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昨天 1ca5584d7e3200a9af65a099bd26d3593e2ba702

 src/main/java/com/ruoyi/ai/config/XiaozhiAgentConfig.java

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package com.ruoyi.ai.config;

import com.ruoyi.ai.store.MongoChatMemoryStore;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.rag.content.retriever.ContentRetriever;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
import dev.langchain4j.store.embedding.EmbeddingStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

/**
 * @author :yys
 * @date : 2025/5/2 20:01
 */
@Configuration
public class XiaozhiAgentConfig {

    @Autowired
    private MongoChatMemoryStore mongoChatMemoryStore;

    @Autowired
    private EmbeddingStore embeddingStore;
    @Autowired
    private EmbeddingModel embeddingModel;

//    @Value("${knowledge.one}")
//    private String one;
//
//    @Value("${knowledge.two}")
//    private String two;
//
//    @Value("${knowledge.three}")
//    private String three;

    @Bean
    ChatMemoryProvider chatMemoryProviderXiaozhi() {
        return memoryId -> MessageWindowChatMemory.builder()
                .id(memoryId)
                .maxMessages(20)
                .chatMemoryStore(mongoChatMemoryStore)
                .build();
    }

//    @Bean
//    ContentRetriever contentRetrieverXiaozhi() {
//        //使用FileSystemDocumentLoader读取指定目录下的知识库文档
//        //并使用默认的文档解析器对文档进行解析
//        Document document1 = FileSystemDocumentLoader.loadDocument(one);
////        Document document2 = FileSystemDocumentLoader.loadDocument(two);
////        Document document3 = FileSystemDocumentLoader.loadDocument(three);
////        List<Document> documents = Arrays.asList(document1, document2, document3);
//
//        List<Document> documents = Collections.singletonList(document1);
////         2. å°†æ•°æ®åº“数据转为LangChain4j的Document对象
////        List<Document> documents = new ArrayList<>();
//
//        //使用内存向量存储
//        InMemoryEmbeddingStore<TextSegment> inMemoryEmbeddingStore = new InMemoryEmbeddingStore<>();
//        //使用默认的文档分割器
//        EmbeddingStoreIngestor.builder()
//                .embeddingModel(embeddingModel)
//                .embeddingStore(inMemoryEmbeddingStore)
//                .build()
//                .ingest(documents);
//        //从嵌入存储（EmbeddingStore）里检索和查询内容相关的信息
//        return EmbeddingStoreContentRetriever.builder()
//                .embeddingModel(embeddingModel)
//                .embeddingStore(inMemoryEmbeddingStore)
//                .build();
//    }

    @Bean
    ContentRetriever contentRetrieverXiaozhiPincone() {
        // åˆ›å»ºä¸€ä¸ª EmbeddingStoreContentRetriever å¯¹è±¡ï¼Œç”¨äºŽä»ŽåµŒå…¥å­˜å‚¨ä¸­æ£€ç´¢å†…容
        return EmbeddingStoreContentRetriever
                .builder()
                // è®¾ç½®ç”¨äºŽç”ŸæˆåµŒå…¥å‘量的嵌入模型
                .embeddingModel(embeddingModel)
                // æŒ‡å®šè¦ä½¿ç”¨çš„嵌入存储
                .embeddingStore(embeddingStore)
                // è®¾ç½®æœ€å¤§æ£€ç´¢ç»“果数量，这里表示最多返回 1 æ¡åŒ¹é…ç»“æžœ
                .maxResults(1)
                // è®¾ç½®æœ€å°å¾—分阈值，只有得分大于等于 0.8 çš„结果才会被返回
                .minScore(0.8)
                // æž„建最终的 EmbeddingStoreContentRetriever å®žä¾‹
                .build();
    }
}