From upstash
Guides usage of Upstash Vector: install SDK, connect, upsert vectors, query, and manage namespaces. Covers TS SDK methods and features like filtering, hybrid/sparse indexes.
How this skill is triggered — by the user, by Claude, or both
Slash command
/upstash:upstash-vector-jsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Vector is a high‑performance vector database for storing, querying, and managing vector embeddings.
Vector is a high‑performance vector database for storing, querying, and managing vector embeddings.
Basic workflow:
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.
sdk-methods: Explains SDK commands: delete, fetch, info, query, range, reset, resumable-query, upsertfeatures/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.
npx claudepluginhub upstash/skills --plugin upstashGuides setup and usage of Upstash Vector DB for semantic search, with namespaces and MixBread AI embeddings. Useful when building vector search features on Vercel.
Implements Cloudflare Vectorize vector database for semantic search, RAG, and AI apps on Workers. Manages indexes, vector CRUD, metadata filtering, embeddings from Workers AI or OpenAI.
Implements vector search solutions using Pinecone, Weaviate, Qdrant, Milvus, and pgvector. Covers embedding strategies, indexing, and hybrid search for RAG and recommendation systems.