Realtime Compute for Apache Flink:Quick start for real-time data analysis with large language models

CREATE MODEL ai_analyze_sentiment
INPUT (`input` STRING)
OUTPUT (`content` STRING)
WITH (
    'provider' = 'dashscope',
    'task' = 'chat/completions',
    'model' = 'qwen3.5-flash',
    'system-prompt' = 'Classify the text below into one of the following labels: [positive, negative, neutral, mixed]. Output only the label.'
);

CREATE MODEL embedding_model
INPUT (`input` STRING)
OUTPUT (`embeddings` ARRAY<FLOAT>)
WITH (
    'provider' = 'dashscope',
    'task' = 'embeddings',
    'model' = 'text-embedding-v4'
);

-- Create a temporary sink table.
CREATE TEMPORARY TABLE print_sink(
  id BIGINT,
  movie_name VARCHAR, 
  predict_label VARCHAR, 
  actual_label VARCHAR
) WITH (
  'connector' = 'print',   -- Use the print connector.
  'logger' = 'true'        -- Display the results in the console.
);

-- Create a temporary data view to construct test data.
-- | id | movie_name | comment   | actual_label |
-- | 1  | Her Story     | My favorite part was when the kid guessed the sounds. It is one of the most romantic narratives I have seen in movies. Very gentle and loving. | POSITIVE |
-- | 2  | The Dumpling Queen   | Unremarkable.  | NEGATIVE |
CREATE TEMPORARY VIEW movie_comment(id, movie_name, user_comment, actual_label)
AS VALUES (1, 'Her Story', 'My favorite part was when the kid guessed the sounds. It is one of the most romantic narratives I have seen in movies. Very gentle and loving.', 'positive'), (2, 'The Dumpling Queen', 'Unremarkable.', 'negative');

INSERT INTO print_sink
SELECT id, movie_name, content as predict_label, actual_label 
FROM ML_PREDICT(
  TABLE movie_comment, 
  MODEL ai_analyze_sentiment,  -- The registered Flink built-in text model.
  DESCRIPTOR(user_comment));   

-- Create a temporary sink table named milvus_sink.
CREATE TEMPORARY TABLE milvus_sink
(
    id STRING,
    movie_name STRING,
    user_comment STRING,
    embeddings ARRAY<FLOAT>,
    PRIMARY KEY (id) NOT ENFORCED
)
WITH (
    'connector' = 'milvus',
    'endpoint' = '<YOUR-ENDPOINT>',
    'port' = '<YOUR-PORT>',
    'userName' = '<YOUR-USERNAME>',
    'password' = '<YOUR-PASSWORD>',
    'databaseName' = 'default',
    'collectionName' = 'movie-comment-embeddings'
);

-- Create a temporary data view to construct test data.
-- | id | movie_name | comment |
-- | 1 | Her Story |My favorite part was when the kid guessed the sounds. It is one of the most romantic narratives I have seen in movies. Very gentle and loving.|
-- | 2 | The Dumpling Queen | Unremarkable. |
CREATE TEMPORARY VIEW movie_comment(id, movie_name,  user_comment)
AS VALUES ('1', 'Her Story', 'My favorite part was when the kid guessed the sounds. It is one of the most romantic narratives I have seen in movies. Very gentle and loving.'), ('2', 'The Dumpling Queen', 'Unremarkable.');

INSERT INTO
    milvus_sink
SELECT
    id,
    movie_name,
    user_comment,
    embeddings
FROM
    ML_PREDICT (
        TABLE movie_comment,
        MODEL embedding_model,  -- The registered Flink built-in embedding model.
        DESCRIPTOR (user_comment)
    );