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Use when generating and managing vector embeddings for semantic search and RAG retrieval across knowledge bases.
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Showing SKILL.md
| name | ruvector |
| description | Use when generating and managing vector embeddings for semantic search and RAG retrieval across knowledge bases. |
| domain | core |
| author | oyi77 |
| license | Apache-2.0 |
| subdomain | core-platform |
| tags | ["infrastructure","memory","ruvector","self-improvement"] |
| persona | {"name":"Edo Liberty","title":"The Vector Search Expert - Master of Similarity Search","expertise":["Vector Databases","Approximate Nearest Neighbors","Embeddings","Similarity Search"],"philosophy":"Similarity search powers the next generation of AI applications.","credentials":["Founder of Pinecone","Former AWS AI Labs director","Published 50+ research papers"],"principles":["Index for speed","Approximate is good enough","Scale to billions","Latency matters"]} |
| version | 1.0.0 |
Self-learning vector database with Graph Neural Networks for autonomous AI memory
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.
| 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. |
Trigger phrases:
Use this skill when you need:
MATCH (a)-[:SIMILAR]->(b) WHERE a.name = "AI" RETURN b
# Quick start
npx ruvector
# Initialize self-learning hooks
npx @ruvector/cli hooks init
# Install optional GNN module
npx ruvector install gnn
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 });
// Enable learning from queries
await db.hooks.enable('self-learning', {
algorithm: 'q-learning',
memorySize: 10000
});
// Run LLMs locally
const { RuvLLM } = require('@ruvector/ruvllm');
const llm = new RuvLLM({ model: 'ruvltra-small' });
const response = await llm.chat('Explain vector databases');
RuVector integrates perfectly with:
runtime-self-improvement - Store learned patternsai-research-agent - Long-term memoryskill-performance-monitor - Track skill usageSKILL.md - This filereferences/ - Additional documentation| 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 |
After completing this skill, confirm: