from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain, ConversationChain
from langchain.llms import HuggingFaceHub

class ConversationChainSingleton:
    _instance = None

    def __new__(cls, *args, **kwargs):
        if not cls._instance:
            cls._instance = super(ConversationChainSingleton, cls).__new__(cls)
            # Initialize your conversation chain here
            cls._instance.conversation_chain = get_conversation_chain()
        return cls._instance

    def get_conversation_chain(self):
        return self.conversation_chain


    def get_conversation_chain( ):
        """
        Create a conversational retrieval chain and a language model.

        """

        llm = HuggingFaceHub(
            repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
            model_kwargs={"max_length": 1048, "temperature":0.2, "max_new_tokens":256, "top_p":0.95, "repetition_penalty":1.0},
        )
        # llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")

        memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
        conversation_chain = ConversationChain(
            llm=llm, verbose=True, memory=memory
        )
        return conversation_chain