feat: integra pipeline PDF→Markdown a 9 stadi e test suite

Porta da main la riscrittura completa di conversione/_pipeline/ (9 stadi
PyMuPDF) e la suite tests/ senza modificare chunks/, step-8/, rag.py,
ollama/, retrieve.py, config.py.

requirements.txt: aggiunge PyMuPDF>=1.24.0 e pytest>=8.0, mantiene chromadb,
rimuove opendataloader-pdf e pymupdf4llm.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

conversione/_pipeline/report.py

@@ -0,0 +1,135 @@
+import json
+import re
+from datetime import datetime
+from pathlib import Path
+
+from .structure import _parse_sections_with_body
+from ._constants import _MATH_SYMBOLS_RE, _EXERCISE_TRIGGER_RE, _MATH_HDR_RE
+
+
+def build_report(
+    stem: str,
+    out_dir: Path,
+    clean_text: str,
+    t_stats: dict,
+    profile: dict,
+    reduction: float,
+) -> Path:
+    text_lines = clean_text.split("\n")
+
+    sections = _parse_sections_with_body(clean_text, 3)
+    lengths  = [len(body) for _, body in sections]
+
+    def _pct(data: list[int], p: float) -> int:
+        if not data:
+            return 0
+        s = sorted(data)
+        return s[max(0, min(len(s) - 1, int(len(s) * p)))]
+
+    distribution = {
+        "min":     min(lengths) if lengths else 0,
+        "p25":     _pct(lengths, 0.25),
+        "mediana": _pct(lengths, 0.50),
+        "p75":     _pct(lengths, 0.75),
+        "max":     max(lengths) if lengths else 0,
+    }
+
+    bare_hdrs = [
+        {"header": hdr, "corpo_inizio": body[:120].replace("\n", " ")}
+        for hdr, body in sections
+        if re.match(r"^### \d+\.\s*$", hdr) and len(body.strip()) < 30
+    ]
+    short_secs = [
+        {"header": hdr, "chars": length, "testo": body[:80].replace("\n", " ")}
+        for (hdr, body), length in zip(sections, lengths)
+        if 0 < length < 150
+    ]
+    long_secs = [
+        {"header": hdr, "chars": length}
+        for (hdr, _), length in zip(sections, lengths)
+        if length > 1500
+    ]
+
+    def _scan(pattern: str, max_n: int = 10) -> list[dict]:
+        hits = []
+        for i, line in enumerate(text_lines):
+            if re.search(pattern, line) and not re.match(r"^#+ ", line):
+                hits.append({"riga": i + 1, "testo": line.strip()[:120]})
+                if len(hits) >= max_n:
+                    break
+        return hits
+
+    def _scan_formula_headers(max_n: int = 10) -> list[dict]:
+        hits = []
+        for i, line in enumerate(text_lines):
+            m = _MATH_HDR_RE.match(line)
+            if not m:
+                continue
+            body = m.group(2)
+            if len(body) <= 100:
+                continue
+            has_math = len(_MATH_SYMBOLS_RE.findall(body)) >= 3
+            has_ex   = bool(_EXERCISE_TRIGGER_RE.search(body))
+            if has_math or has_ex:
+                hits.append({"riga": i + 1, "testo": line.strip()[:120]})
+                if len(hits) >= max_n:
+                    break
+        return hits
+
+    residui = {
+        "backtick":         _scan(r"`"),
+        "dotleader":        _scan(r"(?:\. ){5,}"),
+        "url":              _scan(r"^(https?://|www\.)\S+"),
+        "immagini":         _scan(r"!\[[^\]]*\]\([^)]*\)"),
+        "br_inline":        _scan(r"<br>"),
+        "simboli_encoding": _scan(r'(?<=[0-9A-Za-z])[!"](?=[0-9A-Za-z])'),
+        "formule_inline":   _scan(r"\[\d+\.\d+\]"),
+        "footnote_markers": _scan(r'[¹²³⁰⁴-⁹]'),
+        "pua_markers":      _scan(r'[-]'),
+        "formula_headers":  _scan_formula_headers(),
+    }
+
+    report = {
+        "stem":      stem,
+        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M"),
+        "transforms": {
+            **t_stats,
+            "riduzione_pct": round(reduction),
+        },
+        "structure":    profile,
+        "distribution": distribution,
+        "anomalie": {
+            "bare_headers":        len(bare_hdrs),
+            "short_sections":      len(short_secs),
+            "long_sections":       len(long_secs),
+            "bare_headers_list":   bare_hdrs,
+            "short_sections_list": short_secs,
+            "long_sections_list":  long_secs,
+        },
+        "residui": {
+            "backtick":                   len(residui["backtick"]),
+            "dotleader":                  len(residui["dotleader"]),
+            "url":                        len(residui["url"]),
+            "immagini":                   len(residui["immagini"]),
+            "br_inline":                  len(residui["br_inline"]),
+            "simboli_encoding":           len(residui["simboli_encoding"]),
+            "formule_inline":             len(residui["formule_inline"]),
+            "footnote_markers":           len(residui["footnote_markers"]),
+            "pua_markers":                len(residui["pua_markers"]),
+            "backtick_esempi":            residui["backtick"],
+            "dotleader_esempi":           residui["dotleader"],
+            "url_esempi":                 residui["url"],
+            "immagini_esempi":            residui["immagini"],
+            "br_inline_esempi":           residui["br_inline"],
+            "simboli_encoding_esempi":    residui["simboli_encoding"],
+            "formule_inline_esempi":      residui["formule_inline"],
+            "footnote_markers_esempi":    residui["footnote_markers"],
+            "pua_markers_esempi":         residui["pua_markers"],
+            "formula_headers":            len(residui["formula_headers"]),
+            "formula_headers_esempi":     residui["formula_headers"],
+        },
+    }
+
+    report_path = out_dir / "report.json"
+    report_path.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
+    return report_path