Cascade of Semantically Integrated Layers (CaSIL)

CaSIL_Marble_In_Cup_Question.mov

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🌊 Overview

CaSIL is an advanced natural language processing system that implements a sophisticated four-layer semantic analysis architecture. It processes both user input and knowledge base content through progressive semantic layers, combining:

• Dynamic concept extraction and relationship mapping
• Adaptive semantic similarity analysis
• Context-aware knowledge graph integration
• Multi-layer processing pipeline
• Real-time learning and adaptation

🚀 Quick Start

Prerequisites

• Python 3.10+
• Local LLM server (e.g., Ollama)
• Required Python packages (see requirements.txt)

Installation

1. Clone the repository:

git clone https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers.git

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

cp .env.example .env
# Edit .env with your configuration

4. Start your local LLM server (e.g., Ollama)

5. Run the system:

python main.py --debug # (Shows full internal insight and metrics)

Basic Usage

# Start the system
python main.py --debug

# Available commands:
help      # Show available commands
graph     # Display knowledge graph statistics
concepts  # List extracted concepts
relations # Show concept relationships
exit      # Exit the program

🧠 Core Architecture

1. Semantic Processing Layers

CaSIL processes input through four sophisticated layers:

Layer 1: Initial Understanding (θ=0.7)

• Advanced concept extraction using TF-IDF
• Named entity recognition
• Custom stopword filtering
• Semantic weight calculation

Layer 2: Relationship Analysis (θ=0.7)

• Dynamic similarity matrix computation
• Graph-based relationship discovery
• Concept clustering and community detection
• Temporal relationship tracking

Layer 3: Context Integration (θ=0.9)

• Historical context weighting
• Knowledge base integration
• Dynamic context windows
• Adaptive threshold adjustment

Layer 4: Response Synthesis (θ=0.8)

• Multi-source information fusion
• Style-specific processing
• Dynamic temperature adjustment
• Context-aware response generation

2. Knowledge Graph System

CaSIL maintains two interconnected graph systems:

Session Graph

• Tracks temporary concept relationships
• Maintains conversation context
• Updates in real-time
• Handles recency weighting

Persistent Knowledge Graph

• Stores long-term concept relationships
• Tracks concept evolution over time
• Maintains relationship weights
• Supports community detection

🛠️ Technical Features

Processing Pipeline Architecture

• Multi-Stage Processing

  • Input text → Concept Extraction → Relationship Analysis → Context Integration → Response
  • Each stage maintains its own similarity thresholds and processing parameters
  • Adaptive feedback loop adjusts parameters based on processing results

• Semantic Analysis Engine

  # Example concept extraction flow
  text → TF-IDF Vectorization → Weight Calculation → Threshold Filtering → Concepts
  
  # Relationship discovery process
  concepts → Similarity Matrix → Graph Construction → Community Detection → Relationships

• Dynamic Temperature Control

  temperature = base_temp * layer_modifier['base'] * (
      1 + (novelty_score * layer_modifier['novelty_weight']) *
      (1 + (complexity_factor * layer_modifier['complexity_weight']))
  )

Knowledge Graph Architecture

• Dual-Graph System

  Session Graph (Temporary)      Knowledge Graph (Persistent)
  ├─ Short-term relationships    ├─ Long-term concept storage
  ├─ Recency weighting           ├─ Relationship evolution
  ├─ Context tracking            ├─ Community detection
  └─ Real-time updates           └─ Concept metadata
  

• Graph Update Process

  # Simplified relationship update flow
  new_weight = (previous_weight + (similarity * time_weight)) / 2
  graph.add_edge(concept1, concept2, weight=new_weight)

Unique Features

1. Adaptive Processing

• Dynamic threshold adjustment based on:

  ├─ Input complexity
  ├─ Concept novelty
  ├─ Processing layer
  └─ Historical performance
  

2. Semantic Integration

• Multi-dimensional similarity calculation:

  Combined Similarity = (0.7 * cosine_similarity) + (0.3 * jaccard_similarity)
  

• Weighted by:
  • Term frequency
  • Position importance
  • Historical context

3. Context Management

Context Integration Flow:
Input → Session Context → Knowledge Graph → External Knowledge → Response
     ↑______________________________________________|
     (Feedback Loop)

Data Flow Architecture

                                    ┌────────────────┐
                                    │ Knowledge Base │
                                    └───────┬────────┘
                                            │
                                            ▼
User Input → Concept Extraction → Similarity Analysis → Graph Integration
    ↑              │                        │                  │
    │              ▼                        ▼                  ▼
    │         Session Graph ──────► Relationship Analysis ◄─── Knowledge Graph
    │              │                        │                  │
    │              └────────────────────────┼──────────────────
    │                                       │
    └───────────────────── Response ◄───────┘

Performance Optimizations

• LRU Cache for similarity calculations
• Concurrent processing with thread pooling
• Batched vectorizer updates
• Adaptive corpus management

Key Differentiators

1. Progressive Semantic Analysis

   • Each layer builds upon previous insights
   • Maintains context continuity
   • Adapts processing parameters in real-time

2. Dynamic Knowledge Integration

   • Combines session-specific and persistent knowledge
   • Real-time graph updates
   • Community-based concept clustering

3. Adaptive Response Generation

   • Context-aware temperature adjustment
   • Style-specific processing parameters
   • Multi-source information fusion

📊 System Requirements

• CPU: Multi-core processor recommended
• RAM: 8GB minimum, 16GB recommended
• Storage: 1GB for base system
• Python: 3.8 or higher
• OS: Linux, macOS, or Windows

🔧 Configuration

Environment Variables

DEBUG_MODE=true
USE_EXTERNAL_KNOWLEDGE=false
LLM_URL=http://0.0.0.0:11434/v1/chat/completions
LLM_MODEL=your_model_name

Processing Thresholds

INITIAL_UNDERSTANDING_THRESHOLD=0.7
RELATIONSHIP_ANALYSIS_THRESHOLD=0.7
CONTEXTUAL_INTEGRATION_THRESHOLD=0.9
SYNTHESIS_THRESHOLD=0.8

📚 Advanced Usage

Custom Knowledge Integration

from SemanticCascadeProcessing import CascadeSemanticLayerProcessor

processor = CascadeSemanticLayerProcessor(config)
processor.knowledge_base.load_from_directory("path/to/knowledge")

Graph Analysis

# Get graph statistics
processor.knowledge.print_graph_summary()

# Analyze concept relationships
processor.analyze_knowledge_graph()

🤝 Contributing

Contributions are welcome! Please read our Contributing Guidelines first.

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Thanks to the open-source NLP community
• Special thanks to LocalLLaMA for never letting anything slip by them unnoticed; and for just being an awesome community overall