feat: async score jobs — POST /api/score/async + 评分记录 page

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>

tests/webapp/test_score_jobs_api.py

@@ -0,0 +1,146 @@
+"""Tests for async score jobs API."""
+from __future__ import annotations
+
+import json
+import time
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+from fastapi.testclient import TestClient
+
+
+@pytest.fixture()
+def client(tmp_path, monkeypatch):
+    """TestClient with fresh ScoreJobManager backed by tmp dirs."""
+    import webapp.services.score_job_manager as mgr_mod
+    from webapp.services.score_job_manager import ScoreJobManager
+
+    fresh_mgr = ScoreJobManager(
+        output_dir=tmp_path / "score-async",
+        index_dir=tmp_path / "score-jobs",
+        max_workers=2,
+    )
+    monkeypatch.setattr(mgr_mod, "score_job_manager", fresh_mgr)
+
+    import webapp.api.score_jobs as api_mod
+    monkeypatch.setattr(api_mod, "score_job_manager", fresh_mgr)
+
+    from webapp.server import create_app
+    return TestClient(create_app())
+
+
+class TestAsyncScoreEndpoints:
+    def test_submit_returns_202_with_job_id(self, client):
+        """POST /api/score/async returns 202 immediately."""
+        with patch("webapp.services.score_job_manager.ScoreJobManager._run"):
+            resp = client.post("/api/score/async", json={
+                "question": "q?",
+                "answer": "a.",
+                "metrics": ["answer_relevancy"],
+            })
+        assert resp.status_code == 202
+        data = resp.json()
+        assert "job_id" in data
+        assert data["status"] == "queued"
+
+    def test_list_jobs_empty_initially(self, client):
+        resp = client.get("/api/score/jobs")
+        assert resp.status_code == 200
+        assert resp.json()["jobs"] == []
+
+    def test_get_unknown_job_returns_404(self, client):
+        resp = client.get("/api/score/jobs/nonexistent123")
+        assert resp.status_code == 404
+
+    def test_submitted_job_appears_in_list(self, client):
+        with patch("webapp.services.score_job_manager.ScoreJobManager._run"):
+            resp = client.post("/api/score/async", json={
+                "question": "q?", "answer": "a.", "metrics": ["answer_relevancy"],
+            })
+        job_id = resp.json()["job_id"]
+        time.sleep(0.1)
+        list_resp = client.get("/api/score/jobs")
+        ids = [j["job_id"] for j in list_resp.json()["jobs"]]
+        assert job_id in ids
+
+    def test_get_job_by_id_returns_status(self, client):
+        with patch("webapp.services.score_job_manager.ScoreJobManager._run"):
+            resp = client.post("/api/score/async", json={
+                "question": "q?", "answer": "a.", "metrics": ["answer_relevancy"],
+            })
+        job_id = resp.json()["job_id"]
+        time.sleep(0.1)
+        get_resp = client.get(f"/api/score/jobs/{job_id}")
+        assert get_resp.status_code == 200
+        assert get_resp.json()["job_id"] == job_id
+
+    def test_missing_required_fields_returns_422(self, client):
+        resp = client.post("/api/score/async", json={"question": "q?"})
+        assert resp.status_code == 422
+
+
+class TestScoreJobManager:
+    def test_completed_job_persisted_to_index(self, tmp_path):
+        """Completed job writes index JSON."""
+        from webapp.services.score_job_manager import ScoreJobManager
+        from webapp.models import ScoreRequest
+
+        mgr = ScoreJobManager(
+            output_dir=tmp_path / "runs",
+            index_dir=tmp_path / "index",
+            max_workers=1,
+        )
+        req = ScoreRequest(question="q?", answer="a.", metrics=["answer_relevancy"])
+
+        # Patch _run directly — it uses lazy imports internally
+        def fake_run(job_id, request):
+            mgr._update(job_id, status="completed", finished_at="2026-01-01T00:00:01+00:00",
+                        run_id="fake-run-id", scores={"answer_relevancy": 0.85},
+                        weighted_score=0.85, latency_ms=500)
+
+        with patch.object(mgr, "_run", side_effect=fake_run):
+            status = mgr.submit(req)
+
+        for _ in range(20):
+            s = mgr.get(status.job_id)
+            if s and s.status == "completed":
+                break
+            time.sleep(0.1)
+
+        s = mgr.get(status.job_id)
+        assert s is not None
+        idx_path = tmp_path / "index" / f"{status.job_id}.json"
+        assert idx_path.exists()
+        data = json.loads(idx_path.read_text(encoding="utf-8"))
+        assert data["job_id"] == status.job_id
+        assert data["status"] == "completed"
+
+    def test_loads_existing_index_on_startup(self, tmp_path):
+        """Manager loads persisted jobs from index dir on init."""
+        from webapp.services.score_job_manager import ScoreJobManager
+        from webapp.models import AsyncScoreJobStatus
+
+        idx_dir = tmp_path / "index"
+        idx_dir.mkdir()
+        fake = AsyncScoreJobStatus(
+            job_id="testjob001",
+            status="completed",
+            created_at="2026-01-01T00:00:00+00:00",
+            run_id="some-run-id",
+            scores={"answer_relevancy": 0.9},
+            weighted_score=0.9,
+            latency_ms=1000,
+        )
+        (idx_dir / "testjob001.json").write_text(
+            json.dumps(fake.model_dump(), ensure_ascii=False), encoding="utf-8"
+        )
+        mgr = ScoreJobManager(
+            output_dir=tmp_path / "runs",
+            index_dir=idx_dir,
+            max_workers=1,
+        )
+        loaded = mgr.get("testjob001")
+        assert loaded is not None
+        assert loaded.status == "completed"
+        assert loaded.run_id == "some-run-id"

webapp/api/score_jobs.py

@@ -0,0 +1,89 @@
+"""Routes for async RAGAS scoring jobs (Dify fire-and-forget integration).
+
+Dify calls POST /api/score/async → gets job_id immediately (202).
+Scoring runs in background, result written as a standard run artifact.
+View full report at GET /api/runs/{run_id} or in the 「运行列表」 page.
+"""
+
+from __future__ import annotations
+
+import logging
+
+from fastapi import APIRouter, HTTPException
+
+from webapp.models import AsyncScoreJobResponse, AsyncScoreJobStatus, ScoreRequest
+from webapp.services.score_job_manager import score_job_manager
+
+router = APIRouter(prefix="/api/score", tags=["score"])
+logger = logging.getLogger("webapp.api.score_jobs")
+
+
+@router.post(
+    "/async",
+    status_code=202,
+    response_model=AsyncScoreJobResponse,
+    summary="提交异步评分任务（Dify 推荐方式）",
+    responses={
+        202: {
+            "description": (
+                "任务已排队，立即返回 job_id（202 Accepted）。\n\n"
+                "评分在后台执行，完成后自动生成完整报告（含优化建议）。\n"
+                "通过 `GET /api/score/jobs/{job_id}` 查询状态，"
+                "完成后在「运行列表」页查看完整报告。"
+            ),
+            "content": {
+                "application/json": {
+                    "example": {"job_id": "abc123def456", "status": "queued", "run_id": None}
+                }
+            },
+        },
+    },
+)
+def submit_async_score(request: ScoreRequest) -> AsyncScoreJobResponse:
+    """提交异步 RAGAS 评分任务，立即返回 job_id。
+
+    **适合 Dify 工作流**：HTTP 节点无需等待评分完成（无超时风险），
+    工作流立即继续，评分结果在 RAGAS 平台「运行列表」中查看。
+
+    评分完成后自动生成：
+    - 各指标得分（`scores.csv`）
+    - 摘要报告（`summary.md`）
+    - LLM 优化建议（`optimization_advice.md`）
+    """
+    logger.info(
+        "[score_async] submit  metrics=%s  has_ctx=%s  has_gt=%s",
+        request.metrics, bool(request.contexts), bool(request.ground_truth),
+    )
+    status = score_job_manager.submit(request)
+    logger.info("[score_async] queued  job_id=%s", status.job_id)
+    return AsyncScoreJobResponse(job_id=status.job_id, status=status.status)
+
+
+@router.get(
+    "/jobs",
+    response_model=dict,
+    summary="列出所有异步评分记录",
+)
+def list_score_jobs() -> dict:
+    """返回所有异步评分记录，按创建时间倒序排列。"""
+    jobs = score_job_manager.list_jobs()
+    logger.info("[score_jobs] list  count=%d", len(jobs))
+    return {"jobs": [j.model_dump() for j in jobs]}
+
+
+@router.get(
+    "/jobs/{job_id}",
+    response_model=AsyncScoreJobStatus,
+    summary="查询单个异步评分任务状态",
+    responses={404: {"description": "指定 job_id 的评分任务不存在。"}},
+)
+def get_score_job(job_id: str) -> AsyncScoreJobStatus:
+    """查询单个异步评分任务的状态和结果。
+
+    `status` 为 `completed` 时，`run_id` 字段包含对应的运行 ID，
+    可通过 `GET /api/runs/{run_id}` 获取完整评分报告。
+    """
+    status = score_job_manager.get(job_id)
+    if status is None:
+        raise HTTPException(status_code=404, detail=f"Score job not found: {job_id}")
+    return status

webapp/models.py

@@ -514,3 +514,40 @@ class ScoreResponse(BaseModel):
         default=None,
         description="打分异常时的错误信息（HTTP 200 仍返回，scores 为空）。",
     )
+
+
+# ---------------------------------------------------------------------------
+# 异步评分记录模型
+# ---------------------------------------------------------------------------
+
+class AsyncScoreJobResponse(BaseModel):
+    """Immediate 202 response after submitting an async score job."""
+
+    job_id: str = Field(description="任务唯一标识符，用于后续查询结果。")
+    status: str = Field(default="queued", description="初始状态：queued。")
+    run_id: str | None = Field(
+        default=None,
+        description="评分完成后写入的 Run ID，可在「运行列表」中查看完整报告。",
+    )
+
+
+class AsyncScoreJobStatus(BaseModel):
+    """State of one async score job (queued → running → completed/failed)."""
+
+    job_id: str = Field(description="任务唯一标识符。")
+    status: str = Field(description="queued | running | completed | failed")
+    created_at: str = Field(default="", description="创建时间（ISO 8601 UTC）。")
+    finished_at: str = Field(default="", description="完成时间（ISO 8601 UTC）。")
+    run_id: str | None = Field(
+        default=None,
+        description="完成后对应的 Run ID，可通过 GET /api/runs/{run_id} 查看完整报告。",
+    )
+    request_summary: dict = Field(
+        default_factory=dict,
+        description="请求参数快照（question 前80字、metrics、judge_model 等）。",
+    )
+    scores: dict[str, float | None] = Field(default_factory=dict, description="各指标得分。")
+    weighted_score: float | None = Field(default=None, description="加权综合得分。")
+    latency_ms: int = Field(default=0, description="评分耗时毫秒。")
+    skipped_metrics: list[str] = Field(default_factory=list)
+    error: str | None = Field(default=None)

webapp/server.py

@@ -17,7 +17,7 @@ from fastapi.exceptions import RequestValidationError
 from fastapi.responses import FileResponse, JSONResponse
 from fastapi.staticfiles import StaticFiles
 
-from webapp.api import evaluations, llm_profiles, pipeline, runs, scenarios, score
+from webapp.api import evaluations, llm_profiles, pipeline, runs, scenarios, score, score_jobs
 
 STATIC_DIR = Path(__file__).resolve().parent / "static"
 logger = logging.getLogger("webapp.server")
@@ -69,10 +69,12 @@ OPENAPI_TAGS = [
     {
         "name": "score",
         "description": (
-            "**实时评分 API（Dify 外部 Tool）**\n\n"
+            "**实时评分 API（同步）** — `POST /api/score`\n\n"
-            "接受单条问答记录 `(question, answer, contexts, ground_truth)`，\n"
+            "**异步评分 API（Dify 推荐）** — `POST /api/score/async`\n\n"
-            "同步运行 RAGAS 指标打分，返回各指标得分和加权综合得分。\n\n"
+            "异步方式立即返回 job_id（202），评分在后台执行，完成后自动生成完整报告（含优化建议），"
-            "适用场景：Dify Agent 在回答后即时调用，用于质量监控或自我改进。\n\n"
+            "在「运行列表」页查看。\n\n"
+            "通过 `GET /api/score/jobs` 列出所有异步评分记录，"
+            "`GET /api/score/jobs/{job_id}` 查询单个任务状态。\n\n"
             "**鉴权**：若 `.env` 中配置了 `SCORE_API_TOKEN`，需携带 "
             "`Authorization: Bearer <token>` 请求头。"
         ),
@@ -108,6 +110,7 @@ def create_app() -> FastAPI:
     app.include_router(llm_profiles.router)
     app.include_router(pipeline.router)
     app.include_router(score.router)
+    app.include_router(score_jobs.router)
 
     @app.middleware("http")
     async def access_log_middleware(request: Request, call_next):

webapp/services/score_job_manager.py

@@ -0,0 +1,269 @@
+"""Background task manager for async RAGAS single-sample scoring.
+
+Each job:
+  1. Runs InlineScorer.score() in a thread pool.
+  2. Constructs a minimal EvaluationResult + Scenario in the standard format.
+  3. Calls write_run_artifacts() — produces metadata.json, scores.csv, summary.md.
+  4. Calls run_advisor() — produces optimization_advice.md.
+
+The resulting run directory lands under outputs/score-async/<run_id>/ and is
+automatically picked up by run_reader.list_run_summaries(), so it appears in
+the existing 「运行列表」 and 「报告详情」 pages without any extra wiring.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import threading
+import time
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from webapp.models import AsyncScoreJobStatus, ScoreRequest
+
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+_DEFAULT_JOBS_DIR = _REPO_ROOT / "outputs" / "score-async"
+_DEFAULT_INDEX_DIR = _REPO_ROOT / "outputs" / "score-jobs"  # lightweight job index
+
+
+def _now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+class ScoreJobManager:
+    """Thread-pool manager for async scoring jobs.
+
+    Results are written as standard run artifacts so the report detail page
+    can render them with zero additional code.
+    """
+
+    def __init__(
+        self,
+        output_dir: Path = _DEFAULT_JOBS_DIR,
+        index_dir: Path = _DEFAULT_INDEX_DIR,
+        max_workers: int = 4,
+    ) -> None:
+        self._output_dir = Path(output_dir)
+        self._index_dir = Path(index_dir)
+        self._output_dir.mkdir(parents=True, exist_ok=True)
+        self._index_dir.mkdir(parents=True, exist_ok=True)
+        self._executor = ThreadPoolExecutor(max_workers=max_workers)
+        self._cache: dict[str, AsyncScoreJobStatus] = {}
+        self._lock = threading.Lock()
+        self._load_existing()
+
+    # ------------------------------------------------------------------ #
+    # Public API
+    # ------------------------------------------------------------------ #
+
+    def submit(self, request: ScoreRequest) -> AsyncScoreJobStatus:
+        """Queue one scoring job and return its initial status immediately."""
+        job_id = uuid.uuid4().hex[:12]
+        status = AsyncScoreJobStatus(
+            job_id=job_id,
+            status="queued",
+            created_at=_now_iso(),
+            request_summary={
+                "question": (request.question or "")[:80],
+                "answer": (request.answer or "")[:80],
+                "metrics": list(request.metrics),
+                "judge_model": request.judge_model or "",
+                "embedding_model": request.embedding_model or "",
+                "has_contexts": bool(request.contexts),
+                "has_ground_truth": bool(request.ground_truth),
+            },
+        )
+        with self._lock:
+            self._cache[job_id] = status
+        self._persist_index(status)
+        self._executor.submit(self._run, job_id, request)
+        return status
+
+    def get(self, job_id: str) -> AsyncScoreJobStatus | None:
+        """Return current status or None if unknown."""
+        with self._lock:
+            return self._cache.get(job_id)
+
+    def list_jobs(self) -> list[AsyncScoreJobStatus]:
+        """Return all known jobs, newest first."""
+        with self._lock:
+            jobs = list(self._cache.values())
+        jobs.sort(key=lambda j: j.created_at, reverse=True)
+        return jobs
+
+    # ------------------------------------------------------------------ #
+    # Worker
+    # ------------------------------------------------------------------ #
+
+    def _run(self, job_id: str, request: ScoreRequest) -> None:
+        """Execute scoring, write run artifacts, run advisor."""
+        import logging
+        logger = logging.getLogger("webapp.services.score_job_manager")
+        self._update(job_id, status="running")
+
+        # Lazy imports to keep web server bootable if ragas is not installed.
+        from rag_eval.advisor import run_advisor
+        from rag_eval.metrics.factory import build_models
+        from rag_eval.metrics.weights import compute_weighted_score
+        from rag_eval.reporting.writers import write_run_artifacts
+        from rag_eval.settings import EvaluationSettings
+        from rag_eval.shared.models import (
+            DatasetConfig, EvaluationResult, NormalizedSample,
+            RuntimeConfig, Scenario,
+        )
+        from rag_eval.shared.utils import utc_now_iso
+        from webapp.services.inline_scorer import inline_scorer
+
+        settings = EvaluationSettings()
+        judge_model = request.judge_model or settings.ragas_judge_model
+        embedding_model = request.embedding_model or settings.ragas_embedding_model
+        effective = request.effective_metrics()
+        requested = set(request.metrics)
+        skipped = sorted(requested - set(effective))
+
+        t0 = time.monotonic()
+        started_at = utc_now_iso()
+
+        try:
+            if effective:
+                raw_scores = inline_scorer.score(
+                    question=request.question,
+                    answer=request.answer,
+                    contexts=request.contexts_as_list(),
+                    ground_truth=request.ground_truth,
+                    metrics=effective,
+                    judge_model=judge_model,
+                    embedding_model=embedding_model,
+                    settings=settings,
+                )
+            else:
+                raw_scores = {}
+
+            latency_ms = int((time.monotonic() - t0) * 1000)
+            finished_at = utc_now_iso()
+
+            # Build full scores dict (skipped = None)
+            all_scores: dict[str, float | None] = {m: None for m in request.metrics}
+            all_scores.update(raw_scores)
+            weighted_raw = compute_weighted_score(
+                {k: v for k, v in raw_scores.items() if v is not None}, {}
+            )
+            weighted = round(weighted_raw, 4) if weighted_raw is not None else None
+
+            # Build a score row compatible with report_builder
+            score_row: dict[str, Any] = {
+                "sample_id": "async-score-1",
+                "question": request.question,
+                "answer": request.answer or "",
+                "contexts": request.contexts or "",
+                "ground_truth": request.ground_truth or "",
+                "error": "",
+            }
+            score_row.update(all_scores)
+
+            # Construct minimal EvaluationResult so write_run_artifacts works
+            run_id = finished_at.replace(":", "-")
+            output_dir = self._output_dir
+
+            # Build a minimal Scenario for snapshot + advisor
+            scenario = Scenario(
+                scenario_name=f"async-score-{job_id}",
+                mode="offline",
+                dataset=DatasetConfig(path=output_dir / run_id / "dataset.csv"),
+                judge_model=judge_model,
+                embedding_model=embedding_model,
+                metrics=list(request.metrics),
+                output_dir=output_dir,
+                optimization_advisor=True,  # always generate advice
+            )
+
+            sample = NormalizedSample(
+                sample_id="async-score-1",
+                question=request.question,
+                answer=request.answer or "",
+                contexts=request.contexts_as_list(),
+                ground_truth=request.ground_truth or "",
+            )
+
+            result = EvaluationResult(
+                scenario=scenario,
+                run_id=run_id,
+                started_at=started_at,
+                finished_at=finished_at,
+                valid_samples=[sample],
+                invalid_samples=[],
+                score_rows=[score_row],
+            )
+
+            write_run_artifacts(result)
+            logger.info("[score_job] artifacts written  job_id=%s  run_id=%s", job_id, run_id)
+
+            # Run optimization advisor (builds optimization_advice.md)
+            try:
+                llm, _ = build_models(judge_model, embedding_model, settings)
+                run_advisor(result, scenario, llm)
+                logger.info("[score_job] advisor done  job_id=%s", job_id)
+            except Exception as adv_exc:  # noqa: BLE001
+                logger.warning("[score_job] advisor failed  job_id=%s  err=%s", job_id, adv_exc)
+
+            self._update(
+                job_id,
+                status="completed",
+                finished_at=finished_at,
+                run_id=run_id,
+                scores=all_scores,
+                weighted_score=weighted,
+                latency_ms=latency_ms,
+                skipped_metrics=skipped,
+            )
+
+        except Exception as exc:  # noqa: BLE001
+            latency_ms = int((time.monotonic() - t0) * 1000)
+            logger.error("[score_job] failed  job_id=%s  err=%s", job_id, exc)
+            self._update(
+                job_id,
+                status="failed",
+                finished_at=_now_iso(),
+                latency_ms=latency_ms,
+                error=f"{type(exc).__name__}: {exc}",
+            )
+
+    # ------------------------------------------------------------------ #
+    # Persistence helpers
+    # ------------------------------------------------------------------ #
+
+    def _update(self, job_id: str, **kwargs: Any) -> None:
+        """Merge kwargs into the job status and persist the index."""
+        with self._lock:
+            existing = self._cache.get(job_id)
+            if existing is None:
+                return
+            updated = existing.model_copy(update=kwargs)
+            self._cache[job_id] = updated
+        self._persist_index(updated)
+
+    def _persist_index(self, status: AsyncScoreJobStatus) -> None:
+        """Write a lightweight index JSON for this job (survives restarts)."""
+        path = self._index_dir / f"{status.job_id}.json"
+        path.write_text(
+            json.dumps(status.model_dump(), ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def _load_existing(self) -> None:
+        """Load existing job index files on startup."""
+        for path in sorted(self._index_dir.glob("*.json")):
+            try:
+                data = json.loads(path.read_text(encoding="utf-8"))
+                status = AsyncScoreJobStatus.model_validate(data)
+                self._cache[status.job_id] = status
+            except Exception:  # noqa: BLE001
+                pass
+
+
+# Module-level singleton shared by FastAPI routes.
+score_job_manager = ScoreJobManager()

webapp/static/index.html

@@ -28,6 +28,9 @@
         <button class="nav-item" data-view="profiles">
           <span class="nav-ico">⚙</span><span>LLM 配置</span>
         </button>
+        <button class="nav-item" data-view="scorejobs">
+          <span class="nav-ico">📋</span><span>评分记录</span>
+        </button>
         <button class="nav-item" data-view="apidocs">
           <span class="nav-ico">⎔</span><span>API 文档</span>
         </button>
@@ -234,6 +237,22 @@
         </div>
       </section>
 
+      <!-- 评分记录视图 -->
+      <section class="view" id="view-scorejobs" hidden>
+        <div class="panel">
+          <div class="panel-head">
+            <h2>评分记录</h2>
+            <span class="muted" style="font-size:13px">来自 Dify 异步评分任务（POST /api/score/async）</span>
+          </div>
+          <p class="muted">评分完成后自动生成完整报告（含指标得分与 LLM 优化建议），点击「查看报告」跳转报告详情页。</p>
+        </div>
+        <div id="scorejobs-list"></div>
+        <div class="empty" id="scorejobs-empty" hidden>
+          <p>暂无评分记录。</p>
+          <p class="muted">在 Dify 工作流中调用 <code>POST /api/score/async</code> 后，记录将在此显示。</p>
+        </div>
+      </section>
+
       <!-- API 文档视图 -->
       <section class="view" id="view-apidocs" hidden>
         <iframe
@@ -251,6 +270,7 @@
   <script src="/static/js/report.js"></script>
   <script src="/static/js/profiles.js"></script>
   <script src="/static/js/runner.js"></script>
+  <script src="/static/js/score_jobs.js"></script>
   <script src="/static/js/app.js"></script>
 </body>
 </html>

webapp/static/js/api.js

@@ -66,6 +66,11 @@ const API = {
   },
   applyProfiles(body) { return API.post("/api/llm-profiles/apply", body); },
 
+  // 异步评分记录 API
+  scoreJobsAsync(body) { return API.post("/api/score/async", body); },
+  getScoreJob(jobId) { return API.get(`/api/score/jobs/${encodeURIComponent(jobId)}`); },
+  listScoreJobs() { return API.get("/api/score/jobs"); },
+
   // 测试已保存 profile 的连通性
   testProfile(id) {
     return fetch(`/api/llm-profiles/${encodeURIComponent(id)}/test`, { method: "POST" })

webapp/static/js/app.js

@@ -5,8 +5,8 @@
 const App = {
   currentRunId: null,
   activeView: null,
-  views: ["runs", "new", "report", "profiles", "apidocs"],
+  views: ["runs", "new", "report", "profiles", "scorejobs", "apidocs"],
-  titles: { runs: "运行列表", new: "新建评估", report: "报告详情", profiles: "LLM 配置", apidocs: "API 文档" },
+  titles: { runs: "运行列表", new: "新建评估", report: "报告详情", profiles: "LLM 配置", scorejobs: "评分记录", apidocs: "API 文档" },
 
   // 初始化：绑定导航、从 URL/sessionStorage 恢复上次位置、启动健康检查。
   init() {
@@ -72,6 +72,7 @@ const App = {
     if (view === "new")        Runner.loadScenarios();
     if (view === "report")     Report.render(App.currentRunId);
     if (view === "profiles")   Profiles.load();
+    if (view === "scorejobs")  ScoreJobs.load();
   },
 
   // ----------------------------------------------------------------

webapp/static/js/score_jobs.js

@@ -0,0 +1,125 @@
+// score_jobs.js — 评分记录页面（异步 RAGAS 评分任务列表）
+// 每条评分完成后自动写入标准 Run 产物，点击「查看报告」复用现有报告详情页。
+
+const ScoreJobs = {
+  _pollTimers: {},   // job_id -> setInterval handle
+
+  async load() {
+    const list = document.getElementById("scorejobs-list");
+    const empty = document.getElementById("scorejobs-empty");
+    list.innerHTML = '<p class="muted">加载中…</p>';
+    try {
+      const data = await API.listScoreJobs();
+      const jobs = data.jobs || [];
+      list.innerHTML = "";
+      if (jobs.length === 0) {
+        empty.hidden = false;
+        return;
+      }
+      empty.hidden = true;
+      jobs.forEach(job => list.appendChild(ScoreJobs.renderCard(job)));
+      // Auto-poll any pending jobs
+      jobs.forEach(job => {
+        if (job.status === "queued" || job.status === "running") {
+          ScoreJobs._startPoll(job.job_id);
+        }
+      });
+    } catch (err) {
+      list.innerHTML = `<p class="muted">加载失败：${App.escape(err.message)}</p>`;
+    }
+  },
+
+  renderCard(job) {
+    const card = document.createElement("div");
+    card.className = "run-card";
+    card.id = `score-job-${job.job_id}`;
+    card.innerHTML = ScoreJobs._cardHtml(job);
+    // Bind report button if already completed
+    ScoreJobs._bindReportBtn(card, job);
+    return card;
+  },
+
+  _cardHtml(job) {
+    const time = App.shortTime(job.created_at);
+    const question = App.escape((job.request_summary?.question || "—").slice(0, 60));
+    const metrics = (job.request_summary?.metrics || []).join(", ");
+
+    const statusBadge = `<span class="badge ${job.status}">${job.status}</span>`;
+
+    let scoreHtml = "";
+    if (job.status === "completed") {
+      scoreHtml = Object.entries(job.scores || {})
+        .map(([k, v]) => {
+          const cls = App.scoreClass(v);
+          const text = v === null || v === undefined ? "n/a" : Number(v).toFixed(3);
+          return `<span class="metric-chip" title="${App.escape(k)}">${App.escape(App.shortMetric(k))} <b class="${cls}">${text}</b></span>`;
+        })
+        .join(" ");
+      if (job.weighted_score !== null && job.weighted_score !== undefined) {
+        const cls = App.scoreClass(job.weighted_score);
+        scoreHtml += ` <span class="metric-chip">综合 <b class="${cls}">${Number(job.weighted_score).toFixed(3)}</b></span>`;
+      }
+    } else if (job.status === "failed") {
+      scoreHtml = `<span style="color:var(--bad);font-size:12px">${App.escape((job.error || "").slice(0, 80))}</span>`;
+    } else {
+      scoreHtml = `<span class="muted">评分中，请稍候…</span>`;
+    }
+
+    const reportBtn = job.status === "completed" && job.run_id
+      ? `<button class="btn btn-sm btn-primary score-job-report-btn" data-run-id="${App.escape(job.run_id)}">查看报告</button>`
+      : "";
+
+    return `
+      <div class="run-card-head">
+        <div class="run-card-title">${question}</div>
+        <div style="display:flex;gap:8px;align-items:center">${statusBadge}${reportBtn}</div>
+      </div>
+      <div class="run-card-meta">
+        <div>指标：${App.escape(metrics)} · ${time} · ${job.latency_ms}ms</div>
+      </div>
+      <div class="run-card-metrics">${scoreHtml}</div>
+    `;
+  },
+
+  _bindReportBtn(card, job) {
+    const btn = card.querySelector(".score-job-report-btn");
+    if (!btn) return;
+    btn.addEventListener("click", () => {
+      const runId = btn.dataset.runId;
+      if (runId) {
+        App.enableReportNav();
+        App.navigate("report", runId);
+      }
+    });
+  },
+
+  _startPoll(jobId) {
+    if (ScoreJobs._pollTimers[jobId]) return;
+    ScoreJobs._pollTimers[jobId] = setInterval(async () => {
+      try {
+        const job = await API.getScoreJob(jobId);
+        const card = document.getElementById(`score-job-${jobId}`);
+        if (card) {
+          card.innerHTML = ScoreJobs._cardHtml(job);
+          ScoreJobs._bindReportBtn(card, job);
+        }
+        if (job.status === "completed" || job.status === "failed") {
+          clearInterval(ScoreJobs._pollTimers[jobId]);
+          delete ScoreJobs._pollTimers[jobId];
+          // If completed, pre-enable report nav
+          if (job.status === "completed" && job.run_id) {
+            App.enableReportNav();
+          }
+        }
+      } catch (_e) {
+        clearInterval(ScoreJobs._pollTimers[jobId]);
+        delete ScoreJobs._pollTimers[jobId];
+      }
+    }, 5000);
+  },
+
+  stopAllPolls() {
+    Object.values(ScoreJobs._pollTimers).forEach(t => clearInterval(t));
+    ScoreJobs._pollTimers = {};
+  },
+};