from typing import Dict, Any, List
from langchain_core.documents import Document


def convert_to_basic_types(obj: Any) -> Any:
    """
    递归将对象转换为基本类型，以便Pydantic能够序列化
    
    特殊处理：当字典中的子项包含相同长度的数组时，将其转换为数组对象结构
    例如：{"a": [1,2], "b": [3,4]} -> [{"a":1, "b":3}, {"a":2, "b":4}]
    
    Args:
        obj: 要转换的对象
    
    Returns:
        转换后的基本类型对象
    """
    if obj is None:
        return None
    elif isinstance(obj, (str, int, float, bool)):
        return obj
    elif isinstance(obj, dict):
        # 先递归转换所有值
        converted = {k: convert_to_basic_types(v) for k, v in obj.items()}
        
        # 检查是否需要转换为数组对象结构
        # 条件：所有值都是列表，且长度一致，且长度大于0
        values = list(converted.values())
        if all(isinstance(v, list) for v in values):
            lengths = [len(v) for v in values]
            if len(set(lengths)) == 1 and lengths[0] > 0:
                # 转换为数组对象结构
                result = []
                keys = list(converted.keys())
                for i in range(lengths[0]):
                    item = {}
                    for key in keys:
                        # 处理数组中可能存在的None值
                        if i < len(converted[key]):
                            item[key] = converted[key][i]
                        else:
                            item[key] = None
                    result.append(item)
                return result
        
        return converted
    elif isinstance(obj, (list, tuple)):
        return [convert_to_basic_types(item) for item in obj]
    else:
        # 对于其他类型，尝试将其转换为字符串或字典
        try:
            return dict(obj)
        except:
            return str(obj)

def convert_to_langchain_docs(obj: Any) -> List[Document]:
    """
    将Infinity搜索结果转换为LangChain的Document格式
    
    Args:
        obj: 要转换的对象
    
    Returns:
        转换后的Document列表
    """
    res = convert_to_basic_types(obj=obj)
    # 将数据转换为 LangChain 的 Document 格式
    candidate_docs = [
        Document(page_content=item["content"], 
            metadata={
                "docnm": item["docnm"], 
                "tag_kwd": item["tag_kwd"], 
                "kb_id": item["kb_id"], 
                "doc_id": item["doc_id"]
            }) 
        for item in res[0]
    ]
    return candidate_docs