Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
This commit is contained in:
ruv
524
vendor/ruvector/.claude/intelligence/pretrain-v2.js
vendored
Normal file
524
vendor/ruvector/.claude/intelligence/pretrain-v2.js
vendored
Normal file
@@ -0,0 +1,524 @@
|
||||
#!/usr/bin/env node
|
||||
/**
|
||||
* Pretrain Intelligence System v2 - Enhanced with all v2 features
|
||||
*
|
||||
* Improvements over v1:
|
||||
* - Uses ALL available data (no arbitrary limits)
|
||||
* - Bootstraps Confidence Calibration from performance-metrics
|
||||
* - Adds Pattern Decay timestamps to Q-table
|
||||
* - Identifies Uncertain States for Active Learning
|
||||
* - Prepares A/B Testing baseline metrics
|
||||
*/
|
||||
|
||||
import Database from 'better-sqlite3';
|
||||
import { readFileSync, writeFileSync, existsSync, mkdirSync } from 'fs';
|
||||
import { join, dirname, extname, basename } from 'path';
|
||||
import { fileURLToPath } from 'url';
|
||||
import { createHash } from 'crypto';
|
||||
|
||||
const __dirname = dirname(fileURLToPath(import.meta.url));
|
||||
const DATA_DIR = join(__dirname, 'data');
|
||||
const MEMORY_DB = '/workspaces/ruvector/.swarm/memory.db';
|
||||
|
||||
// Ensure data directory exists
|
||||
if (!existsSync(DATA_DIR)) mkdirSync(DATA_DIR, { recursive: true });
|
||||
|
||||
/**
|
||||
* Text to embedding (same as in index.js)
|
||||
*/
|
||||
function textToEmbedding(text, dims = 128) {
|
||||
const embedding = new Float32Array(dims).fill(0);
|
||||
const normalized = text.toLowerCase().replace(/[^a-z0-9\s]/g, ' ');
|
||||
const words = normalized.split(/\s+/).filter(w => w.length > 1);
|
||||
|
||||
const wordFreq = {};
|
||||
for (const word of words) {
|
||||
wordFreq[word] = (wordFreq[word] || 0) + 1;
|
||||
}
|
||||
|
||||
for (const [word, freq] of Object.entries(wordFreq)) {
|
||||
const hash = createHash('sha256').update(word).digest();
|
||||
const idfWeight = 1 / Math.log(1 + freq);
|
||||
for (let i = 0; i < dims; i++) {
|
||||
const byteIdx = i % hash.length;
|
||||
const val = ((hash[byteIdx] & 0xFF) / 127.5) - 1;
|
||||
embedding[i] += val * idfWeight;
|
||||
}
|
||||
}
|
||||
|
||||
const magnitude = Math.sqrt(embedding.reduce((sum, v) => sum + v * v, 0));
|
||||
if (magnitude > 0) {
|
||||
for (let i = 0; i < dims; i++) embedding[i] /= magnitude;
|
||||
}
|
||||
|
||||
return Array.from(embedding);
|
||||
}
|
||||
|
||||
/**
|
||||
* Main pretraining function
|
||||
*/
|
||||
async function pretrain() {
|
||||
console.log('🧠 RuVector Intelligence Pretraining v2');
|
||||
console.log('========================================\n');
|
||||
|
||||
if (!existsSync(MEMORY_DB)) {
|
||||
console.error('❌ Memory database not found:', MEMORY_DB);
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
const db = new Database(MEMORY_DB, { readonly: true });
|
||||
const stats = { commands: 0, agents: 0, files: 0, patterns: 0, coordination: 0, calibration: 0 };
|
||||
|
||||
// ========== 1. Extract Command Patterns → Q-Table with Decay Metadata ==========
|
||||
console.log('📊 Extracting command patterns (ALL data)...');
|
||||
|
||||
const qTable = {};
|
||||
const trajectories = [];
|
||||
|
||||
// Get ALL commands (no limit)
|
||||
const commands = db.prepare(`
|
||||
SELECT key, value, created_at FROM memory_entries
|
||||
WHERE namespace = 'command-history'
|
||||
ORDER BY created_at DESC
|
||||
`).all();
|
||||
|
||||
for (const row of commands) {
|
||||
try {
|
||||
const data = JSON.parse(row.value);
|
||||
const cmd = data.command || '';
|
||||
const success = data.success === true || data.exitCode === '0';
|
||||
const timestamp = row.created_at ? new Date(row.created_at * 1000).toISOString() : new Date().toISOString();
|
||||
|
||||
// Classify command type
|
||||
let cmdType = 'other';
|
||||
if (cmd.startsWith('cargo')) cmdType = 'cargo';
|
||||
else if (cmd.startsWith('npm')) cmdType = 'npm';
|
||||
else if (cmd.startsWith('git')) cmdType = 'git';
|
||||
else if (cmd.startsWith('wasm-pack')) cmdType = 'wasm';
|
||||
else if (cmd.includes('test')) cmdType = 'test';
|
||||
else if (cmd.includes('build')) cmdType = 'build';
|
||||
|
||||
// Detect context from command
|
||||
let context = 'general';
|
||||
if (cmd.includes('rvlite')) context = 'rvlite';
|
||||
else if (cmd.includes('ruvector-core')) context = 'ruvector-core';
|
||||
else if (cmd.includes('ruvector-graph')) context = 'ruvector-graph';
|
||||
else if (cmd.includes('wasm')) context = 'wasm';
|
||||
else if (cmd.includes('postgres')) context = 'postgres';
|
||||
else if (cmd.includes('mincut')) context = 'mincut';
|
||||
else if (cmd.includes('gnn')) context = 'gnn';
|
||||
else if (cmd.includes('attention')) context = 'attention';
|
||||
else if (cmd.includes('sona')) context = 'sona';
|
||||
|
||||
const state = `${cmdType}_in_${context}`;
|
||||
const action = success ? 'command-succeeded' : 'command-failed';
|
||||
const reward = success ? 1.0 : -0.5;
|
||||
|
||||
// Initialize state with v2 metadata
|
||||
if (!qTable[state]) {
|
||||
qTable[state] = {
|
||||
'command-succeeded': 0,
|
||||
'command-failed': 0,
|
||||
_meta: {
|
||||
lastUpdate: timestamp,
|
||||
updateCount: 0,
|
||||
firstSeen: timestamp
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
const stateCount = (qTable[state]._meta?.updateCount || 0) + 1;
|
||||
qTable[state]._meta.updateCount = stateCount;
|
||||
qTable[state]._meta.lastUpdate = timestamp;
|
||||
|
||||
// Decaying learning rate with Q-value caps
|
||||
const learningRate = Math.max(0.01, 0.3 / Math.sqrt(stateCount));
|
||||
const currentQ = qTable[state][action] || 0;
|
||||
const newQ = currentQ + learningRate * (reward - currentQ);
|
||||
qTable[state][action] = Math.min(0.8, Math.max(-0.5, newQ));
|
||||
|
||||
// Record trajectory with timestamp
|
||||
trajectories.push({
|
||||
id: `pretrain-cmd-${stats.commands}`,
|
||||
state,
|
||||
action,
|
||||
outcome: cmd.slice(0, 100),
|
||||
reward,
|
||||
timestamp
|
||||
});
|
||||
|
||||
stats.commands++;
|
||||
} catch (e) { /* skip malformed */ }
|
||||
}
|
||||
|
||||
console.log(` ✅ Processed ${stats.commands} commands`);
|
||||
|
||||
// ========== 2. Extract Agent Assignments → Q-Table ==========
|
||||
console.log('🤖 Extracting agent assignments (ALL data)...');
|
||||
|
||||
const agentAssignments = db.prepare(`
|
||||
SELECT key, value, created_at FROM memory_entries
|
||||
WHERE namespace = 'agent-assignments'
|
||||
ORDER BY created_at DESC
|
||||
`).all();
|
||||
|
||||
for (const row of agentAssignments) {
|
||||
try {
|
||||
const data = JSON.parse(row.value);
|
||||
const file = data.file || '';
|
||||
const ext = extname(file).slice(1) || 'unknown';
|
||||
const agentType = data.type || 'coder';
|
||||
const recommended = data.recommended === true;
|
||||
const timestamp = row.created_at ? new Date(row.created_at * 1000).toISOString() : new Date().toISOString();
|
||||
|
||||
// Extract crate if applicable
|
||||
const crateMatch = file.match(/crates\/([^/]+)/);
|
||||
const crate = crateMatch ? crateMatch[1] : 'project';
|
||||
|
||||
const state = `edit_${ext}_in_${crate}`;
|
||||
const action = agentType;
|
||||
const reward = recommended ? 1.0 : 0.5;
|
||||
|
||||
// Initialize with v2 metadata
|
||||
if (!qTable[state]) {
|
||||
qTable[state] = {
|
||||
_meta: {
|
||||
lastUpdate: timestamp,
|
||||
updateCount: 0,
|
||||
firstSeen: timestamp
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
const stateCount = (qTable[state]._meta?.updateCount || 0) + 1;
|
||||
qTable[state]._meta.updateCount = stateCount;
|
||||
qTable[state]._meta.lastUpdate = timestamp;
|
||||
|
||||
const learningRate = Math.max(0.01, 0.2 / Math.sqrt(stateCount));
|
||||
const currentQ = qTable[state][action] || 0;
|
||||
qTable[state][action] = Math.min(0.75, currentQ + learningRate * (reward - currentQ));
|
||||
|
||||
trajectories.push({
|
||||
id: `pretrain-agent-${stats.agents}`,
|
||||
state,
|
||||
action,
|
||||
outcome: `recommended for ${basename(file)}`,
|
||||
reward,
|
||||
timestamp
|
||||
});
|
||||
|
||||
stats.agents++;
|
||||
} catch (e) { /* skip */ }
|
||||
}
|
||||
|
||||
console.log(` ✅ Processed ${stats.agents} agent assignments`);
|
||||
|
||||
// ========== 3. Bootstrap Calibration from Performance Metrics ==========
|
||||
console.log('📈 Bootstrapping confidence calibration...');
|
||||
|
||||
const calibrationBuckets = {};
|
||||
const performanceMetrics = db.prepare(`
|
||||
SELECT key, value FROM memory_entries
|
||||
WHERE namespace = 'performance-metrics'
|
||||
AND key LIKE 'command-metrics:%'
|
||||
`).all();
|
||||
|
||||
// Group by complexity (as a proxy for confidence)
|
||||
const complexityToConfidence = { 'low': 0.9, 'medium': 0.7, 'high': 0.5 };
|
||||
|
||||
for (const row of performanceMetrics) {
|
||||
try {
|
||||
const data = JSON.parse(row.value);
|
||||
const success = data.success === true;
|
||||
const complexity = data.complexity || 'medium';
|
||||
const confidence = complexityToConfidence[complexity] || 0.7;
|
||||
|
||||
// Round to bucket (0.5, 0.6, 0.7, 0.8, 0.9)
|
||||
const bucket = (Math.round(confidence * 10) / 10).toFixed(1);
|
||||
|
||||
if (!calibrationBuckets[bucket]) {
|
||||
calibrationBuckets[bucket] = { correct: 0, total: 0 };
|
||||
}
|
||||
calibrationBuckets[bucket].total++;
|
||||
if (success) calibrationBuckets[bucket].correct++;
|
||||
|
||||
stats.calibration++;
|
||||
} catch (e) { /* skip */ }
|
||||
}
|
||||
|
||||
// Calculate calibration - format must match CalibrationTracker expected format
|
||||
// CalibrationTracker expects: { buckets: { "0.9": { total, correct } }, predictions: [] }
|
||||
const calibration = { buckets: {}, predictions: [] };
|
||||
|
||||
for (const [bucket, data] of Object.entries(calibrationBuckets)) {
|
||||
calibration.buckets[bucket] = {
|
||||
total: data.total,
|
||||
correct: data.correct // CalibrationTracker uses "correct", not "accuracy"
|
||||
};
|
||||
}
|
||||
|
||||
console.log(` ✅ Bootstrapped calibration from ${stats.calibration} metrics`);
|
||||
console.log(` 📊 Calibration buckets: ${Object.keys(calibration.buckets).length}`);
|
||||
|
||||
// ========== 4. Extract File History → Vector Memory ==========
|
||||
console.log('📁 Extracting file edit history (ALL data)...');
|
||||
|
||||
const memories = [];
|
||||
|
||||
const fileHistory = db.prepare(`
|
||||
SELECT key, value, created_at FROM memory_entries
|
||||
WHERE namespace = 'file-history'
|
||||
ORDER BY created_at DESC
|
||||
`).all();
|
||||
|
||||
for (const row of fileHistory) {
|
||||
try {
|
||||
const data = JSON.parse(row.value);
|
||||
const file = data.file || '';
|
||||
const ext = extname(file).slice(1);
|
||||
const crateMatch = file.match(/crates\/([^/]+)/);
|
||||
const crate = crateMatch ? crateMatch[1] : null;
|
||||
const timestamp = row.created_at ? new Date(row.created_at * 1000).toISOString() : new Date().toISOString();
|
||||
|
||||
const content = `edit ${ext} file ${basename(file)} in ${crate || 'project'}`;
|
||||
|
||||
memories.push({
|
||||
id: `pretrain-file-${stats.files}`,
|
||||
type: 'edit',
|
||||
content,
|
||||
embedding: textToEmbedding(content),
|
||||
metadata: {
|
||||
file,
|
||||
crate,
|
||||
ext,
|
||||
timestamp
|
||||
}
|
||||
});
|
||||
|
||||
stats.files++;
|
||||
} catch (e) { /* skip */ }
|
||||
}
|
||||
|
||||
console.log(` ✅ Processed ${stats.files} file edits`);
|
||||
|
||||
// ========== 5. Extract Reasoning Patterns ==========
|
||||
console.log('🧩 Extracting reasoning patterns...');
|
||||
|
||||
const patterns = db.prepare(`
|
||||
SELECT id, type, pattern_data, confidence, created_at FROM patterns
|
||||
ORDER BY confidence DESC
|
||||
`).all();
|
||||
|
||||
for (const row of patterns) {
|
||||
try {
|
||||
const data = JSON.parse(row.pattern_data);
|
||||
const content = data.content || data.title || JSON.stringify(data).slice(0, 200);
|
||||
const timestamp = row.created_at || new Date().toISOString();
|
||||
|
||||
memories.push({
|
||||
id: `pretrain-pattern-${stats.patterns}`,
|
||||
type: 'pattern',
|
||||
content,
|
||||
embedding: textToEmbedding(content),
|
||||
metadata: {
|
||||
patternId: row.id,
|
||||
patternType: row.type,
|
||||
confidence: row.confidence,
|
||||
timestamp
|
||||
}
|
||||
});
|
||||
|
||||
stats.patterns++;
|
||||
} catch (e) { /* skip */ }
|
||||
}
|
||||
|
||||
console.log(` ✅ Processed ${stats.patterns} patterns`);
|
||||
|
||||
// ========== 6. Identify Uncertain States for Active Learning ==========
|
||||
console.log('🎯 Identifying uncertain states...');
|
||||
|
||||
const uncertainStates = [];
|
||||
for (const [state, actions] of Object.entries(qTable)) {
|
||||
const qValues = Object.entries(actions)
|
||||
.filter(([k, v]) => k !== '_meta' && k !== '_count' && typeof v === 'number')
|
||||
.map(([k, v]) => ({ action: k, q: v }))
|
||||
.sort((a, b) => b.q - a.q);
|
||||
|
||||
if (qValues.length >= 2) {
|
||||
const gap = qValues[0].q - qValues[1].q;
|
||||
if (gap < 0.1 && qValues[0].q > 0) { // Close Q-values = uncertain
|
||||
uncertainStates.push({
|
||||
state,
|
||||
bestAction: qValues[0].action,
|
||||
secondBest: qValues[1].action,
|
||||
gap: gap.toFixed(4),
|
||||
needsExploration: true
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
console.log(` ✅ Found ${uncertainStates.length} uncertain states for active learning`);
|
||||
|
||||
// ========== 7. Build Swarm Coordination Graph ==========
|
||||
console.log('🔗 Building swarm coordination graph...');
|
||||
|
||||
const nodes = {};
|
||||
const edges = {};
|
||||
|
||||
const agents = new Set();
|
||||
for (const row of agentAssignments) {
|
||||
try {
|
||||
const data = JSON.parse(row.value);
|
||||
if (data.type) agents.add(data.type);
|
||||
} catch (e) { /* skip */ }
|
||||
}
|
||||
|
||||
const agentCapabilities = {
|
||||
'coder': ['rust', 'typescript', 'implementation'],
|
||||
'technical-writer': ['documentation', 'markdown'],
|
||||
'reviewer': ['code-review', 'security'],
|
||||
'tester': ['unit-test', 'integration'],
|
||||
'general-developer': ['general', 'debugging'],
|
||||
'rust-developer': ['rust', 'cargo', 'wasm'],
|
||||
'typescript-developer': ['typescript', 'javascript', 'node'],
|
||||
'ml-developer': ['gnn', 'attention', 'neural'],
|
||||
'documentation-writer': ['docs', 'readme', 'api-docs']
|
||||
};
|
||||
|
||||
for (const agent of agents) {
|
||||
nodes[agent] = {
|
||||
type: agent,
|
||||
capabilities: agentCapabilities[agent] || [agent],
|
||||
load: 0,
|
||||
active: true
|
||||
};
|
||||
stats.coordination++;
|
||||
}
|
||||
|
||||
// Create edges based on common file edits
|
||||
const agentFiles = {};
|
||||
for (const row of agentAssignments) {
|
||||
try {
|
||||
const data = JSON.parse(row.value);
|
||||
const agent = data.type;
|
||||
const file = data.file;
|
||||
if (!agentFiles[agent]) agentFiles[agent] = [];
|
||||
agentFiles[agent].push(file);
|
||||
} catch (e) { /* skip */ }
|
||||
}
|
||||
|
||||
const agentList = Object.keys(agentFiles);
|
||||
for (let i = 0; i < agentList.length; i++) {
|
||||
for (let j = i + 1; j < agentList.length; j++) {
|
||||
const a1 = agentList[i];
|
||||
const a2 = agentList[j];
|
||||
const files1 = new Set(agentFiles[a1].map(f => dirname(f)));
|
||||
const files2 = new Set(agentFiles[a2].map(f => dirname(f)));
|
||||
|
||||
let overlap = 0;
|
||||
for (const dir of files1) {
|
||||
if (files2.has(dir)) overlap++;
|
||||
}
|
||||
|
||||
if (overlap > 0) {
|
||||
edges[`${a1}:${a2}`] = { weight: overlap, interactions: overlap };
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
console.log(` ✅ Built graph with ${Object.keys(nodes).length} agents, ${Object.keys(edges).length} edges`);
|
||||
|
||||
// ========== 8. Save All Data ==========
|
||||
console.log('\n💾 Saving pretrained data (v2)...');
|
||||
|
||||
// Save Q-Table with decay metadata
|
||||
writeFileSync(
|
||||
join(DATA_DIR, 'patterns.json'),
|
||||
JSON.stringify(qTable, null, 2)
|
||||
);
|
||||
console.log(` ✅ Q-Table: ${Object.keys(qTable).length} states (with decay metadata)`);
|
||||
|
||||
// Save Trajectories (keep last 2000 for more history)
|
||||
writeFileSync(
|
||||
join(DATA_DIR, 'trajectories.json'),
|
||||
JSON.stringify(trajectories.slice(-2000), null, 2)
|
||||
);
|
||||
console.log(` ✅ Trajectories: ${Math.min(trajectories.length, 2000)} entries`);
|
||||
|
||||
// Save Memories
|
||||
writeFileSync(
|
||||
join(DATA_DIR, 'memory.json'),
|
||||
JSON.stringify(memories, null, 2)
|
||||
);
|
||||
console.log(` ✅ Vector Memory: ${memories.length} entries`);
|
||||
|
||||
// Save Calibration (NEW)
|
||||
writeFileSync(
|
||||
join(DATA_DIR, 'calibration.json'),
|
||||
JSON.stringify(calibration, null, 2)
|
||||
);
|
||||
console.log(` ✅ Calibration: ${Object.keys(calibration.buckets).length} buckets`);
|
||||
|
||||
// Save Uncertain States for Active Learning (NEW)
|
||||
writeFileSync(
|
||||
join(DATA_DIR, 'uncertain-states.json'),
|
||||
JSON.stringify({ states: uncertainStates, lastUpdated: new Date().toISOString() }, null, 2)
|
||||
);
|
||||
console.log(` ✅ Uncertain States: ${uncertainStates.length} entries`);
|
||||
|
||||
// Save Swarm Graph
|
||||
writeFileSync(
|
||||
join(DATA_DIR, 'coordination-graph.json'),
|
||||
JSON.stringify({ nodes, edges, lastUpdated: new Date().toISOString() }, null, 2)
|
||||
);
|
||||
console.log(` ✅ Swarm Graph: ${Object.keys(nodes).length} nodes`);
|
||||
|
||||
// Save Swarm State
|
||||
writeFileSync(
|
||||
join(DATA_DIR, 'swarm-state.json'),
|
||||
JSON.stringify({
|
||||
tasks: [],
|
||||
optimizations: 0,
|
||||
pretrained: true,
|
||||
pretrainVersion: 2,
|
||||
pretrainedAt: new Date().toISOString(),
|
||||
stats,
|
||||
features: {
|
||||
patternDecay: true,
|
||||
calibration: true,
|
||||
activeLearning: true,
|
||||
uncertainStates: uncertainStates.length
|
||||
}
|
||||
}, null, 2)
|
||||
);
|
||||
|
||||
// Initialize empty feedback tracking (suggestions must be array, followRates must be object)
|
||||
writeFileSync(
|
||||
join(DATA_DIR, 'feedback.json'),
|
||||
JSON.stringify({ suggestions: [], followRates: {}, lastUpdated: new Date().toISOString() }, null, 2)
|
||||
);
|
||||
console.log(` ✅ Feedback tracking initialized`);
|
||||
|
||||
db.close();
|
||||
|
||||
// ========== Summary ==========
|
||||
console.log('\n✅ Pretraining v2 Complete!');
|
||||
console.log('===========================');
|
||||
console.log(` Commands processed: ${stats.commands.toLocaleString()}`);
|
||||
console.log(` Agent assignments: ${stats.agents}`);
|
||||
console.log(` File edits: ${stats.files.toLocaleString()}`);
|
||||
console.log(` Patterns: ${stats.patterns}`);
|
||||
console.log(` Calibration samples: ${stats.calibration.toLocaleString()}`);
|
||||
console.log(` Uncertain states: ${uncertainStates.length}`);
|
||||
console.log(` Swarm nodes: ${Object.keys(nodes).length}`);
|
||||
console.log(` Total Q-states: ${Object.keys(qTable).length}`);
|
||||
console.log(` Total memories: ${memories.length.toLocaleString()}`);
|
||||
console.log('\n🧠 Intelligence system v2 pretrained with:');
|
||||
console.log(' ✅ Pattern decay timestamps');
|
||||
console.log(' ✅ Confidence calibration bootstrap');
|
||||
console.log(' ✅ Active learning uncertain states');
|
||||
console.log(' ✅ All available training data\n');
|
||||
}
|
||||
|
||||
pretrain().catch(console.error);
|
||||
Reference in New Issue
Block a user