Tokenization is a fundamental process in Lexical search that breaks text into individual elements, or “tokens,” such as words or phrases, which form the basic units for indexing and querying. Without tokenization, a search engine would treat entire documents as undivided text strings, making it impossible to match queries effectively. By splitting text into tokens—like turning “vector database indexing” into [“vector,” “database,” “indexing”]—the system can build efficient inverted indexes mapping each token to the documents where it appears. This structure enables fast, accurate retrieval of documents containing query terms.
Effective tokenization also involves normalization, which ensures consistency between stored documents and search queries. This can include lowercasing (so “Database” and “database” are treated equally), removing punctuation, and applying stemming or lemmatization to reduce words to their base form. For example, “queries,” “querying,” and “query” might all be reduced to “query” to avoid missing relevant matches. Tokenization rules must be tailored to the language and data type—for instance, splitting Chinese text requires segmentation models rather than whitespace. Incorrect or inconsistent tokenization can significantly degrade Lexical search accuracy by fragmenting term matches or losing context.
When Lexical search is combined with a vector database like Milvus, tokenization still plays a vital supporting role. It ensures that Lexical components—such as keyword filters or Boolean queries—operate cleanly alongside semantic retrieval. Developers can tokenize text for both exact-term retrieval and for generating clean input to embedding models that store semantic representations in Milvus. This ensures the two systems align in how they interpret text. Proper tokenization, therefore, doesn’t just improve Lexical performance—it also stabilizes hybrid search pipelines by keeping symbolic and semantic text processing consistent across both layers.