Cascade of Semantically Integrated Layers (CaSIL)

Overview

The Cascade of Semantically Integrated Layers (CaSIL) is a pure Python algorithm that enriches responses from any LLM by layering semantic understanding and contextual depth. CaSIL breaks down input through a series of processing layers, adding detail, context, and even a bit of insight along the way. No fancy frameworks, just Python being clever with a little help from nltk, networkx, and scikit-learn.

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How CaSIL Works

CaSIL processes input through four structured layers, each building on the previous to achieve a detailed, context-rich output. This layered approach helps the model not only understand questions but respond in a way that feels more human, intuitive, and informed.

Layer Breakdown

1. Initial Understanding: Grabs the main concepts from user input using TF-IDF and NER (named entity recognition).

2. Relationship Analysis: Discovers connections between concepts and structures them within a knowledge graph.

3. Context Integration: Adds broader contextual knowledge, such as historical or background information, that enriches the response.

4. Response Synthesis: Combines everything into a final answer that feels cohesive and contextually appropriate.

These steps help the LLM approach nuanced topics and complex questions with responses that feel layered and thought-through.

Key Features

• Pure Python Implementation: No LangChain, LangGraph, or other frameworks, just the essentials for lightweight processing.

• Knowledge Graph Integration: Tracks relationships and concept frequency to improve understanding over time.

• Dynamic Corpus Management: Adapts its analysis based on user input, refining concepts with each interaction.

• Adaptive Processing: Configurable thresholds let you adjust response depth and specificity.

• Optional Knowledge Base: The knowledge base can be enabled or disabled depending on user needs, providing flexibility in context integration.

Code Highlights

Semantic Processing Pipeline

Each layer processes the input sequentially, from concept extraction to final synthesis:

def process_semantic_cascade(self, user_input: str) -> Dict[str, Any]:
    """
    Multi-layer processing pipeline.
    
    Layers:
    1. Initial Understanding: Extracts main concepts.
    2. Relationship Analysis: Finds connections between concepts.
    3. Context Integration: Adds contextual knowledge.
    4. Response Synthesis: Assembles a cohesive response.
    
    Args:
        user_input: User query.
        
    Returns:
        Dict with results from each layer.
    """
    try:
        results = {
            'initial_understanding': '',
            'relationships': '',
            'context_integration': '',
            'final_response': '',
            'metadata': {'timestamp': datetime.now().isoformat()}
        }
        
        results['initial_understanding'] = self._process_layer("initial_understanding", user_input)
        results['relationships'] = self._process_layer("relationship_analysis", user_input, results['initial_understanding'])
        results['context_integration'] = self._process_layer("context_integration", user_input, results['relationships'])
        results['final_response'] = self._process_layer("synthesis", user_input, results['context_integration'])
        
        return results
    except Exception as e:
        return {
            'initial_understanding': '',
            'relationships': '',
            'context_integration': '',
            'final_response': f"Error processing query: {str(e)}",
            'error': str(e)
        }

Knowledge Graph Management

CaSIL uses a knowledge graph to capture concepts and connections over time, updating based on input:

def _update_knowledge_graph(self, concepts: List[str], relationships: List[Tuple[str, str, float]]) -> None:
    """
    Updates the knowledge graph with new concepts and relationships.
    
    Args:
        concepts: List of new concepts to add.
        relationships: List of (concept1, concept2, weight) tuples.
    """
    for concept in concepts:
        if concept not in self.knowledge_graph:
            self.knowledge_graph.add_node(concept, first_seen=datetime.now(), frequency=1)
        else:
            self.knowledge_graph.nodes[concept]['frequency'] += 1
            
    for c1, c2, weight in relationships:
        if self.knowledge_graph.has_edge(c1, c2):
            current_weight = self.knowledge_graph[c1][c2]['weight']
            self.knowledge_graph[c1][c2]['weight'] = (current_weight + weight) / 2
        else:
            self.knowledge_graph.add_edge(c1, c2, weight=weight)

Configuration and Adaptability

CaSIL's configuration allows for fine-tuning thresholds, processing options, adaptive response management, and an optional knowledge base toggle:

@dataclass
class CSILConfig:
    """Configuration for CSIL processing."""
    min_keywords: int = 2
    max_keywords: int = 20
    similarity_threshold: float = 0.1
    debug_mode: bool = False
    use_knowledge_base: bool = True  # Toggle for knowledge base

Installation and Usage

Prerequisites

• Python: 3.10+
• Dependencies:
  • nltk >= 3.6
  • scikit-learn >= 0.24
  • numpy >= 1.19
  • networkx >= 2.5

Installation

Clone the repository and install dependencies:

git clone https://github.com/severian42/Cascade-of-Semantically-Integrated-Layers.git
cd Cascade-of-Semantically-Integrated-Layers
pip install -r requirements.txt

Running the CLI Application

Run the CSIL application with debug mode to see detailed processing steps:

python main.py --debug

• --debug: Provides detailed logs for each layer, helping you understand how CSIL processes input.

License

CSIL is licensed under the MIT License. See the LICENSE file for more details.

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With CSIL, every question becomes a layered exploration. Try it out, explore the layers, and see what a little extra semantic depth can do for your AI interactions!