import faiss
import pickle
import os
from PyPDF2 import PdfReader
import glob
from pathlib import Path
import re
import requests

from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain import PromptTemplate

from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings

import dotenv
dotenv.load_dotenv()


def call_semantic_api(query, store_path, k):
    payload = {
        "query": query,
        "store_path": store_path,
        "k": k,
    }

    # response = requests.post("http://localhost:3001/search", json=payload)
    response = semantic_search.search(payload)
    return response


class SemanticSearch():
    def __init__(
        self,
        threshold: float,
        with_source=False,
        k=5,
    ):
        self.threshold = threshold
        self.with_source = with_source
        self.k = k
        
        with open('./data/store.pkl', 'rb') as f:
            self.db = pickle.load(f)

    def __call__(self, query):

        documents = self.db.similarity_search_with_score(query, k=self.k)
        if len(documents) == 0:
            return None

        if not self.with_source:
            output = '\n\n\n'.join([i[0].page_content for i in documents])
        else:
            output = '\n\n\n'.join([i[0].page_content + '\n\nSource:' + os.path.basename(
                str(i[0].metadata['source']) + '\n') for i in documents])

        return output


class ContentSearch():
    def __init__(
            self,
            semantic_search,
            prompt_template,
    ):
        self.semantic_search = semantic_search
        self.prompt_template = prompt_template

    def __call__(self, query):
        content = self.semantic_search(query)
        if content is None:
            return "No results found"
        else:
            return self.prompt_template.format(content=content)