Spring AI ectorStore的使用流程

<!-- Maven 示例 -->
<dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-elasticsearch-store</artifactId>
            <version>${spring-ai.version}</version>
        </dependency>
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-elasticsearch-store-spring-boot-starter</artifactId>
            <version>${spring-ai.version}</version>
        </dependency>

spring:
  profiles:
    active: druid
  elasticsearch:
    uris: http://127.0.0.1:9200/      #请改成自己的路径
  ai:
    ollama:
      base-url: http://localhost:11434
      embedding:
        model: llama3.2
    vectorstore:
      elasticsearch:
        initialize-schema: true         #请不要修改此配置
        index-name: zixiai           #这是 zixiai 默认的索引，请不要修改或删除
        dimensions: 2048                #不要修改这个配置，与具体大模型本身的维度参数有关系
        similarity: cosine
        batching-strategy: TOKEN_COUNT 

    @Override
    public String embed(String msg, Set<String> fileIds) {
        log.debug("embedding... {}", msg);
        Set<String> finalFileIds = (fileIds == null) ? new HashSet<>() : fileIds;
        List<Document> st = vectorStore.similaritySearch(SearchRequest.builder().query(msg).topK(5).build());
//                .similaritySearch(SearchRequest.query(msg).withTopK(5));
        // 首先查询向量库
        String promptContent = null;
        if (!CollectionUtils.isEmpty(st)) {
            promptContent = st.stream()
                    .filter(doc -> {
                        if (CollectionUtils.isEmpty(finalFileIds)) {
                            return true;
                        }
                        Object fileIdObject = doc.getMetadata().get("file_id");
                        String docFileId = fileIdObject != null ? fileIdObject.toString() : null;
                        return finalFileIds.contains(docFileId);
                    })
                    .map(Document::getText)
                    .filter(StringUtils::hasText)
                    .collect(Collectors.joining(" "));
        }
        // 确保 promptContent 不为空
        if (!StringUtils.hasText(promptContent)) {
            promptContent = "No information found in the database.";
        }
        log.debug("Prompt content: {}", promptContent);
        return chatClient
                .prompt(promptContent)
                .user(msg)
                .call()
                .content();
    }