import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS

from langchain.chains import ConversationalRetrievalChain
from langchain.llms import OpenAI


class Agent:
    def __init__(self, openai_api_key: str | None = None) -> None:
        # if openai_api_key is None, then it will look the enviroment variable OPENAI_API_KEY
        self.embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
        self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

        self.llm = OpenAI(temperature=0, openai_api_key=openai_api_key)

        self.chat_history = None
        self.chain = None
        self.db = None

    def ask(self, question: str) -> str:
        if self.chain is None:
            response = "Please, add a document."
        else:
            response = self.chain({"question": question, "chat_history": self.chat_history})
            response = response["answer"].strip()
            self.chat_history.append((question, response))
        return response

    def ingest(self, file_path: os.PathLike) -> None:
        loader = PyPDFLoader(file_path)
        documents = loader.load()
        splitted_documents = self.text_splitter.split_documents(documents)

        if self.db is None:
            self.db = FAISS.from_documents(splitted_documents, self.embeddings)
            self.chain = ConversationalRetrievalChain.from_llm(self.llm, self.db.as_retriever())
            self.chat_history = []
        else:
            self.db.add_documents(splitted_documents)

    def forget(self) -> None:
        self.db = None
        self.chain = None
        self.chat_history = None