blevesearch/bleve
Bleve
Bleve is a search indexing engine library written in Go, designed to provide full-text search and document retrieval capabilities for embedded application data. It functions as a framework for indexing structured or unstructured information, allowing developers to build searchable collections that support complex query logic and data analysis.
The engine distinguishes itself through a pluggable analysis pipeline that normalizes text before indexing, alongside support for vector similarity search to identify semantically related content. It utilizes finite-state transducer automata for efficient prefix and fuzzy matching, while employing term frequency-inverse document frequency scoring to rank results based on statistical relevance.
The library manages the full lifecycle of index data, including segmented disk persistence and periodic merging to maintain performance. It supports advanced retrieval requirements such as boolean logic, geographic proximity filtering, and custom sorting rules, providing the necessary tools to integrate search and autocomplete functionality directly into applications.
Features
- Search & Indexing Engines - Provides a high-performance indexing and retrieval engine written in Go for embedded application data.
- Full-Text Search Engines - Acts as a library for indexing and searching structured or unstructured data with support for complex query logic.
- Full Text Search - Integrates powerful full-text search capabilities into applications for large-scale text retrieval.
- Inverted Index Engines - Maps unique terms to document identifiers using an inverted index to facilitate rapid full-text retrieval.
- Document Retrieval Strategies - Provides a framework for managing, ranking, and querying large datasets with advanced retrieval features.
- Search and Indexing - Builds searchable indexes from structured or unstructured information for full-text retrieval.
- Search and Indexing - Executes complex queries including boolean logic, fuzzy matching, and geographic proximity filtering.
- Data Structures - Modern text indexing library.
- Database Drivers - Modern text indexing and search library.
- Search Engines - Modern text indexing and search library.
- Text Processing - Modern text indexing and search library.
- Vector Similarity Search - Supports vector similarity search to identify semantically related content using high-dimensional embeddings.
- Search Result Filtering - Calculates relevance scores and applies custom sorting rules to organize search results.
- Segmented Storage Architectures - Organizes index data into immutable segments on disk with periodic merging for optimized performance.
- Text Processing Pipelines - Provides a configurable pipeline for tokenization and normalization of raw text before indexing.
- Statistical Scoring Functions - Calculates document relevance using term frequency-inverse document frequency scoring to rank search results.
- Geospatial Data Structures - Organizes location-based information to enable geospatial proximity searches and filtering.
- Finite State Transducers - Uses compact finite-state transducer automata to enable efficient prefix and fuzzy matching during query execution.
