Domain Knowledge Extraction Stage (Stage 2)

The Domain Knowledge Extraction stage retrieves specialized domain knowledge from expert language models and other sources, prioritizes it by relevance, and establishes confidence levels for each knowledge element. This stage is critical for providing accurate, specialized knowledge that serves as the foundation for subsequent reasoning and solution generation stages.

Components

1. Domain Knowledge Service

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

• Coordinating the extraction pipeline flow
• Managing access to domain-specific LLMs
• Prioritizing knowledge by relevance to the query
• Establishing knowledge confidence levels
• Structuring the extracted knowledge for downstream stages

2. Knowledge Extractor

Core component responsible for extracting domain-specific knowledge. Features include:

• Specialized extraction techniques for different domains
• Access to domain-specific knowledge bases
• Identification of formulas, constraints, and relationships
• Hierarchical knowledge representation
• Reference value extraction for specified parameters

3. Knowledge Prioritizer

Prioritizes and ranks extracted knowledge elements. Functionality includes:

• Relevance scoring for each knowledge element
• Dependency mapping between knowledge components
• Confidence level assessment for each element
• Uncertainty quantification across the knowledge set
• Priority weighting based on query requirements

4. LLM Connector

Manages connections to domain-expert language models. Features include:

• Integration with Sprint-LLM domain expert models
• Specialized prompt construction for knowledge extraction
• Response parsing and structured representation
• Error handling and fallback mechanisms
• Performance optimization for model interactions

5. Knowledge Validator

Validates and verifies extracted knowledge. Functionality includes:

• Consistency checking across knowledge elements
• Identification of contradictions or conflicts
• Source reliability assessment
• Cross-validation with multiple sources when available
• Documentation of limitations and caveats

Process Flow

1. Domain Analysis
   • Analyze semantic representation from Stage 1
   • Identify required knowledge domains
   • Determine extraction priorities
   • Select appropriate expert models
2. Model Selection
   • Choose domain-specific expert LLMs
   • Configure model parameters
   • Prepare extraction context
   • Set up fallback options
3. Knowledge Extraction
   • Construct specialized prompts
   • Execute extraction across domains
   • Parse model responses
   • Build initial knowledge structure
4. Validation
   • Check consistency of extracted knowledge
   • Identify conflicts and contradictions
   • Assess source reliability
   • Perform cross-validation
5. Prioritization
   • Score knowledge relevance
   • Map dependencies
   • Calculate confidence levels
   • Quantify uncertainties
6. Knowledge Integration
   • Structure knowledge elements
   • Establish relationships
   • Document dependencies
   • Prepare metadata

Integration Points

Input Requirements

• Semantic representation from Stage 1
• Query context and parameters
• Domain specifications
• Extraction preferences

Output Format

• Structured domain knowledge
• Confidence metrics
• Dependency mappings
• Validation results

Downstream Usage

• Informs reasoning strategies
• Guides solution generation
• Provides validation constraints
• Supports result verification

Performance Considerations

Optimization Goals

• Minimize extraction latency
• Maximize knowledge relevance
• Ensure comprehensive coverage
• Maintain accuracy standards

Monitoring Metrics

• Extraction success rates
• Validation accuracy
• Processing times
• Model performance

Error Handling

Extraction Errors

• Model fallback strategies
• Partial result handling
• Recovery mechanisms
• Error documentation

Validation Failures

• Conflict resolution
• Alternative sources
• Uncertainty documentation
• Quality assurance

Configuration

The stage can be configured through various parameters:

{
  "extraction": {
    "min_confidence": 0.8,
    "max_depth": 3,
    "cross_validation": true
  },
  "models": {
    "primary": "sprint-llm-distilled",
    "fallback": "phi-3-mini",
    "timeout": 30
  },
  "validation": {
    "consistency_threshold": 0.9,
    "min_sources": 2
  }
}

Best Practices

1. Knowledge Quality
   • Validate all extracted knowledge
   • Document confidence levels
   • Track source reliability
   • Maintain knowledge coherence
2. Model Management
   • Monitor model performance
   • Update model selection
   • Optimize prompts
   • Handle failures gracefully
3. Performance Optimization
   • Cache common knowledge
   • Parallelize extraction
   • Prioritize critical paths
   • Monitor resource usage
4. Quality Assurance
   • Regular validation checks
   • Cross-reference sources
   • Update knowledge bases
   • Track extraction metrics