"""
研究流程管理模块
整合关键词提取、文献检索和聚类分析
"""

import asyncio
from typing import Dict, List, Any, Optional
import logging
import time
import json
from pathlib import Path

from backend.utils.api_client import LLMClient, ArxivClient
from backend.utils.keywords import extract_keywords, expand_search_queries
from backend.config import (
    MAX_SEARCH_RESULTS,
    CACHE_DIR,
    ENABLE_CACHE
)

logger = logging.getLogger(__name__)

class ResearchAgent:
    """研究智能体，管理整个研究流程"""
    
    def __init__(self):
        self.llm_client = LLMClient()
        self.arxiv_client = ArxivClient()
    
    async def process_research_intent(
        self, 
        research_intent: str,
        max_results: int = MAX_SEARCH_RESULTS
    ) -> Dict[str, Any]:
        """
        处理研究意图，执行完整的研究流程
        
        Args:
            research_intent: 用户输入的研究意图
            max_results: 最大检索结果数量
            
        Returns:
            包含研究结果的字典
        """
        start_time = time.time()
        
        # 初始化结果字典
        result = {
            "research_intent": research_intent,
            "timestamp": time.time(),
            "keywords": [],
            "papers": [],
            "clusters": [],
            "status": "processing"
        }
        
        try:
            # 1. 提取关键词
            logger.info(f"Extracting keywords from: {research_intent}")
            keywords = await extract_keywords(research_intent, self.llm_client)
            result["keywords"] = keywords
            logger.info(f"Extracted keywords: {keywords}")
            
            # 2. 扩展搜索查询
            search_queries = await expand_search_queries(keywords, self.llm_client)
            result["search_queries"] = search_queries
            logger.info(f"Generated search queries: {search_queries}")
            
            # 3. 检索文献
            all_papers = []
            for query in search_queries:
                logger.info(f"Searching papers with query: {query}")
                papers = await self.arxiv_client.search_papers(
                    query=query,
                    max_results=max_results // len(search_queries)
                )
                all_papers.extend(papers)
            
            # 去重
            unique_papers = []
            paper_ids = set()
            for paper in all_papers:
                if paper["id"] not in paper_ids:
                    unique_papers.append(paper)
                    paper_ids.add(paper["id"])
            
            result["papers"] = unique_papers
            logger.info(f"Found {len(unique_papers)} unique papers")
            
            # 4. 聚类分析（这部分将在clustering.py中实现）
            # TODO: 实现文献聚类
            
            result["status"] = "completed"
            result["processing_time"] = time.time() - start_time
            
            # 缓存结果
            if ENABLE_CACHE:
                self._cache_result(result)
            
            return result
        
        except Exception as e:
            logger.error(f"Error in research process: {str(e)}", exc_info=True)
            result["status"] = "error"
            result["error"] = str(e)
            return result
    
    def _cache_result(self, result: Dict[str, Any]) -> None:
        """缓存研究结果"""
        try:
            cache_file = Path(CACHE_DIR) / f"research_{int(time.time())}.json"
            with open(cache_file, "w", encoding="utf-8") as f:
                json.dump(result, f, ensure_ascii=False, indent=2)
            logger.info(f"Cached result to {cache_file}")
        except Exception as e:
            logger.error(f"Failed to cache result: {str(e)}")