feat(knowledge): 初步完成知识库分片预览

service-ai/service-ai-knowledge/pom.xml

@@ -46,6 +46,14 @@
             <groupId>com.yomahub</groupId>
             <artifactId>liteflow-spring-boot-starter</artifactId>
         </dependency>
+        <dependency>
+            <groupId>org.springframework.ai</groupId>
+            <artifactId>spring-ai-tika-document-reader</artifactId>
+        </dependency>
+        <dependency>
+            <groupId>org.springframework.ai</groupId>
+            <artifactId>spring-ai-pdf-document-reader</artifactId>
+        </dependency>
     </dependencies>
 
     <build>

service-ai/service-ai-knowledge/src/main/java/com/lanyuanxiaoyao/service/ai/knowledge/controller/KnowledgeController.java

@@ -2,7 +2,7 @@ package com.lanyuanxiaoyao.service.ai.knowledge.controller;
 
 import com.lanyuanxiaoyao.service.ai.knowledge.entity.vo.KnowledgeVO;
 import com.lanyuanxiaoyao.service.ai.knowledge.entity.vo.PointVO;
-import com.lanyuanxiaoyao.service.ai.knowledge.reader.TextLineReader;
+import com.lanyuanxiaoyao.service.ai.knowledge.service.EmbeddingService;
 import com.lanyuanxiaoyao.service.ai.knowledge.service.KnowledgeService;
 import io.qdrant.client.QdrantClient;
 import io.qdrant.client.grpc.Points;
@@ -14,7 +14,6 @@ import org.eclipse.collections.api.list.ImmutableList;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 import org.springframework.ai.embedding.EmbeddingModel;
-import org.springframework.ai.reader.TextReader;
 import org.springframework.ai.reader.markdown.MarkdownDocumentReader;
 import org.springframework.ai.reader.markdown.config.MarkdownDocumentReaderConfig;
 import org.springframework.ai.vectorstore.VectorStore;
@@ -37,11 +36,13 @@ public class KnowledgeController {
   private static final Logger logger = LoggerFactory.getLogger(KnowledgeController.class);
 
   private final KnowledgeService knowledgeService;
+  private final EmbeddingService embeddingService;
   private final QdrantClient client;
   private final EmbeddingModel embeddingModel;
 
-  public KnowledgeController(KnowledgeService knowledgeService, VectorStore vectorStore, EmbeddingModel embeddingModel) {
+  public KnowledgeController(KnowledgeService knowledgeService, EmbeddingService embeddingService, VectorStore vectorStore, EmbeddingModel embeddingModel) {
     this.knowledgeService = knowledgeService;
+    this.embeddingService = embeddingService;
     client = (QdrantClient) vectorStore.getNativeClient().orElseThrow();
     this.embeddingModel = embeddingModel;
   }
@@ -89,15 +90,11 @@ public class KnowledgeController {
 
   @PostMapping("preview_text")
   public ImmutableList<PointVO> previewText(
-      @RequestParam("name") String name,
+      @RequestParam(value = "mode", defaultValue = "NORMAL") String mode,
-      @RequestParam(value = "mode", defaultValue = "normal") String mode,
       @RequestParam(value = "type", defaultValue = "text") String type,
       @RequestParam("content") String content
   ) {
-    TextReader reader = new TextLineReader(new ByteArrayResource(content.getBytes(StandardCharsets.UTF_8)));
+    return embeddingService.split(mode, content)
-    return reader.get()
-        .stream()
-        .collect(Collectors.toCollection(Lists.mutable::empty))
         .collect(doc -> {
           PointVO vo = new PointVO();
           vo.setId(doc.getId());

service-ai/service-ai-knowledge/src/main/java/com/lanyuanxiaoyao/service/ai/knowledge/entity/EmbeddingContext.java

@@ -0,0 +1,130 @@
+package com.lanyuanxiaoyao.service.ai.knowledge.entity;
+
+import cn.hutool.core.util.StrUtil;
+import java.io.File;
+import java.nio.file.Path;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import org.eclipse.collections.api.factory.Lists;
+import org.eclipse.collections.api.factory.Maps;
+import org.springframework.ai.document.Document;
+
+/**
+ * @author lanyuanxiaoyao
+ * @version 20250523
+ */
+public class EmbeddingContext {
+  private Config config;
+  private String content;
+  private String file;
+  private List<Document> documents = Lists.mutable.empty();
+  private Map<String, Object> metadata = Maps.mutable.empty();
+
+  public EmbeddingContext(String content) {
+    this(content, new Config());
+  }
+
+  public EmbeddingContext(String content, Config config) {
+    this.content = StrUtil.trim(content);
+    this.config = config;
+  }
+
+  public EmbeddingContext(File file) {
+    this(file, new Config());
+  }
+
+  public EmbeddingContext(File file, Config config) {
+    this.file = file.getAbsolutePath();
+    this.config = config;
+  }
+
+  public EmbeddingContext(Path path) {
+    this(path.toFile());
+  }
+
+  public EmbeddingContext(Path path, Config config) {
+    this(path.toFile(), config);
+  }
+
+  public Config getConfig() {
+    return config;
+  }
+
+  public void setConfig(Config config) {
+    this.config = config;
+  }
+
+  public String getContent() {
+    return content;
+  }
+
+  public void setContent(String content) {
+    this.content = content;
+  }
+
+  public String getFile() {
+    return file;
+  }
+
+  public void setFile(String file) {
+    this.file = file;
+  }
+
+  public List<Document> getDocuments() {
+    return documents;
+  }
+
+  public void setDocuments(List<Document> documents) {
+    this.documents = documents;
+  }
+
+  public Map<String, Object> getMetadata() {
+    return metadata;
+  }
+
+  public void setMetadata(Map<String, Object> metadata) {
+    this.metadata = metadata;
+  }
+
+  @Override
+  public String toString() {
+    return "EmbeddingContext{" +
+           "config=" + config +
+           ", content='" + content + '\'' +
+           ", file='" + file + '\'' +
+           ", documents=" + documents +
+           ", metadata=" + metadata +
+           '}';
+  }
+
+  public static final class Config {
+    private SplitStrategy splitStrategy = SplitStrategy.NORMAL;
+
+    public Config() {
+    }
+
+    public Config(SplitStrategy splitStrategy) {
+      this.splitStrategy = splitStrategy;
+    }
+
+    public SplitStrategy getSplitStrategy() {
+      return splitStrategy;
+    }
+
+    public void setSplitStrategy(SplitStrategy splitStrategy) {
+      this.splitStrategy = splitStrategy;
+    }
+
+    @Override
+    public String toString() {
+      return "Config{" +
+             "splitStrategy=" + splitStrategy +
+             '}';
+    }
+
+    public enum SplitStrategy {
+      NORMAL, LLM, QA
+    }
+  }
+}

service-ai/service-ai-knowledge/src/main/java/com/lanyuanxiaoyao/service/ai/knowledge/reader/TextLineReader.java

@@ -1,34 +0,0 @@
-package com.lanyuanxiaoyao.service.ai.knowledge.reader;
-
-import cn.hutool.core.util.StrUtil;
-import java.util.List;
-import java.util.stream.Stream;
-import org.springframework.ai.document.Document;
-import org.springframework.ai.reader.TextReader;
-import org.springframework.core.io.Resource;
-
-/**
- * @author lanyuanxiaoyao
- * @version 20250522
- */
-public class TextLineReader extends TextReader {
-  public TextLineReader(Resource resource) {
-    super(resource);
-  }
-
-  @Override
-  public List<Document> get() {
-    return super.get()
-        .stream()
-        .flatMap(doc -> {
-          String text = doc.getText();
-          if (StrUtil.isBlank(text)) {
-            return Stream.of(doc);
-          }
-          return Stream.of(text.split("\n\n"))
-              .filter(StrUtil::isNotBlank)
-              .map(line -> new Document(line, doc.getMetadata()));
-        })
-        .toList();
-  }
-}

service-ai/service-ai-knowledge/src/main/java/com/lanyuanxiaoyao/service/ai/knowledge/service/EmbeddingService.java

@@ -1,7 +1,13 @@
 package com.lanyuanxiaoyao.service.ai.knowledge.service;
 
+import com.lanyuanxiaoyao.service.ai.knowledge.entity.EmbeddingContext;
+import com.yomahub.liteflow.core.FlowExecutor;
+import java.nio.file.Path;
+import org.eclipse.collections.api.factory.Lists;
+import org.eclipse.collections.api.list.ImmutableList;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
+import org.springframework.ai.document.Document;
 import org.springframework.stereotype.Service;
 
 /**
@@ -12,5 +18,19 @@ import org.springframework.stereotype.Service;
 public class EmbeddingService {
   private static final Logger logger = LoggerFactory.getLogger(EmbeddingService.class);
 
+  private final FlowExecutor executor;
 
+  @SuppressWarnings("SpringJavaInjectionPointsAutowiringInspection")
+  public EmbeddingService(FlowExecutor executor) {
+    this.executor = executor;
+  }
+
+  public ImmutableList<Document> split(String mode, String content) {
+    EmbeddingContext context = new EmbeddingContext(
+        content,
+        new EmbeddingContext.Config(EmbeddingContext.Config.SplitStrategy.valueOf(mode))
+    );
+    executor.execute2Resp("embedding", null, context);
+    return Lists.immutable.ofAll(context.getDocuments());
+  }
 }

service-ai/service-ai-knowledge/src/main/java/com/lanyuanxiaoyao/service/ai/knowledge/service/node/EmbeddingNodes.java

@@ -0,0 +1,201 @@
+package com.lanyuanxiaoyao.service.ai.knowledge.service.node;
+
+import cn.hutool.core.io.FileUtil;
+import cn.hutool.core.lang.Assert;
+import cn.hutool.core.util.StrUtil;
+import com.lanyuanxiaoyao.service.ai.knowledge.entity.EmbeddingContext;
+import com.yomahub.liteflow.annotation.LiteflowComponent;
+import com.yomahub.liteflow.annotation.LiteflowMethod;
+import com.yomahub.liteflow.core.NodeComponent;
+import com.yomahub.liteflow.enums.LiteFlowMethodEnum;
+import com.yomahub.liteflow.enums.NodeTypeEnum;
+import java.nio.charset.Charset;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.document.Document;
+import org.springframework.ai.document.DocumentReader;
+import org.springframework.ai.reader.ExtractedTextFormatter;
+import org.springframework.ai.reader.pdf.PagePdfDocumentReader;
+import org.springframework.ai.reader.pdf.config.PdfDocumentReaderConfig;
+import org.springframework.ai.reader.tika.TikaDocumentReader;
+import org.springframework.ai.transformer.splitter.TokenTextSplitter;
+import org.springframework.core.io.PathResource;
+
+/**
+ * @author lanyuanxiaoyao
+ * @version 20250523
+ */
+@LiteflowComponent
+public class EmbeddingNodes {
+  private static final Logger logger = LoggerFactory.getLogger(EmbeddingNodes.class);
+
+  private final ChatClient chatClient;
+
+  public EmbeddingNodes(ChatClient.Builder builder) {
+    this.chatClient = builder.build();
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS_BOOLEAN, nodeId = "embedding_check_if_file_needed", nodeName = "判断是否需要读取文件", nodeType = NodeTypeEnum.BOOLEAN)
+  public boolean checkIfFileReadNeeded(NodeComponent node) {
+    EmbeddingContext context = node.getContextBean(EmbeddingContext.class);
+    Assert.notNull(context, "EmbeddingContext is null");
+    if (StrUtil.isNotBlank(context.getFile())) {
+      Assert.isTrue(FileUtil.exist(context.getFile()), "File [{}] not exist", context.getFile());
+      return true;
+    }
+    Assert.notBlank(context.getContent(), "Contents is empty");
+    return false;
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS, nodeId = "test_print", nodeType = NodeTypeEnum.COMMON)
+  public void testPrint(NodeComponent node) {
+    EmbeddingContext context = node.getContextBean(EmbeddingContext.class);
+    logger.info(context.getContent());
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS_SWITCH, nodeId = "file_reader_switch", nodeName = "判断文件格式", nodeType = NodeTypeEnum.SWITCH)
+  public String fileReaderSwitch(NodeComponent node) {
+    EmbeddingContext context = node.getContextBean(EmbeddingContext.class);
+    String extName = FileUtil.extName(context.getFile());
+    return switch (extName.toLowerCase()) {
+      case "txt", "md", "markdown" -> "txt_file_reader";
+      case "pdf" -> "pdf_file_reader";
+      case "doc", "docx", "xls", "xlsx", "ppt", "pptx", "html", "xml", "wps", "et", "dpt" -> "any_file_reader";
+      default -> throw new IllegalStateException("Unsupported ext: " + extName);
+    };
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS, nodeId = "txt_file_reader", nodeName = "读取文本文件", nodeType = NodeTypeEnum.COMMON)
+  public void txtFileReader(NodeComponent node) {
+    EmbeddingContext context = node.getContextBean(EmbeddingContext.class);
+    context.setContent(FileUtil.readString(context.getFile(), Charset.defaultCharset()));
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS, nodeId = "pdf_file_reader", nodeName = "读取pdf文件", nodeType = NodeTypeEnum.COMMON)
+  public void pdfFileReader(NodeComponent node) {
+    EmbeddingContext context = node.getContextBean(EmbeddingContext.class);
+    PagePdfDocumentReader reader = new PagePdfDocumentReader(
+        new PathResource(context.getFile()),
+        PdfDocumentReaderConfig.builder()
+            .withPageTopMargin(0)
+            .withPageExtractedTextFormatter(ExtractedTextFormatter.builder()
+                .withNumberOfTopTextLinesToDelete(0)
+                .build())
+            .build());
+    context.setContent(readBySpringAiReader(reader));
+  }
+
+  /**
+   * <a href="https://tika.apache.org/3.1.0/formats.html#Database_formats">Tika支持的格式</a>
+   */
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS, nodeId = "any_file_reader", nodeName = "使用Tika尝试读取文件", nodeType = NodeTypeEnum.COMMON)
+  public void anyFileReader(NodeComponent node) {
+    EmbeddingContext context = node.getContextBean(EmbeddingContext.class);
+    context.setContent(readBySpringAiReader(new TikaDocumentReader(new PathResource(context.getFile()))));
+  }
+
+  private String readBySpringAiReader(DocumentReader reader) {
+    return reader.get()
+        .stream()
+        .map(Document::getText)
+        .collect(Collectors.joining("\n"))
+        .trim();
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS_SWITCH, nodeId = "split_switch", nodeName = "判断使用什么分段方法", nodeType = NodeTypeEnum.SWITCH)
+  public String splitSwitch(NodeComponent node) {
+    EmbeddingContext context = node.getContextBean(EmbeddingContext.class);
+    return switch (context.getConfig().getSplitStrategy()) {
+      case NORMAL -> "normal_split";
+      case LLM -> "llm_split";
+      case QA -> "qa_split";
+    };
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS, nodeId = "normal_split", nodeName = "使用普通分段", nodeType = NodeTypeEnum.COMMON)
+  public void normalSplit(NodeComponent node) {
+    EmbeddingContext context = node.getContextBean(EmbeddingContext.class);
+    TokenTextSplitter splitter = new TokenTextSplitter(200, 100, 5, 200, true);
+    Document document = Document.builder()
+        .text(context.getContent())
+        .build();
+    context.setDocuments(splitter.split(document));
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS, nodeId = "llm_split", nodeName = "使用大模型分段", nodeType = NodeTypeEnum.COMMON)
+  public void llmSplit(NodeComponent node) {
+    EmbeddingContext context = node.getContextBean(EmbeddingContext.class);
+    context.getDocuments().addAll(llmSplit(
+        """
+            对用户输入的文本，生成高质量的分段。请遵循以下指南：
+            1. 分段原则：
+              分段按文本内容的语义进行分割，每个分段都尽可能保持完整连续的内容表达。
+              避免从词句的中间进行分割。
+            2. 格式：
+              分段之间用两个空行分隔，以提高可读性。
+              避免使用任何Markdown格式
+            3. 内容要求：
+              确保每个分段的内容文字完全依照原文。
+              避免添加任何原文中不存在的文字。
+            """,
+        context.getContent(),
+        context.getMetadata()
+    ));
+  }
+
+  @LiteflowMethod(value = LiteFlowMethodEnum.PROCESS, nodeId = "qa_split", nodeName = "使用Q/A格式分段", nodeType = NodeTypeEnum.COMMON)
+  public void qaSplit(NodeComponent node) {
+    EmbeddingContext context = node.getContextBean(EmbeddingContext.class);
+    context.getDocuments().addAll(llmSplit(
+        """
+            对用户输入的文本，生成一组高质量的问答对。请遵循以下指南：
+            1. 问题部分：
+              为同一个主题创建尽可能多的不同表述的问题，确保问题的多样性。
+              每个问题应考虑用户可能的多种问法，例如：
+              直接询问（如“什么是...？”）
+              请求确认（如“是否可以说...？”）
+              寻求解释（如“请解释一下...的含义。”）
+              假设性问题（如“如果...会怎样？”）
+              例子请求（如“能否举个例子说明...？”）
+              问题应涵盖文本中的关键信息、主要概念和细节，确保不遗漏重要内容。
+            2. 答案部分：
+              提供一个全面、信息丰富的答案，涵盖问题的所有可能角度，确保逻辑连贯。
+              答案应直接基于给定文本，确保准确性和一致性。
+              包含相关的细节，如日期、名称、职位等具体信息，必要时提供背景信息以增强理解。
+            3. 格式：
+              使用"Q:"标记问题集合的开始，所有问题应在一个段落内，问题之间用空格分隔。
+              使用"A:"标记答案的开始，答案应清晰分段，便于阅读。
+              问答对之间用两个空行分隔，以提高可读性。
+              避免使用任何Markdown格式
+            4. 内容要求：
+              确保问答对紧密围绕文本主题，避免偏离主题。
+              避免添加文本中未提及的信息，确保信息的真实性。
+              如果文本信息不足以回答某个方面，可以在答案中说明 "根据给定信息无法确定"，并尽量提供相关的上下文。
+            """,
+        context.getContent(),
+        context.getMetadata()
+    ));
+  }
+
+  private List<Document> llmSplit(String prompt, String content, Map<String, Object> metadata) {
+    String response = chatClient.prompt()
+        .system(prompt)
+        .user(content)
+        .call()
+        .content();
+    Assert.notBlank(response, "LLM response is empty");
+    // noinspection DataFlowIssue
+    return Arrays.stream(StrUtil.trim(response).split("(s?)\\s*\\n\\n"))
+        .map(StrUtil::trim)
+        .map(text -> Document.builder()
+            .text(text)
+            .metadata(metadata)
+            .build())
+        .toList();
+  }
+}

service-ai/service-ai-knowledge/src/main/resources/application.yml

@@ -33,7 +33,7 @@ spring:
       api-key: ENC(K+Hff9QGC+fcyi510VIDd9CaeK/IN5WBJ9rlkUsHEdDgIidW+stHHJlsK0lLPUXXREha+ToQZqqDXJrqSE+GUKCXklFhelD8bRHFXBIeP/ZzT2cxhzgKUXgjw3S0Qw2R)
       chat:
         options:
-          model: 'Qwen3-1.7'
+          model: 'Qwen3-1.7-vllm'
       embedding:
         options:
           model: 'Bge-m3'

service-ai/service-ai-knowledge/src/main/resources/config/flow.xml

@@ -1,14 +1,20 @@
 <?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE flow PUBLIC  "liteflow" "https://liteflow.cc/liteflow.dtd">
 <flow>
-    <chain name="embedding">
+    <chain id="embedding">
         SER(
-            embedding_start,
+            IF(
-            SWITCH(embedding_mode_switch).TO(
+                embedding_check_if_file_needed,
-                normal_embedding,
+                SWITCH(file_reader_switch).TO(
-                llm_embedding,
+                    txt_file_reader,
-                qa_embedding
+                    pdf_file_reader
+                ).DEFAULT(any_file_reader)
             ),
-            embedding_finish
+            SWITCH(split_switch).TO(
-        );
+                normal_split,
+                llm_split,
+                qa_split
+            )
+        )
     </chain>
 </flow>

service-web/client/src/pages/ai/knowledge/DataImport.tsx

@@ -33,21 +33,19 @@ const DataImport: React.FC = () => {
                           name: 'mode',
                           type: 'radios',
                           label: '解析模式',
-                          value: 'normal',
+                          value: 'NORMAL',
                           options: [
                             {
-                              value: 'normal',
+                              value: 'NORMAL',
                               label: '常规模式',
                             },
                             {
-                              value: 'llm',
+                              value: 'LLM',
                               label: '智能模式',
-                              disabled: true,
                             },
                             {
-                              value: 'qa',
+                              value: 'QA',
                               label: 'Q/A模式',
-                              disabled: true,
                             },
                           ],
                         },
@@ -105,7 +103,6 @@ const DataImport: React.FC = () => {
                                 },
                                 dataType: 'form',
                                 data: {
-                                  name: name,
                                   mode: '${mode}',
                                   type: '${type}',
                                   content: '${content}',