Indexeer

Indexeer is a software framework designed to build and operate scalable text indexing and search systems. It provides ingestion pipelines, text extraction, normalization, and index construction to enable fast retrieval over large volumes of data. The framework emphasizes modularity and pluggable components, supporting diverse data sources and data formats.

The core concept behind Indexeer is an inverted index, which maps terms to postings lists that identify the documents containing each term. Documents are stored with metadata, and terms are tokenized and normalized using language-aware analyzers. Optional steps include stop-word removal, stemming or lemmatization, and phrase or proximity handling. A separate ranking component evaluates candidate results using methods such as BM25, vector-space models, or learned ranking models.

Architecturally, Indexeer typically comprises an indexer, a document store, a query processor, and a ranking module. It supports incremental indexing to accommodate near-real-time updates and maintains statistics like term frequency and document frequency to inform ranking. The system is designed for distributed deployment, with support for data sharding, replication, fault tolerance, and scalable storage backends. It exposes APIs (commonly REST or gRPC) and allows pluggable components for analyzers, storage, and ranking.

Common use cases include enterprise search, e-commerce product search, digital libraries, and log analytics. Effective use requires attention to data quality, privacy and security considerations, indexing latency, and resource management to preserve result freshness and relevance.

See also: information retrieval, inverted index, search engine, Lucene, Elasticsearch, BM25.