Qdrant improvements

 - Update docs
 - Allow colleciton auto-creation if missing
 - Add cloud IT : QdrantVectorStoreCloudAutoConfigurationIT
 - Add auto-configuration properties tests: PgVectorStorePropertiesTests

Author: tzolov

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/qdrant.adoc

@@ -9,32 +9,20 @@ link:https://www.qdrant.tech/[Qdrant] is an open-source, high-performance vector
 * Qdrant Instance: Set up a Qdrant instance by following the link:https://qdrant.tech/documentation/guides/installation/[installation instructions] in the Qdrant documentation.
 * If required, an API key for the xref:api/embeddings.adoc#available-implementations[EmbeddingClient] to generate the embeddings stored by the `QdrantVectorStore`.
 
-== Configuration
+To set up `QdrantVectorStore`, you'll need the following information from your Qdrant instance: `Host`, `GRPC Port`, `Collection Name`, and `API Key` (if required).
 
-To set up `QdrantVectorStore`, you'll need the following information from your Qdrant instance:
-
-* Qdrant Host
-* Qdrant GRPC Port
-* Qdrant Collection Name
-* Optional Qdrant API Key (not required for local development)
-
-[NOTE]
-====
-A Qdrant collection has to be link:https://qdrant.tech/documentation/concepts/collections/#create-a-collection[created] in advance with the appropriate dimensions and configurations.
-
-For example if using the OpenAI `text-embedding-ada-002` embedding model, create a collection with a vector size of `1536`.
-====
+NOTE: It is recommended that the Qdrant collection is link:https://qdrant.tech/documentation/concepts/collections/#create-a-collection[created] in advance with the appropriate dimensions and configurations.
+If the collection is not created, the `QdrantVectorStore` will attempt to create one using the `Cosine` similarity and the dimension of the configured `EmbeddingClient`.
 
 == Dependencies
 
-* The Vector Store requires an `EmbeddingClient` instance to calculate embeddings for the documents.
-You can pick one of the available xref:api/embeddings.adoc#available-implementations[EmbeddingClient Implementations]. For example ou can use the OpenAI boot starter:
+Then add the Qdrant boot starter dependency to your project:
 
 [source,xml]
 ----
 <dependency>
 	<groupId>org.springframework.ai</groupId>
-	<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
+	<artifactId>spring-ai-qdrant-store-spring-boot-starter</artifactId>
 </dependency>
 ----
 
@@ -43,21 +31,20 @@ or to your Gradle `build.gradle` build file.
 [source,groovy]
 ----
 dependencies {
-    implementation 'org.springframework.ai:spring-ai-openai-spring-boot-starter'
+    implementation 'org.springframework.ai:spring-ai-qdrant-store-spring-boot-starter'
 }
 ----
 
-TIP: Additionally, you'll need to provide your OpenAI API Key. Set it as an environment variable like so:
-`export SPRING_AI_OPENAI_API_KEY='Your_OpenAI_API_Key`
-
+The Vector Store, also requires an `EmbeddingClient` instance to calculate embeddings for the documents.
+You can pick one of the available xref:api/embeddings.adoc#available-implementations[EmbeddingClient Implementations].
 
-* Add the Qdrant Boot Starter dependency to your project:
+For example to use the xref:api/embeddings/openai-embeddings.adoc[OpenAI EmbeddingClient] add the following dependency to your project:
 
 [source,xml]
 ----
 <dependency>
 	<groupId>org.springframework.ai</groupId>
-	<artifactId>spring-ai-qdrant-store-spring-boot-starter</artifactId>
+	<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
 </dependency>
 ----
 
@@ -66,25 +53,37 @@ or to your Gradle `build.gradle` build file.
 [source,groovy]
 ----
 dependencies {
-    implementation 'org.springframework.ai:spring-ai-qdrant-store-spring-boot-starter'
+    implementation 'org.springframework.ai:spring-ai-openai-spring-boot-starter'
 }
 ----
 
 TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
+Refer to the xref:getting-started.adoc#repositories[Repositories] section to add Milestone and/or Snapshot Repositories to your build file.
+
+To connect to Qdrant and use the `QdrantVectorStore`, you need to provide access details for your instance.
+A simple configuration can either be provided via Spring Boot's _application.properties_,
 
-Please have a look at the list of xref:#qdrant-vectorstore-properties[configuration parameters] for the vector store to learn about the default values and configuration options.
+[source,properties]
+----
+spring.ai.vectorstore.qdrant.host=<host of your qdrant instance>
+spring.ai.vectorstore.qdrant.port=<the GRPC port of your qdrant instance>
+spring.ai.vectorstore.qdrant.api-key=<your api key>
+spring.ai.vectorstore.qdrant.collection-name=<The name of the collection to use in Qdrant>
 
-TIP: Refer to the xref:getting-started.adoc#repositories[Repositories] section to add Milestone and/or Snapshot Repositories to your build file.
+# API key if needed, e.g. OpenAI
+spring.ai.openai.api.key=<api-key>
+----
 
+TIP: Check the list of xref:#qdrant-vectorstore-properties[configuration parameters] to learn about the default values and configuration options.
 
 Now you can Auto-wire the Qdrant Vector Store in your application and use it
 
 [source,java]
 ----
-@Autowired
-VectorStore vectorStore;
+@Autowired VectorStore vectorStore;
+
+// ...
 
-...
 List <Document> documents = List.of(
     new Document("Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!!", Map.of("meta1", "meta1")),
     new Document("The World is Big and Salvation Lurks Around the Corner"),
@@ -97,24 +96,7 @@ vectorStore.add(List.of(document));
 List<Document> results = vectorStore.similaritySearch(SearchRequest.query("Spring").withTopK(5));
 ----
 
-== Configuration
-
-To connect to Qdrant and use the `QdrantVectorStore`, you need to provide access details for your instance.
-A simple configuration can either be provided via Spring Boot's _application.properties_,
-
-[source,properties]
-----
-spring.ai.vectorstore.qdrant.host=<host of your qdrant instance>
-spring.ai.vectorstore.qdrant.port=<port of your qdrant instance>
-spring.ai.vectorstore.qdrant.api-key=<your api key>
-spring.ai.vectorstore.qdrant.collection-name=<The name of the collection to use in Qdrant>
-
-# API key if needed, e.g. OpenAI
-spring.ai.openai.api.key=<api-key>
-----
-
-
-== Manual Configuration
+=== Manual Configuration
 
 Instead of using the Spring Boot auto-configuration, you can manually configure the `QdrantVectorStore`. For this you need to add the `spring-ai-qdrant` dependency to your project:
 
@@ -162,7 +144,7 @@ public VectorStore vectorStore(QdrantVectorStoreConfig config, EmbeddingClient e
 }
 ----
 
-=== Metadata filtering
+== Metadata filtering
 
 You can leverage the generic, portable link:https://docs.spring.io/spring-ai/reference/api/vectordbs.html#_metadata_filters[metadata filters] with the Qdrant vector store.
 
@@ -195,18 +177,18 @@ vectorStore.similaritySearch(SearchRequest.defaults()
 
 NOTE: These filter expressions are converted into the equivalent Qdrant link:https://qdrant.tech/documentation/concepts/filtering/[filters].
 
-
 [[qdrant-vectorstore-properties]]
-== Qdrant VectorStore properties
+== Configuration properties
 
 You can use the following properties in your Spring Boot configuration to customize the Qdrant vector store.
 
+[cols="3,5,1"]
 |===
 |Property| Description | Default value
 
 |`spring.ai.vectorstore.qdrant.host`| The host of the Qdrant server. | localhost
-|`spring.ai.vectorstore.qdrant.port`| The port of the Qdrant server. | 6334
+|`spring.ai.vectorstore.qdrant.port`| The gRPC port of the Qdrant server. | 6334
 |`spring.ai.vectorstore.qdrant.api-key`| The API key to use for authentication with the Qdrant server. | -
 |`spring.ai.vectorstore.qdrant.collection-name`| The name of the collection to use in Qdrant. | -
-|`spring.ai.vectorstore.qdrant.use-tls`| Whether to use TLS(HTTPS). Defaults to false. | false
+|`spring.ai.vectorstore.qdrant.use-tls`| Whether to use TLS(HTTPS). | false
 |===

spring-ai-spring-boot-autoconfigure/src/main/java/org/springframework/ai/autoconfigure/vectorstore/qdrant/QdrantVectorStoreAutoConfiguration.java

@@ -43,7 +43,7 @@ public VectorStore vectorStore(EmbeddingClient embeddingClient, QdrantVectorStor
 			.withCollectionName(properties.getCollectionName())
 			.withHost(properties.getHost())
 			.withPort(properties.getPort())
-			.withTls(properties.useTls())
+			.withTls(properties.isUseTls())
 			.withApiKey(properties.getApiKey())
 			.build();
 

spring-ai-spring-boot-autoconfigure/src/main/java/org/springframework/ai/autoconfigure/vectorstore/qdrant/QdrantVectorStoreProperties.java

@@ -76,7 +76,7 @@ public void setPort(int port) {
 		this.port = port;
 	}
 
-	public boolean useTls() {
+	public boolean isUseTls() {
 		return this.useTls;
 	}
 

spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/vectorstore/qdrant/PgVectorStorePropertiesTests.java

@@ -0,0 +1,56 @@
+/*
+ * Copyright 2023-2023 the original author or authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.springframework.ai.autoconfigure.vectorstore.qdrant;
+
+import org.junit.jupiter.api.Test;
+
+import static org.assertj.core.api.Assertions.assertThat;
+
+/**
+ * @author Christian Tzolov
+ */
+public class PgVectorStorePropertiesTests {
+
+	@Test
+	public void defaultValues() {
+		var props = new QdrantVectorStoreProperties();
+
+		assertThat(props.getCollectionName()).isNull();
+		assertThat(props.getHost()).isEqualTo("localhost");
+		assertThat(props.getPort()).isEqualTo(6334);
+		assertThat(props.isUseTls()).isFalse();
+		assertThat(props.getApiKey()).isNull();
+	}
+
+	@Test
+	public void customValues() {
+		var props = new QdrantVectorStoreProperties();
+
+		props.setCollectionName("MY_COLLECTION");
+		props.setHost("MY_HOST");
+		props.setPort(999);
+		props.setUseTls(true);
+		props.setApiKey("MY_API_KEY");
+
+		assertThat(props.getCollectionName()).isEqualTo("MY_COLLECTION");
+		assertThat(props.getHost()).isEqualTo("MY_HOST");
+		assertThat(props.getPort()).isEqualTo(999);
+		assertThat(props.isUseTls()).isTrue();
+		assertThat(props.getApiKey()).isEqualTo("MY_API_KEY");
+	}
+
+}

spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/vectorstore/qdrant/QdrantVectorStoreAutoConfigurationIT.java

@@ -54,8 +54,6 @@ public class QdrantVectorStoreAutoConfigurationIT {
 
 	private static final String COLLECTION_NAME = "test_collection";
 
-	private static final int EMBEDDING_DIMENSION = 384;
-
 	private static final int QDRANT_GRPC_PORT = 6334;
 
 	@Container
@@ -66,31 +64,6 @@ public class QdrantVectorStoreAutoConfigurationIT {
 			new Document(getText("classpath:/test/data/time.shelter.txt")),
 			new Document(getText("classpath:/test/data/great.depression.txt"), Map.of("depression", "bad")));
 
-	@BeforeAll
-	static void setup() throws InterruptedException, ExecutionException {
-
-		String host = qdrantContainer.getHost();
-		int port = qdrantContainer.getMappedPort(QDRANT_GRPC_PORT);
-		QdrantClient client = new QdrantClient(QdrantGrpcClient.newBuilder(host, port, false).build());
-
-		client
-			.createCollectionAsync(COLLECTION_NAME,
-					VectorParams.newBuilder().setDistance(Distance.Cosine).setSize(EMBEDDING_DIMENSION).build())
-			.get();
-
-		client.close();
-	}
-
-	public static String getText(String uri) {
-		var resource = new DefaultResourceLoader().getResource(uri);
-		try {
-			return resource.getContentAsString(StandardCharsets.UTF_8);
-		}
-		catch (IOException e) {
-			throw new RuntimeException(e);
-		}
-	}
-
 	private final ApplicationContextRunner contextRunner = new ApplicationContextRunner()
 		.withConfiguration(AutoConfigurations.of(QdrantVectorStoreAutoConfiguration.class))
 		.withUserConfiguration(Config.class)
@@ -121,6 +94,16 @@ public void addAndSearch() {
 		});
 	}
 
+	public static String getText(String uri) {
+		var resource = new DefaultResourceLoader().getResource(uri);
+		try {
+			return resource.getContentAsString(StandardCharsets.UTF_8);
+		}
+		catch (IOException e) {
+			throw new RuntimeException(e);
+		}
+	}
+
 	@Configuration(proxyBeanMethods = false)
 	static class Config {