Agent skill

upstash-vector-db-skills

Quellcode ansehen: gocallum/nextjs16-agent-skills

#201Globales Ranking · von 201 SkillsSafe

Installation

npx skills add gocallum/nextjs16-agent-skills --skill upstash-vector-db-skills

342

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MiniMax

MiniMax M3

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Z.ai

GLM 5.3 Flash

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per M tokens

MoonshotAI

Kimi K3

$4.00 / $18.00

per M tokens

DeepSeek

DeepSeek V4.1 Flash

$0.40 / $1.40

per M tokens

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 hybrid)

Recommended: MixBread AI

If using MixBread as your embedding provider:

Create a MixBread API key at https://www.mixbread.ai/
When creating your Upstash index, select MixBread as the embedding model.
MixBread handles tokenization and semantic quality automatically.
No extra setup needed in your code; use index.upsert() / index.query() with text directly.

Best Practices

For Vercel Deployment

Store credentials in Vercel Environment Variables (project settings or .env.local).
Use Edge Functions or Serverless Functions for low-latency access.
Implement request rate limiting to stay within Upstash quotas.

Namespace Strategy

Use namespaces to isolate data by tenant, domain, or use case.
Example: namespace("user-123") for per-user search.
Clean up old namespaces to avoid storage bloat.

Query Performance

Keep topK reasonable (5–10 typically sufficient).
Use metadata filtering to pre-filter results if possible.
Upstash is eventually consistent; expect slight delays after upserts.

Error Handling

try {
  const results = await index.query({
    data: userQuery,
    topK: 5,
    includeMetadata: true,
  });
} catch (error) {
  if (error.status === 401) {
    console.error("Invalid credentials");
  } else if (error.status === 429) {
    console.error("Rate limited");
  } else {
    console.error("Query error:", error);
  }
}

Common Patterns

RAG (Retrieval Augmented Generation)

Upsert documents / knowledge base into Upstash.
On user query, retrieve top-k similar docs via semantic search.
Pass retrieved docs + user query to LLM for better context.

const docs = await index.query({ data: userQuestion, topK: 3 });
const context = docs.map((d) => d.metadata?.text).join("\n");
// Pass context to LLM

Multi-Tenant Search

Use namespaces to isolate each tenant's vectors:

const userNamespace = `tenant-${userId}`;
await index.namespace(userNamespace).upsert({ id, data, metadata });
// Queries only see that tenant's data

Batch Indexing

For bulk imports, upsert in batches:

const batchSize = 100;
for (let i = 0; i < documents.length; i += batchSize) {
  const batch = documents.slice(i, i + batchSize);
  await index.upsert(batch);
  console.log(`Indexed batch ${i / batchSize + 1}`);
}

Troubleshooting

No results returned: Ensure documents are indexed and embedding model is active.
Slow queries: Check quota limits; consider upgrading plan or reducing dataset size.
Stale data: Upstash is eventually consistent; wait 1–2 seconds before querying new inserts.
Namespace not working: Ensure namespace exists (created on first upsert) or use the default "".

Installationen

Installationen342

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Sicherheitsprüfung

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Warnungen: 0Bewertung: 90

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Bewertung: 93

Quellcode ansehen

gocallum/nextjs16-agent-skills

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So verwenden Sie diesen Skill

Install upstash-vector-db-skills by running npx skills add gocallum/nextjs16-agent-skills --skill upstash-vector-db-skills in your project directory. Führen Sie den obigen Installationsbefehl in Ihrem Projektverzeichnis aus. Die Skill-Datei wird von GitHub heruntergeladen und in Ihrem Projekt platziert.

Keine Konfiguration erforderlich. Ihr KI-Agent (Claude Code, Cursor, Windsurf usw.) erkennt installierte Skills automatisch und nutzt sie als Kontext bei der Code-Generierung.

Der Skill verbessert das Verständnis Ihres Agenten für upstash-vector-db-skills, und hilft ihm, etablierte Muster zu befolgen, häufige Fehler zu vermeiden und produktionsreifen Code zu erzeugen.

Was Sie erhalten

Skills sind Klartext-Anweisungsdateien — kein ausführbarer Code. Sie kodieren Expertenwissen über Frameworks, Sprachen oder Tools, das Ihr KI-Agent liest, um seine Ausgabe zu verbessern. Das bedeutet null Laufzeit-Overhead, keine Abhängigkeitskonflikte und volle Transparenz: Sie können jede Anweisung vor der Installation lesen und prüfen.

Kompatibilität

Dieser Skill funktioniert mit jedem KI-Coding-Agenten, der das skills.sh-Format unterstützt, einschließlich Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider und anderen Tools, die projektbezogene Kontextdateien lesen. Skills sind auf Transportebene framework-agnostisch — der Inhalt bestimmt, für welche Sprache oder welches Framework er gilt.

Data sourced from the skills.sh registry and GitHub. Install counts and security audits are updated regularly.

upstash-vector-db-skills

Power your AI agent skills with open-source models.

Links

Quick Setup

1. Create Vector Index (Upstash Console)

2. Install SDK

3. Environment

Code Examples

Initialize Client (Node.js / TypeScript)

Upsert Documents (Auto-Embed)

Query / Semantic Search

Using Namespaces (Data Isolation)

Full Semantic Search Example (Vercel Function)

Index Operations

Embedding Models

Available in Upstash

Recommended: MixBread AI

Best Practices

For Vercel Deployment

Namespace Strategy

Query Performance

Error Handling

Common Patterns

RAG (Retrieval Augmented Generation)

Multi-Tenant Search

Batch Indexing

Troubleshooting

Installationen

Sicherheitsprüfung

Quellcode ansehen

Give agents clean web context

Verwandte Skills

prisma-orm-v7-skills

authjs-skills

nextjs16-skills

ai-sdk-6-skills

clerk-nextjs-skills

resend-integration-skills

So verwenden Sie diesen Skill

Was Sie erhalten

Kompatibilität