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# import os
# from chatBot.common.pdfToText import loadLatestPdf
# os.environ["OPENAI_API_KEY"] = "INSERTYOUROWNAPIKEYHERE"
# from langchain.document_loaders import PyPDFLoader
# from langchain.text_splitter import CharacterTextSplitter
# import pickle
# import faiss
# from langchain.vectorstores import FAISS
# from langchain.embeddings import OpenAIEmbeddings
# from langchain.chains import RetrievalQAWithSourcesChain
# from langchain.chains.question_answering import load_qa_chain
# from langchain import OpenAI
# urls = [
# 'http://en.espn.co.uk/f1/motorsport/story/3836.html', 'https://www.mercedes-amg-hpp.com/formula-1-engine-facts/#' , 'https://www.redbullracing.com/int-en/five-things-about-yas-marina' , 'https://www.redbull.com/gb-en/history-of-formula-1'
# , 'https://www.formula1.com/en/information.abu-dhabi-yas-marina-circuit-yas-island.4YtOtpaWvaxWvDBTItP7s6.html']
# data = loadLatestPdf()
# text_splitter = CharacterTextSplitter(separator='\n',
# chunk_size=1000,
# chunk_overlap=200)
# docs = text_splitter.split_documents(data)
# embeddings = OpenAIEmbeddings()
# vectorStore1_openAI = FAISS.from_documents(docs, embeddings)
# with open("faiss_store_openai.pkl", "wb") as f:
# pickle.dump(vectorStore1_openAI, f)
# with open("faiss_store_openai.pkl", "rb") as f:
# VectorStore = pickle.load(f)
# llm=OpenAI(temperature=0.8, verbose = True)
# gptModel = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=VectorStore.as_retriever()) |