from langchain.chains import ConversationalRetrievalChain
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
import pinecone
from langchain_community.vectorstores import Pinecone
from langchain_community.embeddings import HuggingFaceEmbeddings

import os

openai_api_key = os.environ.get("OPENAI_API_KEY")

class Conversation_RAG:
    def __init__(self, model_name="gpt-3.5-turbo"):
        self.model_name = model_name

    def get_vectordb(self):
        index = pinecone.Index(os.environ.get("PINECONE_INDEX"))
        embeddings = HuggingFaceEmbeddings(
            model_name="sentence-transformers/all-mpnet-base-v2",
        )
        vectordb = Pinecone(index, embeddings, "text")
        
        return vectordb
    
    def create_model(self, max_new_tokens=512, temperature=0.1):
        llm = ChatOpenAI(
            openai_api_key=openai_api_key,
            model_name=self.model_name,
            temperature=temperature,
            max_tokens=max_new_tokens,
            )
    
        return llm
    
    def create_conversation(self, model, vectordb, k_context=5, instruction="Use the following pieces of context to answer the question at the end by. Generate the answer based on the given context only. If you do not find any information related to the question in the given context, just say that you don't know, don't try to make up an answer. Keep your answer expressive."):

        template = instruction + """
        **Document Context Input**:\n
        {context}\n
        **Client Case Input**: {question}\n
        """

        QCA_PROMPT = PromptTemplate(input_variables=["instruction", "context", "question"], template=template)

        qa = ConversationalRetrievalChain.from_llm(
            llm=model,
            chain_type='stuff',
            retriever=vectordb.as_retriever(search_kwargs={"k": k_context}),
            combine_docs_chain_kwargs={"prompt": QCA_PROMPT},
            get_chat_history=lambda h: h,
            verbose=True
        )
        return qa