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.
Features
- Information Retrieval Engines - Provides a high-performance toolkit for building, compressing, and querying inverted indexes for large-scale full-text search.
- Full-Text Inverted Indexes - Maps unique terms to lists of document identifiers to enable rapid full-text searching across massive document collections.
- Full Text Indexing - Constructs and optimizes compressed inverted indexes to enable fast and efficient keyword-based retrieval across massive datasets.
- Boolean Query Languages - Performs logical conjunction and disjunction operations on indexed document collections for precise term matching.
- Document Identifier Reordering - Improves index compression and retrieval performance by reordering document identifiers to optimize delta-encoded sequences.
- Horizontal Scaling - Distributes large datasets across multiple independent index partitions to manage memory constraints and parallelize search operations.
- Index Compression - Provides compressed index creation to reduce storage space and improve retrieval speed by applying quantization and compression settings.
- Inverted Index Builders - Parses raw data into structured inverted indexes with support for sharding and memory-efficient processing.
- Search Indexing Frameworks - Provides a library for constructing and optimizing compressed search indexes for academic research.
- Document Identifier Ordering - Improves compression ratios and search speed by reordering document identifiers within the index.
- Search Query Pipelines - Orchestrates the execution of search requests through index traversal and relevance scoring.
- Index Sharding - Partitions massive document collections into smaller subsets to enable effective scaling and memory management.
- Query Retrieval Algorithms - Processes search requests against indexed data to identify the most relevant document results.
- Search Ranking Algorithms - Scores and orders documents based on term frequency, rarity, and length for improved relevance.
- Large-Scale Index Sharding - Scales search performance by distributing massive document collections across multiple index partitions.
- Integer Identifier Compression - Reduces memory usage by encoding document identifiers with compact, variable-length bit-packing algorithms.
- Ranking - Accelerates retrieval by approximating relevance scores using compressed vector representations.
- Inverted Index Querying - Retrieves search results from inverted indexes for information retrieval tasks and performance measurement.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
