43 lines
1.8 KiB
Python
43 lines
1.8 KiB
Python
# ======================================================================
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# AXIS: Knowledge Expansion Tool (V1.2)
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# This script converts raw text into AXIS-compatible Semantic Lattices.
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# ======================================================================
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import json
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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MODEL_ID = "kofdai/AXIS-Sovereign-Logic-Engine"
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def extract_to_lattice(text):
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print(f"🧐 [AXIS] 知識抽出中: {text[:30]}...")
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype=torch.bfloat16, device_map="auto")
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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':[]}}"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(**inputs, max_new_tokens=512)
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result = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return result
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if __name__ == "__main__":
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# 拡張したい知識の例
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raw_knowledge = [
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"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。",
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"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。"
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]
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knowledge_base = []
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for k in raw_knowledge:
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lattice_piece = extract_to_lattice(k)
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knowledge_base.append(lattice_piece)
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# local_massive_data.json として保存
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with open("local_massive_data.json", "w", encoding="utf-8") as f:
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json.dump(knowledge_base, f, ensure_ascii=False, indent=4)
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print("✅ [SUCCESS] 知識拡張完了。local_massive_data.json を生成しました。") |