File size: 4,514 Bytes
8344211 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | """
export_dataset.py
=================
Esporta tutti i dati da ChromaDB + seed_italian.py in formato JSONL
compatibile con HuggingFace (ChatML / SFTTrainer).
Output: dataset.jsonl (pronto per AutoTrain o finetune.py)
Uso:
python export_dataset.py
python export_dataset.py --out mio_dataset.jsonl --min-chars 50
"""
import argparse
import json
import os
import sys
from pathlib import Path
SYSTEM_PROMPT = (
"Sei GenerAI, un assistente AI specializzato in lingua italiana. "
"Rispondi in modo chiaro, preciso e sempre in italiano."
)
def _row(question: str, answer: str) -> dict:
"""Formato ChatML — compatibile con SFTTrainer e HF AutoTrain."""
return {
"messages": [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": question.strip()},
{"role": "assistant", "content": answer.strip()},
]
}
def load_from_seed() -> list[dict]:
"""Carica le regole di grammatica da seed_italian.py."""
from seed_italian import GRAMMAR_SEED
rows = [_row(item["q"], item["a"]) for item in GRAMMAR_SEED]
print(f"[seed] {len(rows)} esempi caricati da seed_italian.py")
return rows
def load_from_chromadb(min_chars: int = 80) -> list[dict]:
"""Carica i documenti archiviati da ChromaDB (domande web salvate)."""
try:
import chromadb
from chromadb.utils.embedding_functions import SentenceTransformerEmbeddingFunction
except ImportError:
print("[chromadb] chromadb non installato — saltato.")
return []
db_path = "./database"
if not os.path.exists(db_path):
print("[chromadb] Nessun database trovato — saltato.")
return []
ef = SentenceTransformerEmbeddingFunction(
model_name="paraphrase-multilingual-MiniLM-L12-v2"
)
client = chromadb.PersistentClient(path=db_path)
try:
col = client.get_collection(name="generai", embedding_function=ef)
except Exception:
print("[chromadb] Collezione 'generai' non trovata — saltato.")
return []
total = col.count()
if total == 0:
print("[chromadb] Database vuoto — saltato.")
return []
results = col.get(include=["metadatas"], limit=total)
rows = []
skipped = 0
for meta in results["metadatas"]:
source = meta.get("source", "")
if source == "grammatica_italiana":
continue # già in seed, evita duplicati
q = meta.get("query", "").strip()
a = meta.get("answer", "").strip()
if not q or not a or len(a) < min_chars:
skipped += 1
continue
rows.append(_row(q, a))
print(f"[chromadb] {len(rows)} esempi caricati ({skipped} saltati per qualità).")
return rows
def deduplicate(rows: list[dict]) -> list[dict]:
seen = set()
out = []
for row in rows:
key = row["messages"][1]["content"][:80].lower()
if key not in seen:
seen.add(key)
out.append(row)
return out
def main():
parser = argparse.ArgumentParser(description="Esporta dataset per HuggingFace fine-tuning")
parser.add_argument("--out", default="dataset.jsonl", help="File di output (default: dataset.jsonl)")
parser.add_argument("--min-chars", type=int, default=80, help="Lunghezza minima risposta (default: 80)")
parser.add_argument("--no-seed", action="store_true", help="Non includere seed_italian.py")
parser.add_argument("--no-db", action="store_true", help="Non includere ChromaDB")
args = parser.parse_args()
rows = []
if not args.no_seed:
rows += load_from_seed()
if not args.no_db:
rows += load_from_chromadb(min_chars=args.min_chars)
rows = deduplicate(rows)
if not rows:
print("❌ Nessun dato trovato. Aggiungi dati alla KB prima di esportare.")
sys.exit(1)
out_path = Path(args.out)
with open(out_path, "w", encoding="utf-8") as f:
for row in rows:
f.write(json.dumps(row, ensure_ascii=False) + "\n")
print(f"\n✅ Dataset esportato: {out_path.resolve()}")
print(f" Totale esempi : {len(rows)}")
print(f" Formato : ChatML (messages: system/user/assistant)")
print(f"\nProssimo passo:")
print(f" → Fine-tuning locale : python finetune.py --dataset {args.out}")
print(f" → HuggingFace AutoTrain: carica {args.out} su https://huggingface.co/autotrain")
if __name__ == "__main__":
main()
|