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Author SHA1 Message Date
iomgaa afa77173e3 refactor(question_gen): adapt generator/gates/store signatures for strategy 
- generator_v2: _load_prompt_template takes template_name str instead of
  QuestionFamilySpec; _build_v2_prompt takes prompt_template + strategy_name
  + sub_pattern_instruction; generate_one_v2 takes discrete params
  (prompt_template, strategy_name, skill_target, sub_pattern_instruction)
- gates: _gate_leak_test and run_gates take leak_probe_template str
  instead of QuestionFamilySpec
- run_store: add sub_pattern column to DDL + idempotent migration;
  record_item accepts optional sub_pattern param
- Remove QuestionFamilySpec imports from generator_v2 and gates modules
- Update test call sites accordingly

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-07-14 05:50:47 -04:00
iomgaa 4fb7a61f8b fix(question_gen): resolve pipeline integration issues from final review 
1. Apply postprocess shuffle result (pp.options, pp.answer) to final
   GeneratedQuestion output instead of using original candidate values.

2. Record dedup rejection in store via new mark_item_rejected() method,
   preventing items from staying as 'accepted' after dedup rejects them.

3. Add .flatten() to embed_fn outputs in _is_duplicate and embed_pool
   append to handle 2D (1,D) arrays from embedding implementations.

4. Validate exactly 4 options in _validate_parsed_fields (was >= 2),
   matching the A-D answer constraint.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-07-12 00:14:12 -04:00
iomgaa 6d6eb8e3a3 refactor(question_gen): extract _validate_parsed_fields to reduce CC 
Split field validation logic out of _parse_v2_response into a dedicated
_validate_parsed_fields helper. This brings _parse_v2_response from CC=11
(grade C) down to CC=3 (grade A). The extracted validator is CC=9 (grade B).
No grade-C functions remain.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-07-11 23:43:35 -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