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Built-in AI Functions

Databend provides built-in AI functions powered by Azure OpenAI Service for seamless integration of AI capabilities into your SQL workflows.

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Data Privacy Notice: When using built-in AI functions, your data is sent to Azure OpenAI Service. By using these functions, you acknowledge this data transfer and agree to the Azure OpenAI Data Privacy terms.

FunctionDescriptionUse Cases
ai_text_completionGenerates text based on prompts• Content generation
• Question answering
• Summarization
ai_embedding_vectorConverts text to vector representations• Semantic search
• Document similarity
• Content recommendation
cosine_distanceCalculates similarity between vectors• Finding similar documents
• Ranking search results

Vector Storage in Databend

Databend stores embedding vectors using the ARRAY(FLOAT NOT NULL) data type, enabling direct similarity calculations with the cosine_distance function in SQL.

Example: Semantic Search with Embeddings

-- Create a table for documents with embeddings
CREATE TABLE articles (
id INT,
title VARCHAR,
content VARCHAR,
embedding ARRAY(FLOAT NOT NULL)
);

-- Store documents with their vector embeddings
INSERT INTO articles (id, title, content, embedding)
VALUES
(1, 'Python for Data Science', 'Python is a versatile programming language...',
ai_embedding_vector('Python is a versatile programming language...')),
(2, 'Introduction to R', 'R is a popular programming language for statistics...',
ai_embedding_vector('R is a popular programming language for statistics...'));

-- Find semantically similar documents
SELECT
id, title,
cosine_distance(embedding, ai_embedding_vector('How to use Python in data analysis?')) AS similarity
FROM articles
ORDER BY similarity ASC
LIMIT 3;

Example: Text Generation

-- Generate text based on a prompt
SELECT ai_text_completion('Explain the benefits of cloud data warehouses in three points:') AS completion;

Getting Started

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