feat(session-async): add /api/score/session_async with incremental session report aggregation

- 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>

webapp/services/pipeline_task_manager.py

@@ -0,0 +1,257 @@
+"""Background task manager for end-to-end pipeline jobs (build + eval).
+
+Each job runs three sequential phases inside a worker thread:
+  1. parsing_documents  — AliyunDocmind parses every PDF
+  2. generating_questions — LLM generates a draft question bank
+  3. evaluating          — RAGAS online evaluation scores each question
+
+The DatasetBuildJob and Scenario objects are constructed entirely from the
+API request parameters, so no YAML config files are needed.
+"""
+
+from __future__ import annotations
+
+import io
+import threading
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+from contextlib import redirect_stderr, redirect_stdout
+from datetime import datetime, timezone
+from pathlib import Path
+
+from webapp.models import (
+    PipelineJobRequest,
+    PipelineJobStatus,
+    PipelineResult,
+)
+
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+_PIPELINE_OUTPUT_ROOT = _REPO_ROOT / "outputs" / "pipeline"
+
+
+def _now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+class _LineCapture(io.TextIOBase):
+    """Write-only stream that appends complete lines to a task's log buffer."""
+
+    def __init__(self, sink: "PipelineTask") -> None:
+        self._sink = sink
+        self._buffer = ""
+
+    def write(self, text: str) -> int:
+        self._buffer += text
+        while "\n" in self._buffer:
+            line, self._buffer = self._buffer.split("\n", 1)
+            self._sink.append_log(line)
+        return len(text)
+
+    def flush(self) -> None:
+        if self._buffer:
+            self._sink.append_log(self._buffer)
+            self._buffer = ""
+
+
+class PipelineTask:
+    """Mutable state for one pipeline job (build + eval)."""
+
+    def __init__(self, job_id: str, job_name: str) -> None:
+        self.job_id = job_id
+        self.job_name = job_name
+        self.status = "queued"
+        self.phase = "idle"
+        self.logs: list[str] = []
+        self.result: PipelineResult | None = None
+        self.error: str | None = None
+        self.created_at = _now_iso()
+        self.finished_at = ""
+        self._lock = threading.Lock()
+
+    def append_log(self, line: str) -> None:
+        with self._lock:
+            self.logs.append(line)
+
+    def snapshot(self) -> PipelineJobStatus:
+        with self._lock:
+            return PipelineJobStatus(
+                job_id=self.job_id,
+                job_name=self.job_name,
+                status=self.status,
+                phase=self.phase,
+                logs=list(self.logs),
+                result=self.result,
+                error=self.error,
+                created_at=self.created_at,
+                finished_at=self.finished_at,
+            )
+
+
+class PipelineTaskManager:
+    """Owns the thread pool and registry of pipeline jobs."""
+
+    def __init__(self, max_workers: int = 2) -> None:
+        self._executor = ThreadPoolExecutor(max_workers=max_workers)
+        self._tasks: dict[str, PipelineTask] = {}
+        self._lock = threading.Lock()
+
+    def submit(self, request: PipelineJobRequest) -> PipelineTask:
+        """Register and schedule a new pipeline job; return its task object."""
+        job_id = uuid.uuid4().hex[:12]
+        job_name = request.job_name.strip() or f"pipeline-{job_id[:6]}"
+        task = PipelineTask(job_id=job_id, job_name=job_name)
+        with self._lock:
+            self._tasks[job_id] = task
+        self._executor.submit(self._run, task, request)
+        return task
+
+    def get(self, job_id: str) -> PipelineJobStatus | None:
+        with self._lock:
+            task = self._tasks.get(job_id)
+        return task.snapshot() if task is not None else None
+
+    def list_jobs(self) -> list[PipelineJobStatus]:
+        with self._lock:
+            tasks = list(self._tasks.values())
+        snapshots = [t.snapshot() for t in tasks]
+        snapshots.sort(key=lambda s: s.created_at, reverse=True)
+        return snapshots
+
+    # ------------------------------------------------------------------ #
+    # Worker
+    # ------------------------------------------------------------------ #
+
+    def _run(self, task: PipelineTask, request: PipelineJobRequest) -> None:
+        """Execute the full pipeline end to end inside a worker thread."""
+        task.status = "running"
+        task.append_log(f"[{_now_iso()}] 开始 pipeline 任务: {task.job_name}")
+
+        capture = _LineCapture(task)
+        try:
+            with redirect_stdout(capture), redirect_stderr(capture):
+                result = self._execute(task, request)
+            capture.flush()
+            task.result = result
+            task.phase = "done"
+            task.status = "completed"
+            task.append_log(f"[{_now_iso()}] pipeline 任务完成: {task.job_name}")
+        except Exception as exc:  # noqa: BLE001
+            capture.flush()
+            task.error = f"{type(exc).__name__}: {exc}"
+            task.append_log(f"[{_now_iso()}] pipeline 任务失败: {task.error}")
+            task.status = "failed"
+        finally:
+            task.finished_at = _now_iso()
+
+    def _execute(self, task: PipelineTask, req: PipelineJobRequest) -> PipelineResult:
+        """Run build then eval, updating task.phase as we go."""
+
+        # ── resolve paths ──────────────────────────────────────────────
+        docs_path = Path(req.docs_path)
+        if not docs_path.is_absolute():
+            docs_path = (_REPO_ROOT / docs_path).resolve()
+        if not docs_path.is_dir():
+            raise ValueError(f"docs_path is not an existing directory: {docs_path}")
+
+        job_output_dir = _PIPELINE_OUTPUT_ROOT / task.job_id
+        build_artifact_dir = job_output_dir / "build"
+        dataset_csv = job_output_dir / "generated_dataset.csv"
+        eval_output_dir = job_output_dir / "eval"
+
+        # ── phase 1 + 2: dataset build (parse & generate) ─────────────
+        task.phase = "parsing_documents"
+        task.append_log(f"  [build] 扫描文档目录: {docs_path}")
+        build_result = self._run_build(task, req, docs_path, build_artifact_dir, dataset_csv)
+
+        source_chunks_jsonl = build_artifact_dir / "latest" / "source_chunks.jsonl"
+        total_q = len(build_result.draft_samples)
+        parse_failures = len(build_result.parse_failures)
+        task.append_log(f"  [build] 题库生成完毕: {total_q} 道题目, {parse_failures} 份文档解析失败")
+
+        if total_q == 0:
+            raise RuntimeError("题库为空（所有文档均解析或生成失败），中止评估。")
+
+        # ── phase 3: evaluation ────────────────────────────────────────
+        task.phase = "evaluating"
+        task.append_log(f"  [eval] 开始 RAGAS 评估，共 {total_q} 道题目")
+        eval_result = self._run_eval(task, req, dataset_csv, source_chunks_jsonl, eval_output_dir)
+
+        from rag_eval.reporting.artifacts import build_artifact_paths as _build_eval_paths
+        eval_artifact_paths = _build_eval_paths(eval_result.scenario.output_dir, eval_result.run_id)
+
+        return PipelineResult(
+            build_artifact_dir=build_artifact_dir.as_posix(),
+            dataset_csv=dataset_csv.as_posix(),
+            source_chunks_jsonl=source_chunks_jsonl.as_posix(),
+            total_questions=total_q,
+            parse_failures=parse_failures,
+            eval_run_id=eval_result.run_id,
+            eval_output_dir=eval_result.scenario.output_dir.as_posix(),
+            scores_csv=eval_artifact_paths.scores_csv.as_posix(),
+            summary_md=eval_artifact_paths.summary_md.as_posix(),
+        )
+
+    def _run_build(self, task: PipelineTask, req: PipelineJobRequest,
+                   docs_path: Path, artifact_dir: Path, dataset_csv: Path):
+        """Construct DatasetBuildJob and run the build phase."""
+        from rag_eval.dataset_builder.models import DatasetBuildJob, DatasetBuildRuntime
+        from rag_eval.dataset_builder.runner import execute_dataset_build_job
+        from rag_eval.settings import EvaluationSettings
+
+        settings = EvaluationSettings()
+        job = DatasetBuildJob(
+            job_name=task.job_name,
+            input_path=docs_path,
+            input_glob="*.pdf",
+            parser_provider="aliyun_docmind",
+            failure_mode=req.failure_mode,  # type: ignore[arg-type]
+            generation_model=req.generation_model,
+            output_type="online_question_bank",
+            review_mode="draft_with_manual_review",
+            max_questions_per_document=req.max_questions_per_document,
+            max_source_chunks_per_question=req.max_source_chunks_per_question,
+            dataset_path=dataset_csv,
+            artifact_dir=artifact_dir,
+            runtime=DatasetBuildRuntime(max_documents=req.max_documents),
+        )
+        return execute_dataset_build_job(job, settings=settings)
+
+    def _run_eval(self, task: PipelineTask, req: PipelineJobRequest,
+                  dataset_csv: Path, source_chunks_jsonl: Path, eval_output_dir: Path):
+        """Construct Scenario and run the evaluation phase."""
+        from rag_eval.execution.runner import run_scenario_from_scenario_obj
+        from rag_eval.settings import EvaluationSettings
+        from rag_eval.shared.models import (
+            AppAdapterConfig, DatasetConfig, RuntimeConfig, Scenario,
+        )
+
+        settings = EvaluationSettings()
+        scenario = Scenario(
+            scenario_name=task.job_name,
+            mode="online",
+            dataset=DatasetConfig(path=dataset_csv),
+            judge_model=req.judge_model,
+            embedding_model=req.embedding_model,
+            metrics=list(req.metrics),
+            output_dir=eval_output_dir,
+            runtime=RuntimeConfig(
+                batch_size=4,
+                app_concurrency=2,
+                metric_concurrency=2,
+                max_samples=req.max_samples,
+            ),
+            app_adapter=AppAdapterConfig(
+                type="python",
+                callable="apps.siemens_pdf_qa.adapter:run",
+                static_kwargs={
+                    "source_chunks_path": source_chunks_jsonl,
+                    "model": req.answer_model,
+                },
+            ),
+            optimization_advisor=req.optimization_advisor,
+        )
+        return run_scenario_from_scenario_obj(scenario, settings=settings)
+
+
+# Module-level singleton shared by the FastAPI routes.
+pipeline_task_manager = PipelineTaskManager()

webapp/services/report_builder.py

@@ -177,9 +177,11 @@ def build_report(run_dir: Path, metrics: list[str]) -> ReportData:
     w_means = _weighted_metric_means(score_rows_list, metrics, doc_weights)
     rounded_means = {metric: _round_or_none(value) for metric, value in w_means.items()}
 
-    overall_ws = compute_overall_weighted_score_mean(
-        score_rows_list, metric_weights, doc_weights
-    )
+    # 综合加权得分计算（已暂时禁用）
+    # overall_ws = compute_overall_weighted_score_mean(
+    #     score_rows_list, metric_weights, doc_weights
+    # )
+    overall_ws = None
 
     distributions = {
         metric: _distribution(frame, metric)

webapp/services/score_job_manager.py

@@ -149,10 +149,12 @@ class ScoreJobManager:
             # 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
+            # 综合加权得分计算（已暂时禁用）
+            # 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
+            weighted = None
 
             # Build a score row compatible with report_builder
             score_row: dict[str, Any] = {

webapp/services/session_score_manager.py

@@ -0,0 +1,452 @@
+"""Background task manager for session-grouped async RAGAS scoring.
+
+Each session groups multiple scoring calls into one shared run report:
+
+  1. First call: creates outputs/score-session/session-<id>/ and metadata.json.
+  2. Every call: appends a new sample row to scores.csv, rewrites summary.md
+     and optimization_advice.md by re-running write_run_artifacts + run_advisor
+     over ALL accumulated rows.
+  3. The resulting run directory is picked up automatically by run_reader, so the
+     「运行列表」 and 「报告详情」 pages show the live, growing report.
+
+Concurrency model:
+  - Scoring (LLM network I/O) runs freely in the thread pool — different sessions
+    score concurrently; multiple calls to the same session also start scoring in
+    parallel.
+  - File I/O (CSV append, artifact rewrite, advisor) is serialized per session via
+    a per-session threading.Lock, so no two calls corrupt the same session's CSV.
+"""
+
+from __future__ import annotations
+
+import json
+import re
+import threading
+import time
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+
+from webapp.models import AsyncScoreJobStatus, ScoreRequest, SessionStatus
+
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+_DEFAULT_OUTPUT_DIR = _REPO_ROOT / "outputs" / "score-session"
+_DEFAULT_INDEX_DIR = _REPO_ROOT / "outputs" / "score-session-jobs"
+
+# Columns that are sample metadata rather than metric scores (mirrors run_reader.NON_METRIC_COLUMNS)
+_NON_METRIC_COLUMNS = {
+    "sample_id", "question", "contexts", "answer", "ground_truth",
+    "scenario", "language", "retrieval_config", "error",
+    "judge_model", "embedding_model", "run_id", "difficulty",
+    "question_type", "doc_id", "doc_name", "section_path",
+    "page_start", "page_end", "source_chunk_ids", "review_status",
+    "review_notes", "weighted_score", "sample_weight",
+}
+
+
+def _now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def _sanitize_session_id(session_id: str) -> str:
+    """Convert an arbitrary session_id string to a safe directory-name fragment."""
+    return re.sub(r"[^a-zA-Z0-9]", "-", session_id)[:64].strip("-") or "default"
+
+
+class SessionScoreJobManager:
+    """Thread-pool manager for session-grouped async scoring jobs.
+
+    All calls sharing a session_id append to one shared run directory, so the
+    report detail page shows all samples and their aggregate metrics together.
+    """
+
+    def __init__(
+        self,
+        output_dir: Path = _DEFAULT_OUTPUT_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._index_dir / "_sessions").mkdir(parents=True, exist_ok=True)
+        self._executor = ThreadPoolExecutor(max_workers=max_workers)
+
+        # job_id -> AsyncScoreJobStatus; guarded by _lock
+        self._job_cache: dict[str, AsyncScoreJobStatus] = {}
+        # session_id -> [job_ids in order]; guarded by _lock
+        self._session_jobs: dict[str, list[str]] = {}
+        # session_id -> per-session threading.Lock; guarded by _lock
+        self._session_locks: dict[str, threading.Lock] = {}
+        self._lock = threading.Lock()
+
+        self._load_existing()
+
+    # ------------------------------------------------------------------ #
+    # Public API
+    # ------------------------------------------------------------------ #
+
+    def session_run_id(self, session_id: str) -> str:
+        """Return the deterministic run_id for a session (also the dir name)."""
+        return f"session-{_sanitize_session_id(session_id)}"
+
+    def submit(self, session_id: str, request: ScoreRequest) -> tuple[AsyncScoreJobStatus, str]:
+        """Queue one scoring call for a session.
+
+        Returns (job_status, run_id). run_id is deterministic from session_id.
+        """
+        run_id = self.session_run_id(session_id)
+        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),
+                "session_id": session_id,
+            },
+        )
+
+        with self._lock:
+            self._job_cache[job_id] = status
+            if session_id not in self._session_jobs:
+                self._session_jobs[session_id] = []
+            self._session_jobs[session_id].append(job_id)
+
+        self._persist_job_index(status)
+        self._persist_session_index(session_id)
+        self._executor.submit(self._run, job_id, session_id, run_id, request)
+        return status, run_id
+
+    def get_job(self, job_id: str) -> AsyncScoreJobStatus | None:
+        """Return current status of one call, or None if unknown."""
+        with self._lock:
+            return self._job_cache.get(job_id)
+
+    def list_jobs(self) -> list[AsyncScoreJobStatus]:
+        """Return all session job records, newest first."""
+        with self._lock:
+            jobs = list(self._job_cache.values())
+        jobs.sort(key=lambda j: j.created_at, reverse=True)
+        return jobs
+
+    def get_session(self, session_id: str) -> SessionStatus | None:
+        """Return aggregate status for a session, or None if unknown."""
+        with self._lock:
+            job_ids = list(self._session_jobs.get(session_id) or [])
+        if not job_ids:
+            return None
+
+        run_id = self.session_run_id(session_id)
+        run_dir = self._output_dir / run_id
+
+        # Compute live metric means from the CSV (may be mid-update — best effort)
+        metric_means = self._read_metric_means(run_dir)
+
+        with self._lock:
+            jobs = [self._job_cache[jid] for jid in job_ids if jid in self._job_cache]
+
+        latest = max((j.finished_at for j in jobs if j.finished_at), default="")
+        return SessionStatus(
+            session_id=session_id,
+            run_id=run_id,
+            call_count=len(job_ids),
+            metric_means=metric_means,
+            latest_finished_at=latest,
+            jobs=sorted(jobs, key=lambda j: j.created_at),
+        )
+
+    def list_sessions(self) -> list[SessionStatus]:
+        """Return aggregate status for all known sessions."""
+        with self._lock:
+            session_ids = list(self._session_jobs.keys())
+        results = []
+        for sid in session_ids:
+            status = self.get_session(sid)
+            if status is not None:
+                results.append(status)
+        results.sort(key=lambda s: s.latest_finished_at, reverse=True)
+        return results
+
+    # ------------------------------------------------------------------ #
+    # Worker
+    # ------------------------------------------------------------------ #
+
+    def _run(self, job_id: str, session_id: str, run_id: str, request: ScoreRequest) -> None:
+        """Score one sample then append it to the session's shared run artifacts."""
+        import logging
+        logger = logging.getLogger("webapp.services.session_score_manager")
+        self._update_job(job_id, status="running")
+
+        # Lazy imports — 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()
+
+        try:
+            # --- Scoring (can run concurrently for the same session) ----------
+            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 complete scores for this sample (skipped metrics → None)
+            all_scores: dict[str, float | None] = {m: None for m in request.metrics}
+            all_scores.update(raw_scores)
+
+            # 综合加权得分计算（已暂时禁用）
+            # 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
+            weighted = None
+
+            # --- File I/O must be serialized per session ----------------------
+            session_lock = self._get_session_lock(session_id)
+            with session_lock:
+                run_dir = self._output_dir / run_id
+                run_dir.mkdir(parents=True, exist_ok=True)
+
+                # Read all existing rows, then append the new one
+                existing_rows = self._read_score_rows(run_dir)
+                call_number = len(existing_rows) + 1
+
+                new_row: dict[str, Any] = {
+                    "sample_id": f"session-score-{call_number}",
+                    "question": request.question,
+                    "answer": request.answer or "",
+                    "contexts": request.contexts or "",
+                    "ground_truth": request.ground_truth or "",
+                    "error": "",
+                }
+                new_row.update(all_scores)
+
+                all_rows = existing_rows + [new_row]
+
+                # Reconstruct NormalizedSample objects for write_run_artifacts metadata
+                valid_samples = [
+                    NormalizedSample(
+                        sample_id=str(row.get("sample_id", f"session-score-{i + 1}")),
+                        question=str(row.get("question", "")),
+                        answer=str(row.get("answer", "")),
+                        contexts=[
+                            part.strip()
+                            for part in str(row.get("contexts", "")).split(" |||| ")
+                            if part.strip()
+                        ],
+                        ground_truth=str(row.get("ground_truth", "")),
+                    )
+                    for i, row in enumerate(all_rows)
+                ]
+
+                # Determine all metric columns (union of all rows' metric keys)
+                all_metric_names = sorted({
+                    k for row in all_rows
+                    for k in row if k not in _NON_METRIC_COLUMNS
+                })
+
+                scenario = Scenario(
+                    scenario_name=f"session-{_sanitize_session_id(session_id)}",
+                    mode="offline",
+                    dataset=DatasetConfig(path=run_dir / "dataset.csv"),
+                    judge_model=judge_model,
+                    embedding_model=embedding_model,
+                    metrics=all_metric_names,
+                    output_dir=self._output_dir,
+                    optimization_advisor=True,
+                )
+
+                started_at_val = (
+                    existing_rows[0].get("_started_at", finished_at)
+                    if existing_rows else finished_at
+                )
+
+                result = EvaluationResult(
+                    scenario=scenario,
+                    run_id=run_id,
+                    started_at=started_at_val if isinstance(started_at_val, str) else finished_at,
+                    finished_at=finished_at,
+                    valid_samples=valid_samples,
+                    invalid_samples=[],
+                    score_rows=all_rows,
+                )
+
+                write_run_artifacts(result)
+                logger.info(
+                    "[session_job] artifacts written  job_id=%s  session_id=%s  call=%d",
+                    job_id, session_id, call_number,
+                )
+
+                # Regenerate optimization advice over all accumulated rows
+                try:
+                    llm, _ = build_models(judge_model, embedding_model, settings)
+                    run_advisor(result, scenario, llm)
+                    logger.info("[session_job] advisor done  job_id=%s  session=%s", job_id, session_id)
+                except Exception as adv_exc:  # noqa: BLE001
+                    logger.warning(
+                        "[session_job] advisor failed  job_id=%s  err=%s", job_id, adv_exc
+                    )
+
+            self._update_job(
+                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,
+            )
+            self._persist_session_index(session_id)
+
+        except Exception as exc:  # noqa: BLE001
+            latency_ms = int((time.monotonic() - t0) * 1000)
+            import logging as _logging
+            _logging.getLogger("webapp.services.session_score_manager").error(
+                "[session_job] failed  job_id=%s  err=%s", job_id, exc
+            )
+            self._update_job(
+                job_id,
+                status="failed",
+                finished_at=_now_iso(),
+                latency_ms=latency_ms,
+                error=f"{type(exc).__name__}: {exc}",
+            )
+
+    # ------------------------------------------------------------------ #
+    # Helpers
+    # ------------------------------------------------------------------ #
+
+    def _get_session_lock(self, session_id: str) -> threading.Lock:
+        with self._lock:
+            if session_id not in self._session_locks:
+                self._session_locks[session_id] = threading.Lock()
+            return self._session_locks[session_id]
+
+    def _read_score_rows(self, run_dir: Path) -> list[dict[str, Any]]:
+        """Read existing scores.csv rows, returning empty list if file doesn't exist."""
+        scores_path = run_dir / "scores.csv"
+        if not scores_path.is_file():
+            return []
+        try:
+            frame = pd.read_csv(scores_path)
+            return frame.where(pd.notnull(frame), None).to_dict("records")
+        except (OSError, ValueError):
+            return []
+
+    def _read_metric_means(self, run_dir: Path) -> dict[str, float | None]:
+        """Compute per-metric means from the session's scores.csv."""
+        scores_path = run_dir / "scores.csv"
+        if not scores_path.is_file():
+            return {}
+        try:
+            frame = pd.read_csv(scores_path)
+        except (OSError, ValueError):
+            return {}
+        means: dict[str, float | None] = {}
+        for col in frame.columns:
+            if col in _NON_METRIC_COLUMNS:
+                continue
+            if pd.api.types.is_numeric_dtype(frame[col]):
+                val = frame[col].mean(numeric_only=True)
+                means[col] = None if pd.isna(val) else round(float(val), 4)
+        return means
+
+    def _update_job(self, job_id: str, **kwargs: Any) -> None:
+        with self._lock:
+            existing = self._job_cache.get(job_id)
+            if existing is None:
+                return
+            updated = existing.model_copy(update=kwargs)
+            self._job_cache[job_id] = updated
+        self._persist_job_index(updated)
+
+    def _persist_job_index(self, status: AsyncScoreJobStatus) -> None:
+        """Persist a single job's status to the index directory."""
+        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 _persist_session_index(self, session_id: str) -> None:
+        """Persist the session→job_ids mapping."""
+        with self._lock:
+            job_ids = list(self._session_jobs.get(session_id) or [])
+        run_id = self.session_run_id(session_id)
+        data = {"session_id": session_id, "run_id": run_id, "job_ids": job_ids}
+        path = self._index_dir / "_sessions" / f"{_sanitize_session_id(session_id)}.json"
+        path.write_text(
+            json.dumps(data, ensure_ascii=False, indent=2),
+            encoding="utf-8",
+        )
+
+    def _load_existing(self) -> None:
+        """Restore job cache and session mappings from persisted index files on startup."""
+        # Load individual job files
+        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._job_cache[status.job_id] = status
+            except Exception:  # noqa: BLE001
+                pass
+
+        # Load session→job_ids mappings
+        sessions_dir = self._index_dir / "_sessions"
+        if not sessions_dir.is_dir():
+            return
+        for path in sorted(sessions_dir.glob("*.json")):
+            try:
+                data = json.loads(path.read_text(encoding="utf-8"))
+                sid = data.get("session_id", "")
+                job_ids = data.get("job_ids", [])
+                if sid:
+                    self._session_jobs[sid] = job_ids
+            except Exception:  # noqa: BLE001
+                pass
+
+
+# Module-level singleton shared by FastAPI routes.
+session_score_manager = SessionScoreJobManager()