upstash-vector-db-skills▌

Links

• Docs: https://upstash.com/docs/vector
• Getting Started: https://upstash.com/docs/vector/overall/getstarted
• Semantic Search Tutorial: https://upstash.com/docs/vector/tutorials/semantic_search
• Namespaces: https://upstash.com/docs/vector/features/namespaces
• Embedding Models: https://upstash.com/docs/vector/features/embeddingmodels
• MixBread AI: https://www.mixbread.ai/ (preferred embedding provider)

Quick Setup

1. Create Vector Index (Upstash Console)

• Go to Upstash Console
• Create Vector Index: name, region (closest to app), type (Dense for semantic search)
• Select embedding model: MixBread AI recommended (or use Upstash built-in models)
• Copy UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN to .env

2. Install SDK

pnpm add @upstash/vector

3. Environment

UPSTASH_VECTOR_REST_URL=your_url
UPSTASH_VECTOR_REST_TOKEN=your_token

Code Examples

Initialize Client (Node.js / TypeScript)

import { Index } from "@upstash/vector";

const index = new Index({
  url: process.env.UPSTASH_VECTOR_REST_URL,
  token: process.env.UPSTASH_VECTOR_REST_TOKEN,
});

Upsert Documents (Auto-Embed)

When using an embedding model in the index, text is embedded automatically:

// Single document
await index.upsert({
  id: "doc-1",
  data: "Upstash provides serverless vector database solutions.",
  metadata: { source: "docs", category: "intro" },
});

// Batch
await index.upsert([
  { id: "doc-2", data: "Vector search powers semantic similarity.", metadata: { source: "docs" } },
  { id: "doc-3", data: "MixBread AI provides high-quality embeddings.", metadata: { source: "blog" } },
]);

Query / Semantic Search

// Semantic search with auto-embedding
const results = await index.query({
  data: "What is semantic search?",
  topK: 5,
  includeMetadata: true,
});

results.forEach((result) => {
  console.log(`ID: ${result.id}, Score: ${result.score}, Metadata:`, result.metadata);
});

Using Namespaces (Data Isolation)

Namespaces partition a single index into isolated subsets. Useful for multi-tenant or multi-domain apps.

// Upsert in namespace "blog"
await index.namespace("blog").upsert({
  id: "post-1",
  data: "Next.js tutorial for Vercel deployment",
  metadata: { author: "user-123" },
});

// Query only "blog" namespace
const blogResults = await index.namespace("blog").query({
  data: "Vercel deployment",
  topK: 3,
  includeMetadata: true,
});

// List all namespaces
const namespaces = await index.listNamespaces();
console.log(namespaces);

// Delete namespace
await index.deleteNamespace("blog");

Full Semantic Search Example (Vercel Function)

// api/search.ts (Vercel Edge Function or Serverless Function)
import { Index } from "@upstash/vector";

export const config = {
  runtime: "nodejs", // or "edge"
};

const index = new Index({
  url: process.env.UPSTASH_VECTOR_REST_URL,
  token: process.env.UPSTASH_VECTOR_REST_TOKEN,
});

export default async function handler(req, res) {
  if (req.method !== "POST") {
    return res.status(405).json({ error: "Method not allowed" });
  }

  const { query, namespace = "", topK = 5 } = req.body;

  try {
    const searchIndex = namespace ? index.namespace(namespace) : index;
    const results = await searchIndex.query({
      data: query,
      topK,
      includeMetadata: true,
    });

    return res.status(200).json({ results });
  } catch (error) {
    console.error("Search error:", error);
    return res.status(500).json({ error: "Search failed" });
  }
}

Index Operations

// Reset (clear all vectors in index or namespace)
await index.reset();

// Or reset a specific namespace
await index.namespace("old-data").reset();

// Delete a single vector
await index.delete("doc-1");

// Delete multiple vectors
await index.delete(["doc-1", "doc-2", "doc-3"]);

Embedding Models

Available in Upstash

• BAAI/bge-large-en-v1.5 (1024 dim, best performance, ~64.23 MTEB score)
• BAAI/bge-base-en-v1.5 (768 dim, good balance)
• BAAI/bge-small-en-v1.5 (384 dim, lightweight)
• BAAI/bge-m3 (1024 dim, sparse + dense hybri