feat(更新日志):

更新日志

1. 移除个人微信模块
2. 移除直播模块
3. 移除gpts模块
4. 移除应用商店模块
5. 移除套餐管理模块
6. 移除兑换管理模块

## 微信相关
小程序相关功能迁移至企业版
微信公众号/微信机器人迁移至企业版
微信支付迁移至企业版

## 功能模块
智能体模块迁移至企业版
插件管理改为MCP应用并迁移至企业版

知识库：
excel解析迁移至企业版
pdf图片解析迁移至企业版
milvus qdrant扩展 迁移至企业版

ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/PdfImageExtractService.java

@@ -1,41 +0,0 @@
-package org.ruoyi.service;
-
-import java.io.IOException;
-import java.util.List;
-import org.ruoyi.domain.PdfFileContentResult;
-import org.springframework.web.multipart.MultipartFile;
-
-/**
- * PDF图片提取服务接口
- */
-public interface PdfImageExtractService {
-
-  /**
-   * 从PDF文件中提取图片
-   *
-   * @param pdfFile PDF文件
-   * @param imageFormat 输出图片格式 (png, jpeg, gif)
-   * @param allowDuplicates 是否允许重复图片
-   * @return 包含提取图片的ZIP文件的字节数组
-   * @throws IOException 如果文件处理过程中发生错误
-   */
-  byte[] extractImages(MultipartFile pdfFile, String imageFormat, boolean allowDuplicates)
-      throws IOException;
-
-  /**
-   * 处理文件内容
-   *
-   * @param unzip Base64编码的图片数组
-   * @return 文件内容结果列表
-   * @throws IOException 如果API调用过程中发生错误
-   */
-  List<PdfFileContentResult> dealFileContent(String[] unzip) throws IOException;
-
-  /**
-   * 提取PDF中的图片并调用gpt-4o-mini,识别图片内容并返回
-   * @param file
-   * @return
-   * @throws IOException
-   */
-  List<PdfFileContentResult> extractImages(MultipartFile file) throws IOException;
-}

ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/impl/PdfImageExtractServiceImpl.java

@@ -1,149 +0,0 @@
-package org.ruoyi.service.impl;
-
-import java.io.IOException;
-import java.util.ArrayList;
-import java.util.List;
-import java.util.concurrent.TimeUnit;
-import lombok.AllArgsConstructor;
-import lombok.Data;
-import lombok.extern.slf4j.Slf4j;
-import okhttp3.MediaType;
-import okhttp3.MultipartBody;
-import okhttp3.OkHttpClient;
-import okhttp3.OkHttpClient.Builder;
-import okhttp3.Request;
-import okhttp3.RequestBody;
-import okhttp3.Response;
-import org.ruoyi.common.core.domain.R;
-import org.ruoyi.domain.PdfFileContentResult;
-import org.ruoyi.service.PdfImageExtractService;
-import org.ruoyi.utils.ZipUtils;
-import org.springframework.beans.factory.annotation.Value;
-import org.springframework.stereotype.Service;
-import org.springframework.web.multipart.MultipartFile;
-
-/**
- * PDF图片提取服务实现类
- */
-//@Service
-@Slf4j
-@Data
-@AllArgsConstructor
-//public class PdfImageExtractServiceImpl implements PdfImageExtractService {
-public class PdfImageExtractServiceImpl  {
-
-//  @Value("${pdf.extract.service.url}")
-  private String serviceUrl;
-//  @Value("${pdf.extract.ai-api.url}")
-  private String aiApiUrl;
-//  @Value("${pdf.extract.ai-api.key}")
-  private String aiApiKey;
-
-  private final OkHttpClient client = new Builder()
-      .connectTimeout(100, TimeUnit.SECONDS)
-      .readTimeout(150, TimeUnit.SECONDS)
-      .writeTimeout(150, TimeUnit.SECONDS)
-      .callTimeout(300, TimeUnit.SECONDS)
-      .build();
-
-  private static final MediaType JSON = MediaType.parse("application/json; charset=utf-8");
-
-//  @Override
-  public byte[] extractImages(MultipartFile pdfFile, String imageFormat, boolean allowDuplicates)
-      throws IOException {
-    // 构建multipart请求
-    RequestBody requestBody = new MultipartBody.Builder()
-        .setType(MultipartBody.FORM)
-        .addFormDataPart("fileInput", pdfFile.getOriginalFilename(),
-            RequestBody.create(MediaType.parse("application/pdf"), pdfFile.getBytes()))
-        .addFormDataPart("format", imageFormat)
-        .addFormDataPart("allowDuplicates", String.valueOf(allowDuplicates))
-        .build();
-
-    // 创建请求
-    Request request = new Request.Builder()
-        .url(serviceUrl + "/api/v1/misc/extract-images")
-        .post(requestBody)
-        .build();
-
-    // 执行请求
-    try (Response response = client.newCall(request).execute()) {
-      if (!response.isSuccessful()) {
-        throw new IOException("请求失败: " + response.code());
-      }
-      return response.body().bytes();
-    }
-  }
-
-  /**
-   * 处理文件内容
-   *
-   * @param unzip Base64编码的图片数组
-   * @return 文件内容结果列表
-   * @throws IOException 如果API调用过程中发生错误
-   */
-//  @Override
-  public List<PdfFileContentResult> dealFileContent(String[] unzip) throws IOException {
-    List<PdfFileContentResult> results = new ArrayList<>();
-    int i = 0;
-    for (String base64Image : unzip) {
-      // 构建请求JSON
-      String requestJson = String.format("{"
-          + "\"model\": \"gpt-4o\","
-          + "\"stream\": false,"
-          + "\"messages\": [{"
-          + "\"role\": \"user\","
-          + "\"content\": [{"
-          + "\"type\": \"text\","
-          + "\"text\": \"这张图片有什么\""
-          + "}, {"
-          + "\"type\": \"image_url\","
-          + "\"image_url\": {"
-          + "\"url\": \"%s\""
-          + "}}"
-          + "]}],"
-          + "\"max_tokens\": 400"
-          + "}", base64Image);
-
-      // 创建请求
-      Request request = new Request.Builder()
-          .url(aiApiUrl)
-          .addHeader("Authorization", "Bearer " + aiApiKey)
-          .post(RequestBody.create(JSON, requestJson))
-          .build();
-
-      // 执行请求
-      try {
-        log.info("=============call=" + ++i);
-
-        Response response = client.newCall(request).execute();
-        log.info("=============response=" + response);
-        if (!response.isSuccessful()) {
-          throw new IOException("API请求失败: " + response.code() + response.toString());
-        }
-
-        String responseBody = response.body().string();
-        log.info("=============responseBody=" + responseBody);
-        // 使用文件名（这里使用base64的前10个字符作为标识）和API返回内容创建结果对象
-        String filename = base64Image.substring(0, Math.min(base64Image.length(), 10));
-        results.add(new PdfFileContentResult(filename, responseBody));
-      } catch (Exception e) {
-        log.error(e.getMessage());
-        throw new RuntimeException(e);
-      }
-    }
-    return results;
-  }
-
-//  @Override
-  public List<PdfFileContentResult> extractImages(MultipartFile file) throws IOException {
-    String format = "png";
-    boolean allowDuplicates = true;
-    // 获取ZIP数据
-    byte[] zipData = this.extractImages(file, format, allowDuplicates);
-    // 解压文件并识别图片内容并返回
-    String[] unzip = ZipUtils.unzipForBase64(zipData);
-    //解析图片内容
-    return this.dealFileContent(unzip);
-  }
-}

ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/impl/VectorStoreServiceImpl.java

@@ -11,9 +11,6 @@ import dev.langchain4j.store.embedding.EmbeddingSearchRequest;
 import dev.langchain4j.store.embedding.EmbeddingStore;
 import dev.langchain4j.store.embedding.filter.Filter;
 import dev.langchain4j.store.embedding.filter.comparison.IsEqualTo;
-import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
-import dev.langchain4j.store.embedding.milvus.MilvusEmbeddingStore;
-import dev.langchain4j.store.embedding.qdrant.QdrantEmbeddingStore;
 import dev.langchain4j.store.embedding.weaviate.WeaviateEmbeddingStore;
 import lombok.RequiredArgsConstructor;
 import lombok.SneakyThrows;
@@ -23,16 +20,14 @@ import org.ruoyi.domain.bo.QueryVectorBo;
 import org.ruoyi.domain.bo.StoreEmbeddingBo;
 import org.ruoyi.service.VectorStoreService;
 import org.springframework.stereotype.Service;
-
-import static dev.langchain4j.model.openai.OpenAiEmbeddingModelName.TEXT_EMBEDDING_3_SMALL;
 import java.util.ArrayList;
 import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
-import java.util.stream.Collectors;
 
 /**
  * 向量库管理
+ *
  * @author ageer
  */
 @Service
@@ -45,51 +40,23 @@ public class VectorStoreServiceImpl implements VectorStoreService {
     private EmbeddingStore<TextSegment> embeddingStore;
 
     @Override
-    public void createSchema(String kid,String modelName) {
-        switch (modelName) {
-            case "weaviate" -> {
-                String protocol = configService.getConfigValue("weaviate", "protocol");
-                String host = configService.getConfigValue("weaviate", "host");
-                String className = configService.getConfigValue("weaviate", "classname");
-                embeddingStore = WeaviateEmbeddingStore.builder()
-                        .scheme(protocol)
-                        .host(host)
-                        .objectClass(className + kid)
-                        .scheme(protocol)
-                        .avoidDups(true)
-                        .consistencyLevel("ALL")
-                        .build();
-            }
-            case "milvus" -> {
-                String uri = configService.getConfigValue("milvus", "host");
-                String collection = configService.getConfigValue("milvus", "collection");
-                String dimension = configService.getConfigValue("milvus", "dimension");
-                embeddingStore = MilvusEmbeddingStore.builder()
-                        .uri(uri)
-                        .collectionName(collection + kid)
-                        .dimension(Integer.parseInt(dimension))
-                        .build();
-            }
-            case "qdrant" -> {
-                String host = configService.getConfigValue("qdrant", "host");
-                String port = configService.getConfigValue("qdrant", "port");
-                String collectionName = configService.getConfigValue("qdrant", "collectionName");
-                embeddingStore = QdrantEmbeddingStore.builder()
-                        .host(host)
-                        .port(Integer.parseInt(port))
-                        .collectionName(collectionName)
-                        .build();
-            }
-            default -> {
-                //使用内存
-                embeddingStore = new InMemoryEmbeddingStore<>();
-            }
-        }
+    public void createSchema(String kid, String modelName) {
+        String protocol = configService.getConfigValue("weaviate", "protocol");
+        String host = configService.getConfigValue("weaviate", "host");
+        String className = configService.getConfigValue("weaviate", "classname");
+        embeddingStore = WeaviateEmbeddingStore.builder()
+                .scheme(protocol)
+                .host(host)
+                .objectClass(className + kid)
+                .scheme(protocol)
+                .avoidDups(true)
+                .consistencyLevel("ALL")
+                .build();
     }
 
     @Override
     public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
-        createSchema(storeEmbeddingBo.getKid(),storeEmbeddingBo.getVectorModelName());
+        createSchema(storeEmbeddingBo.getKid(), storeEmbeddingBo.getVectorModelName());
         EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(),
                 storeEmbeddingBo.getApiKey(), storeEmbeddingBo.getBaseUrl());
         List<String> chunkList = storeEmbeddingBo.getChunkList();
@@ -101,22 +68,22 @@ public class VectorStoreServiceImpl implements VectorStoreService {
             Embedding embedding = embeddingModel.embed(chunkList.get(i)).content();
             TextSegment segment = TextSegment.from(chunkList.get(i));
             segment.metadata().putAll(dataSchema);
-            embeddingStore.add(embedding,segment);
+            embeddingStore.add(embedding, segment);
         }
     }
 
     @Override
     public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
-        createSchema(queryVectorBo.getKid(),queryVectorBo.getVectorModelName());
+        createSchema(queryVectorBo.getKid(), queryVectorBo.getVectorModelName());
         EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(),
                 queryVectorBo.getApiKey(), queryVectorBo.getBaseUrl());
-       // Filter simpleFilter = new IsEqualTo("kid", queryVectorBo.getKid());
+        // Filter simpleFilter = new IsEqualTo("kid", queryVectorBo.getKid());
         Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
         EmbeddingSearchRequest embeddingSearchRequest = EmbeddingSearchRequest.builder()
                 .queryEmbedding(queryEmbedding)
                 .maxResults(queryVectorBo.getMaxResults())
                 // 添加过滤条件
-         //       .filter(simpleFilter)
+                //       .filter(simpleFilter)
                 .build();
         List<EmbeddingMatch<TextSegment>> matches = embeddingStore.search(embeddingSearchRequest).matches();
         List<String> results = new ArrayList<>();
@@ -126,24 +93,24 @@ public class VectorStoreServiceImpl implements VectorStoreService {
 
 
     @Override
-    public void removeByKid(String kid,String modelName) {
-        createSchema(kid,modelName);
+    public void removeByKid(String kid, String modelName) {
+        createSchema(kid, modelName);
         // 根据条件删除向量数据
         Filter simpleFilter = new IsEqualTo("kid", kid);
         embeddingStore.removeAll(simpleFilter);
     }
 
     @Override
-    public void removeByDocId(String kid, String docId,String modelName) {
-        createSchema(kid,modelName);
+    public void removeByDocId(String kid, String docId, String modelName) {
+        createSchema(kid, modelName);
         // 根据条件删除向量数据
         Filter simpleFilterByDocId = new IsEqualTo("docId", docId);
         embeddingStore.removeAll(simpleFilterByDocId);
     }
 
     @Override
-    public void removeByKidAndFid(String kid, String fid,String modelName) {
-        createSchema(kid,modelName);
+    public void removeByKidAndFid(String kid, String fid, String modelName) {
+        createSchema(kid, modelName);
         // 根据条件删除向量数据
         Filter simpleFilterByKid = new IsEqualTo("kid", kid);
         Filter simpleFilterFid = new IsEqualTo("fid", fid);
@@ -157,25 +124,18 @@ public class VectorStoreServiceImpl implements VectorStoreService {
     @SneakyThrows
     public EmbeddingModel getEmbeddingModel(String modelName, String apiKey, String baseUrl) {
         EmbeddingModel embeddingModel;
-        if(TEXT_EMBEDDING_3_SMALL.toString().equals(modelName)) {
-             embeddingModel = OpenAiEmbeddingModel.builder()
-                    .apiKey(apiKey)
-                    .baseUrl(baseUrl)
-                    .modelName(modelName)
-                    .build();
-        // TODO 添加枚举
-        }else if("quentinz/bge-large-zh-v1.5".equals(modelName)) {
+        if ("quentinz/bge-large-zh-v1.5".equals(modelName)) {
             embeddingModel = OllamaEmbeddingModel.builder()
                     .baseUrl(baseUrl)
                     .modelName(modelName)
                     .build();
-        }else if("baai/bge-m3".equals(modelName)) {
+        } else if ("baai/bge-m3".equals(modelName)) {
             embeddingModel = OpenAiEmbeddingModel.builder()
                     .apiKey(apiKey)
                     .baseUrl(baseUrl)
                     .modelName(modelName)
                     .build();
-        }else {
+        } else {
             throw new ServiceException("未找到对应向量化模型!");
         }
         return embeddingModel;