rag-from-scratch/chunks/chunker.py

#!/usr/bin/env python3
"""
Chunking adattivo

Divide il Markdown revisionato in chunk semantici pronti per la
vettorizzazione. La strategia dipende dal profilo strutturale del documento.

Input:  conversione/<stem>/clean.md + conversione/<stem>/structure_profile.json
Output: chunks/<stem>/chunks.json

Uso:
    python chunks/chunker.py                    # tutti i documenti in conversione/
    python chunks/chunker.py --stem documento   # un solo documento
    python chunks/chunker.py --stem documento --force
"""

import argparse
import json
import re
import sys
from pathlib import Path


# ─── Parametri ────────────────────────────────────────────────────────────────

MIN_CHARS = 200   # sotto questa soglia → accorpa al chunk successivo
MAX_CHARS = 800   # sopra questa soglia → spezza su frasi
OVERLAP_S = 2     # frasi di overlap tra sotto-chunk dello stesso boundary


# ─── Utilità ──────────────────────────────────────────────────────────────────

def split_sentences(text: str) -> list[str]:
    parts = re.split(r'(?<=[.!?»])\s+(?=[A-ZÀÈÉÌÒÙA-Z\"])', text.strip())
    if len(parts) <= 1:
        parts = re.split(r'(?<=[.!?»])\s+', text.strip())
    return [p.strip() for p in parts if p.strip()]


def slugify(s: str, max_len: int = 60) -> str:
    s = s.lower()
    s = re.sub(r'[^\w\s-]', '', s)
    s = re.sub(r'[\s_-]+', '_', s).strip('_')
    return s[:max_len] if s else "section"


_SENT_BOUNDARY = re.compile(r"[.!?»)\]'\u2019\"\u201c\u201d/:|\u2026]$")


def _flush_chunk(
    current: list[str],
    sentences: list[str],
    i: int,
    prefix: str,
    sezione: str,
    titolo: str,
    sub_index: int,
    max_chars: int,
) -> tuple[dict, list[str], int, int]:
    """Emette un chunk, estendendo fino a un confine di frase (max +20%)."""
    hard_limit = int(max_chars * 1.2)
    current_len = sum(len(s) + 1 for s in current)
    while i < len(sentences) and not _SENT_BOUNDARY.search(" ".join(current)):
        nxt = sentences[i]
        if current_len + len(nxt) + 1 > hard_limit:
            break
        current.append(nxt)
        current_len += len(nxt) + 1
        i += 1
    chunk_text = prefix + " ".join(current)
    chunk = {
        "chunk_id": f"{slugify(sezione)}__{slugify(titolo)}__s{sub_index}",
        "text": chunk_text,
        "sezione": sezione,
        "titolo": titolo,
        "sub_index": sub_index,
        "n_chars": len(chunk_text),
    }
    return chunk, current, i, sub_index + 1


def make_sub_chunks(
    body: str,
    prefix: str,
    sezione: str,
    titolo: str,
    max_chars: int,
    overlap_s: int,
) -> list[dict]:
    sentences = split_sentences(body)
    if not sentences:
        return []

    chunks = []
    current: list[str] = []
    current_len = 0
    sub_index = 0

    i = 0
    while i < len(sentences):
        sent = sentences[i]
        if not current or current_len + len(sent) + 1 <= max_chars:
            current.append(sent)
            current_len += len(sent) + (1 if len(current) > 1 else 0)
            i += 1
        else:
            chunk, current, i, sub_index = _flush_chunk(
                current, sentences, i, prefix, sezione, titolo, sub_index, max_chars
            )
            chunks.append(chunk)
            overlap = current[-overlap_s:] if overlap_s and len(current) > overlap_s else []
            current = overlap[:]
            current_len = sum(len(s) + 1 for s in current)

    if current:
        chunk_text = prefix + " ".join(current)
        chunks.append({
            "chunk_id": f"{slugify(sezione)}__{slugify(titolo)}__s{sub_index}",
            "text": chunk_text,
            "sezione": sezione,
            "titolo": titolo,
            "sub_index": sub_index,
            "n_chars": len(chunk_text),
        })

    return chunks


# ─── Parser Markdown ──────────────────────────────────────────────────────────

def parse_h3_sections(text: str) -> list[dict]:
    sections = []
    current_h2 = ""
    current_h3 = ""
    current_body_lines: list[str] = []

    def flush():
        body = "\n".join(current_body_lines).strip()
        if body:
            sections.append({
                "sezione": current_h2,
                "titolo": current_h3,
                "body": body,
            })

    for line in text.splitlines():
        if re.match(r"^# ", line):
            flush()
            current_h2 = line[2:].strip()
            current_h3 = ""
            current_body_lines = []
        elif re.match(r"^## ", line):
            flush()
            current_h2 = line[3:].strip()
            current_h3 = ""
            current_body_lines = []
        elif re.match(r"^### ", line):
            flush()
            current_h3 = line[4:].strip()
            current_body_lines = []
        else:
            current_body_lines.append(line)

    flush()
    return sections


def parse_h2_sections(text: str) -> list[dict]:
    sections = []
    current_h2 = ""
    current_body_lines: list[str] = []

    def flush():
        body = "\n".join(current_body_lines).strip()
        if body:
            sections.append({"sezione": current_h2, "body": body})

    for line in text.splitlines():
        if re.match(r"^## ", line):
            flush()
            current_h2 = line[3:].strip()
            current_body_lines = []
        elif re.match(r"^# ", line):
            flush()
            current_h2 = line[2:].strip()
            current_body_lines = []
        else:
            current_body_lines.append(line)

    flush()
    return sections


# ─── Strategie di chunking ────────────────────────────────────────────────────

def chunk_h3_aware(text: str, stem: str) -> list[dict]:
    sections = parse_h3_sections(text)

    merged: list[dict] = []
    pending: dict | None = None

    for sec in sections:
        if pending is None:
            pending = dict(sec)
            continue

        if (pending["sezione"] == sec["sezione"]
                and len(pending["body"]) < MIN_CHARS):
            sep_title = " / ".join(filter(None, [pending["titolo"], sec["titolo"]]))
            pending = {
                "sezione": pending["sezione"],
                "titolo": sep_title or pending["titolo"],
                "body": pending["body"] + "\n\n" + sec["body"],
            }
        else:
            merged.append(pending)
            pending = dict(sec)

    if pending:
        merged.append(pending)

    chunks = []
    for sec in merged:
        sezione = sec["sezione"] or stem
        titolo = sec["titolo"] or ""
        body = sec["body"]

        prefix = f"[{sezione} > {titolo}]\n" if titolo else f"[{sezione}]\n"
        sub = make_sub_chunks(body, prefix, sezione, titolo, MAX_CHARS, OVERLAP_S)
        chunks.extend(sub)

    return chunks


def chunk_h2_paragraph_split(text: str, stem: str) -> list[dict]:
    sections = parse_h2_sections(text)
    chunks = []

    for sec in sections:
        sezione = sec["sezione"] or stem
        body = sec["body"]
        prefix = f"[{sezione}]\n"

        paragraphs = [
            p.strip()
            for p in re.split(r"\n{2,}", body)
            if p.strip() and not re.match(r"^#+\s", p.strip())
        ]

        merged_pars: list[str] = []
        pending = ""
        for par in paragraphs:
            if pending and len(pending) < MIN_CHARS:
                pending = pending + "\n\n" + par
            else:
                if pending:
                    merged_pars.append(pending)
                pending = par
        if pending:
            merged_pars.append(pending)

        for idx, par in enumerate(merged_pars):
            sub = make_sub_chunks(par, prefix, sezione, f"par{idx}", MAX_CHARS, OVERLAP_S)
            for c in sub:
                c["chunk_id"] = f"{slugify(sezione)}__p{idx}__s{c['sub_index']}"
            chunks.extend(sub)

    return chunks


def chunk_paragraph(text: str, stem: str) -> list[dict]:
    paragraphs = [
        p.strip()
        for p in re.split(r"\n{2,}", text)
        if p.strip() and not re.match(r"^#+\s", p.strip())
    ]
    prefix = f"[Documento: {stem}]\n"

    merged: list[str] = []
    pending = ""
    for par in paragraphs:
        if pending and len(pending) < MIN_CHARS:
            pending = pending + "\n\n" + par
        else:
            if pending:
                merged.append(pending)
            pending = par
    if pending:
        merged.append(pending)

    chunks = []
    for idx, par in enumerate(merged):
        sub = make_sub_chunks(par, prefix, stem, f"par{idx}", MAX_CHARS, OVERLAP_S)
        for c in sub:
            c["chunk_id"] = f"para__{idx}__s{c['sub_index']}"
        chunks.extend(sub)

    return chunks


def chunk_sliding_window(text: str, stem: str) -> list[dict]:
    sentences = split_sentences(text)
    prefix = f"[Documento: {stem}]\n"

    chunks = []
    i = 0
    win_idx = 0

    while i < len(sentences):
        window: list[str] = []
        cur_len = 0

        j = i
        while j < len(sentences):
            s = sentences[j]
            if window and cur_len + len(s) + 1 > MAX_CHARS:
                break
            window.append(s)
            cur_len += len(s) + (1 if len(window) > 1 else 0)
            j += 1

        if not window:
            window = [sentences[i]]
            j = i + 1

        chunk_text = prefix + " ".join(window)
        chunks.append({
            "chunk_id": f"win__{win_idx}",
            "text": chunk_text,
            "sezione": stem,
            "titolo": f"finestra {win_idx}",
            "sub_index": win_idx,
            "n_chars": len(chunk_text),
        })
        win_idx += 1
        i += max(1, len(window) - OVERLAP_S)

    return chunks


# ─── Dispatcher ───────────────────────────────────────────────────────────────

_STRATEGIES: dict[str, callable] = {
    "h3_aware": chunk_h3_aware,
    "h2_paragraph_split": chunk_h2_paragraph_split,
    "paragraph": chunk_paragraph,
    "sliding_window": chunk_sliding_window,
}


def chunk_document(clean_md: Path, profile: dict, stem: str) -> list[dict]:
    text = clean_md.read_text(encoding="utf-8")
    strategia = profile.get("strategia_chunking", "paragraph")
    fn = _STRATEGIES.get(strategia, chunk_paragraph)
    return fn(text, stem)


# ─── Per-document processing ──────────────────────────────────────────────────

def process_stem(stem: str, project_root: Path, force: bool) -> bool:
    conv_dir  = project_root / "conversione" / stem
    out_dir   = project_root / "chunks" / stem
    clean_md  = conv_dir / "clean.md"
    profile_path = conv_dir / "structure_profile.json"
    out_file  = out_dir / "chunks.json"

    print(f"\nDocumento: {stem}")

    if not clean_md.exists():
        print(f"  ✗ clean.md non trovato in conversione/{stem}/ — skip")
        return False
    if not profile_path.exists():
        print(f"  ✗ structure_profile.json non trovato in conversione/{stem}/ — skip")
        return False

    if out_file.exists() and not force:
        print(f"  ⚠️  chunks.json già presente — skip")
        print(f"       (usa --force per rieseguire)")
        return True

    profile   = json.loads(profile_path.read_text(encoding="utf-8"))
    strategia = profile.get("strategia_chunking", "paragraph")
    print(f"  Strategia: {strategia}")

    chunks = chunk_document(clean_md, profile, stem)

    if not chunks:
        print(f"  ✗ Nessun chunk generato — controlla clean.md")
        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"
    )

    lengths = [c["n_chars"] for c in chunks]
    min_c = min(lengths)
    max_c = max(lengths)
    avg_c = int(sum(lengths) / len(lengths))
    short = sum(1 for l in lengths if l < MIN_CHARS)
    long_ = sum(1 for l in lengths if l > MAX_CHARS * 1.5)

    print(f"  Chunk totali: {len(chunks)}")
    print(f"  Min: {min_c} char  Max: {max_c} char  Media: {avg_c} char")
    if short:
        print(f"  ⚠️  {short} chunk sotto MIN_CHARS ({MIN_CHARS})")
    if long_:
        print(f"  ⚠️  {long_} chunk sopra MAX_CHARS×1.5 ({int(MAX_CHARS * 1.5)})")
    print(f"  ✅ chunks.json salvato in chunks/{stem}/")
    return True


# ─── Entry point ─────────────────────────────────────────────────────────────

if __name__ == "__main__":
    project_root = Path(__file__).parent.parent

    parser = argparse.ArgumentParser(description="Chunking adattivo")
    parser.add_argument("--stem", help="Nome del documento (sottocartella di conversione/)")
    parser.add_argument("--force", action="store_true", help="Riesegui anche se già presente")
    args = parser.parse_args()

    if args.stem:
        stems = [args.stem]
    else:
        conv_dir = project_root / "conversione"
        if not conv_dir.exists():
            print(f"Errore: cartella conversione/ non trovata in {project_root}")
            sys.exit(1)
        stems = sorted(
            p.name for p in conv_dir.iterdir()
            if p.is_dir() and (p / "clean.md").exists()
        )
        if not stems:
            print(f"Errore: nessun documento trovato in conversione/")
            sys.exit(1)

    results = [process_stem(s, project_root, args.force) for s in stems]

    ok    = sum(results)
    total = len(results)
    print(f"\n{'✅' if all(results) else '⚠️ '} {ok}/{total} documenti processati")
    sys.exit(0 if all(results) else 1)