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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.
24 lines
592 B
JSON
24 lines
592 B
JSON
{
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"name": "vestige",
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"version": "1.6.0",
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"private": true,
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"description": "Cognitive memory for AI - MCP server with FSRS-6 spaced repetition",
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"author": "Sam Valladares",
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"license": "MIT OR Apache-2.0",
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"repository": {
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"type": "git",
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"url": "https://github.com/samvallad33/vestige"
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},
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"scripts": {
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"build:mcp": "cargo build --release --package vestige-mcp",
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"test": "cargo test --workspace",
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"lint": "cargo clippy -- -D warnings",
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"fmt": "cargo fmt"
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},
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"devDependencies": {
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"typescript": "^5.9.3"
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},
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"engines": {
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"node": ">=18"
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}
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}
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