How does the skill handle security for vector data?

The skill provides templates for Row Level Security (RLS) policies, ensuring that embedding data is filtered by user_id or organization_id to prevent data leakage between users.

Can I implement hybrid search with this setup?

Absolutely. The skill includes a specific phase for setting up hybrid search which combines full-text keyword search with semantic vector search using Reciprocal Rank Fusion (RRF) for balanced results.

What is the difference between HNSW and IVFFlat indexes in this skill?

HNSW is recommended for most applications with fewer than 1 million vectors as it offers higher recall and faster queries. IVFFlat is better for write-heavy workloads or massive datasets where memory usage and insert speed are critical.

Does this skill support OpenAI and Cohere embedding models?

Yes, it includes specific configuration guidance for various dimensions, including OpenAI's 1536 (small) and 3072 (large) dimensions, as well as Cohere's 1024-dimension models.

Supabase pgvector Setup

Name: Supabase pgvector Setup
Author: vanman2024

byvanman2024

Database Management

Configures pgvector on Supabase to enable high-performance semantic search, RAG systems, and hybrid search capabilities.

About

This skill automates the integration of vector databases within Supabase environments, providing developers with pre-configured templates for embedding storage and optimized indexing strategies like HNSW and IVFFlat. It streamlines the development of AI-powered applications by handling complex SQL functions for semantic matching, implementing hybrid search with Reciprocal Rank Fusion (RRF), and ensuring data security through Row Level Security (RLS) policies. Whether you are building a document retrieval system or a recommendation engine, this skill provides the necessary scripts and architectural patterns to deploy production-ready vector search infrastructure.

Key Features

Automated pgvector extension enablement and schema configuration
Support for optimized HNSW and IVFFlat vector indexing strategies
Pre-built templates for semantic search and hybrid RRF functions
Dimension presets for OpenAI, Cohere, and custom embedding models
Integrated validation scripts for performance testing and recall accuracy
0 GitHub stars

Use Cases

Building Retrieval-Augmented Generation (RAG) systems for AI chatbots
Implementing semantic product recommendations in e-commerce platforms
Developing hybrid search systems that combine keyword matching with vector similarity

About

Key Features

Automated pgvector extension enablement and schema configuration
Support for optimized HNSW and IVFFlat vector indexing strategies
Pre-built templates for semantic search and hybrid RRF functions
Dimension presets for OpenAI, Cohere, and custom embedding models
Integrated validation scripts for performance testing and recall accuracy
0 GitHub stars

Use Cases

Building Retrieval-Augmented Generation (RAG) systems for AI chatbots
Implementing semantic product recommendations in e-commerce platforms
Developing hybrid search systems that combine keyword matching with vector similarity