RuVector Skill

Self-learning vector database with Graph Neural Networks for autonomous AI memory

Overview

RuVector is a distributed vector database that learns from every query. Unlike static vector databases, RuVector uses GNN (Graph Neural Network) layers to improve search results over time. It's perfect for building self-improving AI memory systems.

Anti-Rationalization Table

Rationalization	Reality
"I'll figure it out as I go"	A structured approach saves time and reduces errors. Follow the workflow in this skill rather than improvising.
"I already know this topic"	Familiarity breeds shortcuts. Use the checklist to verify you haven't missed critical steps.
"This doesn't apply to my situation"	The patterns here generalize across contexts. Adapt, don't skip — the underlying principles hold.
"One more tool will fix it"	Adding complexity rarely solves process gaps. Master the core workflow first.

When to Use

Trigger phrases:

• "Edo Liberty"
• "Generate and manage vector embeddings for semantic search and RAG retrieval acro"

Use this skill when you need:

• Local vector storage without external API dependencies
• Self-improving memory that gets smarter with usage
• Graph queries with Cypher syntax
• Local LLM integration for RAG without cloud APIs
• Autonomous AI agents that learn from interactions

Key Features

• Automated workflow execution with error recovery
• Configurable parameters for different use cases
• Integration with existing tooling and pipelines
• Detailed logging and status reporting

🧠 Self-Learning Index

• GNN layers learn from every query
• Search results improve over time
• No manual index rebuilding needed

🔍 Graph Queries (Cypher)

MATCH (a)-[:SIMILAR]->(b) WHERE a.name = "AI" RETURN b

💾 Local Embeddings

• Built-in ONNX embedding models
• No API calls needed
• Runs entirely offline

⚡ MCP Tools

• 213+ MCP tools for swarm management
• Memory integration
• GitHub automation

Installation

# Quick start
npx ruvector

# Initialize self-learning hooks
npx @ruvector/cli hooks init

# Install optional GNN module
npx ruvector install gnn

Usage Patterns

• Invoke the skill when the matching domain keywords appear
• Combine with related skills for end-to-end workflows
• Use verification steps to confirm successful execution
• Review output quality before finalizing results

Basic Vector Storage

const ruvector = require('ruvector');

// Create collection
await db.createCollection('memories', { dimension: 384 });

// Add embeddings
await db.insert('memories', {
  id: 'memory_1',
  vector: embedding,
  metadata: { context: 'user_preference', topic: 'coffee' }
});

// Search (improves over time!)
const results = await db.search('memories', queryEmbedding, { topK: 5 });

Self-Learning Hook

// Enable learning from queries
await db.hooks.enable('self-learning', {
  algorithm: 'q-learning',
  memorySize: 10000
});

Local LLM Integration

// Run LLMs locally
const { RuvLLM } = require('@ruvector/ruvllm');
const llm = new RuvLLM({ model: 'ruvltra-small' });

const response = await llm.chat('Explain vector databases');

Integration with 1ai-skills

RuVector integrates perfectly with:

• runtime-self-improvement - Store learned patterns
• ai-research-agent - Long-term memory
• skill-performance-monitor - Track skill usage

Files in This Skill

• SKILL.md - This file
• references/ - Additional documentation

See Also

• RuVector GitHub
• RuVector NPM
• Documentation

When NOT to Use

• When the task requires domain expertise the agent has not been configured with
• When human review is mandated by compliance or regulatory requirements
• When the task is too trivial to warrant this skill
• When a more appropriate skill exists

Common Rationalizations

Rationalization	Reality
"I'll do this later"	Explain why this excuse is wrong for this skill
"This is simple, skip steps"	Even simple tasks benefit from process

Red Flags

• Agent output is not validated against expected quality standards
• Prerequisites are not verified before task execution
• Watch for shortcuts and skipped steps

Verification

After completing this skill, confirm:

• Output meets the defined quality and completeness requirements
• All prerequisites are verified and documented
• All required outputs generated
• Success criteria met

Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality