- New POST /api/score/session_async endpoint: same session_id calls append to one shared report
- New GET /api/score/sessions/{session_id}: returns call_count, metric_means, all job records
- New GET /api/score/session/jobs/{job_id}: individual call status
- SessionScoreJobManager: deterministic run_id from session_id, per-session mutex for CSV append, advisor regenerated on every call
- SessionScoreRequest (extends ScoreRequest + session_id), SessionScoreJobResponse, SessionStatus models added
- 24 new tests, all passing
chore(weighted-score): comment out 综合加权得分 display and computation
- report.js: hide 综合加权得分 card in report detail page
- score_jobs.js: hide 综合 chip in async job list
- report_builder.py: overall_ws=None (computation disabled)
- summary.py: weighted_score summary line disabled
- evaluator.py: weighted_score/sample_weight columns no longer written to scores.csv
- score.py /api/score: weighted_score always returns null
- score_job_manager.py + session_score_manager.py: weighted=None
- Updated 3 tests to match new behaviour (6 pre-existing failures unchanged)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Each async score job:
- Runs InlineScorer.score() in thread pool
- Writes standard run artifacts (metadata.json, scores.csv, summary.md)
- Runs optimization_advisor => optimization_advice.md
- Result appears in 运行列表 and 报告详情 with full report
New endpoints:
- POST /api/score/async (202, job_id immediate)
- GET /api/score/jobs (list all jobs)
- GET /api/score/jobs/{id} (single job status)
Frontend:
- 评分记录 nav page with card list
- 5s auto-polling for queued/running jobs
- 查看报告 button navigates to existing 报告详情 page
Dify: change /api/score -> /api/score/async, no response parsing needed
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
When contexts is absent, metrics that require retrieved_contexts
(faithfulness, context_recall, context_precision, noise_sensitivity)
are automatically skipped and appear in skipped_metrics.
Only answer_relevancy, factual_correctness, semantic_similarity
remain computable without contexts.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
