Vector Search Skill

Implement semantic search and similarity-based ranking using vector embeddings.

What This Skill Provides

• Similarity Metrics: Cosine similarity, Euclidean distance, dot product
• Range Understanding: Mathematical domains for each metric
• Threshold Selection: Setting appropriate minScore/maxDistance values
• Implementation Patterns: Database queries, filtering, ranking

When to Use

• Implementing semantic search (text, images, audio)
• Ranking results by similarity to query
• Recommendation systems (find similar items)
• Clustering or classification based on embeddings
• When user mentions: “vector search”, “embeddings”, “similarity”, “semantic search”

Primitives Included

• Instructions: similarity-metrics.instructions.md – Metric selection and threshold guidance
• Instructions: embedding-integration.instructions.md – OpenAI, Sentence Transformers patterns

Key Concepts

Similarity Metrics Cheat Sheet

Critical: Understand the Range

Most common bug: Setting minScore=0 for cosine similarity and filtering out valid negative scores.

Example:

// ❌ Wrong - filters out negative scores
function search(embedding, minScore = 0) {
  return results.filter(r => r.score >= minScore); 
  // Cosine can be negative!
}

// ✅ Correct - includes full range
function search(embedding, minScore = -1) {
  return results.filter(r => r.score >= minScore);
  // -1 to 1 for cosine similarity
}

Example: Semantic Bird Search

// Generate embedding for query
const queryEmbedding = await openai.embeddings.create({
  model: 'text-embedding-3-small',
  input: 'red bird with black wings'
});

// Search with cosine similarity
const results = await db.query(`
  SELECT 
    id,
    name,
    1 - (embedding <=> $1) as similarity
  FROM birds
  WHERE 1 - (embedding <=> $1) >= $2
  ORDER BY similarity DESC
  LIMIT 10
`, [queryEmbedding.data[0].embedding, 0.3]);

Note: PostgreSQL pgvector uses <=> for cosine distance (0 to 2), so 1 - distance gives similarity (-1 to 1).

Dependencies

• Database: SQLite with BLOB, PostgreSQL with pgvector, or specialized vector DB
• Embedding Model: OpenAI API, Sentence Transformers, or custom model
• Math Library: For similarity calculations if not database-native

Related Skills: fullstack-expertise, claude-framework (E-1 validation)