File size: 5,661 Bytes
281eb38 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | import hashlib
import json
import re
from pathlib import Path
from typing import Dict, List
import gradio as gr
import numpy as np
try:
from sentence_transformers import SentenceTransformer
except Exception: # pragma: no cover
SentenceTransformer = None
STOPWORDS = {
"della",
"delle",
"dello",
"degli",
"dati",
"sono",
"come",
"questa",
"questo",
"nella",
"nelle",
"anche",
"molto",
"dove",
"quando",
"with",
"that",
"from",
"have",
"your",
"will",
"about",
"parlami",
"dimmi",
"spiegami",
"cosa",
"quale",
}
def normalizza_testo(t: str) -> str:
t = (t or "").replace("\n", " ").replace("\t", " ").strip()
return re.sub(r"\s+", " ", t)
def tokenizza(testo: str) -> List[str]:
candidati = re.findall(r"[A-Za-z0-9_]+", testo.lower())
return [t for t in candidati if len(t) >= 4 and t not in STOPWORDS]
class LocalHashEmbedder:
def __init__(self, dim: int = 384):
self.dim = int(dim)
self.name = f"local-hash-{self.dim}"
def _tokens(self, text: str) -> List[str]:
candidati = re.findall(r"[A-Za-z0-9_]+", text.lower())
return [t for t in candidati if len(t) >= 3 and t not in STOPWORDS]
def encode(self, texts):
if isinstance(texts, str):
texts = [texts]
out = np.zeros((len(texts), self.dim), dtype="float32")
for i, text in enumerate(texts):
clean = normalizza_testo(text)
toks = self._tokens(clean) or clean.lower().split()
for tok in toks:
h = int(hashlib.sha1(tok.encode("utf-8", errors="ignore")).hexdigest(), 16)
idx = h % self.dim
sign = -1.0 if ((h >> 8) & 1) else 1.0
out[i, idx] += sign
norm = float(np.linalg.norm(out[i]))
if norm > 0:
out[i] /= norm
return out
def inizializza_embedder():
model_name = "paraphrase-multilingual-MiniLM-L12-v2"
local_path = Path("aio_models") / model_name
if SentenceTransformer is not None and local_path.exists():
try:
model = SentenceTransformer(str(local_path), local_files_only=True)
return model, f"sentence-transformers(local-path): {local_path}"
except Exception:
pass
return LocalHashEmbedder(dim=384), "local-hash-embedder (showcase fallback)"
def vectorizza(model, testi: List[str]) -> np.ndarray:
v = model.encode(testi)
arr = np.array(v, dtype="float32")
norms = np.linalg.norm(arr, axis=1, keepdims=True)
norms[norms == 0] = 1.0
return arr / norms
def carica_corpus() -> List[Dict[str, str]]:
here = Path(__file__).resolve().parent
path = here / "demo_corpus.json"
data = json.loads(path.read_text(encoding="utf-8"))
for r in data:
r["text"] = normalizza_testo(r.get("text", ""))
return data
def prepara_engine():
model, backend = inizializza_embedder()
records = carica_corpus()
texts = [r["text"] for r in records]
mat = vectorizza(model, texts)
return model, backend, records, mat
def cerca(query: str, model, records, mat: np.ndarray, top_k: int = 5):
qv = vectorizza(model, [query])[0]
sims = mat @ qv
qtok = set(tokenizza(query))
out = []
for i, s in enumerate(sims):
rec = records[i]
ttok = set(tokenizza(rec["text"]))
overlap = len(qtok.intersection(ttok)) if qtok else 0
lex = (overlap / max(1, len(qtok))) if qtok else 0.0
score = 0.6 * float(s) + 0.4 * float(lex)
out.append(
{
"score": score,
"sim": float(s),
"lex": float(lex),
"domain": rec.get("domain", "generale"),
"source": rec.get("source", "showcase"),
"text": rec.get("text", ""),
}
)
out.sort(key=lambda x: x["score"], reverse=True)
return out[:top_k]
MODEL, BACKEND, RECORDS, MAT = prepara_engine()
def esegui_ricerca(query: str, top_k: int):
query = normalizza_testo(query)
if not query:
return "Inserisci una domanda.", f"Backend: {BACKEND} | Records demo: {len(RECORDS)}"
risultati = cerca(query, MODEL, RECORDS, MAT, top_k=int(top_k))
lines = []
for i, r in enumerate(risultati, start=1):
lines.append(
f"### {i}. [{r['domain']}] score={r['score']:.3f} sim={r['sim']:.3f} lex={r['lex']:.3f}\n"
f"source: `{r['source']}`\n\n{r['text']}\n"
)
lines.append(
"\n---\nLicenza Showcase: uso e studio liberi, uso commerciale solo su autorizzazione scritta. "
"Contatto: `info@rthitalia.com`"
)
return "\n".join(lines), f"Backend: {BACKEND} | Records demo: {len(RECORDS)}"
with gr.Blocks(title="AIO System Core - Public Showcase") as demo:
gr.Markdown("# AIO System Core - Public Showcase")
gr.Markdown(
"Demo pubblica controllata. Core proprietario e corpus completo restano privati."
)
stato = gr.Markdown(f"Backend: {BACKEND} | Records demo: {len(RECORDS)}")
with gr.Row():
query = gr.Textbox(
label="Inserisci una domanda",
value="Parlami del Mediterraneo e della geopolitica energetica",
lines=3,
)
top_k = gr.Slider(label="Top K", minimum=3, maximum=10, value=5, step=1)
run = gr.Button("Esegui ricerca", variant="primary")
output = gr.Markdown()
run.click(esegui_ricerca, inputs=[query, top_k], outputs=[output, stato])
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860)
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