Caching System For Metric Evaluation Results

LLM evaluation framework — 14+ metrics, faithfulness/hallucination detection, Pytest integration.

Unique: Implements transparent caching via a cache layer that intercepts metric execution before LLM invocation, using content-based hashing of test cases and metric configs as cache keys; supports both local SQLite and cloud-based caching without requiring code changes

vs others: More transparent than manual caching approaches because it's built into the metric execution pipeline, automatically caching results without developer intervention