langchain4j整合milvus实现RAG

langchain4j整合milvus实现RAG

起男 15 2025-04-25

langchain4j整合milvus实现RAG

依赖

	<dependencyManagement>
        <!--langchain包管理-->
        <dependencies>
            <dependency>
                <groupId>dev.langchain4j</groupId>
                <artifactId>langchain4j-bom</artifactId>
                <version>1.0.0-beta3</version>
                <type>pom</type>
                <scope>import</scope>
            </dependency>
            <!--整合阿里百炼-->
            <dependency>
                <groupId>dev.langchain4j</groupId>
                <artifactId>langchain4j-community-bom</artifactId>
                <version>1.0.0-beta3</version>
                <type>pom</type>
                <scope>import</scope>
            </dependency>
        </dependencies>
    </dependencyManagement>
	<dependencies>
        <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-community-dashscope-spring-boot-starter</artifactId>
        </dependency>
        <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-spring-boot-starter</artifactId>
        </dependency>
        <!--流式输出-->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-webflux</artifactId>
        </dependency>
        <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-reactor</artifactId>
        </dependency>
        <!--整合milvus-->
        <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-milvus-spring-boot-starter</artifactId>
            <version>1.0.0-beta3</version>
        </dependency>
    </dependencies>

配置文件

langchain4j:
  community:
    dashscope:
      chat-model:
        api-key: ${ALI_API_KEY}
        model-name: qwen-max
      embedding-model:
        api-key: ${ALI_API_KEY}
        model-name: text-embedding-v3
      streaming-chat-model:
        api-key: ${ALI_API_KEY}
        model-name: qwen-max
  milvus:
    host: 127.0.0.1
    port: 19530
    dimension: 1024
    auto-flush-on-insert: true
    collection-name: default

初始化数据

    @Autowired
    private EmbeddingModel embeddingModel;
    @Autowired
    private EmbeddingStore embeddingStore;
    @Test
    void  test(){
        Document document = ClassPathDocumentLoader.loadDocument("test01.txt",new TextDocumentParser());
        EmbeddingStoreIngestor
                .builder()
                .embeddingStore(store)
                .embeddingModel(embeddingModel)
                .build()
                .ingest(document);
    }

配置类

@Configuration
public class AIConfig {

    @Autowired
    private EmbeddingModel embeddingModel;
    @Autowired
    private MilvusEmbeddingStore store;

    @Bean
    public ContentRetriever contentRetriever(){
        EmbeddingStoreContentRetriever retriever = EmbeddingStoreContentRetriever.builder()
                .embeddingModel(embeddingModel)
                .embeddingStore(store)
                .maxResults(1)
                .build();
        return retriever;
    }
}

AiService

@AiService(wiringMode = AiServiceWiringMode.EXPLICIT,
        chatModel = "qwenChatModel",
        streamingChatModel = "qwenStreamingChatModel",
        contentRetriever = "contentRetriever")
public interface Assistant {

    Flux<String> chat(String message);
}

controller

@RestController
@RequestMapping("chat")
public class ChatController {

    @Autowired
    private Assistant assistant;

    @GetMapping(value = "stream/{message}",produces = "text/stream;charset=utf-8")
    public Flux<String> stream(@PathVariable String message){
        return assistant.chat(message);
    }
}
llm