Free online embedding visualizer that maps text into 2D/3D space. Supports t-SNE and PCA reduction, cosine similarity calculation, and cluster analysis.
Zero dependencies · Works offline⚙️ Settings
Embedding Visualizer - What Can It Do?
Free online embedding visualizer. Reduces text embeddings to 2D/3D space for visualization. Supports t-SNE and PCA reduction, cosine similarity calculation, and cluster analysis. Understand semantic relationships visually.
Key Features
- Visualization: Reduce high-dimensional vectors to 2D/3D space for visual display
- PCA Reduction: Principal component analysis for fast dimensionality reduction
- Cosine Similarity: Calculate text embedding similarity
- Cluster Display: Similar text appears closer in the graph
- No API Key: Uses deterministic hashing, works offline
- Canvas Rendering: Renders multiple text items on canvas
How to Use
- Configure parameters: Set the reduction method and dimensions
- Generate result: Click the Visualize button to get results
- Copy and use: Copy results for use in your projects
Use Cases
- Semantic Analysis: Visualize text embedding relationships
- Model Comparison: Compare the effect of different embedding models
- Cluster Analysis: Group texts by semantic similarity
- Data Display: Present high-dimensional data in 2D/3D space
Learn More
Embedding is an NLP technique that maps text to a high-dimensional space, bringing similar texts closer together. Mainstream models include OpenAI text-embedding-3, BAAI BGE, and Voyage AI voyage-3. Common visualization methods use PCA (fast but only captures linear structure) and t-SNE (preserves local structure but is slower). Cosine similarity is the most common measure of similarity.
FAQ
What is Embedding Visualizer?
Free online Embedding Visualizer. Reduce text embeddings to 2D/3D space for visualization. Support t-SNE and PCA reduction, cosine similarity calculation, and cluster analysis. Understand semantic relationships visually. Pure frontend. Try now!
What is the difference between PCA and t-SNE?
PCA is fast and works well for linearly separable data, while t-SNE preserves local structure and is better for non-linear relationships but takes longer to compute.
Do I need an API key?
No. This tool uses deterministic hashing locally and does not require any API key. You can visualize embeddings for testing and learning purposes. For real production embeddings, use an API such as OpenAI.
How do I read the similarity results?
The similarity matrix shows the cosine similarity between each pair of texts. Values closer to 1 mean the texts are more similar.
Is my data uploaded to a server?
No. All calculation and visualization happens entirely in your browser, locally.