embeddings

majiayu000's avatarfrom majiayu000

Computes semantic embeddings for text and measures distance between sentences. Use for semantic similarity analysis and comparing text.

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[Knowledge Base]

When & Why to Use This Skill

The Embeddings Skill enables AI agents to transform text into high-dimensional numerical vectors using the 'all-MiniLM-L6-v2' sentence-transformer model. By calculating cosine or Euclidean distances, it provides a robust framework for semantic similarity analysis, allowing agents to understand the underlying meaning of sentences beyond simple keyword matching for more accurate information retrieval.

Use Cases

  • Retrieval-Augmented Generation (RAG): Enhance AI responses by retrieving the most semantically relevant documents from a private knowledge base to provide context-aware answers.
  • Content Deduplication: Automatically identify and consolidate redundant text entries or near-duplicate documents in large datasets by measuring semantic proximity.
  • Intelligent FAQ Matching: Map user queries to the most conceptually similar questions in a help center to provide instant, accurate support even when wording differs.
  • Semantic Search Engines: Build search functionality that understands user intent and conceptual relationships, delivering results based on meaning rather than exact string matches.
  • Automated Content Categorization: Group unstructured text, such as customer feedback or research papers, into clusters based on their semantic themes for easier analysis.
nameembeddings
descriptionComputes semantic embeddings for text and measures distance between sentences. Use for semantic similarity analysis and comparing text.
allowed-toolsBash, Read

Embeddings Skill

This skill computes semantic embeddings for text and measures the semantic distance between sentences using sentence transformers.

Instructions

To compute embeddings and measure distance:

  1. Accept one or more sentences as input
  2. Use the sentence-transformers library with the 'all-MiniLM-L6-v2' model
  3. Compute embeddings (384-dimensional vectors)
  4. Calculate cosine distance or Euclidean distance between embeddings
  5. Return the distance metric

What are Embeddings?

Embeddings are numerical vector representations of text that capture semantic meaning. Sentences with similar meanings have embeddings that are close together in vector space.

Distance Metrics

Cosine Distance

  • Range: [0, 2]
  • 0 = identical meaning
  • 2 = opposite meaning
  • Most commonly used for semantic similarity

Euclidean Distance

  • Range: [0, ∞)
  • Measures straight-line distance in embedding space
  • Sensitive to magnitude differences

Python Code Usage

import sys
sys.path.append('.claude/skills/embeddings')
from embedding_utils import (
    compute_embedding,
    cosine_distance,
    semantic_similarity
)

# Compute embeddings
sentence1 = "The cat sits on the mat"
sentence2 = "A feline rests on the rug"

emb1 = compute_embedding(sentence1)
emb2 = compute_embedding(sentence2)

# Calculate distance
distance = cosine_distance(emb1, emb2)
print(f"Semantic distance: {distance:.4f}")

# Or use the convenience function
distance = semantic_similarity(sentence1, sentence2, distance_metric='cosine')

Usage Example

Similar sentences:

  • Sentence 1: "The dog runs in the park"
  • Sentence 2: "A canine sprints through the garden"
  • Cosine Distance: ~0.15 (very similar)

Different sentences:

  • Sentence 1: "The dog runs in the park"
  • Sentence 2: "Quantum physics explains particle behavior"
  • Cosine Distance: ~0.85 (very different)

Functions Available

  1. compute_embedding(sentence) - Get embedding vector for a sentence
  2. compute_embeddings_batch(sentences) - Get embeddings for multiple sentences
  3. cosine_distance(emb1, emb2) - Calculate cosine distance
  4. euclidean_distance(emb1, emb2) - Calculate Euclidean distance
  5. semantic_similarity(sent1, sent2, metric) - End-to-end similarity calculation

Model Information

  • Model: all-MiniLM-L6-v2
  • Embedding dimensions: 384
  • Performance: Fast and efficient
  • Use case: General-purpose sentence embeddings

Notes

  • The model is loaded once and cached for efficiency
  • Requires sentence-transformers package
  • Works well for short texts (sentences, paragraphs)
  • Language: Primarily English, with some multilingual capability