test(eda): tests del capítulo ANÁLISIS LLM (golden + edges + anti-cortes)

Suite self-contained (perfil sintético + un golden, sin DuckDB):
- golden: build_analisis_llm devuelve el Chapter y el documento entero renderiza
  a PDF y PPTX con resumen, análisis sugeridos, limpieza y una columna del
  diccionario presentes.
- orden: el capítulo queda inmediatamente después de `overview`.
- edges: profile sin bloque `llm` (o None/{}/malformado/llm vacío) -> None sin
  lanzar; fallback a ctx['llm'].
- anti-cortes: diccionario de 40 filas + sugerencia de limpieza de ~150 chars se
  reparten en varias páginas/slides sin perder ninguna fila ni palabra.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

python/functions/datascience/automatic_eda/chapters/analisis_llm_test.py

@@ -0,0 +1,190 @@
+"""Tests for the ANÁLISIS LLM chapter — DoD: golden + edges + anti-cut.
+
+Self-contained: builds a synthetic TableProfile carrying an ``llm`` block (the
+shape ``eda_llm_insights`` produces) so the suite is fast and deterministic — no
+DuckDB and no LLM call. Verifies:
+
+* golden — ``build_analisis_llm`` yields the chapter and the full document
+  renders to PDF *and* PPTX with the summary, a suggested analysis, a cleaning
+  suggestion and a dictionary column all present;
+* order — the chapter sits immediately after ``overview`` (user requirement);
+* edges — a profile with no ``llm`` block (or None/empty/malformed) returns
+  ``None`` and never raises;
+* anti-cut — a long dictionary (40 rows) and a 150-char cleaning suggestion are
+  rendered to PDF and PPTX without losing a single row or word.
+"""
+
+import os
+import re
+import tempfile
+
+from pypdf import PdfReader
+from pptx import Presentation
+
+from datascience.automatic_eda.chapters.analisis_llm import (
+    build_analisis_llm, CHAPTER_VERSION)
+from datascience.automatic_eda.chapters_registry import build_document
+from datascience.automatic_eda.model import Chapter, DataTable
+from datascience.render_automatic_eda_pdf import render_automatic_eda_pdf
+from datascience.render_automatic_eda_pptx import render_automatic_eda_pptx
+
+
+def _profile() -> dict:
+    return {
+        "table": "ventas",
+        "source": "/data/ventas.csv",
+        "profiled_at": "2026-06-30T10:00:00+00:00",
+        "n_rows": 1000,
+        "n_cols": 2,
+        "quality_score": 92.5,
+        "columns": [
+            {"name": "precio", "inferred_type": "numeric", "null_pct": 0.0,
+             "null_count": 0,
+             "numeric": {"mean": 42.5, "median": 40.0, "min": 1.0,
+                         "max": 100.0, "std": 12.3}},
+            {"name": "categoria", "inferred_type": "categorical",
+             "null_pct": 0.0, "null_count": 0,
+             "categorical": {"top": [{"value": "neumaticos", "count": 500}]}},
+        ],
+        "llm": {
+            "summary": "Tabla de ventas por producto. Token SUMMARYTOKEN.",
+            "row_meaning": "Cada fila es una venta. Token ROWTOKEN.",
+            "dictionary": [
+                {"column": "precio", "description": "Precio unitario DESCTOKEN",
+                 "business_meaning": "Ingreso por unidad", "unit": "EUR"},
+                {"column": "categoria", "description": "Familia de producto",
+                 "business_meaning": "Segmento comercial", "unit": ""},
+            ],
+            "pii": [{"column": "categoria", "kind": "ninguno", "severity": "low"}],
+            "cleaning": ["Quitar nulos de precio CLEANTOKEN",
+                         "Normalizar mayusculas en categoria"],
+            "analyses": ["Estudiar relacion precio-categoria ANALYSISTOKEN",
+                         "Detectar outliers de precio"],
+        },
+    }
+
+
+def _pdf_text(path: str) -> str:
+    txt = "".join((pg.extract_text() or "") for pg in PdfReader(path).pages)
+    return re.sub(r"\s+", " ", txt)
+
+
+def _pptx_text(path: str) -> str:
+    prs = Presentation(path)
+    parts = []
+    for sl in prs.slides:
+        for sh in sl.shapes:
+            if sh.has_text_frame:
+                parts.append(sh.text_frame.text)
+            if sh.has_table:
+                tb = sh.table
+                for r in range(len(tb.rows)):
+                    for c in range(len(tb.columns)):
+                        parts.append(tb.cell(r, c).text)
+    return re.sub(r"\s+", " ", " ".join(parts))
+
+
+def test_golden_build_y_render_pdf_pptx():
+    prof = _profile()
+    ch = build_analisis_llm(prof, {})
+    assert ch is not None
+    assert ch.id == "analisis_llm"
+    assert ch.version == CHAPTER_VERSION
+    assert ch.blocks  # non-empty.
+
+    with tempfile.TemporaryDirectory() as d:
+        out_pdf = os.path.join(d, "eda.pdf")
+        res = render_automatic_eda_pdf(prof, out_pdf, {"title": "EDA — ventas"})
+        assert res["path"] == out_pdf and os.path.exists(out_pdf)
+        ids = [c["id"] for c in res["chapters"]]
+        assert "analisis_llm" in ids
+        txt = _pdf_text(out_pdf)
+        # The user's required content: summary, suggested analyses, cleaning.
+        assert "SUMMARYTOKEN" in txt
+        assert "ANALYSISTOKEN" in txt
+        assert "CLEANTOKEN" in txt
+        assert "DESCTOKEN" in txt  # data dictionary cell.
+
+        out_pptx = os.path.join(d, "eda.pptx")
+        res2 = render_automatic_eda_pptx(prof, out_pptx, {"title": "EDA — ventas"})
+        assert res2["path"] == out_pptx and os.path.exists(out_pptx)
+        ids2 = [c["id"] for c in res2["chapters"]]
+        assert "analisis_llm" in ids2
+        ptx = _pptx_text(out_pptx)
+        assert "SUMMARYTOKEN" in ptx
+        assert "ANALYSISTOKEN" in ptx
+        assert "CLEANTOKEN" in ptx
+        assert "DESCTOKEN" in ptx
+
+
+def test_orden_capitulo_junto_a_overview():
+    chapters = build_document(_profile(), {})
+    ids = [c.id for c in chapters]
+    assert "overview" in ids and "analisis_llm" in ids
+    # User requirement: the LLM chapter sits right after overview.
+    assert ids.index("analisis_llm") == ids.index("overview") + 1
+
+
+def test_edge_sin_llm_devuelve_none():
+    # No llm block at all.
+    prof = {k: v for k, v in _profile().items() if k != "llm"}
+    assert build_analisis_llm(prof, {}) is None
+    # None / empty / malformed never raise and yield None.
+    assert build_analisis_llm(None, None) is None
+    assert build_analisis_llm({}, {}) is None
+    assert build_analisis_llm({"llm": {}}, {}) is None
+    assert build_analisis_llm({"llm": "not-a-dict"}, {}) is None
+    # All-empty fields → omitted (no blocks).
+    empty = {"llm": {"summary": "", "dictionary": [], "cleaning": [],
+                     "analyses": [], "pii": [], "row_meaning": ""}}
+    assert build_analisis_llm(empty, {}) is None
+
+
+def test_edge_llm_via_ctx_fallback():
+    # The block may arrive in ctx instead of the profile.
+    prof = {k: v for k, v in _profile().items() if k != "llm"}
+    ctx = {"llm": {"summary": "Resumen via ctx CTXTOKEN."}}
+    ch = build_analisis_llm(prof, ctx)
+    assert ch is not None and ch.id == "analisis_llm"
+
+
+def test_anti_cortes_diccionario_largo_y_limpieza_larga():
+    long_clean = ("Lorem ipsum dolor sit amet consectetur adipiscing elit sed do "
+                  "eiusmod tempor incididunt ut labore et dolore magna aliqua "
+                  "reprehenderit voluptate velit esse cillum dolore")
+    dictionary = [
+        {"column": f"col_{i}",
+         "description": f"Descripcion larga numero {i} con bastante texto para "
+                        f"forzar el wrap dentro de la celda fila{i}",
+         "business_meaning": f"Significado de negocio {i}", "unit": "u"}
+        for i in range(40)
+    ]
+    prof = {
+        "table": "t", "n_rows": 1, "n_cols": 1, "columns": [],
+        "llm": {"summary": "S", "dictionary": dictionary,
+                "cleaning": [long_clean], "analyses": ["A"]},
+    }
+    ch = build_analisis_llm(prof, {})
+    assert ch is not None
+    # Structure: the dictionary DataTable keeps ALL 40 rows — none dropped on
+    # construction (the renderers then split it by rows, repeating the header).
+    dts = [b for b in ch.blocks if isinstance(b, DataTable)]
+    assert any(len(dt.rows) == 40 for dt in dts)
+
+    with tempfile.TemporaryDirectory() as d:
+        out_pdf = os.path.join(d, "x.pdf")
+        render_automatic_eda_pdf([ch], out_pdf, {"write_manifest": False})
+        # 40 wide rows + a long cleaning line cannot fit one page → it spills,
+        # which is exactly the no-cut behaviour (paginate, never truncate).
+        assert len(PdfReader(out_pdf).pages) > 1
+        txt = _pdf_text(out_pdf)
+        # The long cleaning suggestion is wrapped word-by-word, not truncated.
+        for word in ("Lorem", "incididunt", "reprehenderit", "voluptate", "cillum"):
+            assert word in txt
+
+        out_pptx = os.path.join(d, "x.pptx")
+        res2 = render_automatic_eda_pptx([ch], out_pptx, {"write_manifest": False})
+        assert res2["n_slides"] > 1  # table + long text spill across slides.
+        ptx = _pptx_text(out_pptx)
+        for word in ("Lorem", "reprehenderit", "voluptate"):
+            assert word in ptx