spring-ai-integration

name	spring-ai-integration
description	Use when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI 2.0 ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.

Spring AI Integration

Dependencies

<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-bom</artifactId>
            <version>2.0.0</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

<dependencies>
    <!-- Choose your model provider — pattern is spring-ai-starter-model-<provider> -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-model-anthropic</artifactId>
    </dependency>
    <!-- OR -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-model-openai</artifactId>
    </dependency>

    <!-- For RAG / vector search -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-vector-store-pgvector</artifactId>
    </dependency>
    <!-- QuestionAnswerAdvisor lives here — 2.0 renamed spring-ai-advisors-vector-store -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-vector-store-advisor</artifactId>
    </dependency>
</dependencies>

Version pairing matters. Spring Boot 4 requires Spring AI 2.0 (spring-ai-bom 2.0.0); the 1.x line targets Boot 3 only. Starter coordinates follow spring-ai-starter-model-<provider> (e.g. -model-anthropic, -model-openai) and spring-ai-starter-vector-store-<store>. Agents trained on pre-1.0 Spring AI emit spring-ai-<x>-spring-boot-starter — those names resolve to nothing in Maven Central. Also gone in 2.0: spring-ai-starter-model-azure-openai (use the OpenAI starter with an Azure base URL instead).

ChatClient — Basic Usage

@Service
@RequiredArgsConstructor
public class DocumentSummaryService {

    private final ChatClient chatClient;

    public String summarize(String conversationId, String content) {
        return chatClient.prompt()
            .advisors(a -> a.param(ChatMemory.CONVERSATION_ID, conversationId))
            .user(u -> u.text("Summarize the following document in 3 bullet points:\n\n{content}")
                .param("content", content))
            .call()
            .content();
    }

    // With system prompt
    public String analyzeFinancial(String conversationId, String document, String language) {
        return chatClient.prompt()
            .advisors(a -> a.param(ChatMemory.CONVERSATION_ID, conversationId))
            .system("You are a financial analyst. Respond in {language}.")
            .system(s -> s.param("language", language))
            .user(document)
            .call()
            .content();
    }
}

Every call using the configured memory advisor must provide a user- or session-scoped ChatMemory.CONVERSATION_ID. Never use one shared conversation ID for all users.

ChatClient Bean Configuration

@Configuration
public class AiConfig {

    @Bean
    public ChatMemory chatMemory() {
        // InMemoryChatMemory is long gone. Use MessageWindowChatMemory —
        // it caps history to a sliding window and defaults to an in-memory repository.
        return MessageWindowChatMemory.builder()
            .maxMessages(20)
            .build();
    }

    @Bean
    public ChatClient chatClient(ChatClient.Builder builder, ChatMemory chatMemory) {
        return builder
            .defaultSystem("You are a helpful assistant for an e-commerce platform.")
            .defaultAdvisors(
                MessageChatMemoryAdvisor.builder(chatMemory).build(), // builder, not new(...)
                new SimpleLoggerAdvisor() // logs prompts/responses
            )
            .build();
    }
}

// 2.0: the conversation id is REQUIRED on every call that goes through a memory advisor.
// ChatMemory.DEFAULT_CONVERSATION_ID is removed — omitting the param throws IllegalArgumentException.
public String chat(String sessionId, String message) {
    return chatClient.prompt()
        .user(message)
        .advisors(a -> a.param(ChatMemory.CONVERSATION_ID, sessionId))
        .call()
        .content();
}

Prompt Templates (externalized)

// src/main/resources/prompts/analyze-order.st
// Analyze this order and identify any anomalies:
// Customer: {customer}
// Items: {items}
// Total: {total}
// Flag any unusual patterns.

@Service
public class OrderAnalysisService {

    @Value("classpath:prompts/analyze-order.st")
    private Resource promptTemplate;

    public String analyzeOrder(String conversationId, Order order) {
        return chatClient.prompt()
            .advisors(a -> a.param(ChatMemory.CONVERSATION_ID, conversationId))
            .user(u -> u.text(promptTemplate)
                .param("customer", order.getCustomerEmail())
                .param("items", order.getItems().toString())
                .param("total", order.getTotal()))
            .call()
            .content();
    }
}

Structured Output

// Define the target record
public record OrderClassification(
    String category,
    String priority,
    List<String> tags,
    boolean requiresManualReview
) {}

@Service
public class OrderClassifier {

    public OrderClassification classify(String orderDescription) {
        return chatClient.prompt()
            .user("Classify this order: " + orderDescription)
            .call()
            .entity(OrderClassification.class); // Spring AI handles JSON parsing
    }
}

RAG Pipeline

@Configuration
public class RagConfig {

    // No manual VectorStore bean — the spring-ai-starter-vector-store-pgvector
    // starter auto-configures one. Just inject it. (The old `new PgVectorStore(...)`
    // constructor is removed; if you must build one, use PgVectorStore.builder(...).)

    @Bean
    public ChatClient ragChatClient(ChatClient.Builder builder, VectorStore vectorStore) {
        return builder
            .defaultAdvisors(
                QuestionAnswerAdvisor.builder(vectorStore)
                    .searchRequest(SearchRequest.builder().topK(5).build()) // builder, not defaults().withTopK()
                    .build()
            )
            .build();
    }
}

@Service
@RequiredArgsConstructor
public class KnowledgeService {

    private final VectorStore vectorStore;
    private final ChatClient ragChatClient;

    // Ingest documents
    public void ingest(List<String> documents) {
        List<Document> docs = documents.stream()
            .map(content -> new Document(content))
            .toList();
        vectorStore.add(docs);
    }

    // Query with RAG
    public String ask(String question) {
        return ragChatClient.prompt()
            .user(question)
            .call()
            .content();
    }
}

Streaming Responses

@GetMapping(value = "/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
public Flux<String> stream(@RequestParam String prompt) {
    return chatClient.prompt()
        .user(prompt)
        .stream()
        .content();
}

application.yml

spring:
  ai:
    anthropic:
      api-key: ${ANTHROPIC_API_KEY}
      chat:
        # 2.0 flattened the properties — the old chat.options.* nesting is dead
        model: ${ANTHROPIC_MODEL}
        max-tokens: 2048
        temperature: 0.7   # 2.0 removed the 0.7 default — set it explicitly if you rely on it
    # OR for OpenAI:
    openai:
      api-key: ${OPENAI_API_KEY}
      chat:
        model: ${OPENAI_MODEL}
    vectorstore:
      pgvector:
        initialize-schema: true
        dimensions: 1536

Gotchas

• Agent uses Spring AI 1.x (spring-ai-bom 1.0.x) on Spring Boot 4 — 1.x targets Boot 3 only; Boot 4 requires Spring AI 2.0
• Agent uses pre-1.0 artifact names (spring-ai-anthropic-spring-boot-starter) — the pattern is spring-ai-starter-model-anthropic
• Agent configures spring.ai.anthropic.chat.options.model — 2.0 flattened properties; drop the .options segment (spring.ai.anthropic.chat.model)
• Agent passes built options to .options(...) — 2.0 takes the builder: .options(AnthropicChatOptions.builder().maxTokens(2048)), no .build()
• Agent writes new MessageChatMemoryAdvisor(new InMemoryChatMemory()) — both long removed; use MessageChatMemoryAdvisor.builder(chatMemory) + MessageWindowChatMemory
• Agent omits the conversation id on a memory-advisor call — mandatory in 2.0 (ChatMemory.DEFAULT_CONVERSATION_ID removed); pass a.param(ChatMemory.CONVERSATION_ID, ...) or get IllegalArgumentException
• Agent uses PromptChatMemoryAdvisor — removed in 2.0; use MessageChatMemoryAdvisor
• Agent adds spring-ai-advisors-vector-store for QuestionAnswerAdvisor — renamed to spring-ai-vector-store-advisor in 2.0
• Agent writes SearchRequest.defaults().withTopK(n) — use SearchRequest.builder().topK(n).build()
• Agent hardcodes API keys — always use environment variables / ${...}
• Agent hardcodes provider model IDs - configure them externally because model catalogs change
• Agent builds prompts with string concatenation — use .param() template variables
• Agent puts prompts inline in code — externalize to src/main/resources/prompts/
• Agent ignores structured output — use .entity(MyClass.class) instead of parsing manually
• Agent uses .entity(List.class) for a list — generics erase; pass new ParameterizedTypeReference<List<X>>() {}
• Agent skips error handling for API calls — wrap in try/catch, handle NonTransientAiException (don't retry) vs TransientAiException (retry)