External AI Functions

Build powerful AI/ML capabilities by connecting TiDB Cloud Lake with your own infrastructure. External functions let you deploy custom models, leverage GPU acceleration, and integrate with any ML framework while keeping your data secure.

Key Capabilities

How It Works

1. Create AI Server: Build your AI/ML server using Python and tidbcloudlake-udf
2. Register Function: Connect your server to TiDB Cloud Lake with CREATE FUNCTION
3. Use in SQL: Call your custom AI functions directly in SQL queries

Example: Text Embedding Function

# Simple embedding UDF server demo
from tidbcloudlake_udf import udf, UDFServer
from sentence_transformers import SentenceTransformer

# Load pre-trained model
model = SentenceTransformer('all-mpnet-base-v2')  # 768-dimensional vectors

@udf(
    input_types=["STRING"],
    result_type="ARRAY(FLOAT)",
)
def ai_embed_768(inputs: list[str], headers) -> list[list[float]]:
    """Generate 768-dimensional embeddings for input texts"""
    try:
        # Process inputs in a single batch
        embeddings = model.encode(inputs)
        # Convert to list format
        return [embedding.tolist() for embedding in embeddings]
    except Exception as e:
        print(f"Error generating embeddings: {e}")
        # Return empty lists in case of error
        return [[] for _ in inputs]

if __name__ == '__main__':
    print("Starting embedding UDF server on port 8815...")
    server = UDFServer("0.0.0.0:8815")
    server.add_function(ai_embed_768)
    server.serve()

-- Register the external function in TiDB Cloud Lake
CREATE OR REPLACE FUNCTION ai_embed_768 (STRING)
    RETURNS ARRAY(FLOAT)
    LANGUAGE PYTHON
    HANDLER = 'ai_embed_768'
    ADDRESS = 'https://your-ml-server.example.com';

-- Use the custom embedding in queries
SELECT
    id,
    title,
    cosine_distance(
        ai_embed_768(content),
        ai_embed_768('machine learning techniques')
    ) AS similarity
FROM articles
ORDER BY similarity ASC
LIMIT 5;