feat(chunks): pipeline AST-based completa — parser → segmenter → packer → validator

Riscrittura completa del chunker con architettura modulare basata su markdown-it-py.

Moduli:
- parser.py     — markdown-it-py + dollarmath plugin → token stream con source map
- segmenter.py  — token stream → Block[] (paragraph, code, table, list, math, html, thematic_break)
- packer.py     — Block[] → Chunk[] con packing min/target/max e content_for_embedding
- validator.py  — invarianti + metriche (size_compliance, overflow, sparse)
- chunker.py    — CLI + orchestrazione pipeline, scrive chunks.json/meta.json/report.json

Flag universali: has_math rileva math_block, $$ e \begin{; has_table rileva pipe table e <table> HTML.
65 test passing.

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

chunks/chunker.py

@@ -1,413 +1,140 @@
 #!/usr/bin/env python3
 """
-chunker.py — Chunking semantico da Markdown pulito
+chunker.py — CLI + orchestrazione pipeline AST-based
-
-Input:  sources/<stem>.md  — Markdown già strutturato (H1/H2/H3 + paragrafi)
-Output: chunks/<stem>/chunks.json
-        chunks/<stem>/meta.json
-
-Regole:
-  - ogni paragrafo diventa un chunk; paragrafi di contesti diversi non si mescolano
-  - se un paragrafo supera MAX_CHARS viene spezzato a confine di frase (mai a metà)
-  - paragrafi brevi (< MIN_CHARS) vengono fusi col successivo finché non raggiungono
-    MIN_CHARS, a patto che abbiano lo stesso contesto heading
-  - tabelle, liste e codice sono blocchi atomici (non si spezzano)
 
 Uso:
     python chunks/chunker.py --stem <stem>
-    python chunks/chunker.py                  # tutti gli stem con .md in sources/
+    python chunks/chunker.py                        # tutti gli stem in sources/
     python chunks/chunker.py --stem <stem> --force
 """
-
+from __future__ import annotations
 import argparse
 import json
-import re
 import sys
+from dataclasses import asdict
+from datetime import datetime, timezone
 from pathlib import Path
 
 _HERE = Path(__file__).resolve().parent
-if str(_HERE) not in sys.path:
+_ROOT = _HERE.parent
-    sys.path.insert(0, str(_HERE))
+if str(_ROOT) not in sys.path:
-import config as cfg
+    sys.path.insert(0, str(_ROOT))
-
+
-
+from chunks.config import ChunkerConfig
-# ─── Utilità ──────────────────────────────────────────────────────────────────
+from chunks.parser import parse
-
+from chunks.segmenter import segment
-def split_sentences(text: str) -> list[str]:
+from chunks.packer import pack
-    parts = re.split(cfg.SENTENCE_SPLIT_RE, text.strip())
+from chunks.validator import validate
-    return [p.strip() for p in parts if p.strip()]
+from chunks.models import ChunkingResult, Diagnostics
-
+
-
+
-def context_to_meta(context: str) -> tuple[str, str]:
+def find_source(stem: str, root: Path) -> Path | None:
-    parts = [p.strip() for p in context.split(" > ") if p.strip()]
+    candidates = [
-    if len(parts) >= 2:
+        root / "sources" / f"{stem}_output" / "auto" / f"{stem}.md",
-        return " > ".join(parts[:-1]), parts[-1]
+        root / "sources" / f"{stem}.md",
-    return (parts[0] if parts else ""), ""
+    ]
-
+    for p in candidates:
-
+        if p.exists():
-# ─── Parser Markdown ──────────────────────────────────────────────────────────
+            return p
-
+    return None
-_SKIP_HEADINGS_LOWER = {h.lower() for h in cfg.SKIP_HEADINGS}
+
-
+
-
+def run_pipeline(stem: str, root: Path = _ROOT,
-def _is_skip_heading(title: str) -> bool:
+                 config: ChunkerConfig | None = None,
-    t = title.lower()
+                 force: bool = False) -> ChunkingResult:
-    return any(t == s or t.startswith(s) for s in _SKIP_HEADINGS_LOWER)
+    config = config or ChunkerConfig()
-
+    out_dir = root / "chunks" / stem
-
+    chunks_path = out_dir / "chunks.json"
-def parse_paragraphs(text: str) -> list[dict]:
+
-    """Estrae blocchi dal Markdown con il loro contesto heading.
+    if chunks_path.exists() and not force:
-
+        print(f"[{stem}] esiste già — usa --force per rigenerare. Skip.")
-    Restituisce: [{"context": "H1 > H2 > H3", "text": "...", "kind": "text|table|list|code"}]
+        return ChunkingResult(stem=stem, source_path="", chunks=[],
-
+                              diagnostics=Diagnostics([], [], {}))
-    - Heading senza corpo non emettono chunk: aggiornano solo il contesto.
+
-    - Tabelle (righe |), liste (righe -/*) e code block (```) sono atomici.
+    source_path = find_source(stem, root)
-    - Sezioni in SKIP_HEADINGS vengono saltate completamente.
+    if source_path is None:
-    - Contenuto pre-heading saltato se SKIP_PRE_HEADING è True.
+        print(f"[{stem}] sorgente non trovata in sources/. Saltato.", file=sys.stderr)
-    """
+        return ChunkingResult(stem=stem, source_path="", chunks=[],
-    headings  = ["", "", ""]   # H1, H2, H3
+                              diagnostics=Diagnostics(
-    result:   list[dict] = []
+                                  errors=[f"sorgente non trovata per stem '{stem}'"],
-    buf:      list[str]  = []
+                                  warnings=[], metrics={}))
-    cur_kind  = "text"
+
-    in_code   = False
+    source = source_path.read_text(encoding="utf-8")
-    skip_level: int | None = None   # livello heading che ha attivato lo skip
+    tokens, lines = parse(source)
-
+    blocks = segment(tokens, lines, config)
-    def current_context() -> str:
+    chunks = pack(blocks, config, stem)
-        parts = [h for h in headings if h]
+
-        return " > ".join(parts[:cfg.CONTEXT_DEPTH]) if parts else "documento"
+    result = ChunkingResult(
-
+        stem=stem,
-    def is_skipping() -> bool:
+        source_path=str(source_path.relative_to(root)),
-        return skip_level is not None
+        chunks=chunks,
-
+        diagnostics=Diagnostics([], [], {}),
-    def flush() -> None:
+    )
-        if is_skipping():
+    result.diagnostics = validate(result, source, config)
-            buf.clear()
-            return
-        if cfg.SKIP_PRE_HEADING and current_context() == "documento":
-            buf.clear()
-            return
-        body = "\n".join(buf).strip()
-        if body:
-            result.append({"context": current_context(), "text": body, "kind": cur_kind})
-        buf.clear()
-
-    for line in text.splitlines():
-        # ── Code block toggle ─────────────────────────────────────────────────
-        if line.strip().startswith("```"):
-            if not in_code:
-                flush()
-                cur_kind = "code"
-                in_code  = True
-                if not is_skipping():
-                    buf.append(line)
-            else:
-                if not is_skipping():
-                    buf.append(line)
-                in_code = False
-                flush()
-                cur_kind = "text"
-            continue
-
-        if in_code:
-            if not is_skipping():
-                buf.append(line)
-            continue
-
-        # ── Heading ───────────────────────────────────────────────────────────
-        m = re.match(r"^(#{1,3}) (.+)", line)
-        if m:
-            flush()
-            level = len(m.group(1))
-            title = m.group(2).strip()
-
-            # chiudi skip se torniamo a livello pari o superiore
-            if skip_level is not None and level <= skip_level:
-                skip_level = None
-
-            headings[level - 1] = title
-            for i in range(level, 3):
-                headings[i] = ""
-            cur_kind = "text"
-
-            # apri skip se questo heading è nella lista
-            if _is_skip_heading(title):
-                skip_level = level
-
-            continue
-
-        # ── Tabella ───────────────────────────────────────────────────────────
-        if line.strip().startswith("|"):
-            if cur_kind != "table":
-                flush()
-                cur_kind = "table"
-            buf.append(line)
-            continue
-
-        # ── Lista ─────────────────────────────────────────────────────────────
-        if re.match(r"^\s*[-*]\s", line):
-            if cur_kind != "list":
-                flush()
-                cur_kind = "list"
-            buf.append(line)
-            continue
-
-        # ── Riga vuota: chiude il paragrafo corrente ──────────────────────────
-        if line.strip() == "":
-            flush()
-            cur_kind = "text"
-            continue
-
-        # ── Testo normale ─────────────────────────────────────────────────────
-        if cur_kind in ("table", "list", "code"):
-            flush()
-            cur_kind = "text"
-        buf.append(line)
-
-    flush()
-    return result
-
-
-# ─── Merge paragrafi brevi ────────────────────────────────────────────────────
-
-def merge_short(paragraphs: list[dict]) -> list[dict]:
-    """Fonde paragrafi di testo consecutivi sotto MIN_CHARS con il successivo,
-    purché condividano lo stesso contesto heading."""
-    if not cfg.MERGE_SHORT_PARAGRAPHS:
-        return paragraphs
-
-    result:   list[dict] = []
-    buf_para: dict | None = None
-    buf_text: str         = ""
-
-    def flush_buf() -> None:
-        nonlocal buf_para, buf_text
-        if buf_para is not None:
-            result.append({**buf_para, "text": buf_text})
-            buf_para = None
-            buf_text = ""
-
-    for para in paragraphs:
-        if para["kind"] != "text":
-            flush_buf()
-            result.append(para)
-            continue
-
-        if buf_para is None:
-            buf_para = para
-            buf_text = para["text"]
-        elif buf_para["context"] == para["context"] and len(buf_text) < cfg.MIN_CHARS:
-            buf_text = buf_text + "\n\n" + para["text"]
-        else:
-            flush_buf()
-            buf_para = para
-            buf_text = para["text"]
-
-    flush_buf()
-    return result
-
-
-# ─── Chunking ─────────────────────────────────────────────────────────────────
-
-def make_chunks(paragraphs: list[dict]) -> list[dict]:
-    """Genera chunk da parse_paragraphs (dopo eventuale merge).
-
-    - Blocchi atomici (table, list, code): un chunk, mai spezzato.
-    - Testo: un chunk per paragrafo; se supera MAX_CHARS, spezza a confine frase.
-    """
-    chunks: list[dict] = []
-    idx    = 0
-
-    for para in paragraphs:
-        text    = para["text"]
-        context = para["context"]
-        kind    = para["kind"]
-        sezione, titolo = context_to_meta(context)
-
-        def emit(body: str) -> None:
-            nonlocal idx
-            chunk_text = f"[{context}]\n{body}"
-            chunks.append({
-                "chunk_id": f"c{idx}",
-                "text":     chunk_text,
-                "sezione":  sezione,
-                "titolo":   titolo,
-                "context":  context,
-                "n_chars":  len(chunk_text),
-            })
-            idx += 1
-
-        # ── Atomici ───────────────────────────────────────────────────────────
-        if kind in ("table", "list", "code"):
-            emit(text)
-            continue
-
-        # ── Testo: split a confine di frase se supera MAX_CHARS ───────────────
-        sents = split_sentences(text)
-        if not sents:
-            continue
-
-        current: list[str] = []
-        for sent in sents:
-            projected = len(" ".join(current + [sent]))
-            if projected <= cfg.MAX_CHARS or not current:
-                current.append(sent)
-            else:
-                emit(" ".join(current))
-                current = [sent]
-
-        if current:
-            emit(" ".join(current))
-
-    return chunks
-
-
-# ─── Merge frasi spezzate ─────────────────────────────────────────────────────
-
-_SENT_END = re.compile(
-    "[.!?;:"
-    + chr(0xBB) + ")\\\\"
-    + chr(0x2018) + chr(0x2019)
-    + chr(0x201C) + chr(0x201D)
-    + "\"'"
-    + chr(0x2014) + chr(0x2013) + chr(0x2026) + chr(0xB7)
-    + "]$"
-    + r"|\d[\d.,/]*$"   # numero o versione
-    + r"|\$$"           # formula LaTeX inline
-    + r"|\}$"           # blocco LaTeX
-    + r"|>$"            # tag HTML
-    + r"|\\\\$"         # \\ LaTeX
-    + r"|\|$"           # riga tabella
-)
-
-
-def _body(chunk: dict) -> str:
-    text = chunk["text"]
-    nl = text.find("\n")
-    return text[nl + 1:] if nl != -1 else text
-
-
-def merge_broken_sentences(chunks: list[dict]) -> list[dict]:
-    """Fonde chunk consecutivi con lo stesso contesto quando il primo termina
-    senza punteggiatura di fine frase (frase spezzata dal sorgente)."""
-    result: list[dict] = []
-    i = 0
-    while i < len(chunks):
-        c    = dict(chunks[i])
-        body = _body(c)
-
-        while (
-            i + 1 < len(chunks)
-            and chunks[i + 1]["context"] == c["context"]
-            and not _SENT_END.search(body.rstrip())
-        ):
-            i += 1
-            next_body = _body(chunks[i])
-            body      = body.rstrip() + " " + next_body.lstrip()
-
-        chunk_text = f"[{c['context']}]\n{body}"
-        c["text"]    = chunk_text
-        c["n_chars"] = len(chunk_text)
-        result.append(c)
-        i += 1
-
-    for idx, c in enumerate(result):
-        c["chunk_id"] = f"c{idx}"
-
-    return result
-
-
-# ─── Pipeline per documento ───────────────────────────────────────────────────
-
-def process_stem(stem: str, project_root: Path, force: bool) -> bool:
-    md_path = project_root / "sources" / f"{stem}_output" / "auto" / f"{stem}.md"
-    if not md_path.exists():
-        md_path = project_root / "sources" / f"{stem}.md"
-    if not md_path.exists():
-        print(f"  ✗ {stem}.md non trovato (cercato in sources/{stem}_output/auto/ e sources/)")
-        return False
-
-    out_dir  = project_root / "chunks" / stem
-    out_file = out_dir / "chunks.json"
-
-    if out_file.exists() and not force:
-        print(f"  ↩  chunks/{stem}/chunks.json già presente — skip (usa --force per rigenerare)")
-        return True
-
-    print(f"[chunker] {stem}")
-
-    text       = md_path.read_text(encoding="utf-8")
-    paragraphs = parse_paragraphs(text)
-
-    if not paragraphs:
-        print(f"  ✗ Nessun paragrafo estratto da {md_path.name}")
-        return False
-
-    if cfg.MERGE_SHORT_PARAGRAPHS:
-        paragraphs = merge_short(paragraphs)
-
-    chunks = make_chunks(paragraphs)
-    chunks = merge_broken_sentences(chunks)
-
-    if not chunks:
-        print(f"  ✗ Nessun chunk generato")
-        return False
 
     out_dir.mkdir(parents=True, exist_ok=True)
-    out_file.write_text(
+
-        json.dumps(chunks, ensure_ascii=False, indent=2), encoding="utf-8"
+    chunks_path.write_text(
+        json.dumps([asdict(c) for c in chunks], ensure_ascii=False, indent=2),
+        encoding="utf-8",
     )
     (out_dir / "meta.json").write_text(
         json.dumps({
-            "stem":        stem,
+            "stem": stem,
-            "source":      str(md_path.relative_to(project_root)),
+            "source_path": result.source_path,
-            "max_chars":   cfg.MAX_CHARS,
+            "total_chunks": len(chunks),
-            "min_chars":   cfg.MIN_CHARS,
+            "total_chars": sum(c.chars for c in chunks),
-            "merge_short": cfg.MERGE_SHORT_PARAGRAPHS,
+            "created_at": datetime.now(timezone.utc).isoformat(),
-            "strategy":    "one_paragraph_per_chunk",
+            "config": {
+                "max_chars": config.max_chars,
+                "min_chars": config.min_chars,
+                "target_chars": config.target_chars,
+                "context_depth": config.context_depth,
+            },
         }, ensure_ascii=False, indent=2),
         encoding="utf-8",
     )
+    (out_dir / "report.json").write_text(
+        json.dumps(asdict(result.diagnostics), ensure_ascii=False, indent=2),
+        encoding="utf-8",
+    )
 
-    lengths   = [c["n_chars"] for c in chunks]
+    n = len(chunks)
-    over_max  = sum(1 for l in lengths if l > cfg.MAX_CHARS)
+    e = len(result.diagnostics.errors)
-    under_min = sum(1 for l in lengths if l < cfg.MIN_CHARS)
+    w = len(result.diagnostics.warnings)
-    avg       = int(sum(lengths) / len(lengths))
+    print(f"[{stem}] {n} chunk  |  errors={e}  warnings={w}  →  {out_dir}")
-
+    return result
-    print(f"  ✅ {len(chunks)} chunk  |  media {avg} char  |  max {max(lengths)} char")
-    if over_max:
-        print(f"  ⚠️  {over_max} chunk superano MAX_CHARS={cfg.MAX_CHARS}")
-    if under_min:
-        print(f"  ℹ️  {under_min} chunk sotto MIN_CHARS={cfg.MIN_CHARS}")
-    print(f"  → chunks/{stem}/chunks.json")
-    return True
 
 
-# ─── Entry point ──────────────────────────────────────────────────────────────
+def _discover_stems(root: Path) -> list[str]:
+    sources = root / "sources"
+    stems: set[str] = set()
+    if not sources.exists():
+        return []
+    for p in sources.iterdir():
+        if p.is_dir() and p.name.endswith("_output"):
+            stem = p.name[: -len("_output")]
+            if (p / "auto" / f"{stem}.md").exists():
+                stems.add(stem)
+        elif p.is_file() and p.suffix == ".md":
+            stems.add(p.stem)
+    return sorted(stems)
 
-if __name__ == "__main__":
-    project_root = Path(__file__).parent.parent
-    sources_dir  = project_root / "sources"
 
-    parser = argparse.ArgumentParser(description="Markdown pulito → chunks.json")
+def main() -> None:
-    parser.add_argument("--stem",  help="Nome documento (es. analisi2)")
+    parser = argparse.ArgumentParser(description="Chunker Markdown AST-based")
-    parser.add_argument("--force", action="store_true",
+    parser.add_argument("--stem", help="Stem documento (es. valute-virtuali)")
-                        help="Rigenera chunks.json anche se già presente")
+    parser.add_argument("--force", action="store_true", help="Rigenera anche se già presente")
     args = parser.parse_args()
 
-    if args.stem:
+    stems = [args.stem] if args.stem else _discover_stems(_ROOT)
-        stems = [args.stem]
+    if not stems:
-    else:
+        print("Nessun sorgente trovato in sources/.", file=sys.stderr)
-        found = set()
+        sys.exit(1)
-        for p in sources_dir.glob("*_output/auto/*.md"):
-            found.add(p.stem)
-        for p in sources_dir.glob("*.md"):
-            found.add(p.stem)
-        stems = sorted(found)
-        if not stems:
-            print("Nessun file .md trovato in sources/")
-            sys.exit(1)
 
-    results = [process_stem(s, project_root, args.force) for s in stems]
+    for stem in stems:
-    ok = sum(results)
+        run_pipeline(stem=stem, force=args.force)
-    print(f"\n{'✅' if all(results) else '⚠️ '} {ok}/{len(results)} documenti processati")
+
-    sys.exit(0 if all(results) else 1)
+
+if __name__ == "__main__":
+    main()

chunks/config.py

@@ -1,64 +1,24 @@
-#!/usr/bin/env python3
+from __future__ import annotations
-"""
+from dataclasses import dataclass, field
-Parametri della pipeline di chunking.
 
-Input atteso: sources/<stem>/<stem>.md — Markdown già pulito e ben strutturato.
-"""
 
-# ─── Dimensione chunk ─────────────────────────────────────────────────────────
+@dataclass
-
+class ChunkerConfig:
-# Caratteri massimi per chunk (prefisso di contesto incluso).
+    max_chars: int = 1200
-# Paragrafi più lunghi vengono spezzati a confine di frase.
+    min_chars: int = 80
-# Una singola frase che supera MAX_CHARS non viene mai spezzata.
+    target_chars: int = 800
-MAX_CHARS: int = 1200
+    context_depth: int = 3
-
+    skip_headings: set[str] = field(default_factory=lambda: {
-# Soglia minima attesa (usata da verify_chunks come warning, non blocca).
+        "indice",
-MIN_CHARS: int = 80
+        "sommario",
-
+        "bibliografia",
-# ─── Spezzatura frasi ─────────────────────────────────────────────────────────
+        "ringraziamenti",
-
+        "abbreviazioni",
-# Regex per rilevare il confine di fine frase.
+    })
-# Split solo prima di lettera maiuscola o virgolette — evita split su abbreviazioni.
+    skip_pre_heading: bool = True
-SENTENCE_SPLIT_RE: str = r"(?<=[.!?»])\s+(?=[A-ZÀÈÉÌÒÙ\"])"
+    merge_short: bool = True
-
+    atomic_types: set[str] = field(default_factory=lambda: {
-# ─── Blocchi atomici ──────────────────────────────────────────────────────────
+        "table", "code", "list", "math", "html",
-
+    })
-# Blocchi Markdown che non vengono mai spezzati, anche se superano MAX_CHARS.
+    fail_on_broken_fence: bool = True
-ATOMIC_TYPES: set[str] = {"table", "code", "list"}
+    fail_on_content_loss: bool = False
-
-# ─── Contesto heading ─────────────────────────────────────────────────────────
-
-# Profondità massima del percorso heading incluso nel prefisso di ogni chunk.
-# 1 = solo H1, 2 = H1 > H2, 3 = H1 > H2 > H3.
-CONTEXT_DEPTH: int = 3
-
-# ─── Sezioni da escludere ────────────────────────────────────────────────────
-
-# Heading (case-insensitive) le cui sezioni vengono saltate completamente.
-# Il match è su prefisso: "indice" salta anche "Indice delle figure".
-# Lasciare vuoto per non escludere nulla.
-SKIP_HEADINGS: set[str] = {
-    "indice",
-    "sommario",
-    "bibliografia",
-    "ringraziamenti",
-    "abbreviazioni",
-}
-
-# Se True, salta il contenuto che precede il primo heading (frontespizio, copertina).
-SKIP_PRE_HEADING: bool = True
-
-# ─── Merge paragrafi corti ────────────────────────────────────────────────────
-
-# Paragrafi consecutivi più corti di MIN_CHARS vengono fusi fino a raggiungerlo,
-# purché appartengano allo stesso contesto heading.
-MERGE_SHORT_PARAGRAPHS: bool = True
-
-# ─── verify_chunks ────────────────────────────────────────────────────────────
-
-# Numero minimo di simboli matematici perché un chunk incompleto sia classificato
-# come "matematico" (warning meno grave rispetto a frase spezzata normale).
-MATH_SYMS_MIN: int = 3
-
-PROTECT_TABLES: bool = True
-PROTECT_MATH:   bool = True

chunks/models.py

@@ -0,0 +1,47 @@
+from __future__ import annotations
+from dataclasses import dataclass, field
+
+
+@dataclass
+class Block:
+    id: str
+    kind: str  # paragraph|heading|table|code|list|math|blockquote|html|thematic_break
+    content: str
+    plain_text: str
+    atomic: bool
+    start_line: int
+    end_line: int
+    header_path: list[dict]
+    chars: int
+
+
+@dataclass
+class Chunk:
+    chunk_id: str
+    chunk_index: int
+    content_original: str
+    content_for_embedding: str
+    content_type: str  # section_fragment | atomic_block | overflow
+    chars: int
+    start_line: int
+    end_line: int
+    header_path: list[dict]
+    block_ids: list[str]
+    flags: dict
+    neighbors: dict
+    assets: list = field(default_factory=list)
+
+
+@dataclass
+class Diagnostics:
+    errors: list[str]
+    warnings: list[str]
+    metrics: dict
+
+
+@dataclass
+class ChunkingResult:
+    stem: str
+    source_path: str
+    chunks: list[Chunk]
+    diagnostics: Diagnostics

chunks/packer.py

@@ -0,0 +1,181 @@
+from __future__ import annotations
+import re
+from chunks.models import Block, Chunk
+from chunks.config import ChunkerConfig
+
+_SENTENCE_RE = re.compile(r"(?<=[.!?»])\s+(?=[A-ZÀÈÉÌÒÙ\"])")
+_CHUNK_COUNTER = 0
+
+
+def _reset_counter() -> None:
+    global _CHUNK_COUNTER
+    _CHUNK_COUNTER = 0
+
+
+def _make_chunk_id() -> str:
+    global _CHUNK_COUNTER
+    _CHUNK_COUNTER += 1
+    return f"chk_{_CHUNK_COUNTER:06d}"
+
+
+def _header_prefix(header_path: list[dict], depth: int) -> str:
+    return " > ".join(h["text"] for h in header_path[:depth])
+
+
+def _build_chunk(blocks: list[Block], index: int, config: ChunkerConfig,
+                 content_type: str = "section_fragment") -> Chunk:
+    content = "\n\n".join(b.content for b in blocks)
+    header_path = blocks[0].header_path
+    prefix = _header_prefix(header_path, config.context_depth)
+    embedding = f"{prefix}\n\n{content}" if prefix else content
+    total_chars = len(content)
+    return Chunk(
+        chunk_id=_make_chunk_id(),
+        chunk_index=index,
+        content_original=content,
+        content_for_embedding=embedding,
+        content_type=content_type,
+        chars=total_chars,
+        start_line=blocks[0].start_line,
+        end_line=blocks[-1].end_line,
+        header_path=header_path,
+        block_ids=[b.id for b in blocks],
+        flags={
+            "has_code":    any(b.kind == "code" for b in blocks),
+            "has_table":   any(
+                b.kind == "table"
+                or (b.kind == "html" and "<table" in b.content.lower())
+                for b in blocks
+            ),
+            "has_math":    any(
+                b.kind == "math"
+                or "$$" in b.content
+                or r"\begin{" in b.content
+                for b in blocks
+            ),
+            "is_overflow": total_chars > config.max_chars,
+            "is_sparse":   total_chars < config.min_chars,
+        },
+        neighbors={"previous_chunk_id": None, "next_chunk_id": None},
+        assets=[],
+    )
+
+
+def _split_paragraph(block: Block, config: ChunkerConfig) -> list[Block]:
+    sentences = _SENTENCE_RE.split(block.content)
+    sub_blocks: list[Block] = []
+    accumulated = ""
+
+    for sent in sentences:
+        candidate = (accumulated + " " + sent).strip() if accumulated else sent
+        if len(candidate) > config.max_chars and accumulated:
+            text = accumulated.strip()
+            sub_blocks.append(Block(
+                id=f"{block.id}_s{len(sub_blocks) + 1}", kind="paragraph",
+                content=text, plain_text=text, atomic=False,
+                start_line=block.start_line, end_line=block.end_line,
+                header_path=block.header_path, chars=len(text),
+            ))
+            accumulated = sent
+        else:
+            accumulated = candidate
+
+    if accumulated:
+        text = accumulated.strip()
+        sub_blocks.append(Block(
+            id=f"{block.id}_s{len(sub_blocks) + 1}", kind="paragraph",
+            content=text, plain_text=text, atomic=False,
+            start_line=block.start_line, end_line=block.end_line,
+            header_path=block.header_path, chars=len(text),
+        ))
+    return sub_blocks if sub_blocks else [block]
+
+
+def pack(blocks: list[Block], config: ChunkerConfig, stem: str) -> list[Chunk]:
+    _reset_counter()
+    chunks: list[Chunk] = []
+    chunk_index = 0
+
+    # Espandi paragrafi sovradimensionati
+    expanded: list[Block] = []
+    for b in blocks:
+        if not b.atomic and b.kind == "paragraph" and b.chars > config.max_chars:
+            expanded.extend(_split_paragraph(b, config))
+        else:
+            expanded.append(b)
+
+    # Raggruppa per header_path
+    groups: list[list[Block]] = []
+    cur_group: list[Block] = []
+    cur_key: str | None = None
+    for b in expanded:
+        key = str(b.header_path)
+        if key != cur_key:
+            if cur_group:
+                groups.append(cur_group)
+            cur_group = [b]
+            cur_key = key
+        else:
+            cur_group.append(b)
+    if cur_group:
+        groups.append(cur_group)
+
+    for group in groups:
+        accumulated: list[Block] = []
+        accumulated_chars = 0
+
+        def flush() -> None:
+            nonlocal accumulated, accumulated_chars, chunk_index
+            if not accumulated:
+                return
+            chunks.append(_build_chunk(accumulated, chunk_index, config))
+            chunk_index += 1
+            accumulated = []
+            accumulated_chars = 0
+
+        for b in group:
+            if b.kind == "thematic_break":
+                flush()
+                continue
+
+            if b.atomic:
+                if accumulated_chars + b.chars <= config.max_chars:
+                    accumulated.append(b)
+                    accumulated_chars += b.chars
+                else:
+                    flush()
+                    ctype = "overflow" if b.chars > config.max_chars else "atomic_block"
+                    chunks.append(_build_chunk([b], chunk_index, config, content_type=ctype))
+                    chunk_index += 1
+            else:
+                # Flush preventivo se aggiungere questo blocco supererebbe max_chars
+                # oppure supererebbe il target (con abbastanza contenuto già accumulato)
+                if accumulated and accumulated_chars >= config.min_chars:
+                    if (accumulated_chars + b.chars > config.max_chars
+                            or accumulated_chars + b.chars > config.target_chars):
+                        flush()
+                accumulated.append(b)
+                accumulated_chars += b.chars
+
+        # Flush residuo — merge con precedente se troppo piccolo
+        if accumulated:
+            if (accumulated_chars < config.min_chars and chunks
+                    and chunks[-1].header_path == accumulated[0].header_path):
+                prev = chunks[-1]
+                merged = prev.content_original + "\n\n" + "\n\n".join(b.content for b in accumulated)
+                prefix = _header_prefix(prev.header_path, config.context_depth)
+                prev.content_original = merged
+                prev.content_for_embedding = f"{prefix}\n\n{merged}" if prefix else merged
+                prev.chars = len(merged)
+                prev.end_line = accumulated[-1].end_line
+                prev.block_ids.extend(b.id for b in accumulated)
+                prev.flags["is_sparse"] = prev.chars < config.min_chars
+            else:
+                flush()
+
+    # Popola neighbors
+    for idx, chunk in enumerate(chunks):
+        chunk.neighbors["previous_chunk_id"] = chunks[idx - 1].chunk_id if idx > 0 else None
+        chunk.neighbors["next_chunk_id"] = chunks[idx + 1].chunk_id if idx < len(chunks) - 1 else None
+
+    return chunks

chunks/parser.py

@@ -0,0 +1,24 @@
+from __future__ import annotations
+from markdown_it import MarkdownIt
+from markdown_it.token import Token
+
+try:
+    from mdit_py_plugins.dollarmath import dollarmath_plugin as _dollarmath
+    _HAS_DOLLARMATH = True
+except ImportError:
+    _HAS_DOLLARMATH = False
+
+
+def parse(source: str) -> tuple[list[Token], list[str]]:
+    """Parsa Markdown in token stream con source map.
+
+    Returns:
+        tokens: lista Token con .map = [start_line, end_line] (0-indexed, end escluso)
+        lines:  righe sorgente (0-indexed) per ricostruzione testo esatto
+    """
+    md = MarkdownIt().enable("table")
+    if _HAS_DOLLARMATH:
+        md = md.use(_dollarmath, allow_labels=False, allow_space=False)
+    tokens = md.parse(source)
+    lines = source.splitlines()
+    return tokens, lines

chunks/segmenter.py

@@ -0,0 +1,234 @@
+from __future__ import annotations
+from markdown_it.token import Token
+from chunks.models import Block
+from chunks.config import ChunkerConfig
+
+_COUNTER = 0
+
+
+def _reset_counter() -> None:
+    global _COUNTER
+    _COUNTER = 0
+
+
+def _make_id() -> str:
+    global _COUNTER
+    _COUNTER += 1
+    return f"blk_{_COUNTER:04d}"
+
+
+def _inline_to_plain(token: Token) -> str:
+    if token.children:
+        parts = []
+        for c in token.children:
+            if c.type in ("text", "code_inline"):
+                parts.append(c.content)
+            elif c.type == "softbreak":
+                parts.append(" ")
+        return "".join(parts).strip()
+    return (token.content or "").strip()
+
+
+def _lines_content(lines: list[str], start: int, end: int) -> str:
+    return "\n".join(lines[start:end]).strip()
+
+
+def _current_path(stack: dict[int, str], depth: int) -> list[dict]:
+    path = []
+    for level in sorted(stack.keys()):
+        path.append({"level": level, "text": stack[level]})
+    return path[:depth]
+
+
+def _is_skip_heading(text: str, skip_set: set[str]) -> bool:
+    t = text.lower().strip()
+    return any(t == s or t.startswith(s) for s in {h.lower() for h in skip_set})
+
+
+def _find_close(tokens: list[Token], start: int, open_type: str, close_type: str) -> int:
+    """Ritorna indice del token close_type corrispondente a tokens[start]."""
+    depth = 1
+    i = start + 1
+    while i < len(tokens) and depth > 0:
+        if tokens[i].type == open_type:
+            depth += 1
+        elif tokens[i].type == close_type:
+            depth -= 1
+        i += 1
+    return i - 1
+
+
+def segment(tokens: list[Token], lines: list[str], config: ChunkerConfig) -> list[Block]:
+    _reset_counter()
+    blocks: list[Block] = []
+    heading_stack: dict[int, str] = {}
+    pre_heading_done = False
+    skip_mode = False
+    skip_level: int | None = None
+
+    i = 0
+    while i < len(tokens):
+        tok = tokens[i]
+
+        # ── Heading ──────────────────────────────────────────────────────────
+        if tok.type == "heading_open":
+            level = int(tok.tag[1])
+            inline = tokens[i + 1] if i + 1 < len(tokens) else None
+            text = (inline.content if inline and inline.type == "inline" else "").strip()
+
+            if _is_skip_heading(text, config.skip_headings):
+                skip_mode = True
+                skip_level = level
+            elif skip_mode and skip_level is not None and level <= skip_level:
+                skip_mode = False
+                skip_level = None
+
+            for lvl in [l for l in list(heading_stack.keys()) if l >= level]:
+                del heading_stack[lvl]
+            heading_stack[level] = text
+            pre_heading_done = True
+            i += 3  # heading_open, inline, heading_close
+            continue
+
+        # ── Skip pre-heading ─────────────────────────────────────────────────
+        if config.skip_pre_heading and not pre_heading_done:
+            i += 1
+            continue
+
+        # ── Skip section ─────────────────────────────────────────────────────
+        if skip_mode:
+            i += 1
+            continue
+
+        header_path = _current_path(heading_stack, config.context_depth)
+
+        # ── Paragraph ────────────────────────────────────────────────────────
+        if tok.type == "paragraph_open":
+            inline = tokens[i + 1] if i + 1 < len(tokens) else None
+            if tok.map:
+                start, end = tok.map
+                content = _lines_content(lines, start, end)
+                plain = _inline_to_plain(inline) if inline and inline.type == "inline" else content
+                blocks.append(Block(
+                    id=_make_id(),
+                    kind="paragraph",
+                    content=content,
+                    plain_text=plain,
+                    atomic=False,
+                    start_line=start,
+                    end_line=end,
+                    header_path=header_path,
+                    chars=len(content),
+                ))
+            i += 3  # paragraph_open, inline, paragraph_close
+            continue
+
+        # ── Code fence ───────────────────────────────────────────────────────
+        if tok.type == "fence":
+            if tok.map:
+                start, end = tok.map
+                content = _lines_content(lines, start, end)
+                plain = f"[codice {tok.info.strip()}]" if tok.info.strip() else "[codice]"
+                blocks.append(Block(
+                    id=_make_id(),
+                    kind="code",
+                    content=content,
+                    plain_text=plain,
+                    atomic=True,
+                    start_line=start,
+                    end_line=end,
+                    header_path=header_path,
+                    chars=len(content),
+                ))
+            i += 1
+            continue
+
+        # ── Container blocks ─────────────────────────────────────────────────
+        _CONTAINERS = {
+            "table_open":        ("table_close",        "table",      True),
+            "bullet_list_open":  ("bullet_list_close",  "list",       True),
+            "ordered_list_open": ("ordered_list_close", "list",       True),
+            "blockquote_open":   ("blockquote_close",   "blockquote", False),
+        }
+        if tok.type in _CONTAINERS:
+            close_type, kind, atomic = _CONTAINERS[tok.type]
+            atomic = atomic or kind in config.atomic_types
+            close_idx = _find_close(tokens, i, tok.type, close_type)
+            close_tok = tokens[close_idx]
+
+            start = tok.map[0] if tok.map else 0
+            end = (close_tok.map[1] if close_tok.map else None) or (tok.map[1] if tok.map else start + 1)
+            content = _lines_content(lines, start, end)
+            blocks.append(Block(
+                id=_make_id(),
+                kind=kind,
+                content=content,
+                plain_text=content,
+                atomic=atomic,
+                start_line=start,
+                end_line=end,
+                header_path=header_path,
+                chars=len(content),
+            ))
+            i = close_idx + 1
+            continue
+
+        # ── Thematic break ────────────────────────────────────────────────────
+        if tok.type == "hr":
+            start = tok.map[0] if tok.map else 0
+            end = tok.map[1] if tok.map else start + 1
+            blocks.append(Block(
+                id=_make_id(),
+                kind="thematic_break",
+                content="---",
+                plain_text="",
+                atomic=False,
+                start_line=start,
+                end_line=end,
+                header_path=header_path,
+                chars=3,
+            ))
+            i += 1
+            continue
+
+        # ── Math block (dollarmath plugin) ───────────────────────────────────
+        if tok.type == "math_block":
+            start = tok.map[0] if tok.map else 0
+            end = tok.map[1] if tok.map else start + 1
+            content = _lines_content(lines, start, end)
+            blocks.append(Block(
+                id=_make_id(),
+                kind="math",
+                content=content,
+                plain_text="[formula]",
+                atomic=True,
+                start_line=start,
+                end_line=end,
+                header_path=header_path,
+                chars=len(content),
+            ))
+            i += 1
+            continue
+
+        # ── HTML block ────────────────────────────────────────────────────────
+        if tok.type == "html_block":
+            start = tok.map[0] if tok.map else 0
+            end = tok.map[1] if tok.map else start + 1
+            content = tok.content.strip()
+            blocks.append(Block(
+                id=_make_id(),
+                kind="html",
+                content=content,
+                plain_text="",
+                atomic="html" in config.atomic_types,
+                start_line=start,
+                end_line=end,
+                header_path=header_path,
+                chars=len(content),
+            ))
+            i += 1
+            continue
+
+        i += 1
+
+    return blocks

chunks/validator.py

@@ -0,0 +1,63 @@
+from __future__ import annotations
+import re
+from chunks.models import ChunkingResult, Diagnostics
+from chunks.config import ChunkerConfig
+
+_OPEN_FENCE_RE = re.compile(r"^(`{3,}|~{3,})", re.MULTILINE)
+
+
+def _has_broken_fence(content: str) -> bool:
+    matches = _OPEN_FENCE_RE.findall(content)
+    return len(matches) % 2 != 0
+
+
+def validate(result: ChunkingResult, source: str, config: ChunkerConfig) -> Diagnostics:
+    errors: list[str] = []
+    warnings: list[str] = []
+    chunks = result.chunks
+
+    if not chunks:
+        warnings.append("Nessun chunk prodotto.")
+        return Diagnostics(errors=errors, warnings=warnings, metrics={"total_chunks": 0})
+
+    # chunk_id unici
+    seen_ids: set[str] = set()
+    for c in chunks:
+        if c.chunk_id in seen_ids:
+            errors.append(f"chunk_id duplicato: {c.chunk_id}")
+        seen_ids.add(c.chunk_id)
+
+    # fence rotto
+    for c in chunks:
+        if _has_broken_fence(c.content_original):
+            msg = f"fence rotto in {c.chunk_id} (righe {c.start_line}-{c.end_line})"
+            if config.fail_on_broken_fence:
+                errors.append(msg)
+            else:
+                warnings.append(msg)
+
+    # size compliance (esclusi overflow)
+    non_overflow = [c for c in chunks if not c.flags.get("is_overflow")]
+    for c in non_overflow:
+        if c.chars > config.max_chars:
+            errors.append(f"chunk {c.chunk_id} supera max_chars ({c.chars} > {config.max_chars})")
+
+    # metriche
+    total = len(chunks)
+    chars_list = [c.chars for c in chunks]
+    avg = sum(chars_list) // total if total else 0
+    compliant = sum(1 for c in non_overflow if config.min_chars <= c.chars <= config.max_chars)
+    compliance = round(compliant / len(non_overflow), 4) if non_overflow else 1.0
+
+    metrics = {
+        "total_chunks":     total,
+        "avg_chars":        avg,
+        "min_chars_actual": min(chars_list),
+        "max_chars_actual": max(chars_list),
+        "overflow_count":   sum(1 for c in chunks if c.flags.get("is_overflow")),
+        "sparse_count":     sum(1 for c in chunks if c.flags.get("is_sparse")),
+        "atomic_count":     sum(1 for c in chunks if c.content_type == "atomic_block"),
+        "size_compliance":  compliance,
+    }
+
+    return Diagnostics(errors=errors, warnings=warnings, metrics=metrics)

chunks/verify_chunks.py

@@ -1,493 +0,0 @@
-#!/usr/bin/env python3
-"""
-Verifica chunk
-
-Analizza chunks/<stem>/chunks.json e segnala ogni anomalia che potrebbe
-degradare la qualità del retrieval. Non modifica nulla.
-
-Input:  chunks/<stem>/chunks.json
-Output: report a schermo + chunks/<stem>/report.json + exit code (0 = OK, 1 = problemi)
-
-Uso:
-    python chunks/verify_chunks.py --stem documento
-    python chunks/verify_chunks.py                    # tutti i documenti in chunks/
-    python chunks/verify_chunks.py --min 200 --max 800
-"""
-
-import argparse
-import json
-import re
-import sys
-from collections import Counter
-from pathlib import Path
-
-_HERE = Path(__file__).resolve().parent
-if str(_HERE) not in sys.path:
-    sys.path.insert(0, str(_HERE))
-import config as cfg
-
-
-# ─── Soglie ───────────────────────────────────────────────────────────────────
-
-MIN_CHARS = cfg.MIN_CHARS
-MAX_CHARS = cfg.MAX_CHARS
-
-_PUNCT_CLS = (
-    "[.!?"
-    + chr(0xBB) + ")\\]"
-    + chr(0x2018) + chr(0x2019)
-    + chr(0x201C) + chr(0x201D)
-    + "\"'"
-    + chr(0x2014) + chr(0x2013) + chr(0x2026)
-    + chr(0xB7)   # punto centrato LaTeX
-    + "]$"
-)
-PUNCT_END = re.compile(
-    _PUNCT_CLS
-    + r"|/$"
-    + r"|\|$"
-    + r"|;$"
-    + r"|:$"
-    + r"|\d[\d.,/]*$"
-    + r"|\$$"
-    + r"|\}$"
-    + r"|>$"
-    + r"|\\\\$"
-)
-_HEX_END   = re.compile(r"[0-9a-fA-F]{8,}$")
-_URL_TAIL  = re.compile(r"(https?://|www\.)\S+(\s+\S+){0,3}$")
-_MATH_SYMS = re.compile(r"[∈∑≤≥≠∀∃∫√∞∂±×÷→←↔⊂⊃⊆⊇∩∪·°]")
-_ROMAN_END = re.compile(r"\b(I{1,3}|IV|VI{0,3}|IX|XI{0,2}|XIV|XV|XVI{0,2}|XIX|XX{0,2})$")
-_TABLE_SEP = re.compile(r"^\s*\|[\s\-|:]+\|\s*$")
-
-
-def _load_thresholds(stem_dir: Path) -> tuple[int, int]:
-    meta = stem_dir / "meta.json"
-    if meta.exists():
-        m = json.loads(meta.read_text(encoding="utf-8"))
-        return m["min_chars"], m["max_chars"]
-    return MIN_CHARS, MAX_CHARS
-
-
-def _strip_prefix(text: str) -> str:
-    text = text.lstrip()
-    if text.startswith("["):
-        end = text.find("]")
-        if end != -1:
-            return text[end + 1:].lstrip("\n")
-    return text
-
-
-# ─── Checks ───────────────────────────────────────────────────────────────────
-
-def is_empty(chunk: dict) -> bool:
-    return not chunk.get("text", "").strip()
-
-
-def has_prefix(chunk: dict) -> bool:
-    return chunk.get("text", "").lstrip().startswith("[")
-
-
-def is_prefix_malformed(chunk: dict) -> bool:
-    """Inizia con [ ma il prefisso non chiude con ] o ha contenuto vuoto."""
-    text = chunk.get("text", "").lstrip()
-    if not text.startswith("["):
-        return False
-    first_line = text.split("\n")[0]
-    end = first_line.find("]")
-    if end == -1:
-        return True
-    return len(first_line[1:end].strip()) == 0
-
-
-def is_body_empty(chunk: dict) -> bool:
-    """Prefisso valido ma nessun testo nel corpo."""
-    text = chunk.get("text", "").lstrip()
-    if not text.startswith("["):
-        return False
-    end = text.find("]")
-    if end == -1:
-        return False
-    return len(text[end + 1:].strip()) == 0
-
-
-def is_too_short(chunk: dict, min_chars: int) -> bool:
-    return chunk.get("n_chars", 0) < min_chars
-
-
-def is_too_long(chunk: dict, max_chars: int) -> bool:
-    return chunk.get("n_chars", 0) > max_chars
-
-
-def ends_incomplete(chunk: dict) -> bool:
-    text = chunk.get("text", "").rstrip()
-    if not text:
-        return False
-    text_check = re.sub(r"[_*]+$", "", text).rstrip()
-    if not text_check:
-        return False
-    if PUNCT_END.search(text_check):
-        return False
-    if _HEX_END.search(text_check):
-        return False
-    if _ROMAN_END.search(text_check):
-        return False
-    if _URL_TAIL.search(text_check[-200:]):
-        return False
-    return True
-
-
-def is_math_incomplete(chunk: dict) -> bool:
-    return ends_incomplete(chunk) and len(_MATH_SYMS.findall(chunk.get("text", ""))) >= cfg.MATH_SYMS_MIN
-
-
-def is_table_broken(chunk: dict) -> bool:
-    """Tabella Markdown (≥2 righe con |) senza riga separatore |---|."""
-    text = chunk.get("text", "")
-    pipe_lines = [l for l in text.splitlines() if "|" in l and l.strip().startswith("|")]
-    if len(pipe_lines) < 2:
-        return False
-    return not any(_TABLE_SEP.match(l) for l in pipe_lines)
-
-
-def find_duplicate_bodies(chunks: list[dict]) -> list[dict]:
-    """Chunk con testo body identico (prefisso escluso). Ignora corpi < 30 char."""
-    seen: dict[str, str] = {}
-    dupes = []
-    for c in chunks:
-        body = _strip_prefix(c.get("text", "")).strip()
-        if len(body) < 30:
-            continue
-        cid = c["chunk_id"]
-        if body in seen:
-            dupes.append({
-                "chunk_id":     cid,
-                "duplicate_of": seen[body],
-                "sezione":      c.get("sezione", ""),
-                "titolo":       c.get("titolo", ""),
-                "n_chars":      c.get("n_chars", 0),
-                "last_text":    body[:120],
-            })
-        else:
-            seen[body] = cid
-    return dupes
-
-
-# ─── Istogramma ───────────────────────────────────────────────────────────────
-
-def _ascii_histogram(lengths: list[int], min_t: int, max_t: int,
-                     n_bins: int = 10, bar_width: int = 28) -> list[str]:
-    if not lengths:
-        return []
-    lo, hi = min(lengths), max(lengths)
-    if lo == hi:
-        return [f"  {lo:>5}–{hi:<5} │{'█' * bar_width}│ {len(lengths)}"]
-    step = (hi - lo) / n_bins
-    bins = [0] * n_bins
-    for l in lengths:
-        idx = min(int((l - lo) / step), n_bins - 1)
-        bins[idx] += 1
-    max_count = max(bins) or 1
-    lines = []
-    for i, count in enumerate(bins):
-        lo_b = int(lo + i * step)
-        hi_b = int(lo + (i + 1) * step)
-        bar  = "█" * round(count / max_count * bar_width)
-        note = ""
-        if lo_b <= min_t < hi_b:
-            note = " ← MIN"
-        elif lo_b <= max_t < hi_b:
-            note = " ← MAX"
-        lines.append(f"  {lo_b:>5}–{hi_b:<5} │{bar:<{bar_width}}│ {count}{note}")
-    return lines
-
-
-# ─── Helpers output ───────────────────────────────────────────────────────────
-
-def _fmt_chunk(c: dict) -> str:
-    cid     = c.get("chunk_id", "?")
-    n       = c.get("n_chars", 0)
-    preview = c.get("text", "")[:60].replace("\n", " ")
-    return f"  [{cid}] ({n} char) «{preview}»"
-
-
-def _chunk_entry(c: dict) -> dict:
-    return {
-        "chunk_id":  c.get("chunk_id", ""),
-        "sezione":   c.get("sezione", ""),
-        "titolo":    c.get("titolo", ""),
-        "n_chars":   c.get("n_chars", 0),
-        "last_text": c.get("text", "").rstrip().split("\n")[-1][-120:],
-    }
-
-
-def _print_list(items: list[dict], limit: int = 5) -> None:
-    for c in items[:limit]:
-        print(_fmt_chunk(c))
-    if len(items) > limit:
-        print(f"  ... e altri {len(items) - limit}")
-
-
-# ─── Core ─────────────────────────────────────────────────────────────────────
-
-def verify_stem(stem: str, project_root: Path, min_chars: int, max_chars: int) -> bool:
-    stem_dir    = project_root / "chunks" / stem
-    chunks_path = stem_dir / "chunks.json"
-    min_chars, max_chars = _load_thresholds(stem_dir)
-
-    print(f"\nDocumento: {stem}")
-
-    if not chunks_path.exists():
-        print(f"  ✗ chunks/{stem}/chunks.json non trovato")
-        print(f"    Esegui prima: python chunks/chunker.py --stem {stem}")
-        return False
-
-    chunks: list[dict] = json.loads(chunks_path.read_text(encoding="utf-8"))
-
-    if not chunks:
-        print(f"  ✗ chunks.json è vuoto")
-        return False
-
-    # ── Raccogli problemi ──────────────────────────────────────────────────────
-
-    empty_chunks     = [c for c in chunks if is_empty(c)]
-    no_prefix        = [c for c in chunks if not is_empty(c) and not has_prefix(c)]
-    malformed_prefix = [c for c in chunks
-                        if not is_empty(c) and has_prefix(c) and is_prefix_malformed(c)]
-    body_empty       = [c for c in chunks
-                        if not is_empty(c) and has_prefix(c)
-                        and not is_prefix_malformed(c) and is_body_empty(c)]
-    too_short        = [c for c in chunks if is_too_short(c, min_chars)]
-    too_long         = [c for c in chunks if is_too_long(c, max_chars)]
-    _incomplete_all  = [c for c in chunks if not is_empty(c) and ends_incomplete(c)]
-    incomplete_math  = [c for c in _incomplete_all if is_math_incomplete(c)]
-    incomplete       = [c for c in _incomplete_all if not is_math_incomplete(c)]
-    broken_tables    = [c for c in chunks if is_table_broken(c)]
-    duplicates       = find_duplicate_bodies(chunks)
-
-    # ── Statistiche ───────────────────────────────────────────────────────────
-
-    lengths  = [c.get("n_chars", 0) for c in chunks]
-    n_total  = len(chunks)
-    blocker_ids = set(
-        c["chunk_id"]
-        for lst in [empty_chunks, no_prefix, malformed_prefix, body_empty, incomplete]
-        for c in lst
-    )
-    n_ok    = n_total - len(blocker_ids)
-    min_l   = min(lengths)
-    max_l   = max(lengths)
-    avg_l   = int(sum(lengths) / n_total)
-    p50     = sorted(lengths)[n_total // 2]
-    n_under = sum(1 for l in lengths if l < min_chars)
-    n_norm  = sum(1 for l in lengths if min_chars <= l <= max_chars)
-    n_over  = sum(1 for l in lengths if l > max_chars)
-
-    section_counts = Counter(c.get("sezione", "—") or "—" for c in chunks)
-
-    # ── Output statistiche ────────────────────────────────────────────────────
-
-    print(f"  Totale: {n_total}  |  ✅ OK: {n_ok}")
-    print()
-    print(f"  Lunghezze — min {min_l}  p50 {p50}  media {avg_l}  max {max_l}")
-    print(f"  Fasce     — <{min_chars}: {n_under}  |  {min_chars}–{max_chars}: {n_norm}  |  >{max_chars}: {n_over}")
-    print()
-    print("  Istogramma:")
-    for line in _ascii_histogram(lengths, min_chars, max_chars):
-        print(line)
-    print()
-    print("  Top sezioni:")
-    for sezione, count in section_counts.most_common(5):
-        bar = "▪" * min(count, 35)
-        print(f"    {bar} {count:>4}  {sezione[:65]}")
-
-    # ── Blockers ──────────────────────────────────────────────────────────────
-
-    if empty_chunks:
-        print(f"\n  🔴 {len(empty_chunks)} chunk VUOTI:")
-        for c in empty_chunks[:5]:
-            print(f"  [{c.get('chunk_id', '?')}]")
-        if len(empty_chunks) > 5:
-            print(f"  ... e altri {len(empty_chunks) - 5}")
-
-    if no_prefix:
-        print(f"\n  🔴 {len(no_prefix)} chunk SENZA PREFISSO DI CONTESTO:")
-        _print_list(no_prefix)
-        print(f"  → Causa probabile: heading mancanti nel clean.md")
-
-    if malformed_prefix:
-        print(f"\n  🔴 {len(malformed_prefix)} chunk con PREFISSO MALFORMATO ([ senza ] o vuoto):")
-        _print_list(malformed_prefix)
-        print(f"  → Causa probabile: heading con caratteri speciali nel clean.md")
-
-    if body_empty:
-        print(f"\n  🔴 {len(body_empty)} chunk con CORPO VUOTO (solo prefisso):")
-        _print_list(body_empty)
-        print(f"  → Causa probabile: sezioni senza testo nel clean.md")
-
-    if incomplete:
-        print(f"\n  🟡 {len(incomplete)} chunk con FRASE SPEZZATA (warning):")
-        for c in incomplete[:5]:
-            last_line = c.get("text", "").rstrip().split("\n")[-1][-80:]
-            print(f"  [{c.get('chunk_id', '?')}] ...{last_line!r}")
-        if len(incomplete) > 5:
-            print(f"  ... e altri {len(incomplete) - 5}")
-        print(f"  → Soluzione: correggi il sorgente .md e rigenera con chunker.py --force")
-
-    # ── Warnings ──────────────────────────────────────────────────────────────
-
-    if too_short:
-        print(f"\n  🟡 {len(too_short)} chunk SOTTO MIN_CHARS ({min_chars}):")
-        _print_list(too_short)
-
-    if too_long:
-        print(f"\n  🟡 {len(too_long)} chunk SOPRA MAX ({max_chars}):")
-        _print_list(too_long)
-        print(f"  → Causa: frasi non suddivisibili o blocchi atomici (tabelle/liste)")
-
-    if incomplete_math:
-        print(f"\n  🟡 {len(incomplete_math)} chunk MATEMATICI senza punteggiatura finale:")
-        for c in incomplete_math[:3]:
-            last_line = c.get("text", "").rstrip().split("\n")[-1][-80:]
-            print(f"  [{c.get('chunk_id', '?')}] ...{last_line!r}")
-        if len(incomplete_math) > 3:
-            print(f"  ... e altri {len(incomplete_math) - 3}")
-
-    if broken_tables:
-        print(f"\n  🟡 {len(broken_tables)} TABELLE senza riga separatore |---|:")
-        _print_list(broken_tables, limit=3)
-        print(f"  → Le tabelle potrebbero non renderizzarsi nel retrieval")
-
-    if duplicates:
-        print(f"\n  🟡 {len(duplicates)} DUPLICATI (corpo identico):")
-        for e in duplicates[:5]:
-            print(f"  [{e['chunk_id']}] ≡ [{e['duplicate_of']}]  «{e['last_text'][:60]}»")
-        if len(duplicates) > 5:
-            print(f"  ... e altri {len(duplicates) - 5}")
-        print(f"  → Causa probabile: sezioni ripetute nel sorgente .md")
-
-    # ── Report.json ───────────────────────────────────────────────────────────
-
-    blockers = empty_chunks + no_prefix + malformed_prefix + body_empty
-    warnings = too_short + too_long + incomplete + incomplete_math + broken_tables
-
-    verdict = "blocked" if blockers else ("warnings_only" if (warnings or duplicates) else "ok")
-
-    report = {
-        "stem":    stem,
-        "verdict": verdict,
-        "stats": {
-            "total":    n_total,
-            "ok":       n_ok,
-            "min_chars": min_l,
-            "max_chars": max_l,
-            "avg_chars": avg_l,
-            "p50_chars": p50,
-            "under_min": n_under,
-            "in_range":  n_norm,
-            "over_max":  n_over,
-            "sections":  [{"sezione": s, "n_chunks": n}
-                          for s, n in section_counts.most_common()],
-        },
-        "thresholds": {
-            "min_chars":    min_chars,
-            "max_chars":    max_chars,
-            "target_chars": cfg.MAX_CHARS,
-        },
-        "blockers": {
-            "empty":            [_chunk_entry(c) for c in empty_chunks],
-            "no_prefix":        [_chunk_entry(c) for c in no_prefix],
-            "malformed_prefix": [_chunk_entry(c) for c in malformed_prefix],
-            "body_empty":       [_chunk_entry(c) for c in body_empty],
-            "incomplete":       [_chunk_entry(c) for c in incomplete],
-        },
-        "warnings": {
-            "too_short":       [_chunk_entry(c) for c in too_short],
-            "too_long":        [_chunk_entry(c) for c in too_long],
-            "incomplete_math": [_chunk_entry(c) for c in incomplete_math],
-            "broken_tables":   [_chunk_entry(c) for c in broken_tables],
-            "duplicate_bodies": duplicates,
-        },
-    }
-
-    out_dir = project_root / "chunks" / stem
-    out_dir.mkdir(parents=True, exist_ok=True)
-    (out_dir / "report.json").write_text(
-        json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8"
-    )
-    print(f"\n  report.json → chunks/{stem}/")
-
-    # ── Prossimi passi ────────────────────────────────────────────────────────
-
-    print(f"\n  {'─' * 50}")
-    print(f"  Verdict: {verdict.upper()}")
-    print(f"  {'─' * 50}")
-
-    if verdict == "ok":
-        print(f"  ✅ Tutto OK — procedi alla vettorizzazione:")
-        print(f"       python ingestion/ingest.py --stem {stem}")
-
-    elif verdict == "warnings_only":
-        print(f"  🟡 Solo avvisi — puoi procedere alla vettorizzazione:")
-        print(f"       python ingestion/ingest.py --stem {stem}")
-        if too_short or too_long:
-            print()
-            print(f"  Per ottimizzare: correggi il sorgente .md e rigenera con --force")
-
-    else:
-        print(f"  🔴 {len(blockers)} problemi bloccanti — correggi prima di procedere:")
-        if empty_chunks or body_empty:
-            print(f"    • chunk vuoti/senza corpo → controlla sources/{stem}/auto/{stem}_clean.md")
-        if no_prefix or malformed_prefix:
-            print(f"    • prefisso mancante/malformato → controlla gli heading in sources/{stem}.md")
-        if incomplete:
-            print(f"    • frasi spezzate → correggi il sorgente e rigenera con --force")
-        print()
-        print(f"  Dopo le correzioni:")
-        print(f"       python chunks/chunker.py --stem {stem} --force")
-        print(f"       python chunks/verify_chunks.py --stem {stem}")
-        if warnings:
-            print()
-            print(f"  🟡 Hai anche {len(warnings)} avvisi — affrontali dopo aver risolto i 🔴.")
-
-    return not blockers
-
-
-# ─── Entry point ──────────────────────────────────────────────────────────────
-
-if __name__ == "__main__":
-    project_root = Path(__file__).parent.parent
-
-    parser = argparse.ArgumentParser(description="Verifica chunk")
-    parser.add_argument("--stem", help="Nome del documento (sottocartella di chunks/)")
-    parser.add_argument(
-        "--min", type=int, default=cfg.MIN_CHARS,
-        help=f"Soglia minima caratteri (default: {cfg.MIN_CHARS})"
-    )
-    parser.add_argument(
-        "--max", type=int, default=cfg.MAX_CHARS,
-        help=f"Soglia massima caratteri (default: {cfg.MAX_CHARS})"
-    )
-    args = parser.parse_args()
-
-    if args.stem:
-        stems = [args.stem]
-    else:
-        chunks_dir = project_root / "chunks"
-        if not chunks_dir.exists():
-            print(f"Errore: cartella chunks/ non trovata in {project_root}")
-            sys.exit(1)
-        stems = sorted(
-            p.name for p in chunks_dir.iterdir()
-            if p.is_dir() and (p / "chunks.json").exists()
-        )
-        if not stems:
-            print("Errore: nessun chunks.json trovato in chunks/")
-            sys.exit(1)
-
-    results = [verify_stem(s, project_root, args.min, args.max) for s in stems]
-
-    ok    = sum(results)
-    total = len(results)
-    print(f"\n{'✅' if all(results) else '⚠️ '} {ok}/{total} documenti senza problemi bloccanti")
-    sys.exit(0 if all(results) else 1)

requirements.txt

@@ -2,3 +2,5 @@ pdfplumber==0.11.9
 PyMuPDF>=1.24.0
 chromadb
 pytest>=8.0
+markdown-it-py>=4.0
+mdit-py-plugins>=0.4

tests/chunks/conftest.py

@@ -0,0 +1,15 @@
+import pytest
+from chunks.parser import parse
+from chunks.config import ChunkerConfig
+
+
+@pytest.fixture
+def cfg():
+    return ChunkerConfig()
+
+
+@pytest.fixture
+def parse_md():
+    def _parse(md: str):
+        return parse(md)
+    return _parse

tests/chunks/test_chunker.py

@@ -0,0 +1,109 @@
+import json
+import pytest
+from pathlib import Path
+from chunks.chunker import run_pipeline, find_source
+
+
+SAMPLE_MD = """# Sezione Principale
+
+## Introduzione
+
+Questo è il primo paragrafo della sezione introduttiva. Contiene testo sufficiente.
+
+## Contenuto
+
+Il secondo paragrafo parla di argomenti tecnici. Continua per alcune righe.
+
+```python
+def esempio():
+    return 42
+```
+
+## Indice
+
+Questo contenuto deve essere saltato.
+
+## Conclusioni
+
+Paragrafo conclusivo del documento di test.
+"""
+
+
+@pytest.fixture
+def tmp_stem(tmp_path):
+    stem = "test_doc"
+    source_dir = tmp_path / "sources" / f"{stem}_output" / "auto"
+    source_dir.mkdir(parents=True)
+    (source_dir / f"{stem}.md").write_text(SAMPLE_MD, encoding="utf-8")
+    (tmp_path / "chunks").mkdir()
+    return tmp_path, stem
+
+
+def test_run_pipeline_produces_output_files(tmp_stem):
+    root, stem = tmp_stem
+    run_pipeline(stem=stem, root=root)
+    out_dir = root / "chunks" / stem
+    assert (out_dir / "chunks.json").exists()
+    assert (out_dir / "meta.json").exists()
+    assert (out_dir / "report.json").exists()
+
+
+def test_chunks_json_schema(tmp_stem):
+    root, stem = tmp_stem
+    run_pipeline(stem=stem, root=root)
+    data = json.loads((root / "chunks" / stem / "chunks.json").read_text())
+    assert isinstance(data, list)
+    assert len(data) > 0
+    c = data[0]
+    for field in ("chunk_id", "content_original", "content_for_embedding",
+                  "header_path", "block_ids", "flags", "neighbors", "assets"):
+        assert field in c, f"campo mancante: {field}"
+
+
+def test_indice_section_skipped(tmp_stem):
+    root, stem = tmp_stem
+    run_pipeline(stem=stem, root=root)
+    data = json.loads((root / "chunks" / stem / "chunks.json").read_text())
+    all_content = " ".join(c["content_original"] for c in data)
+    assert "saltato" not in all_content
+
+
+def test_code_block_preserved(tmp_stem):
+    root, stem = tmp_stem
+    run_pipeline(stem=stem, root=root)
+    data = json.loads((root / "chunks" / stem / "chunks.json").read_text())
+    all_content = " ".join(c["content_original"] for c in data)
+    assert "def esempio" in all_content
+
+
+def test_force_flag_regenerates(tmp_stem):
+    root, stem = tmp_stem
+    run_pipeline(stem=stem, root=root)
+    first = json.loads((root / "chunks" / stem / "chunks.json").read_text())
+    run_pipeline(stem=stem, root=root, force=True)
+    second = json.loads((root / "chunks" / stem / "chunks.json").read_text())
+    assert len(first) == len(second)
+
+
+def test_no_force_skips_existing(tmp_stem, capsys):
+    root, stem = tmp_stem
+    run_pipeline(stem=stem, root=root)
+    run_pipeline(stem=stem, root=root, force=False)
+    captured = capsys.readouterr()
+    assert "skip" in captured.out.lower() or "esiste" in captured.out.lower()
+
+
+def test_find_source_output_auto(tmp_path):
+    stem = "doc"
+    path = tmp_path / "sources" / f"{stem}_output" / "auto" / f"{stem}.md"
+    path.parent.mkdir(parents=True)
+    path.write_text("# T\n")
+    assert find_source(stem, tmp_path) == path
+
+
+def test_find_source_flat(tmp_path):
+    stem = "doc"
+    path = tmp_path / "sources" / f"{stem}.md"
+    path.parent.mkdir(parents=True)
+    path.write_text("# T\n")
+    assert find_source(stem, tmp_path) == path

tests/chunks/test_config.py

@@ -0,0 +1,29 @@
+from chunks.config import ChunkerConfig
+
+
+def test_default_values():
+    c = ChunkerConfig()
+    assert c.max_chars == 1200
+    assert c.min_chars == 80
+    assert c.target_chars == 800
+    assert c.context_depth == 3
+    assert "indice" in c.skip_headings
+    assert c.skip_pre_heading is True
+    assert c.merge_short is True
+    assert "table" in c.atomic_types
+    assert "code" in c.atomic_types
+
+
+def test_custom_values():
+    c = ChunkerConfig(max_chars=2000, min_chars=100, context_depth=2)
+    assert c.max_chars == 2000
+    assert c.min_chars == 100
+    assert c.context_depth == 2
+
+
+def test_skip_headings_case_insensitive_check():
+    c = ChunkerConfig()
+    skip_lower = {h.lower() for h in c.skip_headings}
+    assert "indice" in skip_lower
+    assert "sommario" in skip_lower
+    assert "bibliografia" in skip_lower

tests/chunks/test_models.py

@@ -0,0 +1,51 @@
+from chunks.models import Block, Chunk, Diagnostics, ChunkingResult
+
+
+def test_block_creation():
+    b = Block(
+        id="blk_0001",
+        kind="paragraph",
+        content="Testo di esempio.",
+        plain_text="Testo di esempio.",
+        atomic=False,
+        start_line=0,
+        end_line=1,
+        header_path=[{"level": 1, "text": "Titolo"}],
+        chars=17,
+    )
+    assert b.id == "blk_0001"
+    assert b.kind == "paragraph"
+    assert not b.atomic
+    assert b.chars == 17
+
+
+def test_chunk_creation():
+    c = Chunk(
+        chunk_id="chk_000001",
+        chunk_index=1,
+        content_original="Testo.",
+        content_for_embedding="Titolo\n\nTesto.",
+        content_type="section_fragment",
+        chars=6,
+        start_line=0,
+        end_line=1,
+        header_path=[{"level": 1, "text": "Titolo"}],
+        block_ids=["blk_0001"],
+        flags={"has_code": False, "has_table": False, "has_math": False,
+               "is_overflow": False, "is_sparse": False},
+        neighbors={"previous_chunk_id": None, "next_chunk_id": None},
+        assets=[],
+    )
+    assert c.chunk_id == "chk_000001"
+    assert c.assets == []
+
+
+def test_diagnostics_empty():
+    d = Diagnostics(errors=[], warnings=[], metrics={})
+    assert d.errors == []
+
+
+def test_chunking_result():
+    r = ChunkingResult(stem="doc", source_path="sources/doc.md", chunks=[], diagnostics=Diagnostics([], [], {}))
+    assert r.stem == "doc"
+    assert r.chunks == []

tests/chunks/test_packer.py

@@ -0,0 +1,150 @@
+import pytest
+from chunks.models import Block
+from chunks.config import ChunkerConfig
+from chunks.packer import pack
+
+
+def make_block(idx: int, content: str, kind: str = "paragraph", atomic: bool = False,
+               header_path=None) -> Block:
+    hp = header_path or [{"level": 1, "text": "Titolo"}]
+    return Block(
+        id=f"blk_{idx:04d}", kind=kind, content=content, plain_text=content,
+        atomic=atomic, start_line=idx * 2, end_line=idx * 2 + 1,
+        header_path=hp, chars=len(content),
+    )
+
+
+@pytest.fixture
+def cfg():
+    return ChunkerConfig(min_chars=10, target_chars=50, max_chars=100)
+
+
+def test_single_block_becomes_single_chunk(cfg):
+    blocks = [make_block(1, "Testo breve.")]
+    chunks = pack(blocks, cfg, "test")
+    assert len(chunks) == 1
+    assert chunks[0].content_original == "Testo breve."
+
+
+def test_chunk_id_format(cfg):
+    blocks = [make_block(1, "Testo.")]
+    chunks = pack(blocks, cfg, "test")
+    assert chunks[0].chunk_id.startswith("chk_")
+
+
+def test_neighbors_populated(cfg):
+    blocks = [make_block(i, "x" * 60, header_path=[{"level": 1, "text": "T"}]) for i in range(1, 4)]
+    chunks = pack(blocks, cfg, "test")
+    assert len(chunks) >= 2
+    assert chunks[0].neighbors["next_chunk_id"] == chunks[1].chunk_id
+    assert chunks[1].neighbors["previous_chunk_id"] == chunks[0].chunk_id
+    assert chunks[-1].neighbors["next_chunk_id"] is None
+
+
+def test_blocks_merged_until_target(cfg):
+    blocks = [make_block(i, "x" * 20) for i in range(1, 4)]
+    chunks = pack(blocks, cfg, "test")
+    assert len(chunks) >= 2
+
+
+def test_oversized_paragraph_split(cfg):
+    long_content = ("Questa è la prima frase completa. " * 5).strip()
+    blocks = [make_block(1, long_content)]
+    chunks = pack(blocks, cfg, "test")
+    for c in chunks:
+        assert c.chars <= cfg.max_chars or c.flags["is_overflow"]
+
+
+def test_atomic_block_not_split(cfg):
+    atomic_content = "```python\n" + "codice\n" * 20 + "```"
+    blocks = [make_block(1, atomic_content, kind="code", atomic=True)]
+    chunks = pack(blocks, cfg, "test")
+    assert len(chunks) == 1
+    assert chunks[0].flags["is_overflow"] is True
+    assert chunks[0].content_type == "overflow"
+
+
+def test_heading_path_break_creates_new_chunk(cfg):
+    hp1 = [{"level": 1, "text": "Sezione 1"}]
+    hp2 = [{"level": 1, "text": "Sezione 2"}]
+    blocks = [make_block(1, "x" * 30, header_path=hp1), make_block(2, "x" * 30, header_path=hp2)]
+    chunks = pack(blocks, cfg, "test")
+    assert len(chunks) == 2
+    assert chunks[0].header_path == hp1
+    assert chunks[1].header_path == hp2
+
+
+def test_content_for_embedding_has_header_prefix(cfg):
+    blocks = [make_block(1, "Contenuto.", header_path=[{"level": 1, "text": "Guida"},
+                                                        {"level": 2, "text": "Intro"}])]
+    chunks = pack(blocks, cfg, "test")
+    assert "Guida" in chunks[0].content_for_embedding
+    assert "Intro" in chunks[0].content_for_embedding
+    assert "Contenuto." in chunks[0].content_for_embedding
+
+
+def test_thematic_break_flushes_chunk(cfg):
+    blocks = [
+        make_block(1, "x" * 30),
+        make_block(2, "---", kind="thematic_break"),
+        make_block(3, "x" * 30),
+    ]
+    chunks = pack(blocks, cfg, "test")
+    assert len(chunks) == 2
+
+
+def test_flags_has_code(cfg):
+    blocks = [make_block(1, "```\ncode\n```", kind="code", atomic=True)]
+    chunks = pack(blocks, cfg, "test")
+    assert chunks[0].flags["has_code"] is True
+
+
+def test_flags_has_table(cfg):
+    blocks = [make_block(1, "| A | B |\n|---|---|\n| 1 | 2 |", kind="table", atomic=True)]
+    chunks = pack(blocks, cfg, "test")
+    assert chunks[0].flags["has_table"] is True
+
+
+def test_chunk_index_sequential(cfg):
+    blocks = [make_block(i, "x" * 60) for i in range(1, 5)]
+    chunks = pack(blocks, cfg, "test")
+    for i, c in enumerate(chunks):
+        assert c.chunk_index == i
+
+
+def test_assets_empty(cfg):
+    blocks = [make_block(1, "Testo.")]
+    chunks = pack(blocks, cfg, "test")
+    assert chunks[0].assets == []
+
+
+def test_flags_has_math_via_kind(cfg):
+    blocks = [make_block(1, "$$\nE=mc^2\n$$", kind="math", atomic=True)]
+    chunks = pack(blocks, cfg, "test")
+    assert chunks[0].flags["has_math"] is True
+
+
+def test_flags_has_math_via_content_dollars(cfg):
+    # Paragrafo con $$ inline (senza dollarmath plugin attivo)
+    blocks = [make_block(1, "Vedi la formula:\n$$\nx^2\n$$")]
+    chunks = pack(blocks, cfg, "test")
+    assert chunks[0].flags["has_math"] is True
+
+
+def test_flags_has_math_via_content_begin(cfg):
+    blocks = [make_block(1, r"Equazione: \begin{align} x = 1 \end{align}")]
+    chunks = pack(blocks, cfg, "test")
+    assert chunks[0].flags["has_math"] is True
+
+
+def test_flags_has_table_via_html(cfg):
+    html_content = "<table><tr><td>A</td></tr></table>"
+    blocks = [make_block(1, html_content, kind="html", atomic=True)]
+    chunks = pack(blocks, cfg, "test")
+    assert chunks[0].flags["has_table"] is True
+
+
+def test_flags_no_math_plain_text(cfg):
+    blocks = [make_block(1, "Testo normale senza formule.")]
+    chunks = pack(blocks, cfg, "test")
+    assert chunks[0].flags["has_math"] is False

tests/chunks/test_parser.py

@@ -0,0 +1,69 @@
+from chunks.parser import parse
+
+
+def test_parse_returns_tokens_and_lines():
+    md = "# Titolo\n\nParagrafo.\n"
+    tokens, lines = parse(md)
+    assert len(tokens) > 0
+    assert lines[0] == "# Titolo"
+    assert lines[2] == "Paragrafo."
+
+
+def test_tokens_have_source_map():
+    md = "# Titolo\n\nParagrafo.\n"
+    tokens, lines = parse(md)
+    heading = next(t for t in tokens if t.type == "heading_open")
+    assert heading.map is not None
+    assert heading.map[0] == 0
+
+
+def test_parse_code_fence():
+    md = "# T\n\n```python\ncodice\n```\n"
+    tokens, lines = parse(md)
+    fence = next((t for t in tokens if t.type == "fence"), None)
+    assert fence is not None
+    assert "codice" in fence.content
+
+
+def test_parse_table():
+    md = "| A | B |\n|---|---|\n| 1 | 2 |\n"
+    tokens, lines = parse(md)
+    types = [t.type for t in tokens]
+    assert "table_open" in types
+
+
+def test_parse_list():
+    md = "- item 1\n- item 2\n"
+    tokens, lines = parse(md)
+    types = [t.type for t in tokens]
+    assert "bullet_list_open" in types
+
+
+def test_lines_preserves_source():
+    md = "Riga 1\nRiga 2\nRiga 3\n"
+    _, lines = parse(md)
+    assert lines[0] == "Riga 1"
+    assert lines[1] == "Riga 2"
+
+
+def test_parse_math_block():
+    md = "# T\n\n$$\nE=mc^2\n$$\n"
+    tokens, lines = parse(md)
+    types = [t.type for t in tokens]
+    assert "math_block" in types
+
+
+def test_math_block_has_source_map():
+    md = "# T\n\n$$\nE=mc^2\n$$\n"
+    tokens, lines = parse(md)
+    mb = next(t for t in tokens if t.type == "math_block")
+    assert mb.map is not None
+    assert "E=mc^2" in mb.content
+
+
+def test_parse_math_inline_stays_in_paragraph():
+    md = "Testo con $x^2$ inline.\n"
+    tokens, lines = parse(md)
+    types = [t.type for t in tokens]
+    assert "paragraph_open" in types
+    assert "math_block" not in types

tests/chunks/test_segmenter.py

@@ -0,0 +1,155 @@
+import pytest
+from chunks.parser import parse
+from chunks.config import ChunkerConfig
+from chunks.segmenter import segment
+
+
+@pytest.fixture
+def cfg():
+    return ChunkerConfig()
+
+
+def test_paragraph_block(cfg):
+    tokens, lines = parse("# Titolo\n\nParagrafo di testo.\n")
+    blocks = segment(tokens, lines, cfg)
+    para = next(b for b in blocks if b.kind == "paragraph")
+    assert "Paragrafo di testo." in para.content
+    assert para.header_path == [{"level": 1, "text": "Titolo"}]
+
+
+def test_code_block_is_atomic(cfg):
+    tokens, lines = parse("# T\n\n```python\nprint('hello')\n```\n")
+    blocks = segment(tokens, lines, cfg)
+    code = next(b for b in blocks if b.kind == "code")
+    assert code.atomic is True
+
+
+def test_table_block_is_atomic(cfg):
+    tokens, lines = parse("# T\n\n| A | B |\n|---|---|\n| 1 | 2 |\n")
+    blocks = segment(tokens, lines, cfg)
+    table = next(b for b in blocks if b.kind == "table")
+    assert table.atomic is True
+
+
+def test_list_block_is_atomic(cfg):
+    tokens, lines = parse("# T\n\n- item 1\n- item 2\n")
+    blocks = segment(tokens, lines, cfg)
+    lst = next(b for b in blocks if b.kind == "list")
+    assert lst.atomic is True
+
+
+def test_heading_stack_depth(cfg):
+    md = "# H1\n\n## H2\n\n### H3\n\nTesto.\n"
+    tokens, lines = parse(md)
+    blocks = segment(tokens, lines, cfg)
+    para = next(b for b in blocks if b.kind == "paragraph")
+    assert para.header_path == [
+        {"level": 1, "text": "H1"},
+        {"level": 2, "text": "H2"},
+        {"level": 3, "text": "H3"},
+    ]
+
+
+def test_context_depth_limits_header_path():
+    cfg = ChunkerConfig(context_depth=2)
+    md = "# H1\n\n## H2\n\n### H3\n\nTesto.\n"
+    tokens, lines = parse(md)
+    blocks = segment(tokens, lines, cfg)
+    para = next(b for b in blocks if b.kind == "paragraph")
+    assert len(para.header_path) == 2
+
+
+def test_skip_headings(cfg):
+    md = "# Titolo\n\n## Indice\n\nContenuto saltato.\n\n## Sezione Reale\n\nContenuto reale.\n"
+    tokens, lines = parse(md)
+    blocks = segment(tokens, lines, cfg)
+    contents = [b.content for b in blocks]
+    assert not any("saltato" in c for c in contents)
+    assert any("reale" in c for c in contents)
+
+
+def test_skip_pre_heading(cfg):
+    md = "Testo prima del primo heading.\n\n# Titolo\n\nTesto dopo.\n"
+    tokens, lines = parse(md)
+    blocks = segment(tokens, lines, cfg)
+    contents = [b.content for b in blocks]
+    assert not any("prima del primo" in c for c in contents)
+    assert any("dopo" in c for c in contents)
+
+
+def test_skip_pre_heading_disabled():
+    cfg = ChunkerConfig(skip_pre_heading=False)
+    md = "Testo prima.\n\n# Titolo\n\nTesto dopo.\n"
+    tokens, lines = parse(md)
+    blocks = segment(tokens, lines, cfg)
+    contents = [b.content for b in blocks]
+    assert any("prima" in c for c in contents)
+
+
+def test_block_ids_unique(cfg):
+    md = "# T\n\nPara 1.\n\nPara 2.\n\nPara 3.\n"
+    tokens, lines = parse(md)
+    blocks = segment(tokens, lines, cfg)
+    ids = [b.id for b in blocks]
+    assert len(ids) == len(set(ids))
+
+
+def test_heading_reset_on_same_level(cfg):
+    md = "# H1a\n\n## H2a\n\n# H1b\n\nTesto.\n"
+    tokens, lines = parse(md)
+    blocks = segment(tokens, lines, cfg)
+    para = next(b for b in blocks if b.kind == "paragraph")
+    assert para.header_path == [{"level": 1, "text": "H1b"}]
+
+
+def test_thematic_break(cfg):
+    md = "# T\n\nPara.\n\n---\n\nPara 2.\n"
+    tokens, lines = parse(md)
+    blocks = segment(tokens, lines, cfg)
+    kinds = [b.kind for b in blocks]
+    assert "thematic_break" in kinds
+
+
+def test_blockquote(cfg):
+    md = "# T\n\n> Una citazione.\n"
+    tokens, lines = parse(md)
+    blocks = segment(tokens, lines, cfg)
+    bq = next((b for b in blocks if b.kind == "blockquote"), None)
+    assert bq is not None
+    assert "citazione" in bq.content
+
+
+def test_source_line_numbers(cfg):
+    md = "# Titolo\n\nParagrafo.\n"
+    tokens, lines = parse(md)
+    blocks = segment(tokens, lines, cfg)
+    para = next(b for b in blocks if b.kind == "paragraph")
+    assert para.start_line == 2
+    assert para.end_line == 3
+
+
+def test_math_block_is_atomic(cfg):
+    md = "# T\n\n$$\nE=mc^2\n$$\n"
+    tokens, lines = parse(md)
+    blocks = segment(tokens, lines, cfg)
+    math = next((b for b in blocks if b.kind == "math"), None)
+    assert math is not None
+    assert math.atomic is True
+    assert "E=mc^2" in math.content
+
+
+def test_math_block_plain_text(cfg):
+    md = "# T\n\n$$\n\\int_0^1 f(x) dx\n$$\n"
+    tokens, lines = parse(md)
+    blocks = segment(tokens, lines, cfg)
+    math = next(b for b in blocks if b.kind == "math")
+    assert math.plain_text == "[formula]"
+
+
+def test_html_block_with_table(cfg):
+    md = "# T\n\n<table><tr><td>A</td></tr></table>\n"
+    tokens, lines = parse(md)
+    blocks = segment(tokens, lines, cfg)
+    html = next((b for b in blocks if b.kind == "html"), None)
+    assert html is not None
+    assert "<table" in html.content

tests/chunks/test_validator.py

@@ -0,0 +1,90 @@
+import pytest
+from chunks.models import Chunk, ChunkingResult, Diagnostics
+from chunks.config import ChunkerConfig
+from chunks.validator import validate
+
+
+def make_chunk(idx: int, content: str, chars: int = None,
+               is_overflow: bool = False, content_type: str = "section_fragment") -> Chunk:
+    chars = chars or len(content)
+    return Chunk(
+        chunk_id=f"chk_{idx:06d}", chunk_index=idx,
+        content_original=content, content_for_embedding=content,
+        content_type=content_type, chars=chars,
+        start_line=idx * 3, end_line=idx * 3 + 2,
+        header_path=[{"level": 1, "text": "T"}],
+        block_ids=[f"blk_{idx:04d}"],
+        flags={"has_code": False, "has_table": False, "has_math": False,
+               "is_overflow": is_overflow, "is_sparse": False},
+        neighbors={"previous_chunk_id": None, "next_chunk_id": None},
+        assets=[],
+    )
+
+
+@pytest.fixture
+def cfg():
+    return ChunkerConfig(max_chars=200, min_chars=40, fail_on_content_loss=False)
+
+
+def test_no_errors_on_valid_chunks(cfg):
+    result = ChunkingResult(
+        stem="test", source_path="sources/test.md",
+        chunks=[make_chunk(1, "Testo valido.")],
+        diagnostics=Diagnostics([], [], {}),
+    )
+    diag = validate(result, "# T\n\nTesto valido.\n", cfg)
+    assert diag.errors == []
+
+
+def test_broken_fence_detected(cfg):
+    result = ChunkingResult(
+        stem="test", source_path="sources/test.md",
+        chunks=[make_chunk(1, "```python\ncodice senza chiusura")],
+        diagnostics=Diagnostics([], [], {}),
+    )
+    diag = validate(result, "", cfg)
+    assert any("fence" in e.lower() for e in diag.errors)
+
+
+def test_no_duplicate_chunk_ids(cfg):
+    c1 = make_chunk(1, "Testo A.")
+    c2 = make_chunk(1, "Testo B.")  # stesso chunk_id
+    result = ChunkingResult(
+        stem="test", source_path="sources/test.md",
+        chunks=[c1, c2], diagnostics=Diagnostics([], [], {}),
+    )
+    diag = validate(result, "", cfg)
+    assert any("duplicat" in e.lower() for e in diag.errors)
+
+
+def test_metrics_populated(cfg):
+    chunks = [make_chunk(i, "x" * 50) for i in range(1, 4)]
+    result = ChunkingResult(
+        stem="test", source_path="sources/test.md",
+        chunks=chunks, diagnostics=Diagnostics([], [], {}),
+    )
+    diag = validate(result, "", cfg)
+    assert diag.metrics["total_chunks"] == 3
+    assert diag.metrics["avg_chars"] == 50
+    assert diag.metrics["size_compliance"] == 1.0
+
+
+def test_overflow_not_counted_in_size_compliance(cfg):
+    chunks = [make_chunk(1, "x" * 300, is_overflow=True, content_type="overflow")]
+    result = ChunkingResult(
+        stem="test", source_path="sources/test.md",
+        chunks=chunks, diagnostics=Diagnostics([], [], {}),
+    )
+    diag = validate(result, "", cfg)
+    assert diag.metrics["overflow_count"] == 1
+    assert diag.errors == []
+
+
+def test_empty_chunks_list(cfg):
+    result = ChunkingResult(
+        stem="test", source_path="sources/test.md",
+        chunks=[], diagnostics=Diagnostics([], [], {}),
+    )
+    diag = validate(result, "", cfg)
+    assert diag.warnings
+    assert diag.metrics["total_chunks"] == 0