feat(vector): 新增向量搜索与文本语义检索接口

- DemoController 增加 /testSearch、/tsetSearchText 端点
- QdrantVectorService 补充 searchPoint、searchText、indexText 方法
- 新增 SearchEmbedReq、TextSearchReq、QdrantSearchItem 等 DTO/VO
- 调整 LLM 模型为 qwen3-embedding-0.6b 并开放对应接口免鉴权

src/main/java/com/yolo/keyborad/config/LLMConfig.java

@@ -53,7 +53,8 @@ public class LLMConfig {
                 this.openAiApi(),
                 MetadataMode.EMBED,
                 OpenAiEmbeddingOptions.builder()
-                        .model("qwen/qwen3-embedding-8b")
+                        .model("qwen/qwen3-embedding-0.6b")
+                        .dimensions(2048)
                         .user("user-6")
                         .build(),
                 RetryUtils.DEFAULT_RETRY_TEMPLATE);

src/main/java/com/yolo/keyborad/config/SaTokenConfigure.java

@@ -38,7 +38,9 @@ public class SaTokenConfigure implements WebMvcConfigurer {
                 "/demo/talk",
                 "/user/appleLogin",
                 "/demo/embed",
-                "/demo/testSaveEmbed"
+                "/demo/testSaveEmbed",
+                "/demo/testSearch",
+                "/demo/tsetSearchText"
         };
     }
     @Bean

src/main/java/com/yolo/keyborad/controller/DemoController.java

@@ -1,14 +1,15 @@
 package com.yolo.keyborad.controller;
 
-import cn.hutool.json.JSON;
 import cn.hutool.json.JSONUtil;
 import com.yolo.keyborad.common.BaseResponse;
 import com.yolo.keyborad.common.ResultUtils;
 
 import com.yolo.keyborad.model.dto.EmbedSaveReq;
 import com.yolo.keyborad.model.dto.IosPayVerifyReq;
+import com.yolo.keyborad.model.dto.SearchEmbedReq;
+import com.yolo.keyborad.model.dto.TextSearchReq;
+import com.yolo.keyborad.model.vo.QdrantSearchItem;
 import com.yolo.keyborad.service.impl.QdrantVectorService;
-import io.qdrant.client.QdrantClient;
 import io.swagger.v3.oas.annotations.Operation;
 import io.swagger.v3.oas.annotations.Parameter;
 import io.swagger.v3.oas.annotations.tags.Tag;
@@ -91,4 +92,20 @@ public class DemoController {
                  , JSONUtil.toJsonStr(embedSaveReq.getRecordItem()));
         return ResultUtils.success(true);
     }
+
+
+    @PostMapping("/testSearch")
+    @Operation(summary = "测试搜索向量接口", description = "测试搜索向量接口")
+    @Parameter(name = "userInput",required = true,description = "测试搜索向量接口")
+    public BaseResponse<List<QdrantSearchItem>> testSearch(@RequestBody SearchEmbedReq searchEmbedReq) {
+        return ResultUtils.success(qdrantVectorService.searchPoint(searchEmbedReq.getUserInputEmbed(), 3));
+    }
+
+
+    @PostMapping("/tsetSearchText")
+    @Operation(summary = "测试搜索语义接口", description = "测试搜索语义接口")
+    @Parameter(name = "userInput",required = true,description = "测试搜索语义接口")
+    public BaseResponse<List<QdrantSearchItem>> testSearchText(@RequestBody TextSearchReq textSearchReq) {
+        return ResultUtils.success(qdrantVectorService.searchText(textSearchReq.getUserInput()));
+    }
 }

src/main/java/com/yolo/keyborad/model/dto/SearchEmbedReq.java

@@ -0,0 +1,15 @@
+package com.yolo.keyborad.model.dto;
+
+import lombok.Data;
+
+import java.util.List;
+
+/*
+ * @author: ziin
+ * @date: 2025/11/14 18:12
+ */
+@Data
+public class SearchEmbedReq {
+
+    private List<Float> userInputEmbed;
+}

src/main/java/com/yolo/keyborad/model/dto/TextSearchReq.java

@@ -0,0 +1,12 @@
+package com.yolo.keyborad.model.dto;
+
+import lombok.Data;
+
+/*
+ * @author: ziin
+ * @date: 2025/11/14 19:50
+ */
+@Data
+public class TextSearchReq {
+    private String userInput;
+}

src/main/java/com/yolo/keyborad/model/vo/QdrantSearchItem.java

@@ -0,0 +1,24 @@
+package com.yolo.keyborad.model.vo;
+
+import lombok.Data;
+
+import java.util.List;
+
+// package 自己按项目结构放
+@Data
+public class QdrantSearchItem {
+
+    /** 向量 ID（你插入时用的是 long） */
+    private Long id;
+
+    /** 相似度得分 */
+    private Float score;
+
+    /** 你存进去的 payload 文本（或者 JSON 字符串） */
+    private String payload;
+
+    /** 完整向量（如果不想暴露可以去掉这个字段） */
+    private List<Float> vector;
+
+    // getter / setter 省略，也可以用 Lombok @Data
+}

src/main/java/com/yolo/keyborad/model/vo/VectorSearchResultVO.java

@@ -0,0 +1,18 @@
+package com.yolo.keyborad.model.vo;
+
+import io.qdrant.client.grpc.JsonWithInt;
+import lombok.Data;
+
+import java.util.Map;
+
+/*
+ * @author: ziin
+ * @date: 2025/11/14 18:36
+ */
+@Data
+public class VectorSearchResultVO {
+
+    private String id;
+    private double score;
+    private String payload;
+}

src/main/java/com/yolo/keyborad/service/impl/QdrantVectorService.java

@@ -1,13 +1,16 @@
 package com.yolo.keyborad.service.impl;
 
+import com.google.common.primitives.Floats;
 import com.yolo.keyborad.common.ErrorCode;
 import com.yolo.keyborad.exception.BusinessException;
+import com.yolo.keyborad.model.vo.QdrantSearchItem;
 import io.qdrant.client.QdrantClient;
-import io.qdrant.client.grpc.Collections;
-import io.qdrant.client.grpc.JsonWithInt;
 import io.qdrant.client.grpc.Points;
 import jakarta.annotation.Resource;
 import lombok.extern.slf4j.Slf4j;
+import org.springframework.ai.embedding.Embedding;
+import org.springframework.ai.embedding.EmbeddingModel;
+import org.springframework.ai.embedding.EmbeddingResponse;
 import org.springframework.stereotype.Service;
 
 import java.util.List;
@@ -15,8 +18,10 @@ import java.util.Map;
 import java.util.concurrent.ExecutionException;
 
 import static io.qdrant.client.PointIdFactory.id;
+import static io.qdrant.client.QueryFactory.nearest;
 import static io.qdrant.client.ValueFactory.value;
 import static io.qdrant.client.VectorsFactory.vectors;
+import static io.qdrant.client.WithPayloadSelectorFactory.enable;
 
 
 @Service
@@ -28,6 +33,9 @@ public class QdrantVectorService {
 
     private static final String COLLECTION_NAME = "test_document";
 
+    @Resource
+    private EmbeddingModel embeddingModel;
+
     /**
      * 插入/更新一条向量数据
      *
@@ -37,20 +45,6 @@ public class QdrantVectorService {
      */
     public void upsertPoint(long id, List<Float> vector,String payload){
 
-//        // 1. 确保 collection 存在（没有就创建一次即可）
-//        try {
-//            qdrantClient.createCollectionAsync(
-//                    COLLECTION_NAME,
-//                    Collections.VectorParams.newBuilder()
-//                            .setSize(vector.size())           // 向量维度
-//                            .setDistance(Collections.Distance.Cosine) // 相似度度量
-//                            .build()
-//            ).get();  // 简单起见直接 get()，生产建议在启动时提前创建好
-//        } catch (InterruptedException | ExecutionException e) {
-//            log.error("创建 collection 失败", e);
-//            throw new BusinessException(ErrorCode.OPERATION_ERROR);
-//        }
-
         try {
             qdrantClient.upsertAsync(
                     COLLECTION_NAME,
@@ -66,6 +60,131 @@ public class QdrantVectorService {
             log.error("upsert point 失败", e);
             throw new BusinessException(ErrorCode.OPERATION_ERROR);
         }
+    }
+
+//    public List<VectorSearchResultVO> searchPoint(List<Float> userInput) {
+//        try {
+//            Points.QueryPoints query = Points.QueryPoints.newBuilder()
+//                    .setCollectionName(COLLECTION_NAME)  // ★ 必须设置
+//                    .setQuery(nearest(userInput))
+//                    .build();
+//
+//            List<Points.BatchResult> batchResults = qdrantClient.queryBatchAsync(
+//                    COLLECTION_NAME,
+//                    List.of(query)
+//            ).get();
+//
+//            return batchResults.stream()
+//                    .map(p -> {
+//                        VectorSearchResultVO vo = new VectorSearchResultVO();
+//                        vo.setId(String.valueOf(p.getResult(0).getId()));        // 或者 p.getId().getUuid()
+//                        vo.setScore(p.getResult(0).getScore());
+//                        vo.setPayload(p.getResult(0).getPayloadMap());
+//                        return vo;
+//                    })
+//                    .toList();
+//
+//        } catch (InterruptedException | ExecutionException e) {
+//            log.error("search point 失败", e);
+//            throw new BusinessException(ErrorCode.OPERATION_ERROR);
+//        }
+//    }
+
+    public List<QdrantSearchItem> searchPoint(List<Float> userVector, int limit) {
+        try {
+            Points.QueryPoints query = Points.QueryPoints.newBuilder()
+                    .setCollectionName(COLLECTION_NAME)  // ★ 必须
+                    .setQuery(nearest(userVector))       // ★ 语义向量
+                    .setLimit(limit)                     // TopK
+                    .setWithPayload(enable(true))                         // ★ 带上 payload
+                    .build();
+
+            List<Points.BatchResult> batchResults = qdrantClient.queryBatchAsync(
+                    COLLECTION_NAME,
+                    List.of(query)
+            ).get();
+            Points.BatchResult batchResult = batchResults.get(0);
+
+            // 3. 把 Protobuf 的 ScoredPoint 转成你的 DTO
+            return batchResult.getResultList().stream()
+                    .map(p -> {
+                        QdrantSearchItem item = new QdrantSearchItem();
+
+                        // id：你插入时用的是 setId(id(id))，所以这里取 num
+                        if (p.getId().hasNum()) {
+                            item.setId(p.getId().getNum());
+                        }
+
+                        // score
+                        item.setScore(p.getScore());
+
+                        // payload：你之前是 putAllPayload(Map.of("payload", value(payload)))
+                        // 这里从 Struct 里拿 "payload" 字段
+                        var fieldsMap = p.getPayloadMap();
+                        var payloadValue = fieldsMap.get("payload");
+                        if (payloadValue != null && payloadValue.hasStringValue()) {
+                            item.setPayload(payloadValue.getStringValue());
+                        }
+
+                        // vector：你插入时用的是 vectors(vector)，即 unnamed 单向量
+                        // proto 结构一般是 Vectors.vector.data[]
+                        if (p.getVectors().hasVector()) {
+                            List<Float> vec = p.getVectors().getVector().getDataList();
+                            item.setVector(vec);
+                        }
+
+                        return item;
+                    })
+                    .toList();
+
+        
+        } catch (InterruptedException | ExecutionException e) {
+            log.error("search point 失败", e);
+            throw new BusinessException(ErrorCode.OPERATION_ERROR);
+        }
+    }
+
+
+
+    /**
+     * 把一段文本做 embedding 然后写入 Qdrant
+     *
+     * @param id       业务 ID（比如业务表主键）
+     * @param text     用来做向量的文本（一般是内容）
+     */
+    public void indexText(long id, String text) {
+        // 1. 文本 → 向量
+        List<Float> vector = embedTextToVector(text);
+
+        // 2. 存到 Qdrant，payload 里顺便存原文
+        upsertPoint(id, vector, text);
+    }
+
+    private List<Float> embedTextToVector(String text) {
+        EmbeddingResponse response = embeddingModel.embedForResponse(List.of(text));
+
+
+        Embedding embedding = response.getResult();   // 就一条
+        // Spring AI 里一般是 List<Double>
+        float[] output = embedding.getOutput();
+        // 转成 Qdrant 需要的 List<Float>
+
+
+        return Floats.asList(output);
 
     }
+
+    public List<QdrantSearchItem> searchText(String userInput) {
+        long t0 = System.currentTimeMillis();
+
+        List<Float> floats = this.embedTextToVector(userInput);
+        long t1 = System.currentTimeMillis();
+
+        List<QdrantSearchItem> qdrantSearchItems = this.searchPoint(floats, 3);
+        long t2 = System.currentTimeMillis();
+
+        log.info("embedding = {} ms, qdrant = {} ms, total = {} ms",
+                (t1 - t0), (t2 - t1), (t2 - t0));
+        return qdrantSearchItems;
+    }
 }