What is Sentence Embeddings?
Sentence embeddings are vector representations that encode the semantic meaning of complete sentences into fixed-length numerical arrays. Unlike word embeddings that represent individual tokens, sentence embeddings capture the overall meaning of a sentence including word relationships, grammar, and compositional semantics. These embeddings enable comparing sentences by meaning, clustering similar sentences, or using sentences as units of retrieval in semantic search systems.
Creating effective sentence embeddings is more challenging than word embeddings because the meaning of a sentence emerges from complex interactions between words, not just from averaging word vectors. Modern approaches use transformer models like BERT, RoBERTa, or specialized sentence embedding models like Sentence-BERT, which are trained specifically to produce meaningful sentence-level representations. Training often uses contrastive learning where similar sentences are pulled together in embedding space while dissimilar ones are pushed apart.
Sentence embeddings are widely used in RAG systems and agent memory for chunking and retrieval at the sentence or paragraph level. They enable semantic search where queries and sentence-length passages are compared for meaning. Popular implementations include Sentence-Transformers library, OpenAI's embedding models, and various open-source alternatives. The quality of sentence embeddings significantly impacts retrieval quality, making the choice of embedding model an important consideration in system design.