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Why semantic-search quality depends on the model, language, and corpus — and how Dzen Chat lets you run a local model or switch providers.

Choosing an embedding model determines which documents search finds before a language model begins writing an answer. A general-purpose model offered by a large provider is convenient for getting started. But it is not necessarily the best at understanding Russian, the language of a particular country, specialist vocabulary, or a mixed corpus of instructions, contracts, and code.

For example, a visitor writes, “When will my purchase arrive?”, while the right help-centre section is called “Delivery times and methods”. Keyword search may miss that connection. Semantic search can find it — but only if the model maps these particular phrasings well enough.

An embedding is a numerical vector through which a model represents the meaning of a short piece of text. Phrases with similar meanings receive nearby vectors. That lets a question about delivery match a section about timeframes even when they share almost no words.

A model has at least four important properties: the languages it was trained and evaluated on; its ability to distinguish similar but different intents; the length of the text it can process; and its vector size and speed. A model trained specifically on texts and queries in the relevant language can map its phrasing, morphology, and shades of meaning more accurately than a generic model from an external provider.

The same applies to models for law, medicine, code, or another field: they can make finer distinctions between similar specialist concepts.

This matters especially for Russian-language and multilingual knowledge bases. You can compare a general external model with a specialised model trained for the relevant language group and see a practical difference: the right section appears in the top results more often, while a similar but wrong page ranks lower. That increases the chance that an answer is grounded in the right source.

Embedding quality affects not the wording of an answer, but which evidence reaches its context in the first place.