Vector Documentation Skill
SkillDev toolsProvides quick-start guidance and a unified entry point for Vector features, SDK usage, and integrations. Use when users ask how to work with Vector, its TS SDK, features, or supported frameworks.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Vector Documentation Skill skill
What this skill tells your AI
The instructions your AI receives, as published by upstash/vector-js in skills/SKILL.md and read by ahel’s review.
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.
Signals
- GitHub stars
- 70
- Forks
- 13
- Last commit
- Mar 2026
Advanced
- Catalog kind
- skill
- Gateway key
upstash-vector-js- Source
- github.com/upstash/vector-js