Vect

VecT is a term used to describe a family of models and methods that blend vector embeddings with transformer architectures to enable scalable reasoning over large vector data. The aim is to harness semantic representations from vectors together with the sequence modeling capabilities of transformers, supporting tasks that require retrieving and integrating external knowledge.

VecT systems typically comprise three components: a vector encoder that converts inputs (text, images, or multimodal content) into dense embeddings; a transformer-based context model that processes the sequence and can operate over retrieved context; and a retrieval layer that queries a vector store or index to obtain relevant vectors. In operation, the model can perform cross-attention between input representations and retrieved vectors, allowing it to ground its outputs in external content.

Training approaches for VecT often combine supervised learning with contrastive objectives to align representations across modalities, along with standard sequence modeling losses. Fine-tuning can be task-specific, such as question answering, document summarization, or multimodal reasoning. Many VecT implementations leverage existing vector databases and libraries for scalable similarity search.

Applications include information retrieval, retrieval-augmented generation, open-domain question answering, and multimodal search and recommendation. The approach addresses limits of fixed-context models by enabling dynamic access to external knowledge without prohibitively long input sequences.

Limitations involve computational overhead, dependency on vector quality and indexing, latency considerations, and biases in embedding spaces. See also transformer, vector database, retrieval-augmented generation, and cross-attention.