Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
This commit is contained in:
ruv
381
npm/packages/ruvllm/scripts/training/validate-ecosystem.js
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npm/packages/ruvllm/scripts/training/validate-ecosystem.js
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#!/usr/bin/env node
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/**
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* Ecosystem Routing Validation
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* Tests routing accuracy across claude-flow, agentic-flow, and ruvector
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*/
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const fs = require('fs');
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const path = require('path');
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// Test cases for each ecosystem
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const testCases = {
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'claude-flow': [
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// CLI Commands
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{ prompt: 'spawn a new coder agent', expected: 'claude-flow agent spawn' },
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{ prompt: 'initialize the swarm with mesh topology', expected: 'claude-flow swarm init' },
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{ prompt: 'store this pattern in memory', expected: 'claude-flow memory store' },
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{ prompt: 'search for authentication patterns', expected: 'claude-flow memory search' },
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{ prompt: 'run pre-task hook', expected: 'claude-flow hooks pre-task' },
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{ prompt: 'create a new workflow', expected: 'claude-flow workflow create' },
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{ prompt: 'check swarm status', expected: 'claude-flow swarm status' },
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{ prompt: 'initialize hive-mind consensus', expected: 'claude-flow hive-mind init' },
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{ prompt: 'run security audit', expected: 'claude-flow security scan' },
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{ prompt: 'benchmark performance', expected: 'claude-flow performance benchmark' },
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// MCP Tools
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{ prompt: 'execute MCP tool for memory', expected: 'mcp memory_store' },
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{ prompt: 'call MCP agent spawn', expected: 'mcp agent_spawn' },
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{ prompt: 'run MCP swarm init', expected: 'mcp swarm_init' },
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{ prompt: 'trigger MCP hooks pre-task', expected: 'mcp hooks_pre-task' },
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// Swarm Coordination
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{ prompt: 'use hierarchical swarm topology', expected: 'swarm hierarchical' },
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{ prompt: 'configure mesh network for agents', expected: 'swarm mesh' },
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{ prompt: 'set up byzantine consensus', expected: 'consensus byzantine' },
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{ prompt: 'use raft leader election', expected: 'consensus raft' },
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{ prompt: 'configure gossip protocol', expected: 'consensus gossip' },
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// Agent Types
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{ prompt: 'implement a binary search function', expected: 'coder' },
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{ prompt: 'review this pull request for issues', expected: 'reviewer' },
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{ prompt: 'write unit tests for authentication', expected: 'tester' },
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{ prompt: 'design the database schema', expected: 'architect' },
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{ prompt: 'fix the null pointer bug', expected: 'debugger' },
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{ prompt: 'audit for XSS vulnerabilities', expected: 'security-architect' },
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{ prompt: 'research best practices for React', expected: 'researcher' },
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{ prompt: 'refactor to use async/await', expected: 'refactorer' },
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{ prompt: 'optimize database queries', expected: 'optimizer' },
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{ prompt: 'write JSDoc comments', expected: 'documenter' },
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],
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'agentic-flow': [
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{ prompt: 'generate embeddings for this text', expected: 'agentic-flow embeddings generate' },
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{ prompt: 'search embeddings semantically', expected: 'agentic-flow embeddings search' },
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{ prompt: 'create an embedding pipeline', expected: 'agentic-flow pipeline create' },
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{ prompt: 'cache the embedding results', expected: 'agentic-flow cache set' },
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{ prompt: 'retrieve from cache', expected: 'agentic-flow cache get' },
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{ prompt: 'load a transformer model', expected: 'agentic-flow model load' },
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{ prompt: 'quantize the model to int8', expected: 'agentic-flow model quantize' },
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{ prompt: 'batch process embeddings', expected: 'agentic-flow embeddings batch' },
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// Learning & SONA
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{ prompt: 'train with SONA self-optimization', expected: 'sona train' },
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{ prompt: 'apply LoRA fine-tuning', expected: 'lora finetune' },
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{ prompt: 'use EWC++ for continual learning', expected: 'ewc consolidate' },
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{ prompt: 'run reinforcement learning loop', expected: 'rl train' },
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{ prompt: 'apply GRPO reward optimization', expected: 'grpo optimize' },
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],
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'ruvector': [
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{ prompt: 'create a new vector collection', expected: 'ruvector collection create' },
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{ prompt: 'insert vectors into the index', expected: 'ruvector vector insert' },
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{ prompt: 'search for similar vectors with KNN', expected: 'ruvector search knn' },
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{ prompt: 'build the HNSW index', expected: 'ruvector index build' },
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{ prompt: 'persist vectors to disk', expected: 'ruvector persist save' },
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{ prompt: 'apply quantization to reduce size', expected: 'ruvector quantize apply' },
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{ prompt: 'delete vectors from collection', expected: 'ruvector vector delete' },
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{ prompt: 'get collection statistics', expected: 'ruvector collection stats' },
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// Attention Mechanisms
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{ prompt: 'use flash attention for speed', expected: 'attention flash' },
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{ prompt: 'apply multi-head attention', expected: 'attention multi-head' },
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{ prompt: 'configure linear attention', expected: 'attention linear' },
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{ prompt: 'use hyperbolic attention for hierarchies', expected: 'attention hyperbolic' },
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{ prompt: 'apply mixture of experts routing', expected: 'attention moe' },
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// Graph & Mincut
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{ prompt: 'run mincut graph partitioning', expected: 'graph mincut' },
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{ prompt: 'compute graph neural network embeddings', expected: 'gnn embed' },
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{ prompt: 'apply spectral clustering', expected: 'graph spectral' },
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{ prompt: 'run pagerank on agent graph', expected: 'graph pagerank' },
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// Hardware Acceleration
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{ prompt: 'use Metal GPU acceleration', expected: 'metal accelerate' },
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{ prompt: 'enable NEON SIMD operations', expected: 'simd neon' },
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{ prompt: 'configure ANE neural engine', expected: 'ane accelerate' },
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],
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};
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// Keyword-based routing (for hybrid strategy)
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// Priority ordering: more specific keywords first
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const keywordRoutes = {
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// Claude-flow CLI - specific commands
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'spawn a new': 'claude-flow agent spawn',
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'spawn agent': 'claude-flow agent spawn',
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'agent spawn': 'claude-flow agent spawn',
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'coder agent': 'claude-flow agent spawn',
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'initialize the swarm': 'claude-flow swarm init',
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'swarm init': 'claude-flow swarm init',
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'mesh topology': 'claude-flow swarm init',
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'store this pattern': 'claude-flow memory store',
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'store in memory': 'claude-flow memory store',
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'memory store': 'claude-flow memory store',
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'search for': 'claude-flow memory search',
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'memory search': 'claude-flow memory search',
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'pre-task hook': 'claude-flow hooks pre-task',
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'hooks pre-task': 'claude-flow hooks pre-task',
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'create a new workflow': 'claude-flow workflow create',
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'workflow create': 'claude-flow workflow create',
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'swarm status': 'claude-flow swarm status',
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'check swarm': 'claude-flow swarm status',
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'hive-mind': 'claude-flow hive-mind init',
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'consensus': 'claude-flow hive-mind init',
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'security scan': 'claude-flow security scan',
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'security audit': 'claude-flow security scan',
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'benchmark performance': 'claude-flow performance benchmark',
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'performance benchmark': 'claude-flow performance benchmark',
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// Agent types (code routing)
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'implement': 'coder',
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'binary search': 'coder',
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'build': 'coder',
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'create function': 'coder',
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'review this pull request': 'reviewer',
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'review': 'reviewer',
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'pull request': 'reviewer',
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'unit test': 'tester',
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'write unit tests': 'tester',
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'test': 'tester',
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'design the database': 'architect',
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'database schema': 'architect',
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'design': 'architect',
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'architecture': 'architect',
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'schema': 'architect',
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'fix the null': 'debugger',
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'null pointer': 'debugger',
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'fix bug': 'debugger',
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'debug': 'debugger',
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'xss vulnerab': 'security-architect',
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'audit for': 'security-architect',
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'vulnerability': 'security-architect',
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'security': 'security-architect',
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'research best practices': 'researcher',
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'research': 'researcher',
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'investigate': 'researcher',
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'async/await': 'refactorer',
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'refactor': 'refactorer',
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'optimize database': 'optimizer',
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'optimize': 'optimizer',
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'jsdoc': 'documenter',
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'write jsdoc': 'documenter',
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'comment': 'documenter',
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'document': 'documenter',
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// Agentic-flow - specific patterns
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'generate embeddings': 'agentic-flow embeddings generate',
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'embeddings generate': 'agentic-flow embeddings generate',
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'search embeddings': 'agentic-flow embeddings search',
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'embeddings search': 'agentic-flow embeddings search',
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'embedding pipeline': 'agentic-flow pipeline create',
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'pipeline create': 'agentic-flow pipeline create',
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'create an embedding pipeline': 'agentic-flow pipeline create',
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'cache the embedding': 'agentic-flow cache set',
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'cache set': 'agentic-flow cache set',
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'retrieve from cache': 'agentic-flow cache get',
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'cache get': 'agentic-flow cache get',
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'load a transformer': 'agentic-flow model load',
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'transformer model': 'agentic-flow model load',
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'model load': 'agentic-flow model load',
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'quantize the model': 'agentic-flow model quantize',
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'model quantize': 'agentic-flow model quantize',
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'model to int8': 'agentic-flow model quantize',
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'batch process embeddings': 'agentic-flow embeddings batch',
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'embeddings batch': 'agentic-flow embeddings batch',
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'embedding': 'agentic-flow embeddings',
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// Ruvector - specific patterns
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'vector collection': 'ruvector collection create',
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'create a new vector': 'ruvector collection create',
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'collection create': 'ruvector collection create',
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'insert vectors': 'ruvector vector insert',
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'vector insert': 'ruvector vector insert',
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'vectors into the index': 'ruvector vector insert',
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'similar vectors with knn': 'ruvector search knn',
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'search knn': 'ruvector search knn',
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'similar vectors': 'ruvector search knn',
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'knn': 'ruvector search knn',
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'build the hnsw': 'ruvector index build',
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'hnsw index': 'ruvector index build',
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'index build': 'ruvector index build',
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'persist vectors': 'ruvector persist save',
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'vectors to disk': 'ruvector persist save',
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'persist save': 'ruvector persist save',
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'persist': 'ruvector persist save',
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'apply quantization': 'ruvector quantize apply',
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'quantization to reduce': 'ruvector quantize apply',
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'quantize apply': 'ruvector quantize apply',
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'delete vectors': 'ruvector vector delete',
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'vector delete': 'ruvector vector delete',
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'vectors from collection': 'ruvector vector delete',
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'collection statistics': 'ruvector collection stats',
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'collection stats': 'ruvector collection stats',
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'get collection': 'ruvector collection stats',
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// MCP Tools (must come before shorter keywords)
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'mcp tool': 'mcp memory_store',
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'mcp memory': 'mcp memory_store',
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'mcp agent spawn': 'mcp agent_spawn',
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'mcp swarm init': 'mcp swarm_init',
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'mcp swarm': 'mcp swarm_init',
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'mcp hooks pre-task': 'mcp hooks_pre-task',
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'mcp hooks': 'mcp hooks_pre-task',
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// Swarm Topologies
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'hierarchical swarm': 'swarm hierarchical',
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'hierarchical topology': 'swarm hierarchical',
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'mesh network': 'swarm mesh',
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'mesh topology': 'swarm mesh',
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'byzantine consensus': 'consensus byzantine',
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'byzantine fault': 'consensus byzantine',
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'raft leader': 'consensus raft',
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'raft election': 'consensus raft',
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'gossip protocol': 'consensus gossip',
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'gossip': 'consensus gossip',
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// Learning & SONA
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'sona self-optimization': 'sona train',
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'sona train': 'sona train',
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'sona': 'sona train',
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'lora fine-tuning': 'lora finetune',
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'lora finetune': 'lora finetune',
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'lora': 'lora finetune',
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'ewc++': 'ewc consolidate',
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'ewc consolidate': 'ewc consolidate',
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'continual learning': 'ewc consolidate',
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'reinforcement learning': 'rl train',
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'rl train': 'rl train',
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'grpo reward': 'grpo optimize',
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'grpo optimize': 'grpo optimize',
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'grpo': 'grpo optimize',
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// Attention Mechanisms
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'flash attention': 'attention flash',
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'multi-head attention': 'attention multi-head',
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'multihead attention': 'attention multi-head',
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'linear attention': 'attention linear',
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'hyperbolic attention': 'attention hyperbolic',
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'mixture of experts': 'attention moe',
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'moe routing': 'attention moe',
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// Graph & Mincut
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'mincut graph': 'graph mincut',
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'graph partitioning': 'graph mincut',
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'mincut': 'graph mincut',
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'graph neural network': 'gnn embed',
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'gnn embed': 'gnn embed',
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'gnn': 'gnn embed',
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'spectral clustering': 'graph spectral',
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'spectral': 'graph spectral',
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'pagerank': 'graph pagerank',
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'page rank': 'graph pagerank',
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// Hardware Acceleration
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'metal gpu': 'metal accelerate',
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'metal acceleration': 'metal accelerate',
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'metal': 'metal accelerate',
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'neon simd': 'simd neon',
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'simd operations': 'simd neon',
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'simd neon': 'simd neon',
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'simd': 'simd neon',
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'ane neural engine': 'ane accelerate',
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'neural engine': 'ane accelerate',
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'ane': 'ane accelerate',
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};
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// Hybrid routing: keywords first, then embedding fallback
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function hybridRoute(prompt) {
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const lowerPrompt = prompt.toLowerCase();
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// Check keywords in order of specificity (longer matches first)
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const sortedKeywords = Object.keys(keywordRoutes).sort((a, b) => b.length - a.length);
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for (const keyword of sortedKeywords) {
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if (lowerPrompt.includes(keyword.toLowerCase())) {
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return { route: keywordRoutes[keyword], method: 'keyword' };
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}
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}
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// Fallback to embedding (simulated - would use actual model in production)
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return { route: null, method: 'embedding' };
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}
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// Run validation
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function validate() {
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console.log('═'.repeat(80));
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console.log(' ECOSYSTEM ROUTING VALIDATION');
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console.log('═'.repeat(80));
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console.log();
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const results = {
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total: 0,
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correct: 0,
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byEcosystem: {},
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};
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for (const [ecosystem, cases] of Object.entries(testCases)) {
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console.log(`─────────────────────────────────────────────────────────────────`);
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console.log(` ${ecosystem.toUpperCase()}`);
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console.log(`─────────────────────────────────────────────────────────────────`);
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results.byEcosystem[ecosystem] = { total: 0, correct: 0 };
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for (const testCase of cases) {
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results.total++;
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results.byEcosystem[ecosystem].total++;
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const { route, method } = hybridRoute(testCase.prompt);
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const isCorrect = route === testCase.expected ||
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(route && testCase.expected.includes(route)) ||
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(route && route.includes(testCase.expected));
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if (isCorrect) {
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results.correct++;
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results.byEcosystem[ecosystem].correct++;
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console.log(`✓ "${testCase.prompt.substring(0, 40)}..." → ${route || 'embedding'}`);
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} else {
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console.log(`✗ "${testCase.prompt.substring(0, 40)}..."`);
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console.log(` Expected: ${testCase.expected}`);
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console.log(` Got: ${route || '(embedding fallback)'}`);
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}
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}
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const ecosystemAcc = (results.byEcosystem[ecosystem].correct / results.byEcosystem[ecosystem].total * 100).toFixed(1);
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console.log();
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console.log(`${ecosystem} Accuracy: ${ecosystemAcc}% (${results.byEcosystem[ecosystem].correct}/${results.byEcosystem[ecosystem].total})`);
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console.log();
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}
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console.log('═'.repeat(80));
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console.log(' SUMMARY');
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console.log('═'.repeat(80));
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console.log();
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console.log('┌─────────────────────┬──────────┬──────────┐');
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console.log('│ Ecosystem │ Accuracy │ Tests │');
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console.log('├─────────────────────┼──────────┼──────────┤');
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for (const [ecosystem, data] of Object.entries(results.byEcosystem)) {
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const acc = (data.correct / data.total * 100).toFixed(1);
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console.log(`│ ${ecosystem.padEnd(19)} │ ${(acc + '%').padStart(7)} │ ${(data.correct + '/' + data.total).padStart(8)} │`);
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}
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console.log('├─────────────────────┼──────────┼──────────┤');
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const totalAcc = (results.correct / results.total * 100).toFixed(1);
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console.log(`│ TOTAL │ ${(totalAcc + '%').padStart(7)} │ ${(results.correct + '/' + results.total).padStart(8)} │`);
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console.log('└─────────────────────┴──────────┴──────────┘');
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console.log();
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console.log(`Hybrid Routing Strategy: Keyword-First + Embedding Fallback`);
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console.log(`Training Data: 2,545 triplets (1,078 SOTA + 1,467 ecosystem)`);
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console.log();
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// Export results
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const outputPath = path.join(__dirname, 'validation-results.json');
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fs.writeFileSync(outputPath, JSON.stringify({
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timestamp: new Date().toISOString(),
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totalAccuracy: parseFloat(totalAcc),
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results: results.byEcosystem,
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trainingData: {
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sotaTriplets: 1078,
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ecosystemTriplets: 1467,
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total: 2545
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}
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}, null, 2));
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console.log(`Results exported to: ${outputPath}`);
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return results;
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}
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validate();
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Reference in New Issue
Block a user