmpty()) {
-      int embeddingFieldIndex = schema.fieldIndex(options.embeddingField);
-      float[] embeddings = record.getArray(embeddingFieldIndex).toFloatArray();
+      float[] embeddings = extractFloatArray(record, schema, options.embeddingField);
       // 'options.vectorName' defaults to ""
       vectorsBuilder.mergeFrom(
           namedVectors(Collections.singletonMap(options.vectorName, vector(embeddings))));
@@ -42,9 +40,10 @@ private static Vectors prepareSparseVectors(
 
     for (int i = 0; i < options.sparseVectorNames.length; i++) {
       String name = options.sparseVectorNames[i];
-      List<Float> values = extractFloatArray(record, schema, options.sparseVectorValueFields[i]);
-      List<Integer> indices = extractIntArray(record, schema, options.sparseVectorIndexFields[i]);
-      sparseVectors.put(name, vector(values, indices));
+      float[] values = extractFloatArray(record, schema, options.sparseVectorValueFields[i]);
+      int[] indices = extractIntArray(record, schema, options.sparseVectorIndexFields[i]);
+
+      sparseVectors.put(name, vector(Floats.asList(values), Ints.asList(indices)));
     }
 
     return namedVectors(sparseVectors);
@@ -56,22 +55,21 @@ private static Vectors prepareDenseVectors(
 
     for (int i = 0; i < options.vectorNames.length; i++) {
       String name = options.vectorNames[i];
-      List<Float> values = extractFloatArray(record, schema, options.vectorFields[i]);
+      float[] values = extractFloatArray(record, schema, options.vectorFields[i]);
       denseVectors.put(name, vector(values));
     }
 
     return namedVectors(denseVectors);
   }
 
-  private static List<Float> extractFloatArray(
+  private static float[] extractFloatArray(
       InternalRow record, StructType schema, String fieldName) {
     int fieldIndex = schema.fieldIndex(fieldName);
-    return Floats.asList(record.getArray(fieldIndex).toFloatArray());
+    return record.getArray(fieldIndex).toFloatArray();
   }
 
-  private static List<Integer> extractIntArray(
-      InternalRow record, StructType schema, String fieldName) {
+  private static int[] extractIntArray(InternalRow record, StructType schema, String fieldName) {
     int fieldIndex = schema.fieldIndex(fieldName);
-    return Ints.asList(record.getArray(fieldIndex).toIntArray());
+    return record.getArray(fieldIndex).toIntArray();
   }
 }