What is Document Embeddings?
Document embeddings are vector representations that encode the semantic content and meaning of entire documents into fixed-length numerical arrays. These embeddings are designed to capture the overall topic, themes, and concepts in a document, positioning semantically similar documents near each other in the embedding space. They enable efficient semantic search, document clustering, and similarity comparison across large document collections.
Creating effective document embeddings presents challenges beyond simple sentence embeddings because documents can be lengthy and contain multiple topics or themes. Various approaches exist: simple methods like averaging sentence embeddings or taking the embedding of the document's concatenated text, and more sophisticated techniques that use specialized document-level encoders or hierarchical processing. The optimal approach depends on document length, structure, and the intended use case.
In RAG systems and agent memory, document embeddings (or more commonly, embeddings of document chunks) serve as the foundation for semantic retrieval. When a user query is embedded, the system searches for documents with similar embeddings to find relevant content. The quality of document embeddings directly impacts retrieval performance, making the choice of embedding model and document preprocessing strategy critical decisions in system design. Modern embedding models like those from OpenAI, Cohere, and open-source alternatives provide increasingly sophisticated document-level representations.