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>
gpt-5.4/5.5/5.2/5.4-mini/5.4-nano are incompatible with RAGAS 0.4.3
because they require max_completion_tokens instead of max_tokens.
gpt-5 / gpt-4.1 support max_tokens and json_object mode required by RAGAS.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
max_tokens=1 triggers 'min-output limit' errors on gpt-5.x models.
Using 8 tokens is still cheap but satisfies all known model minimums.
Falls back to max_completion_tokens=8 if max_tokens is not supported.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
text-embedding-* and other embedding models must call /embeddings not
/chat/completions. Added _is_embedding_model() heuristic that checks model
name keywords to route to the correct endpoint automatically.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Each API module now logs:
- evaluations: trigger (scenario path, task_id), status polls, list
- runs: list (count), detail (run_id, metrics, sample counts)
- scenarios: list (total, valid, error counts)
- pipeline: submit (docs_path, models, max_docs), status polls, list
- llm_profiles: CRUD ops (name, model, id), probe/test (model, ok, latency), apply (patched fields)
- score: already had per-request logging
Global middleware (webapp.access logger):
- Every API request: METHOD path -> status (latency_ms) at INFO
- Static file requests demoted to DEBUG to reduce noise
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
- Log incoming request (client, content-type, metrics, has_gt) on each /api/score call
- Log scoring result (latency, skipped metrics, scores) on success
- Register global RequestValidationError handler: logs url/content-type/errors
so 422 causes are visible in server log without checking HTTP response body
- Fix jsonable_encoder for exc.errors() to handle non-serializable ctx objects
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
- Add detailed Chinese route docstring covering all 7 metrics, contexts format,
ground_truth optional behavior, and Bearer auth instructions
- Add 200 response content example for Swagger UI Try-it-out
- Bump app version to 0.3.0
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
