# LANGCHAIN IMPORTS
from langchain import PromptTemplate, LLMChain
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chains.qa_with_sources import load_qa_with_sources_chain


# CLIMATEQA
from climateqa.retriever import ClimateQARetriever
from climateqa.vectorstore import get_pinecone_vectorstore
from climateqa.chains import load_climateqa_chain


class ClimateQA:
    def __init__(
        self,
        hf_embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1",
        show_progress_bar=False,
        batch_size=1,
        max_tokens=1024,
        **kwargs
    ):
        self.llm = self.get_llm(max_tokens=max_tokens, **kwargs)
        self.embeddings_function = HuggingFaceEmbeddings(
            model_name=hf_embedding_model,
            encode_kwargs={
                "show_progress_bar": show_progress_bar,
                "batch_size": batch_size,
            },
        )

    def get_vectorstore(self):
        pass

    def reformulate(self):
        pass

    def retrieve(self):
        pass

    def ask(self):
        pass