Supabase Vector Has a Free API — Build AI Search in Minutes

TL;DR

Supabase now includes pgvector out of the box — giving you a free vector database alongside your PostgreSQL database. Build semantic search, RAG pipelines, and recommendation engines without a separate vector DB.

What Is Supabase Vector?

Supabase is the open-source Firebase alternative built on PostgreSQL. With the addition of pgvector, every Supabase project becomes an AI-ready database:

• pgvector extension — vector similarity search natively in PostgreSQL
• Free tier — 500 MB database with vector support included
• REST API — auto-generated API for all your tables (including vector columns)
• Edge Functions — run embedding generation at the edge
• Auth + RLS — secure your vector data with Row Level Security

Setting Up Vector Search

1. Enable pgvector

-- Enable the vector extension (one-time setup)
CREATE EXTENSION IF NOT EXISTS vector;

-- Create a table with embeddings
CREATE TABLE documents (
  id BIGSERIAL PRIMARY KEY,
  title TEXT NOT NULL,
  content TEXT NOT NULL,
  embedding VECTOR(1536)  -- OpenAI ada-002 dimensions
);

-- Create an index for fast similarity search
CREATE INDEX ON documents
  USING ivfflat (embedding vector_cosine_ops)
  WITH (lists = 100);

2. Store Embeddings via API

import { createClient } from "@supabase/supabase-js";

const supabase = createClient(
  "https://YOUR_PROJECT.supabase.co",
  "YOUR_ANON_KEY"
);

// Generate embedding with OpenAI
const embeddingResponse = await fetch(
  "https://api.openai.com/v1/embeddings",
  {
    method: "POST",
    headers: {
      Authorization: `Bearer ${OPENAI_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      model: "text-embedding-ada-002",
      input: "Your document text here",
    }),
  }
);

const { data } = await embeddingResponse.json();

// Store in Supabase
await supabase.from("documents").insert({
  title: "My Document",
  content: "Your document text here",
  embedding: data[0].embedding,
});

3. Semantic Search with RPC

-- Create a search function
CREATE OR REPLACE FUNCTION match_documents(
  query_embedding VECTOR(1536),
  match_threshold FLOAT,
  match_count INT
)
RETURNS TABLE (
  id BIGINT,
  title TEXT,
  content TEXT,
  similarity FLOAT
)
LANGUAGE SQL STABLE
AS $$
  SELECT
    id, title, content,
    1 - (embedding <=> query_embedding) AS similarity
  FROM documents
  WHERE 1 - (embedding <=> query_embedding) > match_threshold
  ORDER BY (embedding <=> query_embedding)
  LIMIT match_count;
$$;

// Call from your app
const { data: results } = await supabase.rpc("match_documents", {
  query_embedding: queryVector,
  match_threshold: 0.78,
  match_count: 10,
});

RAG Pipeline with Supabase + OpenAI

async function askQuestion(question) {
  // 1. Generate embedding for the question
  const embedding = await generateEmbedding(question);

  // 2. Find relevant documents
  const { data: docs } = await supabase.rpc("match_documents", {
    query_embedding: embedding,
    match_threshold: 0.7,
    match_count: 5,
  });

  // 3. Build context from matched documents
  const context = docs.map((d) => d.content).join("\n\n");

  // 4. Ask GPT with context
  const response = await openai.chat.completions.create({
    model: "gpt-4",
    messages: [
      {
        role: "system",
        content: `Answer based on this context:\n${context}`,
      },
      { role: "user", content: question },
    ],
  });

  return response.choices[0].message.content;
}

Supabase Vector vs Alternatives

Feature	Supabase	Pinecone	Weaviate	Qdrant
Free Tier	500 MB	100K vectors	Self-host	Self-host
SQL Support	✅ Full PostgreSQL	❌	❌	❌
Auth Built-in	✅ RLS	❌	❌	❌
REST API	✅ Auto-generated	✅	✅ REST+GraphQL	✅
Hybrid Search	✅ (SQL + vector)	❌	✅	✅
Managed	✅	✅	✅ Cloud	✅ Cloud

Why Supabase Vector?

1. No separate service — vectors live alongside your regular data
2. SQL + vectors — combine traditional filters with semantic search
3. Built-in auth — RLS policies apply to vector queries too
4. Free forever tier — 500 MB is enough for millions of embeddings
5. Open source — self-host if you need more control

Resources

• Supabase Vector Docs
• pgvector on GitHub
• Supabase AI Examples

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Building an AI app that needs web data? My Apify scraping tools can extract data from any website — feed it into your Supabase vector store for powerful semantic search. Questions? Email [e-mail]