Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
This commit is contained in:
ruv
315
vendor/ruvector/examples/meta-cognition-spiking-neural-network/demos/README.md
vendored
Normal file
315
vendor/ruvector/examples/meta-cognition-spiking-neural-network/demos/README.md
vendored
Normal file
@@ -0,0 +1,315 @@
|
||||
# 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**:
|
||||
```bash
|
||||
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**:
|
||||
```bash
|
||||
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**:
|
||||
```bash
|
||||
node demos/self-discovery/cognitive-explorer.js
|
||||
```
|
||||
|
||||
## 🚀 Quick Start
|
||||
|
||||
### Run All Demonstrations
|
||||
```bash
|
||||
# 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
|
||||
```bash
|
||||
# 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
|
||||
```javascript
|
||||
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
|
||||
```javascript
|
||||
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](../node_modules/agentdb/README.md)
|
||||
- [RuVector Documentation](../node_modules/ruvector/README.md)
|
||||
- [Attention Mechanisms Guide](../node_modules/@ruvector/attention/README.md)
|
||||
|
||||
### Related Demos
|
||||
- [AgentDB Examples](../node_modules/agentdb/examples/)
|
||||
- [Browser Examples](../node_modules/agentdb/examples/browser/)
|
||||
|
||||
## 🤝 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).
|
||||
|
||||
---
|
||||
|
||||
## 🎉 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.
|
||||
|
||||
---
|
||||
|
||||
**Happy Exploring! 🚀**
|
||||
Reference in New Issue
Block a user