Supabase for Generative AI: How to Store, Search & Manage AI Content with pgvector

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Introduction

In the era of artificial intelligence, efficient data storage, retrieval, and management are crucial for building powerful GenAI applications. Supabase, an open-source backend-as-a-service (BaaS), provides scalable real-time databases, authentication, and edge functions, making it an excellent choice for GenAI developers.

In this guide, we’ll explore how to leverage Supabase for tasks like embedding storage, similarity search, and retrieval-augmented generation (RAG). You’ll learn how to:

• Set up Supabase for AI projects
• Store AI-generated content efficiently
• Perform vector similarity searches with pgvector
• Manage user authentication and real-time streaming

By the end, you'll have a solid understanding of integrating Supabase with OpenAI and LangChain to build intelligent applications.

Setting Up Supabase and OpenAI Credentials

Install Required Libraries

To begin, install the necessary Python libraries for interacting with Supabase and handling AI embeddings.

!pip install supabase python-dotenv openai langchain sentence-transformers tiktoken langchain-community

Import Required Libraries

import os
from supabase import create_client, Client
from openai import OpenAI
from langchain.embeddings import OpenAIEmbeddings

Configure Supabase and OpenAI API Keys

from google.colab import userdata
import numpy as np

SUPABASE_URL = userdata.get('SUPABASE_URL')
SUPABASE_KEY = userdata.get('SUPABASE_KEY')
OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')
os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY

supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
embeddings_model = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)

📌 What’s Happening Here?

• The script installs and imports key libraries for database interactions and AI processing.
• Supabase API keys are retrieved securely from Colab’s userdata.
• OpenAI’s embedding model is initialized for vector storage and similarity search.

Creating an AI Content Table in Supabase

Step 1: Create the ai_content Table

Open the SQL Editor in Supabase and run the following:

CREATE TABLE ai_content (
  id SERIAL PRIMARY KEY,
  created_at TIMESTAMPTZ DEFAULT NOW(),
  content TEXT NOT NULL
);

Step 2: Enable Vector Extension & Add Embeddings Column

CREATE EXTENSION IF NOT EXISTS vector;
ALTER TABLE ai_content ADD COLUMN embedding vector(1536);

Step 3: Add User-Specific Content Storage

ALTER TABLE ai_content ADD COLUMN user_id UUID REFERENCES auth.users(id);

📌 Why This Matters?

• The ai_content table stores AI-generated text.
• The embedding column holds vector representations for similarity searches.
• User IDs allow personalized AI content storage.

Storing AI-Generated Content with Embeddings

def store_with_embedding(text, user_id=None):
    """Generate embeddings for the given text and store them in Supabase"""
    embedding = embeddings_model.embed_query(text)  # Generate embedding
    embedding_array = np.array(embedding).tolist()  # Convert to list for storage

    data = {"content": text, "embedding": embedding_array}
    if user_id:
        data["user_id"] = user_id  # Store user-specific content if provided

    response = supabase.table("ai_content").insert(data).execute()
    return response

📌 Breaking It Down:

• Converts input text into an embedding vector using OpenAI’s model.
• Inserts the content and its embedding into Supabase.
• Optionally stores the user ID for personalized content management.

Example Usage:

store_with_embedding("The quick brown fox jumps over the lazy dog.")
store_with_embedding("Artificial Intelligence is transforming the world.")
store_with_embedding("Machine learning is a subset of AI.")

Searching for Similar Content

def search_similar_content(query, top_k=5):
    """Search for similar content using the match_ai_content function"""
    query_embedding = embeddings_model.embed_query(query)

    response = supabase.rpc("match_ai_content", {
        "query_embedding": query_embedding,
        "match_count": top_k
    }).execute()

    return response

📌 How It Works?

• Converts the search query into an embedding vector.
• Calls the Supabase Remote Procedure Call (RPC) function match_ai_content.
• Returns the top k most similar content items.

Example Search Query:

results = search_similar_content("Tell me about AI")
print(results)

Expected Output:

[
    {"content": "Artificial Intelligence is transforming the world.", "similarity": 0.88},
    {"content": "Machine learning is a subset of AI.", "similarity": 0.85},
    {"content": "The quick brown fox jumps over the lazy dog.", "similarity": 0.73}
]

Fetching & Managing AI Content

Fetch All Stored Content

response = supabase.table("ai_content").select("*").execute()
print(response.data)

Insert a New Document

new_doc = {"content": "The AI revolution is transforming industries worldwide."}
response = supabase.table("ai_content").insert(new_doc).execute()
print(response)

Update an Existing Document

updated_doc = {"content": "Updated content about Artificial Intelligence and its applications."}
response = supabase.table("ai_content").update(updated_doc).eq("id", 1).execute()
print(response)

Delete a Document

response = supabase.table("ai_content").delete().eq("id", 1).execute()
print(response)

📌 Why These Operations Matter?

• Fetching allows viewing stored AI content.
• Inserting enables adding new AI-generated insights.
• Updating ensures content relevance.
• Deleting removes unnecessary data.

Conclusion

In this guide, we covered how to:

• Set up Supabase for AI applications.
• Store AI-generated content with embeddings.
• Search for similar content using vector search.
• Manage data operations (insert, update, delete).

By integrating Supabase with OpenAI & LangChain, you can build robust retrieval-augmented generation (RAG) systems, AI-powered knowledge bases, and more!

Further Resources

• Supabase Documentation
• pgvector for PostgreSQL
• OpenAI Embeddings Guide
• LangChain Documentation
• Supabase Build Fast with AI Notebook

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