Fetching metadata from the HF Docker repository...
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Declutter repo root: move DATA_CARD, drop stale gitkeeps and dead scripts/lib
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Apply ruff format across codebase
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Serialize baseline comparison results into eval output dict
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Add eval gate; guard load-test against broken deployments; drop orphaned scripts
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Harden RAGAS eval: 60s timeout, checkpoint/resume, haiku model, --ragas-only flag
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Update and add evaluation, query-bank, and pipeline scripts
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Format load test script
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Expose cache header observability
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Improve validation and resource handling
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Add Qdrant retry logic
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Replace manual dict construction
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Simplified cold-start, API takes query directly
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Use canonical is_refusal() from faithfulness module
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Replace custom percentile with np.percentile
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Extract magic numbers to constants incl other polishing
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Harden summary.py
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Calibrate evidence quality gate thresholds
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Add mixed evidence handling to explanation prompts
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Add bootstrap confidence intervals to evaluation metrics
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Refactor sanity checks with improved adversarial detection
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Track rated_count incrementally
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Add concurrent load testing for SemanticCache
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Add RefusalType enum and query sanitization
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Reorder index creation before upload
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Eliminate duplicate payload.get() calls
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Fix EDA review-level rating distribution and resource cleanup
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Consolidate API reference endpoints
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Fix README accuracy and track Kaggle notebook
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Improve README accuracy
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Fix eval workflow and align README metrics with eval_results JSON
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Add .hfignore to exclude binaries from HuggingFace deployment
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Fix EDA figure paths for GitHub display
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Fix hardcoded HHEM metric
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Restructure Makefile
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Replace EDA with production Qdrant queries
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Switch deployment from Render to Hugging Face Spaces
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Switch deployment from Render to Hugging Face Spaces
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Switch deployment from Render to Hugging Face Spaces
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Add docker-compose, multi-stage builds, and developer tooling
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Add docker-compose, multi-stage builds, and developer tooling
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Fix RAGAS faithfulness scoring (0.64 → 0.82)
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Apply ruff formatting
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Fix all CI lint and type errors
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Initial commit: Sage RAG recommendation system