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# ======================================================================
# AXIS: Knowledge Expansion Tool (V1.2)
# This script converts raw text into AXIS-compatible Semantic Lattices.
# ======================================================================
import json
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL_ID = "kofdai/AXIS-Sovereign-Logic-Engine"
def extract_to_lattice(text):
print(f"🧐 [AXIS] 知識抽出中: {text[:30]}...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype=torch.bfloat16, device_map="auto")
prompt = f"以下のテキストをAXIS立体十字形式（JSON）に変換せよ。論理矛盾があればconflictsに記載せよ。\n入力: {text}\nFormat: {{'nodes':[], 'edges':[], 'conflicts':[]}}"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=512)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
return result
if __name__ == "__main__":
# 拡張したい知識の例
raw_knowledge = [
"意味は計算の結果ではなく、計算が走るための初期構造である。",
"AXISの物理パージは、知能の純粋性を保つための儀式である。"
]
knowledge_base = []
for k in raw_knowledge:
lattice_piece = extract_to_lattice(k)
knowledge_base.append(lattice_piece)
# local_massive_data.json として保存
with open("local_massive_data.json", "w", encoding="utf-8") as f:
json.dump(knowledge_base, f, ensure_ascii=False, indent=4)
print("✅ [SUCCESS] 知識拡張完了。local_massive_data.json を生成しました。") |