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upstash/vector TypeScript SDK Skil

Upsert, query, and delete vectors in Upstash Vector using the TypeScript SDK.

70|13|Updated Jan 11, 2024
Search Tags:#embeddings#ai#search#vector database#data management#typescript sdk
One-click install
npx skills add https://github.com/upstash/vector-js --skill upstash-vector-typescript-sdk-skil
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: upstash/vector TypeScript SDK Skil
Source: https://github.com/upstash/vector-js/tree/main/skills
Command: npx skills add https://github.com/upstash/vector-js --skill upstash-vector-typescript-sdk-skil

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive guide and a unified entry point for interacting with Upstash Vector, enabling users to efficiently manage vector databases, SDK usage, and integrations.

Core Features & Use Cases

  • SDK Usage: Learn how to use the TypeScript SDK for various vector database operations.
  • Vector Operations: Upsert, fetch, query, delete, and manage vectors within your database.
  • Index Management: Understand index structures (dense, sparse, hybrid) and namespace organization.
  • Filtering and Metadata: Implement server-side filtering and leverage metadata for precise queries.
  • Use Case: Developers can quickly integrate Upstash Vector into their serverless applications, using the SDK to store and retrieve embeddings for AI-powered search or recommendation systems.

Quick Start

Use the upstash/vector skill to upsert a new vector with id '1', vector [0.1, 0.2], and metadata {'tag': 'example'} into your Upstash Vector index.

Frequently Asked Questions about upstash/vector TypeScript SDK Skil

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

FAQPage Schema
How do I manage vector database operations using a TypeScript SDK?▼

The SDK supports dense, sparse, and hybrid index structures for vector storage. You can organize and manage your vectors within isolated namespaces to keep your data logically separated and optimized for AI-powered search.

How do I filter vector search results by metadata in TypeScript?▼

You can upsert a new vector by calling the SDK with a specific ID, a numerical array representing the vector, and a JSON object for metadata. This inserts the embedding and its associated data into your Upstash Vector index.

Does this TypeScript SDK support serverless applications for AI search?▼

You can use the SDK to manage vectors across dense, sparse, and hybrid index structures. The difference lies in how each index processes and retrieves embeddings to support various AI search and recommendation use cases.

Can I organize vectors into isolated namespaces within the vector database?▼

Yes, the SDK facilitates data management within isolated namespaces. This allows you to organize vectors into separate logical partitions within your index for more structured data management.

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