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https://github.com/samvallad33/vestige.git
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495a88331f
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.
37 lines
673 B
JSON
37 lines
673 B
JSON
{
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"name": "vestige-mcp-server",
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"version": "1.6.0",
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"description": "Vestige MCP Server - AI Memory System for Claude and other assistants",
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"bin": {
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"vestige-mcp": "bin/vestige-mcp.js",
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"vestige": "bin/vestige.js"
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},
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"scripts": {
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"postinstall": "node scripts/postinstall.js"
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},
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"keywords": [
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"mcp",
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"claude",
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"ai",
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"memory",
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"vestige"
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],
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"author": "Sam Valladares",
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"license": "MIT",
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"repository": {
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"type": "git",
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"url": "git+https://github.com/samvallad33/vestige.git"
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},
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"engines": {
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"node": ">=18"
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},
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"os": [
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"darwin",
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"linux",
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"win32"
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],
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"cpu": [
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"x64",
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"arm64"
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]
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}
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