pgvector

The pgvector adapter wraps any client with a query(text, params) method that resolves to { rows }. A pg Pool or Client works as is. An index is a table.

import { Pool } from "pg";
import { createPgvectorStore } from "vecstore-sdk/pgvector";

const store = createPgvectorStore({
  client: new Pool({ connectionString }),
  schema: "public",
});

Install the client alongside the SDK:

bun add vecstore-sdk pg

For postgres.js, pass { query: (text, params) => sql.unsafe(text, params) } as the client.

Table layout

createIndex runs CREATE EXTENSION IF NOT EXISTS vector, creates the table below, and adds an HNSW (Hierarchical Navigable Small World) index for the metric and a GIN (Generalized Inverted Index) index on metadata:

CREATE TABLE "docs" (
  id text NOT NULL,
  namespace text NOT NULL DEFAULT '',
  embedding vector(1536) NOT NULL,
  metadata jsonb NOT NULL DEFAULT '{}'::jsonb,
  PRIMARY KEY (namespace, id)
);

CREATE EXTENSION needs a role that can create extensions. If your application role cannot, create the extension yourself before you call createIndex.

Metric detection

Queries read the metric from the HNSW or IVFFlat index definition in pg_indexes. When no index exists, they default to cosine. This also works for tables the SDK did not create.

listIndexes returns the tables in the schema that have an embedding vector column.

What the adapter stores