"""
Blog Embeddings Generator
=========================
遍歷 Docusaurus blog 資料夾，為每篇文章產生 embedding 向量，
連同 slug 一起存成 JSON 檔。

支援增量模式：如果 JSON 已存在，只處理新增或內容有變動的文章。
用 --full 強制全部重跑。

需求：
  - ollama 已啟動且已 pull qwen3-embedding:8b
  - pip install pyyaml requests
"""

import os
import sys
import glob
import json
import hashlib
import yaml
import requests
import time

# ============================================================
# 設定區
# ============================================================

# 你的 Docusaurus blog 資料夾路徑
BLOG_DIR = "./blog"

# 輸出檔案路徑
OUTPUT_FILE = "./blog_embeddings.json"

# Ollama 設定
OLLAMA_URL = "http://localhost:11434/api/embed"
OLLAMA_MODEL = "qwen3-embedding:8b"

# ============================================================


def extract_meta(content: str, filepath: str) -> tuple[str | None, str | None]:
    """從 frontmatter 抽出 slug 和 title。"""
    if not content.startswith("---"):
        return None, None
    parts = content.split("---", 2)
    if len(parts) < 3:
        return None, None
    try:
        meta = yaml.safe_load(parts[1])
        if isinstance(meta, dict):
            return meta.get("slug"), meta.get("title")
    except yaml.YAMLError:
        print(f"  ⚠ YAML 解析失敗: {filepath}")
    return None, None


def content_hash(content: str) -> str:
    """算出內容的 hash，用來判斷文章有沒有改過。"""
    return hashlib.sha256(content.encode("utf-8")).hexdigest()


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 load_existing(path: str) -> list[dict]:
    """讀取現有的 JSON，沒有就回空 list。"""
    if not os.path.exists(path):
        return []
    with open(path, encoding="utf-8") as f:
        return json.load(f)


def main():
    full_mode = "--full" in sys.argv

    # 找出所有 .md / .mdx 檔
    patterns = [
        os.path.join(BLOG_DIR, "**", "*.md"),
        os.path.join(BLOG_DIR, "**", "*.mdx"),
    ]
    files = []
    for p in patterns:
        files.extend(glob.glob(p, recursive=True))
    files = sorted(set(files))

    print(f"找到 {len(files)} 個 md/mdx 檔案")

    # 載入現有資料，建立 file -> entry 的索引
    if full_mode:
        existing = {}
        print("模式：完整重建\n")
    else:
        existing_list = load_existing(OUTPUT_FILE)
        existing = {item["file"]: item for item in existing_list}
        print(f"模式：增量更新（現有 {len(existing)} 篇）\n")

    results = []
    skipped = 0
    reused = 0
    processed = 0

    for i, filepath in enumerate(files, 1):
        with open(filepath, encoding="utf-8") as f:
            content = f.read()

        slug, title = extract_meta(content, filepath)
        if not slug:
            print(f"[{i}/{len(files)}] 跳過（無 slug）: {filepath}")
            skipped += 1
            continue

        h = content_hash(content)

        # 如果檔案已存在且 hash 沒變，直接沿用
        if filepath in existing and existing[filepath].get("hash") == h:
            print(f"[{i}/{len(files)}] 沒變，沿用: {slug}")
            results.append(existing[filepath])
            reused += 1
            continue

        print(f"[{i}/{len(files)}] 處理中: {slug}")
        start = time.time()
        embedding = get_embedding(content)
        elapsed = time.time() - start
        print(f"  ✓ {len(embedding)} 維，耗時 {elapsed:.1f}s")

        results.append({
            "slug": slug,
            "title": title,
            "file": filepath,
            "hash": h,
            "embedding": embedding,
        })
        processed += 1

    # 寫出 JSON（只讀不動原檔案）
    with open(OUTPUT_FILE, "w", encoding="utf-8") as f:
        json.dump(results, f, ensure_ascii=False, indent=2)

    print(f"\n完成！")
    print(f"  新增/更新: {processed} 篇")
    print(f"  沿用舊的: {reused} 篇")
    print(f"  跳過:     {skipped} 篇（無 slug）")
    print(f"  輸出至:   {OUTPUT_FILE}")


if __name__ == "__main__":
    main()