Embedding models are algorithms that convert complex data like text, images, or user behavior into numerical vectors (arrays of numbers) that capture meaningful relationships in the data. These vectors, called embeddings, represent items in a lower-dimensional space where similar items are positioned closer together. For example, in natural language processing (NLP), words or sentences are transformed into vectors so that synonyms like "car" and "automobile" have similar vector representations. Embeddings enable machines to process unstructured data efficiently by translating it into a form that machine learning models can work with, such as calculating similarity or making predictions.
Embedding models work by learning patterns from large datasets during training. A common approach involves neural networks trained to adjust vector values so that related items produce similar embeddings. For instance, in Word2Vec—a classic text embedding model—the algorithm learns by predicting surrounding words in sentences. If "dog" often appears near "bark" or "leash," its embedding will be adjusted to reflect those associations. More advanced models like BERT use transformer architectures to generate context-aware embeddings. Instead of fixed word vectors, BERT considers the entire sentence, so the embedding for "bank" in "river bank" differs from "bank account." Training typically involves optimizing a loss function, such as minimizing the distance between related items (e.g., a query and its search result) while maximizing separation for unrelated ones.
Developers use embeddings in applications like search engines, recommendation systems, and clustering. For example, in e-commerce, product embeddings can power recommendations by identifying items with similar vector representations. A user who views a laptop might see related products like laptop bags or mice because their embeddings are nearby in the vector space. Embeddings also enable efficient similarity searches using libraries like FAISS or Annoy, which quickly find the closest vectors in large datasets. A practical example is grouping customer support tickets: by embedding ticket text, developers can cluster similar issues without manual tagging. Pre-trained models (e.g., OpenAI's text-embedding-3-small) simplify integration, while fine-tuning lets teams adapt embeddings to domain-specific tasks, like medical text analysis. The key advantage is transforming raw data into a structured format that algorithms can process at scale.