Commit Graph

3 Commits

Author SHA1 Message Date
iomgaa eaa58604b3 feat(question_gen): add Action Recognition specialized prompt template
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-07-14 06:45:09 -04:00
iomgaa cf51d2de9d feat(question_gen): add v2 generator with per-family prompt templates
Implement generator_v2.py with:
- CandidateQuestion dataclass (canonical location)
- _load_prompt_template: loads per-family .md from store/prompts/
- _build_v2_prompt: constructs system+user messages with material context
- _parse_v2_response: JSON extraction, json_repair, field validation
- generate_one_v2: async VLM call orchestration with reject_reason support

Add 5 family-specific prompt templates:
- retrieval.md: factual recall from visible content
- reasoning.md: multi-hop inference across segments
- enumeration.md: counting/listing entities and actions
- visual.md: visual details requiring frame observation
- spatial.md: spatial relationships between objects/people

Tests: 11 unit tests covering prompt build, parse, and e2e generation.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-07-11 23:41:36 -04:00
iomgaa 271d1682c9 feat(question_gen): add lightweight 4-gate quality check
Implement 4 concurrent LLM-based quality gates for generated questions:
- key_verify: validates answer evidence in source material
- blind_answer: rejects questions answerable without video context
- multi_true: detects ambiguous multi-correct options
- leak_test: per-family shortcut detection (5 probe templates)

Includes run_gates orchestrator with verbatim_ratio short-circuit,
JSON response parsing with fallback, and 9 unit tests (all passing).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-07-11 23:30:13 -04:00