Posts tagged with #fts5

August 6, 2026

The First Real-Scale Evaluation: BM25 Against 657,867 Documents, from 0.170 to 0.366 After Fixing the Set

While the embedding run advances over 6.7 million chunks, we built the full-corpus FTS5 index (2.7 GiB) and ran the first real-scale BM25 evaluation. The v2 query set produced an MRR of 0.170; after fixing fake titles and ambiguous queries, v3 reached 0.366 and the partial hybrid smoke test 0.402. Along the way: a COUNT(*) that lies, 32 documents almost invisible to the index, and a token-pruning step that turned 21 hours of queries into 34 minutes without changing the metrics.

#dof-rag #evaluation #bm25 #fts5 #sqlite #vector-search #embedding

Joaquín Bravo ContrerasRead more

August 3, 2026

Storing 31 GB of Text in 3 GB (and Recovering Every Byte): the Storage Proof of Concept

Fourth installment of the benchmark: we built the compressed corpus on 10,000 real documents. sqlite-zstd compresses 10.9x with random access, chunks are stored as 110-byte recipes instead of text, the word-search index ended up 15x smaller than feared, and TurboQuant quantization ties full-vector quality. Everything fits in ~11 GB.

#dof-rag #storage #sqlite #zstd #fts5 #vector-search #benchmark

Joaquín Bravo ContrerasRead more