Step35 86eb1c9a50 feat: training data pipeline — knowledge entries → JSONL training pairs ...

Add scripts/knowledge_to_training_pairs.py which reads quality-gated
knowledge entries from knowledge/index.json and emits terse→rich
training pairs in JSONL format.

Features:
- Derives terse queries from facts via category-aware heuristics
- Configurable quality filters: min-confidence, model-filter, date range
- Output includes domain, source_confidence, source_model
- Smoke tests added in tests/test_knowledge_to_training_pairs.py

Deliverables for #199:
1. Pipeline script: scripts/knowledge_to_training_pairs.py
2. End-to-end: knowledge/index.json → training_pairs.jsonl (or custom JSONL)
3. Config: min-confidence, model-filter, after/before date filters
4. Test: 9 smoke tests covering conversion, filtering, and end-to-end run

Closes #199

2026-04-26 13:03:06 -04:00

..

test_ci_config.py

test: define CI configuration acceptance for #190

2026-04-15 11:26:08 -04:00

test_dedup.py

feat: knowledge deduplication — content hash + token similarity (#196)

2026-04-21 07:58:09 -04:00

test_freshness.py

feat: Add test_freshness.py (#200)

2026-04-21 11:57:54 +00:00

test_knowledge_gap_identifier.py

test: knowledge gap identifier tests (#172)

2026-04-15 14:42:30 +00:00

test_knowledge_to_training_pairs.py

feat: training data pipeline — knowledge entries → JSONL training pairs

2026-04-26 13:03:06 -04:00

test_perf_bottleneck_finder.py

test: add tests for Performance Bottleneck Finder

2026-04-15 14:48:59 +00:00

test_quality_gate.py

feat: quality gate — score and filter knowledge entries (#198)

2026-04-20 20:31:04 -04:00