import { ChatOpenAI } from 'langchain/chat_models/openai'
import { PromptTemplate } from 'langchain/prompts'
import { StringOutputParser } from 'langchain/schema/output_parser'
import { retriever } from '/utils/retriever'
import { combineDocuments } from '/utils/combineDocuments'
import { RunnablePassthrough, RunnableSequence } from "langchain/schema/runnable"
import { formatConvHistory } from '/utils/formatConvHistory'

document.addEventListener('submit', (e) => {
    e.preventDefault()
    progressConversation()
})

/**
 * Super Challenge:
 * 
 * 1. Pass convHistory into the chain as conv_history at 
 *    the point where we invoke it. Remember to make use 
 *    of our formatConvHistory function!
 * 2. Update the standaloneQuestionTemplate to make use 
 *    of convHistory. 
 * 3. Make sure the answerChain has access to convHistory 
 *    and edit answerTemplate to make use of it.
 * 4. Test by giving the chatbot some information and 
 *    checking in the next question to see if it remembers it.
 * 
 * */ 

const openAIApiKey = process.env.OPENAI_API_KEY
const llm = new ChatOpenAI({ openAIApiKey })

const standaloneQuestionTemplate = 'Given a question, convert it to a standalone question. question: {question} standalone question:'
const standaloneQuestionPrompt = PromptTemplate.fromTemplate(standaloneQuestionTemplate)

const answerTemplate = `You are a helpful and enthusiastic support bot who can answer a given question about Scrimba based on the context provided. Try to find the answer in the context. If you really don't know the answer, say "I'm sorry, I don't know the answer to that." And direct the questioner to email help@scrimba.com. Don't try to make up an answer. Always speak as if you were chatting to a friend.
context: {context}
question: {question}
answer: `
const answerPrompt = PromptTemplate.fromTemplate(answerTemplate)

const standaloneQuestionChain = standaloneQuestionPrompt
    .pipe(llm)
    .pipe(new StringOutputParser())

const retrieverChain = RunnableSequence.from([
    prevResult => prevResult.standalone_question,
    retriever,
    combineDocuments
])
const answerChain = answerPrompt
    .pipe(llm)
    .pipe(new StringOutputParser())

const chain = RunnableSequence.from([
    {
        standalone_question: standaloneQuestionChain,
        original_input: new RunnablePassthrough()
    },
    {
        context: retrieverChain,
        question: ({ original_input }) => original_input.question
    },
    answerChain
])

const convHistory = []

async function progressConversation() {
    const userInput = document.getElementById('user-input')
    const chatbotConversation = document.getElementById('chatbot-conversation-container')
    const question = userInput.value
    userInput.value = ''

    // add human message
    const newHumanSpeechBubble = document.createElement('div')
    newHumanSpeechBubble.classList.add('speech', 'speech-human')
    chatbotConversation.appendChild(newHumanSpeechBubble)
    newHumanSpeechBubble.textContent = question
    chatbotConversation.scrollTop = chatbotConversation.scrollHeight
    const response = await chain.invoke({
        question: question
    })
    convHistory.push(question)
    convHistory.push(response)

    // add AI message
    const newAiSpeechBubble = document.createElement('div')
    newAiSpeechBubble.classList.add('speech', 'speech-ai')
    chatbotConversation.appendChild(newAiSpeechBubble)
    newAiSpeechBubble.textContent = response
    chatbotConversation.scrollTop = chatbotConversation.scrollHeight
}