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向量检索

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Lindorm支持通过搜索索引使用向量检索功能，本文介绍如何通过搜索索引及SQL方式完成向量检索。

前提条件

准备数据

通过MySQL命令行连接并使用宽表引擎，准备SQL表并写入样例数据。

连接宽表引擎创建SQL表

以下示例中，embedding列用于写入向量数据，向量列定义为 VECTOR(n) 类型。

CREATE TABLE product_items (
  id VARCHAR,
  title VARCHAR,
  content VARCHAR,
  category VARCHAR,
  status VARCHAR,
  price DOUBLE,
  embedding VECTOR(8),
  PRIMARY KEY (id)
);

写入样例数据

向量列embedding写入的格式应为'[...]'，由方括号包围的浮点数数组。以下示例写入10条数据，创建索引时使用下文HNSWQ的示例，可以触发离线索引训练。

UPSERT INTO product_items (id, title, content, category, status, price, embedding)
VALUES (
  'item_001',
  '主动降噪蓝牙耳机',
  '无线蓝牙耳机，支持主动降噪、通透模式和长续航，适合通勤和运动使用',
  'audio',
  'ON_SALE',
  399.00,
  '[0.067985594, 0.94134957, 0.9174301, 0.34755236, 0.3318444, 0.30996937, 0.12764448, 0.9909833]'
);

UPSERT INTO product_items (id, title, content, category, status, price, embedding)
VALUES (
  'item_002',
  '轻薄办公笔记本',
  '14英寸轻薄本，适合移动办公、文档编辑和在线会议',
  'computer',
  'ON_SALE',
  5299.00,
  '[0.48320667, 0.12729345, 0.86240918, 0.57118409, 0.09480932, 0.69527483, 0.31873674, 0.23437815]'
);

UPSERT INTO product_items (id, title, content, category, status, price, embedding)
VALUES (
  'item_003',
  '真无线运动耳机',
  '入耳式运动耳机，防水防汗，佩戴稳固，适合跑步、健身和户外训练',
  'audio',
  'ON_SALE',
  299.00,
  '[0.08234191, 0.88452947, 0.90317422, 0.39284715, 0.28741066, 0.34491053, 0.16588271, 0.94621736]'
);

UPSERT INTO product_items (id, title, content, category, status, price, embedding)
VALUES (
  'item_004',
  '机械键盘',
  '87键蓝牙双模机械键盘，茶轴手感，适合程序员和文字办公',
  'computer',
  'ON_SALE',
  269.00,
  '[0.51690823, 0.18346277, 0.80153459, 0.62274118, 0.14639502, 0.73124588, 0.37261894, 0.28194763]'
);

UPSERT INTO product_items (id, title, content, category, status, price, embedding)
VALUES (
  'item_005',
  '4K办公显示器',
  '27英寸4K高清显示器，支持Type-C连接，适合编程、设计和多窗口办公',
  'computer',
  'ON_SALE',
  1599.00,
  '[0.54781264, 0.10893452, 0.83476129, 0.68420137, 0.17266381, 0.76249855, 0.40122976, 0.31508492]'
);

UPSERT INTO product_items (id, title, content, category, status, price, embedding)
VALUES (
  'item_006',
  '空气炸锅',
  '大容量无油空气炸锅，支持薯条、鸡翅和蛋挞等多种烹饪模式',
  'home',
  'ON_SALE',
  329.00,
  '[0.73492115, 0.29481763, 0.21837490, 0.81246537, 0.69021458, 0.14763829, 0.52390741, 0.40831672]'
);

UPSERT INTO product_items (id, title, content, category, status, price, embedding)
VALUES (
  'item_007',
  '扫地机器人',
  '智能扫地机器人，支持激光导航、自动集尘和拖地，适合家庭地面清洁',
  'home',
  'ON_SALE',
  1699.00,
  '[0.78261493, 0.25173984, 0.19652837, 0.84501926, 0.72148305, 0.11947682, 0.56821344, 0.45290631]'
);

UPSERT INTO product_items (id, title, content, category, status, price, embedding)
VALUES (
  'item_008',
  '户外冲锋衣',
  '防风防水透气面料，适合徒步、露营和日常通勤穿着',
  'outdoor',
  'ON_SALE',
  599.00,
  '[0.21469385, 0.67248120, 0.35890274, 0.18673549, 0.82401652, 0.53371908, 0.74258136, 0.29467011]'
);

UPSERT INTO product_items (id, title, content, category, status, price, embedding)
VALUES (
  'item_009',
  '露营帐篷',
  '双层防雨露营帐篷，空间宽敞，适合家庭露营和周末户外活动',
  'outdoor',
  'ON_SALE',
  499.00,
  '[0.23687049, 0.70452813, 0.33147592, 0.15829476, 0.85739241, 0.50468327, 0.79124658, 0.26391844]'
);

UPSERT INTO product_items (id, title, content, category, status, price, embedding)
VALUES (
  'item_010',
  '儿童保温杯',
  '316不锈钢儿童保温杯，带吸管和防漏杯盖，适合学生日常饮水',
  'baby',
  'ON_SALE',
  89.00,
  '[0.39128467, 0.41873925, 0.64208193, 0.24759318, 0.50371684, 0.28645079, 0.19582734, 0.86140952]'
);

创建搜索索引

通过MySQL命令行连接并使用宽表引擎，创建包含向量列的搜索索引。

向量列在索引中通过mapping指定具体的向量索引算法、距离算法和其他算法参数，以下提供HNSWQ和IVFBQ的索引创建示例，更多详细配置可参考通过 curl 命令连接并使用向量引擎。

说明

  • 属性下推，如果有标量参与向量检索，可以对标量列设置属性下推提升检索性能，下推会占用向量内存资源，需要业务评估检索字段。详细说明见 标量属性下推：加速向量+标量查询混合检索。

  • 属性下推必须使用efficient_filter，并且必须要在发起索引训练完成后才可以。

HNSWQ 示例

CREATE INDEX product_vector_idx USING SEARCH ON product_items (
  id,
  title,
  content(type=text, analyzer=ik),
  category(mapping='{"type": "keyword", "meta": {"_vector_filter": "true"}}'),
  status(mapping='{"type": "keyword", "meta": {"_vector_filter": "true"}}'),
  price(mapping='{"type": "double", "meta": {"_vector_filter": "true"}}'),
  embedding(mapping='{
    "type":"knn_vector",
    "dimension":8,
    "data_type":"float",
    "meta":{
      "offline.construction":"true"
    },
    "method":{
      "name":"hnswq",
      "engine":"lvector",
      "space_type":"l2",
      "parameters":{
        "m":32,
        "ef_construction":400
      }
    }
  }')
) WITH (
  INDEX_SETTINGS='{
    "index.knn.vector_empty_value_to_keep": true
  }'
);

  • hnswq写入发起训练数据量要严格大于数据维度，灌入现有离线数据后发起一次训练即可。

  • 属性下推按需设置

IVFBQ 索引示例

IVFBQ 需要先写入足够的训练数据，再触发向量离线训练。

CREATE INDEX product_vector_idx USING SEARCH ON product_items (
  id,
  title,
  content(type=text, analyzer=ik),
  category(mapping='{"type": "keyword", "meta": {"_vector_filter": "true"}}'),
  status(mapping='{"type": "keyword", "meta": {"_vector_filter": "true"}}'),
  price(mapping='{"type": "double", "meta": {"_vector_filter": "true"}}'),
  embedding(mapping='{
    "type":"knn_vector",
    "dimension":8,
    "data_type":"float",
    "meta":{
      "offline.construction":"true"
    },
    "method":{
      "name":"ivfbq",
      "engine":"lvector",
      "space_type":"l2",
      "parameters":{
        "exbits":2,
        "nlist":10000
      }
    }
  }')
) WITH (
  INDEX_SETTINGS='{
    "index.knn.vector_empty_value_to_keep": true
  }'
);

  • 发起 IVFBQ 训练前，向量数据量必须大于 256 条，并且超过 nlist 的 30 倍。建议完成离线数据导入后再触发训练。

  • 属性下推按需设置

为已有搜索索引添加向量列

索引支持后添加向量列，方式和普通索引加列方式一致，可参考新增搜索索引列。新加的列如果有历史数据，需要执行rebuild操作，把历史数据同步到索引中。

以下是增加向量列的示例。

ALTER TABLE product_items
ADD COLUMN image_vector VECTOR(8);

ALTER INDEX product_vector_idx ON product_items
ADD COLUMNS (
  image_vector(mapping='{
    "type":"knn_vector",
    "dimension":8,
    "data_type":"float",
    "meta":{
      "offline.construction":"true"
    },
    "method":{
      "name":"hnswq",
      "engine":"lvector",
      "space_type":"l2",
      "parameters":{
        "m":32,
        "ef_construction":400
      }
    }
  }')
);

离线训练向量索引

采用异步方式发起训练

ALTER INDEX product_vector_idx ON product_items
REBUILD VECTOR INDEX ON embedding
WITH (REMOVE_OLD_INDEX=true);

  • （默认）REMOVE_OLD_INDEX=true：删除旧向量索引后重新训练。训练完成前，该向量列无法提供 KNN 查询；推荐使用该模式。

  • REMOVE_OLD_INDEX=false：训练期间保留旧向量索引，但旧索引会继续占用资源，并可能影响查询性能，建议使用true

查看进度

SHOW INDEX FROM product_items;

通过结果中的 INDEX_VECTOR_BUILD_INFO 查看每个向量列的训练状态。只有状态为 FINISH 才表示训练成功；FAIL 和 ABORT 也属于任务终态，但不表示成功。

终止训练

ALTER INDEX product_vector_idx ON product_items
ABORT REBUILD VECTOR INDEX ON embedding;

同一张表、同一个搜索索引、同一个向量列同一时间只能存在一个训练任务。

查询数据

向量检索支持通过 MySQL 和 OLAP资源组两种方式完成，参考以下示例。

通过MySQL协议执行向量检索

MySQL协议中通过 vector_distance 指定搜索索引向量检索。实际的距离算法以索引 mapping 中定义的 space_type 为准。

纯向量检索

SELECT
  id,
  title,
  category,
  price,
  l_search_score() as score
FROM product_items
ORDER BY vector_distance(
  embedding,
  '[0.067985594, 0.94134957, 0.9174301, 0.34755236, 0.3318444, 0.30996937, 0.12764448, 0.9909833]'
)
LIMIT 10;

向量检索 + 标量过滤

SELECT /*+ _l_force_vector_index_(lvector.filter_type=efficient_filter) */
  id,
  title,
  category,
  price,
  l_search_score() as score
FROM product_items
WHERE status = 'ON_SALE'
  AND category = 'audio'
ORDER BY vector_distance(embedding, '[0.067985594,0.94134957,0.9174301,0.34755236,0.3318444,0.30996937,0.12764448,0.9909833]')
LIMIT 20;

  • _l_force_vector_index_(lvector.filter_type=efficient_filter)用于指定标量过滤的模式。指定为efficient_filter时，服务端会根据标量条件的选择率，选择最优过滤模式。

双路召回

“双路召回”指一次查询中同时使用向量检索和全文检索两条召回链路：向量检索根据 query vector 召回语义相近的数据，全文检索根据关键词/分词匹配召回文本相关的数据。开启 RRF 融合后，系统会分别对两路召回结果进行排序，再基于 RRF 策略融合排序结果，得到最终返回列表。关于RRF计算方式的进一步详细说明，可以参考：融合查询计算方式

当前指定lvector.hybrid_search_type=filter_rrf时，默认会将查询中的全文检索条件作为rrf的全文检索一路。

SELECT /*+ _l_force_vector_index_(
           lvector.hybrid_search_type=filter_rrf
       ) */
       id,
       title,
       content,
       price,
       l_search_score() as score
FROM product_items
WHERE match(content) against('无线蓝牙耳机 降噪')
  AND status = 'ON_SALE'
ORDER BY vector_distance(
           embedding,
           '[0.12,0.08,0.35,0.44,0.19,0.27,0.03,0.91]'
         )
LIMIT 20;

通过OLAP资源组执行向量检索

通过 OLAP 资源组查询宽表数据时，使用 _l_force_vector_index_ 强制选择基础特性中的向量索引。OLAP 资源组的 SQL 使用与距离类型对应的函数：l2_distance、cosine_similarity 或 innerproduct_distance。

纯向量检索

SELECT /*+ _l_force_vector_index_ */
  id,
  title,
  category,
  price,
  _vector_score_(
    embedding,
    '[0.067985594, 0.94134957, 0.9174301, 0.34755236, 0.3318444, 0.30996937, 0.12764448, 0.9909833]'
  ) AS vector_score
FROM product_items
ORDER BY l2_distance(
  embedding,
  '[0.067985594, 0.94134957, 0.9174301, 0.34755236, 0.3318444, 0.30996937, 0.12764448, 0.9909833]'
)
LIMIT 10;

向量检索+标量过滤

SELECT /*+ _l_force_vector_index_(lvector.filter_type=efficient_filter) */
  id,
  title,
  category,
  price,
  _vector_score_(
    embedding,
    '[0.067985594, 0.94134957, 0.9174301, 0.34755236, 0.3318444, 0.30996937, 0.12764448, 0.9909833]'
  ) AS vector_score
FROM product_items
WHERE status = 'ON_SALE'
  AND category = 'audio'
ORDER BY l2_distance(
  embedding,
  '[0.067985594, 0.94134957, 0.9174301, 0.34755236, 0.3318444, 0.30996937, 0.12764448, 0.9909833]'
)
LIMIT 20;

双路召回

OLAP资源组通过lindorm_rrf_rank函数指定双路召回查询。

SELECT /*+ _l_force_vector_index_ */
       id,
       title,
       content,
       price,
       _vector_score_(
         embedding,
         '[0.067985594, 0.94134957, 0.9174301, 0.34755236, 0.3318444, 0.30996937, 0.12764448, 0.9909833]'
       ) AS vector_score
FROM product_items
WHERE status = 'ON_SALE'
ORDER BY lindorm_rrf_rank(
           l2_distance(
             embedding,
             '[0.067985594, 0.94134957, 0.9174301, 0.34755236, 0.3318444, 0.30996937, 0.12764448, 0.9909833]'
           ),
           match_against('无线 降噪 耳机', content)
         )
LIMIT 20;

向量检索HINT参数说明

两种接口的向量检索，均支持通过_l_force_vector_index_(...)HINT指定相关查询参数，下面是针对支持参数的说明，更详细的参数可以参考通过 curl 命令连接并使用向量引擎文档中的说明。

参数

默认值

取值

作用

lvector.ef_search

无

1～1000 的整数

HNSW/HNSWQ 查询参数，控制查询阶段候选规模。

lvector.client_refactor

无

true 或 false

是否开启向量查询客户端重构相关逻辑。

lvector.min_score

无

0.0～1.0 的浮点数

向量相似度过滤阈值。

lvector.nprobe

无

正整数

IVF 类算法查询参数，控制查询的聚类中心数量。

lvector.reorder_factor

无

1～200 的整数

查询结果重排参数。

lvector.filter_type

post_filter

pre_filter、post_filter 或 efficient_filter（推荐）

指定向量检索与过滤条件的组合方式。

lvector.hybrid_search_type

无

filter_rrf

开启全文召回与向量召回的 RRF 融合。

lvector.rrf_rank_constant

无

正整数

RRF 排序公式中的 rank constant，默认由底层 RRF 参数处理。

lvector.rrf_window_size

无

正整数

RRF 文本召回窗口大小，未设置时由底层按 topK 处理。

lvector.rrf_knn_weight_factor

无

0.0～1.0 的浮点数

RRF 中向量召回相对全文召回的权重因子。

lvector.cursor_mode

无

stateless、global_sort 或 region_sort

向量游标模式。

写入自动Embedding

如果原始数据是文本、图片等多模态内容，可以通过搜索引擎Pipeline（同步模式）或流引擎ETL（异步模式） 调用 Embedding 模型，将生成的向量写入宽表向量列。

通过搜索引擎Pipeline实现自动Embedding

通过搜索引擎Pipeline可以实现文本、图片的自动Embedding功能。

创建宽表

text-embedding-v4模型默认维度是1024，创建宽表时，向量维度也需要设置成1024。

CREATE TABLE product_items (
  id VARCHAR,
  title VARCHAR,
  content VARCHAR,
  category VARCHAR,
  status VARCHAR,
  price DOUBLE,
  embedding VECTOR(1024),
  PRIMARY KEY (id)
);

配置写入Embedding Pipeline

创建写入Embedding Pipeline，需要通过搜索引擎接口调用，以下提供通过百炼接口调用text-embedding-v4模型实现文本embedding的pipeline样例。示例中的搜索引擎URL、AI引擎URL分别需要替换成实例的搜索引擎地址和AI引擎地址。

curl -u <username>:<password> -H "Content-Type: application/json" -XPUT "http://<搜索引擎URL>/_ingest/pipeline/write-embedding-pipeline" -d '{
  "description": "demo embedding pipeline",
  "processors": [
    {
      "text-embedding": {
        "inputFields": ["content"], 
        "outputFields": ["embedding"],
        "userName": "root",
        "password": "test****",
        "url": "http://<AI引擎URL>/dashscope/compatible-mode/v1/embeddings",
        "modeName": "text-embedding-v4"
      }
    }
  ]
}'

更详细的参数说明参考 自动Embedding的写入与查询方式。

创建搜索索引引用上述pipeline

创建搜索索引需要引用上一步设置的pipeline，数据写入时会实现自动Embedding，注意向量维度需要和embedding后的向量维度对齐，示例中使用1024维。

CREATE INDEX product_vector_idx USING SEARCH ON product_items (
  id,
  title,
  content(type=text, analyzer=ik),
  category(mapping='{"type": "keyword", "meta": {"_vector_filter": "true"}}'),
  status(mapping='{"type": "keyword", "meta": {"_vector_filter": "true"}}'),
  price(mapping='{"type": "double", "meta": {"_vector_filter": "true"}}'),
  embedding(mapping='{
    "type":"knn_vector",
    "dimension":1024,
    "data_type":"float",
    "meta":{
      "offline.construction":"true"
    },
    "method":{
      "name":"hnswq",
      "engine":"lvector",
      "space_type":"l2",
      "parameters":{
        "m":32,
        "ef_construction":200
      }
    }
  }')
) WITH (
  INDEX_SETTINGS='{
    "index.knn.vector_empty_value_to_keep": true,
    "default_pipeline": "write-embedding-pipeline"
  }'
);

写入样例数据

和前述方式不同，写入不需要显式指定向量列数据，只需要写入文本列即可，索引同步会自动完成向量化。

重要

以下只是写入示例，使用HNSWQ索引时，请注意要写入不小于维度数量（1024）的数据，并触发离线训练后，执行查询测试

UPSERT INTO product_items
  (id, title, content, category, status, price)
VALUES
  ('p001', '无线降噪耳机', '支持主动降噪、通透模式和长续航，适合通勤和办公使用', '数码配件', 'ON_SALE', 399.00);

UPSERT INTO product_items
  (id, title, content, category, status, price)
VALUES
  ('p002', '机械键盘', '87 键蓝牙双模机械键盘，茶轴手感，适合程序员和文字办公', '电脑外设', 'ON_SALE', 269.00);

UPSERT INTO product_items
  (id, title, content, category, status, price)
VALUES
  ('p003', '儿童保温杯', '316 不锈钢材质，带吸管和防漏杯盖，适合学生日常使用', '母婴用品', 'ON_SALE', 89.00);

UPSERT INTO product_items
  (id, title, content, category, status, price)
VALUES
  ('p004', '空气炸锅', '大容量无油空气炸锅，支持薯条、鸡翅、蛋挞等多种烹饪模式', '厨房电器', 'ON_SALE', 329.00);

UPSERT INTO product_items
  (id, title, content, category, status, price)
VALUES
  ('p005', '户外冲锋衣', '防风防水透气面料，适合徒步、露营和日常通勤穿着', '运动户外', 'ON_SALE', 599.00);

通过流引擎ETL实现异步向量化

参考文档：异步向量化。

查询自动Embedding

查询目前支持通过宽表的vector_embedding函数完成文本的向量化，用向量化结果执行向量检索。

SELECT vector_embedding(
  'text-embedding-v4',
  '无线降噪耳机，适合通勤和办公',
  1024
) AS query_vector;
SELECT
  id,
  title,
  category,
  price,
  l_search_score() as score
FROM product_items
ORDER BY vector_distance(
  embedding,
  '[0.02459293, -0.018794596, 0.028363274, -0.024921406, 0.10973988, -0.03221931, -0.035561204, 0.00754783, -0.050128445, 0.07352172, 0.05035695, 0.0067623416, 0.042844824, -0.018237613, 0.025821147, -0.05618385, -0.0080405455, -0.0493858, -0.03684655, 5.6099944E-4, 0.018351866, 0.07375023, 0.07295045, -0.032647755, 0.012446421, 0.045415513, 0.05789764, -0.046215285, -0.048157584, 0.0027724172, 0.054013044, -0.060268387, -0.014195919, -0.02734928, -0.029105918, 0.010539827, 0.024035947, 0.04430155, 0.011625229, 0.050642584, -0.023921695, -0.019065946, 0.01936586, -0.0017611008, -0.034590054, -0.002113678, -2.0529811E-4, -0.030591205, -0.0048450357, -0.05238494, 0.03321902, 0.019908562, -0.014345876, -0.016381005, 0.022150775, -0.0028616772, 0.04353034, 0.02930586, 0.001092543, 0.01358895, -0.0014718983, 0.020979682, -0.026606636, -0.050214134, 0.011253907, -0.077120684, -0.0062124995, 0.042130746, -0.011089669, -0.03664661, 0.017537815, 0.023507528, 0.06895161, -0.02282201, 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)
LIMIT 10;
+------+--------------------+--------------+-------+---------------------+
| id   | title              | category     | price | score               |
+------+--------------------+--------------+-------+---------------------+
| p001 | 无线降噪耳机       | 数码配件     | 399.0 |  0.7290722727775574 |
| p005 | 户外冲锋衣         | 运动户外     | 599.0 | 0.47925230860710144 |
| p003 | 儿童保温杯         | 母婴用品     |  89.0 | 0.46468326449394226 |
| p002 | 机械键盘           | 电脑外设     | 269.0 |  0.4509061872959137 |
| p004 | 空气炸锅           | 厨房电器     | 329.0 |  0.4198461174964905 |
+------+--------------------+--------------+-------+---------------------+
5 rows in set (0.04 sec)
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