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>

webapp/services/text_utils.py

@@ -0,0 +1,47 @@
+"""Self-contained text helpers for the web layer.
+
+These intentionally avoid importing from rag_eval so the web server has no
+import-time dependency on the evaluation engine (and therefore boots even when
+ragas is unavailable). The contexts parser mirrors rag_eval.shared.utils so the
+console interprets serialized CSV context columns the same way the engine does.
+"""
+
+from __future__ import annotations
+
+import ast
+import json
+import math
+from typing import Any
+
+
+def parse_contexts(value: Any) -> list[str]:
+    """Normalize a context payload into a list of non-empty strings.
+
+    Accepts native lists, JSON/Python-literal serialized lists (as written into
+    scores.csv), and plain text, mirroring the engine's own parsing rules.
+    """
+    if isinstance(value, list):
+        return [str(item).strip() for item in value if str(item).strip()]
+    if value is None or (isinstance(value, float) and math.isnan(value)):
+        return []
+
+    text = str(value).strip()
+    if not text:
+        return []
+
+    # Accept serialized lists from CSV exports before falling back to plain text.
+    for parser in (json.loads, ast.literal_eval):
+        try:
+            parsed = parser(text)
+        except (ValueError, SyntaxError, json.JSONDecodeError):
+            continue
+        if isinstance(parsed, list):
+            return [str(item).strip() for item in parsed if str(item).strip()]
+
+    # Preserve paragraph-style context dumps by splitting on blank lines first.
+    if "\n\n" in text:
+        chunks = [chunk.strip() for chunk in text.split("\n\n") if chunk.strip()]
+        if chunks:
+            return chunks
+
+    return [text]