Query Model

When you type something into a search engine, more happens in the background than a simple text comparison. A query model — “query” being the term for a search request — converts your search term into a mathematical representation and compares it with documents represented in the same way. This allows the system to find not only pages that contain exactly your words, but also those that mean the same thing while using different terms. The model has learned to recognize meaning, not just count letters.

Why query models changed search

Older search engines worked on a simple principle: the more often your search term appeared in a document, the more relevant it was considered. This worked for simple queries but failed when it came to language. Someone searching for “heart attack” wouldn’t find a page that only mentioned “myocardial infarction” — even though it’s the same thing.

Query models solve this problem because they operate at the level of meaning. They were trained on vast amounts of text and search histories, learning in the process which queries match which content. This makes search more robust against typos, synonyms, and varying phrasings. For businesses, this is economically crucial: a search engine that delivers more relevant results keeps users on the platform longer.

From search term to hit: the matching process inside

At the heart of a query model is the conversion of text into so-called vectors — long lists of numbers that encode the meaning of a text. Imagine each text as a point in a vast coordinate system. Texts that are similar in content end up as points close to one another. Your search query becomes such a point, and the system looks for the documents whose points lie closest to it.

In practice, there are often two separate models: one for the query and one for the documents. Both learn to transform their texts into vectors in such a way that matching pairs end up close together and non-matching ones end up far apart. This approach is called a bi-encoder architecture. It is fast enough to search through millions of documents in milliseconds — which is essential for a search engine.

There are also more precise but slower variants that analyze the query and document jointly instead of encoding them separately. These so-called cross-encoders are often applied afterward to re-evaluate the most promising hits from the first step in greater detail. This creates a two-stage system combining speed and accuracy.

Query models in products and headlines

Google, Bing, and other search engines have been using query models for years. Google’s model called BERT, introduced in 2019, was a widely discussed step: for the first time, it understood the context of entire sentences, not just individual keywords. Since then, search has noticeably improved for millions of users, especially for long or colloquial queries.

Query models are also embedded in many products outside of classic search engines. Spotify uses similar principles to calculate song recommendations. Online stores suggest items that match a search query even when the exact term is missing from the product title. And in AI assistants such as chatbots, query models are used to retrieve the right passages from a knowledge base before a language model formulates the actual answer — this process is referred to as Retrieval-Augmented Generation.

In financial news, the term comes up when companies such as Cohere, Elasticsearch, or OpenAI introduce new versions of their search and embedding models. The performance of these models is compared on public leaderboards, so-called benchmarks — and improvements there are regarded as a gauge of how well a provider is positioned in the field of information retrieval.