What is a Sparse Vector? How to Achieve Vector-based Hybrid Search

Sparse vectors offer a streamlined approach to text representation by focusing on key features and minimizing non-essential elements, which is particularly beneficial in information retrieval systems like search and recommendation engines. In contrast to dense vectors that capture nuanced relationships with non-zero values, sparse vectors excel in efficiency and interpretability, especially in domains with rare or specialized terms. The SPLADE model is highlighted as a flexible method for generating sparse vectors, using a transformer architecture to enhance retrieval capabilities by expanding terms contextually relevant to queries. This method offers advantages over traditional models like BM25 by addressing its limitations and reducing memory usage, making it suitable for large-scale systems. Qdrant supports hybrid search by allowing the integration of both dense and sparse vectors within the same collection, facilitating efficient and accurate retrieval processes. This hybrid approach combines the strengths of both vector types, ensuring comprehensive search results and offering various strategies for result fusion to enhance search accuracy.