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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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2
packages/vestige-mcp-npm/package.json
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@ -1,6 +1,6 @@
{
"name": "vestige-mcp-server",
"version": "1.5.0",
"version": "1.6.0",
"description": "Vestige MCP Server - AI Memory System for Claude and other assistants",
"bin": {
"vestige-mcp": "bin/vestige-mcp.js",