Skill

upstash-vector-js

Skills for Upstash Vector features, TypeScript/JavaScript SDK usage, and integrations. Use when users ask how to work with Vector, its TS SDK, features, or supported frameworks.

npx claudepluginhub upstash/skills --plugin upstash

Tool Access

This skill uses the workspace's default tool permissions.

Preview

Vector is a high‑performance vector database for storing, querying, and managing vector embeddings.

Supporting Assets

features/filtering-and-metadata.mdfeatures/index-structure.mdfeatures/namespaces.mdsdk-methods.md

SKILL.md

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Vector Documentation Skill

Quick Start

Vector is a high‑performance vector database for storing, querying, and managing vector embeddings.

Basic workflow:

Install the Vector TS SDK.

Connect to a Vector instance.

Upsert vectors, query them, and manage namespaces.

Example (TypeScript):

import { Index } from "@upstash/vector"; const index = new Index({ url: process.env.UPSTASH_VECTOR_REST_URL!, token: process.env.UPSTASH_VECTOR_REST_TOKEN!, }); await index.upsert([{ id: "1", vector: [0.1, 0.2], metadata: { tag: "example" } }]); const results = await index.query({ vector: [0.1, 0.2], topK: 5, });

For full usage, refer to the linked skill files below.

Other Skill Files

TS SDK Reference

sdk-methods: Explains SDK commands: delete, fetch, info, query, range, reset, resumable-query, upsert

Features

features/namespaces: Explains namespaces and dataset organization.

features/index-structure: Covers hybrid and sparse index structures.

features/filtering-and-metadata: Details metadata storage and server-side filtering.

Use these files for deeper guidance on SDK usage, advanced configurations, algorithms, and integrations.

Vector Documentation Skill

Quick Start

Vector is a high‑performance vector database for storing, querying, and managing vector embeddings.

Basic workflow:

Install the Vector TS SDK.
Connect to a Vector instance.
Upsert vectors, query them, and manage namespaces.

Example (TypeScript):

import { Index } from "@upstash/vector";
const index = new Index({
  url: process.env.UPSTASH_VECTOR_REST_URL!,
  token: process.env.UPSTASH_VECTOR_REST_TOKEN!,
});

await index.upsert([{ id: "1", vector: [0.1, 0.2], metadata: { tag: "example" } }]);

const results = await index.query({
  vector: [0.1, 0.2],
  topK: 5,
});

For full usage, refer to the linked skill files below.

Other Skill Files

TS SDK Reference

sdk-methods: Explains SDK commands: delete, fetch, info, query, range, reset, resumable-query, upsert

Features

features/namespaces: Explains namespaces and dataset organization.
features/index-structure: Covers hybrid and sparse index structures.
features/filtering-and-metadata: Details metadata storage and server-side filtering.

Use these files for deeper guidance on SDK usage, advanced configurations, algorithms, and integrations.