[Week 5-6] Complete 4-criteria suite, edge-case hardening, Urdu support, docstrings audit, and fellowship lab note

[Maazkorejo]

Summary of Deliverables — Week 5–6 (Research/Implementation Engineer)

1. 4-Criteria Suite Completion

• Implemented relevance.py (relevance) with cosine similarity topical alignment scoring and relevance_config.json.
• Implemented completeness.py (completeness) with rule-based prompt aspect decomposition and thresholded coverage scoring ($\ge 0.60$) and completeness_config.json.
• Updated llm_eval_kit/__init__.py to automatically register all four criteria (refusal_check, factual_grounding, relevance, completeness) on package import.

2. Edge-Case Hardening

• Empty / Whitespace / None Inputs: Added pre-encoding guards across all criteria to short-circuit directly to score: 0.0 (or score: None if context is missing), preventing BERT tokenization artifacts on empty inputs.
• 5,000-Word Context Benchmarking: Validated $O(N)$ linear-time regex sentence-splitting (< 0.5 ms for 30,000 characters with zero ReDoS risk) and batched transformer inference (batch_size=64, ~350 ms CPU latency).
• Urdu Multilingual Support: Added 13 common Urdu refusal phrases in refusal_config.json, native Urdu punctuation segmentation (۔, ؟), and Eastern Arabic numeral translation (۰–۹ to 0–9).

3. Code Quality & Docstrings Audit

• Removed inline comment noise across all criteria modules.
• Added comprehensive Google-style docstrings with explicit Args:, Returns:, and Known Limitations: sections.

4. Test Verification

• Added tests/test_edge_cases.py and tests/test_criteria_all.py.
• Full suite of 51/51 pytest tests pass cleanly across tests/.

5. 6-Week Fellowship Lab Note

• Submitted lab_note.md (504 words) detailing the full 6-week architecture and implementation journey for Muhammad Maaz (Research/Implementation Engineer).