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vector-search

Retrieve items by comparing query embeddings using cosine similarity, Euclidean distance, or dot product.

Updated Dec 23, 2025
Search Tags:#database#embeddings#pgvector#similarity#ranking#semantic-search#vector-search
One-click install
npx skills add https://github.com/vineethsoma/agent-packages --skill vector-search-vineethsoma
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vector-search
Source: https://github.com/vineethsoma/agent-packages/tree/main/skills/vector-search
Command: npx skills add https://github.com/vineethsoma/agent-packages --skill vector-search-vineethsoma

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables building semantic search capabilities by evaluating vector embeddings and computing similarity or distance between items and queries, replacing brittle keyword matching.

Core Features & Use Cases

  • Similarity Metrics: cosine similarity, Euclidean distance, dot product
  • Range understanding and thresholding for precise retrieval
  • Use cases include document search, product recommendations, multimedia similarity, clustering, and ranking
  • Example: Build a catalog search that ranks items by semantic relevance using text embeddings.

Quick Start

Initialize your embedding model, index items with vector representations, and run a query to retrieve and rank results by the chosen metric.

Frequently Asked Questions about vector-search

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

FAQPage Schema
How do I implement semantic search using vector embeddings?▼

Semantic search with vector embeddings requires an embedding model to generate query vectors and a vector store to index item embeddings, then computes similarity to rank results by relevance.

Can I use pgvector for similarity ranking and clustering?▼

Yes, pgvector supports similarity ranking and clustering by storing embeddings in PostgreSQL and calculating cosine similarity, Euclidean distance, or dot product to retrieve matching items.

What is the best way to compare query embeddings to indexed embeddings?▼

Comparing query embeddings to indexed embeddings uses similarity metrics like cosine similarity, Euclidean distance, or dot product to calculate semantic distance and rank items by relevance.

Does vector search work with image and audio data embeddings?▼

Vector search works with text, image, and audio data by generating embeddings for each modality and applying similarity metrics to retrieve and rank multimedia content by semantic similarity.

How do I apply thresholding to vector similarity search results?▼

Thresholding applies range understanding to similarity scores, filtering indexed embeddings so only items meeting a specified distance or similarity metric threshold are retrieved and returned.

Why use semantic vector search instead of keyword matching?▼

Semantic vector search replaces brittle keyword matching by evaluating embedding vectors, enabling retrieval based on contextual meaning and semantic similarity rather than exact string matches.

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