Embeddings are dense vector representations that capture semantic meaning — transforming text, images, or data into fixed-dimensional numerical vectors where similar items are close together and dissimilar items are far apart, enabling semantic search, clustering, classification, and recommendation systems.

What Are Embeddings?

• Format: Fixed-length float arrays (e.g., 384, 768, 1536 dimensions).
• Property: Semantically similar inputs produce similar vectors.
• Distance: Cosine similarity or Euclidean distance measures relatedness.
• Models: E5, BGE, OpenAI ada-002, Cohere embed, Jina embeddings.

Types of Embeddings

• Word Embeddings: Word2Vec, GloVe — individual word vectors.
• Sentence Embeddings: E5, BGE, all-MiniLM — full sentence meaning.
• Document Embeddings: Longer text representations.
• Multimodal: CLIP — shared space for text and images.

Applications

• Semantic Search: Find relevant documents by meaning, not just keywords.
• RAG: Retrieve context for LLM generation.
• Clustering: Group similar items automatically.
• Classification: Use as features for downstream ML models.
• Recommendation: Find similar products, content, or users.

Top Models (2025)

• OpenAI text-embedding-3-large: 3072d, strong general purpose.
• BGE-M3: Multilingual, multi-granularity, open-source.
• E5-Mistral-7B: Instruction-tuned, state-of-the-art on MTEB.
• Jina Embeddings v3: 8K context, multilingual.
• Cohere Embed v3: Strong multilingual with compression.

Embeddings are the bridge between human language and machine computation — enabling AI systems to understand meaning, find relevance, and make connections across vast amounts of unstructured data.