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Add a complete example demonstrating the LLM memoization system functionality, including: - Basic MemoStore operations with cache hit/miss scenarios - Statistics tracking showing hit rates and performance metrics - Cache invalidation by operation type - Persistence capabilities demonstration - Real-world scenario simulation with cost savings calculation The example showcases how memoization can reduce API costs by caching LLM responses and reusing them when identical requests occur. refactor: update change detection to use fingerprint comparison Replace hash-based content comparison with fingerprint-based comparison in the incremental change detector for improved accuracy and consistency with the memoization system's approach.
Add comprehensive design document outlining performance optimization strategies targeting millisecond-level response times. Includes detailed plans for cache strategy optimization with semantic similarity, incremental indexing with subtree-level updates, parallel retrieval optimization, and memory footprint reduction. The document covers implementation timeline, success criteria, dependencies, and risk mitigation strategies for achieving 90%+ cache hit rates, sub-100ms retrieval latency, and improved memory efficiency.
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