import json
import os
import pathlib
from typing import Dict, List, Tuple

import weaviate
from langchain import OpenAI, PromptTemplate
from langchain.chains import LLMChain
from langchain.chains.base import Chain
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.chains.question_answering import load_qa_chain
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import FewShotPromptTemplate, PromptTemplate
from langchain.prompts.example_selector import \
    SemanticSimilarityExampleSelector
from langchain.vectorstores import FAISS, Weaviate
from pydantic import BaseModel


class CustomChain(Chain, BaseModel):
    vstore: Weaviate
    chain: BaseCombineDocumentsChain
    key_word_extractor: Chain

    @property
    def input_keys(self) -> List[str]:
        return ["question"]

    @property
    def output_keys(self) -> List[str]:
        return ["answer"]

    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
        question = inputs["question"]
        chat_history_str = _get_chat_history(inputs["chat_history"])
        if chat_history_str:
            new_question = self.key_word_extractor.run(
                question=question, chat_history=chat_history_str
            )
        else:
            new_question = question
        print(new_question)
        docs = self.vstore.similarity_search(new_question, k=4)
        new_inputs = inputs.copy()
        new_inputs["question"] = new_question
        new_inputs["chat_history"] = chat_history_str
        answer, _ = self.chain.combine_docs(docs, **new_inputs)
        return {"answer": answer}


def get_new_chain1(vectorstore) -> Chain:
    WEAVIATE_URL = os.environ["WEAVIATE_URL"]
    client = weaviate.Client(
        url=WEAVIATE_URL,
        additional_headers={"X-OpenAI-Api-Key": os.environ["OPENAI_API_KEY"]},
    )

    _eg_template = """## Example:

    Chat History:
    {chat_history}
    Follow Up Input: {question}
    Standalone question: {answer}"""
    _eg_prompt = PromptTemplate(
        template=_eg_template,
        input_variables=["chat_history", "question", "answer"],
    )

    _prefix = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. You should assume that the question is related to LangChain."""
    _suffix = """## Example:

    Chat History:
    {chat_history}
    Follow Up Input: {question}
    Standalone question:"""
    eg_store = Weaviate(
        client,
        "Rephrase",
        "content",
        attributes=["question", "answer", "chat_history"],
    )
    example_selector = SemanticSimilarityExampleSelector(vectorstore=eg_store, k=4)
    prompt = FewShotPromptTemplate(
        prefix=_prefix,
        suffix=_suffix,
        example_selector=example_selector,
        example_prompt=_eg_prompt,
        input_variables=["question", "chat_history"],
    )
    llm = OpenAI(temperature=0.8, model_name="gpt-3.5-turbo")
    key_word_extractor = LLMChain(llm=llm, prompt=prompt)

    EXAMPLE_PROMPT = PromptTemplate(
        template=">Example:\nContent:\n---------\n{page_content}\n----------\nSource: {source}",
        input_variables=["page_content", "source"],
    )
    template = """You are an AI assistant for the open source library LangChain. The documentation is located at https://langchain.readthedocs.io.
You are given the following extracted parts of a long document and a question. Provide a conversational answer with a hyperlink to the documentation.
You should only use hyperlinks that are explicitly listed as a source in the context. Do NOT make up a hyperlink that is not listed.
If the question includes a request for code, provide a code block directly from the documentation.
If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
Question: {question}
=========
{context}
=========
Answer in Markdown:"""
    PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])
    doc_chain = load_qa_chain(
        OpenAI(temperature=0.8, model_name="gpt-3.5-turbo", max_tokens=-1),
        chain_type="stuff",
        prompt=PROMPT,
        document_prompt=EXAMPLE_PROMPT,
    )
    return CustomChain(
        chain=doc_chain, vstore=vectorstore, key_word_extractor=key_word_extractor
    )


def _get_chat_history(chat_history: List[Tuple[str, str]]):
    buffer = ""
    for human_s, ai_s in chat_history:
        human = f"Human: " + human_s
        ai = f"Assistant: " + ai_s
        buffer += "\n" + "\n".join([human, ai])
    return buffer