JavaAI：LangChain4j学习（一）集成SpringBoot和阿里通义千问DashScope

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-dashscope</artifactId>
    <version>${previous version here}</version>
</dependency>

如果使用的langChain4j-dashScope版本大于等于1.0.0-alpha1

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-community-dashscope</artifactId>
    <version>${langchain4j.version}</version>
</dependency>

集成Spring Boot 启动器

Spring Boot 启动器有助于创建和配置语言模型、嵌入模型、嵌入存储、和其他核心 LangChain4j 组件。如果要使用Spring Boot 相关启动器，需要在pom.xml中引入相应依赖项

pom.xml ：

如果使用的langChain4j-dashScope版本小于等于1.0.0-alpha1

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-community-dashscope-spring-boot-starter</artifactId>
    <version>${langchain4j.version}</version>
</dependency>

如果使用的langChain4j-dashScope版本大于等于1.0.0-alpha1

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-community-dashscope-spring-boot-starter</artifactId>
    <version>${latest version here}</version>
</dependency>

或者使用 BOM 管理依赖项：

<dependencyManagement>
    <dependency>
        <groupId>dev.langchain4j</groupId>
        <artifactId>langchain4j-community-bom</artifactId>
        <version>${latest version here}</version>
        <typ>pom</typ>
        <scope>import</scope>
    </dependency>
</dependencyManagement>

配置

引入依赖后，在propertity中配置注册

langchain4j.community.dashscope.api-key=<API Key>
langchain4j.community.dashscope.model-name==<Model Name>

#是否输出日志
langchain4j.community.dashscope.log-requests=true
langchain4j.community.dashscope.log-responses=true
#日志等级
logging.level.dev.langchain4j=DEBUG

其他相关参数也可添加，例如 QwenChatModel参数

langchain4j.community.dashscope.temperature=0.7
langchain4j.community.dashscope.max-tokens=4096

官方参数如下：
langchain4j-community-dashscope 有四个model可使用:

QwenChatModel
QwenStreamingChatModel
QwenLanguageModel
QwenStreamingLanguageModel

QwenChatModel 参数如下，其他也相同

Property	Description	Default Value
baseUrl	The URL to connect to. You can use HTTP or websocket to connect to DashScope	Text Inference and Multi-Modal
apiKey	The API Key
modelName	The model to use.	qwen-plus
topP	The probability threshold for kernel sampling controls the diversity of texts generated by the model. the higher the , the more diverse the generated texts, and vice versa. Value range: (0, 1.0]. We generally recommend altering this or temperature but not both.top_p
topK	The size of the sampled candidate set during the generation process.
enableSearch	Whether the model uses Internet search results for reference when generating text or not.
seed	Setting the seed parameter will make the text generation process more deterministic, and is typically used to make the results consistent.
repetitionPenalty	Repetition in a continuous sequence during model generation. Increasing reduces the repetition in model generation, 1.0 means no penalty. Value range: (0, +inf)repetition_penalty
temperature	Sampling temperature that controls the diversity of the text generated by the model. the higher the temperature, the more diverse the generated text, and vice versa. Value range: [0, 2)
stops	With the stop parameter, the model will automatically stop generating text when it is about to contain the specified string or token_id.
maxTokens	The maximum number of tokens returned by this request.
listeners	Listeners that listen for request, response and errors.

案例使用

例如 QwenChatModel

ChatLanguageModel qwenModel = QwenChatModel.builder()
                    .apiKey("You API key here")
                    .modelName("qwen-max")
                    .build();