Shared Embedding Model Service

Overview

When AI Functions are installed, a shared embedding model is automatically provisioned. This model runs on SingleStore Aura container service and is available to all organizations with AI Functions enabled.

The shared embedding model is powered by Qwen3-Embedding-0.6B, a lightweight yet high-quality embedding model optimized for text vectorization, semantic search, and RAG applications. The shared service is currently available in US East (N. Virginia).

How It Works

The shared embedding model service differs from dedicated embedding models in the following ways:

The shared embedding service runs on GPU-accelerated infrastructure and automatically scales based on demand. SingleStore manages the lifecycle, scaling, and availability of the service.

Prerequisites

• A SingleStore Helios cluster group with AI Functions installed.

• The Shared Embedding Service feature must be enabled for your organization.

Select the Embedding Model

You can view and change your default embedding model in the Cloud Portal:

1. Navigate to AI > AI & ML Functions.

2. Select your cluster group.

3. On the AI Functions tab, in Settings, select Edit.

4. On the Edit AI Functions page, in Models, select the Embedding Model section.

5. The available embedding models include both the shared model (shared-qwen3-embed-0-6b) and the dedicated models provisioned for your organization.

To update the default embedding model used by EMBED_TEXT, select the desired model and save the change.

Usage

Use the shared embedding model with the EMBED_TEXT function by specifying the model name shared-qwen3-embed-0-6b. The following examples use the customer_reviews table.

SELECT cluster.EMBED_TEXT(
    'The headphones have poor sound quality, disconnect frequently, and the battery lasts only a few hours. I would not recommend this product.',
    'shared-qwen3-embed-0-6b'
) AS embedding;

SELECT cluster.EMBED_TEXT(
    'The headphones have poor sound quality, disconnect frequently, and the battery lasts only a few hours. I would not recommend this product.'
) AS embedding;

SET batch_external_functions = AUTO;

UPDATE customer_reviews
SET review_embedding = cluster.EMBED_TEXT(review_text, 'shared-qwen3-embed-0-6b')
WHERE review_embedding IS NULL;

Performance Considerations

• Batching: Use SET batch_external_functions = AUTO, before bulk embedding operations to enable automatic batching, which significantly improves throughput.

• Token limits: The Qwen3-Embedding-0.6B model processes text in token chunks. For large text inputs, the service automatically handles tokenization and batching.

• Region: For lowest latency, use cluster groups in the same region.

Related Topics

• AI Functions

• EMBED_TEXT

• Working with Vector Data