上傳檔案到「/」

blog_search.py

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+"""
+Blog Semantic Search
+====================
+讀取 blog_embeddings.json，讓使用者輸入（或貼上）任意文字，
+用同一個 embedding 模型算出向量，列出最相關的 20 篇文章。
+
+需求：
+  - ollama 已啟動且已 pull qwen3-embedding:8b
+  - pip install numpy requests
+  - 已經跑過 blog_embeddings.py 產生 blog_embeddings.json
+"""
+
+import json
+import numpy as np
+import requests
+
+# ============================================================
+# 設定區
+# ============================================================
+
+EMBEDDINGS_FILE = "./blog_embeddings.json"
+OLLAMA_URL = "http://localhost:11434/api/embed"
+OLLAMA_MODEL = "qwen3-embedding:8b"
+
+# ============================================================
+
+
+def get_embedding(text: str) -> list[float]:
+    """透過 Ollama API 取得 embedding 向量。"""
+    resp = requests.post(OLLAMA_URL, json={
+        "model": OLLAMA_MODEL,
+        "input": text,
+    })
+    resp.raise_for_status()
+    return resp.json()["embeddings"][0]
+
+
+def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
+    """計算兩個向量的 cosine similarity。"""
+    dot = np.dot(a, b)
+    norm = np.linalg.norm(a) * np.linalg.norm(b)
+    if norm == 0:
+        return 0.0
+    return float(dot / norm)
+
+
+def format_row(rank: int, sim: float, title: str, slug: str) -> str:
+    """格式化一行結果。"""
+    bar = "█" * int(sim * 30)
+    return f"  {rank:3d}. {sim:.4f}  {bar}  {title}  ({slug})"
+
+
+def read_query() -> str:
+    """讀取使用者輸入，支援多行貼上（連按兩次 Enter 結束）。"""
+    print("請輸入搜尋文字（連按兩次 Enter 結束）：")
+    lines = []
+    empty_count = 0
+    while True:
+        line = input()
+        if line == "":
+            empty_count += 1
+            if empty_count >= 2:
+                break
+            lines.append(line)
+        else:
+            empty_count = 0
+            lines.append(line)
+    return "\n".join(lines).strip()
+
+
+def main():
+    # 讀取 embeddings
+    with open(EMBEDDINGS_FILE, encoding="utf-8") as f:
+        data = json.load(f)
+
+    if not data:
+        print("找不到任何文章資料。")
+        return
+
+    print(f"已載入 {len(data)} 篇文章的 embeddings\n")
+
+    # 預先轉成 numpy array，之後比較比較快
+    vectors = [np.array(item["embedding"]) for item in data]
+
+    while True:
+        query = read_query()
+        if not query:
+            print("沒有輸入，掰掰！")
+            break
+
+        print(f"\n正在產生 embedding（模型：{OLLAMA_MODEL}）...")
+        query_vec = np.array(get_embedding(query))
+
+        # 計算與所有文章的相似度
+        similarities = []
+        for i, item in enumerate(data):
+            sim = cosine_similarity(query_vec, vectors[i])
+            similarities.append({
+                "slug": item["slug"],
+                "title": item.get("title", "(無標題)"),
+                "similarity": sim,
+            })
+
+        similarities.sort(key=lambda x: x["similarity"], reverse=True)
+
+        top = similarities[:20]
+        print(f"\n🔍 與輸入文字最相關的 20 篇：\n")
+        for rank, s in enumerate(top, 1):
+            print(format_row(rank, s["similarity"], s["title"], s["slug"]))
+
+        print("\n" + "─" * 60 + "\n")
+
+
+if __name__ == "__main__":
+    main()