🔍 Optimize Search Relevance Functions

🔍 Optimize Search Relevance Functions

Issue Description

Performance analysis found that search relevance scoring (BM25, TitleScorer, TerraphimGraph) has computational inefficiencies with repeated calculations, suboptimal data structures, and missing early termination strategies.

📍 Problem Areas

• BM25 implementation with repeated term frequency calculations
• No early termination for low-relevance documents
• Inefficient scoring data structure access patterns
• Missing SIMD optimizations for bulk scoring

🎯 Solution Approach

Optimize relevance functions with better algorithms, early termination, and SIMD operations where beneficial.

📋 Implementation Tasks

• Optimize BM25 implementation with precomputed statistics
• Add early termination strategies for scoring
• Implement SIMD operations for bulk text processing
• Optimize data structures for scoring operations
• Add incremental scoring updates

📊 Expected Performance Improvements

• Scoring speed: 2-3x faster relevance calculations
• Memory usage: 15-25% reduction in scoring overhead
• Query latency: 30-40% faster search response times
• Throughput: Better handling of large document sets

Related to: #193 (Performance Optimization Epic)
Estimated Effort: 5-6 days
Priority: Medium (Search performance)

Start by locating the BM25, TitleScorer, and TerraphimGraph implementations and profiling their current scoring paths. Review the repeated term-frequency calculations, data-structure access, early termination, SIMD operations, and incremental updates; confirm completion against the stated latency, throughput, scoring-speed, and memory targets.