A modern indexing and search library for Go supporting text, numeric, geo-spatial, and vector data.
Bleve is a modern indexing and search library for Go that enables developers to add powerful search capabilities to their applications. It supports indexing of text, numeric, geo-spatial, and vector data, providing a comprehensive query language and hybrid search functionality. The library is designed to be embeddable, allowing search features to be integrated directly into Go applications without external dependencies.
Go developers building applications that require full-text search, faceted navigation, geo-spatial queries, or vector similarity search. It's particularly useful for projects needing an embeddable, self-contained search solution.
Developers choose Bleve for its native Go implementation, extensive feature set covering modern search needs (including vector search), and the ability to index arbitrary Go data structures. Its hybrid search capabilities and support for multiple scoring models make it a versatile alternative to external search services.
A modern text/numeric/geo-spatial/vector indexing library for go
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Supports a wide range of field types including text, numbers, dates, geopoints, and vectors, as explicitly listed in the README under 'Supported field types.'
Offers diverse query types such as term, phrase, fuzzy, geo-spatial, and vector similarity queries, enabling complex search scenarios directly from the features section.
Combines exact and semantic search with support for RRF and RSF score fusion, making it suitable for modern AI-enhanced applications as highlighted in the hybrid search feature.
Includes pre-built analyzers for over 30 languages, simplifying internationalization without custom setup, as noted in the text analysis section.
Advanced features like vector search or custom analyzers require significant configuration and deep understanding, as hinted by the 'intelligent defaults backed up by powerful configuration' philosophy, which can steepen the learning curve.
Compared to established solutions like Elasticsearch, Bleve has fewer third-party integrations, monitoring tools, and administrative UIs, increasing development overhead for comprehensive search systems.
As an embeddable library, it may face performance bottlenecks with very large datasets or high write throughput, lacking native distributed indexing features that dedicated search engines offer.