apache/lucene-solr
Lucene Solr
Dieses Projekt ist eine Volltext-Suchmaschine und Enterprise-Suchinfrastruktur, die für die Indizierung und Abfrage großer Dokumentenmengen entwickelt wurde. Es bietet ein umfassendes Framework für die Informationssuche mittels gerankter Ergebnisse und linguistischer Analyse.
Das System integriert hochdimensionale Vektor-Ähnlichkeitssuche für semantische Abfragen neben traditionellen Volltext-Funktionen. Es zeichnet sich durch Unterstützung für die Suche nach Geodaten, mehrsprachige Textverarbeitung und einen Suchvorschlags-Workflow aus, der fehlertolerante Abfragevervollständigung und Rechtschreibprüfung umfasst.
Die Plattform deckt ein breites Spektrum an Such- und Indizierungsfunktionen ab, einschließlich komplexer Abfrageausführung, Facetten-Aggregation und Ergebnisgruppierung. Sie verarbeitet Textanalysen durch Tokenisierung und Normalisierung und bietet spezialisierte Tools für Document-Joining, das Hervorheben von Suchtreffern sowie benutzerdefiniertes Scoring basierend auf Aktualität und Distanz.
Eine Python-Schnittstelle ist verfügbar, um Indizierungs- und Abfragefunktionen für externe Programmierumgebungen bereitzustellen.
Features
- Full Text Search - Provides a comprehensive engine for indexing and retrieving large sets of documents using ranked lexical search results.
- Full-Text Inverted Indexes - Implements inverted indexes that map individual words to documents to enable efficient full-text retrieval.
- Text Tokenization - Breaks raw text into searchable tokens using linguistic rules to improve search accuracy.
- Enterprise Search - Provides search infrastructure for indexing and retrieval across diverse datasets with support for faceting and grouping.
- Full-Text Search Engines - Provides a full-text search engine for indexing and retrieving text-based content across large datasets.
- Geospatial Search - Implements advanced querying of points and polygons to find documents based on geographic coordinates and spatial relations.
- Linguistic Analysis Frameworks - Provides a framework for applying language-specific processing such as tokenization and stemming during indexing.
- Multilingual Text Processing - Handles language-specific tokenization, stemming, and normalization to ensure accurate search results across different languages.
- Search Index Management - Provides tools for configuring, querying, and maintaining search indices through concurrent indexing and segment merging.
- Complex Search Querying - Implements advanced query mechanisms using a variety of search operators for retrieving filtered and aggregated datasets.
- HNSW Indexes - Uses Hierarchical Navigable Small World indexes for fast approximate nearest-neighbor search on high-dimensional vector data.
- Vector Similarity Search - Performs high-dimensional vector similarity search to enable semantic retrieval and AI-driven content discovery.
- Asynchronous Segment Merging - Organizes index data into immutable chunks and uses background merging to optimize search performance and reclaim space.
- Columnar Formats - Provides field data in a column-oriented format to enable fast sorting, faceting, and aggregation.
- Term Highlighting - Marks matching terms within documents using index offsets to show exactly where search terms appear.
- Faceted Search Engines - Provides backend logic for aggregating document counts into categories to enable faceted navigation.
- Spellchecking - Identifies misspelled words in a query and suggests alternatives based on the indexed content.
- Index Memory Management - Optimizes memory and disk consumption using sparse doc values and columnar batch indexing.
- Search Result Categorizers - Organizes search output using faceting and joins to allow users to narrow findings by specific attributes.
- Search Result Filtering - Refines search hits using numeric ranges and keyword filters to narrow down relevant content.
- Search Suggestions - Implements prefix-based search suggestions and typo-tolerant autocomplete to help users discover correct search terms.
- Ranked Query Completions - Offers typo-tolerant query completions as users type to help discover relevant search terms quickly.
- Vector Quantization - Compresses high-precision vectors into lower-bit representations to reduce memory overhead.
- Relevance Scoring - Modifies result rankings using static features, recency, and distance to calculate document relevance.
- Offset-Based Highlighting - Marks matching terms within documents using character position offsets from the index to highlight search hits.
- Databases and Storage - Search engine library and server for full-text indexing.
- Search Engines - Enterprise search platform built on top of Lucene.
