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feat(ruvector-cli): integrate tiny-dancer router (train/route/info)
npx ruvector tiny-dancer train <draco.json> --out model.safetensors trains a FastGRNN cost-optimal router from a DRACO {embedding,scores} dataset; `route` loads + routes; `info` reports availability. @ruvector/tiny-dancer added as an optionalDependency (native binary, lazy-required with install hint). Bump 0.2.31. Co-Authored-By: claude-flow <ruv@ruv.net>
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‎npm/packages/ruvector/bin/cli.js‎

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@@ -1875,6 +1875,97 @@ program
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console.log('');
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});
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// =============================================================================
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// Tiny Dancer - cost-optimal FastGRNN model router (train + route)
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// =============================================================================
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const tinyDancer = program
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.command('tiny-dancer')
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.alias('td')
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.description('Cost-optimal FastGRNN model router — train from a DRACO dataset and route with it (requires @ruvector/tiny-dancer)');
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function loadTinyDancer() {
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try {
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return require('@ruvector/tiny-dancer');
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} catch (e) {
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console.error(chalk.red('\n This command requires @ruvector/tiny-dancer'));
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console.error(chalk.yellow(' Install it: npm install @ruvector/tiny-dancer'));
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console.error(chalk.dim(' (native router; ships for linux/macos/windows incl. musl + arm64)\n'));
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process.exit(1);
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}
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}
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tinyDancer
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.command('train <draco>')
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.description('Train a FastGRNN router from a DRACO dataset (rows of {embedding, scores}) into a .safetensors model')
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.requiredOption('--out <path>', 'Output .safetensors model path')
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.option('--input-dim <n>', 'Embedding/feature dimension (default: inferred from the first row)')
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.option('--prices <json>', 'Price table as JSON or @file, e.g. \'{"haiku":1,"opus":15}\'')
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.option('--epochs <n>', 'Training epochs', '40')
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.option('--lr <n>', 'Learning rate', '0.05')
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.option('--hidden <n>', 'Hidden dimension', '12')
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.option('--tolerance <n>', 'Cheap-model "good enough" tolerance', '0.05')
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.action(async (draco, options) => {
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const td = loadTinyDancer();
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const parsed = JSON.parse(fs.readFileSync(draco, 'utf8'));
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const rows = Array.isArray(parsed) ? parsed : parsed.rows;
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const prices = options.prices
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? JSON.parse(options.prices.startsWith('@') ? fs.readFileSync(options.prices.slice(1), 'utf8') : options.prices)
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: (parsed.prices || {});
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if (!Array.isArray(rows) || rows.length === 0) {
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console.error(chalk.red(' DRACO file must contain rows of { embedding, scores }')); process.exit(1);
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}
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if (!prices || Object.keys(prices).length === 0) {
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console.error(chalk.red(' Provide a price table via --prices or a "prices" field in the file')); process.exit(1);
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}
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const inputDim = options.inputDim ? parseInt(options.inputDim, 10) : (rows[0].embedding || []).length;
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console.log(chalk.cyan(`\n Training FastGRNN router: ${rows.length} rows, dim ${inputDim}`));
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const res = await td.trainRouter(rows, prices, {
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outputPath: options.out,
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inputDim,
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hiddenDim: parseInt(options.hidden, 10),
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epochs: parseInt(options.epochs, 10),
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learningRate: parseFloat(options.lr),
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tolerance: parseFloat(options.tolerance),
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});
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console.log(chalk.green(` ✓ trained: acc=${res.trainAccuracy.toFixed(3)} val=${res.valAccuracy.toFixed(3)} loss=${res.trainLoss.toFixed(4)}`));
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console.log(chalk.white(` ✓ saved: ${res.modelPath} (${res.modelBytes} bytes, ${res.epochsRun} epochs)`));
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console.log(chalk.gray(` Load it: new Router({ modelPath: '${res.modelPath}' })\n`));
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});
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tinyDancer
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.command('route <model>')
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.description('Route a query through a trained model. --query: JSON embedding array; --candidates: JSON file of [{id, embedding}]')
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.requiredOption('--query <json>', 'Query embedding as a JSON array or @file')
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.requiredOption('--candidates <file>', 'Candidates JSON file: [{ id, embedding }]')
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.option('--threshold <n>', 'Confidence threshold', '0.85')
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.action(async (model, options) => {
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const td = loadTinyDancer();
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const queryEmbedding = JSON.parse(options.query.startsWith('@') ? fs.readFileSync(options.query.slice(1), 'utf8') : options.query);
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const candidates = JSON.parse(fs.readFileSync(options.candidates, 'utf8'));
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const router = new td.Router({ modelPath: model, confidenceThreshold: parseFloat(options.threshold) });
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const resp = await router.route({ queryEmbedding, candidates });
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console.log(chalk.cyan('\n Routing decisions (best first):'));
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for (const d of resp.decisions) {
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console.log(` ${chalk.white(d.candidateId)} conf=${d.confidence.toFixed(3)} light=${d.useLightweight} unc=${d.uncertainty.toFixed(3)}`);
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}
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console.log(chalk.gray(` inference ${resp.inferenceTimeUs}µs over ${resp.candidatesProcessed} candidates\n`));
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});
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tinyDancer
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.command('info')
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.description('Show tiny-dancer availability and version')
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.action(() => {
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try {
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const td = require('@ruvector/tiny-dancer');
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console.log(chalk.green(`\n @ruvector/tiny-dancer ${td.version()} — ${td.hello()}`));
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console.log(chalk.gray(' train: npx ruvector tiny-dancer train <draco.json> --out model.safetensors'));
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console.log(chalk.gray(' route: npx ruvector tiny-dancer route <model.safetensors> --query <emb> --candidates <file>\n'));
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} catch {
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console.log(chalk.yellow('\n @ruvector/tiny-dancer not installed. npm install @ruvector/tiny-dancer\n'));
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}
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});
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// =============================================================================
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// Server Commands - HTTP/gRPC server
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// =============================================================================

‎npm/packages/ruvector/package.json‎

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{
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"name": "ruvector",
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"version": "0.2.30",
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"description": "Self-learning vector database for Node.js — hybrid search, Graph RAG, FlashAttention-3, HNSW, 50+ attention mechanisms",
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"version": "0.2.31",
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"description": "Self-learning vector database for Node.js \u2014 hybrid search, Graph RAG, FlashAttention-3, HNSW, 50+ attention mechanisms",
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"main": "dist/index.js",
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"types": "dist/index.d.ts",
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"bin": {
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"ora": "^5.4.1"
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},
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"optionalDependencies": {
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"@ruvector/rvf": "^0.1.0"
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"@ruvector/rvf": "^0.1.0",
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"@ruvector/tiny-dancer": "^0.1.21"
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},
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"devDependencies": {
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"@types/node": "^20.10.5",

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