Agent skill · AI & Agents

vector-db-patterns

Embedding strategies, ANN algorithms, hybrid search, RAG chunking strategies, and reranking for semantic search and retrieval.

vibeeval521★ · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add vibeeval/vibecosystem --skill vector-db-patterns --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 9 KB
Bundled scripts: none
Path: skills/vector-db-patterns/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 521
Language: C#

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Vector DB Patterns Semantic search and retrieval-augmented generation (RAG) patterns with vector databases. ## Embedding Strategies ```typescript import { OpenAI } from 'openai' const openai = new OpenAI() // Batch embedding for efficiency (max 2048 inputs per request for text-embedding-3-small) async function embedTexts(texts: string[]): Promise<number[][]> { const BATCH_SIZE = 2048 const allEmbeddings: number[][] = [] for (let i = 0; i < texts.length; i += BATCH_SIZE) { const batch = texts.slice(i, i + BATCH_SIZE) const response = await openai.embeddings.create({ model: 'text-embedding-3-small', // 1536 dimensions, good cost/quality input: batch, dimensions: 512, // Reduce dims for speed (Matryoshka) }) allEmbeddings.push(...response.data.map(d => d.embedding)) } return allEmbeddings } // Embed with prefix for asymmetric retrieval async function embedForSearch(query: string): Promise<number[]> { const [embedding] = await embedTexts([`search_query: ${query}`]) return embedding } async function embedForStorage(document: string): Promise<number[]> { const [embedding] = await embedTexts([`search_document: ${document}`]) return embedding } ``` ## Chunking Strategies for RAG ```types

What's inside
Steps it walks through
  1. Embedding Strategies
  2. Chunking Strategies for RAG
  3. Vector Search with Metadata Filtering
  4. Hybrid Search (Vector + Keyword)
  5. Reranking
  6. Checklist
  7. Anti-Patterns
More from vibecosystem
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About this skill
What does the vector-db-patterns skill do?

Embedding strategies, ANN algorithms, hybrid search, RAG chunking strategies, and reranking for semantic search and retrieval.

How do I install it?

Run `npx skills add vibeeval/vibecosystem --skill vector-db-patterns --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From vibeeval/vibecosystem, a repository with 521 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going