💾 Add Intelligent Caching Layer

💾 Add Intelligent Caching Layer

Issue Description

Performance analysis revealed that repeated expensive operations (graph traversals, search results, document processing) are being recalculated unnecessarily. There's no caching layer to store and reuse results, causing significant performance degradation for common queries.

📍 Problem Areas

• Graph traversal results recalculated for similar queries
• Document processing repeated for same content
• Search results not cached for repeated searches
• LLM API responses not cached for similar prompts

🎯 Solution Approach

Implement multi-layer caching system with LRU eviction, TTL policies, and intelligent invalidation.

Core Caching Components:

1. LRU Cache for frequently accessed search results
2. Graph Cache for traversal results and path computations
3. Document Cache for processed document metadata
4. API Response Cache for LLM and external service calls

📋 Implementation Tasks

• Design cache key generation strategies
• Implement LRU caching with configurable sizes
• Add TTL-based cache invalidation
• Implement cache warming strategies
• Add cache metrics and monitoring

📊 Expected Performance Improvements

• Repeated queries: 50-70% faster response times
• Graph operations: 3-5x faster for common traversals
• Document processing: 60-80% reduction in reprocessing
• API calls: 40-60% reduction in external service calls

Related to: #193 (Performance Optimization Epic)
Estimated Effort: 4-5 days
Priority: Medium (Performance optimization)

The issue names no files, tests, or entry points. Start by mapping where graph traversals, document processing, searches, and LLM or external-service calls are implemented, then establish cache-key, TTL, invalidation, warming, and metrics requirements for each path. Done means the proposed multi-layer cache works across all listed problem areas and its performance claims can be verified.