Spring AI 开发指南概述

Spring AI 是 Spring 官方提供的 AI 应用开发框架，简化了与各种大语言模型（LLM）的集成，包括 OpenAI、Anthropic、Azure OpenAI 等。

核心功能 1. 项目创建

依赖：

org.springframework.ai spring-ai-openai-spring-boot-starter

或使用 Gradle：

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

Chat Client

配置：

spring: ai: openai: api-key: ${OPENAI_API_KEY} chat: options: model: gpt-4 temperature: 0.7

使用 ChatClient：

@Service public class ChatService { private final ChatClient chatClient;

public ChatService(ChatClient chatClient) {
    this.chatClient = chatClient;
}

public String chat(String message) {
    return chatClient.call(message);
}

public String chatWithPrompt(String userMessage) {
    Prompt prompt = new Prompt(new UserMessage(userMessage));
    ChatResponse response = chatClient.call(prompt);
    return response.getResult().getOutput().getContent();
}

}

流式响应：

@Service public class ChatService { private final StreamingChatClient streamingChatClient;

public ChatService(StreamingChatClient streamingChatClient) {
    this.streamingChatClient = streamingChatClient;
}

public Flux<String> streamChat(String message) {
    return streamingChatClient.stream(message)
        .map(response -> response.getResult().getOutput().getContent());
}

}

Prompt Template

定义模板：

@Service public class PromptService { private final PromptTemplate promptTemplate;

public PromptService() {
    this.promptTemplate = new PromptTemplate(
        "请用{style}风格回答以下问题：{question}"
    );
}

public String generatePrompt(String style, String question) {
    Map<String, Object> variables = Map.of(
        "style", style,
        "question", question
    );
    return promptTemplate.render(variables);
}

}

使用 ChatClient：

@Service public class ChatService { private final ChatClient chatClient; private final PromptTemplate promptTemplate;

public ChatService(ChatClient chatClient) {
    this.chatClient = chatClient;
    this.promptTemplate = new PromptTemplate(
        "请用{style}风格回答以下问题：{question}"
    );
}

public String chatWithStyle(String style, String question) {
    Prompt prompt = promptTemplate.create(Map.of(
        "style", style,
        "question", question
    ));
    ChatResponse response = chatClient.call(prompt);
    return response.getResult().getOutput().getContent();
}

}

Embedding

配置：

spring: ai: openai: embedding: options: model: text-embedding-ada-002

使用 EmbeddingClient：

@Service public class EmbeddingService { private final EmbeddingClient embeddingClient;

public EmbeddingService(EmbeddingClient embeddingClient) {
    this.embeddingClient = embeddingClient;
}

public List<Double> embed(String text) {
    EmbeddingResponse response = embeddingClient.embedForResponse(
        List.of(text)
    );
    return response.getResult().getOutput();
}

public List<List<Double>> embedBatch(List<String> texts) {
    EmbeddingResponse response = embeddingClient.embedForResponse(texts);
    return response.getResult().getOutput();
}

}

Vector Store

配置：

spring: ai: vectorstore: pgvector: index-type: HNSW distance-type: COSINE_DISTANCE

使用 VectorStore：

@Service public class VectorStoreService { private final VectorStore vectorStore; private final EmbeddingClient embeddingClient;

public VectorStoreService(
    VectorStore vectorStore,
    EmbeddingClient embeddingClient
) {
    this.vectorStore = vectorStore;
    this.embeddingClient = embeddingClient;
}

public void addDocument(String id, String content) {
    List<Double> embedding = embeddingClient.embed(content);
    Document document = new Document(id, content, Map.of());
    vectorStore.add(List.of(document));
}

public List<Document> searchSimilar(String query, int topK) {
    List<Double> queryEmbedding = embeddingClient.embed(query);
    return vectorStore.similaritySearch(
        SearchRequest.query(query)
            .withTopK(topK)
    );
}

}

Function Calling

定义函数：

@Bean public Function weatherFunction() { return request -> { // 调用天气 API WeatherResponse response = weatherService.getWeather( request.getLocation() ); return response; }; }

配置 Function Calling：

@Configuration public class FunctionCallingConfig { @Bean public Function weatherFunction() { return request -> { // 实现天气查询逻辑 return new WeatherResponse(/ ... /); }; } }

使用 Function Calling：

@Service public class ChatService { private final ChatClient chatClient; private final FunctionCallbackRegistry functionCallbackRegistry;

public ChatService(
    ChatClient chatClient,
    FunctionCallbackRegistry functionCallbackRegistry
) {
    this.chatClient = chatClient;
    this.functionCallbackRegistry = functionCallbackRegistry;
}

public String chatWithFunction(String message) {
    Prompt prompt = new Prompt(
        new UserMessage(message),
        functionCallbackRegistry.getFunctionCallbacks()
    );
    ChatResponse response = chatClient.call(prompt);
    return response.getResult().getOutput().getContent();
}

}

多模型支持

配置多个模型：

spring: ai: openai: api-key: ${OPENAI_API_KEY} anthropic: api-key: ${ANTHROPIC_API_KEY} azure: openai: api-key: ${AZURE_OPENAI_API_KEY} endpoint: ${AZURE_OPENAI_ENDPOINT}

使用特定模型：

@Service public class MultiModelService { private final ChatClient openAiChatClient; private final ChatClient anthropicChatClient;

public MultiModelService(
    @Qualifier("openAiChatClient") ChatClient openAiChatClient,
    @Qualifier("anthropicChatClient") ChatClient anthropicChatClient
) {
    this.openAiChatClient = openAiChatClient;
    this.anthropicChatClient = anthropicChatClient;
}

public String chatWithOpenAI(String message) {
    return openAiChatClient.call(message);
}

public String chatWithAnthropic(String message) {
    return anthropicChatClient.call(message);
}

}

最佳实践 1. 配置管理使用环境变量存储 API Key 区分开发和生产环境配置配置合理的超时和重试策略 2. 错误处理 @Service public class ChatService { private final ChatClient chatClient;

public String chat(String message) {
    try {
        return chatClient.call(message);
    } catch (Exception e) {
        // 处理错误
        log.error("Chat error", e);
        return "抱歉，处理请求时出现错误";
    }
}

}

性能优化使用流式响应提升用户体验合理使用缓存减少 API 调用批量处理 Embedding 请求
成本控制选择合适的模型（GPT-3.5 vs GPT-4）限制 Token 使用量监控 API 调用情况常用依赖

org.springframework.ai spring-ai-openai-spring-boot-starter

org.springframework.ai spring-ai-anthropic-spring-boot-starter

org.springframework.ai spring-ai-azure-openai-spring-boot-starter

org.springframework.ai spring-ai-pgvector-store-spring-boot-starter

示例 Prompt "如何使用 Spring AI 集成 OpenAI？" "Spring AI 中如何实现流式响应？" "如何在 Spring AI 中使用 Embedding 和 Vector Store？" "Spring AI 中如何实现 Function Calling？" "如何配置 Spring AI 支持多个模型？"

安装