What are Embeddings? - Definition & Meaning

Definition

Embeddings are dense numerical vectors that represent text, images, or other data in a continuous vector space. Similar items lie close together, allowing semantic similarity to be computed via distance metrics.

Technical explanation

Embeddings transform discrete symbols (words, pixels) into continuous vectors of typically 128 to 1536 dimensions. Word embeddings like Word2Vec and GloVe learn that semantically similar words lie close together. Modern sentence embeddings (Sentence-BERT, OpenAI Embeddings) encode entire sentences. Embeddings are obtained via neural networks where the embedding layer acts as a bottleneck. Cosine similarity or Euclidean distance measures similarity. Vector databases index embeddings for fast similarity search. Embeddings are the foundation for RAG, recommendation systems, and semantic search.

How AVARC Solutions applies this

AVARC Solutions uses embeddings in RAG systems for document retrieval, in recommendation systems for product and content matching, and in semantic search features. We integrate embedding APIs (OpenAI, Cohere, open-source models) and vector databases for scalable similarity search.

Practical examples

• A knowledge base storing documents as embeddings and matching user queries by semantic similarity for targeted RAG retrieval.
• A recommendation system comparing product and user embeddings to find "similar items" based on behavior and attributes.
• A chatbot system storing previous conversations as embeddings for retrieving relevant context on follow-up questions.

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