langchain4j笔记-05

AI Services

简单使用

java 复制代码

public static StreamingChatModel STREAMING_BASE_MODEL = OpenAiStreamingChatModel.builder()
            .baseUrl("https://api.deepseek.com")
            .apiKey(System.getenv("DS_API_KEY"))
            .modelName("deepseek-v4-flash")
            .logRequests(true)
            .logResponses(true)
            .build();

java 复制代码

interface Assistant {
    String chat(String userMessage);
}

java 复制代码

@Test
void test01() {
    /**
     * 您将接口的Class与底层组件一起提供给AiServices，然后AiServices会创建一个实现该接口的代理对象。
     * 目前这一过程使用的是反射机制，但我们也在考虑其他替代方案。
     * 这个代理对象会处理所有输入和输出的转换。在本例中，输入是一个简单的String，但我们使用的是以ChatMessage作为输入的ChatModel。
     * 因此，AiService会自动将其转换为UserMessage并调用ChatModel。
     * 由于chat方法的输出类型是String，当ChatModel返回AiMessage后，该消息会在chat方法返回之前被转换为String类型。
     */
    Assistant assistant = AiServices.create(Assistant.class, BASE_MODEL);
    String answer = assistant.chat("Hello");
    System.out.println(answer);
}

添加SystemMessage

方式一：通过注解，但是注解可以是文本，也可以从配置文件获取

java 复制代码

interface Friend {

    //@SystemMessage("你是一个AI建模专家，只能回答关于脑建立的建模问题。")
    @SystemMessage(fromResource = "my-prompt-template.txt")
    String chat(String userMessage);
}

@Test
void test02() {
    Friend friend = AiServices.create(Friend.class, BASE_MODEL);
    String answer = friend.chat("腿抽筋怎么办");
    System.out.println(answer);
    // 我专注于脑建立的建模问题，无法提供医疗建议。建议你咨询医生或查看可靠的健康资源来处理腿抽筋。如果你有关于脑建模（例如神经网络、认知架构等）的问题，我很乐意帮助！
}

方式二：通过代码配置（可以给不同的chatMemoryId提供不同的SystemMessage）

java 复制代码

@Test
void test03() {
    Friend friend = AiServices.builder(Friend.class)
            .chatModel(BASE_MODEL)
            .systemMessageProvider(chatMemoryId -> "你是一个AI建模专家，只能回答关于脑建立的建模问题。")
            .build();
    String answer = friend.chat("腿抽筋怎么办");
    System.out.println(answer);
    // 抱歉，我无法回答关于腿抽筋的医学建议。。。。。。。。。
}

在运行时修改系统提示词, 使用 System Message Transformer

java 复制代码

@Test
void test04() {
    Friend friend = AiServices.builder(Friend.class)
            .chatModel(BASE_MODEL)
            .systemMessageProvider(chatMemoryId -> "你是我的好朋友，请用俚语回答.")
            .systemMessageTransformer(systemMessage -> systemMessage + " Today's date is " + LocalDate.now() + ".")
            .build();
    String answer = friend.chat("腿抽筋怎么办");
    System.out.println(answer);
}

请求的部分json如下：

json 复制代码

{
    "role" : "system",
    "content" : "你是我的好朋友，请用俚语回答. Today's date is 2026-05-12."
}

获取上下文参数

java 复制代码

@Test
void test05() {
    Friend friend = AiServices.builder(Friend.class)
            .chatModel(BASE_MODEL)
            .systemMessageProvider(chatMemoryId -> "你是我的好朋友，请用俚语回答.")
            .systemMessageTransformer((systemMessage, context) ->
                    systemMessage + " Tenant: " + context.invocationParameters().get("tenant") + ".")
            .build();

    String answer = friend.chat("腿抽筋怎么办");
    System.out.println(answer);

}
// "content" : "你是我的好朋友，请用俚语回答. Tenant: null."

UserMessage

java 复制代码

interface Friend {

    @UserMessage("你是我的好朋友，请用俚语回答. {{it}}")
    String chat(String userMessage);
}

java 复制代码

interface Friend {

    @UserMessage("You are a good friend of mine. Answer using slang. {{message}}")
    String chat(@V("message") String userMessage);
}

配置 ChatMemory

java 复制代码

@Test
    void test01() {

        //RedisChatMemoryStore chatMemoryStore = new RedisChatMemoryStore("192.168.6.15:6379", 6349, 11, userId, "redis@2025");
        ChatMemory chatMemory = MessageWindowChatMemory.builder()
                .id(UUID.randomUUID().toString())
                .chatMemoryStore(new InMemoryChatMemoryStore())
                .maxMessages(10).build();

        Friend friend = AiServices.builder(Friend.class)
                .chatModel(BASE_MODEL)
                .systemMessageProvider(chatMemoryId -> "你是一个智能助手")
                .chatMemory(chatMemory)
                .build();

        List<String> questions = new ArrayList<>();
        questions.add("北京大学是211么？");
        questions.add("是985么？");
        questions.add("是双一流么？");

        for (String userMessage : questions) {
            String answer = friend.chat(userMessage);
            System.out.println(answer);
        }
    }

通过程序对 ChatRequest 重写

java 复制代码

/**
 * 通过程序对 ChatRequest 重写
 */
@Test
void test02() {
    Assistant assistant = AiServices.builder(Assistant.class)
            .chatModel(BASE_MODEL)
            .chatRequestTransformer(new UnaryOperator<ChatRequest>() {
                @Override
                public ChatRequest apply(ChatRequest chatRequest) {
                    List<ChatMessage> messages = chatRequest.messages();
                    System.out.println(messages.getLast().toString());
                    return chatRequest;
                }
            })  // Configures the transformation function to be applied to the ChatRequest
            .build();

    String res = assistant.chat("你好");
    System.out.println(res);

}

获取ChatRequest同时也需要获取ChatMemory，不过这里存在问题

java 复制代码

/**
 * 如果需要同时访问 ChatMemory 来实现所需的 ChatRequest 转换，
 * 还可以通过配置 chatRequestTransformer 方法并传入一个
 * BiFunction<ChatRequest, Object, ChatRequest> 来实现，
 * 其中传递给该函数的第二个参数是 memory ID。
 */
@Test
void test03() {
    ChatMemory chatMemory = MessageWindowChatMemory.builder()
            .id(UUID.randomUUID().toString())
            .chatMemoryStore(new InMemoryChatMemoryStore())
            .maxMessages(10).build();

    System.out.println(chatMemory.id()); // 这里输出的uuid字符串

    Assistant assistant = AiServices.builder(Assistant.class)
            .chatModel(BASE_MODEL)
            .chatMemory(chatMemory)
            .chatRequestTransformer(new BiFunction<ChatRequest, Object, ChatRequest>() {

                /**
                 *
                 * @param chatRequest ChatRequest
                 * @param o memoryID
                 * @return
                 */
                @Override
                public ChatRequest apply(ChatRequest chatRequest, Object o) {
                    System.out.println(o.toString()); // 这里输出的是default，和上面的ChatMemoryId 不一样
                    //todo: 这里需要排查为什么？？？？
                    return chatRequest;
                }
            })
            .build();

    String res = assistant.chat("你好");
    System.out.println(res);

}

请求的时候携带请求参数

java 复制代码

interface AssistantWithChatParams {

    String chat(@UserMessage String userMessage, ChatRequestParameters params);
}

java 复制代码

@Test
void test04() {

    AssistantWithChatParams assistant = AiServices.builder(AssistantWithChatParams.class)
            .chatModel(BASE_MODEL)  // or whichever model
            .build();

    ChatRequestParameters customParams = ChatRequestParameters.builder()
            .temperature(0.85)
            .build();

    String answer = assistant.chat("Hi there!", customParams);

}

SystemMessage 和 UserMessage 注解混合

java 复制代码

interface Friend {

    @UserMessage("你是我的好朋友，请用俚语回答. {{it}}")
    String chat(String userMessage);


    @SystemMessage("Given a name of a country, {{answerInstructions}}")
    @UserMessage("{{country}}")
    String chat(@V("answerInstructions") String answerInstructions, @V("country") String country);
}

多模态

java 复制代码

String chat(@UserMessage AudioContent audio, @UserMessage ImageContent image);

返回类型

java 复制代码

interface Assistant {

    String chat(String message);

    @UserMessage("产生一个给定主题的文章的大纲: {{it}}")
    Result<List<String>> generateOutlineFor(String topic);
}

@Test
void test05() {
    Assistant assistant = AiServices.builder(Assistant.class)
            .chatModel(BASE_MODEL)  // or whichever model
            .build();
    Result<List<String>> result = assistant.generateOutlineFor("软件架构设计");
    List<String> outline = result.content();
    TokenUsage tokenUsage = result.tokenUsage();
    List<Content> sources = result.sources();
    List<ToolExecution> toolExecutions = result.toolExecutions();
    FinishReason finishReason = result.finishReason();
}

返回pojo

java 复制代码

import dev.langchain4j.service.UserMessage;
import lombok.Data;

import java.time.LocalDate;

interface PersonExtractor {

    @UserMessage("Extract information about a person from {{it}}")
    Person extractPersonFrom(String text);
}

@Data
class Person {

    String firstName;
    String lastName;
    LocalDate birthDate;
    Address address;
}

@Data
class Address {
    String street;
    Integer streetNumber;
    String city;
}

测试

java 复制代码

    @Test
    void test06() {

        PersonExtractor personExtractor = AiServices.create(PersonExtractor.class, BASE_MODEL);

        String text = """
            In 1968, amidst the fading echoes of Independence Day,
            a child named John arrived under the calm evening sky.
            This newborn, bearing the surname Doe, marked the start of a new journey.
            He was welcomed into the world at 345 Whispering Pines Avenue
            a quaint street nestled in the heart of Springfield
            an abode that echoed with the gentle hum of suburban dreams and aspirations.
            """;

        Person person = personExtractor.extractPersonFrom(text);

        //Person(firstName=John, lastName=Doe, birthDate=1968-07-04, address=Address(street=Whispering Pines Avenue, streetNumber=345, city=Springfield))
        System.out.println(person);

    }

大模型的JSON模式： the LLM will be forced to respond with a valid JSON.

java 复制代码

public static final ChatModel BASE_MODEL = OpenAiChatModel.builder()
        .baseUrl("https://api.deepseek.com")
        .apiKey(System.getenv("DS_API_KEY"))
        .modelName("deepseek-v4-flash")
        .strictJsonSchema(true) // 指定为json格式
        .logRequests(true)
        .logResponses(true)
        .build();

streaming

java 复制代码

interface Assistant {
    TokenStream chat(String message);
}

java 复制代码

@Test
void test01() {

    Assistant assistant = AiServices.create(Assistant.class, STREAMING_BASE_MODEL);

    TokenStream tokenStream = assistant.chat("给我讲个笑话");

    CompletableFuture<ChatResponse> futureResponse = new CompletableFuture<>();

    tokenStream
            .onPartialResponse((String partialResponse) -> System.out.println(partialResponse))
            //.onPartialThinking((PartialThinking partialThinking) -> System.out.println(partialThinking))
            //.onRetrieved((List<Content> contents) -> System.out.println(contents))
            //.onIntermediateResponse((ChatResponse intermediateResponse) -> System.out.println(intermediateResponse))
            // This will be invoked every time a new partial tool call (usually containing a single token of the tool's arguments) is available.
            //.onPartialToolCall((PartialToolCall partialToolCall) -> System.out.println(partialToolCall))
            // This will be invoked right before a tool is executed. BeforeToolExecution contains ToolExecutionRequest (e.g. tool name, tool arguments, etc.)
            //.beforeToolExecution((BeforeToolExecution beforeToolExecution) -> System.out.println(beforeToolExecution))
            // This will be invoked right after a tool is executed. ToolExecution contains ToolExecutionRequest and tool execution result.
            //.onToolExecuted((ToolExecution toolExecution) -> System.out.println(toolExecution))
            .onCompleteResponse((ChatResponse response) -> futureResponse.complete(response))
            .onError((Throwable error) -> futureResponse.completeExceptionally(error))
            .start();

    futureResponse.join(); // Blocks the main thread until the streaming process (running in another thread) is complete
}

返回Flux

xml 复制代码

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-reactor</artifactId>
    <version>1.14.1-beta24</version>
</dependency>

java 复制代码

interface Assistant {
    //TokenStream chat(String message);
    Flux<String> chat(String message);
}

java 复制代码

@Test
void test01() {
    Assistant assistant = AiServices.create(Assistant.class, STREAMING_BASE_MODEL);
    Flux<String> chat = assistant.chat("给我讲个笑话,200字以上");
    CompletableFuture<ChatResponse> futureResponse = new CompletableFuture<>();
    chat.doOnNext(System.out::print).subscribe();  // 触发执行
    futureResponse.join();
}