indexingsanan

Indexingsanan is a term used in information science to describe a modular, multi-layer indexing framework designed to improve retrieval performance on large-scale text collections. It combines lexical, positional, and semantic indexing into a single coherent system, enabling fast phrase and similarity searches while remaining adaptable to updates.

Architecture: The core components typically include a lexical inverted index that maps terms to document identifiers, a positional or phrase index that records term positions for exact and near-phrase queries, and a semantic index built from embeddings or vector representations. A ranking module blends signals from these layers, often with tunable weights, and a metadata layer tracks versioning to support incremental indexing.

Operation: During indexing, documents are parsed to extract tokens, positions, and features; each layer is updated incrementally. For queries, a planner retrieves candidates from one or more indices and scores them with a hybrid function that integrates lexical similarity, phrase compatibility, and embedding-based semantic similarity. The final ranking merges results and applies filtering and normalization.

Applications: Indexingsanan is used in digital libraries, enterprise search, e-commerce catalogs, and code search environments, where robust phrase querying and semantic matching are valuable and data volumes are large.

Advantages and limitations: Potential benefits include higher recall for multi-term queries, resilience to typographical errors, and faster access through specialized indexes. Limitations include implementation complexity, the need for careful tuning of component weights, and higher storage and maintenance costs compared with single-index approaches.

See also: inverted index, phrase index, semantic search, vector space model.