QBit is a new column type added to ClickHouse for storing floats as bit planes, which enhances vector search capabilities. It allows users to adjust precision and performance at query time without needing to decide upfront on parameters. This flexibility is key, as choosing the wrong settings can waste resources and complicate adjustments later. The article highlights the importance of vector search in various applications, including recommendations and retrieval-augmented generation, while pointing out that specialized databases often struggle with structured data. ClickHouse already offered brute-force vector search and approximate nearest neighbor (ANN) methods like HNSW. However, these methods require upfront decisions about parameters and can suffer from memory constraints. QBit addresses these issues by storing floating-point numbers as bit planes, enabling the system to read only the necessary bits during a search. This avoids the downsides of traditional quantization, such as duplicated data and irreversible precision loss, while also eliminating the memory bottlenecks associated with HNSW. The article illustrates how QBit can be implemented using a sample dataset of fruits and animals. By using a table that employs the QBit type, users can conduct searches that dynamically balance accuracy and speed. While QBit improves the speed of vector searches, it still operates within a computational complexity of O(n), meaning that for small datasets, HNSW may still be the fastest option. The comparison table succinctly outlines the trade-offs between brute-force, HNSW, and QBit in terms of precision, speed, and other considerations.