Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
This commit is contained in:
ruv
393
vendor/ruvector/.claude/intelligence/pretrain.js
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393
vendor/ruvector/.claude/intelligence/pretrain.js
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#!/usr/bin/env node
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/**
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* Pretrain Intelligence System from memory.db
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*
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* Extracts learned patterns from existing swarm memory:
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* - Command success/failure patterns → Q-Table
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* - Agent assignments → Neural Router training
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* - File edit history → Vector Memory
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* - Coordination patterns → Swarm Graph
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*/
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import Database from 'better-sqlite3';
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import { readFileSync, writeFileSync, existsSync, mkdirSync } from 'fs';
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import { join, dirname, extname, basename } from 'path';
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import { fileURLToPath } from 'url';
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import { createHash } from 'crypto';
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const __dirname = dirname(fileURLToPath(import.meta.url));
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const DATA_DIR = join(__dirname, 'data');
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const MEMORY_DB = '/workspaces/ruvector/.swarm/memory.db';
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// Ensure data directory exists
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if (!existsSync(DATA_DIR)) mkdirSync(DATA_DIR, { recursive: true });
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/**
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* Text to embedding (same as in index.js)
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*/
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function textToEmbedding(text, dims = 128) {
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const embedding = new Float32Array(dims).fill(0);
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const normalized = text.toLowerCase().replace(/[^a-z0-9\s]/g, ' ');
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const words = normalized.split(/\s+/).filter(w => w.length > 1);
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const wordFreq = {};
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for (const word of words) {
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wordFreq[word] = (wordFreq[word] || 0) + 1;
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}
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for (const [word, freq] of Object.entries(wordFreq)) {
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const hash = createHash('sha256').update(word).digest();
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const idfWeight = 1 / Math.log(1 + freq);
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for (let i = 0; i < dims; i++) {
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const byteIdx = i % hash.length;
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const val = ((hash[byteIdx] & 0xFF) / 127.5) - 1;
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embedding[i] += val * idfWeight;
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}
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}
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const magnitude = Math.sqrt(embedding.reduce((sum, v) => sum + v * v, 0));
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if (magnitude > 0) {
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for (let i = 0; i < dims; i++) embedding[i] /= magnitude;
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}
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return Array.from(embedding);
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}
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/**
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* Main pretraining function
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*/
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async function pretrain() {
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console.log('🧠 RuVector Intelligence Pretraining');
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console.log('=====================================\n');
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if (!existsSync(MEMORY_DB)) {
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console.error('❌ Memory database not found:', MEMORY_DB);
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process.exit(1);
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}
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const db = new Database(MEMORY_DB, { readonly: true });
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const stats = { commands: 0, agents: 0, files: 0, patterns: 0, coordination: 0 };
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// ========== 1. Extract Command Patterns → Q-Table ==========
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console.log('📊 Extracting command patterns...');
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const qTable = {};
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const trajectories = [];
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const commands = db.prepare(`
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SELECT key, value FROM memory_entries
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WHERE namespace = 'command-history'
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ORDER BY created_at DESC
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LIMIT 5000
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`).all();
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for (const row of commands) {
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try {
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const data = JSON.parse(row.value);
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const cmd = data.command || '';
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const success = data.success === true || data.exitCode === '0';
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// Classify command type
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let cmdType = 'other';
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if (cmd.startsWith('cargo')) cmdType = 'cargo';
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else if (cmd.startsWith('npm')) cmdType = 'npm';
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else if (cmd.startsWith('git')) cmdType = 'git';
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else if (cmd.startsWith('wasm-pack')) cmdType = 'wasm';
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else if (cmd.includes('test')) cmdType = 'test';
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else if (cmd.includes('build')) cmdType = 'build';
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// Detect context from command
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let context = 'general';
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if (cmd.includes('rvlite')) context = 'rvlite';
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else if (cmd.includes('ruvector-core')) context = 'ruvector-core';
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else if (cmd.includes('ruvector-graph')) context = 'ruvector-graph';
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else if (cmd.includes('wasm')) context = 'wasm';
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else if (cmd.includes('postgres')) context = 'postgres';
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const state = `${cmdType}_in_${context}`;
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const action = success ? 'command-succeeded' : 'command-failed';
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const reward = success ? 1.0 : -0.5;
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// Update Q-table with strong regularization to prevent overfitting
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if (!qTable[state]) qTable[state] = { 'command-succeeded': 0, 'command-failed': 0 };
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const stateCount = (qTable[state]._count || 0) + 1;
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qTable[state]._count = stateCount;
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// Decaying learning rate: starts at 0.3, decays to 0.01
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const learningRate = Math.max(0.01, 0.3 / Math.sqrt(stateCount));
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const currentQ = qTable[state][action] || 0;
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// Update with capped value (max 0.8) to prevent overconfidence
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const newQ = currentQ + learningRate * (reward - currentQ);
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qTable[state][action] = Math.min(0.8, Math.max(-0.5, newQ));
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// Record trajectory
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trajectories.push({
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id: `pretrain-cmd-${stats.commands}`,
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state,
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action,
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outcome: cmd.slice(0, 100),
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reward,
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timestamp: data.timestamp || new Date().toISOString()
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});
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stats.commands++;
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} catch (e) { /* skip malformed */ }
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}
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console.log(` ✅ Processed ${stats.commands} commands`);
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// ========== 2. Extract Agent Assignments → Q-Table ==========
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console.log('🤖 Extracting agent assignments...');
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const agentAssignments = db.prepare(`
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SELECT key, value FROM memory_entries
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WHERE namespace = 'agent-assignments'
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ORDER BY created_at DESC
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LIMIT 1000
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`).all();
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for (const row of agentAssignments) {
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try {
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const data = JSON.parse(row.value);
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const file = data.file || '';
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const ext = extname(file).slice(1) || 'unknown';
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const agentType = data.type || 'coder';
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const recommended = data.recommended === true;
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// Extract crate if applicable
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const crateMatch = file.match(/crates\/([^/]+)/);
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const crate = crateMatch ? crateMatch[1] : 'project';
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const state = `edit_${ext}_in_${crate}`;
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const action = agentType;
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const reward = recommended ? 1.0 : 0.5;
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// Anti-overfitting: cap Q-values and use count-based decay
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if (!qTable[state]) qTable[state] = {};
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const stateCount = (qTable[state]._count || 0) + 1;
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qTable[state]._count = stateCount;
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const learningRate = Math.max(0.01, 0.2 / Math.sqrt(stateCount));
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const currentQ = qTable[state][action] || 0;
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qTable[state][action] = Math.min(0.75, currentQ + learningRate * (reward - currentQ));
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trajectories.push({
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id: `pretrain-agent-${stats.agents}`,
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state,
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action,
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outcome: `recommended for ${basename(file)}`,
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reward,
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timestamp: new Date().toISOString()
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});
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stats.agents++;
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} catch (e) { /* skip */ }
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}
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console.log(` ✅ Processed ${stats.agents} agent assignments`);
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// ========== 3. Extract File History → Vector Memory ==========
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console.log('📁 Extracting file edit history...');
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const memories = [];
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const fileHistory = db.prepare(`
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SELECT key, value FROM memory_entries
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WHERE namespace = 'file-history'
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ORDER BY created_at DESC
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LIMIT 2000
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`).all();
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for (const row of fileHistory) {
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try {
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const data = JSON.parse(row.value);
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const file = data.file || '';
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const ext = extname(file).slice(1);
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const crateMatch = file.match(/crates\/([^/]+)/);
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const crate = crateMatch ? crateMatch[1] : null;
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const content = `edit ${ext} file ${basename(file)} in ${crate || 'project'}`;
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memories.push({
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id: `pretrain-file-${stats.files}`,
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type: 'edit',
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content,
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embedding: textToEmbedding(content),
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metadata: {
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file,
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crate,
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ext,
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timestamp: data.timestamp || new Date().toISOString()
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}
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});
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stats.files++;
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} catch (e) { /* skip */ }
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}
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console.log(` ✅ Processed ${stats.files} file edits`);
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// ========== 4. Extract Patterns → Vector Memory ==========
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console.log('🧩 Extracting reasoning patterns...');
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const patterns = db.prepare(`
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SELECT id, type, pattern_data, confidence FROM patterns
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ORDER BY confidence DESC
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LIMIT 100
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`).all();
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for (const row of patterns) {
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try {
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const data = JSON.parse(row.pattern_data);
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const content = data.content || data.title || JSON.stringify(data).slice(0, 200);
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memories.push({
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id: `pretrain-pattern-${stats.patterns}`,
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type: 'pattern',
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content,
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embedding: textToEmbedding(content),
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metadata: {
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patternId: row.id,
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patternType: row.type,
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confidence: row.confidence,
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timestamp: new Date().toISOString()
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}
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});
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stats.patterns++;
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} catch (e) { /* skip */ }
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}
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console.log(` ✅ Processed ${stats.patterns} patterns`);
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// ========== 5. Extract Coordination → Swarm Graph ==========
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console.log('🔗 Building swarm coordination graph...');
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const nodes = {};
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const edges = {};
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// Extract unique agents from assignments
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const agents = new Set();
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for (const row of agentAssignments) {
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try {
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const data = JSON.parse(row.value);
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if (data.type) agents.add(data.type);
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} catch (e) { /* skip */ }
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}
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// Create nodes for each agent type
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const agentCapabilities = {
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'coder': ['rust', 'typescript', 'implementation'],
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'technical-writer': ['documentation', 'markdown'],
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'reviewer': ['code-review', 'security'],
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'tester': ['unit-test', 'integration'],
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'general-developer': ['general', 'debugging'],
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'rust-developer': ['rust', 'cargo', 'wasm'],
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'ml-developer': ['gnn', 'attention', 'neural']
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};
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for (const agent of agents) {
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nodes[agent] = {
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type: agent,
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capabilities: agentCapabilities[agent] || [agent],
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load: 0,
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active: true
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};
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stats.coordination++;
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}
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// Create edges based on common file edits (simplified)
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const agentFiles = {};
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for (const row of agentAssignments) {
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try {
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const data = JSON.parse(row.value);
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const agent = data.type;
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const file = data.file;
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if (!agentFiles[agent]) agentFiles[agent] = [];
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agentFiles[agent].push(file);
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} catch (e) { /* skip */ }
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}
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// Connect agents that work on similar files
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const agentList = Object.keys(agentFiles);
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for (let i = 0; i < agentList.length; i++) {
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for (let j = i + 1; j < agentList.length; j++) {
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const a1 = agentList[i];
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const a2 = agentList[j];
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const files1 = new Set(agentFiles[a1].map(f => dirname(f)));
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const files2 = new Set(agentFiles[a2].map(f => dirname(f)));
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// Count overlapping directories
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let overlap = 0;
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for (const dir of files1) {
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if (files2.has(dir)) overlap++;
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}
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if (overlap > 0) {
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edges[`${a1}:${a2}`] = { weight: overlap, interactions: overlap };
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}
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}
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}
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console.log(` ✅ Built graph with ${Object.keys(nodes).length} agents, ${Object.keys(edges).length} edges`);
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// ========== 6. Save All Data ==========
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console.log('\n💾 Saving pretrained data...');
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// Save Q-Table (patterns.json)
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writeFileSync(
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join(DATA_DIR, 'patterns.json'),
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JSON.stringify(qTable, null, 2)
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);
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console.log(` ✅ Q-Table: ${Object.keys(qTable).length} states`);
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// Save Trajectories
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writeFileSync(
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join(DATA_DIR, 'trajectories.json'),
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JSON.stringify(trajectories.slice(-1000), null, 2)
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);
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console.log(` ✅ Trajectories: ${Math.min(trajectories.length, 1000)} entries`);
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// Save Memories
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writeFileSync(
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join(DATA_DIR, 'memory.json'),
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JSON.stringify(memories, null, 2)
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);
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console.log(` ✅ Vector Memory: ${memories.length} entries`);
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// Save Swarm Graph
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writeFileSync(
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join(DATA_DIR, 'coordination-graph.json'),
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JSON.stringify({ nodes, edges, lastUpdated: new Date().toISOString() }, null, 2)
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);
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console.log(` ✅ Swarm Graph: ${Object.keys(nodes).length} nodes`);
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// Save Swarm State
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writeFileSync(
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join(DATA_DIR, 'swarm-state.json'),
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JSON.stringify({
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tasks: [],
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optimizations: 0,
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pretrained: true,
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pretrainedAt: new Date().toISOString(),
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stats
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}, null, 2)
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);
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db.close();
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// ========== Summary ==========
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console.log('\n✅ Pretraining Complete!');
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console.log('========================');
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console.log(` Commands processed: ${stats.commands}`);
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console.log(` Agent assignments: ${stats.agents}`);
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console.log(` File edits: ${stats.files}`);
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console.log(` Patterns: ${stats.patterns}`);
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console.log(` Swarm nodes: ${Object.keys(nodes).length}`);
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console.log(` Total Q-states: ${Object.keys(qTable).length}`);
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console.log(` Total memories: ${memories.length}`);
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console.log('\n🧠 Intelligence system is now pretrained!');
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}
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pretrain().catch(console.error);
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