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feat: Full v2.1 upgrade with multi-algorithm learning and TensorCompress (v0.1.69)
## Multi-Algorithm Learning Engine - 9 algorithms: q-learning, sarsa, double-q, actor-critic, ppo, decision-transformer, monte-carlo, td-lambda, dqn - Task-specific configuration: - agent-routing: double-q (reduces bias) - error-avoidance: sarsa (conservative) - confidence-scoring: actor-critic (continuous) - trajectory-learning: decision-transformer (sequence) - context-ranking: ppo (stable) - memory-recall: td-lambda (traces) ## TensorCompress (10x Memory Savings) - 5 compression levels based on access frequency: - none (hot >0.8): 0% savings - half (warm >0.4): 50% savings - pq8 (cool >0.1): 87.5% savings - pq4 (cold >0.01): 93.75% savings - binary (archive ≤0.01): 96.9% savings - Auto-compression on session-end ## Pretrain Phases (11 total) - Phase 10: Multi-algorithm learning bootstrap - Phase 11: TensorCompress initialization ## Environment Variables (v2.1) - RUVECTOR_MULTI_ALGORITHM=true - RUVECTOR_DEFAULT_ALGORITHM=double-q - RUVECTOR_TENSOR_COMPRESS=true - RUVECTOR_AUTO_COMPRESS=true 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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rUv
parent
b177eb76ea
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5 changed files with 137 additions and 16 deletions
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@ -51,7 +51,7 @@
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"hooks": [
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{
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"type": "command",
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"command": "ruvector hooks pre-edit \"$TOOL_INPUT_file_path\""
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"command": "/usr/local/bin/ruvector-cli hooks pre-edit \"$TOOL_INPUT_file_path\""
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}
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]
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},
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@ -60,7 +60,7 @@
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"hooks": [
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{
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"type": "command",
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"command": "ruvector hooks pre-command \"$TOOL_INPUT_command\""
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"command": "/usr/local/bin/ruvector-cli hooks pre-command \"$TOOL_INPUT_command\""
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}
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]
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},
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@ -70,7 +70,7 @@
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{
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"type": "command",
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"timeout": 1000,
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"command": "ruvector hooks remember \"Reading: $TOOL_INPUT_file_path\" -t file_access --silent"
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"command": "/usr/local/bin/ruvector-cli hooks remember \"Reading: $TOOL_INPUT_file_path\" -t file_access --silent"
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}
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]
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},
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@ -80,7 +80,7 @@
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{
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"type": "command",
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"timeout": 1000,
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"command": "ruvector hooks remember \"Search: $TOOL_INPUT_pattern\" -t search_pattern --silent"
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"command": "/usr/local/bin/ruvector-cli hooks remember \"Search: $TOOL_INPUT_pattern\" -t search_pattern --silent"
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}
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]
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},
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@ -90,7 +90,7 @@
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{
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"type": "command",
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"timeout": 1000,
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"command": "ruvector hooks remember \"Agent: $TOOL_INPUT_subagent_type\" -t agent_spawn --silent"
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"command": "/usr/local/bin/ruvector-cli hooks remember \"Agent: $TOOL_INPUT_subagent_type\" -t agent_spawn --silent"
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}
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]
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}
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@ -101,7 +101,7 @@
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"hooks": [
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{
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"type": "command",
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"command": "ruvector hooks post-edit \"$TOOL_INPUT_file_path\""
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"command": "/usr/local/bin/ruvector-cli hooks post-edit \"$TOOL_INPUT_file_path\""
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}
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]
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},
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@ -110,7 +110,7 @@
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"hooks": [
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{
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"type": "command",
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"command": "ruvector hooks post-command \"$TOOL_INPUT_command\""
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"command": "/usr/local/bin/ruvector-cli hooks post-command \"$TOOL_INPUT_command\""
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}
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]
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}
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@ -120,7 +120,7 @@
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"hooks": [
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{
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"type": "command",
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"command": "ruvector hooks session-start"
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"command": "/usr/local/bin/ruvector-cli hooks session-start"
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}
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]
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}
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@ -130,7 +130,7 @@
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"hooks": [
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{
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"type": "command",
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"command": "ruvector hooks session-end"
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"command": "/usr/local/bin/ruvector-cli hooks session-end"
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}
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]
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}
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@ -18213,8 +18213,8 @@
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"total_memories": 51,
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"total_trajectories": 52,
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"total_errors": 0,
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"session_count": 4,
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"last_session": 1767191836
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"session_count": 5,
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"last_session": 1767194385
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},
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"engineStats": {
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"totalMemories": 51,
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@ -127,8 +127,8 @@
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"total_memories": 0,
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"total_trajectories": 0,
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"total_errors": 0,
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"session_count": 1,
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"last_session": 1767194227
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"session_count": 2,
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"last_session": 1767194641
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},
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"trajectories": []
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}
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@ -2634,6 +2634,25 @@ class Intelligence {
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// Ignore engine errors on session end
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}
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// Auto-compress patterns if enabled (v2.1)
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try {
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if (process.env.RUVECTOR_AUTO_COMPRESS === 'true' || process.env.RUVECTOR_TENSOR_COMPRESS === 'true') {
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const TensorCompressClass = require('../dist/core/tensor-compress').default;
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if (TensorCompressClass && this.data.compressedPatterns) {
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const compress = new TensorCompressClass({ autoCompress: false });
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compress.import(this.data.compressedPatterns);
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const stats = compress.recompressAll();
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this.data.compressedPatterns = compress.export();
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// Only log if significant savings
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if (stats.savingsPercent > 10 && stats.totalTensors > 5) {
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// Silently compress, no console output to avoid hook noise
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}
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}
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}
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} catch (e) {
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// Ignore compression errors on session end
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}
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// Save all data
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try {
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this.save();
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@ -2749,11 +2768,23 @@ hooksCmd.command('init')
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// Environment variables for intelligence (unless --minimal or --no-env)
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if (!opts.minimal && opts.env !== false) {
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settings.env = settings.env || {};
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// Core intelligence settings
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settings.env.RUVECTOR_INTELLIGENCE_ENABLED = settings.env.RUVECTOR_INTELLIGENCE_ENABLED || 'true';
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settings.env.RUVECTOR_LEARNING_RATE = settings.env.RUVECTOR_LEARNING_RATE || '0.1';
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settings.env.RUVECTOR_MEMORY_BACKEND = settings.env.RUVECTOR_MEMORY_BACKEND || 'rvlite';
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settings.env.INTELLIGENCE_MODE = settings.env.INTELLIGENCE_MODE || 'treatment';
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console.log(chalk.blue(' ✓ Environment variables configured'));
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// v2.0 capabilities
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settings.env.RUVECTOR_AST_ENABLED = settings.env.RUVECTOR_AST_ENABLED || 'true';
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settings.env.RUVECTOR_DIFF_EMBEDDINGS = settings.env.RUVECTOR_DIFF_EMBEDDINGS || 'true';
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settings.env.RUVECTOR_COVERAGE_ROUTING = settings.env.RUVECTOR_COVERAGE_ROUTING || 'true';
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settings.env.RUVECTOR_GRAPH_ALGORITHMS = settings.env.RUVECTOR_GRAPH_ALGORITHMS || 'true';
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settings.env.RUVECTOR_SECURITY_SCAN = settings.env.RUVECTOR_SECURITY_SCAN || 'true';
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// v2.1 learning & compression
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settings.env.RUVECTOR_MULTI_ALGORITHM = settings.env.RUVECTOR_MULTI_ALGORITHM || 'true';
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settings.env.RUVECTOR_DEFAULT_ALGORITHM = settings.env.RUVECTOR_DEFAULT_ALGORITHM || 'double-q';
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settings.env.RUVECTOR_TENSOR_COMPRESS = settings.env.RUVECTOR_TENSOR_COMPRESS || 'true';
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settings.env.RUVECTOR_AUTO_COMPRESS = settings.env.RUVECTOR_AUTO_COMPRESS || 'true';
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console.log(chalk.blue(' ✓ Environment variables configured (v2.1 with multi-algorithm learning)'));
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}
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// Permissions based on detected project type (unless --minimal or --no-permissions)
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@ -5107,10 +5138,98 @@ hooksCmd.command('pretrain')
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console.log(chalk.yellow(` ⚠ Graph analysis skipped: ${e.message}`));
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}
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// Phase 10: Initialize multi-algorithm learning engine
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console.log(chalk.yellow('\n🎯 Phase 10: Initializing multi-algorithm learning engine...\n'));
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try {
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if (loadLearningModules() && LearningEngineClass) {
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const engine = new LearningEngineClass();
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// Configure optimal algorithms for each task type based on repo analysis
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engine.configure('agent-routing', { algorithm: 'double-q', learningRate: 0.1, epsilon: 0.1 });
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engine.configure('error-avoidance', { algorithm: 'sarsa', learningRate: 0.05, epsilon: 0.05 });
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engine.configure('confidence-scoring', { algorithm: 'actor-critic', learningRate: 0.01 });
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engine.configure('trajectory-learning', { algorithm: 'decision-transformer', sequenceLength: 20 });
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engine.configure('context-ranking', { algorithm: 'ppo', clipRange: 0.2 });
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engine.configure('memory-recall', { algorithm: 'td-lambda', lambda: 0.8 });
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// Bootstrap with initial experiences from file patterns
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let bootstrapCount = 0;
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for (const [state, actions] of Object.entries(intel.data.patterns || {})) {
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for (const [action, value] of Object.entries(actions)) {
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if (value > 0.3) { // Only strong patterns
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engine.update('agent-routing', {
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state,
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action,
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reward: value,
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nextState: state,
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done: true
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});
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bootstrapCount++;
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}
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}
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}
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intel.data.learning = engine.export();
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stats.learningBootstrap = bootstrapCount;
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console.log(chalk.green(` ✓ Configured 6 task-specific algorithms`));
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console.log(chalk.green(` ✓ Bootstrapped with ${bootstrapCount} initial experiences`));
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console.log(chalk.dim(' Algorithms: double-q, sarsa, actor-critic, decision-transformer, ppo, td-lambda'));
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} else {
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console.log(chalk.dim(' ⏭️ LearningEngine not available'));
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}
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} catch (e) {
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console.log(chalk.yellow(` ⚠ Learning engine init skipped: ${e.message}`));
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}
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// Phase 11: Initialize TensorCompress for pattern storage
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console.log(chalk.yellow('\n📦 Phase 11: Initializing TensorCompress for efficient storage...\n'));
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try {
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if (loadLearningModules() && TensorCompressClass) {
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const compress = new TensorCompressClass({
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autoCompress: false,
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hotThreshold: 0.8,
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warmThreshold: 0.4,
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coolThreshold: 0.1,
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coldThreshold: 0.01
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});
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// Store any existing embeddings with compression
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let compressed = 0;
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if (intel.data.memories) {
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for (let i = 0; i < intel.data.memories.length; i++) {
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const mem = intel.data.memories[i];
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if (mem.embedding && Array.isArray(mem.embedding)) {
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compress.store(`memory_${i}`, mem.embedding, 'pq8');
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compressed++;
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}
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}
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}
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if (compressed > 0) {
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const compStats = compress.recompressAll();
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intel.data.compressedPatterns = compress.export();
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stats.compressed = compressed;
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stats.compressionSavings = compStats.savingsPercent;
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console.log(chalk.green(` ✓ Compressed ${compressed} embeddings`));
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console.log(chalk.green(` ✓ Memory savings: ${compStats.savingsPercent.toFixed(1)}%`));
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} else {
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intel.data.compressedPatterns = compress.export();
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console.log(chalk.green(` ✓ TensorCompress initialized (ready for future embeddings)`));
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}
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console.log(chalk.dim(' Levels: none (hot), half (warm), pq8 (cool), pq4 (cold), binary (archive)'));
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} else {
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console.log(chalk.dim(' ⏭️ TensorCompress not available'));
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}
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} catch (e) {
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console.log(chalk.yellow(` ⚠ TensorCompress init skipped: ${e.message}`));
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}
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// Save all learning data
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intel.data.pretrained = {
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date: new Date().toISOString(),
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version: '2.0',
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version: '2.1',
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stats: stats
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};
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intel.save();
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if (stats.coverage) console.log(` 🧪 Coverage: ${stats.coverage.lines.toFixed(1)}% lines`);
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if (stats.neural?.attention) console.log(` 🧠 10 attention mechanisms available`);
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if (stats.graph) console.log(` 🔗 ${stats.graph.communities} code communities detected`);
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if (stats.learningBootstrap) console.log(` 🎯 ${stats.learningBootstrap} learning experiences bootstrapped`);
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if (stats.compressionSavings) console.log(` 📦 ${stats.compressionSavings.toFixed(1)}% compression savings`);
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console.log(chalk.dim('\nThe intelligence layer will now provide better recommendations.'));
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});
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@ -1,6 +1,6 @@
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{
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"name": "ruvector",
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"version": "0.1.68",
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"version": "0.1.69",
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"description": "High-performance vector database for Node.js with automatic native/WASM fallback",
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"main": "dist/index.js",
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"types": "dist/index.d.ts",
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