diff --git a/context_retrieve.py b/context_retrieve.py
deleted file mode 100644
index f4c4fc2..0000000
--- a/context_retrieve.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import os
-import openai
-from langchain.document_loaders.csv_loader import CSVLoader
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain.document_loaders import TextLoader
-
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chat_models import ChatOpenAI
-from langchain.chains import RetrievalQA
-from langchain import PromptTemplate
-
-import re
-import requests
-import xml.etree.ElementTree as ET
-
-from fragment import Fragment
-from VectorDatabase import Latern
-
-
-
-# OpenAI Setup
-OPEN_API_KEY = "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"
-# openai.api_key = os.getenv(openai_api_key)
-os.environ['OPENAI_API_KEY'] = OPEN_API_KEY
-
-def getPmcPaper(pmcid):
-    """
-    """
-    url = f'https://www.ebi.ac.uk/europepmc/webservices/rest/{pmcid}/fullTextXML'
-    req = requests.get(url)
-    res = req.text
-    return res
-
-def extractMethodsFromPmcPaper(paper):
-    """
-    """
-    tree = ET.fromstring(paper)
-    mtext = []
-    for sec in tree.iter('sec'):
-        for title in sec.iter('title'):
-            if isinstance(title.text, str):
-                if re.search('methods', title.text, re.IGNORECASE):
-                    mtext.extend(list(sec.itertext()))
-    return " ".join(mtext)
-
-def preprocess(input_text):
-    """
-    """
-    processed_data = input_text.replace("\n","")
-    return processed_data
-
-def get_embeddings(fname):
-    """
-    """
-    loader = TextLoader(fname)
-    documents = loader.load()
-    text_splitter = CharacterTextSplitter(separator = ".",chunk_size = 1000, chunk_overlap=0)
-    
-    docs = text_splitter.split_documents(documents)
-    
-    emb = OpenAIEmbeddings()
-    input_texts = [d.page_content for d in docs]
-
-    input_embeddings = emb.embed_documents(input_texts)
-    text_embeddings = list(zip(input_texts, input_embeddings))
-
-    return text_embeddings, emb
-
-def saveFassIndex(fname, sname, ):
-    """
-    """
-    txt_embs, emb = get_embeddings(docs)
-    faissIndex = FAISS.from_embeddings(text_embeddings=txt_embs, embedding=emb)
-    faissIndex.save_local(sname)
-    # faissIndex = FAISS.from_documents(docs, OpenAIEmbeddings())
-    # faissIndex.save_local("input_doc")
-
-def Query(input_query, faiss_obj):
-    chatbot = RetrievalQA.from_chain_type(
-        llm=ChatOpenAI(
-            openai_api_key=OPEN_API_KEY,
-            temperature=0, model_name="gpt-3.5-turbo", max_tokens=50
-        ),
-        chain_type="stuff",
-        retriever=faiss_obj.as_retriever(search_type="similarity", search_kwargs={"k":1})
-    ) 
-    template = """ {query}? """
-    prompt = PromptTemplate(
-        input_variables=["query"],
-        template=template,
-    )
-    print(chatbot.run(
-        prompt.format(query=input_query)
-    ))
-
-
-def main():
-    text = getPmcPaper(pmcid)
-    
-    methods_text = preprocess(extractMethodsFromPmcPaper(text))
-    fname = 'input_file.txt'
-    sname = 'input_doc'
-    with open(fname, 'w') as file:
-        file.write(methods_text)
-    # print(methods_text)
-    txt_embs, emb = get_embeddings(fname) 
-    
-    fragments = []
-    for txt, embs in txt_embs:
-        fragment = Fragment(pmcid, 'methods', txt, embs)
-        fragments.append(fragment)
-        
-    latern = Latern()
-    latern.insertEmbeddings(fragments)
-    
-    # retreieve. PMC
-    faissIndex = FAISS.from_embeddings(text_embeddings=txt_embs, embedding=emb)
-    inp_query = "Does the paper report a new structure of a biomolecule or biomolecular complex modeled using experimental data"
-    Query(inp_query, faissIndex)
-
-if __name__ == '__main__':  
-    main()
\ No newline at end of file