validation-engine.md

Validation Engine

Overview

The DataHive Validation Engine is a critical component of the LN1 pipeline that ensures data quality, accuracy, and reliability through automated validation processes and multi-node consensus.

Core Components

Engine Architecture

class ValidationEngine:
    def __init__(self):
        self.validators = []
        self.consensus_threshold = 0.67  # 2/3 majority required
        self.validation_rules = ValidationRules()

    async def process_validation(self, legal_data):
        initial_validation = await self.validate_content(legal_data)
        consensus_result = await self.achieve_consensus(initial_validation)
        return self.generate_validation_report(consensus_result)

Validation Process

Content Validation

• Source authenticity verification
• Document structure analysis
• Legal reference validation
• Metadata completeness checks

Consensus Mechanism

• Multi-node validation distribution
• Response aggregation
• Threshold verification
• Conflict resolution

Integration Points

Pipeline Components

• Interfaces with document indexer
• Connects to pattern recognition module
• Feeds into storage system
• Updates knowledge models

Cross-Node Operations

• Validation request broadcasting
• Result collection and aggregation
• State synchronization
• Error handling

Quality Metrics

Performance Monitoring

• Validation success rates
• Processing time tracking
• Error frequency analysis
• Consensus achievement rates

Quality Assurance

• Automated validation checks
• Manual review triggers
• Performance benchmarking
• Continuous improvement tracking

Note: This documentation is subject to updates as the validation engine evolves.