import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI

# Set API key from environment
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")

def split_text(text):
    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    return splitter.split_text(text)

def create_vector_store(chunks):
    embeddings = OpenAIEmbeddings()
    vectorstore = FAISS.from_texts(chunks, embedding=embeddings)
    return vectorstore

def answer_question(vectorstore, query):
    docs = vectorstore.similarity_search(query)
    llm = OpenAI(temperature=0)
    chain = load_qa_chain(llm, chain_type="stuff")
    return chain.run(input_documents=docs, question=query)