pisa-engine/pisa
Pisa
Pisa is a high-performance toolkit and framework designed for building, compressing, and querying inverted indexes to facilitate large-scale information retrieval. It provides the necessary infrastructure to parse raw data into structured indexes, enabling efficient full-text search across massive document collections.
The engine distinguishes itself through advanced optimization techniques that reduce memory footprints and accelerate retrieval speeds. It employs document identifier reordering to maximize the effectiveness of delta-encoded integer sequences and utilizes vector quantization to approximate relevance scores. These capabilities are supported by horizontal sharding, which allows for the distribution of large datasets across multiple partitions to manage memory constraints and parallelize search operations.
The framework encompasses a comprehensive suite of tools for index construction and query execution. It supports various retrieval algorithms, boolean logic operations, and ranking mechanisms based on term frequency and document characteristics. The project is structured as a command-line tool and library, providing a foundation for both production-scale search indexing and academic information retrieval research.
