Domain Knowledge Extraction Stage (Stage 2) - Dual-Model Architecture

The Domain Knowledge Extraction stage retrieves specialized domain knowledge from multiple expert language models, performs intelligent multi-model fusion, and establishes confidence levels for each knowledge element through consensus validation. This stage is critical for providing accurate, specialized knowledge that serves as the foundation for subsequent reasoning and solution generation stages.

Dual-Model Expert Architecture

The enhanced stage now employs a sophisticated dual-model architecture:

Primary Domain Expert

Secondary Domain Expert

Multi-Model Fusion Engine

Components

1. Enhanced Domain Knowledge Service

The main service orchestrating the dual-model domain knowledge extraction process. Key functionality includes:

2. Advanced Knowledge Extractor

Core component responsible for extracting domain-specific knowledge from both expert models. Features include:

3. Multi-Model Knowledge Prioritizer

Enhanced prioritizer and ranks extracted knowledge elements with fusion capabilities. Functionality includes:

4. Enhanced LLM Connector

Enhanced connector that manages connections to both primary and secondary domain-expert language models. Features include:

5. Cross-Model Knowledge Validator

Enhanced validator that performs cross-model validation of extracted knowledge. Functionality includes:

Enhanced Process Flow

  1. Domain Analysis & Model Selection
    • Analyze semantic representation from Stage 1
    • Identify required knowledge domains
    • Determine extraction priorities
    • Select appropriate expert models (primary and/or secondary)
  2. Dual-Model Knowledge Extraction
    • Construct specialized prompts for each expert model
    • Execute simultaneous extraction across both expert models
    • Parse model responses with source attribution
    • Build initial knowledge structure with multi-model insights
  3. Cross-Model Validation
    • Check consistency of extracted knowledge across models
    • Identify conflicts and contradictions between expert outputs
    • Assess source reliability and model confidence
    • Perform cross-validation between expert models
  4. Multi-Model Fusion & Prioritization
    • Perform intelligent fusion of insights from multiple experts
    • Score knowledge relevance with model source weighting
    • Map dependencies across model sources
    • Calculate confidence levels with consensus validation
    • Quantify uncertainties with multi-model analysis
    • Detect and boost consensus insights
  5. Enhanced Knowledge Integration
    • Structure knowledge elements with multi-model attribution
    • Establish relationships across expert model outputs
    • Document dependencies and model sources
    • Prepare metadata with consensus and complementary insight analysis

Integration Points

Input Requirements

Output Format

Downstream Usage

Performance Considerations

Optimization Goals

Monitoring Metrics

Error Handling

Extraction Errors

Validation Failures

Configuration

The stage can be configured through various parameters supporting dual-model operation:

{
  "extraction": {
    "min_confidence": 0.8,
    "max_depth": 3,
    "cross_validation": true,
    "enable_dual_models": true,
    "consensus_threshold": 0.9
  },
  "models": {
    "primary": "sprint-domain-expert",
    "secondary": "sprint-domain-expert-secondary", 
    "fallback": "phi-3-mini",
    "timeout": 30,
    "enable_multi_model_fusion": true
  },
  "validation": {
    "consistency_threshold": 0.9,
    "min_sources": 2,
    "consensus_boost": 0.1,
    "cross_model_validation": true
  }
}

Best Practices

  1. Multi-Model Knowledge Quality
    • Validate all extracted knowledge across expert models
    • Document confidence levels with consensus indicators
    • Track source reliability for each expert model
    • Maintain knowledge coherence across model sources
  2. Dual-Model Management
    • Monitor performance of both primary and secondary experts
    • Update model selection based on domain requirements
    • Optimize prompts for each expert model’s strengths
    • Handle failures gracefully with cross-model fallbacks
  3. Performance Optimization
    • Cache common knowledge across model sources
    • Parallelize extraction from multiple expert models
    • Prioritize critical paths with model consensus
    • Monitor resource usage across dual-model operations
  4. Quality Assurance
    • Regular validation checks across expert models
    • Cross-reference sources between models
    • Update knowledge bases for both expert models
    • Track extraction metrics with multi-model attribution

Model Specifications

Primary Sprint Expert

Secondary Sprint Expert (sprint-llm-distilled-20250324-040451)

Benefits of Dual-Model Architecture