wifi-densepose/vendor/ruvector/examples/meta-cognition-spiking-neural-network/demos

AgentDB Comprehensive Demonstrations

This directory contains a comprehensive exploration of AgentDB's capabilities, showcasing the full power of the 2.0.0-alpha.2.11 release.

🎯 What's Included

1. Vector Search (vector-search/)

File: semantic-search.js

Demonstrates AgentDB's blazing-fast vector search capabilities using RuVector:

• 150x faster than cloud-based alternatives
• Sub-millisecond query latency
• Semantic similarity search
• Filtered search by metadata
• Performance benchmarking

Key Features:

• HNSW indexing
• Cosine similarity
• Real-time search
• Native Rust performance

Run it:

node demos/vector-search/semantic-search.js

2. Attention Mechanisms (attention/)

File: all-mechanisms.js

Showcases all 5 attention mechanisms included in AgentDB:

1. Multi-Head Attention - Standard transformer attention with parallel heads
2. Flash Attention - Memory-efficient block-wise computation
3. Linear Attention - O(N) complexity using kernel approximations
4. Hyperbolic Attention - Poincaré ball model for hierarchical data
5. MoE Attention - Mixture of Experts with dynamic routing

Key Features:

• All 5 mechanisms demonstrated
• Performance comparisons
• Use case explanations
• Expert routing visualization

Run it:

node demos/attention/all-mechanisms.js

3. Self-Discovery System (self-discovery/)

File: cognitive-explorer.js

A cognitive system that autonomously explores its own capabilities:

What It Does:

• Discovers and catalogs its own abilities
• Stores discoveries in semantic memory
• Reflects on performance patterns
• Builds hierarchical knowledge graphs
• Generates insights from experience

Cognitive Patterns Demonstrated:

• Self-awareness through performance monitoring
• Pattern recognition across discoveries
• Hierarchical knowledge organization
• Continuous learning and improvement
• Meta-cognition (thinking about thinking)

Key Features:

• Autonomous exploration
• Semantic memory storage
• Knowledge graph construction
• Performance analysis
• Insight generation

Run it:

node demos/self-discovery/cognitive-explorer.js

🚀 Quick Start

Run All Demonstrations

# Make the runner executable
chmod +x demos/run-all.js

# Run all demos in sequence
node demos/run-all.js

This will execute all demonstrations automatically, showing you the full capabilities of AgentDB.

Run Individual Demos

# Vector search only
node demos/vector-search/semantic-search.js

# Attention mechanisms only
node demos/attention/all-mechanisms.js

# Self-discovery system only
node demos/self-discovery/cognitive-explorer.js

📊 Expected Output

Vector Search Demo

🔎 AgentDB Vector Search Demonstration
======================================================================

📚 Creating Vector Database...

✅ Vector database created with 128 dimensions
📊 Using RuVector (Rust backend) - 150x faster than cloud alternatives

📝 Indexing documents...
  ✓ Indexed: Introduction to Neural Networks (AI)
  ...

🔍 Running Semantic Search Queries...

📊 Performance Statistics:
  Average Search Latency: 0.234ms
  Queries per Second: 4273

Attention Mechanisms Demo

🧠 AgentDB Attention Mechanisms Demonstration
======================================================================

🔵 1. DOT PRODUCT ATTENTION (Basic)
✅ Initialized Dot Product Attention
⚡ Computation time: 1.234ms

🔵 2. MULTI-HEAD ATTENTION (Standard Transformer)
✅ Initialized with 4 attention heads
⚡ Computation time: 2.456ms
...

Self-Discovery Demo

🧠 AgentDB Self-Discovery System
======================================================================

🚀 Beginning Self-Discovery Process...

🔍 Exploring: Vector Search
✅ Discovery recorded: Vector Search
   Duration: 1.234ms
   Category: Core Systems

🤔 SELF-REFLECTION: Analyzing Discoveries
📊 Total Discoveries: 6
✅ Successful: 6

💡 Generated Insights:
   1. Average capability execution: 2.145ms
   2. Fastest category: Core Systems (1.234ms avg)
   ...

🎓 What You'll Learn

About AgentDB

1. Performance: See the 150x speedup in action
2. Attention Mechanisms: Understand when to use each mechanism
3. Cognitive Patterns: Learn how AI systems can be self-aware
4. Vector Search: Master semantic similarity search
5. Memory Systems: Store and retrieve semantic memories

About AI Architecture

1. Attention is Key: Different problems need different attention mechanisms
2. Hyperbolic Geometry: Natural representation of hierarchies
3. Self-Reflection: AI systems can monitor and improve themselves
4. Knowledge Graphs: Organize information hierarchically
5. Semantic Memory: Store meaning, not just data

🛠️ Technical Details

Dependencies

All demonstrations use:

• agentdb@2.0.0-alpha.2.11 - Main package
• ruvector@0.1.26 - Vector search
• @ruvector/attention@0.1.1 - Attention mechanisms

Generated Files

The demonstrations create several database files:

• demos/vector-search/semantic-db.bin - Vector search index
• demos/self-discovery/memory.bin - Cognitive memory storage

These files persist across runs, so subsequent executions will be faster.

System Requirements

• Node.js 16+
• ~100MB RAM per demo
• ~10MB disk space for generated files

📚 Code Examples

Using Vector Search

const { VectorDB } = require('ruvector');

const db = new VectorDB({
  dimensions: 128,
  maxElements: 1000
});

const vector = new Float32Array(128).fill(0.1);
await db.insert({ id: 'doc1', vector, metadata: { title: 'Example' } });

const results = await db.search(vector, 5);

Using Attention Mechanisms

const { MultiHeadAttention, HyperbolicAttention } = require('@ruvector/attention');

// Multi-head for general tasks
const mha = new MultiHeadAttention(64, 4);
const output = mha.compute(query, keys, values);

// Hyperbolic for hierarchies
const hyp = new HyperbolicAttention(64, -1.0);
const hierOutput = hyp.compute(query, keys, values);

🎯 Use Cases

Vector Search

• Semantic document search
• RAG (Retrieval-Augmented Generation)
• Recommendation systems
• Duplicate detection
• Content clustering

Attention Mechanisms

• Multi-Head: General transformers, language models
• Flash: Long sequences, production systems
• Linear: Real-time processing, streaming data
• Hyperbolic: Knowledge graphs, taxonomies, org charts
• MoE: Multi-task learning, domain-specific routing

Self-Discovery

• AI agent introspection
• Autonomous capability mapping
• Performance monitoring
• Knowledge base construction
• Continuous learning systems

🔬 Advanced Topics

Performance Optimization

• Vector dimension tuning
• Batch processing
• Index configuration
• Memory management

Integration Patterns

• RAG pipelines
• Agent memory systems
• Semantic caching
• Knowledge graphs

Research Applications

• Cognitive architectures
• Meta-learning
• Self-improving systems
• Emergent behaviors

📖 Further Reading

Official Documentation

• AgentDB README
• RuVector Documentation
• Attention Mechanisms Guide

Related Demos

• AgentDB Examples
• Browser Examples

🤝 Contributing

These demonstrations are designed to be:

• Educational: Learn by example
• Extensible: Build on these patterns
• Practical: Production-ready code

Feel free to:

• Modify and extend these demos
• Create new demonstrations
• Share your discoveries
• Build applications using these patterns

📝 License

These demonstrations follow the same license as AgentDB (MIT OR Apache-2.0).

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🎉 Credits

Package: agentdb@2.0.0-alpha.2.11 Session: AgentDB Exploration & Self-Discovery Date: December 2, 2025

Built with ❤️ using AgentDB's cognitive capabilities.

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Happy Exploring! 🚀