@ragpipe/plugin-supabase

Supabase vector store plugin for ragpipe, powered by @supabase/supabase-js.

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({
    supabaseUrl: process.env.SUPABASE_URL ?? "",
    supabaseKey: process.env.SUPABASE_SERVICE_ROLE_KEY ?? "",
    tableName: "documents",   // default
    queryName: "match_documents", // default RPC function name
  }),
});

API

supabaseVectorStore(options)

Returns a VectorStorePlugin backed by Supabase using the official JS SDK.

| Option | Type | Default | Description | |---|---|---|---| | supabaseUrl | string | — | Supabase project URL (required) | | supabaseKey | string | — | Service role key (required) | | tableName | string | "documents" | Table to store documents | | queryName | string | "match_documents" | PostgreSQL function for vector search |

Methods

| Method | Description | |---|---| | search(vector, topK) | Calls supabase.rpc() for cosine similarity search | | upsert(source, content, vector) | Inserts via supabase.from().upsert() with dedup on source,content | | clear() | Deletes all rows from the documents table | | disconnect() | No-op (Supabase JS client manages connections automatically) |

Database Setup

Run the following in your Supabase SQL editor:

-- 1. Enable pgvector
CREATE EXTENSION IF NOT EXISTS vector;

-- 2. Create the documents table
CREATE TABLE documents (
  id BIGSERIAL PRIMARY KEY,
  source TEXT NOT NULL,
  content TEXT NOT NULL,
  vector VECTOR(3072),
  UNIQUE(source, content)
);

-- 3. Create the similarity search function
CREATE OR REPLACE FUNCTION match_documents(
  query_embedding VECTOR(3072),
  match_count INT DEFAULT 5
)
RETURNS TABLE (
  source TEXT,
  content TEXT,
  similarity FLOAT
)
LANGUAGE plpgsql
AS $$
BEGIN
  RETURN QUERY
  SELECT
    d.source,
    d.content,
    1 - (d.vector <=> query_embedding) AS similarity
  FROM documents d
  ORDER BY d.vector <=> query_embedding
  LIMIT match_count;
END;
$$;

-- 4. Create an index for faster search
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