wangwei
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91c0dab4f9
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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>
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2026-06-16 17:12:32 +08:00 |
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wangwei
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e89695e490
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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>
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2026-06-15 15:53:57 +08:00 |
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