code: 
  from dotenv import load_dotenv
  load_dotenv()
  
  from llama_index import GPTVectorStoreIndex, TrafilaturaWebReader
  import chromadb
  
  
  def create_embedding_store(name):
      chroma_client = chromadb.Client()
      return chroma_client.create_collection(name)
  
  def query_pages(collection, urls, questions):
      docs = TrafilaturaWebReader().load_data(urls)
      index = GPTVectorStoreIndex.from_documents(docs, chroma_collection=collection)
      query_engine = index.as_query_engine()
      for question in questions:
          print(f"Question: {question} \n")
          print(f"Answer: {query_engine.query(question)}")
  
  if __name__ == "__main__":
      url_list = ["https://supertype.ai", "https://supertype.ai/about-us"]
      questions = [
          "Who are the members of Supertype.ai", 
          "What problems are they trying to solve?",
          "What are the important values at the company?"
      ]
  
      collection = create_embedding_store("supertype")
  
      query_pages(
          collection,
          url_list,
          questions
      )
  
apis: llama_index.GPTVectorStoreIndex.from_documents llama_index.TrafilaturaWebReader
extract_api: [((32, 45), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (43, 45), False, 'from dotenv import load_dotenv\n'), ((185, 202), 'chromadb.Client', 'chromadb.Client', ([], {}), '()\n', (200, 202), False, 'import chromadb\n'), ((361, 431), 'llama_index.GPTVectorStoreIndex.from_documents', 'GPTVectorStoreIndex.from_documents', (['docs'], {'chroma_collection': 'collection'}), '(docs, chroma_collection=collection)\n', (395, 431), False, 'from llama_index import GPTVectorStoreIndex, TrafilaturaWebReader\n'), ((310, 332), 'llama_index.TrafilaturaWebReader', 'TrafilaturaWebReader', ([], {}), '()\n', (330, 332), False, 'from llama_index import GPTVectorStoreIndex, TrafilaturaWebReader\n')]