添加依赖：

   <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-tika-document-reader</artifactId>
        </dependency>

Java代码：

package com.zkwm.springai.rag;

import org.springframework.ai.document.Document;
import org.springframework.ai.reader.tika.TikaDocumentReader;
import org.springframework.ai.transformer.splitter.TokenTextSplitter;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.core.io.FileSystemResource;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;

import java.util.List;

@RestController
@RequestMapping("/docChunk")
public class DocumentChunkController {
    @Autowired
    private VectorStore vectorStore;

    @GetMapping("/processAndStore")
    public String processAndStore(String filePath) {

        TokenTextSplitter splitter = TokenTextSplitter.builder()
                .withChunkSize(20)    // 每块最大 Token 数
                .withMinChunkSizeChars(5)   // 最小字符数
                .withMaxNumChunks(10000)     // 最大分块数
                .withKeepSeparator(true)     // 保留分隔符
                .build();
        // 1. 读取文档（PDF/Word/TXT）
        TikaDocumentReader reader = new TikaDocumentReader(new FileSystemResource(filePath));
        List<Document> documents = reader.read();
        // 2. 文档切片（核心）
        List<Document> chunks = splitter.apply(documents);
        // 3. 向量化并存入向量库
        int batchSize = 10;
        for (int i = 0; i < chunks.size(); i += batchSize) {
            int end = Math.min(i + batchSize, chunks.size());
            List<Document> batch = chunks.subList(i, end);
            vectorStore.add(batch); // 每次只发 10 条给模型
        }
        return "ok";
    }
}