AI function

ai_filter

ai_filter(text)
ai_filter(model_name, text)

• Description: Checks if the input text is factual or compliant. Returns true if the content is credible, reasonable, or compliant with predefined rules. Otherwise, it returns false.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • text: The input text. Data type: VARCHAR.

• Return value: BOOLEAN.

Example 1

SELECT ai_filter("Is Zhejiang in China?")

Result

+---------------------------------+
| ai_filter("Is Zhejiang in China?")       |
+---------------------------------+
| 1                               | 

Example 2

SELECT ai_filter("qwen_plus_external", "Is Zhejiang in China?")

Result

+----------------------------------------------+
| ai_filter("qwen_plus_external", "Is Zhejiang in China?")       |
+----------------------------------------------+
| 1                                            | 

ai_translate

ai_translate(text, targetLang)
ai_translate(model_name, text, targetLang)

• Description: Translates the input text into the specified language.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • text: The input text. Data type: VARCHAR.

  • targetLang: The target language for translation. Data type: VARCHAR.

    Important

    The model must support the target language.

• Return value: VARCHAR.

Example

SELECT ai_translate("AnalyticDB for MySQL is a data analytics platform based on a lakehouse architecture.", "cn") as translate_text

Result

+-------------------------------------------------------+
| translate_text                                        |
+-------------------------------------------------------+
| AnalyticDB for MySQL is a data analytics platform based on a lakehouse architecture. | 

ai_classify

ai_classify(text, labels)
ai_classify(model_name, text, labels)

• Description: Classifies input text into a predefined category or label based on semantic analysis.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • text: The input text. Data type: VARCHAR.

  • labels: A list of predefined category labels. Data type: VARCHAR or Array<VARCHAR>.

• Return value: VARCHAR.

Example 1

SELECT ai_classify('Last night, I tried a new pasta recipe in the kitchen. The sauce was rich, and my family loved it!', "['travel', 'cooking', 'reading', 'driving']")
AS predicted_label;

Result

+----------------------------------------------+
| predicted_label                              |
+----------------------------------------------+
| cooking                                      | 

Example 2

SELECT ai_classify('Last night, I tried a new pasta recipe in the kitchen. The sauce was rich, and my family loved it!', ARRAY['travel', 'cooking', 'reading', 'driving'])
AS predicted_label;

Result

+----------------------------------------------+
| predicted_label                              |
+----------------------------------------------+
| cooking                                      | 

ai_extract

ai_extract(text, labels)
ai_extract(model_name, text, labels)

• Description: Extracts specified types of structured information, such as names, dates, addresses, and keywords, from unstructured text.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • text: The input text. Data type: VARCHAR.

  • labels: A list of predefined category labels. Data type: VARCHAR or Array<VARCHAR>.

• Return value: VARCHAR.

Example

select ai_extract('Yesterday (June 15, 2024), I bought an iPhone 15 Pro on Taobao. Its titanium body and A17 chip are truly amazing!', "['product_name', 'date', 'key_feature']") as result

Result

+----------------------------------------------+
| result                                       |
+----------------------------------------------+
| product_name=iPhone 15 Pro, date=June 15 2024, key_feature=titanium body and A17 chip                                         | 

ai_generate

ai_generate(text)
ai_generate(model_name, text)

• Description: Generates text based on the input prompt.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • text: The input text. Data type: VARCHAR.

• Return value: VARCHAR.

Example

select ai_generate('Briefly introduce the TPC-H benchmark in one sentence.') as result

Result

+----------------------------------------------+
| result                                       |
+----------------------------------------------+
| TPC-H is a standard benchmark used to evaluate the decision support capabilities of a database. | 

ai_sentiment

ai_sentiment(text)
ai_sentiment(model_name, text)

• Description: Analyzes the sentiment of the input text and returns one of the following four labels: positive, negative, neutral, or mixed.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • text: The input text. Data type: VARCHAR.

• Return value: VARCHAR.

Example

SELECT ai_sentiment('These headphones have excellent sound quality and are comfortable to wear. Highly recommended!') AS sentiment;

Result

+----------------------------------------------+
| sentiment                                    |
+----------------------------------------------+
| positive                                     | 

ai_similarity

ai_similarity(text1, text2)
ai_similarity(model_name, text1, text2)

• Description: Computes the semantic similarity between two pieces of text. Returns a similarity score from 0 to 10, where a higher score indicates greater similarity.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • text1 and text2: The input texts. Data type: VARCHAR.

• Return value: VARCHAR.

Example

SELECT ai_similarity(
  'How do I reset my account password?',
  'I forgot my login password, how can I retrieve it?'
) AS result;

Result

+----------------------------------------------+
| result                                       |
+----------------------------------------------+
| 9.3                                          | 

ai_mask

ai_mask(text, labels)
ai_mask(model_name, text, labels)

• Description: Masks sensitive information, such as ID card numbers, phone numbers, and email addresses, in the input text.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • text: The input text. Data type: VARCHAR.

  • labels: A list of predefined category labels. Data type: VARCHAR or Array<VARCHAR>.

• Return value: VARCHAR.

Example

SELECT ai_mask(
  'Contact me at 1381234****, my email is user@example.com, and my ID card number is 110101199003072316',
  "['phone', 'email', 'id_card']"
) AS result;

Result

+----------------------------------------------+
| result                                       |
+----------------------------------------------+
| Contact me: [MSKED], email is [MSKED], ID number [MSKED]   | 

ai_summarize

ai_summarize(text)
ai_summarize(model_name, text)

• Description: Generates a concise semantic summary of long text.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • text: The input text. Data type: VARCHAR.

• Return value: VARCHAR.

Example

SELECT ai_summarize('Above the winding lotus pond, all you see are vast leaves. The leaves rise high out of the water, like the skirts of graceful dancers. Among the layers of leaves, a few white flowers are scattered—some opening softly, some shyly in bud—like pearls, like stars in a blue sky, like beauties fresh from a bath. A breeze passes and brings faint fragrance, like a distant song from a tall building. At that moment, leaves and flowers tremble slightly, and the ripple flashes across the pond like lightning. The leaves, packed shoulder to shoulder, now seem to hold a wave of deep green. Beneath them flows quiet water, hidden from view, while the leaves look all the more charming. Moonlight pours down like water, gently over the leaves and flowers. A thin mist floats above the pond. Leaves and flowers look as if washed in milk, as if wrapped in a light veil of dream. It is full moon, yet a pale cloud hangs in the sky, so the light is not too bright; but I find it just right—deep sleep is necessary, yet a light nap has its own flavor. The moonlight comes filtered through trees. Dense shrubs cast jagged dark shadows like ghosts, while the sparse silhouettes of willows look like paintings on the lotus leaves. The moonlight in the pond is uneven, but light and shadow form a harmonious rhythm, like a famous tune played on a violin.') AS result;

Result

+---------------------------------------------------------+
| result                                                  |
+---------------------------------------------------------+
| The lotus pond under moonlight is quiet and beautiful: broad lotus leaves rise like dancers’ skirts, dotted with white flowers like pearls, stars, or beauties fresh from a bath. A breeze carries fragrance; leaves and blossoms tremble and form green ripples, while water flows hidden below. Moonlight pours down like water and a thin mist softens the scene into a dream. Clouds veil the full moon and cast mottled light and shadow: shrubs throw sharp dark shapes, willows leave delicate silhouettes, and together they move in harmony like a famous melody. | 

ai_embed

ai_embed(text)
ai_embed(model_name, text)
ai_embed(model_name, text, options)

• Description: Generates a text embedding.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • text: The input text. Data type: VARCHAR.

  • options: Optional parameters. Data type: VARCHAR. The supported parameters are as follows.

    Parameter	Description
    dimension	The embedding dimension.
    type	The embedding type. The model must support the specified type. document (default) query

• Return value: Array.

Example

SELECT ai_embed("text_embedding_v4", "What should I have for lunch today?", "{'dimension':1024}")

Result

[-0.02406704,-0.09344522,-0.021460138,-0.059166607,0.027912581,...]

ai_pdf_extract

ai_pdf_extract(url, options)
ai_pdf_extract(model_name, url, options)

• Description: Extracts information from a PDF document.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • url: The URL of the source PDF file. Data type: VARCHAR.

  • options: Optional parameters. Data type: VARCHAR. The supported parameters are as follows.

    Parameter	Description
    page_index	The page number of the file.
    role_arn	(Optional) The role ARN of the account that owns the OSS file. If you use the OSS service of the account that owns the AnalyticDB for MySQL cluster, this parameter is not required.
    schema_only	Specifies whether to extract the file structure. true: The return value includes the file structure. false: The return value does not include the file structure.
    components	(Optional) Defines a custom structure for data extraction.

    The components parameter is a JSON array. Each element has the following structure:

    Parameter type	Description
    name	The name of the structure.
    type	The type of the structure: METADATA: Metadata FORM: Form TABLE: Table CUSTOM: Custom
    instruction	A description of the structure's content.

• Return value: JSON.

Example

SELECT ai_pdf_extract(
  'qwen35_plus',
  'oss://my-bucket/invoices/invoice_001.pdf',
  '{"page_index": 1}'
);

Result

{"data":[{"content":[{"description":"Services\nCost of various services.","rate":"$50.00\n+Tax","qty":"10","amount":"$500.00"},{"description":"Consulting\nConsultation for your business.","rate":"$75.00\n+Tax","qty":"15","amount":"$1,125.00"},{"description":"Materials\nCost of materials and supplies to complete job.","rate":"$122.39\n+Tax","qty":"1","amount":"$122.39"}],"name":"table","type":"table"},{"content":{"billed_to":"Your Client\n1234 Clients Street\nCity, California\n90210\nUnited States\n1-888-123-8910","date_issued":"26/2/2021","invoice_number":"INV-10012","amount_due":"$1,699.48","due_date":"25/4/2021","subtotal":"$1,798.39","discount":"-$179.84","tax":"$80.93","total":"$1,699.48","deposit_requested":"$169.95","deposit_due":"$169.95"},"name":"form","type":"form"},{"content":{"filename":"stocklist.pdf","size":117249,"creation_date":"2026-03-09 06:37:58","modification_date":"2026-03-09 06:37:58","page_count":1,"url":"oss://test-oss-hk/ai/pdf_test/stocklist.pdf","page_index":1},"name":"metadata","type":"metadata"}]}

ai_audio_transcribe

This function supports only built-in models. To create a model service, see Create an inference service.

ai_audio_transcribe(url)
ai_audio_transcribe(model_name, url)
ai_audio_transcribe(model_name, url, options)

• Description: Transcribes an audio clip into text with configurable segmentation mode and target language. Returns the transcribed text or a URL to the text file.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • url: The URL of the input audio file. Data type: VARCHAR.

  • options: Optional parameters in a JSON string. The supported parameters are as follows.

    Parameter	Description	Valid values	Default
    language	The target language of the transcribed text.	cn (Chinese) en (English) ja (Japanese) yue (Cantonese) fspk (Mixed Chinese and English)	cn
    diarization_mode	The mode for segmenting the text into paragraphs.	word (by word) sentence (by sentence) speaker (by speaker)	word
    output_type	The output format.	json (returns the transcribed text directly as a JSON object) url (saves the text to OSS and returns the URL of the text file)	json

• Return value: VARCHAR.

Example 1

SELECT ai_audio_transcribe("https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_female2.wav") as result

Result

+----------------------------------------------+
| result                                       |
+----------------------------------------------+
| {"TaskId": "xxxx", "Transcription": {"AudioInfo": {"Size": 128480, "Duration": 3834, "SampleRate": 16000, "Language": "cn"}, "Paragraphs": [{"ParagraphId": "1768447804076500000", "SpeakerId": "1", "Words": [{"Id": 10, "SentenceId": 1, "Start": 100, "End": 595, "Text": "Hello, "}, {"Id": 20, "SentenceId": 1, "Start": 596, "End": 841, "Text": "world, "}, {"Id": 30, "SentenceId": 1, "Start": 844, "End": 1588, "Text": "this is the"}, {"Id": 40, "SentenceId": 1, "Start": 1588, "End": 2580, "Text": "Alibaba"}, {"Id": 50, "SentenceId": 1, "Start": 2580, "End": 3076, "Text": "speech"}, {"Id": 60, "SentenceId": 1, "Start": 3076, "End": 3820, "Text": "lab."}]}], "AudioSegments": [[100, 3820]]}}                                | 

Example 2

SELECT ai_audio_transcribe("tingwu", "https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_female2.wav", "{'diarization_mode':'sentence'}") as result

Result

+----------------------------------------------+
| result                                       |
+----------------------------------------------+
| {"TaskId": "xxxx", "Transcription": {"AudioInfo": {"Size": 128480, "Duration": 3834, "SampleRate": 16000, "Language": "cn"}, "Speakers": [{"SpeakerId": "1", "Sentences": [{"SentenceId": 1, "Start": 100, "End": 3820, "Text": "Hello, world, this is the Alibaba speech lab."}]}]}}| 

ai_audio_embed

This function supports only built-in models. To create a model service, see Create an inference service.

ai_audio_embed(url)
ai_audio_embed(model_name, url)
ai_audio_embed(model_name, url, options)

• Description: Converts an audio segment into an embedding vector. You can specify the segment using start and end times.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • text: The input text. Data type: VARCHAR.

  • options: Optional parameters in a JSON string. The supported parameters are as follows.

    Parameter	Description	Note
    access_id	The AccessKey ID for OSS.	If a role ARN is specified, you do not need to provide access_id, access_secret, or token.
    access_secret	The AccessKey secret for OSS.
    token	The OSS token credential.
    role_arn	The role credential.
    start_time	The start time of the audio.	If not specified, defaults to the beginning of the audio.
    end_time	The end time of the audio.	If not specified, defaults to the end of the audio.

• Return value: Array<FLOAT>.

Example

SELECT ai_audio_embed("https://dashscope.oss-cn-beijing.aliyuncs.com/samples/audio/paraformer/hello_world_female2.wav")

Result

[0.4234241,0.3575562,0.022631245,-0.09829298,-0.40605977,-0.72465694,-1.8039973,2.4773192,0.91702485,0.15146817,0.7209677,0.78260005,0.8019302,2.7577147,-0.86063147,-0.8249354,0.28732437,0.9009849,0.8899265,-1.5320694,-2.0866642,0.5051579,1.3553369,2.0348034,-0.74032015,-0.66065377,0.2756823,0.15714298,-2.8250875,0.8162984,0.20878711,0.8285897,1.4863579,1.0645012,-1.2933203,0.6919463,0.87850314,1.2212433,-0.44420764,-1.0146322,-1.8209833,0.16996253,-1.534067,-0.10253409,0.07611574,2.4005992,-1.2903963,0.6613166,-0.2554945,0.97570425,-0.108424075,0.82061934,-0.18102498,-0.27137256,-1.2723781,-0.9188018,-0.027503261,-0.15346082,-0.9564006,-0.4618776,-1.077228,-0.27756396,0.28749034,0.2570488,-0.39753774,0.5270934,0.6970718,-1.2984067,2.4312186,1.485573,1.6031872,-0.6158852,-1.1512791,0.49305362,-1.1939837,0.72933173,0.2659372,2.5559616,-0.41886744,0.6304756,-0.6171451,-0.5282561,0.78426796,-0.57570314,-0.91166306,-0.531768,0.96598166,0.17423655,-0.9133532,-0.09258405,-0.26951456,-0.34667063,-0.21612991,-0.054430377,0.052798696,0.5198285,1.9431844,-0.5638291,-2.1043544,-0.47198293,0.21959576,0.5975169,0.3951007,0.28112686,1.792782,-0.15896009,-0.71152246,1.1540072,0.37563428,0.0016163457,0.6544563,0.043974634,-1.6375796,-0.3698572,1.5310435,-0.20282944,0.73641545,2.1867669,1.4115202,-0.8114418,-0.36265984,0.2191038,-0.8933491,0.6616433,1.545423,0.10970531,-0.109743424,0.44605017,-0.087265715,0.29077715,-2.4435875,-1.1484046,0.078253455,0.02283861,-0.29482847,2.0237377,-0.6009212,-1.2377249,0.05194488,-0.16573223,0.8370868,-0.43602425,-0.4706988,0.17996503,0.88799554,0.6562157,-0.7520023,0.64789253,0.36865348,-1.4820247,0.5299587,1.1884397,1.4351603,0.21073115,-0.1712549,2.9410155,1.7485802,-0.6828801,-0.4833641,-0.4477328,2.3307724,-0.35595444,-0.61682695,-0.5370858,-0.8068234,1.2195143,-1.0834758,0.45275012,1.6243625,-1.3629726,-0.2959109,0.05071621,-0.7280639,-0.1713935,-0.43650395,-0.2131698,0.25380868,-0.16288652,2.0921175,0.3555297,-0.10752705,0.5716714,-0.09943808,-1.9066161,0.12683412,0.8566219,-0.20323975,1.4260024,-0.28789783,0.32412016,0.88897985,0.86555415,0.5940666,0.6645551,0.046334613,-1.5640875,-0.3510718,0.55363727,-0.13503519,0.67008746,-0.9184686,0.025278697,1.0128921,-0.61241907,-0.01107134,-0.8309523,-0.51025873,1.1141272,0.28813183,-0.10634196,-0.9954202,-0.07171043,-0.5856978,-1.2660285,2.1327019,-0.60268104,-0.6227884,-0.42101067,0.059599742,-0.09640202,-1.258711,0.5054924,-1.1847625,-0.044398762,-0.98595804,0.8883682,0.6221085,-2.5484293,0.7249505,0.69930685,0.7739025,-0.8139478,-1.1988907,-1.0416493,2.0153732,-1.7091763,-0.5611238,-1.2147603,0.9113469,1.5113174,0.23810485,-1.702736,-0.3295935,-0.41867778,-1.0378691,-0.45600057,-0.43525052,-0.1078409,-1.2993969,0.12842774,0.026097976,-0.7705405,-1.0907317,0.28274077,1.236289,1.6190177,2.1874366,0.16072829,-0.33150536,2.218483,-1.1703843,0.10300327,1.1994884,0.48275462,-0.40795812,0.5020531,1.1787555,-0.082187966,0.6315653,0.36654752,-0.24940589,-0.8652801,1.6283739,0.41405886,0.6377814,0.08396838,-1.0169003,1.2100558,1.4457762,-0.07999261,-0.012102162,0.85055244,-0.09711141,-1.0452846,0.13768612,-2.0506873,-1.6474499,0.043265514,-1.0009454,-0.0111249415,-1.2523409,-0.080719866,-0.6187693,-1.398226,0.6425289,-0.4808641,-0.06030046,-0.10275636,-0.31625932,-1.5993032,-0.20966552,-1.4618409,0.34925935,-0.5034448,0.100028045,0.25327235,-1.078896,-0.23394233,1.2247928,-2.6050038,-0.71609926,-0.77765155,-1.2089496,0.8526703,-0.1358416,1.1074059,1.1545771,-0.94525933,0.41012967,0.9361201,-0.14788401,-0.29333082,1.5782444,1.1100405,-0.4074414,-0.3862537,-0.5779069,-0.88644946,0.2233385,1.3612705,1.2413827,-1.3625424,-1.3623037,0.3056319,-1.4446377,0.64613384,0.15064861,-0.61473364,-1.3611295,-0.1975697,-1.0701923,0.7591377,-1.2106745,-2.067824,0.45041704,-0.71582735,-1.743847,1.169414,2.0158787,0.4734838,-0.3133036,-1.9916989,1.1441987,0.9155275,-1.3003027,0.82898057,-0.7439868,1.2072865,0.46877453,0.6648313,0.80477613,1.6927507,0.5842916,0.36608973,2.259473,-1.1628797,-0.3311869,0.36989415,-0.25035658,-0.28012496,1.092324,-0.40238732,0.0046352614,-2.0625768,1.161326,0.92277956,-0.20316431,-0.15164377,1.1715052,-0.7067665,-0.8608931,0.079684004,-0.89916384,0.02488108,-0.57668805,-0.879138,-1.7274998,0.824049,1.5638052,0.28415555,-0.17347385,-2.085917,0.4987632,-0.031601395,-1.0190377,-0.7815816,0.69643223,-0.33574122,2.1242745,0.19785832,0.55690974,-1.3932099,-0.44952002,-0.7719798,1.1326215,0.43839702,0.996999,-0.55692834,1.4014084,-2.3939395,-1.0112343,-1.4143248,1.1514114,1.1233637,-0.3385779,-0.23665123,-0.100857966,0.4633971,1.6215613,-0.0692888,-0.031505972,1.0472811,0.57112235,0.6015763,0.07582237,0.52702487,-1.3809607,-1.7482765,0.38386008,0.99316126,0.95603,0.40644804,-1.5072054,-0.34419048,0.63205683,-1.0854999,-0.92245156,-0.2712947,-0.75696105,0.996232,-0.10738732,-0.4674776,-1.2149413,-1.5094053,1.6796608,0.21961057,-0.35295358,-1.2609407,-0.040009048,-0.38785484,0.7788784,-0.65823495,2.0559616,-1.0074826,1.2282485,1.2540467,0.4914942,-0.47057188,-0.47061247,-0.16255763,-0.6718562,-0.53630847,0.4804698,-0.3134068,0.6407026,-0.727981,0.0481851,0.06927338,0.8321921,-0.6639807,0.74932885,0.23291564,0.76362675,-1.2966217,0.8806557,-1.2141875,0.6996881,-0.8293652,0.9085288,1.8878758,0.11363957,0.148718,0.5030497,-1.0422761,0.08673843,-0.80342984,-0.8046266,-0.18026677,0.28900644,0.76534355,-1.3163859,-0.72775376,0.36529016,-0.3660175,0.31056792,0.052575216,-1.11831,0.7895246,1.1172394,-0.31374845,0.17143561,-0.42633826,0.16579832,-0.012790448,-1.4290546,-0.47322562,0.7557427,1.0487514,-0.14331971,0.30455515,0.6938429,0.5565799]

ai_agg

ai_agg(text)
ai_agg(model_name, text)

• Description: Aggregates multiple rows of text data within a group and submits them to an AI model to generate a conclusive summary.

• Parameters:

  • model_name: The model name. Data type: VARCHAR.

  • text: The input text. Data type: VARCHAR.

• Return value: VARCHAR.

Example 1 (without specifying a model name, using the default model)

SELECT product_name, ai_agg(comment) AS summary
FROM product_reviews
GROUP BY product_name;

Result

+--------------+------------------------------------------------------+
| product_name | summary                                              |
+--------------+------------------------------------------------------+
| Headphones A | Users generally praised the sound quality and comfort, but some reported insufficient battery life. |
+--------------+------------------------------------------------------+

Example 2 (specifying a model name)

SELECT product_name, ai_agg('qwen-plus', comment) AS summary
FROM product_reviews
GROUP BY product_name;

Result

+--------------+------------------------------------------------------+
| product_name | summary                                              |
+--------------+------------------------------------------------------+
| Headphones A | Users generally praised the sound quality and comfort, but some reported insufficient battery life. |
+--------------+------------------------------------------------------+

Example 3 (aggregating data from the entire table)

SELECT ai_agg(feedback) AS overall_summary
FROM customer_feedback;

Result

+------------------------------------------------------------+
| overall_summary                                            |
+------------------------------------------------------------+
| Customer feedback is concentrated in the following areas: product quality satisfaction is high, while logistics speed needs improvement. |
+------------------------------------------------------------+

Use case 1: Text retrieval

Scenario: In RAG or customer service systems, search a text library by semantics to retrieve the most relevant results.

Use ai_embed to convert the search query and library text into embeddings, then calculate cosine similarity to find the ten most similar texts.

SELECT cosine_similarity(ai_embed("The semantic text to search for"), ai_embed(text)) AS cos
FROM text_table
ORDER BY cos
LIMIT 10

To further optimize vector search performance, see vector search.

Use case 2: Speaker recognition

Scenario: For meetings or daily conversations, transcribe recordings into text and differentiate dialogue by speaker.

To further analyze speakers, segment the audio by speaker and compare each segment's embedding with a voice dataset. For the complete solution, see speaker recognition.

• Transcription and segmentation: Use the AI_AUDIO_TRANSCRIBE function to transcribe audio into text and segment it by speaker.

  SELECT ai_audio_transcribe("oss://xxx", "{'diarization_mode':'speaker'}")

• Speaker recognition: Use the AI_AUDIO_EMBED function to generate embedding vectors for the input audio and the original audio library. Then, calculate the l2_distance and select the 10 closest matches.

  SELECT l2_distance(input_embedding, origin_embedding) AS ld
  FROM(
    SELECT ai_audio_embed("oss://xxx") AS input_embedding
    JOIN
    SELECT ai_audio_embed(url) AS origin_embedding
    FROM audio_table)
  ORDER BY ld
  LIMIT 10

Use case 3: Extract negative customer reviews

Scenario: Filter out negative feedback from user comments.

The customer_comment table stores daily customer comments. Use ai_sentiment to identify the sentiment of each comment and filter for all negative comments from January 1, 2026.

-- The qwen_plus_external model, which corresponds to the qwen-plus model from Model Studio, has been created in advance.
SELECT name, comment
FROM customer_comment
WHERE ai_sentiment("qwen_plus_external", comment)='negative'
AND dt='20260101'