feat(chat): 新增保存润色结果向量接口并重构向量类型

- ChatController 新增 /save_embed 接口，接收 ChatSaveReq 保存用户选中润色句子的向量
- 统一向量参数由 List<Float> 改为 float[]，降低 GC 压力
- 向量搜索增加 ≥0.9 相似度过滤，仅返回高置信结果
- 精简 DemoController 测试接口，下线冗余的 testSaveEmbed/testSearch
- 调整 Embedding 模型为 qwen3-embedding-4b，降低资源占用
- 放开 /chat/save_embed 匿名访问，适配前端直调

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

@@ -53,7 +53,7 @@ public class LLMConfig {
                 this.openAiApi(),
                 MetadataMode.EMBED,
                 OpenAiEmbeddingOptions.builder()
-                        .model("qwen/qwen3-embedding-8b")
+                        .model("qwen/qwen3-embedding-4b")
                         .dimensions(1536)
                         .user("user-6")
                         .build(),

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

@@ -84,7 +84,8 @@ public class SaTokenConfigure implements WebMvcConfigurer {
                 "/api/apple/validate-receipt",
                 "/character/list",
                 "/user/resetPassWord",
-                "/chat/talk"
+                "/chat/talk",
+                "/chat/save_embed"
         };
     }
     @Bean

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

@@ -1,7 +1,11 @@
 package com.yolo.keyborad.controller;
 
 import cn.dev33.satoken.stp.StpUtil;
+import cn.hutool.core.util.IdUtil;
+import com.yolo.keyborad.common.BaseResponse;
+import com.yolo.keyborad.common.ResultUtils;
 import com.yolo.keyborad.model.dto.chat.ChatReq;
+import com.yolo.keyborad.model.dto.chat.ChatSaveReq;
 import com.yolo.keyborad.model.dto.chat.ChatStreamMessage;
 import com.yolo.keyborad.model.entity.KeyboardCharacter;
 import com.yolo.keyborad.service.KeyboardCharacterService;
@@ -12,6 +16,7 @@ import io.swagger.v3.oas.annotations.tags.Tag;
 import jakarta.annotation.Resource;
 import lombok.extern.slf4j.Slf4j;
 import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.embedding.EmbeddingResponse;
 import org.springframework.ai.openai.OpenAiChatOptions;
 import org.springframework.ai.openai.OpenAiEmbeddingModel;
 import org.springframework.boot.context.properties.bind.DefaultValue;
@@ -47,15 +52,11 @@ public class ChatController {
 
     @PostMapping("/talk")
     @Operation(summary = "聊天润色接口", description = "聊天润色接口")
-    @Parameter(name = "userInput",required = true,description = "测试聊天接口",example = "talk to something")
     public Flux<ServerSentEvent<ChatStreamMessage>> testTalk(@RequestBody ChatReq chatReq){
-
         KeyboardCharacter character = keyboardCharacterService.getById(chatReq.getCharacterId());
-
         // 1. LLM 流式输出
         Flux<ChatStreamMessage> llmFlux = client
-                .prompt(character.getPrompt() +
+                .prompt(character.getPrompt())
-                        "\nUser message: %s".formatted(chatReq.getMessage()))
                 .system("""
                 Format rules:
                 - Return EXACTLY 3 replies.
@@ -97,8 +98,9 @@ public class ChatController {
     @PostMapping("/save_embed")
     @Operation(summary = "保存润色后的句子", description = "保存润色后的句子")
     @Parameter(name = "userInput",required = true,description = "测试聊天接口",example = "talk to something")
-    public Flux<String> testTalkWithVector(@RequestBody ChatReq chatReq) {
+    public BaseResponse<Boolean> testTalkWithVector(@RequestBody ChatSaveReq chatSaveReq) {
-
+        float[] embed = embeddingModel.embed(chatSaveReq.getUserInputMessage());
-        return null;
+        qdrantVectorService.upsertPoint(IdUtil.getSnowflakeNextId(), embed, chatSaveReq.getUserSelectMessage());
+        return ResultUtils.success(true);
     }
 }

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

@@ -97,23 +97,23 @@ public class DemoController {
     }
 
 
-    @PostMapping("/testSaveEmbed")
+//    @PostMapping("/testSaveEmbed")
-    @Operation(summary = "测试存储向量接口", description = "测试存储向量接口")
+//    @Operation(summary = "测试存储向量接口", description = "测试存储向量接口")
-    @Parameter(name = "userInput",required = true,description = "测试存储向量接口")
+//    @Parameter(name = "userInput",required = true,description = "测试存储向量接口")
-    public BaseResponse<Boolean> testSaveEmbed(@RequestBody EmbedSaveReq embedSaveReq) {
+//    public BaseResponse<Boolean> testSaveEmbed(@RequestBody EmbedSaveReq embedSaveReq) {
-         qdrantVectorService.upsertPoint(embedSaveReq.getRecordItem().getId()
+//         qdrantVectorService.upsertPoint(embedSaveReq.getRecordItem().getId()
-                 , embedSaveReq.getVector()
+//                 , embedSaveReq.getVector()
-                 , JSONUtil.toJsonStr(embedSaveReq.getRecordItem()));
+//                 , JSONUtil.toJsonStr(embedSaveReq.getRecordItem()));
-        return ResultUtils.success(true);
+//        return ResultUtils.success(true);
-    }
+//    }
 
 
-    @PostMapping("/testSearch")
+//    @PostMapping("/testSearch")
-    @Operation(summary = "测试搜索向量接口", description = "测试搜索向量接口")
+//    @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")
+//    @Parameter(name = "userInput",required = true,description = "测试搜索向量接口")
-    public BaseResponse<List<QdrantSearchItem>> testSearch(@RequestBody SearchEmbedReq searchEmbedReq) {
+//    public BaseResponse<List<QdrantSearchItem>> testSearch(@RequestBody SearchEmbedReq searchEmbedReq) {
-        return ResultUtils.success(qdrantVectorService.searchPoint(searchEmbedReq.getUserInputEmbed(), 3));
+//        return ResultUtils.success(qdrantVectorService.searchPoint(searchEmbedReq.getUserInputEmbed(), 3));
-    }
+//    }
 
 
     @PostMapping("/tsetSearchText")

src/main/java/com/yolo/keyborad/model/dto/chat/ChatSaveReq.java

@@ -0,0 +1,16 @@
+package com.yolo.keyborad.model.dto.chat;
+
+import lombok.Data;
+
+/*
+ * @author: ziin
+ * @date: 2025/12/8 19:26
+ */
+@Data
+public class ChatSaveReq {
+
+    private String userInputMessage;
+
+    private String userSelectMessage;
+
+}

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

@@ -13,6 +13,7 @@ import org.springframework.ai.embedding.EmbeddingModel;
 import org.springframework.ai.embedding.EmbeddingResponse;
 import org.springframework.stereotype.Service;
 
+import java.util.Arrays;
 import java.util.List;
 import java.util.Map;
 import java.util.concurrent.ExecutionException;
@@ -43,7 +44,7 @@ public class QdrantVectorService {
      * @param vector    向量（和 collection 中定义的 size 一致）
      * @param payload   额外信息，例如原文、标题、userId 等
      */
-    public void upsertPoint(long id, List<Float> vector,String payload){
+    public void upsertPoint(long id, float[] vector,String payload){
 
         try {
             qdrantClient.upsertAsync(
@@ -90,12 +91,12 @@ public class QdrantVectorService {
 //        }
 //    }
 
-    public List<QdrantSearchItem> searchPoint(List<Float> userVector, int limit) {
+    public List<QdrantSearchItem> searchPoint(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
+                    .setLimit(limit)                     // 限制返回数量
                     .setWithPayload(enable(true))                         // ★ 带上 payload
                     .build();
 
@@ -107,37 +108,31 @@ public class QdrantVectorService {
 
             // 3. 把 Protobuf 的 ScoredPoint 转成你的 DTO
             return batchResult.getResultList().stream()
+                    .filter(p -> p.getScore() >= 0.9)   // ★ 只要相似度 ≥ 90%
                     .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(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);
@@ -149,38 +144,29 @@ public class QdrantVectorService {
     /**
      * 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) {
+//    public void indexText(long id, String text) {
-        // 1. 文本 → 向量
+//        // 1. 文本 → 向量
-        List<Float> vector = embedTextToVector(text);
+//         embedTextToVector(text);
+//
+//        // 2. 存到 Qdrant，payload 里顺便存原文
+//        upsertPoint(id, vector, text);
+//    }
 
-        // 2. 存到 Qdrant，payload 里顺便存原文
+    private float[] embedTextToVector(String text) {
-        upsertPoint(id, vector, text);
-    }
 
-    private List<Float> embedTextToVector(String text) {
+        return embeddingModel.embed(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);
+        float[] floats = this.embedTextToVector(userInput);
         long t1 = System.currentTimeMillis();
 
-        List<QdrantSearchItem> qdrantSearchItems = this.searchPoint(floats, 3);
+        List<QdrantSearchItem> qdrantSearchItems = this.searchPoint(floats, 1);
         long t2 = System.currentTimeMillis();
 
         log.info("embedding = {} ms, qdrant = {} ms, total = {} ms",