import asyncio
import json
from datetime import datetime
from typing import List, Set, Optional
from langchain_core.tools import tool
from tavily import TavilyClient

from src.core.config import settings

# 模拟配置加载
TAVILY_API_KEY = settings.TAVILY_API_KEY


@tool
async def topic_research(topic: str, max_urls: int = 15) -> str:
    """
    深度调研工具。该工具会利用 Tavily 搜索引擎针对特定主题进行多维度搜索。
    它会自动生成针对性的搜索词（包含年份和趋势），并返回去重后的高质量 URL 列表。
    """
    if not TAVILY_API_KEY:
        return "❌ 错误: 未配置 TAVILY_API_KEY。"

    client = TavilyClient(api_key=TAVILY_API_KEY)

    # 1. 自动生成多维度搜索词 (在工具内部快速生成)
    current_year = datetime.now().strftime("%Y")
    queries = [
        f"{topic} trends {current_year}",
        f"{topic} market analysis {current_year}",
        f"top selling {topic} styles {current_year}",
        f"best {topic} materials and colors {current_year}"
    ]

    # 2. 并行执行搜索
    async def perform_search(q: str):
        # 使用 asyncio.to_thread 运行同步的 Tavily SDK
        def sync_search():
            try:
                response = client.search(
                    query=q,
                    search_depth="advanced",
                    max_results=5,
                    include_answer=False
                )
                return response.get('results', [])
            except Exception as e:
                print(f"Search error: {e}")
                return []

        return await asyncio.to_thread(sync_search)

    search_tasks = [perform_search(q) for q in queries]
    search_results_list = await asyncio.gather(*search_tasks)

    # 3. 结果去重与过滤
    seen_urls: Set[str] = set()
    final_urls = []

    # 常见的非内容页面过滤
    skip_extensions = ('.pdf', '.jpg', '.png', '.zip', '.exe')

    for results in search_results_list:
        for item in results:
            url = item.get('url')
            if url and url not in seen_urls:
                if not url.lower().endswith(skip_extensions):
                    seen_urls.add(url)
                    final_urls.append(url)

    # 4. 结果截断
    selected_urls = final_urls[:max_urls]

    # 返回 JSON 字符串，便于 Agent 下一步调用批量爬虫 (Crawl4ai)
    return json.dumps(selected_urls, ensure_ascii=False)