chore: bump version

README.md

@@ -30,7 +30,7 @@ For use with Java and Scala projects, the package can be found [here](https://ce
 <dependency>
     <groupId>io.qdrant</groupId>
     <artifactId>spark</artifactId>
-    <version>1.12</version>
+    <version>1.13</version>
 </dependency>
 ```
 
@@ -43,7 +43,7 @@ from pyspark.sql import SparkSession
 
 spark = SparkSession.builder.config(
         "spark.jars",
-        "spark-1.12-jar-with-dependencies.jar",  # specify the downloaded JAR file
+        "spark-1.13-jar-with-dependencies.jar",  # specify the downloaded JAR file
     )
     .master("local[*]")
     .appName("qdrant")
@@ -73,7 +73,7 @@ To load data into Qdrant, a collection has to be created beforehand with the app
 You can use the `qdrant-spark` connector as a library in Databricks to ingest data into Qdrant.
 - Go to the `Libraries` section in your cluster dashboard.
 - Select `Install New` to open the library installation modal.
-- Search for `io.qdrant:spark:1.12` in the Maven packages and click `Install`.
+- Search for `io.qdrant:spark:1.13` in the Maven packages and click `Install`.
 
 <img width="1064" alt="Screenshot 2024-01-05 at 17 20 01 (1)" src="https://github.com/qdrant/qdrant-spark/assets/46051506/d95773e0-c5c6-4ff2-bf50-8055bb08fd1b">
 

pom.xml

@@ -6,7 +6,7 @@
 	<modelVersion>4.0.0</modelVersion>
 	<groupId>io.qdrant</groupId>
 	<artifactId>spark</artifactId>
-	<version>1.12</version>
+	<version>1.13</version>
 	<name>qdrant-spark</name>
 	<url>https://github.com/qdrant/qdrant-spark</url>
 	<description>An Apache Spark connector for the Qdrant vector database</description>