Enable Native Multimodal AI Search with SQL on PolarDB

1. Introduction

In today’s AI‑driven intelligent commerce, traditional search systems cannot meet the demand for "sub‑second response + multimodal interaction". Current AI retrieval systems face two major challenges: data must be moved from databases to vector stores, causing redundancy and version chaos; and toolchains are fragmented, making model invocation and deployment difficult.

The proposed solution is to use native SQL to achieve multimodal intelligent retrieval. By integrating an AI engine with PolarDB, AI capabilities are natively embedded in the database, allowing standard SQL syntax to call multimodal AI services for efficient image feature extraction and vectorization without data migration or separate services.

2. Solution Advantages

Low‑threshold AI native integration : Manage the full AI lifecycle via standard SQL, eliminating complex algorithm development and enabling developers to invoke multimodal AI capabilities through simple SQL interfaces.

Flexible adaptation to various scenarios : Customizable search dimensions and generic multimodal vector models can be easily applied to e‑commerce, healthcare, industrial, and other domain‑specific retrieval needs.

Full‑link data security loop : The "AI computation inside the database" architecture keeps raw data within PolarDB for feature extraction and model inference, combined with fine‑grained permission control and encryption to significantly reduce data leakage risk.

Pay‑as‑you‑go and serverless : A serverless model lets users pay only for actual compute resources, lowering operational costs and freeing developers from managing underlying infrastructure.

3. Architecture

The solution is built on PolarDB PostgreSQL combined with Alibaba Cloud Baidu AI model services, creating an out‑of‑the‑box intelligent multimodal search application that supports text‑to‑image, image‑to‑image, and other scenarios. The application is deployed via the Serverless AI development platform Function AI, which handles model deployment, scaling, and high availability.

4. Deployment Steps

1) Provision resources : Enable Function Compute, create a VPC and subnet, an OSS bucket, and a PolarDB PostgreSQL cluster.

2) Deploy the application :

Log in to the PolarDB cluster console, select the appropriate region, and configure the cluster parameters.

Obtain an API key from the Alibaba Cloud Baidu AI console.

Open the provided Function AI project template and configure parameters (default settings are recommended).

3) Validate the solution : Access the sample application, import image data, and test both text‑search and image‑search modes.

5. Resource Cleanup

After testing, delete the created resources: one Baidu AI API key, one VPC, one subnet, one PolarDB PostgreSQL cluster, and one OSS bucket to avoid further charges.

For detailed steps and reference links, see the original documentation.