"""
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()