PGvector

本部分将引导您设置 PGvector VectorStore 以存储文档嵌入并执行相似性搜索。

This section walks you through setting up the PGvector VectorStore to store document embeddings and perform similarity searches.

PGvector 是 PostgreSQL 的开源扩展，支持存储和搜索机器学习生成的嵌入。它提供了不同的功能，让用户能够识别精确最近邻和近似最近邻。它设计用于与其他 PostgreSQL 特性（包括索引和查询）无缝协作。

PGvector is an open-source extension for PostgreSQL that enables storing and searching over machine learning-generated embeddings. It provides different capabilities that let users identify both exact and approximate nearest neighbors. It is designed to work seamlessly with other PostgreSQL features, including indexing and querying.

Prerequisites

首先，您需要访问启用了 vector、hstore 和 uuid-ossp 扩展的 PostgreSQL 实例。

First you need an access to PostgreSQL instance with enabled vector, hstore and uuid-ossp extensions.

setup local Postgres/PGVector 附录显示如何通过 Docker 容器在本地设置数据库。

The appendix_a appendix shows how to set up a DB locally with a Docker container.

在启动时，PgVectorStore 会尝试安装所需的数据库扩展名并使用索引创建所需的 vector_store 表。

On startup, the PgVectorStore will attempt to install the required database extensions and create the required vector_store table with an index.

您还可以按如下所示手动执行此操作：

Optionally, you can do this manually like so:

CREATE EXTENSION IF NOT EXISTS vector;
CREATE EXTENSION IF NOT EXISTS hstore;
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";

CREATE TABLE IF NOT EXISTS vector_store (
	id uuid DEFAULT uuid_generate_v4() PRIMARY KEY,
	content text,
	metadata json,
	embedding vector(1536) // 1536 is the default embedding dimension
);

CREATE INDEX ON vector_store USING HNSW (embedding vector_cosine_ops);

如果使用不同的维度，请用实际嵌入维度替换 1536。

replace the 1536 with the actual embedding dimension if you are using a different dimension.

下一步，如果需要，为 EmbeddingClient提供 API 密钥，以便生成由 `PgVectorStore`存储的嵌入。

Next if required, an API key for the EmbeddingClient to generate the embeddings stored by the PgVectorStore.

Dependencies

然后，将 PgVectorStore 引导启动程序依赖项添加到您的项目：

Then add the PgVectorStore boot starter dependency to your project:

<dependency>
	<groupId>org.springframework.ai</groupId>
	<artifactId>spring-ai-pgvector-store-spring-boot-starter</artifactId>
</dependency>

或添加到 Gradle build.gradle 构建文件中。

or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-pgvector-store-spring-boot-starter'
}

向量存储还要求 EmbeddingClient 实例为文档计算嵌入。您可以选择一个可用的 EmbeddingClient Implementations。

The Vector Store, also requires an EmbeddingClient instance to calculate embeddings for the documents. You can pick one of the available EmbeddingClient Implementations.

例如，要使用 OpenAI EmbeddingClient，请将以下依赖项添加到你的项目中：

For example to use the OpenAI EmbeddingClient add the following dependency to your project:

<dependency>
	<groupId>org.springframework.ai</groupId>
	<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>

或添加到 Gradle build.gradle 构建文件中。

or to your Gradle build.gradle build file.

dependencies {
    implementation 'org.springframework.ai:spring-ai-openai-spring-boot-starter'
}

请参阅 Dependency Management 部分，将 Spring AI BOM 添加到您的构建文件中。请参阅 Repositories 部分，将 Milestone 和/或快照存储库添加到您的构建文件中。

Refer to the Dependency Management section to add the Spring AI BOM to your build file. Refer to the Repositories section to add Milestone and/or Snapshot Repositories to your build file.

要连接并配置 PgVectorStore，您需要提供实例的访问详细信息。可以使用 Spring Boot 的 application.yml 提供一个简单的配置

To connect to and configure the PgVectorStore, you need to provide access details for your instance. A simple configuration can either be provided via Spring Boot’s application.yml

spring:
  datasource:
    url: jdbc:postgresql://localhost:5432/postgres
    username: postgres
    password: postgres
  ai:
	vectorstore:
	  pgvector:
		index-type: HNSW
		distance-type: COSINE_DISTANCE
		dimension: 1536

查看 configuration parameters 的列表以了解默认值和配置选项。

Check the list of pgvector-properties to learn about the default values and configuration options.

现在，您可以在应用程序中自动连接 PgVector Store 并使用它

Now you can Auto-wire the PgVector Store in your application and use it

@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"),
    new Document("You walk forward facing the past and you turn back toward the future.", Map.of("meta2", "meta2")));

// Add the documents to PGVector
vectorStore.add(List.of(document));

// Retrieve documents similar to a query
List<Document> results = vectorStore.similaritySearch(SearchRequest.query("Spring").withTopK(5));

Manual Configuration

您可以手动配置 PgVectorStore，而不是使用 Spring Boot 自动配置。为此，您需要向您的项目添加 PostgreSQL 连接和 JdbcTemplate 自动配置依赖项：

Instead of using the Spring Boot auto-configuration, you can manually configure the PgVectorStore. For this you need to add the PostgreSQL connection and JdbcTemplate auto-configuration dependencies to your project:

<dependency>
	<groupId>org.springframework.boot</groupId>
	<artifactId>spring-boot-starter-jdbc</artifactId>
</dependency>

<dependency>
	<groupId>org.postgresql</groupId>
	<artifactId>postgresql</artifactId>
	<scope>runtime</scope>
</dependency>

<dependency>
	<groupId>org.springframework.ai</groupId>
	<artifactId>spring-ai-pgvector-store</artifactId>
</dependency>

  1. 参见 Dependency Management 部分，将 Spring AI BOM 添加到你的构建文件中。

Refer to the Dependency Management section to add the Spring AI BOM to your build file.

要在应用程序中配置 PgVector，可以使用以下设置：

To configure PgVector in your application, you can use the following setup:

@Bean
public VectorStore vectorStore(JdbcTemplate jdbcTemplate, EmbeddingClient embeddingClient) {
	return new PgVectorStore(jdbcTemplate, embeddingClient);
}

Metadata filtering

您可以利用 PgVector 存储中通用的可移植过滤器 metadata filters。

You can leverage the generic, portable metadata filters with the PgVector store.

例如，你可以使用文本表达式语言：

For example, you can use either the text expression language:

vectorStore.similaritySearch(
    SearchRequest.defaults()
    .withQuery("The World")
    .withTopK(TOP_K)
    .withSimilarityThreshold(SIMILARITY_THRESHOLD)
    .withFilterExpression("author in ['john', 'jill'] && article_type == 'blog'"));

或使用 Filter.Expression DSL 以编程方式：

or programmatically using the Filter.Expression DSL:

FilterExpressionBuilder b = new FilterExpressionBuilder();

vectorStore.similaritySearch(SearchRequest.defaults()
    .withQuery("The World")
    .withTopK(TOP_K)
    .withSimilarityThreshold(SIMILARITY_THRESHOLD)
    .withFilterExpression(b.and(
        b.in("john", "jill"),
        b.eq("article_type", "blog")).build()));

这些筛选表达式被转换为等同的 PgVector 筛选。

These filter expressions are converted into the equivalent PgVector filters.

PgVectorStore properties

您可以在 Spring Boot 配置中使用以下属性来自定义 PGVector 向量存储。

You can use the following properties in your Spring Boot configuration to customize the PGVector vector store.

Property Description Default value

spring.ai.vectorstore.pgvector.index-type

Nearest neighbor search index type. Options are NONE - exact nearest neighbor search, IVFFlat - index divides vectors into lists, and then searches a subset of those lists that are closest to the query vector. It has faster build times and uses less memory than HNSW, but has lower query performance (in terms of speed-recall tradeoff). HNSW - creates a multilayer graph. It has slower build times and uses more memory than IVFFlat, but has better query performance (in terms of speed-recall tradeoff). There’s no training step like IVFFlat, so the index can be created without any data in the table.

HNSW

spring.ai.vectorstore.pgvector.distance-type

Search distance type. Defaults to COSINE_DISTANCE. But if vectors are normalized to length 1, you can use EUCLIDEAN_DISTANCE or NEGATIVE_INNER_PRODUCT for best performance.

COSINE_DISTANCE

spring.ai.vectorstore.pgvector.dimension

Embeddings dimension. If not specified explicitly the PgVectorStore will retrieve the dimensions form the provided EmbeddingClient. Dimensions are set to the embedding column the on table creation. If you change the dimensions your would have to to re-create the vector_store table as well.

-

spring.ai.vectorstore.pgvector.remove-existing-vector-store-table

Deletes the existing vector_store table on start up.

false

Run Postgres & PGVector DB locally

docker run -it --rm --name postgres -p 5432:5432 -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=postgres ankane/pgvector

您可以像这样连接到此服务器：

You can connect to this server like this:

psql -U postgres -h localhost -p 5432