diff --git a/main.py b/main.py
deleted file mode 100644
index 91a7e3d2..00000000
--- a/main.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# pip install langchain openai pypdf2 faiss-cpu python-dotenv tiktoken
-
-from dotenv import load_dotenv
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chains.question_answering import load_qa_chain
-from langchain.llms import OpenAI
-
-
-def get_text_chunks_from_pdf(pdf_path):
-    # initialize pdf reader
-    pdf = PdfReader('gpt4-in-medicine.pdf')
-
-    # get raw text from pdf
-    raw_text = ""
-    for page in pdf.pages:
-        raw_text += page.extract_text()
-    
-    # split text into chunks for token limit
-    text_splitter = CharacterTextSplitter(        
-        separator = "\n",
-        chunk_size = 1000,
-        chunk_overlap  = 200,
-        length_function = len,
-    )
-    return text_splitter.split_text(raw_text)
-
-
-def setup_text_search(text_chunks):
-    # get embeddings from openai
-    embeddings = OpenAIEmbeddings()
-    
-    # perform docsearch
-    docsearch = FAISS.from_texts(text_chunks, embeddings)
-    chain = load_qa_chain(OpenAI(), chain_type='stuff')
-    
-    return docsearch, chain
-    
-    
-def answer_question(question, docsearch, chain):
-    # ask question to llm using langchain
-    docs = docsearch.similarity_search(question)
-    response = chain.run(input_documents=docs, question=question)
-    return response
-
-
-def main():
-    # get api key
-    load_dotenv()
-    
-    text_chunks = get_text_chunks_from_pdf('gpt4-in-medicine.pdf')
-    
-    docsearch, chain = setup_text_search(text_chunks)
-    
-    response = answer_question("What is this document about?", docsearch, chain)
-    
-    print(response)
-
-
-if __name__ == '__main__':
-    main()