Embeddings

The Embeddings page lets you generate text embeddings and visually compare how semantically similar two pieces of text are.

Sidebar item: Embeddings Route: /embeddings

What Are Embeddings?

An embedding is a list of numbers (a vector) that represents the meaning of a piece of text. Texts with similar meanings produce vectors that point in similar directions. LLMxRay uses this concept to let you explore semantic similarity hands-on.

Embedding Text

1. Select an embedding model from the dropdown. Only models with embedding capabilities appear (e.g., nomic-embed-text, all-minilm).
2. Enter text in the input area.
3. Click Embed. The resulting vector is displayed as a visual bar chart.

No embedding model?

If no models appear in the dropdown, you need to pull one:

ollama pull nomic-embed-text

Comparing Two Texts

1. Enter text in both input areas.
2. Click Compare. Both texts are embedded and compared.
3. The cosine similarity meter shows how semantically close the two texts are:
   • 1.0 — Identical meaning
   • 0.7+ — Very similar
   • 0.3–0.7 — Somewhat related
   • < 0.3 — Unrelated

Use Cases

• Understanding embeddings — See what these numbers actually represent
• Testing RAG relevance — Check if your documents will match user queries
• Exploring synonyms — See how the model understands semantic relationships
• Educational — Great for AI/ML coursework on vector representations

Tips

• Different embedding models produce different vector dimensions and similarity scores.
• Short, focused texts produce more meaningful comparisons than long paragraphs.
• Results are stored in the session and can be reviewed later.