import re
from fastapi import Body, HTTPException
from fastapi.concurrency import run_in_threadpool
from typing import List
import logging
from configs import (
    SELF_TOP_K,
    SCORE_THRESHOLD,
)
from configs.kb_config import DEFAULT_JOURNAL_BASE
from server.knowledge_base.kb_doc_api import search_docs
from server.utils import BaseResponse
from pydantic import BaseModel, Field  # 用于请求模型验证

# 配置日志记录
logger = logging.getLogger(__name__)

# 请求模型验证（可选增强）
class ArticleRequest(BaseModel):
    file_name: str = Field(..., min_length=1, description="文件标题", example="智能制造技术的发展与应用")
    key_words: str = Field(..., description="逗号分隔的关键词", example="智能制造，工业互联网，物联网")

async def relevant_articles(
    request: ArticleRequest = Body(...)  # 使用Pydantic模型进行输入验证
):
    """
        相关文献搜索：\n
        - 输入：文件标题、关键词\n
        - 输出：相关文献列表\n
        返回值数据结构：\n
            {\n
                "code": 状态码（200表示成功，404表示未找到相关文档，500表示服务出现错误）,\n
                "msg": 消息描述,\n
                "data": {\n
                    "total": 相关文献的总数,\n
                    "articles": [\n
                        {\n
                            "doc_id": 文件ID\n
                            "title": 标题\n
                            "author_info": 作者\n
                            "publish_date": 发表时间\n
                            "data_source": 链接\n
                        },\n
                    ]\n
                }\n
            }\n
    """
    try:
        # 构造规范化查询（标题重复两次加强权重，关键词空格分隔）
        keywords_list = [kw.strip() for kw in request.key_words.split(',') if kw.strip()]
        processed_keywords = ' '.join(set(keywords_list)).strip("暂无关键词")  # 去重处理
        
        # 构造带权重的查询字符串
        query = f"{request.file_name}{processed_keywords}"
        logger.info(f"相关文献检索query：{query}")
        # 合并所有知识库的搜索结果
        all_docs = []
        for knowledge_name in [DEFAULT_JOURNAL_BASE]:
            docs = await run_in_threadpool(
                search_docs,
                query=query,
                fileName=[],
                knowledge_base_name=knowledge_name,
                top_k=SELF_TOP_K,
                score_threshold=SCORE_THRESHOLD
            )
            for doc in docs:
                if doc.metadata.get("publish_date", None) == 17010101:
                    doc.metadata.pop("publish_date", None)
            all_docs.extend(docs)
        docs = all_docs[:5]
        
        # ========== 结果后处理 ==========
        if not docs:
            logger.info(f"未找到相关文档：{query}")
            return BaseResponse(code=404, msg="未找到相关文档")
        
        # 提取链接
        # pattern = r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'
        formatted_docs = []
        for doc in docs:
            # data_source = doc.metadata.get("data_source", "")
            # links = re.findall(pattern, data_source)
            formatted_doc = {
                "doc_id": doc.metadata.get("ID", ""),
                "title": doc.metadata.get("title", ""),
                "author_info": doc.metadata.get("author_info", ""),
                "publish_date": doc.metadata.get("publish_date", ""),
                "data_source": doc.metadata.get("data_source", ""),
            }
            formatted_docs.append(formatted_doc)
        
        logger.info(f"成功返回 {len(docs)} 篇相关文档")
        return BaseResponse(
            code=200,
            msg="成功检索到相关文档",
            data={
                "total": len(formatted_docs),
                "articles": formatted_docs,
            }
        )

    except Exception as e:
        logger.error(f"文档检索失败: {str(e)}", exc_info=True)
        return BaseResponse(
            code=500,
            msg=f"文档检索服务暂时不可用，错误信息：{str(e)}"
        )