SQLite is Enough. Lexical, Semantic, and Hybrid Search with scrydb

arXiv:2608.24060 · cs.IR, cs.CL, cs.DB · Submitted 2026-08-25 · Read on arXiv

cs.IR, cs.CL, cs.DB

Submitted: 2026-08-25

Updated: 2026-08-25

Code: https://github.com/breuert/scrydb

Terminology

Embeddings — 34× in this paper · explained in 2 episodes
Quantization — 9× in this paper · explained in 12 episodes
Benchmark — 8× in this paper · explained in 2 episodes
Cosine Similarity — 6× in this paper · explained in 3 episodes
Hamming Distance — 6× in this paper · explained in 2 episodes
Reciprocal Rank — 3× in this paper
Core — 2× in this paper · explained in 2 episodes
Hierarchical Navigable Small World — 2× in this paper
Massive Text Embedding Benchmark — 2× in this paper
Reciprocal Rank Fusion — 2× in this paper
Transformer — 2× in this paper · explained in 2 episodes
Agentic Search — 1× in this paper · explained in 1 episode
Average Precision — 1× in this paper
Decoupling — 1× in this paper · explained in 2 episodes
Downstream Tasks — 1× in this paper · explained in 2 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
Representation Learning — 1× in this paper · explained in 2 episodes
Semantic Similarity — 1× in this paper · explained in 2 episodes
Trace — 1× in this paper · explained in 2 episodes

Sources

Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

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