embed 添加redis缓存

core/src/main/java/com/tzld/videoVector/service/impl/EmbeddingServiceImpl.java

@@ -1,11 +1,16 @@
 package com.tzld.videoVector.service.impl;
 
+import com.alibaba.fastjson.JSON;
+import com.alibaba.fastjson.TypeReference;
 import com.tzld.videoVector.api.DashScopeEmbeddingApiService;
 import com.tzld.videoVector.model.po.pgVector.DeconstructVectorConfig;
 import com.tzld.videoVector.service.EmbeddingService;
+import com.tzld.videoVector.util.Md5Util;
+import com.tzld.videoVector.util.RedisUtils;
 import lombok.extern.slf4j.Slf4j;
 import org.springframework.beans.factory.annotation.Value;
 import org.springframework.stereotype.Service;
+import org.springframework.util.StringUtils;
 
 import javax.annotation.PostConstruct;
 import javax.annotation.Resource;
@@ -26,15 +31,26 @@ public class EmbeddingServiceImpl implements EmbeddingService {
     @Value("${embedding.mode:dashscope}")
     private String embeddingMode;
 
+    /**
+     * embedding 缓存过期时间（秒），默认 1 小时
+     */
+    @Value("${embedding.cache.expire:3600}")
+    private long cacheExpireSeconds;
+
+    private static final String CACHE_KEY_PREFIX = "embedding:cache:";
+
     @Resource
     private com.tzld.videoVector.api.EmbeddingApiService embeddingApiService;
 
     @Resource
     private DashScopeEmbeddingApiService dashScopeEmbeddingApiService;
 
+    @Resource
+    private RedisUtils redisUtils;
+
     @PostConstruct
     public void init() {
-        log.info("向量化服务初始化完成，模式: {}", embeddingMode);
+        log.info("向量化服务初始化完成，模式: {}, 缓存过期: {}s", embeddingMode, cacheExpireSeconds);
     }
 
     @Override
@@ -44,17 +60,49 @@ public class EmbeddingServiceImpl implements EmbeddingService {
             return Collections.emptyList();
         }
 
-        // 根据配置选择向量化方式
+        // 构建缓存 key：基于文本MD5 + 模型 + 维度
+        String model = config != null ? config.getEmbeddingModel() : null;
+        Integer dim = config != null ? config.getDimension() : null;
+        String cacheKey = buildCacheKey(text, model, dim);
+
+        // 查询 Redis 缓存
+        if (cacheKey != null) {
+            try {
+                String cached = redisUtils.get(cacheKey);
+                if (StringUtils.hasText(cached)) {
+                    List<Float> vector = JSON.parseObject(cached, new TypeReference<List<Float>>() {});
+                    if (vector != null && !vector.isEmpty()) {
+                        log.debug("命中 embedding Redis 缓存，key={}", cacheKey);
+                        return vector;
+                    }
+                }
+            } catch (Exception e) {
+                log.warn("读取 embedding 缓存失败，key={}，继续调用 API", cacheKey, e);
+            }
+        }
+
+        // 调用 embedding API
+        List<Float> vector;
         if ("dashscope".equalsIgnoreCase(embeddingMode)) {
-            String model = config != null ? config.getEmbeddingModel() : null;
-            Integer dim = config != null ? config.getDimension() : null;
-            return dashScopeEmbeddingApiService.embed(text, model, dim);
+            vector = dashScopeEmbeddingApiService.embed(text, model, dim);
         } else if ("api".equalsIgnoreCase(embeddingMode)) {
-            return embeddingApiService.embed(text);
+            vector = embeddingApiService.embed(text);
         } else {
             log.error("不支持的 embedding 模式: {}", embeddingMode);
             return Collections.emptyList();
         }
+
+        // 写入 Redis 缓存
+        if (vector != null && !vector.isEmpty() && cacheKey != null) {
+            try {
+                redisUtils.set(cacheKey, JSON.toJSONString(vector), cacheExpireSeconds);
+                log.debug("embedding 结果已缓存，key={}, 维度={}", cacheKey, vector.size());
+            } catch (Exception e) {
+                log.warn("写入 embedding 缓存失败，key={}", cacheKey, e);
+            }
+        }
+
+        return vector;
     }
 
     @Override
@@ -63,17 +111,26 @@ public class EmbeddingServiceImpl implements EmbeddingService {
             return Collections.emptyList();
         }
 
-        // 根据配置选择向量化方式
-        if ("dashscope".equalsIgnoreCase(embeddingMode)) {
-            String model = config != null ? config.getEmbeddingModel() : null;
-            Integer dim = config != null ? config.getDimension() : null;
-            return dashScopeEmbeddingApiService.batchEmbed(texts, model, dim);
-        } else if ("api".equalsIgnoreCase(embeddingMode)) {
-            return embeddingApiService.batchEmbed(texts);
-        } else {
-            log.error("不支持的 embedding 模式: {}", embeddingMode);
-            return Collections.emptyList();
+        // 批量场景逐条走 embed 方法，复用缓存逻辑
+        List<List<Float>> results = new java.util.ArrayList<>(texts.size());
+        for (String text : texts) {
+            results.add(embed(text, config));
+        }
+        return results;
+    }
+
+    /**
+     * 构建 embedding 缓存 key
+     * 格式：embedding:cache:{md5(text)}:{model}:{dimension}
+     */
+    private String buildCacheKey(String text, String model, Integer dimension) {
+        String textHash = Md5Util.encoderByMd5(text);
+        if (!StringUtils.hasText(textHash)) {
+            return null;
         }
+        String modelPart = StringUtils.hasText(model) ? model : "default";
+        String dimPart = dimension != null ? String.valueOf(dimension) : "0";
+        return CACHE_KEY_PREFIX + textHash + ":" + modelPart + ":" + dimPart;
     }
 
 }