Embeddings Visualizer

Text embeddings are dense numerical vectors that represent the semantic meaning of text. Similar sentences have vectors that point in similar directions in high-dimensional space. Unlike TF-IDF (which this tool uses), neural embeddings from models like OpenAI text-embedding-3 or Sentence Transformers capture deeper semantic relationships — 'king' and 'monarch' have high similarity even though they share no words.

Cosine similarity measures the angle between two vectors, ignoring their magnitude. A score of 1.0 means the vectors point in the same direction (identical content). A score of 0.0 means they are perpendicular (completely unrelated). A score of -1.0 means opposite directions (rare in text embeddings since most values are non-negative). In practice, scores above 0.7 indicate high semantic similarity.

This tool uses TF-IDF (Term Frequency-Inverse Document Frequency) vectors — a classic sparse representation. Real embedding models like OpenAI text-embedding-3-large produce dense 1536-dimensional vectors using neural networks trained on billions of documents. Neural embeddings understand synonyms, context, and language nuances that TF-IDF misses. TF-IDF only detects shared vocabulary, making it a good approximation for keyword-heavy text but poor for paraphrases.

Embeddings enable: (1) Semantic search — find documents by meaning, not just keywords. (2) RAG (Retrieval Augmented Generation) — retrieve relevant context chunks for LLM prompts. (3) Clustering — group similar documents automatically. (4) Recommendation systems — suggest similar items. (5) Duplicate detection — find near-duplicate content. (6) Classification — train lightweight classifiers on top of embeddings.

OpenAI text-embedding-3-small ($0.02/1M tokens) is excellent for most use cases. text-embedding-3-large offers higher accuracy at 3x cost. Cohere Embed v3 excels at multilingual text. Sentence Transformers (all-MiniLM-L6-v2) runs locally for free — good for private data. For RAG applications, the embedding model should match your retrieval use case — some are optimized for query-document pairs (asymmetric search) vs document-document similarity.

RAG (Retrieval-Augmented Generation) augments LLM prompts with relevant context retrieved from a knowledge base. The process: (1) Embed all documents in your knowledge base and store vectors in a vector database (Pinecone, Qdrant, pgvector). (2) When a user asks a question, embed the query. (3) Find the most similar document chunks using cosine similarity. (4) Inject those chunks into the LLM prompt as context. This lets LLMs answer questions about private data without fine-tuning.