Analyze and optimize vector embeddings used in machine learning, semantic search, and recommendation systems. These tools help you measure similarity, visualize embeddings, and improve clustering performance.
Measure similarity between vectors using cosine similarity.
Compute Euclidean and other distance metrics between embeddings.
Compare embeddings across multiple similarity methods.
Group embeddings into clusters to detect patterns.
Visualize high-dimensional vectors in 2D/3D space.
Test how embeddings perform in semantic search scenarios.
Cosine similarity measures the angle between two vectors, indicating how similar they are regardless of magnitude.
Cosine similarity focuses on direction, while Euclidean distance measures absolute distance between points.
Embeddings transform data into numerical vectors that capture semantic meaning, enabling similarity search and clustering.
Semantic search retrieves results based on meaning rather than exact keyword matches using embeddings.
Cosine similarity is common for text embeddings, while Euclidean distance is often used in geometric contexts.
These tools are essential for machine learning workflows, vector databases, recommendation systems, and AI-powered search engines.