chore: rimuove chunker legacy, aggiunge dipendenze AST-based pipeline

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
2026-06-08 16:16:07 +02:00
parent b321a51c5c
commit be8ae9f6b8
6 changed files with 2 additions and 970 deletions
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#!/usr/bin/env python3
"""
chunker.py — Chunking semantico da Markdown pulito
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 --stem <stem> --force
"""
import argparse
import json
import re
import sys
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
# ─── Utilità ──────────────────────────────────────────────────────────────────
def split_sentences(text: str) -> list[str]:
parts = re.split(cfg.SENTENCE_SPLIT_RE, text.strip())
return [p.strip() for p in parts if p.strip()]
def context_to_meta(context: str) -> tuple[str, str]:
parts = [p.strip() for p in context.split(" > ") if p.strip()]
if len(parts) >= 2:
return " > ".join(parts[:-1]), parts[-1]
return (parts[0] if parts else ""), ""
# ─── Parser Markdown ──────────────────────────────────────────────────────────
_SKIP_HEADINGS_LOWER = {h.lower() for h in cfg.SKIP_HEADINGS}
def _is_skip_heading(title: str) -> bool:
t = title.lower()
return any(t == s or t.startswith(s) for s in _SKIP_HEADINGS_LOWER)
def parse_paragraphs(text: str) -> list[dict]:
"""Estrae blocchi dal Markdown con il loro contesto heading.
Restituisce: [{"context": "H1 > H2 > H3", "text": "...", "kind": "text|table|list|code"}]
- Heading senza corpo non emettono chunk: aggiornano solo il contesto.
- Tabelle (righe |), liste (righe -/*) e code block (```) sono atomici.
- Sezioni in SKIP_HEADINGS vengono saltate completamente.
- Contenuto pre-heading saltato se SKIP_PRE_HEADING è True.
"""
headings = ["", "", ""] # H1, H2, H3
result: list[dict] = []
buf: list[str] = []
cur_kind = "text"
in_code = False
skip_level: int | None = None # livello heading che ha attivato lo skip
def current_context() -> str:
parts = [h for h in headings if h]
return " > ".join(parts[:cfg.CONTEXT_DEPTH]) if parts else "documento"
def is_skipping() -> bool:
return skip_level is not None
def flush() -> None:
if is_skipping():
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"
)
(out_dir / "meta.json").write_text(
json.dumps({
"stem": stem,
"source": str(md_path.relative_to(project_root)),
"max_chars": cfg.MAX_CHARS,
"min_chars": cfg.MIN_CHARS,
"merge_short": cfg.MERGE_SHORT_PARAGRAPHS,
"strategy": "one_paragraph_per_chunk",
}, ensure_ascii=False, indent=2),
encoding="utf-8",
)
lengths = [c["n_chars"] for c in chunks]
over_max = sum(1 for l in lengths if l > cfg.MAX_CHARS)
under_min = sum(1 for l in lengths if l < cfg.MIN_CHARS)
avg = int(sum(lengths) / len(lengths))
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" {under_min} chunk sotto MIN_CHARS={cfg.MIN_CHARS}")
print(f" → chunks/{stem}/chunks.json")
return True
# ─── Entry point ──────────────────────────────────────────────────────────────
if __name__ == "__main__":
project_root = Path(__file__).parent.parent
sources_dir = project_root / "sources"
parser = argparse.ArgumentParser(description="Markdown pulito → chunks.json")
parser.add_argument("--stem", help="Nome documento (es. analisi2)")
parser.add_argument("--force", action="store_true",
help="Rigenera chunks.json anche se già presente")
args = parser.parse_args()
if args.stem:
stems = [args.stem]
else:
found = set()
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]
ok = sum(results)
print(f"\n{'' if all(results) else '⚠️ '} {ok}/{len(results)} documenti processati")
sys.exit(0 if all(results) else 1)