import json
import asyncio
import unicodedata

from server.chat.KgoSearchAPIWrapper import KgoSearchAPIWrapper
from server.chat.search_engine_chat import search_engine_chat
from configs import VECTOR_SEARCH_TOP_K, MAX_TOKENS
from server.agent import model_container
from pydantic import BaseModel, Field
from configs import LLM_MODELS, TEMPERATURE
from configs.basic_config import *

# def get_kgo_search_type(query: str = "全部"):
#     # 过滤掉所有非汉字的字符
#     query = ''.join(char for char in query if unicodedata.category(char) == 'Lo')
#     search_map = KgoSearchAPIWrapper().search_map

#     if "论文" in query:
#         return "1001"
#     elif "外文" in query or "英文" in query:
#         return "1013"
#     elif "期刊" in query or "研究进展" in query:
#         return "1002"
#     else:
#         matched_types = [value for key, value in search_map.items() if key in query]
#         if matched_types:
#             return ','.join(matched_types)
#         else:
#             print("未找到匹配的搜索类型，返回默认值：1000")
#             return "1000"

@timing_decorator
async def search_engine_iter(query: str , uid: str):
    response = await search_engine_chat(uid = uid,
                                        query=query,
                                        search_engine_name="zhipu_search",
                                        model_name=LLM_MODELS[0],
                                        temperature=TEMPERATURE,  # Agent搜索互联网的时候，温度设为0.1
                                        history=[],
                                        top_k=VECTOR_SEARCH_TOP_K,
                                        max_tokens=MAX_TOKENS,
                                        prompt_name="search",
                                        stream=False,
                                        kgo_search_type="1000")

    contents = ""
    docs = []

    async for data in response.body_iterator:  # 这里的data是一个json字符串
        data = json.loads(data)
        contents = data.get("answer", [])
        current_docs = data.get("docs", [])
        if current_docs:
            docs.extend(current_docs)
        else:
            logging.error("No docs found in the response")
            return docs
    # print("contents：", contents)
    # print("docs：", docs)
    # 回复搜索结果和搜索结果的总结
    return contents,docs


def search_internet(query: str, uid: str):
    # filter_words = {
    #     "统计数据", "视频", "数据集", "新闻", "专利", "期刊", "图书", "报告", "项目", "成果", "会议论文",
    #     "政策", "外文期刊论文", "学位论文", "期刊论文", "全部论文", "原文", "全文", "pdf", "资料", " ",
    #     "进展", "研究", "最新", "外文", "英文", "最新", "文件", "资料", "论文"
    # }
    # filtered_query = query
    # for word in filter_words:
    #     if word in query:
    #         filtered_query = filtered_query.replace(word, "")
    # print("filtered query:", filtered_query)

    # kgo_search_type = get_kgo_search_type(query)
    # print("kgo_search_type:", kgo_search_type)

    # 使用过滤后的查询字符串进行搜索
    return asyncio.run(search_engine_iter(query, uid))


class SearchInternetInput(BaseModel):
    location: str = Field(description="Query for Internet search")


if __name__ == "__main__":
    result = search_internet("人工智能领域的政策")
    print("答案:", result)