ZhiPuAI Embeddings

Spring AI支持智谱AI的文本嵌入模型。智谱AI的文本嵌入衡量文本字符串的相关性。嵌入是一个浮点数向量（列表）。两个向量之间的距离衡量它们的相关性。小距离表示高度相关，大距离表示低相关。

Spring AI supports the ZhiPuAI’s text embeddings models. ZhiPuAI’s text embeddings measure the relatedness of text strings. An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.

Prerequisites

你需要使用智谱AI创建一个API来访问智谱AI语言模型。

You will need to create an API with ZhiPuAI to access ZhiPu AI language models.

在` ZhiPu AI registration page 创建一个账户并在 API Keys page `生成令牌。

Create an account at ZhiPu AI registration page and generate the token on the API Keys page.

Spring AI项目定义了一个名为` spring.ai.zhipu.api-key 的配置属性，你应该将其设置为从API密钥页面获得的 API Key `的值。

The Spring AI project defines a configuration property named spring.ai.zhipu.api-key that you should set to the value of the API Key obtained from the API Keys page.

你可以在` application.properties `文件中设置此配置属性：

You can set this configuration property in your application.properties file:

spring.ai.zhipu.api-key=<your-zhipu-api-key>

为了在处理API密钥等敏感信息时增强安全性，你可以使用Spring表达式语言（SpEL）引用环境变量：

For enhanced security when handling sensitive information like API keys, you can use Spring Expression Language (SpEL) to reference an environment variable:

# In application.yml
spring:
  ai:
    zhipu:
      api-key: ${ZHIPU_API_KEY}
# In your environment or .env file
export ZHIPU_API_KEY=<your-zhipu-api-key>

你也可以在应用程序代码中以编程方式设置此配置：

You can also set this configuration programmatically in your application code:

// Retrieve API key from a secure source or environment variable
String apiKey = System.getenv("ZHIPU_API_KEY");

Add Repositories and BOM

Spring AI 工件发布在 Maven Central 和 Spring Snapshot 存储库中。请参阅“添加 Spring AI 仓库”部分，将这些仓库添加到您的构建系统。

Spring AI artifacts are published in Maven Central and Spring Snapshot repositories. Refer to the Artifact Repositories section to add these repositories to your build system.

为了帮助进行依赖项管理，Spring AI 提供了一个 BOM（物料清单）以确保在整个项目中使用一致版本的 Spring AI。有关将 Spring AI BOM 添加到你的构建系统的说明，请参阅 Dependency Management 部分。

To help with dependency management, Spring AI provides a BOM (bill of materials) to ensure that a consistent version of Spring AI is used throughout the entire project. Refer to the Dependency Management section to add the Spring AI BOM to your build system.

Auto-configuration

Spring AI 自动配置、启动器模块的工件名称发生了重大变化。请参阅 upgrade notes 以获取更多信息。

There has been a significant change in the Spring AI auto-configuration, starter modules' artifact names. Please refer to the upgrade notes for more information.

Spring AI为Azure智谱AI嵌入模型提供Spring Boot自动配置。要启用它，请将以下依赖项添加到项目的Maven ` pom.xml `文件中：

Spring AI provides Spring Boot auto-configuration for the Azure ZhiPuAI Embedding Model. To enable it add the following dependency to your project’s Maven pom.xml file:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-model-zhipuai</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-starter-model-zhipuai'
}

  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.

Embedding Properties

Retry Properties

前缀` spring.ai.retry `用作属性前缀，允许你配置智谱AI嵌入模型的重试机制。

The prefix spring.ai.retry is used as the property prefix that lets you configure the retry mechanism for the ZhiPuAI Embedding model.

Property Description Default

spring.ai.retry.max-attempts

Maximum number of retry attempts.

10

spring.ai.retry.backoff.initial-interval

Initial sleep duration for the exponential backoff policy.

2 sec.

spring.ai.retry.backoff.multiplier

Backoff interval multiplier.

5

spring.ai.retry.backoff.max-interval

Maximum backoff duration.

3 min.

spring.ai.retry.on-client-errors

If false, throw a NonTransientAiException, and do not attempt retry for 4xx client error codes

false

spring.ai.retry.exclude-on-http-codes

List of HTTP status codes that should not trigger a retry (e.g. to throw NonTransientAiException).

empty

spring.ai.retry.on-http-codes

List of HTTP status codes that should trigger a retry (e.g. to throw TransientAiException).

empty

Connection Properties

前缀` spring.ai.zhipuai `用作属性前缀，允许你连接到智谱AI。

The prefix spring.ai.zhipuai is used as the property prefix that lets you connect to ZhiPuAI.

Property Description Default

spring.ai.zhipuai.base-url

The URL to connect to

[role="bare"]https://open.bigmodel.cn/api/paas

spring.ai.zhipuai.api-key

The API Key

-

Configuration Properties

嵌入自动配置的启用和禁用现在通过前缀为 spring.ai.azure.openai.embedding 的顶级属性进行配置。

Enabling and disabling of the embedding auto-configurations are now configured via top level properties with the prefix spring.ai.model.embedding.

为了启用，spring.ai.model.embedding=zhipuai（默认已启用）

To enable, spring.ai.model.embedding=zhipuai (It is enabled by default)

要禁用，spring.ai.model.embedding=none（或任何不匹配 zhipuai 的值）

To disable, spring.ai.model.embedding=none (or any value which doesn’t match zhipuai)

此更改旨在允许配置多个模型。

This change is done to allow configuration of multiple models.

spring.ai.zhipuai.embedding 前缀是配置智谱 AI 的嵌入实现的属性前缀。

The prefix spring.ai.zhipuai.embedding is property prefix that configures the EmbeddingModel implementation for ZhiPuAI.

Property Description Default

spring.ai.zhipuai.embedding.enabled (Removed and no longer valid)

Enable ZhiPuAI embedding model.

true

spring.ai.model.embedding

Enable ZhiPuAI embedding model.

zhipuai

spring.ai.zhipuai.embedding.base-url

Optional overrides the spring.ai.zhipuai.base-url to provide embedding specific url

-

spring.ai.zhipuai.embedding.api-key

Optional overrides the spring.ai.zhipuai.api-key to provide embedding specific api-key

-

spring.ai.zhipuai.embedding.options.model

The model to use

embedding-2

spring.ai.zhipuai.embedding.options.dimensions

The number of dimensions, the default value is 2048 when the model is embedding-3

-

您可以覆盖智谱 AI 嵌入和聊天实现的通用 spring.ai.zhipuai.base-url 和 spring.ai.zhipuai.api-key。如果设置了 spring.ai.zhipuai.embedding.base-url 和 spring.ai.zhipuai.embedding.api-key 属性，它们将优先于通用属性。同样，如果设置了 spring.ai.zhipuai.chat.base-url 和 spring.ai.zhipuai.chat.api-key 属性，它们将优先于通用属性。如果您希望为不同的模型和不同的模型端点使用不同的智谱 AI 账户，这会很有用。

You can override the common spring.ai.zhipuai.base-url and spring.ai.zhipuai.api-key for the ChatModel and EmbeddingModel implementations. The spring.ai.zhipuai.embedding.base-url and spring.ai.zhipuai.embedding.api-key properties if set take precedence over the common properties. Similarly, the spring.ai.zhipuai.chat.base-url and spring.ai.zhipuai.chat.api-key properties if set take precedence over the common properties. This is useful if you want to use different ZhiPuAI accounts for different models and different model endpoints.

所有以 spring.ai.zhipuai.embedding 为前缀的属性都可以在运行时通过向 EmbeddingClient 调用添加请求特定的 EmbeddingOptions 来覆盖。

All properties prefixed with spring.ai.zhipuai.embedding.options can be overridden at runtime by adding a request specific Runtime Options to the EmbeddingRequest call.

Runtime Options

ZhipuAiEmbeddingOptions 提供了智谱 AI 配置，例如要使用的模型等等。

The ZhiPuAiEmbeddingOptions.java provides the ZhiPuAI configurations, such as the model to use and etc.

默认选项也可以使用 spring.ai.zhipuai.embedding 属性进行配置。

The default options can be configured using the spring.ai.zhipuai.embedding.options properties as well.

在启动时，使用 ZhipuAiEmbeddingOptions 构造函数设置用于所有嵌入请求的默认选项。在运行时，您可以使用 EmbeddingOptions 实例作为 EmbeddingRequest 的一部分来覆盖默认选项。

At start-time use the ZhiPuAiEmbeddingModel constructor to set the default options used for all embedding requests. At run-time you can override the default options, using a ZhiPuAiEmbeddingOptions instance as part of your EmbeddingRequest.

例如，要覆盖特定请求的默认模型名称：

For example to override the default model name for a specific request:

EmbeddingResponse embeddingResponse = embeddingModel.call(
    new EmbeddingRequest(List.of("Hello World", "World is big and salvation is near"),
        ZhiPuAiEmbeddingOptions.builder()
            .model("Different-Embedding-Model-Deployment-Name")
        .build()));

Sample Controller

这将创建一个 EmbeddingModel 实现，您可以将其注入到您的类中。这是一个使用 EmbeddingModel 实现的简单 @Controller 类的示例。

This will create a EmbeddingModel implementation that you can inject into your class. Here is an example of a simple @Controller class that uses the EmbeddingModel implementation.

spring.ai.zhipuai.api-key=YOUR_API_KEY
spring.ai.zhipuai.embedding.options.model=embedding-2
@RestController
public class EmbeddingController {

    private final EmbeddingModel embeddingModel;

    @Autowired
    public EmbeddingController(EmbeddingModel embeddingModel) {
        this.embeddingModel = embeddingModel;
    }

    @GetMapping("/ai/embedding")
    public Map embed(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
        EmbeddingResponse embeddingResponse = this.embeddingModel.embedForResponse(List.of(message));
        return Map.of("embedding", embeddingResponse);
    }
}

Manual Configuration

如果您不使用 Spring Boot，可以手动配置智谱 AI 嵌入模型。为此，请将 spring-ai-zhipuai 依赖项添加到您的项目的 Maven pom.xml 文件中：

If you are not using Spring Boot, you can manually configure the ZhiPuAI Embedding Model. For this add the spring-ai-zhipuai dependency to your project’s Maven pom.xml file:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-zhipuai</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-zhipuai'
}

  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.

spring-ai-zhipuai 依赖项还提供了对 ZhipuAiChatClient 的访问。有关 ZhipuAiChatClient 的更多信息，请参阅 ChatClient 部分。

The spring-ai-zhipuai dependency provides access also to the ZhiPuAiChatModel. For more information about the ZhiPuAiChatModel refer to the ZhiPuAI Chat Client section.

接下来，创建 EmbeddingClient 实例并使用它来计算两个输入文本之间的相似度：

Next, create an ZhiPuAiEmbeddingModel instance and use it to compute the similarity between two input texts:

var zhiPuAiApi = new ZhiPuAiApi(System.getenv("ZHIPU_AI_API_KEY"));

var embeddingModel = new ZhiPuAiEmbeddingModel(api, MetadataMode.EMBED,
				ZhiPuAiEmbeddingOptions.builder()
						.model("embedding-3")
						.dimensions(1536)
						.build());

EmbeddingResponse embeddingResponse = this.embeddingModel
	.embedForResponse(List.of("Hello World", "World is big and salvation is near"));

EmbeddingRequest 提供了嵌入请求的配置信息。选项类提供了一个 with 方法，便于选项创建。

The ZhiPuAiEmbeddingOptions provides the configuration information for the embedding requests. The options class offers a builder() for easy options creation.