olivernn/lunr.js

Features

• Search and Indexing - Provides a full-text search engine for building searchable indexes in browser or Node.js environments.
• Client-Side Search Engines - Provides a lightweight search engine for fast, offline-capable keyword searching directly within web applications.
• Tokenization Pipelines - Utilizes a pipeline-based tokenization process to normalize text, stem words, and remove stop words before indexing.
• Client-Side Search Indices - Allows static websites to implement search by indexing content into portable JSON files.
• Inverted Index Engines - Employs an inverted index engine to map individual terms to documents for constant-time keyword retrieval.
• Index Serialization - Provides JSON serialization of search indexes to enable storage and distribution of pre-built search data.
• Full Text Search - Implements a full-text search system using specialized inverted indexing for fast keyword retrieval.
• Full-Text Search Engines - Provides a full-text search engine capable of querying indices across multiple languages with ranked results.
• Fuzzy Matching - Implements edit distance calculations to find documents despite typographical errors in search queries.
• In-Memory Search Indices - Creates an in-memory inverted index for fast keyword retrieval and ranked document matching.
• JSON Indexing - Supports the serialization of search indexes to and from JSON format for efficient state storage.
• Search and Indexing - Provides a full-text search engine for building searchable indexes in browser or Node.js environments.
• Keyword Search - Implements keyword search to retrieve and rank documents based on term frequency.
• Search-Index-Based Retrieval - Enables fast, client-side keyword searching of documents without requiring a backend server.
• Statistical Scoring Functions - Uses TF-IDF statistical scoring functions to calculate document relevance based on term frequency.
• Stop Word Filters - Includes a customizable stop-word filter to remove non-informative words from the indexing pipeline.
• Field-Scoped Indexing - Implements field-scoped indexing to allow targeted queries and specific weighting of different data types within documents.
• Index Serialization - Converts the internal search structure to and from JSON for easy storage and distribution of pre-built search data.
• Multilingual Text Processing - Provides specialized processing for different languages to handle stemming and normalization during indexing and search.
• Advanced Query Types - Supports advanced query types including fuzzy matching, wildcards, field scoping, and term boosting.
• Matching Algorithms - Implements matching algorithms that support wildcards and edit-distance based fuzzy matching.
• Dynamic Search Scopes - Supports dynamic search scopes by allowing queries to be limited to specific document fields.
• Multilingual Search Engines - Supports language-specific tokenization and stop-word removal to enable search across multiple languages.
• Search Result Ranking - Implements logic for determining result priority through term boosting and document weighting.
• Search and Databases - Provides client-side full-text search.

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