import os
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from langchain.embeddings.base import Embeddings
from openai import OpenAI
import requests
import httpx
import logging

from extraction_info import info_data_txt, info_faiss_archived

class SiliconFlowEmbeddings(Embeddings):
    """SiliconFlow嵌入模型封装"""
    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):
        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):
        return self._embed(texts)

    def embed_query(self, text):
        return self._embed([text])[0]


# embeddings = Embedding(url="http://10.1.16.39:9995/v1", api_key="xxx", model_name="bge-m3")
embeddings  = SiliconFlowEmbeddings(api_key="xxx")

with open(info_data_txt, 'r', encoding='utf-8') as file:
    txt_list = [line.strip() for line in file]

# embedding_path = "/data/Z_LLM_data/Embed_data/bge-m3"
# embeddings = HuggingFaceEmbeddings(model_name=embedding_path)

# faiss_archived = "./data/faiss_data/data"
vectorstore_txt_faiss = FAISS.from_texts(txt_list, embeddings)
vectorstore_txt_faiss.save_local(info_faiss_archived)

retriever_txt_faiss1 = vectorstore_txt_faiss.as_retriever(search_kwargs={"k":3})
retriever_txt_faiss2 = vectorstore_txt_faiss.as_retriever(
    search_type="mmr",
    search_kwargs={"k": 3,  # 检索结果
                   "fetch_k": 1,  # 候选结果数量
                   "lambda_mult": 0.5}  # 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": 0.5}
)

def intersection_of_three_lists(input_str):
    list1 = retriever_txt_faiss1.invoke(input_str)
    list2 = retriever_txt_faiss2.invoke(input_str)
    list3 = retriever_txt_faiss3.invoke(input_str)
    
    def _intersection_of_three_lists(retrieval_results):
        return [doc.page_content for doc in retrieval_results]
        
    list11 = _intersection_of_three_lists(list1)
    list22 = _intersection_of_three_lists(list2)
    list33 = _intersection_of_three_lists(list3)
        
    return list(set(list11) & set(list22) & set(list33))