queryvector

A queryvector is a vector representation of a user query used in information retrieval and search systems. It encodes the semantic content of the query in a continuous embedding space, enabling similarity-based ranking against document representations rather than relying solely on keyword matching.

Construction commonly begins with a raw text query and applies embedding techniques to produce a fixed-length vector. Methods range from traditional bag-of-words or TF-IDF weighted representations to modern neural encoders, such as sentence embeddings from transformer models (for example, BERT-derived or SBERT-based encoders). The resulting vector typically resides in a high-dimensional space, with its dimensions aligned to the chosen embedding model.

In practice, queryvectors are used in dense vector search. Documents are represented as vectors in the same space, and retrieval proceeds by measuring similarity (such as cosine similarity or inner product) between the queryvector and document vectors. This enables semantic matching, capturing relationships beyond exact word overlap. Implementations often use vector indices or approximate nearest neighbor libraries to support scalable, low-latency retrieval.

Variants of the approach include dual-encoder models, which separately encode queries and documents, and cross-encoder models, which jointly encode both to produce a relevance score but at higher computational cost. Normalization, query expansion, and re-ranking stages are common post-processing steps.

Advantages of using queryvectors include improved handling of synonyms and paraphrases and better performance on semantically related queries. Limitations involve dependency on embedding quality, potential latency, domain adaptation needs, and resource requirements for large-scale indexing. Queryvectors are a core component of modern neural information retrieval and semantic search systems.