القائمة
Use when generating and managing vector embeddings for semantic search and RAG retrieval across knowledge bases.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
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يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/oyi77/1ai-skills --skill ruvectorيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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استنادا إلى تصنيف SOC المهني
عرض 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: