Implement Qdrant vector store
- Implement QdrantVectorStore. Uses a custom parser for converting Spring AI metadata(Map<String, Object>) to Qdrant GRPC payload. - Implement Qdrant Expression Filter support. Uses a custom parser for converting Spring AI filters to Qdrant-compatible GRPC filters. - Add ITs using testcontainers. - Add antora docs adrant.adoc. - Add Qdrant vector store auto-configuraton and boot starter. Additional (review) change: - Fix poms parent to 0.8.1-SNAPSHOT. - Rename ObjectFactory into QdrantObjectFactor. - Rename ValueFactory into QdrantValueFactory. - Move the org.springframework.ai.vectorstore package into org.springframework.ai.vectorstore.qdrant. - Add missing Autoconfigure definition. - Add missing license and JavaDocs. - Minor code style improvmentes. - Move the qdrant version to the main pom - Add QdrantVectorStoreAutoConfigurationIT - Remove guava dependency - Improve gdrant.adoc conent and structure. - Remove the grpc-protobuf dependency Resolves #331
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Anush008
committed by
Christian Tzolov
Christian Tzolov
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e1462b86e3
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@@ -43,6 +43,7 @@
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*** xref:api/vectordbs/weaviate.adoc[]
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*** xref:api/vectordbs/redis.adoc[]
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*** xref:api/vectordbs/pinecone.adoc[]
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*** xref:api/vectordbs/qdrant.adoc[]
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** xref:api/etl-pipeline.adoc[]
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** xref:api/testing.adoc[]
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** xref:api/generic-model.adoc[]
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@@ -93,6 +93,7 @@ These are the available implementations of the `VectorStore` interface:
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* xref:api/vectordbs/neo4j.adoc[Neo4jVectorStore] - The https://neo4j.com/[Neo4j] vector store.
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* xref:api/vectordbs/pgvector.adoc[PgVectorStore] - The https://github.com/pgvector/pgvector[PostgreSQL/PGVector] vector store.
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* xref:api/vectordbs/pinecone.adoc[PineconeVectorStore] - https://www.pinecone.io/[PineCone] vector store.
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* xref:api/vectordbs/qdrant.adoc[QdrantVectorStore] - https://www.qdrant.tech/[Qdrant] vector store.
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* xref:api/vectordbs/redis.adoc[RedisVectorStore] - The https://redis.io/[Redis] vector store.
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* xref:api/vectordbs/weaviate.adoc[WeaviateVectorStore] - The https://weaviate.io/[Weaviate] vector store.
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* link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/vectorstore/SimpleVectorStore.java[SimpleVectorStore] - A simple implementation of persistent vector storage, good for educational purposes.
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@@ -0,0 +1,212 @@
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= Qdrant
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This section walks you through setting up the Qdrant `VectorStore` to store document embeddings and perform similarity searches.
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link:https://www.qdrant.tech/[Qdrant] is an open-source, high-performance vector search engine/database.
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== Prerequisites
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* Qdrant Instance: Set up a Qdrant instance by following the link:https://qdrant.tech/documentation/guides/installation/[installation instructions] in the Qdrant documentation.
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* If required, an API key for the xref:api/embeddings.adoc#available-implementations[EmbeddingClient] to generate the embeddings stored by the `QdrantVectorStore`.
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== Configuration
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To set up `QdrantVectorStore`, you'll need the following information from your Qdrant instance:
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* Qdrant Host
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* Qdrant GRPC Port
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* Qdrant Collection Name
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* Optional Qdrant API Key (not required for local development)
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[NOTE]
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====
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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.
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For example if using the OpenAI `text-embedding-ada-002` embedding model, create a collection with a vector size of `1536`.
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====
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== Dependencies
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* The Vector Store requires an `EmbeddingClient` instance to calculate embeddings for the documents.
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You can pick one of the available xref:api/embeddings.adoc#available-implementations[EmbeddingClient Implementations]. For example ou can use the OpenAI boot starter:
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[source,xml]
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----
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
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</dependency>
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----
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or to your Gradle `build.gradle` build file.
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[source,groovy]
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----
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dependencies {
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implementation 'org.springframework.ai:spring-ai-openai-spring-boot-starter'
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}
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----
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TIP: Additionally, you'll need to provide your OpenAI API Key. Set it as an environment variable like so:
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`export SPRING_AI_OPENAI_API_KEY='Your_OpenAI_API_Key`
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* Add the Qdrant Boot Starter dependency to your project:
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[source,xml]
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----
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-qdrant-store-spring-boot-starter</artifactId>
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</dependency>
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----
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or to your Gradle `build.gradle` build file.
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[source,groovy]
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----
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dependencies {
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implementation 'org.springframework.ai:spring-ai-qdrant-store-spring-boot-starter'
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}
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----
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TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
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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.
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TIP: Refer to the xref:getting-started.adoc#repositories[Repositories] section to add Milestone and/or Snapshot Repositories to your build file.
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Now you can Auto-wire the Qdrant Vector Store in your application and use it
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[source,java]
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----
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@Autowired
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VectorStore vectorStore;
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...
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List <Document> documents = List.of(
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new Document("Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!!", Map.of("meta1", "meta1")),
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new Document("The World is Big and Salvation Lurks Around the Corner"),
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new Document("You walk forward facing the past and you turn back toward the future.", Map.of("meta2", "meta2")));
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// Add the documents to Qdrant
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vectorStore.add(List.of(document));
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// Retrieve documents similar to a query
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List<Document> results = vectorStore.similaritySearch(SearchRequest.query("Spring").withTopK(5));
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----
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== Configuration
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To connect to Qdrant and use the `QdrantVectorStore`, you need to provide access details for your instance.
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A simple configuration can either be provided via Spring Boot's _application.properties_,
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[source,properties]
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----
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spring.ai.vectorstore.qdrant.host=<host of your qdrant instance>
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spring.ai.vectorstore.qdrant.port=<port of your qdrant instance>
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spring.ai.vectorstore.qdrant.api-key=<your api key>
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spring.ai.vectorstore.qdrant.collection-name=<The name of the collection to use in Qdrant>
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# API key if needed, e.g. OpenAI
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spring.ai.openai.api.key=<api-key>
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----
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== Manual Configuration
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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:
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[source,xml]
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----
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-qdrant</artifactId>
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</dependency>
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----
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or to your Gradle `build.gradle` build file.
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[source,groovy]
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----
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dependencies {
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implementation 'org.springframework.ai:spring-ai-qdrant'
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}
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----
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To configure Qdrant in your application, you can use the following setup:
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[source,java]
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----
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@Bean
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public QdrantVectorStoreConfig qdrantVectorStoreConfig() {
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return QdrantVectorStoreConfig.builder()
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.withHost("<QDRANT_HOSTNAME>")
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.withPort(<QDRANT_GRPC_PORT>)
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.withCollectionName("<QDRANT_COLLECTION_NAME>")
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.withApiKey("<QDRANT_API_KEY>")
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.build();
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}
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----
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Integrate with OpenAI's embeddings by adding the Spring Boot OpenAI starter to your project.
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This provides you with an implementation of the Embeddings client:
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[source,java]
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----
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@Bean
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public VectorStore vectorStore(QdrantVectorStoreConfig config, EmbeddingClient embeddingClient) {
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return new QdrantVectorStore(config, embeddingClient);
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}
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----
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=== Metadata filtering
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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.
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For example, you can use either the text expression language:
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[source,java]
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----
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vectorStore.similaritySearch(
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SearchRequest.defaults()
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.withQuery("The World")
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.withTopK(TOP_K)
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.withSimilarityThreshold(SIMILARITY_THRESHOLD)
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.withFilterExpression("author in ['john', 'jill'] && article_type == 'blog'"));
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----
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or programmatically using the `Filter.Expression` DSL:
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[source,java]
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----
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FilterExpressionBuilder b = new FilterExpressionBuilder();
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vectorStore.similaritySearch(SearchRequest.defaults()
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.withQuery("The World")
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.withTopK(TOP_K)
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.withSimilarityThreshold(SIMILARITY_THRESHOLD)
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.withFilterExpression(b.and(
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b.in("john", "jill"),
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b.eq("article_type", "blog")).build()));
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----
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NOTE: These filter expressions are converted into the equivalent Qdrant link:https://qdrant.tech/documentation/concepts/filtering/[filters].
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[[qdrant-vectorstore-properties]]
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== Qdrant VectorStore properties
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You can use the following properties in your Spring Boot configuration to customize the Qdrant vector store.
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|===
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|Property| Description | Default value
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|`spring.ai.vectorstore.qdrant.host`| The host of the Qdrant server. | localhost
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|`spring.ai.vectorstore.qdrant.port`| The port of the Qdrant server. | 6334
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|`spring.ai.vectorstore.qdrant.api-key`| The API key to use for authentication with the Qdrant server. | -
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|`spring.ai.vectorstore.qdrant.collection-name`| The name of the collection to use in Qdrant. | -
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|`spring.ai.vectorstore.qdrant.use-tls`| Whether to use TLS(HTTPS). Defaults to false. | false
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|===
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@@ -164,6 +164,7 @@ Each of the following sections in the documentation shows which dependencies you
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** xref:api/vectordbs/neo4j.adoc[Neo4jVectorStore] - The https://neo4j.com/[Neo4j] vector store.
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** xref:api/vectordbs/pgvector.adoc[PgVectorStore] - The https://github.com/pgvector/pgvector[PostgreSQL/PGVector] vector store.
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** xref:api/vectordbs/pinecone.adoc[PineconeVectorStore] - https://www.pinecone.io/[PineCone] vector store.
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** xref:api/vectordbs/qdrant.adoc[QdrantVectorStore] - https://www.qdrant.tech/[Qdrant] vector store.
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** xref:api/vectordbs/redis.adoc[RedisVectorStore] - The https://redis.io/[Redis] vector store.
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** xref:api/vectordbs/weaviate.adoc[WeaviateVectorStore] - The https://weaviate.io/[Weaviate] vector store.
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** link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/vectorstore/SimpleVectorStore.java[SimpleVectorStore] - A simple (in-memory) implementation of persistent vector storage, good for educational purposes.
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@@ -15,7 +15,7 @@ Spring AI provides the following features:
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* Supported Model types are Chat and Text to Image with more on the way.
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* Portable API across AI providers for Chat and for Embedding models. Both synchronous and stream API options are supported. Dropping down to access model specific features is also supported.
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* Mapping of AI Model output to POJOs.
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* Support for all major Vector Database providers such as Azure Vector Search, Chroma, Milvus, Neo4j, PostgreSQL/PGVector, PineCone, Redis, and Weaviate
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* Support for all major Vector Database providers such as Azure Vector Search, Chroma, Milvus, Neo4j, PostgreSQL/PGVector, PineCone, Qdrant, Redis, and Weaviate
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* Portable API across Vector Store providers, including a novel SQL-like metadata filter API that is also portable.
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* Function calling
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* Spring Boot Auto Configuration and Starters for AI Models and Vector Stores.
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