TF-IDF, short for Term Frequency–Inverse Document Frequency, plays a central role in Lexical search ranking by quantifying how important a word is within a document relative to the entire corpus. It helps distinguish meaningful terms from common or insignificant ones, ensuring that search results prioritize documents containing terms that best represent the query’s intent. The term frequency (TF) component measures how often a term appears in a specific document, while the inverse document frequency (IDF) reduces the weight of terms that appear too frequently across many documents, such as “the” or “data.” The resulting TF-IDF score provides a balanced metric that captures both relevance and distinctiveness.
In a Lexical search workflow, TF-IDF is used to calculate a numeric relevance score between the user’s query and each document in the corpus. For instance, if a developer searches “vector index optimization,” documents that frequently contain “vector” and “index” but rarely appear with those terms elsewhere will receive a higher TF-IDF score. This weighting ensures that documents with distinctive and contextually relevant terms rank higher, while overly generic documents are ranked lower. This is why TF-IDF remains foundational in search engines, despite newer models—its interpretability and simplicity make it both efficient and explainable.
When integrated with a vector database like Milvus, TF-IDF can serve as the first stage in a hybrid retrieval pipeline. Developers can use TF-IDF-based Lexical ranking to generate an initial set of candidate documents, then pass those candidates to Milvus for semantic re-ranking using vector similarity. This approach combines the precision of keyword matching with the contextual understanding of embeddings. The synergy between TF-IDF and Milvus ensures that results are not only relevant by text frequency but also meaningful by concept, making the hybrid model effective for applications like knowledge retrieval, document search, and chat-based question answering.