异步向量化

创建宽表

创建样例宽表，其中待向量化的文本字段定义为text_field，向量化的结果会存储在宽表的text_field_embedding列中。history_text_field是text_field的历史值拷贝，用于同步过程中判断text_field是否发生更新，避免重复向量化。

创建样例宽表SQL使用方式可参考：通过MySQL命令行连接并使用宽表引擎。

CREATE TABLE
  embedded_table (
    id BIGINT,
    text_field VARCHAR,
    history_text_field VARCHAR,
    text_field_embedding VARCHAR,
    PRIMARY KEY (id)
  );

写入样例数据

用户写入不需要操作history_text_field和text_field_embedding两列，流引擎ETL任务会自动补全内容。

UPSERT INTO embedded_table(id, text_field) VALUES (1, '测试数据');
UPSERT INTO embedded_table(id, text_field) VALUES (2, '商品列表');
UPSERT INTO embedded_table(id, text_field) VALUES (3, '新闻速递');
UPSERT INTO embedded_table(id, text_field) VALUES (4, '参考书籍');

创建AI Embedding模型

用户可选用由Lindorm AI引擎部署的模型服务接口，也可使用阿里云百炼或其他第三方平台提供的模型服务接口。默认情况下，流引擎提供的内置函数会调用当前集群上AI引擎的模型服务接口，如果需要选用其他模型服务接口，则需要通过开发流引擎JAR作业的方式上传使用。

在提交流引擎ETL任务之前，需要在Lindorm AI引擎或其他模型服务上预先创建Embedding模型。通过Lindorm AI引擎创建模型服务的详细方式可以参考文档：模型管理。

curl -X POST "http://<url>/v1/ai/models/create" \
-H "x-ld-ak: <username>" \
-H "x-ld-sk: <password>" \
-H "Content-Type: application/json" \
-d '{
    "model_name": "bge_m3_model",
    "model_path": "huggingface://BAAI/bge-m3",
    "task": "FEATURE_EXTRACTION",
    "algorithm": "BGE_M3"
}' 

提交流引擎ETL任务

ETL任务中指定当text_field发生更新或text_field_embedding为空时进行向量化。

CREATE ETL embedding_task
WITH
  (
    `ai.embedding` = 'bge_m3_model',
    `sink.null-mode`='SKIP',
    `source.parallelism` = '4',
    `parallelism` = '8'
  ) AS
INSERT INTO
  `lindorm_table`.`default`.`embedded_table` (id, text_field_embedding, history_text_field)
SELECT
  id,
  bge_m3_model(text_field) AS text_field_embedding,
  text_field AS history_text_field
FROM
  `lindorm_table`.`default`.`embedded_table`
WHERE text_field_embedding IS NULL OR history_text_field IS NULL OR text_field != history_text_field;

观察向量化进度

通过以上步骤，向量化结果会保存在宽表的text_field_embedding列中。业务可以通过流引擎的ETL状态观察向量化的进度，参考：SHOW ETLS。其中EVENT_TIME_LAG表示ETL任务当前处理数据的延迟，单位为毫秒（ms）。

mysql> SHOW ETLS;
+----------------+---------------+---------+-----------------+-------------+-----------------+-----------------+---------------+----------------+
| ETL_ID         | ETL_VERSION   | STATUS  | JOB_RESTART_NUM | NUM_RECORDS | RECORDS_PER_SEC | NUM_BYTES       | BYTES_PER_SEC | EVENT_TIME_LAG |
+----------------+---------------+---------+-----------------+-------------+-----------------+-----------------+---------------+----------------+
| embedding_task | 1765187467350 | RUNNING | 0               | 508663.0    | 0.0             | 1.9333789595E10 | 0.0           | 691.0          |
+----------------+---------------+---------+-----------------+-------------+-----------------+-----------------+---------------+----------------+
1 row in set (0.05 sec)

创建搜索索引

为embedded_table创建搜索索引，在索引列配置中通过mapping定义的方式，指定text_field_embedding列对应的向量列属性。

CREATE INDEX idx_search USING SEARCH ON embedded_table (
  id,
  text_field(type=text, analyzer=ik),
  text_field_embedding(mapping='{
    "type": "knn_vector",
    "dimension": 1024,
    "data_type": "float",
    "method": {
        "engine": "lvector",
        "name": "hnsw",
        "space_type": "cosinesimil",
        "parameters": {
            "m": 24,
            "ef_construction": 500
        }
    }
  }')
) WITH (
  SOURCE_SETTINGS='{
      "excludes": ["text_field_embedding"]
  }',
  INDEX_SETTINGS='{
      "index": {
          "knn": "true",
          "knn.vector_empty_value_to_keep": true,
          "search.default_pipeline": "embedded_table_search_pipeline"
      }
  }'
);

以上示例中，指定向量列构建索引时使用的算法为hnsw，维度为1024，距离计算方式为cosinesimil，业务实际使用时应结合业务需求设计向量列索引的各项参数。

• 向量列构建索引的各属性说明及可选值可以参考：基础特性。

• 搜索索引非向量列的配置、使用方式可以参考搜索索引相关文档：管理搜索索引、分区索引。

查询向量化结果

搜索索引同步完成后，可以通过如下curl命令实现简易向量检索。更详细的融合检索方式，可参考文档：全文向量混合检索。

以下示例中的vector内容，是字符串“新闻”向量化后的结果。

curl -u <username>:<password> -H "Content-Type: application/json" -XGET "http://<URL>/default.embedded_table.idx_search/_search" -d '{
  "size": 1,
  "_source": ["text_field"],
  "query": {
    "knn": {
      "text_field_embedding": {
        "k": 10,
        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