wangwei
|
e4d4e4968b
|
feat: add InlineScorer service with LLM client caching
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
|
2026-06-22 15:03:43 +08:00 |
|
wangwei
|
36e5506e2a
|
feat: report_builder uses weighted means; ReportData gains weighted_score_mean
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
|
2026-06-18 17:16:09 +08:00 |
|
wangwei
|
835614189e
|
feat: ScenarioInfo exposes metric_weights and doc_weights from YAML
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
|
2026-06-18 17:05:26 +08:00 |
|
wangwei
|
ce0d2291b0
|
feat: yaml_patcher and ProfileApplyRequest support metric_weights and doc_weights
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
|
2026-06-18 17:02:21 +08:00 |
|
wangwei
|
91c0dab4f9
|
fix(advisor): fix LLM API call, wire advice_markdown to webapp, update .env.example timeouts
- llm_analyzer.py: use llm.langchain_llm.ainvoke() (correct RAGAS 0.4.3 API)
- webapp/models.py: add advice_markdown field to ReportData
- webapp/services/run_reader.py: add read_advice_markdown() reading optimization_advice.md
- webapp/services/report_builder.py: pass advice_markdown into ReportData
- .env.example: OPENAI_TIMEOUT_SECONDS 30→180, RAGAS_METRIC_TIMEOUT_SECONDS 45→300
Co-Authored-By: Claude <noreply@anthropic.com>
|
2026-06-16 17:12:32 +08:00 |
|
wangwei
|
e329f59139
|
feat: add yaml_patcher service to apply LLM profiles to scenario YAML
|
2026-06-16 16:21:19 +08:00 |
|
wangwei
|
5d09deb420
|
feat: add ProfileManager service with JSON persistence
|
2026-06-16 16:14:31 +08:00 |
|
wangwei
|
e89695e490
|
Add RAGAS evaluation web console (FastAPI + vanilla JS)
- webapp/: FastAPI backend with runs/scenarios/evaluations API routers;
services for run_reader, report_builder, scenario_scanner, task_manager
(lazy ragas import — server boots even without ragas); Pydantic models
- webapp/static/: single-page console (layout A: left-nav + main area);
report detail with metric cards, Chart.js distribution histogram,
grouping table, lowest-score sample review; trigger evaluation + log polling
- webmain.py: uvicorn entry point (alongside existing main.py CLI)
- start.bat: Windows one-click launcher with env checks and auto-browser open
- rag_eval/datasets/: implement missing loader + normalizer modules
(load_dataset_records, normalize_records) required by evaluator
- scripts/seed_sample_run.py: generate realistic demo run artifacts
- .gitignore: exclude datasets/ data files but keep rag_eval/datasets/ source
Co-Authored-By: Claude Sonnet 4 <noreply@anthropic.com>
|
2026-06-15 15:53:57 +08:00 |
|