Legalese-Node-LN1

Document Indexer Pipeline

The Document Indexer Pipeline is a core component of the LN1 node that processes, indexes, and makes legal documents searchable within the DataHive ecosystem. This pipeline ensures efficient document processing, accurate indexing, and fast retrieval capabilities.

Pipeline Architecture

Core Components

class IndexerPipeline:
    def __init__(self):
        self.document_processor = DocumentProcessor()
        self.index_manager = IndexManager()
        self.search_engine = SearchEngine()
        self.metadata_indexer = MetadataIndexer()

Processing Stages

1. Document intake
2. Content extraction
3. Metadata generation
4. Index creation
5. Search optimization

Document Processing

Content Extraction

class ContentExtractor:
    def extract_content(self, document):
        return {
            'text': self.extract_text(),
            'structure': self.analyze_structure(),
            'citations': self.extract_citations(),
            'metadata': self.extract_metadata()
        }

Metadata Generation

• Document classification
• Citation mapping
• Legal entity recognition
• Jurisdiction identification

Indexing Strategy

Index Structure

interface IndexStructure {
    document: {
        id: string;
        content: string;
        metadata: object;
        vectors: number[];
    };
    mappings: {
        fields: string[];
        analyzers: string[];
        settings: object;
    }
}

Optimization Techniques

class IndexOptimizer:
    def optimize_index(self):
        return {
            'segment_merge': self.merge_segments(),
            'cache_warmup': self.warm_cache(),
            'field_optimization': self.optimize_fields(),
            'analyzer_tuning': self.tune_analyzers()
        }

Search Capabilities

Query Processing

class QueryProcessor:
    def process_query(self, query):
        return {
            'parsed_query': self.parse_query(),
            'expanded_terms': self.expand_terms(),
            'filters': self.apply_filters(),
            'ranking': self.configure_ranking()
        }

Ranking Algorithms

• TF-IDF scoring
• Citation importance
• Document freshness
• Legal relevance

Performance Optimization

Indexing Performance

class PerformanceOptimizer:
    def optimize_performance(self):
        return {
            'batch_processing': self.configure_batching(),
            'memory_management': self.optimize_memory(),
            'concurrent_indexing': self.manage_concurrency(),
            'resource_allocation': self.allocate_resources()
        }

Caching Strategy

• Document cache
• Query cache
• Filter cache
• Aggregation cache

Integration Points

External Systems

class SystemIntegration:
    def configure_integrations(self):
        return {
            'storage_system': self.connect_storage(),
            'search_api': self.setup_search_api(),
            'monitoring': self.configure_monitoring(),
            'analytics': self.setup_analytics()
        }

Monitoring and Metrics

Performance Metrics

class IndexerMonitor:
    def collect_metrics(self):
        return {
            'indexing_rate': self.measure_indexing_speed(),
            'query_performance': self.measure_query_speed(),
            'index_size': self.measure_index_size(),
            'resource_usage': self.track_resources()
        }

Health Checks

• Index health
• Query performance
• Resource utilization
• Error rates

Error Handling

Error Recovery

class ErrorHandler:
    def handle_error(self, error):
        return {
            'error_type': self.classify_error(error),
            'recovery_action': self.determine_action(error),
            'notification': self.notify_stakeholders(error),
            'logging': self.log_error(error)
        }

Maintenance Procedures

Index Maintenance

class IndexMaintenance:
    def maintain_index(self):
        return {
            'optimization': self.optimize_index(),
            'cleanup': self.cleanup_old_segments(),
            'backup': self.backup_index(),
            'health_check': self.verify_index_health()
        }

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