Skip to main content

Supabase

LangDB integrates AI models like OpenAI to generate embeddings and store them in Supabase for efficient data retrieval.

Preparing Database​

Step 1: Create a Supabase Project​

Go to Supabase and create a new project.

Step 2: Enable PgVector​

create extension vector;

Step 3: Create a table to store Embeddings​

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

Generating and Storing Embeddings​

Step 1: Install Libraries​

pip install openai python-dotenv supabase

Step 2: Generate Embeddings​

from openai import OpenAI
from dotenv import load_dotenv
from supabase import create_client, Client
import os

load_dotenv()
api_key = os.getenv("LANGDB_API_KEY")
project_id = os.getenv("LANGDB_PROJECT_ID")
base_url = f"https://api.us-east-1.langdb.ai/{project_id}/v1"


client = OpenAI(
    base_url=base_url,
    api_key=api_key,
)

text = "Hello LangDB"
response = client.embeddings.create(
    model="text-embedding-ada-002",
    input=text,
)

embedding = response.data[0].embedding

Step 3: Store in Supabase​

# # Store in Supabase
result = supabase.table('embeddings').insert({
    "content": text,
    "embedding": embedding
}).execute()