mindsdb 的一些sql command
主要是学习中的一个记录,发现mindsdb 中的一些
项目操作相关sql
- 创建项目&&查看&&移除
CREATE PROJECT [IF NOT EXISTS] project_name;
SHOW DATABASES
WHERE type = 'project';
DROP PROJECT [IF EXISTS] project_name;
数据源操作相关sql
- 创建database&&查看&&移除
实际上就是data handlers 的实例对象的使用,当然也有反向操作的DROP 命令
CREATE DATABASE [IF NOT EXISTS] datasource_name
[WITH] [ENGINE [=] engine_name] [,]
[PARAMETERS [=] {
"key": "value",
...
}];
SELECT *
FROM information_schema.databases;
或者
SHOW DATABASES;
SHOW FULL DATABASES;
DROP DATABASE [IF EXISTS] database_name;
- 查看handlers
对于type 指定不同的可以查看不同的,比如ml,data 等
SELECT *
FROM information_schema.handlers
WHERE type = 'data';
或者
SHOW HANDLERS
WHERE type = 'data';
模型操作相关sql
- 创建ml engine && 查看&&移除
CREATE ML_ENGINE [IF NOT EXISTS] ml_engine_name
FROM handler_name
[USING argument_key = argument_value];
SELECT *
FROM information_schema.ml_engines;
-- or
SHOW ML_ENGINES;
DROP ML_ENGINE [IF EXISTS] ml_engine_name;
- 创建模型&&训练&&部署&&查看&&移除
注意早期有 CREATE PREDICTOR 的名字,为了兼容,目前还能使用
CREATE [OR REPLACE] MODEL [IF NOT EXISTS] project_name.predictor_name
[FROM [integration_name | project_name]
(SELECT [sequential_column,] [partition_column,] column_name, ...
FROM [integration_name. | project_name.]table_name
[JOIN model_name])]
PREDICT target_column
[ORDER BY sequential_column]
[GROUP BY partition_column]
[WINDOW int]
[HORIZON int]
[USING engine = 'engine_name',
tag = 'tag_name',
...];
DROP MODEL [IF EXISTS] predictor_name;
SHOW MODELS;
SELECT * FROM project_name.models;
RETRAIN [MODEL] project_name.predictor_name
[FROM [integration_name | project_name]
(SELECT column_name, ...
FROM [integration_name. | project_name.]table_name)
PREDICT target_name
USING engine = 'engine_name',
tag = 'tag_name',
active = 0/1];
FINETUNE [MODEL] project_name.model_name
FROM [integration_name | project_name]
(SELECT column_name, ...
FROM [integration_name. | project_name.]table_name
[WHERE incremental_column > LAST])
[USING
key = value,
...];
- 预测
SELECT target_name, target_name_explain
FROM mindsdb.predictor_name
WHERE column_name = value
AND column_name = value;
SELECT d1.column_name,
d2.column_name,
m1.column_name,
m2.column_name,
...
FROM integration_name.table_name_1 [AS] d1
[JOIN integration_name.table_name_2 [AS] d2 ON ...]
[JOIN ...]
JOIN project_name.model_name_1 [AS] m1
[JOIN project_name.model_name_2 [AS] m2]
[JOIN ...]
[ON d1.input_data = m1.expected_argument];
table操作相关sql
包含了create table,create view, drop table,join table,insert into ,delete from 都相对比较简单,阅读官方文档可以很快就掌握了
job操作相关sql
job 实现任务的自动处理,包含了调度能力
- 创建&&查看
CREATE JOB [IF NOT EXISTS] [project_name.]job_name [AS] (
<statement_1>[; <statement_2>][; ...]
)
[START <date>]
[END <date>]
[EVERY [number] <period>]
[IF (<statement_1>[; <statement_2>][; ...])];
SHOW JOBS;
SELECT * FROM [project_name.]jobs WHERE name = 'job_name';
SELECT * FROM log.jobs_history WHERE project = 'mindsdb' AND name = 'job_name';
DROP JOB [IF EXISTS] [project_name.]job_name;
SHOW JOBS WHERE project = 'project-name';
SELECT * FROM project-name.jobs;
触发器操作相关sql
定于基于事件的任务处理
- 创建&&查看&&删除
CREATE TRIGGER trigger_name
ON integration_name.table_name
[COLUMNS column_name1, column_name2, ...]
(
sql_code
)
DROP TRIGGER trigger_name;
SHOW TRIGGERS;
ai agent 操作相关sql
包含了agent,知识库,chatbot, skill, 里边比较有意思,后边专门介绍下
说明
以上是一些简单说明,关于agent 部分,后边单独介绍,里边会使用到不少RAG,大模型的玩法,只是基于了sql 进行集成
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