Integrating Gemini Pro with Supabase Vector Store

The Power of Postgres pgvector

Supabase simplifies semantic applications by supporting pgvector columns. Using Gemini Pro embeddings, we can easily match user queries to document contexts.

1. Designing the SQL Vector Table

Let's initialize the vector extension and table layout in Supabase SQL editor: ``sql -- Enable extension create extension if not exists vector; -- Create vector table create table document_embeddings ( id uuid primary key default gen_random_uuid(), title text not null, body text not null, embedding vector(768) -- Gemini Pro embedding dimensions ); `

2. Injecting Gemini Embeddings API

Obtain embeddings using official Google generative packages: `javascript const { GoogleGenAI } = require('@google/genai'); const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY }); const response = await ai.models.embedContent({ model: 'text-embedding-004', contents: 'Better Call Bvk AI services scaling details.', }); ``