Cognitive Engine

Cognitive intelligence system for advanced abstraction, pattern recognition, and insight generation.

Overview

Cognitive Engine is a backend AI system that processes information through multiple abstraction layers to discover patterns, generate insights, and create novel connections. It's the "cognitive engine" of the SuperInstance ecosystem.

Key Features

• 5-Level Abstraction - Process data through hierarchical cognitive layers
• Pattern Recognition - Detect complex patterns across datasets
• Insight Generation - Generate novel insights and hypotheses
• Knowledge Synthesis - Combine disparate information into coherent understanding
• Dream Mode - Generative exploration of idea spaces
• Memory Integration - Work with MemorySystem for persistent knowledge
• Tensor Operations - Knowledge tensor manipulation
• Streaming API - Real-time cognitive processing

Architecture

                    ┌─────────────────────────┐
                    │     Cognitive Engine    │
                    │      Cognitive Core     │
                    └───────────┬─────────────┘
                                │
    ┌───────────────────────────┼───────────────────────────┐
    │                           │                           │
┌───▼────┐              ┌──────▼──────┐              ┌────▼─────┐
│ Level 1│              │   Level 2   │              │  Level 3 │
│Raw Data│ ─────────▶   │  Patterns   │  ─────────▶  │ Concepts │
└────────┘              └─────────────┘              └──────────┘
                                                        │
    ┌───────────────────────────┼───────────────────────────┐
    │                           │                           │
┌───▼──────┐              ┌─────▼──────┐              ┌────▼─────┐
│ Level 4  │              │   Level 5  │              │  Dream   │
│Contextual│  ─────────▶  │ Abstract   │  ─────────▶  │  Mode    │
│Meanings  │              │ Principles │              │Generator │
└──────────┘              └────────────┘              └──────────┘

Quick Start

Prerequisites

• Node.js 18+
• PostgreSQL (for knowledge storage)
• pnpm 8+

Installation

# Clone the repository
git clone https://github.com/SuperInstance/CognitiveEngine.git
cd CognitiveEngine

# Install dependencies
pnpm install

# Start PostgreSQL
docker-compose up -d

# Configure environment
cp .env.example .env
# Edit .env with your settings

# Start the service
pnpm start

Running in Production

# Build
pnpm build

# Start with PM2
npx pm2 start dist/index.js --name cognitive-engine

Usage

Basic Abstraction

import { CognitiveEngine } from '@superinstance/cognitive-engine';

const engine = new CognitiveEngine({
  connectionString: process.env.DATABASE_URL
});

// Process data through abstraction levels
const insights = await engine.dream({
  input: 'User engagement metrics showing 30% drop',
  context: { domain: 'product-analytics' }
});

console.log(insights);
// {
//   patterns: ['engagement drops correlate with feature changes'],
//   concepts: ['user friction points'],
//   hypotheses: ['new UI may be causing confusion']
// }

Pattern Recognition

// Detect patterns in data
const patterns = await engine.recognizePatterns({
  data: [
    { timestamp: '2024-01-01', metric: 100 },
    { timestamp: '2024-01-02', metric: 95 },
    { timestamp: '2024-01-03', metric: 90 }
  ],
  patternTypes: ['trend', 'anomaly', 'cycle']
});

Dream Mode

// Generative exploration
const dreams = await engine.enterDreamMode({
  seed: 'sustainable energy solutions',
  explorationDepth: 5,
  noveltyThreshold: 0.7
});

// Returns novel idea combinations

Project Structure

cognitive-engine/
├── src/
│   ├── core/                   # Core cognitive engine
│   │   ├── cognitive-engine.ts # Main CognitiveEngine class
│   │   ├── abstraction.ts      # Abstraction layer processor
│   │   └── consciousness.ts    # Consciousness simulation
│   ├── levels/                 # Abstraction levels
│   │   ├── level1-data.ts      # Raw data processing
│   │   ├── level2-patterns.ts  # Pattern recognition
│   │   ├── level3-concepts.ts  # Concept extraction
│   │   ├── level4-context.ts   # Contextual meaning
│   │   └── level5-principles.ts# Abstract principles
│   ├── patterns/               # Pattern detection
│   │   ├── detector.ts         # Pattern detection engine
│   │   └── clustering.ts       # Clustering algorithms
│   ├── insights/               # Insight generation
│   │   ├── generator.ts        # Insight generator
│   │   └── scorer.ts           # Insight relevance scoring
│   ├── dream/                  # Dream mode
│   │   ├── explorer.ts         # Idea space explorer
│   │   └── synthesizer.ts      # Idea synthesizer
│   ├── storage/                # Knowledge persistence
│   │   ├── postgres.ts         # PostgreSQL interface
│   │   └── tensor.ts           # Tensor operations
│   ├── api/                    # HTTP API
│   │   ├── routes.ts           # API routes
│   │   └── middleware.ts       # Express middleware
│   └── types/                  # TypeScript definitions
├── docker/
│   └── docker-compose.yml
├── migrations/                 # Database migrations
└── tests/                      # Test suite

API Reference

REST API

Endpoint	Method	Description
/health	GET	Health check
/api/dream	POST	Process input through dreamer
/api/patterns	POST	Detect patterns
/api/insights	GET	Retrieve recent insights
/api/levels/:id	GET	Get abstraction level output
/api/dream-mode	POST	Enter generative dream mode

WebSocket API

const ws = new WebSocket('ws://localhost:4000/ws');

// Subscribe to insight stream
ws.send(JSON.stringify({
  type: 'subscribe',
  channel: 'insights'
}));

// Receive real-time insights
ws.onmessage = (event) => {
  const insight = JSON.parse(event.data);
  console.log('New insight:', insight);
};

Configuration

Environment Variables

# Server
PORT=4000
HOST=0.0.0.0
NODE_ENV=development

# Database
DATABASE_URL=postgresql://user:pass@localhost:5432/luciddreamer

# Abstraction
MAX_ABSTRACTION_LEVELS=5
PATTERN_CONFIDENCE_THRESHOLD=0.7
INSIGHT_NOVELTY_THRESHOLD=0.6

# Dream Mode
DREAM_EXPLORATION_DEPTH=5
DREAM_MAX_COMBINATIONS=100

# Caching
REDIS_ENABLED=true
REDIS_HOST=localhost
REDIS_PORT=6379

# LLM Integration
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...

Cognitive Levels

Level 1: Raw Data

• Input normalization
• Data cleaning
• Feature extraction

Level 2: Patterns

• Statistical patterns
• Temporal patterns
• Correlation detection

Level 3: Concepts

• Concept extraction
• Semantic clustering
• Category formation

Level 4: Contextual Meanings

• Context integration
• Situational awareness
• Pragmatic interpretation

Level 5: Abstract Principles

• First principles
• Universal patterns
• Meta-insights

Technologies

• Node.js 18+ - Runtime
• TypeScript 5 - Type safety
• Express - Web framework
• PostgreSQL - Knowledge storage
• Redis - Caching
• Tensor Ops - Knowledge tensor operations
• OpenAI/Anthropic - LLM integration

License

MIT

Contributing

Contributions welcome! Please see CONTRIBUTING.md

Support

• GitHub Issues: https://github.com/SuperInstance/CognitiveEngine/issues
• Documentation: https://docs.superinstance.dev/cognitive-engine

---

SuperInstance - Modular toolkit ecosystem for intelligent applications.