Add builder pattern to OpenSearchVectorStore and refactor package name
Add builder pattern to OpenSearchVectorStore Introduces a builder pattern for OpenSearchVectorStore configuration and refactors the package structure to org.springframework.ai.vectorstore.opensearch for better organization and consistency with other vector stores. The builder pattern improves usability by: * Providing a fluent API for configuring store instances * Making configuration options more discoverable through method names * Enabling better validation of configuration parameters * Supporting optional parameters with sensible defaults * The package refactoring aligns with the project's standard package naming conventions and improves code organization. All constructors are deprecated in favor of the new builder pattern to guide users toward the preferred configuration approach.
This commit is contained in:
Soby Chacko
committed by
Mark Pollack
Mark Pollack
parent
f69d879ec9
commit
d3d34c9215
@@ -1,50 +1,89 @@
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= OpenSearch
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This section guides you through setting up the OpenSearch `VectorStore` to store document embeddings and perform similarity searches.
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This section walks you through setting up `OpenSearchVectorStore` to store document embeddings and perform similarity searches.
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link:https://opensearch.org[OpenSearch] is an open-source search and analytics engine originally forked from Elasticsearch, distributed under the Apache License 2.0. It enhances AI application development by simplifying the integration and management of AI-generated assets. OpenSearch supports vector, lexical, and hybrid search capabilities, leveraging advanced vector database functionalities to facilitate low-latency queries and similarity searches as detailed on the link:https://opensearch.org/platform/search/vector-database.html[vector database page]. This platform is ideal for building scalable AI-driven applications and offers robust tools for data management, fault tolerance, and resource access controls.
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link:https://opensearch.org[OpenSearch] is an open-source search and analytics engine originally forked from Elasticsearch, distributed under the Apache License 2.0. It enhances AI application development by simplifying the integration and management of AI-generated assets. OpenSearch supports vector, lexical, and hybrid search capabilities, leveraging advanced vector database functionalities to facilitate low-latency queries and similarity searches as detailed on the link:https://opensearch.org/platform/search/vector-database.html[vector database page].
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The link:https://opensearch.org/docs/latest/search-plugins/knn/index/[OpenSearch k-NN] functionality allows users to query vector embeddings from large datasets. An embedding is a numerical representation of a data object, such as text, image, audio, or document. Embeddings can be stored in the index and queried using various similarity functions.
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== Prerequisites
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* A running OpenSearch instance. The following options are available:
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** link:https://opensearch.org/docs/latest/opensearch/install/index/[Self-Managed OpenSearch]
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** link:https://docs.aws.amazon.com/opensearch-service/[Amazon OpenSearch Service]
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* `EmbeddingModel` instance to compute the document embeddings. Several options are available:
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- If required, an API key for the xref:api/embeddings.adoc#available-implementations[EmbeddingModel] to generate the
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embeddings stored by the `OpenSearchVectorStore`.
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* If required, an API key for the xref:api/embeddings.adoc#available-implementations[EmbeddingModel] to generate the embeddings stored by the `OpenSearchVectorStore`.
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== Dependencies
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== Auto-configuration
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Add the OpenSearch Vector Store dependency to your project:
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Spring AI provides Spring Boot auto-configuration for the OpenSearch Vector Store.
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To enable it, add the following dependency to your project's Maven `pom.xml` file:
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[tabs]
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======
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Maven::
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+
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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-opensearch-store</artifactId>
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<artifactId>spring-ai-opensearch-store-spring-boot-starter</artifactId>
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</dependency>
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----
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Gradle::
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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-opensearch-store'
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implementation 'org.springframework.ai:spring-ai-opensearch-store-spring-boot-starter'
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}
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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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== Configuration
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For Amazon OpenSearch Service, use these dependencies instead:
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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-aws-opensearch-store-spring-boot-starter</artifactId>
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</dependency>
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----
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or for Gradle:
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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-aws-opensearch-store-spring-boot-starter'
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}
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----
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Please have a look at the list of xref:#_configuration_properties[configuration parameters] for the vector store to learn about the default values and configuration options.
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Additionally, you will need a configured `EmbeddingModel` bean. Refer to the xref:api/embeddings.adoc#available-implementations[EmbeddingModel] section for more information.
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Now you can auto-wire the `OpenSearchVectorStore` as a vector store in your application:
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[source,java]
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----
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@Autowired 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!!", 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 OpenSearch
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vectorStore.add(documents);
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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 Properties
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To connect to OpenSearch and use the `OpenSearchVectorStore`, 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.yml`,
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A simple configuration can be provided via Spring Boot's `application.yml`:
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[source,yaml]
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----
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@@ -55,146 +94,105 @@ spring:
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uris: <opensearch instance URIs>
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username: <opensearch username>
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password: <opensearch password>
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indexName: <opensearch index name>
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mappingJson: <JSON mapping for opensearch index>
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aws:
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index-name: spring-ai-document-index
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initialize-schema: true
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similarity-function: cosinesimil
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batching-strategy: TOKEN_COUNT
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aws: # Only for Amazon OpenSearch Service
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host: <aws opensearch host>
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serviceName: <aws service name>
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accessKey: <aws access key>
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secretKey: <aws secret key>
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service-name: <aws service name>
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access-key: <aws access key>
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secret-key: <aws secret key>
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region: <aws region>
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# API key if needed, e.g. OpenAI
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openai:
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apiKey: <api-key>
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----
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TIP: Check the list of xref:#_configuration_properties[configuration parameters] to learn about the default values and configuration options.
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== Auto-configuration
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Properties starting with `spring.ai.vectorstore.opensearch.*` are used to configure the `OpenSearchVectorStore`:
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=== Self-Managed OpenSearch
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[cols="2,5,1",stripes=even]
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|===
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|Property | Description | Default Value
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Spring AI provides Spring Boot auto-configuration for the OpenSearch Vector Store.
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To enable it, add the following dependency to your project's Maven `pom.xml` or Gradle `build.gradle` build files:
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|`spring.ai.vectorstore.opensearch.uris`| URIs of the OpenSearch cluster endpoints | -
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|`spring.ai.vectorstore.opensearch.username`| Username for accessing the OpenSearch cluster | -
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|`spring.ai.vectorstore.opensearch.password`| Password for the specified username | -
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|`spring.ai.vectorstore.opensearch.index-name`| Name of the index to store vectors | `spring-ai-document-index`
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|`spring.ai.vectorstore.opensearch.initialize-schema`| Whether to initialize the required schema | `false`
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|`spring.ai.vectorstore.opensearch.similarity-function`| The similarity function to use | `cosinesimil`
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|`spring.ai.vectorstore.opensearch.batching-strategy`| Strategy for batching documents when calculating embeddings. Options are `TOKEN_COUNT` or `FIXED_SIZE` | `TOKEN_COUNT`
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|`spring.ai.vectorstore.opensearch.aws.host`| Hostname of the OpenSearch instance | -
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|`spring.ai.vectorstore.opensearch.aws.service-name`| AWS service name | -
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|`spring.ai.vectorstore.opensearch.aws.access-key`| AWS access key | -
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|`spring.ai.vectorstore.opensearch.aws.secret-key`| AWS secret key | -
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|`spring.ai.vectorstore.opensearch.aws.region`| AWS region | -
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|===
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The following similarity functions are available:
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* `cosinesimil` - Default, suitable for most use cases. Measures cosine similarity between vectors.
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* `l1` - Manhattan distance between vectors.
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* `l2` - Euclidean distance between vectors.
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* `linf` - Chebyshev distance between vectors.
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== Manual Configuration
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Instead of using the Spring Boot auto-configuration, you can manually configure the OpenSearch vector store. For this you need to add the `spring-ai-opensearch-store` to your project:
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[tabs]
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======
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Maven::
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+
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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-opensearch-store-spring-boot-starter</artifactId>
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<artifactId>spring-ai-opensearch-store</artifactId>
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</dependency>
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----
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Gradle::
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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-opensearch-store-spring-boot-starter'
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implementation 'org.springframework.ai:spring-ai-opensearch-store'
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}
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----
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======
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Then use the `spring.ai.vectorstore.opensearch.*` properties to configure the connection to the self-managed OpenSearch instance.
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=== Amazon OpenSearch Service
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To enable Amazon OpenSearch Service., add the following dependency to your project's Maven `pom.xml` or Gradle `build.gradle` build files:
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[tabs]
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======
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Maven::
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+
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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-aws-opensearch-store-spring-boot-starter</artifactId>
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</dependency>
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----
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Gradle::
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+
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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-aws-opensearch-store-spring-boot-starter'
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}
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----
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======
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Then use the `spring.ai.vectorstore.opensearch.aws.*` properties to configure the connection to the Amazon OpenSearch Service.
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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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Here is an example of the needed bean:
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Create an OpenSearch client bean:
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[source,java]
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----
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@Bean
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public EmbeddingModel embeddingModel() {
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// Can be any other EmbeddingModel implementation
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return new OpenAiEmbeddingModel(new OpenAiApi(System.getenv("SPRING_AI_OPENAI_API_KEY")));
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public OpenSearchClient openSearchClient() {
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RestClient restClient = RestClient.builder(
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HttpHost.create("http://localhost:9200"))
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.build();
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return new OpenSearchClient(new RestClientTransport(
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restClient, new JacksonJsonpMapper()));
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}
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----
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Now you can auto-wire the `OpenSearchVectorStore` as a vector store in your application.
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Then create the `OpenSearchVectorStore` bean using the builder pattern:
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[source,java]
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----
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@Autowired 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 OpenSearch
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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 = this.vectorStore.similaritySearch(SearchRequest.query("Spring").withTopK(5));
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----
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=== Configuration properties
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You can use the following properties in your Spring Boot configuration to customize the OpenSearch vector store.
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[cols="2,5,1",stripes=even]
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|===
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|Property| Description | Default value
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|`spring.ai.vectorstore.opensearch.uris`| URIs of the OpenSearch cluster endpoints. | -
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|`spring.ai.vectorstore.opensearch.username`| Username for accessing the OpenSearch cluster. | -
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|`spring.ai.vectorstore.opensearch.password`| Password for the specified username. | -
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|`spring.ai.vectorstore.opensearch.indexName`| Name of the default index to be used within the OpenSearch cluster. | `spring-ai-document-index`
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|`spring.ai.vectorstore.opensearch.mappingJson`| JSON string defining the mapping for the index; specifies how documents and their
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fields are stored and indexed. Refer link:https://opensearch.org/docs/latest/search-plugins/vector-search/[here] for some sample configurations |
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{
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"properties":{
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"embedding":{
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"type":"knn_vector",
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"dimension":1536
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}
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}
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@Bean
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public VectorStore vectorStore(OpenSearchClient openSearchClient, EmbeddingModel embeddingModel) {
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return OpenSearchVectorStore.builder()
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.openSearchClient(openSearchClient)
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.embeddingModel(embeddingModel)
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.index("custom-index") // Optional: defaults to "spring-ai-document-index"
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.similarityFunction("l2") // Optional: defaults to "cosinesimil"
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.initializeSchema(true) // Optional: defaults to false
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.batchingStrategy(new TokenCountBatchingStrategy()) // Optional: defaults to TokenCountBatchingStrategy
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.build();
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}
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|`spring.ai.vectorstore.opensearch.aws.host`| Hostname of the OpenSearch instance. | -
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|`spring.ai.vectorstore.opensearch.aws.serviceName`| AWS service name for the OpenSearch instance. | -
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|`spring.ai.vectorstore.opensearch.aws.accessKey`| AWS access key for the OpenSearch instance. | -
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|`spring.ai.vectorstore.opensearch.aws.secretKey`| AWS secret key for the OpenSearch instance. | -
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|`spring.ai.vectorstore.opensearch.aws.region`| AWS region for the OpenSearch instance. | -
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|===
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=== Customizing OpenSearch Client Configuration
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In cases where the Spring Boot auto-configured OpenSearchClient with `Apache HttpClient 5 Transport` bean is not what
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you want or need, you can still define your own bean.
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Please read the link:https://opensearch.org/docs/latest/clients/java/[OpenSearch Java Client Documentation]
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// This can be any EmbeddingModel implementation
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@Bean
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public EmbeddingModel embeddingModel() {
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return new OpenAiEmbeddingModel(new OpenAiApi(System.getenv("OPENAI_API_KEY")));
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}
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----
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== Metadata Filtering
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@@ -202,34 +200,30 @@ You can leverage the generic, portable xref:api/vectordbs.adoc#metadata-filters[
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For example, you can use either the text expression language:
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[tabs]
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======
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SQL filter syntax::
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+
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[source,java]
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----
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vectorStore.similaritySearch(SearchRequest.defaults()
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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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`Filter.Expression` DSL::
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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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.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("author", "john", "jill"),
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b.eq("article_type", "blog")).build()));
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----
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======
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NOTE: Those (portable) filter expressions get automatically converted into the proprietary OpenSearch link:https://opensearch.org/docs/latest/query-dsl/full-text/query-string/[Query string query].
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@@ -40,7 +40,7 @@ import software.amazon.awssdk.regions.Region;
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import org.springframework.ai.embedding.BatchingStrategy;
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import org.springframework.ai.embedding.EmbeddingModel;
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import org.springframework.ai.embedding.TokenCountBatchingStrategy;
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import org.springframework.ai.vectorstore.OpenSearchVectorStore;
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import org.springframework.ai.vectorstore.opensearch.OpenSearchVectorStore;
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import org.springframework.ai.vectorstore.observation.VectorStoreObservationConvention;
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import org.springframework.beans.factory.ObjectProvider;
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import org.springframework.boot.autoconfigure.AutoConfiguration;
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@@ -78,9 +78,17 @@ public class OpenSearchVectorStoreAutoConfiguration {
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var indexName = Optional.ofNullable(properties.getIndexName()).orElse(OpenSearchVectorStore.DEFAULT_INDEX_NAME);
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var mappingJson = Optional.ofNullable(properties.getMappingJson())
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.orElse(OpenSearchVectorStore.DEFAULT_MAPPING_EMBEDDING_TYPE_KNN_VECTOR_DIMENSION);
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return new OpenSearchVectorStore(indexName, openSearchClient, embeddingModel, mappingJson,
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properties.isInitializeSchema(), observationRegistry.getIfUnique(() -> ObservationRegistry.NOOP),
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customObservationConvention.getIfAvailable(() -> null), batchingStrategy);
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return OpenSearchVectorStore.builder()
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.index(indexName)
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.openSearchClient(openSearchClient)
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.embeddingModel(embeddingModel)
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.mappingJson(mappingJson)
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.initializeSchema(properties.isInitializeSchema())
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.observationRegistry(observationRegistry.getIfUnique(() -> ObservationRegistry.NOOP))
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.customObservationConvention(customObservationConvention.getIfAvailable(() -> null))
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.batchingStrategy(batchingStrategy)
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.build();
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}
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@Configuration(proxyBeanMethods = false)
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@@ -36,7 +36,7 @@ import org.springframework.ai.autoconfigure.retry.SpringAiRetryAutoConfiguration
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import org.springframework.ai.document.Document;
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import org.springframework.ai.embedding.EmbeddingModel;
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import org.springframework.ai.transformers.TransformersEmbeddingModel;
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import org.springframework.ai.vectorstore.OpenSearchVectorStore;
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import org.springframework.ai.vectorstore.opensearch.OpenSearchVectorStore;
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import org.springframework.ai.vectorstore.SearchRequest;
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import org.springframework.boot.autoconfigure.AutoConfigurations;
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import org.springframework.boot.test.context.runner.ApplicationContextRunner;
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@@ -36,7 +36,7 @@ import org.springframework.ai.document.Document;
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import org.springframework.ai.embedding.EmbeddingModel;
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import org.springframework.ai.observation.conventions.VectorStoreProvider;
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import org.springframework.ai.transformers.TransformersEmbeddingModel;
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import org.springframework.ai.vectorstore.OpenSearchVectorStore;
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import org.springframework.ai.vectorstore.opensearch.OpenSearchVectorStore;
|
||||
import org.springframework.ai.vectorstore.SearchRequest;
|
||||
import org.springframework.ai.vectorstore.observation.VectorStoreObservationContext;
|
||||
import org.springframework.boot.autoconfigure.AutoConfigurations;
|
||||
|
||||
@@ -14,7 +14,7 @@
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
package org.springframework.ai.vectorstore;
|
||||
package org.springframework.ai.vectorstore.opensearch;
|
||||
|
||||
import java.text.ParseException;
|
||||
import java.text.SimpleDateFormat;
|
||||
@@ -14,7 +14,7 @@
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
package org.springframework.ai.vectorstore;
|
||||
package org.springframework.ai.vectorstore.opensearch;
|
||||
|
||||
import java.io.IOException;
|
||||
import java.io.StringReader;
|
||||
@@ -37,8 +37,6 @@ import org.opensearch.client.opensearch.core.search.Hit;
|
||||
import org.opensearch.client.opensearch.indices.CreateIndexRequest;
|
||||
import org.opensearch.client.opensearch.indices.CreateIndexResponse;
|
||||
import org.opensearch.client.transport.endpoints.BooleanResponse;
|
||||
import org.slf4j.Logger;
|
||||
import org.slf4j.LoggerFactory;
|
||||
|
||||
import org.springframework.ai.document.Document;
|
||||
import org.springframework.ai.document.DocumentMetadata;
|
||||
@@ -48,6 +46,8 @@ import org.springframework.ai.embedding.EmbeddingOptionsBuilder;
|
||||
import org.springframework.ai.embedding.TokenCountBatchingStrategy;
|
||||
import org.springframework.ai.observation.conventions.VectorStoreProvider;
|
||||
import org.springframework.ai.observation.conventions.VectorStoreSimilarityMetric;
|
||||
import org.springframework.ai.vectorstore.AbstractVectorStoreBuilder;
|
||||
import org.springframework.ai.vectorstore.SearchRequest;
|
||||
import org.springframework.ai.vectorstore.filter.Filter;
|
||||
import org.springframework.ai.vectorstore.filter.FilterExpressionConverter;
|
||||
import org.springframework.ai.vectorstore.observation.AbstractObservationVectorStore;
|
||||
@@ -57,7 +57,84 @@ import org.springframework.beans.factory.InitializingBean;
|
||||
import org.springframework.util.Assert;
|
||||
|
||||
/**
|
||||
* An ObservationVectorStore implementation that stores vectors in OpenSearch.
|
||||
* OpenSearch-based vector store implementation using OpenSearch's vector search
|
||||
* capabilities.
|
||||
*
|
||||
* <p>
|
||||
* The store uses OpenSearch's k-NN functionality to persist and query vector embeddings
|
||||
* along with their associated document content and metadata. The implementation supports
|
||||
* various similarity functions and provides efficient vector search operations.
|
||||
* </p>
|
||||
*
|
||||
* <p>
|
||||
* Features:
|
||||
* </p>
|
||||
* <ul>
|
||||
* <li>Automatic schema initialization with configurable index creation</li>
|
||||
* <li>Support for multiple similarity functions: Cosine, L1, L2, and Linf</li>
|
||||
* <li>Metadata filtering using OpenSearch query expressions</li>
|
||||
* <li>Configurable similarity thresholds for search results</li>
|
||||
* <li>Batch processing support with configurable strategies</li>
|
||||
* <li>Observation and metrics support through Micrometer</li>
|
||||
* </ul>
|
||||
*
|
||||
* <p>
|
||||
* Basic usage example:
|
||||
* </p>
|
||||
* <pre>{@code
|
||||
* OpenSearchVectorStore vectorStore = OpenSearchVectorStore.builder()
|
||||
* .openSearchClient(openSearchClient)
|
||||
* .embeddingModel(embeddingModel)
|
||||
* .initializeSchema(true)
|
||||
* .build();
|
||||
*
|
||||
* // Add documents
|
||||
* vectorStore.add(List.of(
|
||||
* new Document("content1", Map.of("key1", "value1")),
|
||||
* new Document("content2", Map.of("key2", "value2"))
|
||||
* ));
|
||||
*
|
||||
* // Search with filters
|
||||
* List<Document> results = vectorStore.similaritySearch(
|
||||
* SearchRequest.query("search text")
|
||||
* .withTopK(5)
|
||||
* .withSimilarityThreshold(0.7)
|
||||
* .withFilterExpression("key1 == 'value1'")
|
||||
* );
|
||||
* }</pre>
|
||||
*
|
||||
* <p>
|
||||
* Advanced configuration example:
|
||||
* </p>
|
||||
* <pre>{@code
|
||||
* OpenSearchVectorStore vectorStore = OpenSearchVectorStore.builder()
|
||||
* .openSearchClient(openSearchClient)
|
||||
* .embeddingModel(embeddingModel)
|
||||
* .index("custom-index")
|
||||
* .mappingJson(customMapping)
|
||||
* .similarityFunction("l2")
|
||||
* .initializeSchema(true)
|
||||
* .batchingStrategy(new TokenCountBatchingStrategy())
|
||||
* .filterExpressionConverter(new CustomFilterExpressionConverter())
|
||||
* .build();
|
||||
* }</pre>
|
||||
*
|
||||
* <p>
|
||||
* Similarity Functions:
|
||||
* </p>
|
||||
* <ul>
|
||||
* <li>cosinesimil: Default, suitable for most use cases. Measures cosine similarity
|
||||
* between vectors.</li>
|
||||
* <li>l1: Manhattan distance between vectors.</li>
|
||||
* <li>l2: Euclidean distance between vectors.</li>
|
||||
* <li>linf: Chebyshev distance between vectors.</li>
|
||||
* </ul>
|
||||
*
|
||||
* <p>
|
||||
* For more information about available similarity functions, see: <a href=
|
||||
* "https://opensearch.org/docs/latest/search-plugins/knn/approximate-knn/#spaces">OpenSearch
|
||||
* KNN Spaces</a>
|
||||
* </p>
|
||||
*
|
||||
* @author Jemin Huh
|
||||
* @author Soby Chacko
|
||||
@@ -83,10 +160,6 @@ public class OpenSearchVectorStore extends AbstractObservationVectorStore implem
|
||||
}
|
||||
""";
|
||||
|
||||
private static final Logger logger = LoggerFactory.getLogger(OpenSearchVectorStore.class);
|
||||
|
||||
private final EmbeddingModel embeddingModel;
|
||||
|
||||
private final OpenSearchClient openSearchClient;
|
||||
|
||||
private final String index;
|
||||
@@ -101,40 +174,106 @@ public class OpenSearchVectorStore extends AbstractObservationVectorStore implem
|
||||
|
||||
private String similarityFunction;
|
||||
|
||||
/**
|
||||
* Creates a new OpenSearchVectorStore with default mapping and collection name.
|
||||
* @deprecated Use {@link #builder()} instead
|
||||
* @param openSearchClient The OpenSearch client
|
||||
* @param embeddingModel The embedding model to use
|
||||
* @param initializeSchema Whether to initialize the schema
|
||||
* @since 1.0.0
|
||||
*/
|
||||
@Deprecated(since = "1.0.0-M5", forRemoval = true)
|
||||
public OpenSearchVectorStore(OpenSearchClient openSearchClient, EmbeddingModel embeddingModel,
|
||||
boolean initializeSchema) {
|
||||
this(openSearchClient, embeddingModel, DEFAULT_MAPPING_EMBEDDING_TYPE_KNN_VECTOR_DIMENSION, initializeSchema);
|
||||
}
|
||||
|
||||
/**
|
||||
* Creates a new OpenSearchVectorStore with custom mapping.
|
||||
* @deprecated Use {@link #builder()} instead
|
||||
* @param openSearchClient The OpenSearch client
|
||||
* @param embeddingModel The embedding model to use
|
||||
* @param mappingJson The JSON mapping for the index
|
||||
* @param initializeSchema Whether to initialize the schema
|
||||
* @since 1.0.0
|
||||
*/
|
||||
@Deprecated(since = "1.0.0-M5", forRemoval = true)
|
||||
public OpenSearchVectorStore(OpenSearchClient openSearchClient, EmbeddingModel embeddingModel, String mappingJson,
|
||||
boolean initializeSchema) {
|
||||
this(DEFAULT_INDEX_NAME, openSearchClient, embeddingModel, mappingJson, initializeSchema);
|
||||
}
|
||||
|
||||
/**
|
||||
* Creates a new OpenSearchVectorStore with custom index name and mapping.
|
||||
* @deprecated Use {@link #builder()} instead
|
||||
* @param index The name of the index
|
||||
* @param openSearchClient The OpenSearch client
|
||||
* @param embeddingModel The embedding model to use
|
||||
* @param mappingJson The JSON mapping for the index
|
||||
* @param initializeSchema Whether to initialize the schema
|
||||
* @since 1.0.0
|
||||
*/
|
||||
@Deprecated(since = "1.0.0-M5", forRemoval = true)
|
||||
public OpenSearchVectorStore(String index, OpenSearchClient openSearchClient, EmbeddingModel embeddingModel,
|
||||
String mappingJson, boolean initializeSchema) {
|
||||
this(index, openSearchClient, embeddingModel, mappingJson, initializeSchema, ObservationRegistry.NOOP, null,
|
||||
new TokenCountBatchingStrategy());
|
||||
}
|
||||
|
||||
/**
|
||||
* Creates a new OpenSearchVectorStore with all configuration options.
|
||||
* @deprecated Use {@link #builder()} instead
|
||||
* @param index The name of the index
|
||||
* @param openSearchClient The OpenSearch client
|
||||
* @param embeddingModel The embedding model to use
|
||||
* @param mappingJson The JSON mapping for the index
|
||||
* @param initializeSchema Whether to initialize the schema
|
||||
* @param observationRegistry The observation registry for metrics
|
||||
* @param customObservationConvention Custom observation convention
|
||||
* @param batchingStrategy The strategy for batching operations
|
||||
* @since 1.0.0
|
||||
*/
|
||||
@Deprecated(since = "1.0.0-M5", forRemoval = true)
|
||||
public OpenSearchVectorStore(String index, OpenSearchClient openSearchClient, EmbeddingModel embeddingModel,
|
||||
String mappingJson, boolean initializeSchema, ObservationRegistry observationRegistry,
|
||||
VectorStoreObservationConvention customObservationConvention, BatchingStrategy batchingStrategy) {
|
||||
|
||||
super(observationRegistry, customObservationConvention);
|
||||
this(builder().openSearchClient(openSearchClient)
|
||||
.embeddingModel(embeddingModel)
|
||||
.index(index)
|
||||
.mappingJson(mappingJson)
|
||||
.initializeSchema(initializeSchema)
|
||||
.observationRegistry(observationRegistry)
|
||||
.customObservationConvention(customObservationConvention)
|
||||
.batchingStrategy(batchingStrategy));
|
||||
}
|
||||
|
||||
Objects.requireNonNull(embeddingModel, "RestClient must not be null");
|
||||
Objects.requireNonNull(embeddingModel, "EmbeddingModel must not be null");
|
||||
this.openSearchClient = openSearchClient;
|
||||
this.embeddingModel = embeddingModel;
|
||||
this.index = index;
|
||||
this.mappingJson = mappingJson;
|
||||
this.filterExpressionConverter = new OpenSearchAiSearchFilterExpressionConverter();
|
||||
/**
|
||||
* Creates a new OpenSearchVectorStore using the builder pattern.
|
||||
* @param builder The configured builder instance
|
||||
*/
|
||||
protected OpenSearchVectorStore(OpenSearchBuilder builder) {
|
||||
super(builder);
|
||||
|
||||
Assert.notNull(builder.openSearchClient, "OpenSearchClient must not be null");
|
||||
|
||||
this.openSearchClient = builder.openSearchClient;
|
||||
this.index = builder.index;
|
||||
this.mappingJson = builder.mappingJson;
|
||||
this.filterExpressionConverter = builder.filterExpressionConverter;
|
||||
// the potential functions for vector fields at
|
||||
// https://opensearch.org/docs/latest/search-plugins/knn/approximate-knn/#spaces
|
||||
this.similarityFunction = COSINE_SIMILARITY_FUNCTION;
|
||||
this.initializeSchema = initializeSchema;
|
||||
this.batchingStrategy = batchingStrategy;
|
||||
this.similarityFunction = builder.similarityFunction;
|
||||
this.initializeSchema = builder.initializeSchema;
|
||||
this.batchingStrategy = builder.batchingStrategy;
|
||||
}
|
||||
|
||||
/**
|
||||
* Creates a new builder instance for configuring an OpenSearchVectorStore.
|
||||
* @return A new OpenSearchBuilder instance
|
||||
*/
|
||||
public static OpenSearchBuilder builder() {
|
||||
return new OpenSearchBuilder();
|
||||
}
|
||||
|
||||
public OpenSearchVectorStore withSimilarityFunction(String similarityFunction) {
|
||||
@@ -306,4 +445,132 @@ public class OpenSearchVectorStore extends AbstractObservationVectorStore implem
|
||||
public record OpenSearchDocument(String id, String content, Map<String, Object> metadata, float[] embedding) {
|
||||
}
|
||||
|
||||
/**
|
||||
* Builder class for creating OpenSearchVectorStore instances.
|
||||
*/
|
||||
public static class OpenSearchBuilder extends AbstractVectorStoreBuilder<OpenSearchBuilder> {
|
||||
|
||||
private OpenSearchClient openSearchClient;
|
||||
|
||||
private String index = DEFAULT_INDEX_NAME;
|
||||
|
||||
private String mappingJson = DEFAULT_MAPPING_EMBEDDING_TYPE_KNN_VECTOR_DIMENSION;
|
||||
|
||||
private boolean initializeSchema = false;
|
||||
|
||||
private BatchingStrategy batchingStrategy = new TokenCountBatchingStrategy();
|
||||
|
||||
private FilterExpressionConverter filterExpressionConverter = new OpenSearchAiSearchFilterExpressionConverter();
|
||||
|
||||
private String similarityFunction = COSINE_SIMILARITY_FUNCTION;
|
||||
|
||||
/**
|
||||
* Sets the OpenSearch client.
|
||||
* @param openSearchClient The OpenSearch client to use
|
||||
* @return The builder instance
|
||||
* @throws IllegalArgumentException if openSearchClient is null
|
||||
*/
|
||||
public OpenSearchBuilder openSearchClient(OpenSearchClient openSearchClient) {
|
||||
Assert.notNull(openSearchClient, "OpenSearchClient must not be null");
|
||||
this.openSearchClient = openSearchClient;
|
||||
return this;
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets the embedding model.
|
||||
* @param embeddingModel The embedding model to use
|
||||
* @return The builder instance
|
||||
* @throws IllegalArgumentException if embeddingModel is null
|
||||
*/
|
||||
public OpenSearchBuilder embeddingModel(EmbeddingModel embeddingModel) {
|
||||
Assert.notNull(embeddingModel, "EmbeddingModel must not be null");
|
||||
this.embeddingModel = embeddingModel;
|
||||
return this;
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets the index name.
|
||||
* @param index The name of the index to use
|
||||
* @return The builder instance
|
||||
* @throws IllegalArgumentException if index is null or empty
|
||||
*/
|
||||
public OpenSearchBuilder index(String index) {
|
||||
Assert.hasText(index, "index must not be null or empty");
|
||||
this.index = index;
|
||||
return this;
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets the JSON mapping for the index.
|
||||
* @param mappingJson The JSON mapping to use
|
||||
* @return The builder instance
|
||||
* @throws IllegalArgumentException if mappingJson is null or empty
|
||||
*/
|
||||
public OpenSearchBuilder mappingJson(String mappingJson) {
|
||||
Assert.hasText(mappingJson, "mappingJson must not be null or empty");
|
||||
this.mappingJson = mappingJson;
|
||||
return this;
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets whether to initialize the schema.
|
||||
* @param initializeSchema true to initialize schema, false otherwise
|
||||
* @return The builder instance
|
||||
*/
|
||||
public OpenSearchBuilder initializeSchema(boolean initializeSchema) {
|
||||
this.initializeSchema = initializeSchema;
|
||||
return this;
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets the batching strategy.
|
||||
* @param batchingStrategy The batching strategy to use
|
||||
* @return The builder instance
|
||||
* @throws IllegalArgumentException if batchingStrategy is null
|
||||
*/
|
||||
public OpenSearchBuilder batchingStrategy(BatchingStrategy batchingStrategy) {
|
||||
Assert.notNull(batchingStrategy, "batchingStrategy must not be null");
|
||||
this.batchingStrategy = batchingStrategy;
|
||||
return this;
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets the filter expression converter.
|
||||
* @param converter The filter expression converter to use
|
||||
* @return The builder instance
|
||||
* @throws IllegalArgumentException if converter is null
|
||||
*/
|
||||
public OpenSearchBuilder filterExpressionConverter(FilterExpressionConverter converter) {
|
||||
Assert.notNull(converter, "filterExpressionConverter must not be null");
|
||||
this.filterExpressionConverter = converter;
|
||||
return this;
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets the similarity function for vector comparison. See
|
||||
* https://opensearch.org/docs/latest/search-plugins/knn/approximate-knn/#spaces
|
||||
* for available functions.
|
||||
* @param similarityFunction The similarity function to use
|
||||
* @return The builder instance
|
||||
* @throws IllegalArgumentException if similarityFunction is null or empty
|
||||
*/
|
||||
public OpenSearchBuilder similarityFunction(String similarityFunction) {
|
||||
Assert.hasText(similarityFunction, "similarityFunction must not be null or empty");
|
||||
this.similarityFunction = similarityFunction;
|
||||
return this;
|
||||
}
|
||||
|
||||
/**
|
||||
* Builds a new OpenSearchVectorStore instance with the configured properties.
|
||||
* @return A new OpenSearchVectorStore instance
|
||||
* @throws IllegalStateException if the builder is in an invalid state
|
||||
*/
|
||||
@Override
|
||||
public OpenSearchVectorStore build() {
|
||||
validate();
|
||||
return new OpenSearchVectorStore(this);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
@@ -14,7 +14,7 @@
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
package org.springframework.ai.vectorstore;
|
||||
package org.springframework.ai.vectorstore.opensearch;
|
||||
|
||||
import java.util.Date;
|
||||
import java.util.List;
|
||||
@@ -14,7 +14,7 @@
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
package org.springframework.ai.vectorstore;
|
||||
package org.springframework.ai.vectorstore.opensearch;
|
||||
|
||||
import org.testcontainers.utility.DockerImageName;
|
||||
|
||||
@@ -14,7 +14,7 @@
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
package org.springframework.ai.vectorstore;
|
||||
package org.springframework.ai.vectorstore.opensearch;
|
||||
|
||||
import java.io.IOException;
|
||||
import java.net.URISyntaxException;
|
||||
@@ -47,6 +47,8 @@ import org.springframework.ai.document.Document;
|
||||
import org.springframework.ai.embedding.EmbeddingModel;
|
||||
import org.springframework.ai.openai.OpenAiEmbeddingModel;
|
||||
import org.springframework.ai.openai.api.OpenAiApi;
|
||||
import org.springframework.ai.vectorstore.SearchRequest;
|
||||
import org.springframework.ai.vectorstore.VectorStore;
|
||||
import org.springframework.beans.factory.annotation.Qualifier;
|
||||
import org.springframework.boot.SpringBootConfiguration;
|
||||
import org.springframework.boot.autoconfigure.EnableAutoConfiguration;
|
||||
@@ -396,9 +398,13 @@ class OpenSearchVectorStoreIT {
|
||||
@Qualifier("vectorStore")
|
||||
public OpenSearchVectorStore vectorStore(EmbeddingModel embeddingModel) {
|
||||
try {
|
||||
return new OpenSearchVectorStore(new OpenSearchClient(ApacheHttpClient5TransportBuilder
|
||||
.builder(HttpHost.create(opensearchContainer.getHttpHostAddress()))
|
||||
.build()), embeddingModel, true);
|
||||
return OpenSearchVectorStore.builder()
|
||||
.openSearchClient(new OpenSearchClient(ApacheHttpClient5TransportBuilder
|
||||
.builder(HttpHost.create(opensearchContainer.getHttpHostAddress()))
|
||||
.build()))
|
||||
.embeddingModel(embeddingModel)
|
||||
.initializeSchema(true)
|
||||
.build();
|
||||
}
|
||||
catch (URISyntaxException e) {
|
||||
throw new RuntimeException(e);
|
||||
@@ -409,12 +415,15 @@ class OpenSearchVectorStoreIT {
|
||||
@Qualifier("anotherVectorStore")
|
||||
public OpenSearchVectorStore anotherVectorStore(EmbeddingModel embeddingModel) {
|
||||
try {
|
||||
return new OpenSearchVectorStore("another_index",
|
||||
new OpenSearchClient(ApacheHttpClient5TransportBuilder
|
||||
.builder(HttpHost.create(opensearchContainer.getHttpHostAddress()))
|
||||
.build()),
|
||||
embeddingModel, OpenSearchVectorStore.DEFAULT_MAPPING_EMBEDDING_TYPE_KNN_VECTOR_DIMENSION,
|
||||
true);
|
||||
return OpenSearchVectorStore.builder()
|
||||
.index("another_index")
|
||||
.openSearchClient(new OpenSearchClient(ApacheHttpClient5TransportBuilder
|
||||
.builder(HttpHost.create(opensearchContainer.getHttpHostAddress()))
|
||||
.build()))
|
||||
.embeddingModel(embeddingModel)
|
||||
.mappingJson(OpenSearchVectorStore.DEFAULT_MAPPING_EMBEDDING_TYPE_KNN_VECTOR_DIMENSION)
|
||||
.initializeSchema(true)
|
||||
.build();
|
||||
}
|
||||
catch (URISyntaxException e) {
|
||||
throw new RuntimeException(e);
|
||||
@@ -14,7 +14,7 @@
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
package org.springframework.ai.vectorstore;
|
||||
package org.springframework.ai.vectorstore.opensearch;
|
||||
|
||||
import java.io.IOException;
|
||||
import java.net.URISyntaxException;
|
||||
@@ -47,6 +47,8 @@ import org.springframework.ai.observation.conventions.VectorStoreProvider;
|
||||
import org.springframework.ai.observation.conventions.VectorStoreSimilarityMetric;
|
||||
import org.springframework.ai.openai.OpenAiEmbeddingModel;
|
||||
import org.springframework.ai.openai.api.OpenAiApi;
|
||||
import org.springframework.ai.vectorstore.SearchRequest;
|
||||
import org.springframework.ai.vectorstore.VectorStore;
|
||||
import org.springframework.ai.vectorstore.observation.DefaultVectorStoreObservationConvention;
|
||||
import org.springframework.ai.vectorstore.observation.VectorStoreObservationDocumentation.HighCardinalityKeyNames;
|
||||
import org.springframework.ai.vectorstore.observation.VectorStoreObservationDocumentation.LowCardinalityKeyNames;
|
||||
@@ -205,12 +207,18 @@ public class OpenSearchVectorStoreObservationIT {
|
||||
public OpenSearchVectorStore vectorStore(EmbeddingModel embeddingModel,
|
||||
ObservationRegistry observationRegistry) {
|
||||
try {
|
||||
return new OpenSearchVectorStore(OpenSearchVectorStore.DEFAULT_INDEX_NAME,
|
||||
new OpenSearchClient(ApacheHttpClient5TransportBuilder
|
||||
.builder(HttpHost.create(opensearchContainer.getHttpHostAddress()))
|
||||
.build()),
|
||||
embeddingModel, OpenSearchVectorStore.DEFAULT_MAPPING_EMBEDDING_TYPE_KNN_VECTOR_DIMENSION, true,
|
||||
observationRegistry, null, new TokenCountBatchingStrategy());
|
||||
return OpenSearchVectorStore.builder()
|
||||
.index(OpenSearchVectorStore.DEFAULT_INDEX_NAME)
|
||||
.openSearchClient(new OpenSearchClient(ApacheHttpClient5TransportBuilder
|
||||
.builder(HttpHost.create(opensearchContainer.getHttpHostAddress()))
|
||||
.build()))
|
||||
.embeddingModel(embeddingModel)
|
||||
.mappingJson(OpenSearchVectorStore.DEFAULT_MAPPING_EMBEDDING_TYPE_KNN_VECTOR_DIMENSION)
|
||||
.initializeSchema(true)
|
||||
.observationRegistry(observationRegistry)
|
||||
.customObservationConvention(null)
|
||||
.batchingStrategy(new TokenCountBatchingStrategy())
|
||||
.build();
|
||||
}
|
||||
catch (URISyntaxException e) {
|
||||
throw new RuntimeException(e);
|
||||
@@ -14,7 +14,7 @@
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
package org.springframework.ai.vectorstore;
|
||||
package org.springframework.ai.vectorstore.opensearch;
|
||||
|
||||
import java.io.IOException;
|
||||
import java.net.URISyntaxException;
|
||||
@@ -46,6 +46,8 @@ import org.springframework.ai.ollama.api.OllamaOptions;
|
||||
import org.springframework.ai.ollama.management.ModelManagementOptions;
|
||||
import org.springframework.ai.ollama.management.OllamaModelManager;
|
||||
import org.springframework.ai.ollama.management.PullModelStrategy;
|
||||
import org.springframework.ai.vectorstore.SearchRequest;
|
||||
import org.springframework.ai.vectorstore.VectorStore;
|
||||
import org.springframework.beans.factory.annotation.Qualifier;
|
||||
import org.springframework.boot.SpringBootConfiguration;
|
||||
import org.springframework.boot.test.context.runner.ApplicationContextRunner;
|
||||
@@ -167,9 +169,13 @@ class OpenSearchVectorStoreWithOllamaIT {
|
||||
@Qualifier("vectorStore")
|
||||
public OpenSearchVectorStore vectorStore(EmbeddingModel embeddingModel) {
|
||||
try {
|
||||
return new OpenSearchVectorStore(new OpenSearchClient(ApacheHttpClient5TransportBuilder
|
||||
.builder(HttpHost.create(opensearchContainer.getHttpHostAddress()))
|
||||
.build()), embeddingModel, true);
|
||||
return OpenSearchVectorStore.builder()
|
||||
.openSearchClient(new OpenSearchClient(ApacheHttpClient5TransportBuilder
|
||||
.builder(HttpHost.create(opensearchContainer.getHttpHostAddress()))
|
||||
.build()))
|
||||
.embeddingModel(embeddingModel)
|
||||
.initializeSchema(true)
|
||||
.build();
|
||||
}
|
||||
catch (URISyntaxException e) {
|
||||
throw new RuntimeException(e);
|
||||
@@ -180,12 +186,15 @@ class OpenSearchVectorStoreWithOllamaIT {
|
||||
@Qualifier("anotherVectorStore")
|
||||
public OpenSearchVectorStore anotherVectorStore(EmbeddingModel embeddingModel) {
|
||||
try {
|
||||
return new OpenSearchVectorStore("another_index",
|
||||
new OpenSearchClient(ApacheHttpClient5TransportBuilder
|
||||
.builder(HttpHost.create(opensearchContainer.getHttpHostAddress()))
|
||||
.build()),
|
||||
embeddingModel, OpenSearchVectorStore.DEFAULT_MAPPING_EMBEDDING_TYPE_KNN_VECTOR_DIMENSION,
|
||||
true);
|
||||
return OpenSearchVectorStore.builder()
|
||||
.index("another_index")
|
||||
.openSearchClient(new OpenSearchClient(ApacheHttpClient5TransportBuilder
|
||||
.builder(HttpHost.create(opensearchContainer.getHttpHostAddress()))
|
||||
.build()))
|
||||
.embeddingModel(embeddingModel)
|
||||
.mappingJson(OpenSearchVectorStore.DEFAULT_MAPPING_EMBEDDING_TYPE_KNN_VECTOR_DIMENSION)
|
||||
.initializeSchema(true)
|
||||
.build();
|
||||
}
|
||||
catch (URISyntaxException e) {
|
||||
throw new RuntimeException(e);
|
||||
Reference in New Issue
Block a user