Redis一鱼两吃：向量数据库与缓存的完美结合

docker run -p 6379:6379 --name redis-stack redis/redis-stack:latest

HSET docs:01 doc_embedding <vector_bytes> category sports

JSON.SET docs:01 $ '{"doc_embedding":[0.34,0.63,-0.54,-0.69,0.98,0.61], "category": "sports"}'

import io.lettuce.core.RedisClient;
import io.lettuce.core.api.StatefulRedisConnection;
import io.lettuce.core.api.sync.RedisCommands;
/**
 * @author lengleng
 */
public class LettuceVectorExample {
    public static void main(String[] args) {
        // 创建 Redis 客户端
        RedisClient redisClient = RedisClient.create("redis://localhost:6379");
        StatefulRedisConnection<String, String> connection = redisClient.connect();
        RedisCommands<String, String> commands = connection.sync();

        // 创建包含向量字段的索引
        String createIndex = "FT.CREATE myIndex ON HASH PREFIX 1 doc: SCHEMA embedding VECTOR FLAT 6 TYPE FLOAT32 DIM 4 DISTANCE_METRIC COSINE";
        commands.sendCommand("FT.CREATE", "myIndex", "ON", "HASH", "PREFIX", "1", "doc:", "SCHEMA", "embedding", "VECTOR", "FLAT", "6", "TYPE", "FLOAT32", "DIM", "4", "DISTANCE_METRIC", "COSINE");

        // 存储向量数据
        float[] vector = {0.1f, 0.2f, 0.3f, 0.4f};
        String vectorStr = floatArrayToString(vector); // 转换为字符串表示
        commands.hset("doc:1", "embedding", vectorStr);

        // 执行向量搜索（KNN）
        float[] queryVector = {0.15f, 0.25f, 0.35f, 0.45f};
        String query = "(@embedding:[VECTOR_RANGE 0.5 " + floatArrayToString(queryVector) + "])=>{$KNN: 1}";
        String result = commands.sendCommand("FT.SEARCH", "myIndex", query).toString();
        System.out.println("Search result: " + result);

        // 关闭连接
        connection.close();
        redisClient.shutdown();
    }

    // 辅助方法：将 float 数组转换为字符串
    private static String floatArrayToString(float[] vector) {
        StringBuilder sb = new StringBuilder();
        for (float v : vector) {
            sb.append(v).append(" ");
        }
        return sb.toString().trim();
    }
}