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feat: Vestige v1.6.0 — 6x storage reduction, neural reranking, instant startup

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Four internal optimizations for dramatically better performance:

1. F16 vector quantization (ScalarKind::F16 in USearch) — 2x storage savings
2. Matryoshka 256-dim truncation (768→256) — 3x embedding storage savings
3. Convex Combination fusion (0.3 keyword / 0.7 semantic) replacing RRF
4. Cross-encoder reranker (Jina Reranker v1 Turbo via fastembed TextRerank)

Combined: 6x vector storage reduction, ~20% better retrieval quality.
Cross-encoder loads in background — server starts instantly.
Old 768-dim embeddings auto-migrated on load.

614 tests pass, zero warnings.
This commit is contained in:
Sam Valladares 2026-02-19 01:09:39 -06:00
parent 5b7d22d427
commit 495a88331f
19 changed files with 195 additions and 98 deletions
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@ -3655,7 +3655,7 @@ checksum = "0b928f33d975fc6ad9f86c8f283853ad26bdd5b10b7f1542aa2fa15e2289105a"
[[package]]
name = "vestige-core"
version = "1.5.0"
version = "1.6.0"
dependencies = [
"chrono",
"directories",
@ -3689,7 +3689,7 @@ dependencies = [
[[package]]
name = "vestige-mcp"
version = "1.5.0"
version = "1.6.0"
dependencies = [
"anyhow",
"axum",