100 lines
2.7 KiB
Python
100 lines
2.7 KiB
Python
"""
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Blog Similarity Finder
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======================
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讀取 blog_embeddings.json，用模糊搜尋選一篇文章，
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列出最相似的 10 篇和最不相似的 10 篇。
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需求：
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- pip install numpy iterfzf
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- 已經跑過 blog_embeddings.py 產生 blog_embeddings.json
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"""
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import json
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import numpy as np
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from iterfzf import iterfzf
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# ============================================================
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# 設定區
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# ============================================================
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# embedding 檔案路徑（blog_embeddings.py 的輸出）
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EMBEDDINGS_FILE = "./blog_embeddings.json"
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# ============================================================
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def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
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"""計算兩個向量的 cosine similarity。"""
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dot = np.dot(a, b)
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norm = np.linalg.norm(a) * np.linalg.norm(b)
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if norm == 0:
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return 0.0
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return float(dot / norm)
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def format_row(rank: int, sim: float, title: str, slug: str) -> str:
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"""格式化一行結果。"""
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bar = "█" * int(sim * 30)
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return f" {rank:3d}. {sim:.4f} {bar} {title} ({slug})"
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def main():
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# 讀取 embeddings
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with open(EMBEDDINGS_FILE, encoding="utf-8") as f:
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data = json.load(f)
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if not data:
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print("找不到任何文章資料。")
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return
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# 建立選項：title + slug，方便搜尋
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choices = []
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for item in data:
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title = item.get("title", "(無標題)")
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choices.append(f"{title} | {item['slug']}")
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# 模糊搜尋選擇
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selected = iterfzf(choices, prompt="選擇文章 > ")
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if not selected:
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return
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idx = choices.index(selected)
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target = data[idx]
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target_vec = np.array(target["embedding"])
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target_title = target.get("title", "(無標題)")
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print(f"\n以「{target_title}」為基準：\n")
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# 計算與所有其他文章的相似度
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similarities = []
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for i, item in enumerate(data):
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if i == idx:
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continue
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sim = cosine_similarity(target_vec, np.array(item["embedding"]))
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similarities.append({
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"slug": item["slug"],
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"title": item.get("title", "(無標題)"),
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"similarity": sim,
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})
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# 由高到低排序
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similarities.sort(key=lambda x: x["similarity"], reverse=True)
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# 最相似 10 篇
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top = similarities[:20]
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print("🔥 最相似的 20 篇：")
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for rank, s in enumerate(top, 1):
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print(format_row(rank, s["similarity"], s["title"], s["slug"]))
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# 最不相似 10 篇
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bottom = similarities[-5:]
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bottom.reverse()
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print(f"\n🧊 最不相似的 5 篇：")
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for rank, s in enumerate(bottom, 1):
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print(format_row(rank, s["similarity"], s["title"], s["slug"]))
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if __name__ == "__main__":
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main()
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