import os
from langchain_community.vectorstores import FAISS
# from langchain_huggingface import HuggingFaceEmbeddings

# embedding_path = "/data/Z/Z_llm_dm/vector_data/bge-m3"
# embeddings = HuggingFaceEmbeddings(model_name=embedding_path)


from typing import List
import requests
from langchain.embeddings.base import Embeddings


class SiliconFlowEmbeddings(Embeddings):
    def __init__(self, api_key: str, model: str = "bge-m3"):
        self.api_key = api_key
        self.model = model
        self.url = "http://10.1.16.39:9995/v1/embeddings"
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }

    def _embed(self, input: List[str]) -> List[List[float]]:
        payload = {
            "model": self.model,
            "input": input,
            "encoding_format": "float"
        }
        response = requests.post(self.url, json=payload, headers=self.headers)
        response.raise_for_status()
        data = response.json()
        return [item["embedding"] for item in data["data"]]

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        return self._embed(texts)

    def embed_query(self, text: str) -> List[float]:
        return self._embed([text])[0]

embeddings = SiliconFlowEmbeddings(api_key="sk-ftnofbucchwnscojohyxwmfzgaykdxihafnlphohsinftkbr")

def Mixed_retrieval(input_path):
    file_name = os.path.splitext(os.path.basename(input_path))[0]
    faiss_archived = f"./faiss_data/{file_name}"

    txt_list = []
    with open(input_path, 'r', encoding='utf-8') as file:
        txt_list = [line.strip() for line in file]
    vectorstore_txt_faiss = FAISS.from_texts(txt_list, embeddings)
    vectorstore_txt_faiss.save_local(faiss_archived)

    # vectorstore_txt_faiss = FAISS.load_local(vectorstore_txt_faiss,
    #                                          embeddings=embeddings,
    #                                          allow_dangerous_deserialization=True)

    retriever_txt_faiss1 = vectorstore_txt_faiss.as_retriever(search_kwargs={"k": 5})
    retriever_txt_faiss2 = vectorstore_txt_faiss.as_retriever(
        search_type="mmr",
        search_kwargs={"k": 5,  # 检索结果
                       "fetch_k": 2,  # 候选结果数量
                       "lambda_mult": 0.1}  # 平衡指数，1为相关性；0为多样性
    )
    retriever_txt_faiss3 = vectorstore_txt_faiss.as_retriever(
        search_type="similarity_score_threshold",
        search_kwargs={"score_threshold": 0.3}
    )

    return retriever_txt_faiss1, retriever_txt_faiss2, retriever_txt_faiss3



def interface_search(input_str, retriever_txt_faiss1, retriever_txt_faiss2, retriever_txt_faiss3):
    index_keyword1 = []
    for i in retriever_txt_faiss1.invoke(input_str):
        index_keyword1.append(i.page_content)
    index_keyword2 = []
    for i in retriever_txt_faiss2.invoke(input_str):
        index_keyword2.append(i.page_content)
    index_keyword3 = []
    for i in retriever_txt_faiss3.invoke(input_str):
        index_keyword3.append(i.page_content)

    return list(set(index_keyword1) & set(index_keyword2) & set(index_keyword3))


def Building_search_dictionary(input_csv_path1, input_csv_path2, index_keyword):
    import pandas as pd

    df1 = pd.read_csv(input_csv_path1, encoding='utf-8')
    df2 = pd.read_csv(input_csv_path2, encoding='utf-8', names=['path', 'id'])
    #df2 = pd.read_csv(input_csv_path2, encoding='utf-8')

    matching_path = df1.loc[df1['name'] == index_keyword, 'index']

    # print(matching_path)

    # print(matching_path.tolist()[0] )

    # todo: bug修改: 避免matching_path和matching_ids没有映射
    if matching_path.empty:
        return(None, None)
    else:
        matching_ids = df2.loc[df2['path'] == matching_path.tolist()[0], 'id']

    # print(matching_ids)
    if matching_ids.empty:
        return (matching_path.tolist()[0], None)
    else:
        return (matching_path.tolist()[0], int(matching_ids.values[0]))


def Official_website_kg_search(input_id):
    # info = WikijsTool.get_all_documents()

    import re
    from bs4 import BeautifulSoup
    from booway_kg_api.WikijsTool import WikijsTool

    html_text = WikijsTool.query_doc_info(input_id)['content']
    cleaned_img_text = re.sub(r'<img\s+[^>]*>', '', html_text)

    soup = BeautifulSoup(cleaned_img_text, "html.parser")
    plain_text = soup.get_text()

    return plain_text