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This article presents scrydb, a lightweight Python library that unifies lexical and semantic search capabilities directly within SQLite using FTS5 and vector extensions. By enabling hybrid retrieval and result reranking in a single database layer, it offers an efficient alternative to heavier infrastructure for agentic search and information retrieval tasks. The work provides practical benchmarks on latency and effectiveness trade-offs, demonstrating how standard database tools can be leveraged for advanced AI-driven data access without external dependencies.
SQLite is Enough. Lexical, Semantic, and Hybrid Search with scrydb
arXiv:2608.24060v1 Announce Type: cross Abstract: This work introduces scrydb, a Python library that enables lexical, semantic, and hybrid search within SQLite. For lexical search, scrydb leverages SQLite's full-text search extension FTS5. Semantic search builds on sqlite-vec, a SQLite extension for vector search. Furthermore, the library allows users to rerank and fuse retrieval results to combine both lexical and semantic approaches, providing a lightweight solution for downstream tasks in information retrieval (IR) or agentic search. We evaluate scrydb on various IR benchmark datasets and demonstrate its effectiveness in text retrieval based on keyword matching, semantic similarity, and rank fusion. In addition, we provide insights into query latency and the trade-off between efficiency and effectiveness. scrydb is available under the MIT license.
Title:SQLite is Enough. Lexical, Semantic, and Hybrid Search with scrydb
Abstract:This work introduces scrydb, a Python library that enables lexical, semantic, and hybrid search within SQLite. For lexical search, scrydb leverages SQLite's full-text search extension FTS5. Semantic search builds on sqlite-vec, a SQLite extension for vector search. Furthermore, the library allows users to rerank and fuse retrieval results to combine both lexical and semantic approaches, providing a lightweight solution for downstream tasks in information retrieval (IR) or agentic search. We evaluate scrydb on various IR benchmark datasets and demonstrate its effectiveness in text retrieval based on keyword matching, semantic similarity, and rank fusion. In addition, we provide insights into query latency and the trade-off between efficiency and effectiveness. scrydb is available under the MIT license.
Information Retrieval (cs.IR); Computation and Language (cs.CL); Databases (cs.DB)
arXiv:2608.24060 [cs.IR]
(or arXiv:2608.24060v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2608.24060 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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