How to Create a Vector Database in Supabase (Step-by-Step Guide)

In the world of AI, vector databases are becoming essential. They allow you to store and search embeddings — numerical representations of text, images, or audio — so that your applications can understand meaning, not just keywords.

If you’re building an AI chatbot, recommendation engine, or semantic search tool, you’ll likely need one.

The good news? You can easily set up a vector database using Supabase — an open-source alternative to Firebase built on top of PostgreSQL.

Let’s go step by step 👇

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🧠 What is a Vector Database?

A vector database stores embeddings — high-dimensional vectors generated by AI models.

For example:

• “AI in hospitals” → [0.12, -0.45, 0.88, ...]

• “Artificial intelligence for healthcare” → [0.11, -0.44, 0.86, ...]

These numbers represent meaning.
A vector database can compare these embeddings and find which texts are semantically similar, not just identical in words.

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🧩 Step 1: Create a Supabase Account

1. Go to https://supabase.com

2. Click Start your project or Sign in

3. Sign up using GitHub or your email

4. You’ll land on the Supabase dashboard

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🗄️ Step 2: Create a New Project

1. Click New Project

2. Fill in:

   • Name: my-vector-db

   • Password: any secure password

   • Region: pick one closest to you

3. Click Create project

Supabase will now spin up a PostgreSQL database for you (takes about a minute).

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⚙️ Step 3: Enable the pgvector Extension

Supabase uses the PostgreSQL extension pgvector to store and search vectors.

To enable it:

1. Go to the SQL Editor in the left sidebar

2. Run this command:

   create extension if not exists vector;
   

✅ Now your database supports vector embeddings!

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🧱 Step 4: Create a Table for Vectors

Next, create a table to store your documents and their embeddings.

In the SQL Editor, run:

create table documents (
  id bigserial primary key,
  content text,
  embedding vector(1536)
);

🧮 Note: 1536 is the dimension size for OpenAI’s text-embedding-3-small model.
Change it if you use a different model.

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🧮 Step 5: Add a Search Function (Optional but Recommended)

This function makes it easy to find similar documents.

create or replace function match_documents(
  query_embedding vector(1536),
  match_threshold float,
  match_count int
)
returns table(id bigint, content text, similarity float)
language sql stable
as $$
  select
    id,
    content,
    1 - (documents.embedding <=> query_embedding) as similarity
  from documents
  where 1 - (documents.embedding <=> query_embedding) > match_threshold
  order by similarity desc
  limit match_count;
$$;

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🔑 Step 6: Get Your API Keys

To connect from your app:

1. Go to Project Settings → API

2. Copy:

   • Project URL

   • anon (public) API key

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🧠 Step 7: Connect and Insert Data (Python Example)

Install the required libraries:

pip install supabase openai

Then run this code:

from supabase import create_client
from openai import OpenAI

# Supabase setup
url = "https://YOUR_PROJECT.supabase.co"
key = "YOUR_SUPABASE_ANON_KEY"
supabase = create_client(url, key)

# OpenAI setup
client = OpenAI(api_key="YOUR_OPENAI_KEY")

# Create an embedding
text = "Artificial intelligence for healthcare"
embedding = client.embeddings.create(
    model="text-embedding-3-small",
    input=text
).data[0].embedding

# Insert into Supabase
supabase.table("documents").insert({
    "content": text,
    "embedding": embedding
}).execute()

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🔍 Step 8: Search by Meaning (Semantic Search)

Now you can find similar content based on meaning — not just words:

query = "AI in hospitals"
query_embedding = client.embeddings.create(
    model="text-embedding-3-small",
    input=query
).data[0].embedding

response = supabase.rpc("match_documents", {
    "query_embedding": query_embedding,
    "match_threshold": 0.7,
    "match_count": 5
}).execute()

print(response.data)

This returns the most semantically similar rows from your database.
You just built your own vector search engine 🎯

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💡 Real-World Use Cases

• Chatbots with your data (RAG systems)

• Semantic search engines

• AI recommendation systems

• Document Q&A bots

• Personalized knowledge assistants

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✅ Conclusion

That’s it! You’ve created a fully functional vector database using Supabase and pgvector — all in a few steps.

It’s open-source, scalable, and easy to integrate with any AI workflow.

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