AI & Prompt Engineering 100% Local Processing Limit: 5 MB

Cosine Similarity Vector Calculator

Compute cosine similarity, dot product, and Euclidean distance between text or numeric vectors.

Sponsor Space

1. Text or Vector Inputs

2. Similarity Metrics

Cosine Similarity Score
88.4%
High Semantic Proximity
Dot Product
0.884
Euclidean Distance
0.481
Sponsor Space

How to Use the Cosine Similarity Vector Calculator

  1. 1
    Enter two text snippets or comma-separated numeric embedding vectors.
  2. 2
    Click "Compute Semantic Similarity".
  3. 3
    Inspect percentage similarity score, dot product, and Euclidean distance.

Practical Example & Use Case

Common Real-World Scenario

Testing similarity between search queries and FAQ titles to determine the optimal embedding retrieval threshold.

Technical Notes & Limitations

Browser Engine Architecture

Text mode uses n-gram frequency vectors; for high-dimensional 1536-dim embeddings, paste raw vector numbers.

Frequently Asked Questions

What is a good cosine similarity score?

Generally, scores above 0.80 indicate strong semantic relevance in vector databases.