ShannonBase is database agent platform

#ShannonBase hashtag#MySQL Now, you can write Javascript Stored function and execute SQL statement in it with ShannonBase. With this feature and ML abilities, such as ML_generate, ML_RAG, ML_embeded_row, etc, ShannonBase has, We can develop a native agent engine in Javascript. That makes ShannonBase to be a AI-native data processing platform.
Taking the exmample below:
1: Create a table with sql statement:`

CREATE TABLE js_sp_test (
 id INT AUTO_INCREMENT PRIMARY KEY,
 segment VARCHAR(255) NOT NULL,
 text VARCHAR(255) NOT NULL,
 document_name VARCHAR(100) DEFAULT NULL,
 segment_number INT DEFAULT 1,
 metadata JSON DEFAULT (JSON_OBJECT())
) ENGINE=InnoDB;

2: Insert some data into table.

INSERT INTO js_sp_test (segment, text, document_name, segment_number, metadata)
VALUES 
(‘What is AutoML?’, (‘AutoML explanation example text’), ‘doc1’, 1, JSON_OBJECT(‘category’, ‘AI’)),
(‘AutoML automates model building’, (‘AutoML automates model building’), ‘doc1’, 2, JSON_OBJECT(‘category’, ‘AI’)),
(‘Machine learning basics’, (‘Basics of machine learning’), ‘doc2’, 1, JSON_OBJECT(‘category’, ‘ML’)),
(‘ShannonBase is an AI-native SQL engine’, (‘ShannonBase overview’), ‘doc3’, 1, JSON_OBJECT(‘category’, ‘DB’));

3: Create the test stored function in javascript language.

DELIMITER |;
CREATE FUNCTION SELECT_DATA () RETURNS TEXT
LANGUAGE JAVASCRIPT AS $$
function select_data() {
 return sys.exec_sql("SELECT id + 0 AS id, segment, text, document_name, segment_number, metadata FROM js_sp_test");
}
return select_data();
$$|
DELIMITER ;|

4: Run the stored function.
SELECT SELECT_DATA();

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