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upstash-vector-db-skills

Configure Upstash Vector DB for semantic search with namespaces in Node.js/TypeScript.

Updated Jun 16, 2017
One-click install
npx skills add https://github.com/vuanhtu1993/vuanhtu1993.github.io --skill upstash-vector-db-skills-vuanhtu1993
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: upstash-vector-db-skills
Source: https://github.com/vuanhtu1993/vuanhtu1993.github.io/tree/main/.gemini/skills/upstash-vector-db-skills
Command: npx skills add https://github.com/vuanhtu1993/vuanhtu1993.github.io --skill upstash-vector-db-skills-vuanhtu1993

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps developers configure and operate Upstash Vector DB for semantic search, enabling fast vector similarity, namespace-based isolation, and easy embedding model integration in modern apps.

Core Features & Use Cases

  • Vector index creation and management with Upstash
  • Namespaces for multi-tenant separation and data isolation
  • End-to-end examples for upserting and querying with embedded text (Node.js/TypeScript)

Quick Start

Install the Upstash Vector client: pnpm add @upstash/vector Create and configure a vector index in the Upstash Console, selecting a suitable embedding model (MixBread recommended) Set environment variables UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN Run sample code to upsert documents and perform semantic search across namespaces

Frequently Asked Questions about upstash-vector-db-skills

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up Upstash Vector DB for semantic search in a Node.js application?▼

To set up Upstash Vector DB for semantic search, install the @upstash/vector client, create a vector index in the Upstash Console, configure your REST URL and token environment variables, and run the provided upsert and query code samples.

Can I use namespaces for data isolation in Upstash Vector DB?▼

Yes, Upstash Vector DB supports namespaces for data isolation. You can use namespaces to achieve multi-tenant separation within your vector index, allowing distinct data segments to be queried independently.

What embedding models work with Upstash Vector DB for similarity matching?▼

Upstash Vector DB works with MixBread and Upstash embedding models. When creating your vector index in the Upstash Console, you can select a suitable model like MixBread to generate embeddings for your text data.

How does vector similarity matching work in serverless and edge deployments?▼

Vector similarity matching in serverless and edge deployments uses a REST API to query an Upstash Vector index. You upsert embedded text documents into the index and perform fast similarity searches using the configured REST URL and token.

Do I need specific environment variables to connect my TypeScript project to Upstash Vector?▼

Yes, you need to set the UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN environment variables. These credentials allow your Node.js or TypeScript application to authenticate and communicate with your Upstash Vector index.

What is the best way to upsert and query embedded text in Upstash Vector DB?▼

The best way to upsert and query embedded text is using the @upstash/vector client in Node.js or TypeScript. The Skill provides end-to-end code examples demonstrating how to insert documents and perform semantic searches across namespaces.

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