@ragpipe/plugin-supabase

@ragpipe/plugin-supabase

Supabase pgvector vector store plugin for ragpipe.

Install

pnpm add ragpipe @ragpipe/plugin-supabase

Usage

import { defineConfig } from "ragpipe";
import { supabaseVectorStore } from "@ragpipe/plugin-supabase";

export default defineConfig({
  // ... embedding, generation
  vectorStore: supabaseVectorStore({
    databaseUrl: process.env.DATABASE_URL ?? "",
    tableName: "documents", // default
    dimensions: 3072,
  }),
});

API

supabaseVectorStore(options)

Returns a VectorStorePlugin backed by Supabase PostgreSQL with pgvector.

Option	Type	Default	Description
databaseUrl	string	—	PostgreSQL connection string (required)
tableName	string	"documents"	Table to store/query vectors
dimensions	number	—	Vector dimensions (for documentation; table must match)

Methods

Method	Description
search(vector, topK)	Cosine similarity search, returns top-K results with scores
upsert(source, content, vector)	Insert a document if it doesn't already exist
clear()	Truncate the documents table
disconnect()	Close the PostgreSQL connection

Database Setup

Enable pgvector and create the documents table in your Supabase SQL editor:

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE documents (
  id BIGSERIAL PRIMARY KEY,
  source TEXT NOT NULL,
  content TEXT NOT NULL,
  vector VECTOR(3072)
);

CREATE INDEX ON documents
  USING ivfflat (vector vector_cosine_ops)
  WITH (lists = 100);

Adjust VECTOR(3072) to match your embedding model's dimensions.

License

MIT